104843

iv

Master Thesis ǀ Tesis de Maestría submitted within the UNIGIS MSc programme presented UNIGIS MSc Programme at/en Interfaculty Department of Geoinformatics- Z_GIS Departamento de Geomática – Z_GIS University of Salzburg ǀ Universidad de Salzburg Application of Cybernetic Hydrology Balance to three catchments in California, United States. by/por Ing Agr Paúl Gonzalo Benalcázar Vergara 06155499 A thesis submitted in partial fulfillment of the requirements of the degree of Master of Science– MSc

Advisor: Ph.D. Carlos Mena Thunder Bay, July 5th, 2019

v

Compromiso de Ciencia.

Through this document, including my form, I certify and assure that my thesis is entirely the result of my work. I have cited all the sources that I have used in my thesis and all cases I have indicated their origin. Through this document, including my form, I certify and assure that my thesis is entirely the result of my work. I have cited all the sources that I have used in my thesis and all cases I have indicated their origin.

Thunder Bay , July 5th, 2019 (pace and date)

(signature)

iv

ACKNOWLEDGE This study would not be possible without the help of Dad and Mom since at every moment they have been the ones who have driven me to continue with my studies and in every academic decision, I have dreamed.

But mainly, this work is dedicated to my wife and daughter, Criss, and Mel. Since carrying out this study, I have sacrificed my family time. They have known that every achievement and success entail a sacrifice, and, in this case, it has been our time, but that they have decided to understand and to accompany in each step.

In additions, my thanks to Dr. Juan León Ruiz, Director of the School of Engineering of Renewable Resources of the Polytechnic School of Chimborazo and his friend Dr. Víctor Ponce, professor and Director of Visualab, at San Diego State University in California. They allowed me to carry out my research in California and have the scientific support to carry out the study, and my gratitude to the entire UNIGIS Latin America Team – The University of Salzburg.

Finally, God manifested in that source of infinite energy represented and present in every great moment that allowed me and allows me to have my physical, mental and spiritual faculties to continue sounding and conquering my dreams with my family.

Paul 2019.

v

ABSTRACT The present study showed the application of the cybernetic hydrologic balance model the following three catchments in California: Russian River, Salinas River, and Whitewater River. The precipitation and runoff data were obtained from the United States Geological Survey. For the detailed study, ASTER data were used for preparing a digital elevation model (DEM), and Geographic Information System (GIS). ArcMapArc Hydro was used to obtain relief aspect of morphometric parameters such as watershed boundaries, flow direction, flow accumulation and flow length. Eleven morphometric parameters were measured, calculated using the Arc Hydro tool for each catchment the data analysis was performed using an online calculator and the United States Geology Services PAST software. Also, runoff and baseflow coefficients temporal and spatial analysis were used to analyze and create different thematic maps for the three catchments from 1990 to 2017. The study provides details on runoff and baseflow parameters and Horton index to understand the effects on climate change over the three catchments in California.

Keywords: cybernetic water balance, conventional water balance, Russian River, Salinas River, Whitewater River, Horton Index

vi

RESUMEN El presente estudio mostró la aplicación del Modelo cibernético hidrológico para la determinación de cantidad de agua en cuencas hidrográficas en California, Estados Unidos.

Estas cuencas son: El Río “Russian”, el río Salinas y el río “Whitewater”. Los datos de precipitación y escorrentía se obtuvieron del Servicio Geológico de los Estados Unidos.

Para el estudio detallado, los datos ASTER fueron utilizados para preparar el modelo de elevación digital (DEM) con la utilización de los Sistema de Información Geográfica. El software ArcMap-Arc Hydro se usó para obtener los parámetros geomorfológicos de las cuencas, así como la definición de sus límites, dirección de flujo, acumulación de flujo y longitud del flujo. Se midieron once parámetros geo morfométricos, para lo cual se utilizó la herramienta Arc Hydro. El análisis de los datos se realizó mediante la calculadora “online” y la utilización del software PAST, de los Servicios de Geología de los Estados Unidos. De igual forma, los coeficientes de escorrentía y de flujo base analizados fueron utilizados para la creación de diferentes mapas temáticos de las tres cuencas objeto de estudio desde 1990 hasta 2017. El estudio proporciona detalles sobre los parámetros de escorrentía, flujo de base e índice de Horton para comprender los efectos de cambio climático sobre tres cuencas de California.

Palabras claves: Balance hidrológico cibernético, balance hidrológico convencional, Río Russian, Río Salinas, Río Whitewater, Índice de Horton.

vii

GLOSSARY ASTER Data:

Advanced Spaceborne Thermal emission and Reflection Radiometer data

Flow accumulation: Raster of accumulated flow to each cell, as determined by accumulating

the weight for all cells that flow into each downslope cell.

Flow direction:

Integer raster whose values range from 1 to 255.

Flow length:

Calculation of the length of the longest flow path within a given

basin Groundwater:

Water held underground in the soil or pores and crevices in the rock

Hydrography:

The science of surveying and charting bodies of water, such as seas, lakes, and rivers.

Index:

An indicator, sign, or measure of something.

Infiltration:

Permeation of a liquid into something by filtration.

Physiology:

The branch of biology that deals with the normal functions of living organisms and their parts.

NSW:

National Weather Services

Surface water:

Water that collects on the surface of the ground.

US EPA:

The United States Environmental Protection Agency.

Visualab:

Software Hydrology Laboratory located in the San Diego States University

Water yield:

An estimate of water income and water expenditure for a set time.

viii

TABLE OF CONTENTS. 1

INTRODUCTION .......................................................................................................... 5

1.1 BACKGROUND ............................................................................................................ 5 1.2 GENERAL OBJECTIVE................................................................................................ 6 1.3 SPECIFIC OBJECTIVES ............................................................................................... 6 1.4 RESEARCH QUESTION............................................................................................... 6 1.5 HYPOTHESIS ................................................................................................................ 6 1.6 JUSTIFICATION ........................................................................................................... 6 1.7 SCOPE ............................................................................................................................ 8 2

LITERATURE REVIEW ............................................................................................... 9

2.1 HYDROLOGY ............................................................................................................... 9 2.1.1 Catchment. .......................................................................................................... 9 2.1.2 Hydrologic model.............................................................................................. 10 2.1.3 Conceptual Model. ............................................................................................ 11 2.1.4 Cybernetic hydrologic balance .......................................................................... 12 2.2 HORTON INDEX (HI) ................................................................................................ 14 2.3 GIS TECHNIQUES AND COMPUTER POWER ...................................................... 15 2.3.1 GIS Model ......................................................................................................... 15 2.3.2 Data model ........................................................................................................ 15 2.3.3 Spatial model ..................................................................................................... 16 2.3.4 Types of spatial models ..................................................................................... 16 2.4 REASONS FOR SPATIAL MODELING .................................................................... 18 2.5 HYDROLOGY MODELING DATA REPRESENTATION ....................................... 18 2.5.1 Arc Hydro data model characteristics ............................................................... 18 2.5.2 Arc Hydro attributes .......................................................................................... 19 2.5.3 Arc Hydro data model characteristics ............................................................... 20 2.6 HYDROLOGIC CHARACTERISTICS OF CATCHMENTS .................................... 21

ix

2.6.1 Catchment area .................................................................................................. 22 2.6.2 Catchment shape ............................................................................................... 22 2.6.3 Compactness ratio ............................................................................................. 22 2.6.4 Catchment relief ................................................................................................ 23 2.6.5 Linear measures ................................................................................................ 23 2.6.6 Basin Topology ................................................................................................. 23 2.6.7 Drainage density................................................................................................ 24 2.6.8 Drainage patterns .............................................................................................. 24 2.7 DESCRIPTION OF ECOSYSTEM IN CALIFORNIA ............................................... 25 2.7.1 The North and the Coast hydrology regions ..................................................... 26 2.7.2 North and South Coast Hydrologic California .................................................. 27 2.7.3 California precipitation ..................................................................................... 28 3

METHODOLOGY ....................................................................................................... 29

3.1 STUDY AREA ............................................................................................................. 29 3.1.1 Selection of Basins ............................................................................................ 29 3.1.2 The geographic location of studies catchment in California state .................... 30 3.2 GENERAL CHARACTERISTIC OF THE CATCHMENTS ...................................... 31 3.2.1 Russian River near Guerneville, California. ..................................................... 31 3.2.2 Salinas River near Spreckles, California. .......................................................... 33 3.2.3 Whitewater river near Mecca, California .......................................................... 35 3.3 CYBERNETIC HYDROLOGIC BALANCE MODEL ............................................... 37 3.4 GIS APPLICATION ..................................................................................................... 37 3.5 APPLICATION OF THE CYBERNETIC HYDROLOGIC MODEL DIAGRAM .... 39 4

RESULTS AND DISCUSSION ................................................................................... 40

4.1 RESULTS OF CALIFORNIA’S CATCHMENT ........................................................ 40 4.1.1 Data sources of Russian River near Guerneville, California ............................ 40 4.1.2 Data Sources of Salinas River Catchment ........................................................ 40

x

4.1.3 data sources of Whitewater River catchment, California .................................. 41 4.2 RESULTS OF THE BASEFLOW AND RUNOFF COEFFICIENT TO EACH CATCHMENT. ................................................................................................................... 41 4.2.1 Russian River .................................................................................................... 41 4.2.2 Salinas River ..................................................................................................... 43 4.2.3 Whitewater River .............................................................................................. 44 4.3 RESULTS OF ARCHYDRO GEOMORPHOLOGICAL MODEL ............................ 45 4.3.1 Russian River .................................................................................................... 46 4.3.2 Salinas River ..................................................................................................... 46 4.3.3 Whitewater River .............................................................................................. 47 4.4 RESULTS OF CYBERNETIC HYDROLOGY MODEL MAPS-GIS APPLICATION 48 4.4.1 Russian River baseflow coefficient ................................................................... 48 4.4.2 Russian River runoff coefficient ....................................................................... 49 4.4.3 Salinas River baseflow coefficient (Ku) ........................................................... 51 4.4.4 Salinas River runoff coefficient ........................................................................ 52 4.4.5 Whitewater River baseflow coefficient (Ku) .................................................... 54 4.4.6 Whitewater River runoff coefficient (Kr) ......................................................... 55 4.5 DISCUSSION OF PRECIPITATION, RUNOFF AND BASEFLOW FOR THE CATCHMENTS .................................................................................................................. 56 4.5.1 Precipitation ...................................................................................................... 56 4.5.2 Base flow coefficient......................................................................................... 56 4.5.3 Runoff coefficient ............................................................................................. 57 4.5.4 Horton Index ..................................................................................................... 57 5

CONCLUSIONS AND RECOMMENDATIONS ....................................................... 59

5.1 CONCLUSIONS .......................................................................................................... 59 5.2 RECOMMENDATION ................................................................................................ 60

xi

6

REFERENCES ............................................................................................................. 62

7

ANNEXES .................................................................................................................... 68

7.1 DISCHARGE AND PRECIPITATION DATA FROM THE WEATHER STATIONS. ...................................................................................................................................... 68 7.2 RESULT OF ONLINE CALCULATOR FOR U, W, V KU AND KR .......................... 73 7.3 GEOMORPHOLOGICAL ANALYSIS MAPS OF CATCHMENT ........................... 76 7.3.1 Russian River geomorphological analysis ........................................................ 76 7.3.2 Salinas River geomorphological analysis ......................................................... 77 7.3.3 Russian River geomorphological analysis ....................................................... 78 7.4 ARCHYDRO AND TOOLBOX PROBLEMS ............................................................ 79 7.4.1 Error 999999. Using the resampling Raster Clip. ............................................. 79 Error 010005 Unable to allocate memoy . .................................................................... 79 7.4.2 Error 010168. Running Topo to raster .............................................................. 79 7.4.3 Reducing the size (area km2 ) of the stream definition po to raster................... 80 7.4.4 Adjoint catchment tool in ArcHydro................................................................. 80 7.4.5 Archydro is not creating an attribute field related to hydroID, mextID, joint ID . ........................................................................................................................... 80 7.4.6 The polygon processing .................................................................................... 81

xii

LIST OF TABLES Table 1. California’s catchment selected for application of the cybernetic hydrologic balance. ................................................................................................................................ 29 Table 2. Input feature data used to Topo to Raster tool. ..................................................... 38 Table 3. Russian River data sources .................................................................................... 40 Table 4. Salinas River data sources ..................................................................................... 40 Table 5. Whitewater River data sources .............................................................................. 41 Table 6. Statistical analysis of Russian River in mm .......................................................... 41 Table 7. Statistical analysis of Salinas River....................................................................... 43 Table 8. Statistical analysis of Whitewater River................................................................ 44 Table 9.Geomorphological parameters of the Russian River. ............................................. 46 Table 10.Geomorphological parameters of the Salinas River ............................................. 46 Table 11.Geomorphological parameters of the Whitewater River ...................................... 47 Table 12. Precipitation and discharge data information for Russian River ......................... 68 Table 13. Precipitation and discharge data information for Salinas River .......................... 70 Table 14. Precipitation and discharge data information for Whitewater River ................... 72 Table 15. U, W, V, Horton Index, Ku and Kr of Russian River near Guerneville catchment. ............................................................................................................................................. 73 Table 16. U, W, V, Horton Index Ku and Kr of Salinas River. ............................................ 74 Table 17. U, W, V, Horton Index, Ku and Kr of Whitewater River ..................................... 75

xiii

LIST OF FIGURES Figure 1. Water cycle .......................................................................................................... 10 Figure 2. Elements of L’vovich’s water balance ................................................................. 14 Figure 3. classification of the models according to abstraction .......................................... 16 Figure 4. Arc Hydro identifiers and connections to the geodatabase .................................. 19 Figure 5. Layers from the ArcHydro Model....................................................................... 21 Figure 6. The aquatic ecosystem in California .................................................................... 25 Figure 7. Water storage and distribution in California. ....................................................... 27 Figure 8. California average precipitation from 1900-1960 ................................................ 28 Figure 9 Studies catchment in California state .................................................................... 30 Figure 10. Russian River Valley ......................................................................................... 31 Figure 11. The geographic location of the Russian River. .................................................. 32 Figure 12. Salinas River in the foreground.......................................................................... 33 Figure 13. The geographic location of Salinas River .......................................................... 34 Figure 14. Whitewater River – Whitewater Canyon ........................................................... 35 Figure 15. The geographic location Whitewater River. ...................................................... 36 Figure 16. Cybernetic Hydrology Model scheme. .............................................................. 39 Figure 17. Russian River Horton Index from 1990 to 2017 ................................................ 42 Figure 18. Salinas River Horton index from 1993 to 2017. ................................................ 43 Figure 19. Whitewater River Horton Index from 1990 to 2009. ......................................... 45 Figure 20. Russian River baseflow coefficient.................................................................... 48 Figure 21. Russian River runoff coefficient ........................................................................ 49 Figure 22. Salinas River baseflow coefficient. .................................................................... 51 Figure 23. Salinas River runoff coefficient. ........................................................................ 52 Figure 24. Whitewater River baseflow coefficient. ............................................................. 54 Figure 25. Whitewater River runoff coefficient. ................................................................. 55 Figure 26. Morphological analysis of the Russian River. ................................................... 76 Figure 27. Morphological analysis of Salinas River ........................................................... 77 Figure 28. Morphological analysis of Whitewater River .................................................... 78

5

1

INTRODUCTION

1.1

BACKGROUND

Climate change may cause more severe droughts and prolonged floods in the future (Ishida and Kavvas, 2017). Knowing the amount of water available in a catchment is one of the many challenges faced by water managers (Lyon, 2003). A catchment's water yield is a significant problem that must be solved in the hydrology field (Ponce and Shetty, 1995a). A water balance help manager to predict the behaviour of a catchment Precipitation as an indicator of input for hydrologic water processes could help to understand droughts, floods formation, and water budget. Understanding intensity, duration, and frequency may help to view possible consequences that catchments could have under various climate change scenarios (Ishida, Gorguner, Ercan, Trinh, and Kavvas, 2017). The hydrologic systems of California face a threat due to climate change. Projected anomalies from modelling scenarios indicate that by 2090, the temperature will increase by 2.1°C, resulting in environmental consequences (Knowles and Cayan., 2002). Therefore, watersheds need to be evaluated carefully not only for the habitats of a wide variety of species (flora and fauna) but also for freshwater requirements, production, consumption (cities and industry) and for the environment of the delta of the Sacramento and San Joaquin Rivers.

There are various computational models -from simple to sophisticated, that help to understand the complexity of a catchment. One of the available tools for understanding the complexity of a catchment is Geographic Information Systems, and its spatial data management analysis (Gericke and Du Plessis, 2012). Even though there are different methods for measuring water balance that requires a significant amount of hydrologic-meteorological data, a practical alternative represented is “the cybernetic hydrologic model,” which is called the L’vovich’s catchment wetting approach (L’vovich’s, 1979). This method separates annual precipitations into three components: surface runoff, base flow, and vaporization.

6

1.2

GENERAL OBJECTIVE

Define the water balance of three catchments in California using the Cybernetic Hydrologic Model by applying the catchment wetting online calculator and the Geographic Information System (ArcMap-Arc Hydro tool).

1.3

SPECIFIC OBJECTIVES

•

Apply the cybernetic hydrologic model to basins in California using GIS

•

Calculate related geomorphological parameters using GIS- Hydrology tools

•

Define the Horton Index to three catchments

•

Produce a set of maps (runoff and baseflow coefficients) derived by the online wetting calculator and GIS analysis

1.4

RESEARCH QUESTION

Does the cybernetic hydrologic model and GIS improve the analysis of the water budget in the three selected catchments?

1.5

HYPOTHESIS

The cybernetic hydrologic balance improves the understanding of water budget using hydrologic parameters and GIS techniques in California.

1.6

JUSTIFICATION Geographic Information Systems have application in almost every field in

engineering, social and natural sciences, helping to collect and analyze spatial information. Climate change is a multi-dimensional impact on living species on Earth.

The potential impact of increased temperature could alter the spatial and temporal variation of precipitations (Wang and Kotamarthi, 2015). Consequently, it is essential to look at the multidimensional phenomenon’s perspective that affects and causes alteration on available resources such as water yield in catchments.

7

Water management studies have conducted to know the amount of water availability in a catchment, so that manager can assess and identify hydrologic issues that could happen in the future as floods or droughts (Ishida & Kavvas, 2017; Maurer, Brekke, & Pruitt, 2010).

In 2015, The California Natural Resources Agency wrote a report related to California's most significant drought, comparing historical and recent conditions. The reports described periods of drought from 1920 until 2014. The last drought that this area suffered were from 2012 until 2014 in which due to climate change, droughts have occurred at a time of record warmth in California. Sierra Nevada mountain headwaters are principal sources of California water. It accumulates water during winters as precipitation, and then its water is used for both municipality and agricultural water supply through the entire state (Mao, Nijssen, and Lettenmaier, 2015).

The California Department of Water (2015) indicates that climate change has impacted California water resources because of extreme weather conditions and population. Reduced snowpack, higher sea levels, and river courses have changed.

Models predict more precipitation than snow so that that food supplies could be impacted in counties, and created challenges for water security in the future (California Department of Water, 2018). Climate change will continue to do so because California’s population is increasing, and they will demand more and more water.

The snowpack has decreased because of increasing global average temperature.

The mountain snowpack provides almost 75% of water availability. It is accumulated during wet winter and released slowly during spring, and summer, but because of the temperature, snowpack will melt fast and early. By the end of 2100, the Sierra Nevada of California is projected to loss 48-65% of its snow cover; therefore, the impact of water supply is and will be affected (California Department of Water, 2018).

The aim to this study is to assess the water budget in three catchments using the cybernetic hydrology model integrating the Geographic Information System-ArcMap Temporal and multi-spatial analysis (Horton Index), and thematic maps will help to understand hydrologic patterns and climate change over the years. As a result, the water

8

manager will count with a new tool that could be used for planning or implementing strategies regarding future climate change scenarios in California.

1.7

SCOPE This study encompasses the application of the cybernetic hydrologic model to three

basins in California using: information of the USGS department, and the online catchment wetting calculator developed by Dr. Victor Ponce at the Visualab in San Diego States University. In addition to the model, Arc Hydro tools are going to incorporate into the model to support, analyze, and represent multitemporal-especial data, plus establishing geomorphological parameters to the three selected California’s catchments.

9

2

LITERATURE REVIEW

2.1

HYDROLOGY Hydrology studies water on the Earth, from occurrence, circulation, distribution

to chemical, physical, and relation to living organisms. Hydrology encompasses surface and groundwater. Hydrology cycles are re-circulatory transport of water of the Earth into the atmosphere, land, and oceans. Elements of the hydrology cycle are the atmosphere, vegetation, snowpack and icecaps, land surface, soil, streams, lakes and rivers, aquifers, and oceans (Ponce, 1994).

The liquid phases of the hydrologic cycle are precipitation from the atmosphere onto the land surface, through fall from vegetation onto the land surface. Melt from snow and ice to land surface. Surface runoff from land surface to streams, lakes, and rivers, and from streams, lakes, and rivers to oceans. Infiltration from land to surface soil, interflow from soil to streams lakes, and rivers and vice versa. Percolation from soil to aquifers, capillary rise from aquifers to the soil. Groundwater flows from streams, lakes, and rivers to aquifers and vice versa, and from aquifers to oceans and vice versa (Ponce, 1994).

Vapour-transport phases of hydrologic cycle are (1) Evaporation from the land surface, streams, lakes, rivers, and oceans to atmosphere. (2) Evapotranspiration from vegetation to the atmosphere and vapour diffusion soil land and surface (Ponce, 1994).

The U.S. Hydrology Department often uses the term watershed and basin to refers a catchment. The small catchment is called stream watershed, whereas large catchment is called river basin. Hydrology budget refers to an accounting of various transport phases of hydrology cycle (Ponce, 1994).

2.1.1

Catchment.

A catchment is part of the Earth's surface that collects runoff and concentrates

water to release in downstream points. Runoff concentrated by catchment flows into either large catchments or oceans (Ponce, 1994). The terms watershed and basin are commonly used to refers to a catchment. Sometimes, a watershed is used to describe

10

small catchment while a basin is used to describe large catchment. The interpretation of the hydrology cycle within catchment's boundaries leads to the concept of hydrology budget. Hydrologic budget is the account of all transport phases that occur in a hydrologic cycle (Ponce, 1994).

2.1.2

Hydrologic model Due to the constant degradation of ecosystems, catchment models have been

developed from traditional hydrology models to more precise and comprehensive models to enhance water management and maximize land productivity (Gericke and Du Plessis, 2012). Mathematical models have developed to understand a complicated process that has happened in hydrology that has a direct link to geology, geomorphology, topography, weather, land use, and human activities, which must be analyzed and quantified (Ghoraba, 2015). Water, one of the elements of nature, is primal for the survival of living things on the Earth. Elements that govern the economics of countries are essential for developing agriculture and industry. The ability of using and keeping water has become the core of nations, where nations have developed strategies and regulations for conserving the water (Ghoraba, 2015).

Water balance has been used around the world to understand and study hydrologic effects under climate change scenarios. Thornthwaite and Mather created a model (Alemaw and Chaoka, 2003) to analyze long-term monthly climate conditions.

Typical examples are (1) South America, a grid-based model (Vorosmarty et al., 1989; cited in Alemaw and Chaoka, 2003).

Source: Ponce (1994)

Figure 1. Water cycle

11

2.1.3

Conceptual Model.

The water cycle has three main components: runoff, precipitation, and

evapotranspiration. Runoff is the amount of water that flows through the surface, and the ground in a catchment. It is usually expressed in litre per second or millimetre per year.

Having runoff information is necessary for the water cycle to show how wet a catchment is. Runoff data is very variable year by year, and pressure over water resources is variable over the years. Seasonal differences are a good indicator of water sensitivity during the lowest monthly runoff expresses under water scarcity, and drought (Frantar and Brancelj, 2013).

Precipitation, snowfall and hail are part of the rainfall. Rainfall is used to describe precipitation. Generally, a catchment has an abstractive capability that acts to reduce total rainfall into effective rainfall. The differences between total rainfall and effective rainfall are the losses or hydrology abstraction. Its includes interception, infiltration, surface storage, evaporation, and evapotranspiration (Ponce, 2014).

Discharge regime is an indicator of average river discharge fluctuations over periods (years). Multiple factors influence discharge regimes such as vegetation, climate, topography, bedrock, soil, and society. The primary factor is climate under precipitation, evapotranspiration, temperature, and snow cover duration (Frantar and Brancelj, 2013). The following formula is a hydrology budget equation for both surface and groundwater proposed by Ponce (1994): [Eq.1] ∆S = Change in storage P = Precipitation E= Evaporation T= Evapotranspiration G = Groundwater outflow Q = Surface runoff A hydrology budget equation that considers only surface water is:

12

[Eq.2] I = Infiltration.

Within a given period, under equilibrium conditions (∆S=0), equation 2 reduces to: [Eq.3] Equation (3) is imperfect. It assumes that infiltration is lost from the surface water budget. It is returned to control volume as evaporation from lakes and ponds, evapotranspiration or surface runoff (Ponce and Shetty, 1995a). In equation (3), hydrology losses L are defined as the sum of evaporation, evapotranspiration, and infiltration.

[Eq.4] Given the fundamental equation of flood hydrology.

[Eq.5] 2.1.4

Cybernetic hydrologic balance Ponce and Shetty (1995a) provide a background related to one of the

fundamental problems in hydrology, which is how much water could have a catchment over a defined period. The authors mentioned that there are: empirical models described

by Ghoraba (2015); and continuous simulation models described by Crawford and Linsley, (1966 cited in Ghoraba, 2015) and practical alternative models called conceptual model described by L'vovich's (1979).

The conceptual model uses the hydrology budget in which the precipitation is divided into various components. It divides annual precipitation into surface runoff, baseflow, and vaporization (L'vovich’s, 1979). The normal water balance applicable to the individual storm is: [Eq.6] The losses for an individual storm are an interception, surface storage, and infiltration. A water balance equation applicable on an annual basis is: [Eq.7]

13

R is runoff that includes surface, and subsurface runoff (vegetated surface, nonvegetated surface, and water bodies). Annual precipitation P can be separated into two components: [Eq.8] Then equation (8) includes more variables related to surface runoff and catchment wetting. The equation (8) is divided into two main components. Catchment wetting consists of baseflow and vaporization. Wetting consists of two components: [Eq.9] Where U is baseflow, and V is vaporization, the fraction of wetting returned to the atmosphere as water vapour. Vaporization, which comprises all moisture returned to the atmosphere, has two components: .

[Eq.10]

V has two main components: the nonproductive evapotranspiration, hereafter referred to as evaporation and T = productive evaporation that is part of transpiration from a vegetated surface such as surfaces and other parts of the plant used for their physiological needs, hereafter as evapotranspiration. In turn, evaporation has two components: [Eq.11] In which 1) Eg, evaporation from non-vegetated areas of Earth’s surface and nearsurface 2) Ew, evaporation from sizable water bodies such as reservoirs, lakes and rivers.

Runoff, (the total runoff) is the sum of surface and baseflow: [Eq.12] Combining equation 8, 9 and 12: [Eq.13] Equation 8 to 13 are set of water balance equations, combining equations 12 and 13 lead to: [Eq.14] Equation (14) separates annual precipitation of catchment into three major components:(a) Surface runoff, (b) Baseflow, and (c) Vaporization. Significantly, Eq 14

14

assumes that soil moisture changes year by year; therefore, it is negligible. Under L'vovich's hydrology budget, two water balance coefficients may be defined: (1) Runoff coefficient, and (2) Baseflow coefficient. The runoff coefficient is: [Eq.15] According to the California Water Boards, (2011) runoff coefficient is a dimensionless coefficient relating to the amount of precipitation received. Kr is going to be high in areas with low infiltration and high runoff, and low for permeable, well-vegetated areas. Kr indicates flash flooding areas during storms as water moves fast overland on its way to a river channel or a valley floor.

The baseflow coefficient is: [Eq.16] The set of equations above described are the L’vovich’s water balance equations.

Sources (Ponce 1994)

Figure 2. Elements of L’vovich’s water balance

2.2

HORTON INDEX (HI) Horton Index is a dimensionless number between 0 to 1 that describes a fraction of

a catchment wetting (Eq 9) uses in evaporation known as vaporation (Horton, 1933). HI is defined as: [Eq 17] According to Troch et al. (2009), HI provides a measure of vegetation water limitation in

15

response to changes in precipitation. Voepel et al. (2011) indicate that HI values are significantly related both to climate and topographic characteristics. Thus, HI represents competition for W between plant water use (ET), and drainage to baseflow. Understanding how the precipitation is divided into three components ( evapotranspiration, storage and streamflow) is a fundamental question in hydrology. From a catchment perspective, hydrologic partitioning is manifested in various spatial and temporal scales of a catchment.

Runoff responses related to climatic forcing, catchment morphological, and pedological characteristics (Botter, Porporato, Rodriguez‐Iturbe, and Rinaldo, 2007; Wagener, Sivapalan, Troch, and Woods, 2007).

2.3

GIS TECHNIQUES AND COMPUTER POWER Geographic Information System (GIS) and Remote Sensing (RS) are two fields that

have been improved application of catchment models around the world. For example, GIS is a tool for managing many databases and proving visual representation of catchment’s characteristics (Alemaw and Chaoka, 2003; Ghoraba, 2015). Besides, catchment models using GIS improve them efficiently and increase the functionality of hydrologic models.

GIS software have increased time-saving, data analysis and data display for temporal and spatial digital hydrology data (Thomas & Huggett, 1980).

2.3.1

GIS Model A model is a representation of essential aspects of the real world, such as process,

objects, systems, and phenomenon. Regarding GIS, it is a computer representation of spatial information. According to Longley, Goodchild, Maguire, & Rhind, (2005) is a computational model that can be divided into two: data model and spatial model.

2.3.2

Data model It is the representation of how the world looks likes in a digital data structure.

After, this is used for GIS spatial analysis that could be represented in maps or spatial analysis into a model of natural processes (Domniţa, Craciun, Haidu, & Magyari-Saska, 2012).

16

2.3.3

Spatial model It is a spatial simulation model that shows natural, social, and economical process

dynamically (Domniţa et al., 2012). GIS techniques allow various analyzes of geographic elements of catchments that are part of landscapes (Frantar and Brancelj, 2013). All hydrologic models depend on the type of data available. It could be defined as internal variables and be constant (catchment geometry characteristics). The spatial model could be a spatial variable such as rainfall or meteorological parameters.

2.3.4

Types of spatial models

Ackoff (1964, cited by Rana, 2015), classified spatial models in three main areas: •

Iconic. Uses the same materials that are applied to small scale for studying the phenomenon.

•

Analogue. Uses various materials that are applied in small scale.

•

Symbolic. Uses typical mathematic’s symbols

Sources (Thomas & Huggett, 1980)

Figure 3. classification of the models according to abstraction

On the other hand, Nir (1987) defined the following spatial model classification: • Analogy models. It uses events from the past to show in present events or similar events from other places.

• Physical models. It is a physical setup that uses similar materials to study process, evolution, and influences of physical factors.

• Mathematical models. It is showed as a conjunction of a series of mathematical relations (equations, functions, or statistical laws) to represent phenomenon and its evolutions.

17

Due to technological advancements, Brimicombe, (2009) added to the previous models in the following category: •

Computational models. It is written in a programming language to express phenomenon and implications. Models can be stochastic or deterministic that have rulers such as logical (in, or, and), conditional (if), operators, and artificial components.

Domniţa et al., (2012) mentioned that other factors could consider time, a degree of specification, and how it is being used. Spatial models can be classified in: • Statics. Elements are fixed during the time. It shows a single state.

• Dynamics. Models change according to time.

• White box. Model of internal working is fully specified.

• Gray Box. Model of internal working is partially specified.

• Black box. Model internal working is unspecified, and inputs are transformed to outputs through correlations.

There are two essential criterions to consider in hydrology model according to Beven and Young (2003).

• Spatial aggregated models. It uses both whole catchment and principal values throughout catchment calculations. However, spatial distribution is unknown.

• Spatially distributed models. It is spatially distributed throughout the catchment in which cells have values and can be characterized in many elements such as hillslopes, and sub-catchments. Cells can have states variable characteristics.

According to Serban (1995 cited by (Rana, 2015), there are two types of models: stochastic and deterministic model.

• Stochastic model. It is a black box that uses mathematical and statistic concepts to use input data (precipitations) to output data (discharge). It does not count the physical mechanisms of the results process.

• Determinist models. It represents the physical process by observing the real-world base on knowledge of the physical process in a catchment; for example, runoff, baseflow, surface runoff, and evapotranspiration. Deterministic models can be more complicated where models could be an integration of an entire system.

18

2.4 •

REASONS FOR SPATIAL MODELING Simplify a tool for beginner users. Beginner users can create a simple model using the default values of some parameters.

•

Chain together many process tools. Some geoprocessing workflows can be executed following a specific order that can be chained in a single model.

•

Parameters more flexibles. Parameters can be used in different ways in a model, and it can use datasets of multiples kinds of parameters.

•

Sharing models between users. Many users can use spatial models to use different datasets or different locations.

In addition to the above mentioned, there are other benefits to GIS automation; for example, automation makes work more comfortable, faster and accurately (Domniţa et al., 2012).

2.5

HYDROLOGY MODELING DATA REPRESENTATION One of the hydrologic models available throughout ESRI is called Arc Hydro,

developed at the Center for Research in Water Resources in Texas by Maidment (2002).

He considers that the main characteristic of the model is its structure (hydrology layers in GIS), and application in water resources.

2.5.1

Arc Hydro data model characteristics

Arc Hydro runs in ArcGIS, and it is composed of two parts. The first one, it is geospatial. The second one is a temporal model. Arc Hydro has a spatial dataset that connects features in it; also, it includes tools that allow for manipulating and analyzing hydrology data or creation of the new spatial model.

Arc Hydro uses a data structure for hydrology simulation models, but it does not contain any function to simulate hydrologic processes (Domniţa et al., 2012). The modelling of hydrology process can be exchanged through data exchange, and independent hydrology model, through the model attached to Arc Hydro as a dynamic link library (dll)

19

or through extending Arc Hydro objects. As a result, functions and data available can be used directly in ArcGIS or external programs calling library methods (Domniţa et al., 2012).

Another characteristic of Arc Hydro is that geospatial data can be combined, which stand for where hydrology process occurs in time. Arc Hydro was developed to use in water resources problems in any scale, and any specific modelling task. Arc Hydro consists of a set of layers that includes different terrains characteristics and structures to be modelled in. The model information is stored in a series of simple data tables as part of feature classes, raster datasets, and attributes (Domniţa et al., 2012). Water modelling can be applied for different purposes such as floods, water for supplied and quality, infrastructure design, and landscape studies. Also, it requires to account for water behaviour as well as water regulations that could be included in a model. Arc Hydro creates object attributes to show the relationship between different elements to the same layer or other layers; consequently, ArcObjects uses ObjectId to identify a feature in feature class uniquely.

Sources (Domniţa et al., 2012).

Figure 4. Arc Hydro identifiers and connections to the geodatabase

2.5.2

Arc Hydro attributes Arc Hydro has two attributes: HydroId and Hydrocode. HydroId. It is an integer

attribute that identifies feature inside the database, while in each feature, ObjectId can vary because of the spatial analysis or copying from one feature class to another. HydroId identifies the relations between distinctive features class and different types of hydrologic

20

structures that can be used in the same analysis. HydroId identifiers management at database level has two tables, called: LayerKeyTable and LayerIdTable, which are created automatically. HydroCode. It is a text attribute standing for a public identifier of the feature. It is an external identifier for each element in a feature class, and GIS managers can use HydroCode to combine gauging station data to feature class from a model.

2.5.3

Arc Hydro data model characteristics

Digital elevation model (DEM) is a primary data structure for extraction and processing. It has a grid of cell, and every cell has a specific altitude value. Figure 5 is organized into five categories to show how Arc Hydro has organized: Flow elements, hydrography, flow network elements, flow channels, and time series (Zeiler, 2000).

• Flow elements. These are derived either from DEM or surface topography. Lines stand for streams or rivers. Points represent outlets, and polygons represent catchment delineations.

• Hydrography. It is obtained from topographic maps by digitalizing, which has natural or anthropic details. Hydrography can contain points (gauge, pump stations, etc.), lines (streams and rivers), and polygons (lakes, lagoons).

• Flow networks. These have graph’s data structure, nodes (contain points), and edges (connected to lines) that show a path of flows in drainage networks. For streams HydroEdges, for nodes, Hydro Junctions and HydroNetwork_Junctions Schematic Node is an alternative representation of flow areas and the connection between these areas represented by Schematic Links.

• Flow channels. These do not represent exact locations in the field, but they are represented in 3D dimensional of channel bottom with cross-sections (measured in the field), banks and flooding areas.

• Time series. It is used for periodical measurement from gauging stations and other facilities. The TSType table contains metadata for each time series.

21

Sources (Maidment, 2002)

Figure 5. Layers from the ArcHydro Model

2.6

HYDROLOGIC CHARACTERISTICS OF CATCHMENTS Essential characteristics of a catchment are: area, shape, relief, linear measures,

topology, density, and drainage patterns.

22

2.6.1

Catchment area

It is the most fundamental characteristic because it determines the potential runoff volume, where a storm could cover the whole area. The direction of surface runoff is perpendicular to contour lines. All peaks and saddle are identified at the outset. Runoff in peak runs in all direction while runoff in the saddle is in two opposite directions perpendicular to the saddle axis (Ponce, 1994). Several formulas have been proposed. One of them is the following: [Eq.18] Qp = Peak flow A= Area of the catchment c and n = Parameter defined the regression analysis.

2.6.2

Catchment shape

Catchment shape is outline described by the horizontal projection of a catchment.

The following formula describes it: [Eq.19] Kf = Form ratio A= Catchment area L= Catchment length, measure along the longest watercourse. Area and length are given in square kilometres and kilometres.

2.6.3

Compactness ratio

There is another parameter derived from the catchment perimeter that is compactness ratio. It is the ratio of catchment perimeter that equivalent circle. The following formula describes it:

23

[Eq.20] Kc = Compactness ratio P= Perimeter of catchment A= Catchment area Compactness ratio close to 1 describes a catchment having a fast and peaked catchment response. On the other hand, the compactness ratio larger than 1 describes a catchment with a delayed runoff response. However, other elements that are part of a catchment should be considered like catchment relief, vegetation, drainage density (Ponce, 2014).

2.6.4

Catchment relief

Relief is the differences between two references points. Maximum catchment relief is the elevation difference between the highest point in a catchment divide and a catchment outlet. The mainstream is the central and large watercourse of a catchment, and one conveying the runoff to the outlet relief. The hypsometric analysis describes the overall relief of catchments. The hypsometric curve is used when hydrologic variables such as precipitation, vegetation cover or snowfall shows differences according to altitude. The relief ratio helps to measure the intensity of erosional process active in a catchment (Ponce, 2014).

2.6.5

Linear measures

These describe a one-dimensional feature of a catchment. The hydraulic length (L) is the length measured along the principal watercourse (Ponce, 2014).

2.6.6

Basin Topology

Basin topology refers to regional anatomy of a stream network. Distributed rainfallrunoff modelling needs a hierarchical description of stream connectivity of its topology.

24

Therefore, stream order classified streams in hierarchical numerical order starting from zero (overland flow). Two first order streams create second-order streams. Large catchment could have stream orders of 10 or more (Ponce, 2014).

Catchment geomorphology plays a role in the relationship between vegetation cover, landscape evolution and water status (Yetemen, Istanbulluoglu, & Vivoni, 2010). In semiarid basins, differences in vegetation between the north and south facing slopes in association with different soil moisture states (catchment wetting) posed a dominant control over the geomorphology of the basin (Yetemen et al., 2010). The two main parameters from geomorphology of a catchment are: slope and elevation that control the annual precipitation, and these two parameters can be retained long enough in a catchment for the available vegetation (Voepel et al., 2011).

2.6.7

Drainage density

Drainage density is the total radio of the stream length (the sum of the length of the streams) to the catchment area. High density indicates fast and peaked runoff response, whereas low density reflects delayed runoff response (Ponce, 2014).

2.6.8

Drainage patterns

Drainage patterns in catchments vary widely. The more intricate patterns are the result of high drainage density. They reflect geology, soil, and vegetation effects. Drainage patterns are often related to hydrology properties such as runoff response or annual water yield. The most common drainage patterns are dendritic, rectangular, radial, and trellis (Ponce, 2014).

Catchment’s analysis model is essential for two main reasons. First, it helps to delineate catchment boundaries and corresponding discharge points. Secondly, it helps to understand surface runoff and base flow processes. Also, using GIS models help to determine a suitable location and volume of reservoirs for yield hydrology budget (Dixon, Uddameri, & Ray, 2015).

25

The evolution of landscape models confirm the dominant role of vegetation in climates limited by water (Collins & Bras, 2010; Istanbulluoglu & Bras, 2005), It suggests that response of vegetation to climatic gradients affects the density of drainage, relief, and concavity of channels, which reproduces empirical patterns in the density of drainage with climate (Abrahams & Ponczynski, 1984). Rasmussen et al. (2011) used catchment data from different climates and typical lithology to demonstrate that energy and mass flow associated with primary production. Effective precipitation explains substantial variation in the structure, and functions of the catchment.

2.7

DESCRIPTION OF ECOSYSTEM IN CALIFORNIA California is a house of diverse ecosystem and biota, covering more than 150,000

square miles. Its territory has mountains, valleys, coasts, and desert. It gives different climate conditions around it. From east to west, there is the Sierra Nevada region, coastal mountain ranges to the Pacific Ocean (US EPA, 2012). California’s water quality management has defined eight ecological regions: North Coast, Central Valley, Coast and interior Chaparral, South Coast, West Sierra, and Central Lahontan, Desert-Modoc (USA EPA, 2013).

Sources : (California Department of Water, 2003b).

Figure 6. The aquatic ecosystem in California

26

The San Francisco Bay-Delta watershed’s extension is 75,000 square miles. For the north, the watershed extends to 500 miles to the Cascade Range in the north to the Tehachapi Mountains. For the south and east, it is bounded by Sierra mountain, and for the west to the Coast Range. According to the United States Environmental Protection Agency: “The San Francisco Bay-Delta provides water to 25 million Californians, irrigation for 7000 square miles of agriculture, and includes important economic resources such as California’s water supply infrastructure, port, deep-water shipping channels, a major highway and railroad corridors and energy lines” (California Department of Water, 2003b, ¶1).

2.7.1

The North and the Coast hydrology regions The North region covers approximately 14,470 square miles including Modoc,

Siskiyou, Del Norte, Trinity, Humboldt, Mendocino Lake, and Sonoma counties. In 1995, the population was 606,000 inhabitants with most of them living in the center along the Pacific Coast and the inland valleys north of the San Francisco Bay Area. The rural area is extended for a northern mountainous portion of the region. The land is covered densely with forest.

There are irrigation areas along narrow river valleys. The principal crops are alfalfa, grain, and pasture. In the southern portion of the Coast hydrology region, the primary plants close to the urban areas are wine grapes, nursery, stock, orchards, and truck crops (California Department of Water, 2003a).

27

Sources (California Department of Water,2003a)

Figure 7. Water storage and distribution in California

2.7.2

North and South Coast Hydrologic California

The South Coast Region covers approximately 10,600 square miles of the South California catchment that drains to the Pacific Ocean. Between the most important and significant geographic feature are the coastal plain, the central Transverse Range, the Peninsular Ranges and the San Fernando, San Gabriel and Santa Ana River and Santa Clara Rivers valleys (California Department of Water, 2003b). Seventeenth million people are living within the boundaries of South Coast Hydrologic Region that represents 50% of California’s population located over Metropolitan areas surrounding Ventura, Los Angeles, San Diego, San Bernando, and Riverside. (California Department of Water, 2003b).

28

Sources (CA State Climatologist, 2017)

Figure 8. California average precipitation from 1900-1960

In California, water is finite resources because of the Mediterranean climate.

Precipitation and streamflows are variable from year to year. The primary water sources in California states are the California Central Valley Project and the State Water Project, and the Colorado River, which supplies to cities like Los Angeles, Riverside, San Bernardino, San Diego and parts of Southern California (California Department of Water, 2003b). \

2.7.3

California precipitation The precipitation behaviour is variable year by year in California states, and

understanding this behaviour is critical for water managers and policy-makers. The California climate is Mediterranean (cold, wet winters and warm and dry summers). Cold season and precipitation falls are present from October to April. In the state, southeast areas could receive less than 5 inches while in the north coast areas it could receive 100 inches in a year. According to the Northern California Resources, the average precipitation is 50 inches per year in Sacramento River (CA State Climatologist, 2017).

29

3

METHODOLOGY To apply the cybernetic hydrology model, geographical rainfall-runoff data was

used. This information was obtained through the California states’ websites (National Oceanic and Atmospheric Administration, 2018). Digital elevation models were used from the USGS virtual platform (Earth Explore) and (Alaska Satellite Facilities). Precipitation and discharge data are available in the National Centers for Environmental Information (National Oceanic and Atmospheric Administration, 2018).

3.1 3.1.1

STUDY AREA Selection of Basins

Table 1. California’s catchment selected for application of the cybernetic hydrologic balance.

Item

Catchment/Location

Area/Km2

Counties

1

Russian River near Guerneville, CA

3,463

Mendocino and Sonoma, CA

2

Salinas River near Spreckles, CA

11,777

Monterrey, San Benito, San Luis Obispo and Kern, CA

3

Whitewater River near Mecca, CA

3,849

Riverside and San Bernardino, CA

30

3.1.2

The geographic location of studies catchment in California state

Figure 9 Studies catchment in California state

31

3.2 3.2.1

GENERAL CHARACTERISTIC OF THE CATCHMENTS Russian River near Guerneville, California.

Sources (Eloquent & Meadow, 2019)

Figure 10. Russian River Valley

The Russian River Catchment has 1,500 square miles of agricultural, forest, and urban area in the northern Sonoma County and southern Mendocino County California. People supply their demands using surface and groundwater for irrigation, municipal, private and industrial-commercial uses. In the rural part, they apply for wineries and recreation (United States Geological Services, 2018a). The Russian River is prone to droughts due to hydrology variation responding to extreme climate variation plus increasing water demands which is a challenge for environmental, and water resources managers to ensure that water would not have depletion.

32

Figure 11. The geographic location of the Russian River.

33

3.2.2

Salinas River near Spreckles, California.

Sources (Central Coast Regional Water Quality Control Board, 2017).

Figure 12. Salinas River in the foreground

The Salinas River is part of the riparian corridor in the Central Coast of California.

It has 4,600 square miles of land in the Monterrey and San Obispo Counties. The catchment includes 200,000 acres of irrigated agriculture, fish, and wildlife habitat. Urban expansion, agricultural runoff have been affected wildlife, native fish and water quality in the catchment (Central Coast Regional Water Quality Control Board, 2017).

34

Figure 13. Geographic location of Salinas River

35

3.2.3

Whitewater river near Mecca, California

Sources (Media New Group, 2016)

Figure 14. Whitewater River – Whitewater Canyon

The Whitewater River is a small permanent stream in western Riverside CountyCalifornia, with a small upstream section in southwestern San Bernardino County. It is located close to San Gregorio Mountain, San Bernardino Mountains, and San Bernardino County. Before Palm Spring, the Whitewater River is fed imported water from the Colorado River aqueduct by the Metropolitan Water District of Southern California (Greystone Environmental Consultants, 2015).

36

Figure 15. Geographic location Whitewater River.

37

3.3

CYBERNETIC HYDROLOGIC BALANCE MODEL The following equations were used to formulate the hydrologic model using

L’vovich’s model. Equations 8 to 12 were applied. Discharge and precipitation are in millimetres (Ponce & Shetty, 1995a). The following steps were applied: a. Assembly an array annual precipitation data P (mm) from 1990 to 2017 within the catchment. This data is available by using the webpage ‘Water Resources of the United States in which by typing the weather location code or by map location, the information can be downloaded (United States Geological Services, 2018a). b. Assembly the discharge hydrographs data m3/s from 1990 to 2017 at the mount of the catchment. The information available in the (United States Geological Services, 2018b) website. c. Using the “PART” software (United States Geological Services, 2018b), which separates the discharge hydrographs into surface runoff S (mm) and U (mm). d. Obtaining the R (mm), the discharge hydrographs is divided by the area of the Thiessen polygons area of the catchment. e. With P and S, using Eq. 8 to calculate catchment wetting W (mm) f. With U and W, using Eq. 9 to calculate vaporization V (mm) g. With P and R, using Eq. 15 to calculate the runoff coefficient Kr h. With U and W, using Eq.16 to calculate the baseflow coefficient Ku After applied the above steps, P, R, S, U, W, and V hydrologic values were obtained for the overall indicator of the catchments.

3.4

GIS APPLICATION The DEM of catchments were obtained using the Earthexplore.usgs.gov website

services from USGS platform of the NASA LPDSSC Collection-Aster Collections-ASTER global (Ariza, 2013). It was extracted, projected and transformed for each catchment of the study area. Specific GIS data features such as line, polygons, and points were removed and created using the Clip and Extract analysis toolbox.

38

Before starting to run the Arc Hydro packages, the DEM needed a pre-conditioning hydrology process. Maidment (2002) describes that the pre-conditioning process is known as the Anedum (Topo to Raster). The preconditioning process is used to reduce the possible errors in digital elevation models like sinks in areas of low elevation. To correct it, the Topo to Raster tool in ArcMap was used. The DEM was prepared, and it was converted to points using the raster to points tool, then adding to contour, breakpoint-streams (called streams), and boundaries were added to the Topo to Raster tool (Table 2). After that, the new DEM was obtained.

Table 2. Input feature data used to Topo to Raster tool.

Input feature data

Field

Type

Raster to point DEM

Grid code

Point elevation

Contour 15 m

Contour

Contour

Streams

Streams

Boundary

Boundary

The output cell was 15 m as the original DEM. In the environment tab/ processing extent, the DEM was used in Snap Raster. Catchment polygons were used to cut out pieces of feature class as a cookie cutter (Gericke & Du Plessis, 2012). Data extraction was followed by transformation, attribute table editions and calculation of the catchment geometry (areas, perimeters, and distances). After that, Venkatesh (2012) describes the process to obtain watershed and stream network delineation using Arc Hydro tools.

39

3.5

APPLICATION OF THE CYBERNETIC HYDROLOGIC MODEL DIAGRAM

Figure 16. Cybernetic Hydrology Model scheme.

40

4

RESULTS AND DISCUSSION

4.1

RESULTS OF CALIFORNIA’S CATCHMENT

4.1.1

Data sources of Russian River near Guerneville, California

Table 3. Russian River data sources Description

Longitude

Latitude

Agency

Code

Station name

Elevation (m)

pluviometer

123° 47’59

38° 47’59’’

NSW

041838

Cloverdale

100

pluviometer

123°16’19’’

39°07’36’’

NSW

049126

Ukiah 4 WSW

404.8

pluviometer

123°07’36’’

39°21’43’’

NSW

047109

Potter Valley PH

310

pluviometer

122.713

38.45579

NSW

047965

Santa Rosa

50.6

pluviometer

122.865

38.4305

USC

043578

Graton

61

pluviometer

122.858

38.62136

USC

043875

Healdsburg

53

discharge

122°55’36’’

38°30’31’’

USGS

11467000

Russian River near

6

Guerneville Sources: (United States Geological Services, 2018a)

In Russian River, there are six pluviometer stations located to different altitudes from 50.6 m to 404 m and additional discharge gauge station situated at the end of the catchment whose agency are the USGS, USC and NSW (table 3).

4.1.2

Data Sources of Salinas River Catchment

Table 4. Salinas River data sources Description

Longitude

Latitude

Agency

Code

Station name

Elevation (m)

pluviometer

121°08’16’’

36°12’25’’

NSW

044555

King City

98

pluviometer

120°37’42’’

35°40’11’’

NSW

046742

Paso Robles Muni

247

pluviometer

120°38’15’’

35°22’27’’

NSW

047933

Santa Margarita

350

Booster pluviometer

120°30’14’’

35°20’14’’

NSW

047672

Salinas Dam

424.3

pluviometer

121.1822°

36.4819

USC

046926

Pinnacles Nm, Ca

398.4

pluviometer

121.666°

36.6594

USC

047668

Salinas number 2

13.7

discharge

121°40’17’’

36°37’52’’

USGS

111525000

Salinas River near

6

Spreckels Sources: (United States Geological Services, 2018a)

In Salinas River, there are six pluvimetric stations and one discharge station for the Russian River catchment (Table 4). Most of the pluviometric stations are managed by National Services Weather and USGS Department of the United States, located from 6 m to 404.8 m.

41

4.1.3

Data sources of Whitewater River catchment, California

Table 5. Whitewater River data sources Description

Longitude

Latitude

Agency

Code

Station name

Elevation (m)

pluviometer

116° 10’00’’

33°38 10

NSW

048892

Desert

-36

Resorts

RGNL AP pluviometer

116° 12’55’’

33°42’31’’

NSW

044259

Indio Fire STN

-6.4

pluviometer

116°22’35’’

33°39’05’’

NSW

042327

Deep Canyon LAB

366

pluviometer

116°30’35’’

33°31’29’’

NSW

046635

Palm Spring

130

discharge

116°04’36’’

33°31’29’’

USGS

10259540

Whitewater

river

-69

near Mecca Sources: (United States Geological Services, 2018)

In Whitewater River, there are four pluviometric stations and one discharge station around Whitewater River catchment located from -69 m to 366 m above the sea level.

4.2

RESULTS OF THE BASEFLOW AND RUNOFF COEFFICIENT TO EACH CATCHMENT.

4.2.1

Russian River

Table 6. Statistical analysis of Russian River in mm Descriptive statistics

P

R

S

U

W

V

Mean

975.76

287.48

145.79

141.69

829.97

688.28

Standard Error

66.20

31.59

16.59

16.11

51.79

42.88

Median

957.41

284.67

144.51

139.55

807.72

663.88

Standard Deviation (SD)

350.28

350.28

87.80

85.24

274.05

226.92

0.37

0.59

0.61

0.61

0.34

0.34

Minimum

202.36

60.72

3.05

46.95

199.31

141.64

Maximum

1722.34

704.50

406.85

330.24

1410.51

1080.27

Coefficient of variation (Cv)

Horton Index (V/W)

0.82

Descriptive statistics were used to analyze 28 years of diary precipitations and discharge data from 1990-2017 (Table 12). Precipitation, discharge, surface runoff, base flow, catchment wetting, vaporation, Horton index, base flow coefficient, and runoff coefficient were obtained using the catchment wetting online calculator (see annexes, Table 15) for the Russian River catchment.

42

Figure 17. Russian River Horton Index from 1990 to 2017

According to the climatic spectrum in subtropical regions, published in Ponce, (2012), the Russian River is located in a Sub humid region (average P = 975.76 mm, SD = 350.28) (Table 6). The mean catchment wetting is 829.97 mm, SD 51.79 mm. The average Horton Index was 0.82; indicating that 82% of catchment wetting (W) was used as vaporation (known as evapotranspiration),

Figure 17. The Cv of P = 0.37,

which is the interannual variability of precipitation that leads to more drought (Steinscheneider, 2018). The vaporation was 688.28 mm from 1990-2017 being similar to (Flint, Flint, Thorne, & Boynton, 2013) as part of the potential evapotranspiration for water years 1981-2010, calculated by the Basin characterization model for California.

It is crucial to notice that S and U are almost similar (SD >85), corresponding to a sub-humid region of the catchment.

43

4.2.2

Salinas River

Table 7. Statistical analysis of Salinas River Descriptive statistics

P

R

S

U

W

V

Mean

366.19

26.21

14.15

12.06

352.04

339.98

Standard Error

29.66

6.63

3.40

3.44

27.19

25.37

Median

378.97

19.60

10.37

6.63

358.39

342.75

Standard Deviation

135.90

30.37

15.58

15.76

124.58

116.28

Coefficient of variation

0.36

1.55

1.50

2.38

0.35

0.34

Minimum

56.82

0.82

0.48

0.26

56.34

55.38

Maximum

590.54

120.75

54.91

65.84

535.63

501.26

Horton Index (V/W)

0.97

Descriptive statistics were analyzed using 24 years of diary precipitations and discharge data from 1993-2013 and 2017 (see annexes, Table 13), in which precipitation, discharge, surface runoff, base flow, catchment wetting, vaporation, Horton Index, base flow coefficient, and runoff coefficient where obtained using the catchment wetting online calculator (see annexes, Table 16) for Salinas River catchment.

Figure 18. Salinas River Horton index from 1993 to 2017.

44

According to the climatic spectrum in subtropical regions, published in Ponce, (2012). Salinas River is located in Arid region (average P= 366.19 mm, SD of 135.90) (Salinas River

Table 7, table 7). The mean catchment wetting is 352.04 mm, SD 25.37 mm) (see Table 7).

The average Horton Index was 0.97, indicating that 97 % of the catchment wetting is used to evapotranspiration (Figure 18). The Cv of P= 0.36, which is the interannual variability of precipitation that leads to more drought (Steinscheneider, 2018). The vaporation was 339.98 mm from 1993-2013 and 2017 which is similar to Flint et al. (2013) as part of the potential evapotranspiration for water years 1981-2010, calculated by the Basin Characterization model for California.

The amount of surface and base flow to the arid region is 14.15 mm (SD = 15.58) and 12.06 mm (SD= 15.76); as a result, there is not much variation. The surface runoff is negligible to this area.

4.2.3

Whitewater River

Table 8. Statistical analysis of Whitewater River Descriptive statistics

P

R

S

U

W

V

Mean

86.03

11.35

1.06

10.30

84.98

74.68

Standard Error

17.61

0.50

0.13

0.48

17.60

17.49

Median

60.27

11.05

1.02

10.06

59.50

47.38

Standard Deviation

65.87

1.86

0.47

1.78

65.86

65.43

Coefficient of variation

1.09

0.17

0.46

0.18

1.11

1.38

Minimum

22.42

7.90

0.51

6.88

21.41

11.84

Maximum

226.12

14.55

2.30

13.78

225.10

213.37

Horton Index (V/W)

0.87

Descriptive statistics were analyzed using 14 years of diary precipitations and discharge data from 1990-1992, 1991,1996 1999-2003 and 2006-2009 (Table 14), in which precipitation, discharge, surface runoff, baseflow, catchment wetting, vaporation, Horton index, baseflow coefficient, and runoff coefficient where obtained using the catchment wetting online calculator (Table 17) in the Whitewater River catchment.

45

Figure 19. Whitewater River Horton Index from 1990 to 2009.

According to the climatic spectrum in subtropical regions, published in Ponce (2012), Whitewater River is located in Super Arid region (average P=86.03 mm, SD = 17.61 mm). The mean catchment wetting is 84.98 mm, SD 17.60 mm (Table 8)). The average Horton Index was 0.87, indicating that 87 % of the catchment wetting is used for vegetation (Figure 19). The Cv of P = 1.09, which is the interannual variability of precipitation leading to more drought (Steinscheneider, 2018). The vaporation was 74.68 mm from 1993-2013 and 2017 which is similar to Flint et al. (2013) as part of the potential evapotranspiration for water years 1981-2010, calculated by the Basin Characterization model for California.

The amount of surface runoff water available to this catchment is 1.06 mm (SD = 0.47), and the amount of baseflow is 10.30 (SD = 1.78 mm) which is more representative compared to surface runoff water. Therefore, groundwater to this area is essential.

4.3

RESULTS OF ARCHYDRO GEOMORPHOLOGICAL MODEL Catchment area and its geomorphology analysis were obtained using Arc Hydro

and data analysis tool for hydrology. The following parameters were obtained: drainage area, perimeter, catchment hydraulic length, form ratio, compactness ratio, maximum elevation, minimum elevation, average land surface slope, stream slope, total channel length, and drainage density (Gericke & Du Plessis, 2012).

46

The following geomorphological characteristics for each catchment of California are described here:

4.3.1

Russian River

Table 9.Geomorphological parameters of the Russian River.

Parameters

Symbol

Units 2

Value

Drainage area

A

km

4463.37

Drainage perimeter

P

km

685

Catchment hydraulic length

L

km

161.20

Form Ratio

Kf

-

0.17

Compactness ratio

Kc

-

0.08

Maximum elevation

Emax

masl

1425

Minimum elevation

E min

masl

8

Average land surface slope

S0

m/m

33.22

Stream Slope (0 to 100%)

S1

m/m

0.005

km

2194.14

km/km2

0.49

Total stream network channel length

ÎŁ

Drainage density

D

L

The catchment is located between 8 to 1425 mls. Its drainage is 4463,37 km2, and its average land surface slope is 33.22 m/m with form ratio of 0.17, and compactness ratio (Kc) of 0.08 (Figure 26).

4.3.2

Salinas River

Table 10.Geomorphological parameters of the Salinas River Parameters

Symbol

Units

Value

Drainage area

A

km2

13 167

Drainage perimeter

P

km

968

Catchment hydraulic length

L

km

269

Form Ratio

Kf

-

0.18

Compactness ratio

Kc

-

0.04

Maximum elevation

Emax

m(mls)

1783

Minimum elevation

E min

m(mls)

6

Average land surface slope

S0

m/m

26.21

Stream Slope (0 to 100%)

S1

m/m

0.003

A total stream channel length

ÎŁL

km

Drainage density

D

7233 2

km/km

0.55

47

The catchment is located between 6 to 1783 mls. Its drainage area is 13167 km2, and its average land surface slope is 26.21 m/m with form ratio of 0.18, and compactness ratio (kc) of 0.04 (see annexes, Figure 27).

4.3.3

Whitewater River

Table 11.Geomorphological parameters of the Whitewater River Parameters

Symbol

Units

Value

Drainage area

A

km2

3884.18

Drainage perimeter

P

km

334.47

Catchment hydraulic length

L

km

126.92

Form Ratio

Kf

-

0.24

Compactness ratio

Kc

-

2.10

Maximum elevation

Emax

m(mls)

3490

Minimum elevation

E min

m(mls)

-69

Average land surface slope

S0

m/m

0.26

Stream Slope (0 to 100%)

S1

m/m

0.028

Total stream network channel length

ÎŁL

km

Drainage density

D

294.48 2

km/km

0.08

The catchment is located between -69 to 3490 mls. Its drainage area is 3884.18. km2 and its average land surface slope is 0.26 m/m with a form ratio of 0.24 and compactness ratio (kc) of 2.10 ( Figure 28).

48

4.4 RESULTS OF CYBERNETIC HYDROLOGY MODEL MAPS-GIS APPLICATION Using values of Ku and Kr from the online calculator, a table of attributes were built in ArcMap to analyze runoff and baseflow for each catchment temporally.

4.4.1

Russian River baseflow coefficient (KU)

Figure 20. Russian River baseflow coefficient.

49

4.4.2

Russian River runoff coefficient (Kr)

Figure 21. Russian River runoff coefficient

50

Multitemporal analysis of the Russian River baseflow coefficient from 1990, 1994, 1998, 2002, 2006, 2010, 2014 and 2017 was portrayed. In 2006 and 2017, the baseflow coefficient shows high values, which means that more baseflow water was available to those years for the region ( Figure 20). It was due to precipitation

conditions available to the same year and the geomorphology of the catchment (Ku = 0.320.43). On the other hand, during the 1990 and 1994, baseflow coefficient showed the worst scenarios in which Ku was between 0.09 to 0.07 (Table 15).

In figure 21, there are two primary years where Kr coefficient is 0.36-0.57. It means that those years were wet. On the other hand, in 1990, 1994 were dried. Years 1998, 2002, 2010, 2017 show Kr coefficient values between 0.28 to 0.15.

51

4.4.3

Salinas River baseflow coefficient (Ku)

Figure 22. Salinas River baseflow coefficient.

52

4.4.4 Salinas River runoff coefficient (Kr)

Figure 23. Salinas River runoff coefficient.

53

Multitemporal analysis over Salinas River shows different baseflow coefficient due to climate conditions, and geomorphology of the catchment. In 1999 had the highest Ku coefficient available to the catchment Ku=.04 - 0.096 (Figure 22). On the other hand, during 2009, the temporal analysis shows a value of 0.001, which means that groundwater during that year was less than 10 % of the surface water (Brooks, Ffolliott, Gregersen, & DeBano, 2003).

The figure 24 shows in 1993, 1997, 2006, and 2017 values of Kr = 0.08 to 0.15.Those values did not represent a problem for a flood because of the amount of surface runoff available for that year, and Kr is not high enough to cause problems to infrastructure (The California Water Boards, 2011). For the other years, the runoff coefficient values were less than 0.005, indicating that the Salinas River is an arid region, and there is not enough runoff water available that could have an economical worth.

54

4.4.5

Whitewater River baseflow coefficient (Ku)

Figure 24. Whitewater River baseflow coefficient.

55

4.4.6

Whitewater River runoff coefficient (Kr)

Figure 25. Whitewater River runoff coefficient.

56

Multitemporal analysis of the Whitewater river baseflow coefficient from 1996 to 2009 shows different behaviours due to climate conditions, and geomorphology of the catchment. There were four necessary periods in which Ku = 0.22-0.32. Baseflow as part of the groundwater, denoted a valuable resource for communities around the Whitewater River. In contrast, during 1992, 2003 were less than Kr =0.05 as a result of climate conditions variability (see Figure 24) (Knowles & Cayan, 2002).

During the period of 1999, figure 25 shows that the Whitewater River had high values of Kr = 0.32 to 0.44, but during the rest of periods, especially 1992, 2011, and 2003, these Kr values were less than 0.1; indicating that the Whitewater River is a Super Arid region where runoff doesn’t exist, and baseflow is the primary sources of groundwater (Ponce, 2012).

4.5 DISCUSSION OF PRECIPITATION, RUNOFF AND BASEFLOW FOR THE CATCHMENTS 4.5.1

Precipitation

Precipitation across California’s catchment was variable (Table 15, Table 16, Table 17) during the periods 1990-2017. Rainfall variations were associated with the phenomenon called Atmospheric River floods (AR), which contributed between 20-50% of the state’s precipitation and streamflow (Dettinger, Ralph, Das, Neiman, & Cayan, 2011).

Year by year, the variability shows a coefficient of variation (Cv) higher than 0.35 for all catchments. This Cv was high in the Whitewater River Cv= 1.04 (Table 17). Floods of the Russian River in 1998 and 2010 were associated directly to AR floods in central California (Dettinger et al., 2011).

4.5.2

Base flow coefficient

Due to climate spectrum of the two catchments (Salinas River (arid region), and Whitewater River (super arid region), base flow is an essential element of the groundwater

57

available (98% of groundwater appears as base flow) (Ponce, 2012). Its reduction could cause drying up of spring and wetlands areas, the die-off of riparian vegetation, and the reduction of base flow in nearby streams. In these two catchments, the mean U are 12.06 (SD= 3.44) and 10.30 (SD=2.38) respectively, so base flow is a crucial water supply in these regions. On the contrary, the Russian River had an average U= 141.69 (SD=85.24) where the surface runoff is the main water supplied; then the base flow is the second water supplied for the region (United States Geological Services, 2018a).

4.5.3

Runoff coefficient

In arid region (Salinas River and Whitewater River) climatic spectrum is less than 300 mm of mean annual precipitation, the surface water is little (scarce runoff). The runoff coefficient is 10-15% of the precipitation. In the humid region (Russian River), the middle of the climatic spectrum is 800 mm of mean annual rainfall, about 39% of the precipitation is part of streamflow(runoff). Values that agree with previous studies conducted by Lสนvovich's (1979) and Ponce (2012).

4.5.4

Horton Index

There are consistent patterns found in the two arid catchments. Driest areas and driest year exhibited both higher and less variable Horton Index (Table 16 Table 16 and Table 17) whereas the humid catchment (Table 16) had less Horton index value (Horton, 1933; Voepel et al., 2011). The HI increase as a catchment becomes drier and become closer to HI =1 in drier regions and during dry years (Sivapalan, Yaeger, Harman, Xu, & Troch, 2011). The Horton Index observed during the periods of study 1990-2017 shows a direct correlation between the climate spectrum in subtropical regions and the HI. Horton index values match to arid and sup arid regions were the HI = 1 for most of the years. On the other hand, the sub-humid region was HI = 0.52 due to the northern location in the California county. That information shows general pattern due to climate change in which arid region will become driest, and wet areas will become wettest as the data indicates (Cayan, Maurer, Dettinger, Tyree, & Hayhoe, 2008).

58

Climate change has been affected by snow catchment hydrology as its part of the Russian River, where the temperature has increased (Gleick, 1987), and snow decreased (Cayan et al., 2008). The timing of snowmelt stressing water managers for water supplies or flooding events through annual precipitation (Huntington & Niswonger, 2012). The ability to predict the water balance in a catchment depends on different factors such as climate spatial-temporal variability (by looking to GIS and Remote sensing techniques), calibration of parameters to catchment models, and determination of variability and distribution of vegetation (Xu, Yang, & Sivapalan, 2012).

The study shows that Russian River located in Northwest of California is primary wet with significant values of Kr and Ku, whereas arid zone (Salinas River) and super arid (Whitewater River) located in the Southernmost quarter of California are prevailing dry with negligible runoff and few wet years as Moran (2017) found on his work. Although analyzing three catchments in California states is not statistical significance, the analysis of periods from 1990 to 2017 showed a reduction of HI into arid regions and HI increases in trends in semi-humid regions. Climate change poses a significant effort to predict water balance to areas, but it is essential to take into account that indirect impacts could be more significant than direct effects (Jones, 2011).

59

5 5.1

CONCLUSIONS AND RECOMMENDATIONS CONCLUSIONS The fundamental approach of cybernetic water balance is inductive and additive. It

means that catchment is a whole system in which runoff is equal to precipitation minus losses, and it is similar to runoff plus vaporization.

The cybernetic hydrology model is a model used since 1979, and it has been applied for water balance using the concept of the wetting catchment. Vegetation and water availability are considered to define how much surface flow and baseflow a catchment has by looking at the water pressure to surface storage and groundwater resources. This model has been applied in the region to estimate distribution runoff (fresh water) and shown the potential of water deficits to California catchments.

The cybernetic hydrologic model developed by L’vovich’s (1979), and Ponce and Shetty (1995b) incorporate water budget analysis, which is divided into two main components. 1) Highly interconnected nature of a catchment associated with a geomorphological characteristic of a catchment. 2) Spatial and temporal representation of water balances of catchment helps water managers to understand what the current behaviour of the area is. Also, by adding the GIS and RS technology, not only geomorphological characteristics can be studied but also ecological, pedological and anthropological process in multiscale variability could be done.

The cybernetic hydrology model helps to predict regional patterns of annual water balance, as well as interannual variability in individual catchments. The analysis shows that there is close symmetry between spatial variability of mean annual balance and general trends of temporal variability in individual catchments. Consequently, the cybernetic hydrology model provides an excellent framework to evaluate not only how the plant responds to water availability but also how this water availability is linked to the geomorphology of a catchment described by geomorphological GIS analysis.

The application and incorporation of GIS technology (ArcHydro) have proven to be a significant advance in hydrology science not only for preprocessing but also for post-

60

processing data analysis and visualization of hydrology models. Also, features such as Kr and Ku may change over time because of adding new contributing factors or geospatial analysis that can support further studies for the cybernetic hydrology model.

The amount of water available to vegetation (HI) is quantified using spatial and temporal GIS analysis tools. As a result, a new body of knowledge related to GIS-and Ecohydrology has developed for further studies.

The scale of information needed for water managers is often beyond numbers and statistical results, as the catchment wetting online calculator had shown. The fusion between the online calculator and Arc-Hydro tools and its functionality integrate spatial, temporal, and statistical analysis for the catchments so that managers and policymakers can see, analyze, and take decision geo-spatially for water availability in the three basins under the study.

Runoff and baseflow values are complex mechanisms affected by precipitation and its distribution in time and space. Two parameters are linked to the geomorphology (drainage, hillslope, land use, soil infiltration rate, snow cover, vegetation, and human intervention), which significant; and they need to be considered for better water yield results.

5.2

RECOMMENDATION The cybernetic hydrologic model can be applied in areas like Ecuador due to the

few data requirements for the model (precipitation and discharge). Weather information that could be compiled using local data for a catchment or satellite, plus adding remote sensing and GIS tools.

Due to use of ArcHydro tools into ArcMap 6.2, some errors were identified during the analysis process. As a result, there is a list of the most critical issues founded during this study. 1)Error 999999-Error is executing the function. 2)Error 010005-Unable to allocate memory. 3) Error 010168. 4)Reducing the size (area Km2) of the stream definition.

61

5) Adjoint catchment tool in ArcHydro. 6)ArcHydro is not creating an attribute field related to HydroID MextID, JoinID. 7)The polygon processing. Error descriptions and solutions are described in the annexes 7.4.

62

6

REFERENCES

Abrahams, A. D., & Ponczynski, J. J. (1984). Drainage density in relation to precipitation intensity in the USA. Journal of Hydrology, 75(1–4), 383–388.

Alemaw, B. F., & Chaoka, T. R. (2003). A continental scale water balance model: a GISapproach for Southern Africa. Physics and Chemistry of the Earth, Parts A/B/C, 28(20–27), 957–966.

Ariza, G. (2013). How to use ArcHydro tools with GIS. Retrieved from http://ponce.sdsu.edu/ariza_thesis_final.html, March 23, 2019.

Beven, K., & Young, P. (2003). Comment on “Bayesian recursive parameter estimation for hydrologic models” by M. Thiemann, M. Trosset, H. Gupta, and S. Sorooshian.

Water Resources Research, 39(5).

Botter, G., Porporato, A., Rodriguez‐Iturbe, I., & Rinaldo, A. (2007). Basin‐scale soil moisture dynamics and the probabilistic characterization of carrier hydrologic flows: Slow, leaching‐prone components of the hydrologic response. Water Resources Research, 43(2).

Brimicombe, A. (2009). GIS, environmental modeling and engineering. CRC Press.

Brooks, K. N., Ffolliott, P. F., Gregersen, H. M., & DeBano, L. F. (2003). Hydrology and the management of watersheds. Iowa State University Press.

CA State Climatologist. (2017). CA_Precip_climatology_Final.pdf, (p. 2). Retrieved from https://water.ca.gov/LegacyFiles/climatechange/docs/2017/DWR_Hydroclimate_R eport_2016.pdf, January 25, 2019 California Department of Water. (2003a). North Coast Hydrologic Region.

California Department of Water. (2003b). South Coast Hydrologic Region.

California Department of Water. (2015). California basin drought period. Retrieved from https://water.ca.gov/-/media/DWR-Website/Web-Pages/WaterBasics/Drought/Files/Publications-And-Reports/Drought-in-California.pdf. 9 (p.136). March 21, 2019California Department of Water. (2018). Winter Storm Provides Much-Needed Boost to Sierra Snowpack, but Water Content Still Below Average. Retrieved from https://water.ca.gov/News/News-Releases/2018/March-18/Winter-Storm-Provides-

63

Much-Needed-Boost-to-Sierra-Snowpack-but-Water-Content-Still-Below-Average.

June 4, 2018.

Cayan, D. R., Maurer, E. P., Dettinger, M. D., Tyree, M., & Hayhoe, K. (2008). Climate change scenarios for the California region. Climatic Change, 87(1), 21–42.

Central Coast Regional Water Quality Control Board. (2017). Salinas River Watershed.

Retrieved May 21, 2018, from www.rcdmonterrey.org/salinas-river Collins, D. B. G., & Bras, R. L. (2010). Climatic and ecological controls of equilibrium drainage density, relief, and channel concavity in drylands. Water Resources Research, 46(4).

Dettinger, M. D., Ralph, F. M., Das, T., Neiman, P. J., & Cayan, D. R. (2011).

Atmospheric rivers, floods and the water resources of California. Water, 3(2), 445– 478.

Domniţa, M., Craciun, A., Haidu, I., & Magyari-Saska, Z. (2012). GIS used for determination of the maximum discharge in smaller basins (under 2 km 2). WSEAS Trans. Environ. Dev, 6, 468–477.

Dixon, B., Uddameri, V., & Ray, C. (2015). GIS and Geocomputation for Water Resource Science and Engineering. John Wiley & Sons.

Eloquent, U., & Meadow. (2019). Explore Russian River Valley | Visit | Gary Farrell Vineyards & Winery Reviewed from https://www.garyfarrellwinery.com/visit/russian-river-valley. January 12, 2019.

Flint, L. E., Flint, A. L., Thorne, J. H., & Boynton, R. (2013). Fine-scale hydrologic modeling for regional landscape applications: the California Basin Characterization Model development and performance. Ecological Processes, 2(1), 25.

Frantar, P., & Brancelj, I. R. (2013). Gis Analysis of Average Monthly Specific Runoff in Slovenia. Analele Stiintifice Ale Universitatii “Al. I. Cuza” Din Iasi. Serie Noua.

Geografie; Iasi, 59(2), 49.

Gericke, O. J., & Du Plessis, J. A. (2012). Catchment parameter analysis in flood hydrology using GIS applications. Journal of the South African Institution of Civil Engineering= Joernaal van Die Suid-Afrikaanse Instituut van Siviele Ingenieurswese, 54(2), 15–26.

Ghoraba, S. M. (2015). Hydrological modeling of the Simly Dam watershed (Pakistan) using GIS and SWAT model. Alexandria Engineering Journal, 54(3), 583–594.

64

Gleick, P. H. (1987). The development and testing of a water balance model for climate impact assessment: modeling the Sacramento basin. Water Resources Research, 23(6), 1049–1061.

Greystone Environmental Consultants. (2015). Water Resources. Retrieved from http://www.blm.gov/ca/pdfs/palmsprings_pdfs/IID-EIS/FINAL-3. 4-Water Resources.pdf, May 20, 2019 Horton, R. E. (1933). The relation of hydrology to the botanical sciences. Eos, Transactions American Geophysical Union, 14(1), 23–25.

Huntington, J. L., & Niswonger, R. G. (2012). Role of surface‐water and groundwater interactions on projected summertime streamflow in snow-dominated regions: An integrated modelling approach. Water Resources Research, 48(11).

Ishida, K., Gorguner, M., Ercan, A., Trinh, T., & Kavvas, M. L. (2017). Trend analysis of watershed-scale precipitation over Northern California by means of dynamicallydownscaled CMIP5 future climate projections. Science of The Total Environment, 592, 12–24.

Ishida, K., & Kavvas, M. L. (2017). Climate change analysis on historical watershed-scale precipitation by means of long-term dynamical downscaling: Climate Change Analysis on Historical Watershed-scale Precipitation. Hydrological Processes, 31(1), 35–50.

Istanbulluoglu, E., & Bras, R. L. (2005). Vegetation‐modulated landscape evolution: Effects of vegetation on landscape processes, drainage density, and topography.

Journal of Geophysical Research: Earth Surface, 110(F2).

Jones, J. A. (2011). Hydrologic responses to climate change: considering geographic context and alternative hypotheses. Hydrological Processes, 25(12), 1996–2000.

Knowles N., & Cayan D. R. (2002). Potential effects of global warming on the Sacramento/San Joaquin watershed and the San Francisco estuary. Geophysical Research Letters, 29(18), 38–1.

Lʹvovich's, M. I. (1979). World water resources and their future. American Geophysical Union.

Longley, P. A., Goodchild, M. F., Maguire, D. J., & Rhind, D. W. (2005). Geographic information systems and science. John Wiley & Sons.

Lyon, J. G. (2003). GIS for Water Resource and Watershed Management. CRC Press.

65

Maidment, D. R. (2002a). Arc Hydro: GIS for water resources. Redlands, Calif: ESRI Press.

Mao, Y., Nijssen, B., & Lettenmaier, D. P. (2015). Is climate change implicated in the 2013–2014 California drought? A hydrologic perspective. Geophysical Research Letters, 42(8), 2805–2813.

Maurer, E. P., Brekke, L. D., & Pruitt, T. (2010). Contrasting Lumped and Distributed Hydrology Models for Estimating Climate Change Impacts on California Watersheds1. JAWRA Journal of the American Water Resources Association, 46(5), 1024–1035.

Media New Group. (2016). WHITEWATER: Life-sustaining river can be deadly [Blog].

Retrieved from https://www.pe.com/2016/03/25/whitewater-life-sustaining-rivercan-be-deadly/. April 16, 2019.

Merwade, V., & Maidment, D. C. (2004). Watershed and Stream Network Delineation.

GIS Water Resources.

Moran, T. C. (2017). Development and Application of a Parsimonious Water Balance Model for Mediterranean Climate Conditions. UC Berkeley.

National Oceanic and Atmospheric Administration. (2018). National Oceanic and Atmospheric Administration's. National Weather Service. Retrieved from https://water.weather.gov/ahps2/index.php?wfo=sto. June 13, 2018.

Nir, D. (1987). Regional geography considered from the systems’ approach. Geoforum, 18(2), 187–202.

Ponce, V. (1994). Engineering Hydrology. Principles and Practices (1st ed., Vol. 1).

Ponce, V. (2012). Cumulative impacts on water Resources of large-scale energy projects in Boulevard and surrounding communities, San Diego, California. Retrieved from https://www.sandiegocounty.gov/content/dam/sdc/dplu/ceqa/SoitecDocuments/Record-Documents/2014-01-30-WayneSkains-email-CommentBoulevard-Solar-Including. April 23, 2018.

Ponce, V. (2014). Engineering Hydrologic, Principles and Practices (Second edition).

Retrieved from pon.sdsu.edu/enghydro/index.html. January 25, 2018.

Ponce, V. M., & Shetty, A. V. (1995a). A conceptual model of catchment water balance: 2.

Application to runoff and baseflow modelling. Journal of Hydrology(Amsterdam), 173(1), 41–50.

66

Ponce, V. M., & Shetty, A. V. (1995b). A conceptual model of catchment water balance: 1.

Formulation and calibration. Journal of Hydrology, 173(1–4), 27–40.

Rana, L. (2015). Models, theory & systems analysis in geography. The Association for Geographical Studies, 1–33.

Rasmussen, C., Troch, P. A., Chorover, J., Brooks, P., Pelletier, J., & Huxman, T. E. (2011). An open system framework for integrating critical zone structure and function. Biogeochemistry, 102(1–3), 15–29.

Sampson, K., & Gochis, D. (2018). WRF Hydro Gis Pre*Processing Tool, Version 5.0 Documentation.

Sivapalan, M., Yaeger, M. A., Harman, C. J., Xu, X., & Troch, P. A. (2011). Functional model of water balance variability at the catchment scale: 1. Evidence of hydrologic similarity and space-time symmetry. Water Resources Research, 47(2).

Steinscheneider, S. (2018). Water management and Climate Extremes in the SacramentoSan Joaquin River Basin. 2018 Fall Cyberseminar Series on Major Challenges in key river basins around the world presented at the CUAHSI. The California Water Boards. (2011). Runoff Coefficient (C) Fact Sheet. Retrieved from https://www.waterboards.ca.gov/water_issues/programs/swamp/docs/cwt/guidance/ 513.pdf. May 31, 2018.

Thomas, R. W., & Huggett, R. J. (1980). Modelling in geography: a mathematical approach. Rowman & Littlefield.

Troch, P. A., Martinez, G. F., Pauwels, V. R., Durcik, M., Sivapalan, M., Harman, C., …

Huxman, T. (2009). Climate and vegetation water use efficiency at catchment scales. Hydrological Processes: An International Journal, 23(16), 2409–2414.

United States Geological Services. (2018a). California Water Science Center. Determining Water Availability in the Russian River Watershed. Retrieved from https://www.usgs.gov/centers/ca-water/science/determining-water-availabilityrussian-river-watershed?qt-science_center_objects=0#qt-science_center_objects.

May 24, 2018.

United States Geological Services. (2018b). USGS Groundwater Information: PART - A computerized method of base-flow-record estimation.

United States Geological Services. (2018c). Water Resources of the United States.

Retrieved June 13, 2018, from National Water Information System (NWIS)

67

US EPA, (2012). About the Watershed [Overviews and Factsheets]. US EPA website Retrieved from https://www.epa.gov/sfbay-delta/about-watershed. May 24, 2018, USA EPA, T. C. (2013). California Integrated Assessment of Watershed Health. Retrieved from https://www.epa.gov/sites/production/files/201511/documents/ca_hw_report_111213_0.pdf. May 24, 2018.

Venkatesh, M. (2012). Watershed and Stream Network Delineation using ArcHydro Tools.

Voepel, H., Ruddell, B., Schumer, R., Troch, P. A., Brooks, P. D., Neal, A., … Sivapalan, M. (2011). Quantifying the role of climate and landscape characteristics on hydrologic partitioning and vegetation response. Water Resources Research, 47(10).

Wagener, T., Sivapalan, M., Troch, P., & Woods, R. (2007). Catchment classification and hydrologic similarity. Geography Compass, 1(4), 901–931.

Wang, J., & Kotamarthi, V. R. (2015). High‐resolution dynamically downscaled projections of precipitation in the mid and late 21st century over North America.

Earth’s Future, 3(7), 268–288.

Xu, X., Yang, D., & Sivapalan, M. (2012). Assessing the impact of climate variability on catchment water balance and vegetation cover. Hydrology and Earth System Sciences, 16(1), 43–58.

Yetemen, O., Istanbulluoglu, E., & Vivoni, E. R. (2010). The implications of geology, soils, and vegetation on landscape morphology: Inferences from semi-arid basins with complex vegetation patterns in Central New Mexico, USA. Geomorphology, 116(3), 246–263. https://doi.org/10.1016/j.geomorph.2009.11.026 Zeiler, M. (2000). Modelling our world: the ESRI guide to geodatabase design. ESRI, Inc.

68

7 7.1

ANNEXES DISCHARGE AND PRECIPITATION DATA FROM THE WEATHER STATIONS.

Table 12. Precipitation and discharge data information for Russian River

Year

Graton

Santa Rosa

Healdsburg

Cloverdalle

Potter Valley

1990

583.1

489.5

564.4

522.7

744.4

1991

835.2

667.7

847.4

147.5

1992

1051.2

792.5

1029.9

1993

1150.7

882.5

1994

754.1

1995

Ukia

Average (mm)

Thiessen polygons P (mm)

815.4

619.92

612.13

753.4

902.4

692.27

455.6

1177.1

1337.6

1138.4

1131.7

1129.3

558.9

811.5

800.8

1553.7

1155.9

1741.4

1996

1435.5

1137.7

1997

975.3

1998

Discharge m3/s

R(mm)

U(mm)

S(mm)

6813.48

92.86

54.04

38.82

640.50

10972.31

149.53

61.18

88.35

973.98

925.75

14110.52

192.30

75.96

116.35

1382.3

1135.82

1152.66

28472.63

388.03

208.75

179.29

769.8

911.6

767.78

786.51

7760.98

105.77

60.51

45.26

1842.5

1745.2

1984

1670.45

1722.34

51693.56

704.50

297.65

406.85

1473.4

1524.3

1645.4

1811.2

1504.58

1523.89

33822.97

460.95

199.62

261.33

759.1

968.1

941.5

1106.2

1186.8

989.50

993.04

23907.76

325.82

157.45

168.37

1666.3

1246.5

1730.2

1606.4

1822.8

1980

1675.37

1690.20

44754.36

609.93

330.24

279.69

1999

938

753.9

791.2

831.5

1142.2

1120.2

929.50

914.17

20582.75

280.51

156.01

124.50

2000

994.7

684.8

977.6

900.6

997.1

1077.1

938.65

946.91

18000.06

245.31

142.97

102.34

2001

1193.9

845.8

1235.5

1253.4

1188.2

1306.8

1170.60

1199.10

16362.03

222.99

86.86

136.13

2002

926.3

780.7

1036.6

1031.9

1120.8

1168.6

1010.82

1025.23

21192.58

288.82

119.39

169.43

2003

961.5

759.7

1125.8

1071.5

1313.7

1294.4

1087.77

1099.76

22865.36

311.62

157.21

154.41

2004

971.5

768.1

1063.6

1016.6

1027.3

873.5

953.43

968.05

22564.12

307.51

145.50

162.01

2005

1398.3

1100.2

1503.9

1357.4

1349.5

1130.4

1306.62

1322.45

25864.42

352.49

154.66

197.83

2006

1195.7

870.3

1207.1

1212.6

1278.8

1133.4

1149.65

1164.06

39953.96

544.51

298.92

245.59

69

2007

693.7

515.2

804.3

633

695.3

436.1

629.60

634.26

8473.85

115.48

60.66

54.82

2008

762.6

548.5

831.9

772

752.9

293.2

660.18

669.94

13785.98

187.88

91.91

95.97

2009

753.1

602.6

816.7

682.4

770.9

533.3

693.17

693.06

8312.05

113.28

58.67

54.61

2010

1510.6

1040.1

1333.3

1294.2

1737.4

1426.7

1390.38

1374.16

25964.41

353.85

165.88

187.98

2011

772.5

680

692

870.7

1057.7

986

843.15

840.95

21355.09

291.03

178.02

113.01

2012

1260.6

911.8

657.2

1199.7

1311.2

1392.3

1122.13

1111.28

18708.53

254.97

102.09

152.88

2013

321

14.4

220.2

444.6

290.4

258.12

202.36

4455.68

60.72

57.67

3.05

2014

1070.5

1114.9

1205.1

1187.7

1144.55

779.51

11285.26

153.80

46.95

106.85

2015

452.4

636.5

701.4

817.6

651.98

450.71

6142.53

83.71

47.69

36.02

2016

1199.5

1279.3

1452.6

1430.4

1340.45

910.37

21596.71

294.33

136.12

158.21

2017

1301.2

1338.7

1297.1

1361.3

942.52

967.90

40857.86

556.82

314.74

242.09

164.2

192.6

70

Table 13. Precipitation and discharge data information for Salinas River Year

Salinas

Paso

King

Pinnacl

Margari

Salinas

Annual

Average

Discharge

U

S

s

Dam

Robles

City

es

ta

number 2

Average

Thiessen P (mm)

m3/s

R (mm)

(mm)

(mm)

1990

230.7

169.6

106.4

225.6

335.1

176.7

207.35

161.14

1.95

0.0128

0.01

1991

628.5

385.3

339.5

430.8

804.8

220.2

468.18

390.15

445.83

2.9154

2.92

1992

626.9

368.7

292.3

468.4

953.6

316.9

504.47

384.53

364.50

2.3835

2.38

1993

693.5

478.8

379.1

493.3

930.9

352.7

554.72

460.12

5614.58

36.7147

1994

472.4

290.2

256.7

400.9

582

232.5

372.45

308.15

82.91

0.5421

1995

977.7

649.4

430.4

645.2

1399.4

316.5

736.43

580.36

12096.34

79.1000

27.98

51.12

1996

835.2

449.3

442.5

576

1420.7

539.2

710.48

533.20

5048.92

33.0158

12.70

20.32

1997

548.5

308.8

292.2

373.7

758.4

422.7

450.72

352.73

8183.31

53.5120

31.96

21.56

1998

896.1

473

553

478.7

1263.7

764.8

738.22

590.54

18464.81

120.744

65.84

54.91

1999

320.8

173.2

184.1

267

548.7

295.2

298.17

224.00

988.19

6.4619

3.62

2.84

2000

572.4

284.5

297.1

437.1

845.8

458.1

482.50

363.93

3239.57

21.1841

8.17

13.02

2001

677.3

418.6

391.1

535.8

942

441.4

567.70

460.85

2088.62

13.6578

3.29

10.37

2002

419

192.4

235.8

359.2

611.5

275.6

348.92

269.19

309.72

2.0253

0.25

1.77

2003

357.4

194.7

217.2

285.4

545.2

308.8

318.12

249.26

254.72

1.6657

0.71

0.95

2004

650.6

369.6

358

446

884.4

357

510.93

411.62

306.85

2.0065

0.50

1.50

2005

610

360.8

328.7

479.4

884.9

444.1

517.98

405.82

7150.32

46.7572

24.90

21.85

2006

709.7

351.8

360.7

484

867.4

442.3

535.98

422.79

5076.50

33.1961

18.50

14.70

2007

281

106.7

109.6

176.4

420.1

218.4

218.70

149.53

125.25

0.8190

0.27

0.55

2008

523

201.3

239.3

395.8

747.1

318.8

404.22

293.25

1093.71

7.1520

3.32

3.83

2009

550.7

215.2

223.8

274

745

300.4

384.85

275.83

193.42

1.2648

0.25

1.01

2010

900.6

427.6

383.7

573.4

1161.9

550.4

666.27

497.01

2997.85

19.6035

10.95

8.66

2011

576.4

320.3

316.5

498

805.5

353.1

478.30

378.97

5634.85

36.8472

16.26

20.58

2012

391.8

230.8

282

413.6

701.2

360

396.57

314.59

361.30

2.3626

1.05

1.31

15.19

21.52 0.54

71

2013

74.8

48.9

45.9

48.1

133.9

98.7

75.05

56.82

220.65

1.4429

0.96

2014

387.7

258.9

295.1

380.4

560.1

440.8

387.17

324.51

0.043

0.0003

0.00

2015

214

187.3

141.4

241.1

342.8

195.7

220.38

183.33

0.000

0.0000

0.00

2016

502.8

246.3

267.5

318.1

728.3

381.2

407.37

311.03

0.000

0.0000

0.00

2017

727.4

324.3

351.5

272

1110.7

477.7

543.93

399.59

4716.31

30.8407

6.63

0.48

24.21

72

Table 14. Precipitation and discharge data information for Whitewater River Palm_ Desert

Deep

Sprin

Indio

Average P

Years

resort

Canyon

gs

Fire

(mm)

1990

11.4

50.1

49.2

58

42.18

1991

115.2

137.5

195.7

308.3

1992

163.5

233.9

215.2

303.7

1993

170.6

213.5

284.6

447.2

1994

56.8

91.5

48

87.8

1995

79.5

178.4

184.3

295.6

1996

28.2

42.7

33.5

63.7

1997

123

224.6

113.1

1998

74.7

145.3

1999

26.8

53.7

2000

35.1

86.1

2001

69.2

91.4

2002

11

20.1

19.3

44.3

2003

66

65.7

124.1

165.6

2004

115.2

101.7

198.4

271.3

2005

159.5

182.9

237

2006

5.9

44.4

30.5

2007

36

58.3

2008

101.1

180.6

Average Thiessen P

Discharge

R

U

S(m

m3/s

(mm)

(mm)

m)

46.14

868.08

13.28

12.26

1.02

189.18

194.42

885.55

13.55

11.76

1.79

229.08

226.12

833.17

12.75

11.73

1.02

278.98

285.14

948.70

14.52

-

71.03

62.55

950.62

14.55

13.78

184.45

188.38

874.54

13.38

-

42.03

39.19

841.26

12.87

11.61

148.5

152.30

138.17

716.70

10.97

-

118

118

114.00

117.17

747.14

11.43

-

19.3

47.5

36.83

30.35

751.67

11.50

9.20

2.30

52.1

67.1

60.10

57.98

733.63

11.23

10.46

0.77

103.9

130.3

98.70

101.86

710.08

10.87

10.11

0.76

23.68

22.42

691.59

10.58

9.57

1.01

105.35

114.13

639.83

9.79

9.04

0.75

171.65

184.15

646.98

9.90

-

377.2

239.15

241.18

636.25

9.74

-

30.5

27.83

29.76

598.18

9.15

8.64

0.51

47.7

47.7

47.43

48.00

663.51

10.15

9.14

1.02

160.4

229

167.78

167.45

704.25

10.78

10.01

0.77 1.02

(mm)

2009

30

38.2

69.4

97.8

58.85

64.05

516.57

7.90

6.88

2010

113.5

190.4

283.2

412.2

249.83

268.02

436.41

6.68

-

2011

48.9

85.8

64.4

102

75.28

72.03

465.04

7.12

-

2012

64.6

107

74.6

121.1

91.83

86.12

475.35

7.27

-

2013

48.7

72.7

65.8

127.2

78.60

74.68

490.93

7.51

-

2014

17.3

63

61.7

76.6

54.65

58.88

547.70

8.38

-

2015

51.5

115.2

75.8

103.9

86.60

83.77

608.55

9.31

-

2016

89.6

110

137.2

204.2

135.25

137.69

658.61

10.08

-

2017

85.4

126

203

253.9

167.08

184.24

645.70

9.88

-

0.77

1.26

73

7.2

RESULT OF ONLINE CALCULATOR FOR U, W, V KU AND KR

Table 15. U, W, V, Horton Index, Ku and Kr of Russian River near Guerneville catchment.

Year

P

R

%

S

U

W

V

Horton

Ku

Kr

Index 1990

612.13

92.86

15%

38.82

54.04

573.31

519.27

0.91

0.094

0.152

1991

640.5

149.53

23%

88.35

61.18

552.15

490.97

0.89

0.111

0.233

1992

925.75

192.3

21%

116.35

75.95

809.4

733.45

0.91

0.094

0.208

1993

1152.66

388.03

34%

179.29

208.74

973.37

764.63

0.79

0.214

0.337

1994

786.51

105.77

13%

45.26

60.51

741.25

680.74

0.92

0.082

0.134

1995

1722.34

704.5

41%

406.85

297.65

1315.49

1017.84

0.77

0.226

0.409

1996

1523.89

460.95

30%

261.33

199.62

1262.56

1062.94

0.84

0.158

0.302

1997

993.04

325.82

33%

168.37

157.45

824.67

667.22

0.81

0.191

0.328

1998

1690.2

609.93

36%

279.69

330.24

1410.51

1080.27

0.77

0.234

0.361

1999

914.17

280.51

31%

124.5

156.01

789.67

633.66

0.80

0.198

0.307

2000

946.91

245.31

26%

102.34

142.97

844.57

701.6

0.83

0.169

0.259

2001

1199.1

222.99

19%

136.13

86.86

1062.97

976.11

0.92

0.082

0.186

2002

1025.23

288.82

28%

169.43

119.39

855.8

736.41

0.86

0.14

0.282

2003

1099.76

311.62

28%

154.41

157.21

945.35

788.14

0.83

0.166

0.283

2004

968.05

307.51

32%

162.01

145.5

806.04

660.54

0.82

0.181

0.318

2005

1322.45

352.49

27%

197.83

154.66

1124.62

969.96

0.86

0.138

0.267

2006

1164.06

544.51

47%

245.59

298.92

918.47

619.55

0.67

0.325

0.468

2007

634.26

115.48

18%

54.82

60.66

579.44

518.78

0.90

0.105

0.182

2008

669.94

187.88

28%

95.97

91.91

573.97

482.06

0.84

0.16

0.28

2009

693.06

113.28

16%

54.61

58.67

638.45

579.78

0.91

0.092

0.163

2010

1374.16

353.85

26%

187.98

165.87

1186.18

1020.31

0.86

0.14

0.258

2011

840.95

291.03

35%

113.01

178.02

727.94

549.92

0.76

0.245

0.346

2012

1111.28

254.97

23%

152.88

102.09

958.4

856.31

0.89

0.107

0.229

2013

202.36

60.72

30%

3.05

57.67

199.31

141.64

0.71

0.289

0.3

2014

779.51

153.8

20%

106.85

46.95

672.66

625.71

0.93

0.07

0.197

2015

450.71

83.71

19%

36.02

47.69

414.69

367

0.88

0.115

0.186

2016

910.37

294.33

32%

158.21

136.12

752.16

616.04

0.82

0.181

0.323

2017

967.9

556.82

58%

242.09

314.73

725.81

411.08

0.57

0.434

0.575

averag

975.758

287.4757

145.787

141.688

829.971

688.283

0.16932

0.28117

e

929

1

6

8

2

1

9

74

Table 16. U, W, V, Horton Index Ku and Kr of Salinas River.

Year

P

R

%

S

U

W

V

Horton

Ku

Kr

Index 1993

460.12

36.715

8%

21.52

15.195

438.6

423.405

0.97

0.035

0.08

1995

580.36

79.1

14%

51.12

27.98

529.24

501.26

0.95

0.053

0.136

1996

533.2

33.016

6%

20.32

12.696

512.88

500.184

0.98

0.025

0.062

352.73 53.512

15%

21.56

31.952

331.17

299.218

0.90

0.096

0.152

1997 1998

590.54

120.745

20%

54.91

65.835

535.63

469.796

0.88

0.123

0.204

1999

224

6.462

3%

2.84

3.622

221.16

217.538

0.98

0.016

0.029

2000

363.93

21.184

6%

13.02

8.164

350.91

342.746

0.98

0.023

0.058

2001

460.85

13.658

3%

10.37

3.288

450.48

447.192

0.99

0.007

0.03

2002

269.19

2.025

1%

1.77

0.255

267.42

267.165

1.00

0.001

0.008

2003

249.26

1.666

1%

0.95

0.716

248.31

247.594

1.00

0.003

0.007

2004

411.62

2.007

0%

1.5

0.507

410.12

409.614

1.00

0.001

0.005

2005

405.82

46.757

12%

21.85

24.907

383.97

359.063

0.94

0.065

0.115

2006

422.79

33.196

8%

14.7

18.496

408.09

389.594

0.95

0.045

0.079

2007

149.53

0.819

1%

0.55

0.269

148.98

148.711

1.00

0.002

0.005

2008

293.25

7.152

2%

3.83

3.322

289.42

286.098

0.99

0.011

0.024

2009

275.83

1.265

0%

1.01

0.255

274.82

274.565

1.00

0.001

0.005

2010

497.01

19.604

4%

8.66

10.944

488.35

477.407

0.98

0.022

0.039

2011

378.97

36.847

10%

20.58

16.267

358.39

342.123

0.95

0.045

0.097

2012

314.59

2.363

1%

1.31

1.053

313.28

312.227

1.00

0.003

0.008

2013

56.82

1.443

3%

0.48

0.963

56.34

55.377

0.98

0.017

0.025

2017

399.59

30.841

8%

24.21

6.631

375.38

368.749

0.98

0.018

0.077

average

366.190

26.2084

14.1457

12.0627

352.044

339.982

0.97

0.02914

0.05928

75

Table 17. U, W, V, Horton Index, Ku and Kr of Whitewater River Year

P

R

%

S

U

W

V

Horton Index

Ku

Kr

1990

46.14

13.28

29%

1.02

12.26

45.12

32.86

0.73

0.272

0.288

1991

194.42

13.55

7%

1.79

11.76

192.63

180.87

0.94

0.061

0.07

1992

226.12

12.75

6%

1.02

11.73

225.1

213.37

0.95

0.052

0.056

1994

62.55

14.55

23%

0.77

13.78

61.78

48

0.78

0.223

0.233

1996

39.19

12.87

33%

1.26

11.61

37.93

26.32

0.69

0.306

0.328

1999

30.35

11.5

38%

2.3

9.2

28.05

18.85

0.67

0.328

0.379

2000

57.98

11.23

19%

0.77

10.46

57.21

46.75

0.82

0.183

0.194

2001

101.86

10.87

11%

0.76

10.11

101.1

90.99

0.90

0.1

0.107

2002

22.42

10.58

47%

1.01

9.57

21.41

11.84

0.55

0.447

0.472

2003

114.13

9.79

9%

0.75

9.04

113.38

104.34

0.92

0.08

0.086

2006

29.76

9.15

31%

0.51

8.64

29.25

20.61

0.70

0.295

0.307

2007

48

10.15

21%

1.02

9.13

46.98

37.85

0.81

0.194

0.211

2008

167.45

10.78

6%

0.77

10.01

166.68

156.67

0.94

0.06

0.064

2009

64.05

7.9

12%

1.02

6.88

63.03

56.15

0.89

0.109

0.123

average

86.03

11.35357

0.20846753

1.055

10.29857

84.975

74.67643

0.81

0.193571

0.208429

76

7.3 7.3.1

GEOMORPHOLOGICAL ANALYSIS MAPS OF CATCHMENT Russian River geomorphological analysis

Figure 26. Morphological analysis of the Russian River.

77

7.3.2

Salinas River geomorphological analysis

Figure 27. Morphological analysis of Salinas River

78

7.3.3

Russian River geomorphological analysis

Figure 28. Morphological analysis of Whitewater River

79

7.4 7.4.1

ARCHYDRO AND TOOLBOX PROBLEMS Error 999999. Using the resampling Raster Clip.

SOLUTION: Activate the input feature for clipping geometry windows.

Error 010005 Unable to allocate memoy .

Solution: User must be an administrator of the computer.

7.4.2

Error 010168. Running Topo to raster

Solution : Erase zpa410 in the C:\windows\system32\zpa410 as administrator. (“How Topo to Raster works—Help | ArcGIS Desktop,” n.d.)

80

7.4.3

Reducing the size (area km2 ) of the stream definition to raster

Solution. Increase the size of the pixel to be able to run all process.

7.4.4 Adjoint catchment tool in ArcHydro I have run the adjoint catchment tool in arcHydro for many times, it appears to be complete the process, but then I opened the attribute table, and it had blank adjoint catchments feature class, all of them have zero shape areas. The tool only creates polygons but not information associated within. I thought that It happened because of the ArcHydro version or because of the target location or temporary files.

Solution.

The only way to work is to go back to the stream definition tool in which the threshold has had a higher river threshold, then start the steps again from the stream definition tool.

Usually, the river threshold is by default 10% to define the stream. Several processes were done until, area catchment = 0.001 Km2 was adjusted (Merwade & Maidment, 2004).

7.4.5

Archydro is not creating an attribute field related to hydroID, mextID, joint ID Several times while I was running the ArcHydro tools suddenly, the software and application stopped. It forced me to start all the process again.

Solution: a. Make sure that Geoprocessing extents include the whole project. Erasing all temporary folders into ArcMap and Archydro is necessary. b.

Another way to do this is to erase the temporary folders using the ArcHydro toolbar under ApUtilities\additional utilities\Clean User’s Temp Folder.

81

c. The target location in arcHydro should be correct. Under ApUtilities\Set target locations, the hydro configuration must be ckeched and being sure that it is correct according to the project folder. Raster data should go in a folder into the project folder with the data frame named and the vector data to a feature data set with the data frame named as well. Project folder vector data to a geodatabase (GeoNet. The ESRI community, 2018)

7.4.6

The polygon processing

Catchment polygon processing, drainage Line Processing, and Adjoint catchment processing are rasters that later are converted to vectors located in layer folder according to APUutilities specified in Set Target Locations.Using polygon processing in ArcHydro, I had problems to execute and create vector information that creates “layer” feature dataset.

“It happens because one property of a feature dataset is its X/Y domains, and feature dataset cannot hold data outside of its x/y domain” shapefile (Sampson & Gochis, 2018).

In other words, the catchment Polygon Processing inherits the extent from the top layer in ArcMap document display. Therefore, the top layer should be one that occupies the fill extent needed. If the layer on top is of lesser extent, then some polygons may be omitted, and the adjoint catchment processing that will be done later will fail.

Solution: To avoid the problem is necessary to add (above the Cat layer created before the polygon processing) a functional feature dataset with the “contour” shapefile (Sampson & Gochis, 2018).