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Purpose:

To forecast urban water demand and project potential savings from conservation and use of alternative water sources over varying climatic conditions and land uses.

Description:

Water supply and demand assessment under alternative climate, land use and population scenarios is an area of great interest among urban planners and water managers. The Integrated Urban Water Model (IUWM) was developed for urban water demand and savings forecasting with urban water conservation and recycling practices. The purpose of the mass balance model is to allow evaluation of alternative urban water management strategies under varying climatic conditions at a municipal or regional scale. IUWM has been deployed as an online tool and as a web service, thus enabling accessibility, ease of use and applicability at the municipal scale. IUWM facilitates the development of urban water demand forecasts through automated retrieval of publicly available data inputs through a geographical information system (GIS) interface, thus relieving the need for manual input of data. Indoor residential demands are forecast based on end-use at the census block level with population and household data retrieved from the United States census. Combined residential/commercial, industrial, and institutional (CII) irrigation demands are forecast based on daily evapotranspiration and land cover data. Water management strategies included in IUWM are:

  • Indoor conservation
  • Irrigation conservation
  • Graywater reuse for toilet flushing and irrigation
  • Stormwater capture and use
  • Wastewater treatment plant (WWTP) effluent reuse

Users Guide:

1. Go to erams.com/iuwm.
2. Select a desired “base layer” from options under the map tab from the left dashboard.

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3. Zoom to the area of interest using the zoom area in the map canvas.

4. From left dashboard, select the IUWM tab.

5. Define your service area by one of the following methods:

  • Draw on map: The options for selection of the geographic region of interest, i.e. service area, include the area within a desired radius of a point (point buffer); area within a buffer of a line; area within a rectangle; or area within a free-hand polygon.
  • Select from a user uploaded layer extent. For this option, first upload your own shapefile using the shapefile upload option for “Geospatial Layers” under the map tab from the left dashboard, go to Map > Geospatial Layers. Click Spatial layers > Select Add Layer > Add Shapefile.
  • Select a region from known boundaries, e.g., states, counties, hydrologic unit codes (HUCs).

Figure below shows the extent of the Denver County in Colorado from selection from known boundaries.

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6. Select your analysis subunits. The IUWM model forecasts water demand for each subunit. These subunits could be selected from nationally available polygon databases such as US Census Counties, Tracts, Block Groups, or Blocks, or they can be defined using a user uploaded polygon shapefile.

7. Select a dataset for population of each subunit. US Census 2000 and 2010 population and demographic data at Block, Block Group, and Tract levels are available as options for the US National Datasets drop menu. However, any user provided layer as a polygon feature class (i.e., shapefile) can be used to estimate population, demographics, and housing units for each subunit.

8. Select a dataset for land use land cover consistent with the USGS National Land Use Land Cover (NLCD) classifications. US National Datasets including NLCD 2001, NLCD 2006, and NLCD 2011 are availed for analysis within the US. For other regions, users must provide a raster class layer for land use land cover. To upload your own raster layer, go to Map > Geospatial Layers. Click Spatial layers > Select Add Layer > Add Raster.

9. Select a dataset for climate information. Several options for regions within the US are available and can be selected from US Datasets drop menu, including the PRISM and NARR datasets. For all other regions, the user must provide the data as tables. To upload your own climate data, go to Map > Geospatial Layers. Click Tables > Select Add Table. Two tables must be provided: (i) a metadata file table that provides information about  station ID, agency, latitude, longitude, and elevation of each station in the user datasets; and (ii) a separate table for each station in the metadata file.

10. Once all datasets are selected, click Generate to process the data for each subunit in the analysis.

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11. Click the “Edit” button to create scenarios for your service area. Note that a default scenario is automatically created for the service area, which can be modified to simulate baseline water demand projections. Click Create to generate new scenarios of alternative water management strategies or changes in climate, land use or population..

12. Scenarios can include options for “Home Profiles”, “Landscape Irrigation Demand and Conservation”; “Graywater Reuse”; and “Stormwater Use”. The user can also modify the parameters for practices including: “Graywater Reuse”; “Stormwater Reuse”; “Wastewater Reuse”; “Irrigation Conservation”; “Projections” for changes in population and land use; and “Costs” associated with conservation and demand reduction practices.

  • The “Climate tab” allows users to change precipitation by a percentage and temperature by an increment, or specify new climate files for the scenario (e.g., climate change scenarios).
  • The Subunits tab enables the user to manually change any attribute of interest for any subunit.

           IUWM4-1     IUWM4-2

           IUWM4-3      IUWM4-4

  • Indoor Conservation: Indoor conservation is modeled by modifying “Home Profiles”
    • Under “Home Profiles”, the percentage of each older homes (i.e. Average Homes (before 1995)), average home in 2016 and high efficiency homes can be modified to simulate installation of high efficiency fixtures in the home. In the example below, 50% are estimated to be average new homes and 50% are assumed to be high efficiency homes.

iuwm-Cons-Scen1

  • Graywater Reuse: IUWM enables users to estimate the impact of graywater use for several end uses including:
    • Residential Flushing: Use of graywater to flush toilets in residences.
    • Irrigation: Use of graywater for landscape irrigation.
    • Combined Flushing and Irrigation: Use of graywater to both flush toilets in residences and irrigate landscape. Note that it is assumed that graywater will first be used to flush toilets, and excess is used for irrigation.
    • Residential Potable: Graywater is treated to potable quality and blended with municipal water to meet indoor water demand.
    • Combined Potable and Irrigation: Some graywater is used to meet irrigation demand, while some graywater is treated to potable quality to supply water for indoor demand, and indoor demand is met first, with excess source water being used to meet irrigation demand.
    • Commercial, Industrial and Institutional (CII): Graywater is used to meet demands for CII use. Treatment would vary based on the end use for water (e.g. irrigation, toilet flushing, cooling tower etc.).
  • For each use of graywater, the user needs to specify percentage of population adopting (“% Adoption”) and “Storage Capacity per Household in Gallons”. Storage could be on a household basis, or centralized in a neighborhood or multi-residential building. Either way, storage is entered on a per household basis. In the example below, it assumed that 40% of the service area population adopts graywater use for irrigation, and 10% of the service area population adopts graywater use for toilet flushing and irrigation with a storage capacity of 150 gallons per household.

iuwm-Cons-Scen2

  • Stormwater Use: IUWM enables users to estimate the impact of stormwater capture and use for several end uses. The end uses are the same as those defined above for “Graywater Reuse”. In the example below, is assumed that 50% of the impervious area is available for stormwater capture and that 10% of the service area population will use stormwater for toilet flushing and 80% will use stormwater for irrigation. A storage volume of 1500 gallons per household is assumed. As is the case for graywater reuse, storage could be on a household basis, or centralized in a neighborhood or multi-residential building. Either way, storage is entered on a per household basis.

iuwm-Cons-Scen3

  • Wastewater Reuse: The quantity of wastewater available for reuse is specified as a percentage of total wastewater. The user then selects an end use, which are the same as those defined above for “Graywater Reuse”. In the example below, 80% of generated wastewater is available for reuse and 80% of the population will use that water for flushing toilets in residence.

iuwm-Cons-Scen4

  • Irrigation Conservation:There are several approaches by which irrigation conservation can be achieved. IUWM enables simulations of the following irrigation modifications:
    • Evapotranspiration (ET) demand met: Irrigators may have different behaviors for how much water is applied for irrigation. This is represented via the “Evaporation demand met” parameter where the user can adjust the percentage of evapotranspiration demand met.
    • Decrease in irrigation achieved via advanced irrigation systems: Advanced irrigation systems can be installed that include moisture sensors or rain gauges to ensure irrigation does not exceed plant requirements. This option allows the user to estimate a percentage decrease achieved via this practice.
    • Irrigation efficiency: All irrigation approaches have an irrigation efficiency associated with them. The “irrigation efficiency” option enables the user to modify efficiency of irrigation practices. The default value is 78%, which is a typical efficiency for sprinkler irrigation.
    • Percent of precipitation for which residents account when making an irrigation decision: Some irrigators turn off their irrigation systems when there is rain. This option enables the user to modify the percentage or rainfall that is decreased from the irrigation demand.
    • Plant Factor: This enables adjustment of irrigation demand based on type of landscape (e.g. xeriscape landscape). Plant factors are taken from SLIDE: Simplified Landscape Irrigation Demand Estimation.j

    In the example below, a 10% decrease in irrigation demand is achieved via advanced irrigation systems, and the plant factor is decreased to an average of 0.5 to represent modification of landscape to more drought tolerant species.

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  • Population and land use change: Changes in population, number of households or density of development (impact to % pervious) can be simulated under “Projections”. Projections are input as a percent change from the baseline inputs for population and land use.

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13. Once all desired scenarios are created, click Save and close the dialog window for editing the service area scenarios and parameters.

14. The bottom panel provides several options for visualization of the model outputs for the entire service area, individual subunits, or a combination of selected subunits. Outputs can be summarized on an annual basis, monthly time step, or average monthly responses.

The figure below shows the comparison of a Baseline scenario ( landscape plant) and a Reduce Irrigation Scenario based on Desert Adapted Plants for a service area:

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15. All inputs and results are saved in two tables under the Map tab in the left dashboard (Map > Geospatial Layers > Tables):

  • IUWM Service Area Results
  • IUWM Service Area Scenarios

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Glossary:

Census Tract: Census Tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity that are updated by local participants prior to each decennial census as part of the Census Bureau’s Participant Statistical Areas Program.  The Census Bureau delineates census tracts in situations where no local participant existed or where state, local, or tribal governments declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of statistical data.

Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people.  A census tract usually covers a contiguous area; however, the spatial size of census tracts varies widely depending on the density of settlement.  Census tract boundaries are delineated with the intention of being maintained over a long time so that statistical comparisons can be made from census to census.  Census tracts occasionally are split due to population growth or merged as a result of substantial population decline.

For more information, click here.

Census Block Group: Block Groups (BGs) are statistical divisions of census tracts, are generally defined to contain between 600 and 3,000 people, and are used to present data and control block numbering.  A block group consists of clusters of blocks within the same census tract that have the same first digit of their four-digit census block number.  For example, blocks 3001, 3002, 3003, . . ., 3999 in census tract 1210.02 belong to BG 3 in that census tract.  Most BGs were delineated by local participants in the Census Bureau’s Participant Statistical Areas Program.  The Census Bureau delineated BGs only where a local or tribal government declined to participate, and a regional organization or State Data Center was not available to participate.

A BG usually covers a contiguous area.  Each census tract contains at least one BG, and BGs are uniquely numbered within the census tract.  Within the standard census geographic hierarchy, BGs never cross state, county, or census tract boundaries but may cross the boundaries of any other geographic entity.

For more information, click here.

Census Block: Blocks are statistical areas bounded by visible features, such as streets, roads, streams, and railroad tracks, and by nonvisible boundaries, such as selected property lines and city, township, school district, and county limits and short line-of-sight extensions of streets and roads.  Generally, census blocks are small in area; for example, a block in a city bounded on all sides by streets. Census blocks insuburban and rural areas may be large, irregular, and bounded by a variety of features, such as roads,streams, and transmission lines. In remote areas, census blocks may encompass hundreds of square miles.   Census blocks cover the entire territory of the United States, Puerto Rico, and the Island Areas.  Census blocks nest within all other tabulated census geographic entities and are the basis for all tabulated data.

For more information, click here.

National Land Use Land Cover Dataset (NLCD): The National Land Cover Database (NLCD) Land Cover Collection is produced through a cooperative project conducted by the Multi-Resolution Land Characteristics (MRLC) Consortium (www.mrlc.gov). The MRLC Consortium is a partnership of Federal agencies, consisting of the U.S. Geological Survey, the National Oceanic and Atmospheric Administration, the U.S. Environmental Protection Agency, the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service, the U.S. Forest Service, the National Park Service, the U.S. Fish and Wildlife Service, the Bureau of Land Management, NASA, and the U.S. Army Corps of Engineers. A primary goal of the MRLC Consortium is to generate current, consistent, and seamless national datasets of land cover, percent developed imperviousness, and percent tree canopy. NLCD 2001 land cover was created by partitioning the conterminous United States into 66 mapping zones, based on ecoregion and geographical characteristics, edge matching features, and the size requirement of Landsat mosaics. NLCD 2001 represents a seamless assembly of land cover for all 66 MRLC mapping zones. NLCD 2001 land cover was developed for all 50 states and Puerto Rico / U.S. Virgin Islands. NLCD 2006 land cover was created on a path/row basis and mosaicked to create a seamless national product. NLCD 2006 land cover was developed for the conterminous United States. NLCD 2011 land cover was created on a path/row basis and mosaicked to create a seamless national product. The data in NLCD 2011 are completely integrated with NLCD 2001 and NLCD 2006. As part of the NLCD 2011 project, the NLCD 2001 and 2006 land cover data products were revised and reissued to provide full compatibility with the new NLCD 2011 products. NLCD 2011 land cover was developed for the conterminous United States and Alaska. Questions about the NLCD Land Cover Collection can be directed to the NLCD land cover mapping team at USGS EROS, Sioux Falls, SD (605) 594-6151 or mrlc@usgs.gov.

For more information, click here.

PRISM Climate Date: PRISM datasets provide estimates of six basic climate elements: precipitation (ppt), minimum temperature (tmin), maximum temperature (tmax), dew point (tdmean), minimum vapor pressure deficit (vpdmin), and maximum  vapor pressure deficit (vpdmax).  Two derived variables, mean temperature (tmean) and vapor pressure (vpr), are sometimes included, depending on the dataset.

For more information, click here.

NARR Climate Data: The NARR project is an extension of the NCEP Global Reanalysis which is run over the North American Region. The NARR model uses the very high resolution NCEP Eta Model (32km/45 layer) together with the Regional Data Assimilation System (RDAS) which, significantly, assimilates precipitation along with other variables. The improvements in the model/assimilation have resulted in a dataset with substantial improvements in the accuracy of temperature, winds and precipitation compared to the NCEP-DOE Global Reanalysis 2. We currently have output which includes 8 times daily data at 29 levels and most of the variables.

For  more information, click here.