Rainwater Cistern Resizing

RAINWATER CISTERN SIZING A Web-Based Tool

RAINWATER CISTERN SIZING A Web-Based Tool Corey Squire

2016-2017

How can the sizing of rainwater cisterns respond to more detailed rainfall data and to recent climate change?

ABSTRACT This project attempts to solve two problems simultaneously.

The first question is how can a cistern be sized using specific, historic rainfall data rather than long term averages. The second question is how can a calculation tool be best packaged and distributed for widespread use.

The project resulted in a calculator that can be loaded with up to 30 years of daily rainfall data for any given location and will calculate the water level of a hypothetical cistern for any given day. The calculator was originally created in excel and then converted to HTML using an Excel plugin called “spreadsheet converter.� This HTML based tool can be hosted on either an intranet or internet site and can be available for widespread use without the possibility of breaking the internal calculations.

KEYWORDS Rainwater Cistern Sizing, Web Tool, Historic Rainfall, Climate Change, Big Data

INTRODUCTION Two trends over the past few decades should cause us to rethink the way we use weather data for design. The first trend is climate change. Since around 2000, the climate has been changing rapidly enough to make some long term climate averages obsolete. The last three years have seen each successive year break the record for the hottest and 16 of the 17 hottest years on record have accrued since 2001. In conjunction with this warming trend, rainfall patterns have been shifting significantly.

From 2009 to 2012, Texas experienced one of its worst droughts on record. This was followed by a severe drought in California, which was then followed by record rains and flooding. Despite these recent drastic changes in weather, many designers still base the designs of building systems on long term weather averages that stretch back to the 1970s.

The second trend is big data. Averages were developed as a way to reduce error in relatively small data sets.

Today, with huge quantities of easily accessible data and inexpensive ways of manipulating it, averages might be on its way out in favor of precise, actual metrics. For example, before mass manufacturing, individual goods were made one at a time for individual purposes. Shirts could be sized to the individual because there was little cost in making individual items slightly different.

Manufacturing created a need for averages and today, the huge range of human body forms that exist are divided into a handful of shirt sizes: small, medium, large, etc. In the future, the experience of shopping for clothing might include stepping into a 3D scanner and having the perfect fit 3D printed on the spot. Data and technology could eliminate the need for an average.

Similarly, in the past, systems were sized using long term averages because more detailed information was unavailable or costly to manipulate. Going forward, specific data can be used to answer specific questions. In the case of cistern, this means sizing a cistern that can respond to recent extremes, both to droughts and floods, rather than a historically typical year.

THE TOOL The cistern sizer takes three metrics into account: The size of the cistern, the daily water demand, and the size of the catchment area. It uses daily rainwater data from the previous 30 years. For each day in the data set, the tool calculates the net water projected to enter or leave the cistern, keeping the minimum as zero and the maximum

4

Variable 1 - Catchment

Variable 2 - Use

Variable 3 - Rain

5

as the size of the cistern. The equation used is as follows: Water in cistern at time n+1= IF((rainwater + use) + water in cistern at time n < 0 then 0 otherwise (IF( (rainwater + use) + water in cistern at time n > Cistern size then cistern size otherwise (rainwater + use) + water in cistern at time n)))

sizing that is missed when using monthly averages. If a location is expected to receive 3” of rain in a month, it’s important know if that means 0.1” per day, or 30 dry days followed by a 3” event. This difference would have a major impact on the size of the cistern but could only be

In addition to the amount of water in the hypothetical cistern on any day in the dataset, the tool also calculates the following composite metrics: • • • • • •

Percent of water captured Percent of days when the cistern is empty Percent of days when the cistern is full Percent of demand met Longest drought in the dataset Longest period where the cistern is empty understood through the use of daily data.

The output of the tool is not an optimized solution, but a collection of both graphic and numeric information to help designers make informed decisions. The tool will also help to determine the limiting factor of the system.

For example, a dry climate might receive occasional heavy storms that will fill a cistern during one event. In this case it’s important to know when the size of the cistern or the catchment area would need to be increased to take advantage of this. In a rainy climate, the cistern and the catchment area could be relatively small, and the daily demand, more than the rainfall, might determine the cistern size.

Micro droughts are another consideration for cistern 6

WEB TOOLS The other question explored through this project is how best to package and distribute calculation tools. Ideally, any tool would be easily accessible, intuitive to use, difficult to break and provide results that are instantly actionable. This way, the calculation can become part of a standard workflow. Previously, calculation tools lived in an Excel spreadsheet. To use a calculator, a designer needed to know that the calculator existed, know where in the server file structure it lived, know how to use it, and finally, not break or alter it so that it would still work for the next user. This system of filed spreadsheets is a poor

way to store and distribute knowledge.

A few different paths for creating a calculator web tool were explored. These included hiring a coder to write the software (too expensive), learning to code (too time consuming), using free conversion web apps (too simplistic), and then finally purchasing a plug in for converting an excel spreadsheet to HTML.

CONCLUSIONS Spreadsheet Converter produced useful tools that can be self hosted. The software allows for limited graphic

manipulation, but does have a nicer and more controlled user interface than Excel. Currently, there is no logical central location for the calculators to live, but this will change when we upgrade to a new intranet. At that point I will be able see how often these tools are used and whether or not this is a successful way of distributing information. Going forward, a suite of web based calculator tools can be created and hosted in an easily accessible location. After an internal beta test period, these tools can be hosted publicly at lakeflato.com to benefit the profession.

Lake|Flato Architects 311 Third Street, San Antonio, Texas 78205 210.227.3335 www.lakeflato.com 8