DEM AGGREGATION FOR WATERSHED MODELING1

ABSTRACT: The widely available USGS 7.5‐minute Digital Elevation Model (DEM) has a cell size of approximately 30 m × 30 m. This high resolution topographic information is impractical for many applications of distributed hydrologic and water quality models. In this study, cells were aggregated into coarse‐resolution areal units, termed grids, and a method to approximate flow direction for coarse‐resolution grids from 30 m DEM cells was developed. The method considers the flow path defined from the fine‐resolution DEM in determining a grid's flow direction and makes flow directions for grids closely follow the flow pattern suggested by the DEM. The aggregation method was applied to a DEM of Goodwater Creek, a nearly flat watershed that is located in central Missouri. The drainage networks derived for different levels of cell aggregations showed that grid aggregates of the Goodwater Creek watershed provided an adequate representation of the landscape topography.     

UNCERTAINTY ANALYSIS OF RUNOFF ESTIMATES FROM A RUNOFF CONTOUR MAP1

ABSTRACT: The U.S. Environmental Protection Agency (EPA) in cooperation with the U.S. Geological Survey (USGS) conducted an analysis to quantify the uncertainty associated with interpolating runoff to specific sites using a runoff contour map. We interpolated runoff to 93 gaged watersheds from a runoff contour map using (1) hand interpolation to the watershed outlet, (2) a computer interpolation to the watershed outlet, and (3) hand interpolation to the watershed centroid. We compared the interpolated values to the actual gaged values and found that there was a bias in the average interpolated value for runoff estimated at basin outlets, with interpolated values being less than the actual. We found no significant difference between the hand interpolation method and the computer interpolation method except that the computer method tended to have higher variability due to factors inherent to the software used. There were no strong spatial correlations or regional patterns in the runoff interpolations, which indicates that there are no regional biases introduced in the development of the contour map. We determined that we could estimate runoff, on the average, within approximately 8.9 cm (3.5 in; 15 percent) of the measured value using the three methods. The results of this work indicate that runoff contour maps can he used in regional studies to estimate runoff to ungaged systems with quantifiable uncertainty.     

INTEGRATING GEOGRAPHIC INFORMATION SYSTEMS IN GROUND-WATER APPLICATIONS USING NUMERICAL MODELING TECHNIQUES1

ABSTRACT: The Sand and Gravel Aquifer is the sole source of potable water in Escambia County, Florida. In order to better understand the hydraulics of the aquifer, a numerical computer model of the aquifer was developed. The model applied a finite element technique which allowed for density-dependent transport and flow in three dimensions. The modeling technique was integrated with GIS to develop a system for optimal management of the resource. The GIS was the primary tool in the development of the model grid, as well as being the integral component in the modeling procedure. Multiple model grids were developed for simulating regional flow and local flow/transport phenomena. The model grids were generated by the GIS where nodal and element sequencing were recorded. The grid topology was stored in the GIS with the element numbers, node numbers, and the related hydrogeologic attributes. The Triangulated Irregular Network (TIN) module was used for transferring interpolated value between GIS coverages. TIN allowed a fit of the model grid to the physical dimensions of the aquifer and for interpolating boundary values for telescopically refined grids. Calculations between TIN surfaces provided the residuals of the dependent variable from observed TIN surfaces. Model calibration was conducted within the GIS environment through a combination of visual and relational querying. The GIS provided an integrated environment which facilitated model analyses and data storage and retrieval.     

基于地统计的区域气象要素空间插值与分布研究

利用地统计学的理论与方法,以黄土高原南部地区为例,分别运用径向基函数法、普通克里格法和反距离权重法对研究区内139个气象台站2010年的年平均气温和年降水量进行了空间插值,并利用交叉检验方法对插值精度进行了评估。评估结果表明,3种空间插值方法对研究区域内的气象要素进行统计内插的效果都较好。对气温来说,径向基函数法最好,其均方根预测误差值仅为2.263,其次是反距离权重法和普通克里格最好,均方根预测误差值分别仅为2.377和2.38;对降水来说,则是普通克里格法最优,均方根预测误差最小,且其标准均方根预测误差值为0.8205,接近于1。由此可见,对年均降水量采用普通克里格插值法的精度较高。     

GIS‐Based Spatial Precipitation Estimation: A Comparison of Geostatistical Approaches1

Abstract: As one of the primary inputs that drive watershed dynamics, the estimation of spatial variability of precipitation has been shown to be crucial for accurate distributed hydrologic modeling. In this study, a Geographic Information System program, which incorporates Nearest Neighborhood (NN), Inverse Distance Weighted (IDW), Simple Kriging (SK), Ordinary Kriging (OK), Simple Kriging with Local Means (SKlm), and Kriging with External Drift (KED), was developed to facilitate automatic spatial precipitation estimation. Elevation and spatial coordinate information were used as auxiliary variables in SKlm and KED methods. The above spatial interpolation methods were applied in the Luohe watershed with an area of 5,239 km², which is located downstream of the Yellow River basin, for estimating 10 years’ (1991‐2000) daily spatial precipitation using 41 rain gauges. The results obtained in this study show that the spatial precipitation maps estimated by different interpolation methods have similar areal mean precipitation depth, but significantly different values of maximum precipitation, minimum precipitation, and coefficient of variation. The accuracy of the spatial precipitation estimated by different interpolation methods was evaluated using a correlation coefficient, Nash‐Sutcliffe efficiency, and relative mean absolute error. Compared with NN and IDW methods that are widely used in distributed hydrologic modeling systems, the geostatistical methods incorporated in this GIS program can provide more accurate spatial precipitation estimation. Overall, the SKlm_EL_X and KED_EL_X, which incorporate both elevation and spatial coordinate as auxiliary into SKlm and KED, respectively, obtained higher correlation coefficient and Nash‐Sutcliffe efficiency, and lower relative mean absolute error than other methods tested. The GIS program developed in this study can serve as an effective and efficient tool to implement advanced geostatistics methods that incorporate auxiliary information to improve spatial precipitation estimation for hydrologic models.     

WATER QUALITY MODEL OUTPUT UNCERTAINTY AS AFFECTED BY SPATIAL RESOLUTION OF INPUT DATA1

ABSTRACT: Resolution of the input GIS data used to parameterize distributed‐parameter hydrologic/water quality models may affect uncertainty in model outputs and impact the subsequent application of model results in watershed management. In this study we evaluated the impact of varying spatial resolutions of DEM, land use, and soil data (30 × 30 m, 100 × 100 m, 150 × 150 m, 200 × 200 m, 300 × 300 m, 500 × 500 m, and 1,000 × 1,000 m) on the uncertainty of SWAT predicted flow, sediment, NO₃‐N, and TP transport. Inputs included measured hydrologic, meteorological, and watershed characteristics as well as water quality data from the Moores Creek watershed in Washington County, Arkansas. The SWAT model output was most affected by input DEM data resolution. A coarser DEM data resolution resulted in decreased representation of watershed area and slope and increased slope length. Distribution of pasture, forest, and urban areas within the watershed was significantly affected at coarser resolution of land use and resulted in significant uncertainty in predicted sediment, NO₃‐N, and TP output. Soils data resolution had no significant effect on flow and NO₃‐N predictions; however, sediment was overpredicted by 26 percent, and TP was underpredicted by 26 percent at 1,000 m resolution. This may be due to change in relative distribution of various hydrologic soils groups (HSGs) in the watershed. Minimum resolution for input GIS data to achieve less than 10 percent model output error depended upon the output variable of interest. For flow, sediment, NO₃‐N, and TP predictions, minimum DEM data resolution should range from 30 to 300 m, whereas minimum land use and soils data resolution should range from 300 to 500 m.     

EFFECTS OF IMPROVED PRECIPITATION ESTIMATES ON AUTOMATED RUNOFF MAPPING: EASTERN UNITED STATES1

ABSTRACT: We evaluated maps of runoff created by means of two automated procedures. We implemented each procedure using precipitation estimates of both 5-km and 10-km resolution from PRISM (Parameter-elevation Regressions on Independent Slopes Model). Our goal was to determine if using the 5-km PRISM estimates would improve map accuracy. Visual inspection showed good general agreement among our runoff maps, as well as between our maps and one produced using a manual method. A quantitative uncertainty analysis comparing runoff interpolated from our maps with gage data that had been withheld showed slightly smaller actual and percentage interpolation errors for the 5-km PRISM-based maps. Our analyses suggest a modest region-wide improvement in runoff map accuracy with the use of PRISM-based precipitation estimates of 5-km (compared to 10-km) resolution.     

A CyberGIS Integration and Computation Framework for High‐Resolution Continental‐Scale Flood Inundation Mapping

We present a Digital Elevation Model‐based hydrologic analysis methodology for continental flood inundation mapping (CFIM), implemented as a cyberGIS scientific workflow in which a 1/3rd arc‐second (10 m) height above nearest drainage (HAND) raster data for the conterminous United States (CONUS) was computed and employed for subsequent inundation mapping. A cyberGIS framework was developed to enable spatiotemporal integration and scalable computing of the entire inundation mapping process on a hybrid supercomputing architecture. The first 1/3rd arc‐second CONUS HAND raster dataset was computed in 1.5 days on the cyberGIS Resourcing Open Geospatial Education and Research supercomputer. The inundation mapping process developed in our exploratory study couples HAND with National Water Model forecast data to enable near real‐time inundation forecasts for CONUS. The computational performance of HAND and the inundation mapping process were profiled to gain insights into the computational characteristics in high‐performance parallel computing scenarios. The establishment of the CFIM computational framework has broad and significant research implications that may lead to further development and improvement of flood inundation mapping methodologies.     

Prediction of Streamflow Regime and Annual Runoff for Ungauged Basins Using a Distributed Monthly Water Balance Model1

Moore, R.D. (Dan), J.W. Trubilowicz, and J.M. Buttle, 2011. Prediction of Streamflow Regime and Annual Runoff for Ungauged Basins Using a Distributed Monthly Water Balance Model. Journal of the American Water Resources Association (JAWRA) 48(1): 32‐42. DOI: 10.1111/j.1752‐1688.2011.00595.x Abstract: Prediction of streamflow in ungauged basins is a global challenge, but is particularly an issue in physiographically complex regions like British Columbia (BC), Canada. The objective of this study was to assess the accuracy of a simple water balance model that can be run using existing spatial datasets. The model was developed by modifying an existing monthly water balance model to account for interception loss from forest canopy, glacier melt, and evaporation from lakes. The model was run using monthly climate normals from the ClimateBC application, which have a horizontal resolution of 400 m. Each ClimateBC grid cell was classified as forest, open land, glacier or water surface based on provincial scale digital maps of biogeoclimatic zones, glaciers, and water. The output was monthly mean runoff from each grid cell. These values were integrated within the catchment boundaries for streams gauged by the Water Survey of Canada. Annual runoff was predicted with modest accuracy: after updating the predicted runoff by interpolating errors from neighboring gauged streams, the mean absolute error was 25.4% of the gauged value, and 52% of the streams had errors less than 20%. However, the model appears to be quite robust in distinguishing between pluvial, hybrid, and melt‐dominated hydroclimatic regimes, and therefore has promise as a tool for catchment classification.     

Understanding the Large‐Scale Influence of Levees on Floodplain Connectivity Using a Hydrogeomorphic Approach

The widespread construction of levees has reduced river–floodplain connectivity and altered associated fluvial processes in many river systems. Despite the recognition that levees can alter floodplain connectivity, few studies have examined the role of levees in reducing floodplain areas at large watershed scales. This paper explores the application of a hydrogeomorphic floodplain inundation model in the Wabash Basin, located in the Midwestern United States, to assess changes in floodplain area in levee‐protected areas. We evaluate 10‐ and 30‐m topographic resolutions and spatially examine the influence of levees on floodplain area in relation to river network attributes. Generally, floodplains in levee‐protected areas were influenced by topographic resolution, stream order, and elevation details of levees found in topography datasets. We show, when compared to Federal Emergency Management Agency maps, our approach underpredicts floodplain area when using 10‐m resolution topography data but only slightly overpredicts when using 30‐m resolution data. After removing details of levees from topography data, we found changes in floodplain area varied spatially, but basin‐aggregate results changed little compared to topography datasets that contain levees, though larger floodplain areas were produced in some regions where levees were removed. This work contributes to a growing research emphasis on using hydrogeomorphic floodplain models to understand floodplain disconnectivity.     

Evaluation of Methods for Delineating Riparian Zones in a Semi‐Arid Montane Watershed

Riparian zones in semi‐arid, mountainous regions provide a disproportionate amount of the available wildlife habitat and ecosystem services. Despite their importance, there is little guidance on the best way to map riparian zones for broad spatial extents (e.g., large watersheds) when detailed maps from field data or high‐resolution imagery and terrain data are not available. Using well‐established accuracy metrics (e.g., kappa, precision, computational complexity), we evaluated eight methods commonly used to map riparian zones. Focusing on a semi‐arid, mountainous watershed, we found that the most accurate and robust method for mapping riparian zones combines data on upstream drainage area and valley topography. That method performed best regardless of stream order, and was most effective when implemented with fine resolution topographic and stream line data. Other commonly used methods to model riparian zones, such as those based on fixed‐width buffers, yielded inaccurate results. We recommend that until very‐high resolution (<1 m) elevation data are available at broad extents, models of riparian zones for semi‐arid mountainous regions should incorporate drainage area, valley topography, and quantify uncertainty.     

Application of Multivariate Statistical Methods and Water-Quality Index to Evaluation of Water Quality in the Kashkan River

The Kashkan River (KR), located in the west of Iran, is a major source of water supply for residential and agricultural areas as well as livestock. The objective of this study was to assess the spatial and long temporal variations of surface water quality of the KR based on measured chemical ions. The Canadian Council of Ministers of Environment Water Quality Index (CCME WQI) technique was utilized using measurements from 10 sampling stations during a period of 36 years (1974–2009). The measured data included cations (Na⁺, K⁺, Ca²⁺, Mg²⁺), anions (HCO₃ ^?, Cl^?, SO₄ ^2?), pH, and electrical conductivity. Principal component analysis was performed to identify which of the parameters to be included in the CCME WQI calculations were actually correlated and which ones were responsible for most of the variance observed in the water-quality data. In addition, KR water quality was evaluated for its suitability for drinking and irrigation purposes using conventional methods. Last, trend detection in the WQI time series of the KR showed water-quality degradation at all sampling stations, whereas the Jelhool sub-basin more adversely affects the quality of KR water in the watershed. Nonetheless, on average, the water quality of the KR was rated as fair.     

Investigating the Fate and Transport of Escherichia coli in the Charles River,Boston, Using High‐Resolution Observation and Modeling1

Abstract: The processes affecting the fate and transport of Escherichia coli in surface waters were investigated using high‐resolution observation and modeling. The concentration patterns in Boston’s Charles River were observed during four sampling events with a total of 757 samples, including two spatial surveys with two along‐river (1,500 m length) and three across‐river (600 m length) transects at approximately 25‐m intervals, and two temporal surveys at a fixed location (Community Boating) over seven days at hourly intervals. The data reveal significant spatial and temporal structure at scales not resolved by typical monitoring programs. A mechanistic, time‐variable, three‐dimensional coupled hydrodynamic and water quality model was developed using the ECOMSED and RCA modeling frameworks. The computational grid consists of 3,066 grid cells with average length dimension of 25 m. Forcing functions include upstream and downstream boundary conditions, Stony Brook, and Muddy River (major tributaries) combined sewer overflow (CSO) and non‐CSO discharge and wind. The model generally reproduces the observed spatial and temporal patterns. This includes the presence and absence of a plume in the study area under similar loading, but different hydrodynamic conditions caused by operation of the New Charles River Dam (downstream) and wind. The model also correctly predicts an episode of high concentrations at the time‐series station following seven days of no rainfall. The model has an overall root mean square error (RMSE) of 250 CFU/100 ml and an error rate (above or below the USEPA‐recommended single sample criteria value of 235 CFU/100 ml) of 9.4%. At the time series station, the model has an RMSE of 370 CFU/100 ml and an error rate of 15%.     

Utility of Gridded Rainfall for IHACRES Daily River Flow Predictions in Southern California Watersheds1

Abstract: Studies to regionalize conceptual hydrologic models generally require rainfall and river flow data from multiple watersheds. Besides the considerable time (cost) to assemble and process rainfall data for many watersheds, investigators often need to choose from a number of candidate gauges, subjectively weighing the relative importance of proximity and elevation to select a representative rainfall dataset. The Unified Raingauge Dataset (URD) is a gridded daily rainfall dataset that covers the conterminous United States at 0.25 × 0.25 degrees spatial resolution and is available from 1948 to present. The objective of this study was to determine whether uncertainty in daily river flow predictions using the conceptual hydrologic model IHACRES in small to moderate size watersheds (50‐400 km²) in southern California would increase if URD gridded rainfall data were used in place of single rain gauge data to calibrate the model. Rain gauge data were obtained from the gauge nearest the watershed centroid and the gauge closest in elevation to the watershed mean elevation. Results from 20 randomly selected watersheds indicated that IHACRES calibration performance was similar using rainfall data from the URD grids and rain gauge data. There was some evidence of greater uncertainties associated with the URD calibrations in areas where topography may affect rainfall amounts. In contrast to the URD data, monthly gridded data produced by the Parameter‐Elevation Regressions on Independent Slopes Model (PRISM) includes adjustments for elevation and produces gridded values at a finer spatial resolution (4 km²). A limited test on two watersheds demonstrated that scaling the URD daily rainfall estimates to match the PRISM monthly values may improve rainfall estimates and model simulation performance.     

A Fully Distributed Implementation of Mean Annual StreamFlow Regional Regression Equations1

Abstract: Estimates of mean annual streamflow are needed for a variety of hydrologic assessments. Away from gage locations, regional regression equations that are a function of upstream area, precipitation, and temperature are commonly used. Geographic information systems technology has facilitated their use for projects, but traditional approaches using the polygon overlay operator have been too inefficient for national scale applications. As an alternative, the Elevation Derivatives for National Applications (EDNA) database was used as a framework for a fully distributed implementation of mean annual streamflow regional regression equations. The raster “flow accumulation” operator was used to efficiently achieve spatially continuous parameterization of the equations for every 30 m grid cell of the conterminous United States (U.S.). Results were confirmed by comparing with measured flows at stations of the Hydro‐Climatic Data Network, and their applications value demonstrated in the development of a national geospatial hydropower assessment. Interactive tools at the EDNA website make possible the fast and efficient query of mean annual streamflow for any location in the conterminous U.S., providing a valuable complement to other national initiatives (StreamStats and the National Hydrography Dataset Plus).     

OPEN CHANNEL INTERPOLATION OF CROSS SECTIONAL PROPERTIES1

ABSTRACT: This paper confirms the use of interpolated data to refine water surface profiles. Sources of error within these computations are due to truncation error, inaccuracies in geometric data, and improper modeling. Confirmation includes the development of an equation that models the effect of data measurement error on the computed water surface profile. A review of interpolation procedures includes a proposed method based upon geometric properties.     

PRISM vs. CFSR Precipitation Data Effects on Calibration and Validation of SWAT Models

Precipitation is one of the most important drivers in watershed models. Our objective was to compare two sources of interpolated precipitation data in terms of their effect on calibration and validation of two Soil and Water Assessment Tool (SWAT) models. One model was a suburban watershed in metropolitan Atlanta, Georgia. The precipitation sources were Parameter‐elevation Relationships on Independent Slopes Model (PRISM) data on a 4‐km grid and climate forecast system reanalysis (CFSR) data on a 38‐km grid. The PRISM data resulted in a better fit to the calibration data (Nash Sutcliffe efficiency [NSE] = 0.64, Kling‐Gupta efficiency [KGE] = 0.74, p‐factor = 0.84, and r‐factor = 0.43) than the CFSR data (NSE = 0.47, KGE = 0.53, p‐factor = 0.67, and r‐factor = 0.39). Validation results were similar. Sensitive parameters were similar in both the PRISM and CFSR models, but fitted values indicated more rapid groundwater flow to the streams with the PRISM data. The same comparison was made in the Big Creek watershed located approximately 1,000 km away, in central Louisiana. Results were similar with a more responsive groundwater system indicating PRISM data may produce better predictions of streamflow because of a more accurate estimate of rainfall within a watershed or because of a denser grid. Our study implies PRISM is providing a better estimate than CFSR of precipitation within a watershed when rain gauge data are not available, resulting in more accurate simulations of streamflows at the watershed outlet. Editor's note: This paper is part of the featured series on SWAT Applications for Emerging Hydrologic and Water Quality Challenges. See the February 2017 issue for the introduction and background to the series.     

Downward shortwave radiation estimation and spatial assessment on sites over complex terrain applying integrative approach of MTCLIM-XL,interpolation, RS and GIS

Downward shortwave radiation (DSR) is a highly variable solar source on spatiotemporal basis and essential for energy and agriculture systems, while its calculations are helpful in the environment-related studies, climatology, and monitoring fire risk. Statistical methods developed to extrapolate values of climatic variables and radiation could fail to generate reliable findings of DSR over a complex terrain without considering local topographic factors. In the present study, we proposed an integrative approach of MTCLIM-XL extrapolation with remote sensing (RS) and geographic information system (GIS) to estimate real-time DSR and its spatial potential over surfaces of contrasting elevated sites on a mountainous terrain of Quetta (Pakistan).Based on methodological approach, remote sensing data product of high-resolution DEM (SRTM 30m) was processed to extract topographic data, and meteorological data were obtained from a base site, Subsequently, MTCLIM-XL executed the simulation to calculate the daily-based DSR (W/m²).Spatial distribution of DSR was generated by applying deterministic interpolation with complementing quantification of Hillshade analysis for spatially obstructive surfaces, and resultant spatial hotspot-based potential was assessed on basis of specified threshold level (above 250 W/m² = 2 kW h/m²) over the specified area. We observed usable potential of DSR at target sites and its spatial distribution during the study period of 2015 to April 2016. Using EUMETSAT CMSAF data as a standard, the validation demonstrates agreeable results of low RMSE and high correlation coefficient values for selected sites, except some sites with relatively high elevations and irregular gradients. Analysis of solar zenith angle to evaluate its inverse relation with increment in DSR values shows agreeable high inverse relation, while the negative trend for only some sites features relatively high rugged topography. In conclusion, MTCLIM-XL with RS and GIS integration manifests as a reliable approach for estimation and spatial potential assessment-based exploration of DSR over complex terrain having no ground data, while prospectively it will complement to the environment-related studies on local to mesoscale.     

Assessing the Impact of Areal Precipitation Input on Streamflow Simulations Using the SWAT Model1

Masih Ilyas, Shreedhar Maskey, Stefan Uhlenbrook, and Vladimir Smakhtin, 2011. Assessing the Impact of Areal Precipitation Input on Streamflow Simulations Using the SWAT Model. Journal of the American Water Resources Association (JAWRA) 47(1):179‐195. DOI: 10.1111/j.1752‐1688.2010.00502.x Abstract: Reduction of input uncertainty is a challenge in hydrological modeling. The widely used model Soil Water Assessment Tool (SWAT) uses the data of a precipitation gauge nearest to the centroid of each subcatchment as an input for that subcatchment. This may not represent overall catchment precipitation conditions well. This paper suggests an alternative – using areal precipitation obtained through interpolation. The effectiveness of this alternative is evaluated by comparing its simulations with those based on the standard SWAT precipitation input procedure. The model is applied to mountainous semiarid catchments in the Karkheh River basin, Iran. The model performance is evaluated at daily, monthly, and annual scales by using a number of performance indicators at 15 streamflow gauging stations each draining an area in the range of 590‐42,620 km². The comparison suggests that the use of areal precipitation improves model performance particularly in small subcatchments in the range of 600‐1,600 km². The modified areal precipitation input results in increased reliability of simulated streamflows in the areas of low rain gauge density. Both precipitation input methods result in reasonably good simulations for larger catchments (over 5,000 km²). The use of areal precipitation input improves the accuracy of simulated streamflows with spatial resolution and density of rain gauges having significant impact on results.     

Identifying Urban Features from LiDAR for a High‐Resolution Urban Hydrologic Model

Light Detection and Ranging (LiDAR), is relatively inexpensive, provides high spatial resolution sampling at great accuracy, and can be used to generate surface terrain and land cover datasets for urban areas. These datasets are used to develop high‐resolution hydrologic models necessary to resolve complex drainage networks in urban areas. This work develops a five‐step algorithm to generate indicator fields for tree canopies, buildings, and artificial structures using Geographic Resources Analysis Support System (GRASS‐GIS), and a common computing language, Matrix Laboratory. The 54 km² study area in Parker, Colorado consists of twenty‐four 1,500 × 1,500 m LiDAR subsets at 1 m resolution with varying degrees of urbanization. The algorithm correctly identifies 96% of the artificial structures within the study area; however, application success is dependent upon urban extent. Urban land use fractions below 0.2 experienced an increase in falsely identified building locations. ParFlow, a three‐dimensional, grid‐based hydrological model, uses these building and artificial structure indicator fields and digital elevation model for a hydrologic simulation. The simulation successfully develops the complex drainage network and simulates overland flow on the impervious surfaces (i.e., along the gutters and off rooftops) made possible through this spatial analysis process.