GLACIAL FLOODING AND DISASTER RISK MANAGEMENT KNOWLEDGE EXCHANGE AND FIELD TRAINING July 11-24, 2013 Huaraz, Peru
July 16, 2013 Day 4 - Field Methods and Modules I
Hydrology and Water Quality of Quilcay Watershed: End-Point Water Sampling and Calculations
Jeff La Frenierre, The Ohio State University Raúl Loayza-Muro, Universidad Peruana Cayetano Heredia Molly H. Polk, University of Texas at Austin Oliver Wigmore, The Ohio State University
A Brief Primer on Mountain Mire Ecology
Molly H. Polk Department of Geography and the Environment University of Texas at Austin email@example.com
Throughout wetland science, nomenclature is a chronic source of confusion and peatlands are no exception. Terms with the same meaning have evolved in many languages leading to communication problems among scientists (Clymo 1983; Mitsch and Gosselink 2007; Keddy 2010). There is agreement, however, that a peatland is a type of wetland where the accumulation of organic material exceeds the decomposition rate. The term peatland is shorthand for peat landform. A mire is an active peat system and a mire complex is a catch-all phrase that describes a mosaic of peat landforms (Charman 2002). The terms fen and bog can be easily confused, but they differ in their sources of water and nutrients. Fens are fed by groundwater and are considered to be minerogenous and minerotrophic. Their pH is generally more neutral and nutrient levels tend to be higher than bogs (Evans and Warburton 2007). Fed by rainwater, bogs are ombrogenous and ombotrophic. Their pH is more acidic and nutrient levels are generally lower (Charman 2002; Rydin and Jeglum 2006). There are 3 types of minerogenous peats. Topogenous fens are found in basins or depressions where there is no inlet or outlet for water flow. Soligenous fens are in valley bottoms and on the slopes of valley walls; they are maintained by water flowing down valley walls (Charman 2002). Limnogenous fens derive water from lakes or rivers; found in riparian or lacustrine margins (Clymo 1983). In our walk up the Quilcayhuanca valley, we will see a mire complex with examples of several types of peatlands. Locally, these wetlands are known as bofedales, humedales or oconales. Soil Taxonomy Peats fall into the Histosol soil order, one of 12 currently recognized soil orders (United States Department of Agriculture 2006). To be a Histosol, soil must contain high organic matter, it must be > 80 cm thick, and it can have no permafrost. Low bulk densities and high carbon content are additional defining features. All Histosols occur in wetlands, but wetlands also contain other soil orders. There are 4 Histosol suborders that are broadly divided by the degree of decomposition: Fibrists, Folists, Hemists, Saprists. (Soil Survey Staff 1999). Peat Initiation A number of conditions must be present for peat initiation to commence. These conditions interact over time and result in the creation of peat soil. • Climate – positive water balance is required. This can be attained through high precipitation and low evapotranspiration or low precipitation and low temperatures or high precipitation and high temperatures (Charman 2002). • Organisms – mosses, herbaceous plants, woody and aquatic plants are specifically adapted to wet conditions. Litter from these plants determines the type of peat that develops (Rydin and Jeglum 2006; Brady and Weil 2002). Micro-organisms that facilitate decomposition include fungi, bacteria, and worms specialized for anaerobic conditions (Moore and Bellamy 1974). • Relief – peat develops in basins and depressions and on slopes up to 20°. Glacial till plains or valleys where glacier recession has occurred are especially conducive to peat initiation. In these areas, the natural drainage system is blocked by moraines (Clymo 1983; Zinck 2011). • Parent material – there must be an impermeable layer upon which peat can begin to form (Charman 2002). • Time – plant litter production and decomposition takes time and accumulation rates vary (Brady and Weil 2002). Tropical mountain peats accumulate faster than other mountain peats but slower
than boreal or lowland tropical peats. (0.25 mm/year in Colorado Rocky Mountains vs. 1.3 mm/year in Ecuadorian páramo vs. 8 mm/yr in lowland tropics (Chimner and Karberg 2008)). There are three processes of peat formation. Primary peat formation permits peat to form directly over freshly exposed mineral soil without any open or standing water and no deposition of aquatic sediment. It occurs on poorly drained, flat expanses in shallow basins or slopes. Infilling or terrestrialization allows peat to develop on the margins of slow moving water then fill in towards the center. Open waters become partially or totally converted to bogs or fens. Paludification occurs when a shift in local hydrology promotes waterlogged conditions (i.e. change in climate or plant colonization); the term also refers to peat encroachment onto adjacent mineral soils (Charman 2002; Rydin and Jeglum 2006). All three processes are at work to create mire complexes in most valleys in HNP. General Physical and Chemical Characteristics In terms of physical characteristics, peatlands exhibit vertical and horizontal structure. The acrotelm is the top 10 – 15 cm of the mire including surface vegetation. The catotelm is the deeper layer where conditions are always anoxic. Soil in the catotelm is always darker, more decomposed and constantly waterlogged (Rydin and Jeglum 2006). Peat is known for high water storage capacity and most water is stored in the catotelm where hydraulic conductivity is low (Pfadenhauer et al. 1993). Mires can exhibit large and small-scale horizontal patterns, but these are more common in the boreal latitudes. Wide color variation exists, but peat is usually dark brown to black. Fibrosity depends on state of decomposition, bulk density, and porosity. All of these characteristics vary over time as the decomposition state changes. Chemically, peats have low pH levels, high cation exchange capacity, and low gas content (higher in the acrotelm than in the catotelm) (Rydin and Jeglum 2006). Carbon typically comprises ½ of the organic matter. Peatlands store tremendous volumes of carbon: the best current estimate of global carbon storage is 600 gigatons, most located north of 45° N (Yu et al. 2010). Peat is a source of methane and nitrous oxide. Peat chemistry is heavily influenced by the sources of water and throughflow patterns (Moore and Bellamy 1974).
Field Exercise – Von Post System of Humification Scale
Mostly undecomposed Very weakly decomposed Weakly decomposed Moderately decomposed Strongly decomposed Strongly decomposed Well decomposed Almost completely decomposed Completely decomposed
H3 H4 H5 H6 H7 H8 H9
Expressed fluid Colorless, clear Yellow-brown clear Turbid
Peat lost when squeezed None
Consistency in the hand n.d.
Noticeably turbid Strongly turbid
Up to 1/3
Only fibrous matter and roots remain Almost all peat escapes
Distinct Distinct Clear, becoming indistinct Somewhat indistinct Indistinct but recognizable Vague
All peat escapes
Coordinates Depth to Surface (cm) Plant Structure Expressed Fluid Peat lost Consistency in hand Description Munsell Soil Color Scale Notes
References Brady, N. C., and R. R. Weil. 2002. The Nature and Properties of Soils 13th ed. Upper Saddle River, N.J.: Prentice Hall. Charman, D. J. 2002. Peatlands and Environmental Change. New York: J. Wiley & Sons Ltd. Chimner, R. A., and J. M. Karberg. 2008. Long-term carbon accumulation in two tropical mountain peatlands, Andes Mountains, Ecuador. Mires and Peat 3:1–10. Clymo, R. S. 1983. Peat. In Mires -- Swamp, Bog, Fen, and Moor, Ecosystems of the World., ed. A. J. P. Gore, 159–224. Amsterdam: Elsevier Scientific Pub. Co. Evans, M., and J. Warburton. 2007. Geomorphology of Upland Peat: Erosion, Form, and Landscape Change. Malden, MA: Blackwell Publishing. Keddy, P. A. 2010. Wetland Ecology: Principles and Conservation. Cambridge University Press. Mitsch, W. J., and J. G. Gosselink. 2007. Wetlands 4th ed. Hoboken, N.J.: Wiley. Moore, P. D., and D. J. Bellamy. 1974. Peatlands. London: Elek Science. Pfadenhauer, J., H. Schneekloth, R. Schneider, and S. Schneider. 1993. Mire distribution. In Mires: Process, Exploitation, and Conservation, eds. A. L. Heathwaite and K. Göttlich, 71–121. Chichester: Wiley. Rydin, H., and J. K. Jeglum. 2006. The Biology of Peatlands. Oxford: Oxford University Press. Soil Survey Staff. 1999. Soil taxonomy: A basic system of soil classification for making and interpreting soil surveys 2nd ed. Washington, D.C.: USDA-Natural Resources Conservation Service Handbook 436. United States Department of Agriculture. 2006. Keys to Soil Taxonomy 10th ed. Washington, D.C.: U.S. Dept. of Agriculture, Natural Resources Conservation Service. Yu, Z., J. Loisel, D. P. Brosseau, D. W. Beilman, and S. J. Hunt. 2010. Global peatland dynamics since the Last Glacial Maximum. Geophysical Research Letters 37:L13402. Zinck, J. A. 2011. Tropical and subtropical peats: an overview. In Peatlands of the Western Guayana Highlands, Venezuela: Properties and Paleogeographic Significance of Peats, eds. J. A. Zinck and O. Huber, 5–28. Berlin: Springer-Verlag.
Jeff La Frenierre Department of Geography and Byrd Polar Research Center The Ohio State University
A Brief Primer on Estimating the Contribution of Glacier Melt to Watershed Discharge Background Mountain glaciers can greatly influence the hydrology of their watersheds and thus play an important role in downstream water resources management. Glaciers store excess water during periods of increased precipitation and release water downstream when precipitation is minimal and social demand is higher (Viviroli et al., 2007; Kaser et al., 2010). This buffering service helps maintain the viability of downstream irrigation, hydroelectric generation and domestic water supply. Worldwide, nearly 120 million people live in watersheds where glacier meltwater comprises at least 50% of total discharge for at least one month per year (Schaner et al., 2012), thus widespread glacier retreat is a significant concern for water managers (Bradley et al., 2006; Immerzeel et al., 2010). The ability to reasonably estimate the contribution of glacier meltwater to total watershed discharge is thus an integral part of climate change risk assessment and sustainable water management in high mountain glacierized watersheds (Viviroli et al., 2011). Five broad methodological approaches for estimating the contribution of glacier meltwater have been presented in the scientific literature. The following is a brief overview of each method, the spatial and temporal scales of analysis in which it is most appropriate, its advantages, and its key sources of uncertainty. Direct Discharge Measurement Description: A comparison of streamflow measurements taken immediately downstream of the glacier tongue with those taken simultaneously at points farther downstream (accounting for water transit time) Spatial Scale: Micro watersheds (less than 100 km2) Temporal Scale: Hourly (longer with repeated measurements or automated gauges) Advantages: Simple technique; no existing data sets needed Sources of Uncertainty: Discharge measurement error; difficulty in routing all meltwater past a measuring gauge Glaciological Approaches Description: Calculate the volume of ice melting from glaciers in a watershed, convert to a volumetric water equivalent, and compare to downstream measured streamflow Spatial Scale: Micro to meso (100 km2 to 10,000 km2) Temporal Scale: Typically annual to decadal Advantages: Potentially the most precise technique; can utilize existing glacier mass balance data Sources of Uncertainty: Glacier mass balance measurement error; extrapolation of point mass balance or energy mass balance data across all glaciers; accuracy estimating ice volume; unquantified loss of ice mass to groundwater infiltration or sublimation; discharge measurement error Hydrological Balance Equations Description: Quantify other components of the local hydrological cycle (e.g. precipitation, groundwater exchange, evapotranspiration), solve the hydrological balance equation with glacier meltwater as the missing term, and compare with downstream measured Spatial Scale: Micro to meso Temporal Scale: Monthly to decadal (longer time scales usually more accurate) Advantages: Existing data may be available; measurement of some other hydrological terms may be simpler than measuring glacier mass balance
Jeff La Frenierre Department of Geography and Byrd Polar Research Center The Ohio State University Sources of Uncertainty: Measurement error (especially precipitation); interpolation of point measurements to represent conditions throughout the watershed; difficulty in quantifying evapotranspiration, sublimation and groundwater exchange; discharge measurement error Hydrochemical Tracers Description: Identify the unique chemical signatures of different hydrological end-members (e.g. precipitation runoff, groundwater, glacier melt), then use a mixing model to estimate the proportional contribution of each Spatial Scale: Daily; longer with repeated measurements Temporal Scale: Micro to meso Advantages: Does not require existing data; water sampling is easy and inexpensive (though laboratory analysis is required); very effective at capturing temporal and spatial variability Sources of Uncertainty: Tracers that are not conservative (undergo further chemical reactions); end members that do not have homogenous chemical signatures; inability to differentiate some end members Hydrological Modeling Description: A set of nested equations that solve the water balance while simulating the spatial and temporal variation of various hydrologic components such as quick flow, groundwater fluxes and snow and ice melt runoff Spatial Scale: Any (depends on input data) Temporal Scale: Any (depends on model structure and input data) Advantages: Potentially useful at all temporal/spatial scales; provides hypothesis testing capabilities; can identify areas where additional hydrological research is necessary Sources of Uncertainty: Quality of input data; interpolation/rescaling of point measurements used as input data; equifinality (when a model arrives at the ‘right’ solution using the ‘wrong recipe’
References Bradley RS, Vuille M, Diaz HF, et al. (2006) Threats to Water Supplies in the Tropical Andes Science 312: 1755-1756. Immerzeel WW, van Beek LPH and Bierkens MFP. (2010) Climate Change Will Affect the Asian Water Towers. Science 328: 1382-1385. Kaser G, Groshauser M and Marzeion B. (2010) Contribution potential of glaciers to water availability in different climate regimes. Proceedings of the National Academy of Sciences 107: 20223–20227. Schaner N, Voisin N, Nijssen B, et al. (2012) The contribution of glacier melt to streamflow. Environmental Research Letters 7: 034029. Viviroli D, Archer DR, Buytaert W, et al. (2011) Climate change and mountain water resources: overview and recommendations for research, management and policy. Hydrology and Earth System Sciences 15: 471-504. Viviroli D, Dürr HH, Messerli B, et al. (2007) Mountains of the world, water towers for humanity: Typology, mapping, and global significance. Water Resources Research 43: W07447.
Jeff La Frenierre Department of Geography and Byrd Polar Research Center The Ohio State University
A Brief Primer on Hydrological Tracers Waters originating from different components of the hydrological system will tend to have different chemical signatures by virtue of their contact with different materials (rock, organic material, etc.) and by virtue of the processes to which they have been exposed (evaporation, infiltration, etc.) (Drever, 1997). Thus, hydrological tracers provide empirical information about the sources and flow patterns of waters within a catchment (Leibundgut et al., 2009), and are especially valuable in hydrological research because they are very effective at capturing the temporal and spatial variation of different water sources within a catchment. Numerous studies employing hydrochemical tracers have been undertaken in glacierized mountain watersheds, including in the Himalaya (Jeelani et al., 2010), Tibetan Plateau (Meng et al., 2013), Tien Shan (Kong and Pang, 2012), French Pyrenees (Brown et al., 2006), American Rockies (Cable et al., 2011) and here in the Peruvian Andes (Mark and Seltzer, 2003; Mark and McKenzie, 2007; Baraer et al., 2009). Background Hydrological tracers can be classified as artificial or natural (aka environmental tracers). Artificial tracers include chemicals such as salt slugs and dyes that are injected into a hydrological system in order to identify flow patterns and residence times both above and below the ground surface. Natural tracers utilize naturally-occurring chemicals such as stable isotopes of water and major solutes to determine both water sourcing (i.e. precipitation vs. groundwater vs. glacier melt), flow patterns, and mixing characteristics. Key Terms
Isotope: Isotopes are “flavors” of an element that have different numbers of neutrons, which results in atoms of the same element having slightly different weights (Kendall and McDonnell, 1998). A ‘heavy’ isotope was one or more extra neutrons, which results in different fractionation behavior than that of a ‘light’ isotope. For example, the light oxygen isotope, 16O (99.76% of all O atoms), has 8 electrons, 8 protons and 8 neutrons. The heavy isotope, 18O (0.2% of all O atoms) has 8 electrons, 8 protons and 10 neutrons. The two extra neutrons give these atoms a slightly greater mass. Radioactive isotopes decay over time to form new isotopes; stable isotopes do not decay. Fractionation: Fractionation is the partitioning of ‘light’ and ‘heavy’ isotopes so that the relative proportions of different isotopes of the same element exists in a given compound (Kendall and McDonnell, 1998). In hydrology, fractionation occurs as water undergoes phase changes and/or transfer from one hydrological component to another (e.g. evaporation from sea water to atmospheric vapor). The greater mass of heavier isotopes results in different chemical and physical properties than that of lighter isotopes. Solute: Atoms/molecules dissolved in water. Ion: Where an isotope has a varying number of neutrons, an ion is an atom or molecule with a varying of electrons (relative to the number of protons) such that the atoms has a net positive or negative electrical charge. Molecules with a net positive charge are called
Jeff La Frenierre Department of Geography and Byrd Polar Research Center The Ohio State University
cations (e.g. Na+, Ca2+) while those with a net negative charge are anions (e.g. Cl-; SO42-). Different components of the hydrological system can be expected to have different ionic concentrations due to the different materials and processes to which they have been exposed. For example, atmospheric vapor origination from evaporation of sea water will typically have higher concentrations of Na and Cl, but very low concentrations of other ions. Groundwater on a volcano will have high concentrations of Mg and SO4, since these are prevalent in basalt and other volcanic rocks. End member: An end member is a ‘pure’ unmixed water with a chemical signature that is unique from other end members as well as mixed models. In glacierized watersheds, end members typically include precipitation runoff, groundwater discharge, seasonal snowmelt and glacier ice melt. Conservative tracer: A conservative tracer is one that does not undergo further chemical reaction (fractionation or change in ionic concentration) after it has entered some component of the hydrological system. A fundamental assumption of tracer hydrology is that the chemical signatures of different end members remains constant as the waters mix, thus allowing the proportional contribution of each to be measured. Mixing model: A set of mass balance equations used to determine the relative proportion of various end members within a sample of mixed water.
References Baraer M, McKenzie J, Mark BG, et al. (2009) Characterizing contributions of glacier melt and groundwater during the dry season in a poorly gauged catchment of the Cordillera Blanca (Peru). Advances in Geosciences 22: 41. Brown LE, Hannah DM, Milner AM, et al. (2006) Water source dynamics in a glacierized alpine river basin (Taillon-Gabiétous, French Pyrénées). Water Resources Research 42: W08404. Cable J, Ogle K and Williams D. (2011) Contribution of glacier meltwater to streamflow in the Wind River Range, Wyoming, inferred via a Bayesian mixing model applied to isotopic measurements. Hydrological Processes 25: 2228-2236. Drever JI. (1997) Geochemistry of Natural Waters, Upper Saddle River, NJ: Prentice-Hall. Jeelani G, Bhat NA and Shivanna K. (2010) Use of d18O tracer to identify stream and spring origins of a mountainous catchment: A case study from Liddar watershed, Western Himalaya, India. Journal of Hydrology 393: 257-264. Kendall C and McDonnell JJ. (1998) Isotope tracers in catchment hydrology: Elsevier Amsterdam. Kong Y and Pang Z. (2012) Evaluating the sensitivity of glacier rivers to climate change based on hydrograph separation of discharge. Journal of Hydrology 434-435: 121-129. Leibundgut C, Maloszewski P and Kulls C. (2009) Tracers in Hydrology: John Wiley & Sons, Ltd. Mark BG and McKenzie JM. (2007) Tracing increasing tropical Andean glacier melt with stable isotopes in water. Environmental Science & Technology 41: 6955-6960. Mark BG and Seltzer GO. (2003) Tropical glacier meltwater contribution to stream discharge: a case study in the Cordillera Blanca, Peru. Journal of Glaciology 49: 271-281. Meng Y, Liu G and Zhang L. (2013) A comparative study on stable isotopic composition in waters of the glacial and nonglacial rivers in Mount Gongga, China. Water and Environment Journal: In Press.
Hydrologic Sampling Method Nutrients and Isotopes: 1. Take one 30ml sample bottle and cap and label clearly with date, time and location. Use labelling tape if bottles are to be reused in the future. 2. Rinse sample bottle and cap three times with water to be sampled. Be sure to dump rinse water away from sampling location. 3. Collect 30ml sample with bottle. 4. Secure cap and tape closed for transport. 5. Keep sample refrigerated or in a cooler where possible. Ions/Cations: 1. Take one 30ml sample bottle and cap and label clearly with date, time and location. Use labelling tape if bottles are to be reused in the future. 2. Take one 60ml syringe. 3. Rinse syringe 3 times with water to be sampled. Be sure to dump rinse water away from sampling location. 4. Secure 0.45Âľm filter to syringe. 5. Rinse filter by pushing ~2ml of water through. 6. Rinse sample bottle and cap three times with filtered water from syringe. 7. Collect 30ml sample in bottle from syringe. 8. Secure cap and tape closed for transport. 9. Keep sample refrigerated or in a cooler where possible. NOTE: In cases of high suspended sediment load multiple filters may be necessary. In this case they must be rinsed each time following step 5.
Metals, deglaciation and biomonitoring in the Cordillera Blanca RaĂşl Loayza-Muro Laboratory of Ecotoxicology Universidad Peruana Cayetano Heredia firstname.lastname@example.org
Origin of heavy metals in the Cordillera Blanca Metals are natural, ubiquitous but unevenly distributed constituents of the Earth's crust. In mountain areas like the Andes, metals are readily mobilized by acid conditions produced by natural oxidation of mineral layers. The Cordillera Blanca shows a unique and diverse geomorphology dominated by the Chicama formation consisting of a granodioritic batholit formed by plagioclase and biotite minerals presenting aluminum, iron, nickel, cobalt, strontium and zinc (Rivera et al., 2008; Sevink, 2009). At the upper sections in the proglacial zone of the Cordillera Blanca, metamorphic sedimentary rocks characterized by pyrite (Fe2S) are well oxidized, generating protons that lower the pH below 4. As a result, rocks are readily weathered resulting in high metal concentrations being mobilized into streams (Burns, 2010). Thus, the heterogenous morphology results in a prominent spatial diversity in leaching along the Andes. Mining exploitation has been one of the most important economical activities developed at high altitudes in Andean countries and is still continuously growing. In the past, mining practices were performed without environmental protection and mineral waste was stored in large piles exposed to rainfall (Romero et al., 2008). Currently, these abandoned dumps and mine tailings are the most important contributor to metal pollution, representing a standing threat for Andean rivers and streams due to the continuous mobilization of metals and acid drainage, changing water chemistry and biotic communities (Ministerio de EnergĂa y Minas, 1998). Moreover, since in several cases metal levels exceed the permissible limits for human or agricultural water use, it is deemed that such toxic contaminants have critically deteriorated important freshwater sources in the region during decades (UNEP, 2003).
Hydrochemical consequences of glacier retreat The Cordillera Blanca in the Peruvian Andes the world's most extensively glacier-covered (~70%) tropical mountain range. These glaciers are particularly sensitive to and the most visible indicator of global climate changes because they are constantly close to melting conditions. The Cordillera Blanca, located within the Huascaran National Park, drains the Santa River basin, where large cities above 2500 m rely to a great extent on high altitude snow and water stocks, lagoons and natural wetlands for domestic, agricultural or industrial use when rainfall is low or absent. Environmental conditions are rapidly changing in this region because of reduced cloud cover allowing the penetration of high levels of solar radiation, and the general increase of temperature due to climate change. This is leading to an increased melting of glaciers and altered hydrological cycles, being ~40% of the present discharge in the Santa River watershed during the dry season originated from non-renewed glacier melt. Simultaneously, the retreat of glaciers is resulting in the weathering and leaching of metal-rich rocks, producing natural acid drainage and mobilization of high metal concentrations, such as aluminum, iron nickel, cobalt, strontium and zinc into water bodies. These acid and metal inputs usually exceed the Peruvian and international standards for
protection of aquatic biodiversity, agriculture and human use, thus impairing the quality of headwater sources and streams at high altitudes.
Macrofauna as an early warning indicator of water quality Benthic macroinvertebrates are directly or indirectly impacted by metals in the water (Kiffney & Clements, 1996), substratum and food resources (Kiffney & Clements, 1993; Farag et al., 1998; Courtney & Clements, 2002), and show different responses to metal exposure. Metals may therefore explain much of the variability in assemblage structure between polluted and unpolluted sites, and reveal species-specific sensitivities to metals (Clements et al., 2000). Studies on the effects of metals in natural and artificial polluted streams have shown a loss of sensitive species, resulting in a significant reduction of richness and a shift towards more tolerant taxa (Gerhardt et al., 2004). Ephemeroptera are among the most sensitive group, whereas Plecoptera, Trichoptera and Diptera may survive under high metal concentrations (Clements, 1994; Kiffney & Clements, 1994; Clements et al., 2000). Indirect effects of metal pollution include smothering of the streambed by metal oxyhydroxide precipitates, restricting available habitats for benthic fauna, impoverishing food quality, and modifying interactions between functional feeding groups (Kiffney, 1996; Clements, 1999; O’Halloran et al., 2008). Water chemical analysis are widely used for evaluating impacts of pollutants, although they represent instantaneous conditions and seldom evidence biological effects. A more reliable approach for estimating water quality are biotic indices, such as the Biological Monitoring Working Party (BMWP), based on benthic macrofauna. These organisms are representative of a wide range of habitats and thus have been used as early warning indicators of ecosystem deterioration or restoration (Alba-Tercedor & Sánchez-Ortega, 1988; Armitage et al., 1983). For determining the BMWP index, macroinvertebrate Families are ranked according to the a list where very sensitive taxa have 10 points and very tolerant ones have 1 point. The total sum of punctuations corresponding to each Family in a sample produces the BMWP score. Such values are then compared with the corresponding water quality class according to the following score ranges: good (≥100), passable (61-100), dubious (36-60), critical (16-35) and very critical (<15). A ‘good’ quality indicates very clean waters (pristine or not sensibly altered system) and is represented by a blue color; ‘passable’ indicates evidences of mild pollution effects (green); ‘dubious’ means polluted waters (altered system; yellow); ‘critical’ indicates very polluted waters (very altered system; orange); and ‘very critical’ corresponds to strongly polluted waters (strongly altered system; red). This monitoring tool can reveal past and present water conditions, it has a lower cost, and requires a basic training for organism identification. Moreover, it allows an easy representation and mapping of water quality using colors, which may improve large scale basin evaluation and management.
References Alba-Tercedor, J, Sánchez-Ortega, A.1988. Un método rápido y simple para evaluar la calidad biológica de las aguas corrientes basado en Hellawell. Limnetica 4, 51–6. Armitage, PD, Moss, D, Wright, JF, Furse, MT. 1983. The performance of a new biological water quality score system based on macroinvertebrates over a wide range of unpolluted running-water sites. Water Res 17, 333–347.
Burns PJ. 2010. Geochemical analysis of waters in a tropical glacial valley, Cordillera Blanca, Peru (Senior Honors Thesis). Ohio State University, Columbus, Ohio, USA. Clements WH. 1994. Benthic invertebrate community responses to heavy metals in the Upper Arkansas River, Colorado. J N Am Benthol Soc 13, 30–44. Clements WH. 1999. Metal tolerance and predator–prey interactions in benthic macroinvertebrate stream communities. Ecol Appl 9, 1073–1084. Clements WH, Carlisle DM, Lazorchak JM, Johnson PC. 2000. Heavy metals structure benthic communities in Colorado Mountain streams. Ecol Appl 10, 626–638. Courtney LA, Clements WH. 2002. Assessing the influence of water and substratum quality on benthic macroinvertebrate communities in a metal-polluted stream: an experimental approach. Freshwater Biol 47, 1766–1778. Farag AM, Woodward DF, Goldstein JN, Brumbaugh W, Meyer JS. 1998. Concentrations of metals associated with mining waste in sediments, biofilm, benthic macroinvertebrates, and fish from the Coeur d’Alene River Basin, Idaho. Arch Environ Contam Toxicol 34, 119–127. Gerhardt A, Janssens de Bisthoven L, Soares AMVM. 2004. Macroinvertebrate response to acid mine drainage: community metrics and on-line behavioural toxicity bioassay. Environ Pollut 130, 263–274. Kiffney PM. 1996. Main and interactive effects of invertebrate density, predation, and metals on a Rocky Mountain stream macroinvertebrate community. Can J Fish Aquat Sci 53, 1595–1601. Kiffney PM, Clements WH. 1993. Bioaccumulation of heavy metals by benthic invertebrates at the Arkansas River, Colorado. Environ Toxicol Chem 12, 1507–1517. Kiffney PM, Clements WH. 1994. Effects of heavy metals on a macroinvertebrate assemblage from a Rocky Mountain stream in experimental microcosms. J N Am Benthol Soc 13, 511–523. Kiffney PM, Clements WH. 1996. Effects of metals on stream macroinvertebrate assemblages from different altitudes. Ecol Appl 6, 472–481. Ministerio de Energía y Minas. 1998. Estudio de evaluación ambiental territorial y de planeamiento para la reducción o eliminación de la contaminación de origen minero en la cuenca del río Santa. Dirección General de Asuntos Ambientales, Lima. O’Halloran K, Cavanagh J-A, Harding JS. 2008. Response of a New Zealand mayfly (Deleatidium spp.) to acid mine drainage: implications for mine remediation. Environ Toxicol Chem 27, 1135– 1140. Rivera H, Chira J, Campián M, Cornelio F. 2008. Análisis correlacional y evolutivo de los metales pesados en sedimentos del río Santa entre Huaraz–Carhuaz, Departamento de Áncash. Rev Inst Invest FIGMMG, UNMSM 11, 19–24. Romero A, Flores S, Medina R. 2008. Estudio de los metales pesados en el relave abandonado de Ticapampa. Rev Inst Invest FIGMMG, UNMSM 11, 13–16. Sevink J. 2009. The Cordillera Blanca Guide. University of Amsterdam – The Mountain Institute. Lima, Peru. United Nations Environment Program (UNEP). 2003. Water resources management in Latin America and the Caribbean. UNEP / LAC-IGWG.XIV / Inf.5. pp. 31. Nairobi, Kenya.
Handout for field workshop for High Mountains Adaptation Partnership in Huaraz, Peru, in July 2013.