Investigation of the Curve Number Method For Surface Runoff Estimation In Tropical Regions

This study tests the applicability of the curve number (CN) method within the Soil and Water Assessment Tool (SWAT) to estimate surface runoff at the watershed scale in tropical regions. To do this, surface runoff simulated using the CN method was compared with observed runoff in numerous rainfall‐runoff events in three small tropical watersheds located in the Upper Blue Nile basin, Ethiopia. The CN method generally performed well in simulating surface runoff in the studied watersheds (Nash‐Sutcliff efficiency [NSE] > 0.7; percent bias [PBIAS] < 32%). Moreover, there was no difference in the performance of the CN method in simulating surface runoff under low and high antecedent rainfall (PBIAS for both antecedent conditions: ~30%; modified NSE: ~0.4). It was also found that the method accurately estimated surface runoff at high rainfall intensity (e.g., PBIAS < 15%); however, at low rainfall intensity, the CN method repeatedly underestimated surface runoff (e.g., PBIAS > 60%). This was possibly due to low infiltrability and valley bottom saturated areas typical of many tropical soils, indicating that there is scope for further improvements in the parameterization/representation of tropical soils in the CN method for runoff estimation, to capture low rainfall‐intensity events. In this study the retention parameter was linked to the soil moisture content, which seems to be an appropriate approach to account for antecedent wetness conditions in the tropics.     

Long‐Term High‐Resolution Radar Rainfall Fields for Urban Hydrology

Accurate records of high‐resolution rainfall fields are essential in urban hydrology, and are lacking in many areas. We develop a high‐resolution (15 min, 1 km²) radar rainfall data set for Charlotte, North Carolina during the 2001‐2010 period using the Hydro‐NEXRAD system with radar reflectivity from the National Weather Service Weather Surveillance Radar 1988 Doppler weather radar located in Greer, South Carolina. A dense network of 71 rain gages is used for estimating and correcting radar rainfall biases. Radar rainfall estimates with daily mean field bias (MFB) correction accurately capture the spatial and temporal structure of extreme rainfall, but bias correction at finer timescales can improve cold‐season and tropical cyclone rainfall estimates. Approximately 25 rain gages are sufficient to estimate daily MFB over an area of at least 2,500 km², suggesting that robust bias correction is feasible in many urban areas. Conditional (rain‐rate dependent) bias can be removed, but at the expense of other performance criteria such as mean square error. Hydro‐NEXRAD radar rainfall estimates are also compared with the coarser resolution (hourly, 16 km²) Stage IV operational rainfall product. Stage IV is adequate for flood water balance studies but is insufficient for applications such as urban flood modeling, in which the temporal and spatial scales of relevant hydrologic processes are short. We recommend the increased use of high‐resolution radar rainfall fields in urban hydrology.     

Improvement in HSPF's Low‐Flow Predictions by Implementation of a Power Law Groundwater Storage‐Discharge Relationship

We have enhanced the ability of a widely used watershed model, Hydrologic Simulation Program — FORTRAN (HSPF), to predict low flows by reconfiguring the algorithm that simulates groundwater discharge. During dry weather periods, flow in most streams consists primarily of base flow, that is, groundwater discharged from underlying aquifers. In this study, HSPF's groundwater storage‐discharge relationship is changed from a linear to a more general nonlinear relationship which takes the form of a power law. The nonlinear algorithm is capable of simulating streamflow recession curves that have been found in some studies to better match observed dry weather hydrographs. The altered version of HSPF is implemented in the Chesapeake Bay Program's Phase 5 Model, an HSPF‐based model that simulates nutrient and sediment loads to the Chesapeake Bay, and is tested in the upper Potomac River basin, a 29,950 km² drainage area that is part of the Bay watershed. The nonlinear relationship improved median Nash‐Sutcliffe efficiencies for log daily flows at the model's 45 calibration points. Mean absolute percent error on low‐flow days dropped in five major Potomac River tributaries by up to 12 percentage points, and in the Potomac River itself by four percentage points, where low‐flow days were defined as days when observed flows were in the lowest 5th percentile range. Percent bias on low‐flow days improved by eight percentage points in the Potomac River, from ?11 to ?3%.     

Evaluation of CFSR climate data for hydrologic prediction in data‐scarce watersheds: an application in the Blue Nile River Basin

Data scarcity has been a huge problem in modeling the water resources of the Upper Blue Nile basin, Ethiopia. Satellite data and different statistical methods have been used to improve the quality of conventional meteorological data. This study assesses the applicability of the National Centers for Environmental Prediction's Climate Forecast System Reanalysis (CFSR) climate data in modeling the hydrology of the region. The Soil and Water Assessment Tool was set up to compare the performance of CFSR weather with that of conventional weather in simulating observed streamflow at four river gauging stations in the Lake Tana basin — the upper part of the Upper Blue Nile basin. The conventional weather simulation performed satisfactorily (e.g., NSE ≥ 0.5) for three gauging stations, while the CFSR weather simulation performed satisfactorily for two. The simulations with CFSR and conventional weather yielded minor differences in the water balance components in all but one watershed, where the CFSR weather simulation gave much higher average annual rainfall, resulting in higher water balance components. Both weather simulations gave similar annual crop yields in the four administrative zones. Overall the simulation with the conventional weather performed better than the CFSR weather. However, in data‐scarce regions such as remote parts of the Upper Blue Nile basin, CFSR weather could be a valuable option for hydrological predictions where conventional gauges are not available.     

Effectiveness of Multi‐Site Weather Generator for Hydrological Modeling1

Khalili, Malika, François Brissette, and Robert Leconte, 2011. Effectiveness of Multi‐site Weather Generator for Hydrological Modeling. Journal of the American Water Resources Association (JAWRA) 1‐12. DOI: 10.1111/j.1752‐1688.2010.00514.x Abstract: A multi‐site weather generator has been developed using the concept of spatial autocorrelation. The multi‐site generation approach reproduces the spatial autocorrelations observed between a set of weather stations as well as the correlations between each pair of stations. Its performance has been assessed in two previous studies using both precipitation and temperature data. The main objective of this paper is to assess the efficiency of this multi‐site weather generator compared to a uni‐site generator with respect to hydrological modeling. A hydrological model, known as Hydrotel, was applied over the Chute du Diable watershed, located in the Canadian province of Quebec. The distributed nature of Hydrotel accounts for the spatial variations throughout the watershed, and thus allows a more in‐depth assessment of the effect of spatially dependent meteorological input on runoff generation. Simulated streamflows using both the multi‐site and uni‐site generated weather data were statistically compared to flows modeled using observed data. Overall, the hydrological modeling using the multi‐site weather generator significantly outperformed that using the uni‐site generator. This latter combined to Hydrotel resulted in a significant underestimation of extreme streamflows in all seasons.     

Implications of Conceptual Channel Representation on SWAT Streamflow and Sediment Modeling

Hydrologic modeling outputs are influenced by how a watershed system is represented. Channel routing is a typical example of the mathematical conceptualization of watershed landscape and processes in hydrologic modeling. We investigated the sensitivity of accuracy, equifinality, and uncertainty of Soil and Water Assessment Tool (SWAT) modeling to channel dimensions to demonstrate how a conceptual representation of a watershed system affects streamflow and sediment modeling. Results showed the amount of uncertainty and equifinality strongly responded to channel dimensions. On the other hand, the model performance did not significantly vary with the changes in the channel representation due to the degree of freedom allowed by the conceptual nature of hydrologic modeling in the parameter calibration. Such findings demonstrated good modeling performance statistics do not necessarily mean small output uncertainty, and partial improvements in the watershed representation may neither increase modeling accuracy nor reduce uncertainty. We also showed the equifinality and uncertainty of hydrologic modeling are case‐dependent rather than specific to models or regions, suggesting great caution should be used when attempting to transfer uncertainty analysis results to other modeling studies, especially for ungauged watersheds. 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.     

Evaluation of the Global Precipitation Measurement (GPM) Satellite Rainfall Products over the Lower Colorado River Basin,Texas

Quality of precipitation products from the Integrated Multi‐satellitE Retrievals for Global Precipitation Measurement mission (IMERG) was evaluated over the Lower Colorado River Basin of Texas. Observations of several rainfall events of a wide range of magnitudes during May 2015 by a very dense network of 241 rain gauges over the basin were used as a reference. The impact of temporal and spatial downscaling of different satellite products (near/post‐real‐time) on their accuracy was studied. Generally, all IMERG products perform better when the temporal and spatial resolutions are downscaled. The Final product shows relatively better performance compared to the near‐real‐time products in terms of basic performance measures; however, regarding rainfall detection, all products show nearly similar performance. When considering rainfall detection, IMERG adequately captures the precipitation events; however, in terms of spatial patterns and accuracy, more improvements are needed. IMERG products analysis results may help developers gain insight into the regional performance of the product, improve the product algorithms, and provide information to end users on the products’ suitability for potential hydrometeorological applications. Overall, the IMERG products, even the uncalibrated product at its finest resolution, showed reasonable performance indicating their great potential for applications such as water resources management, prevention of natural disasters, and flood forecasting.     

Monitored and Modeled Correlations of Sediment and Nutrients with Chesapeake Bay Water Clarity

This article analyzes the correlations of the observed and modeled light attenuation coefficient, Kd, with in situ total suspended solids (TSS) and chlorophyll‐a concentrations in Chesapeake Bay (CB) tidal waters, and with sediment and nutrient loads from the Chesapeake watershed. Light attenuation is closely related to in situ TSS and chlorophyll‐a concentrations, however, the strength of the correlation differs among the CB segments. There are distinct differences between saline and tidal fresh segments in the main Bay, but less distinction among saline and tidal fresh segments in the tidal tributaries. The correlation between Kd with sediment and nutrient loads is complicated by the lag times of TSS and the chlorophyll‐a responses to reductions in nutrient and sediment loads from the watershed, and also due to the diverse load sources. Three sets of model sensitivity scenarios were performed with: (1) differential sediment and nutrient loads; (2) selective sediment source types; and (3) geographically isolated inputs. The model results yield similar findings as those based on observed data and provide information regarding the effect of sediment on specific water bodies. Based on the model results a method was developed to determine sediment and nutrient load reductions needed to achieve the water clarity standards of the CB segments.     

HYSTAR Sediment Model: Distributed Two‐Dimensional Simulation of Watershed Erosion and Sediment Transport Using Time‐Area Routing

An erosion and sediment transport component incorporated in the HYdrology Simulation using Time‐ARea method (HYSTAR) upland watershed model provides grid‐based prediction of erosion, transport and deposition of sediment in a dynamic, continuous, and fully distributed framework. The model represents the spatiotemporally varied flow in sediment transport simulation by coupling the time‐area routing method and sediment transport capacity approach within a grid‐based spatial data model. This avoids the common, and simplistic, approach of using the Universal Soil Loss Equation (USLE) to estimate erosion rates with a delivery ratio to relate gross soil erosion to sediment yield of a watershed, while enabling us to simulate two‐dimensional sediment transport processes without the complexity of numerical solution of the partial differential governing equations. In using the time‐area method for routing sediment, the model offers a novel alternative to watershed‐scale sediment transport simulation that provides detailed spatial representation. In predicting four‐year sediment hydrographs of a watershed in Virginia, the model provided good performance with R² of 0.82 and 0.78 and relative error of ?35% and 11% using the Yalin and Yang's sediment transport capacity equations, respectively. Prediction of spatiotemporal variation in sediment transport processes was evaluated using maps of sediment transport rates, concentrations, and erosion and deposition mass, which compare well with expected behavior of flow hydraulics and sediment transport processes.     

Use of SWAT to Estimate Spatial Scaling of Phosphorus Export Coefficients and Load Reductions Due to Agricultural BMPS

Phosphorus export coefficients (kg/ha/yr) from selected land covers, also called phosphorus yields, tend to get smaller as contributing areas get larger because some of the phosphorus mobilized on local fields gets trapped during transport to regional watershed outlets. Phosphorus traps include floodplains, wetlands, and lakes, which can then become impaired by eutrophication. The Sunrise River watershed in east central Minnesota, United States, has numerous lakes impaired by excess phosphorus. The Sunrise is tributary to the St. Croix River, whose much larger watershed is terminated by Lake St. Croix, also impaired by excess phosphorus. To support management of these impairments at both local and regional scales, a Soil and Water Assessment Tool (SWAT) model of the Sunrise watershed was constructed to estimate load reductions due to selected best management practices (BMPs) and to determine how phosphorus export coefficients scaled with contributing area. In this study, agricultural BMPs, including vegetated filter strips, grassed waterways, and reduction of soil‐phosphorus concentrations reduced phosphorus loads by 4‐20%, with similar percentage reductions at field and watershed spatial scales. Phosphorus export coefficients from cropland in rotation with corn, soybeans, and alfalfa decreased as a negative power function of contributing area, from an average of 2.12 kg/ha/yr at the upland field scale (~0.6 km²) to 0.63 kg/ha/yr at the major river basin scale (20,000 km²). 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.     

Testing of the Modified Streambank Erosion and Instream Phosphorus Routines for the SWAT Model

In some watersheds, streambanks are a source of two major pollutants, phosphorus (P) and sediment. P originating from both uplands and streambanks can be transported and stored indefinitely on floodplains, streambanks, and in closed depressions near the stream. The objectives of this study were to (1) test the modified streambank erosion and instream P routines for the Soil and Water Assessment Tool (SWAT) model in the Barren Fork Creek watershed in northeast Oklahoma, (2) predict P in the watershed with and without streambank‐derived P, and (3) determine the significance of streambank erosion P relative to overland P sources. Measured streambank and channel parameters were incorporated into a flow‐calibrated SWAT model and used to estimate streambank erosion and P for the Barren Fork Creek using modified streambank erosion and instream P routines. The predicted reach‐weighted streambank erosion was 40 kg/m vs. the measured 42 kg/m. Streambank erosion contributed 47% of the total P to the Barren Fork Creek and improved P predictions compared to observed data, especially during the high‐flow events. Of the total P entering the stream system, approximately 65% was removed via the watershed outlet and 35% was stored in the floodplain and stream system. This study successfully applied the SWAT model's modified streambank erosion and instream P routines and demonstrated that streambank‐derived P can improve P modeling at the watershed scale. 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.     

Effects of Spatial and Temporal Data Aggregation on the Performance of the Multi‐Radar Multi‐Sensor System

The objectives of this study were to (1) evaluate the performance of the Multi‐Radar Multi‐Sensor (MRMS) system in capturing precipitation compared to gauge data, and (2) assess the effects of spatial (1–50 km) and temporal (15–120 min) data aggregation scales on the performance of the MRMS system. Point‐to‐grid comparisons were conducted between 215 rain gauges and the MRMS system. The MRMS system at 1 km spatial and 15 min temporal resolutions captured precipitation reasonably well with average R², root mean square error (RMSE), and percent bias (PBIAS) values of 0.65, 0.5 mm, and 11.9 mm; whereas Threat Score, probability of detection, and false alarm ratio were 0.57, 0.92, and 0.40, respectively. Decreasing temporal resolution from 15 min to two hours resulted in an increase in R² and a decrease in RMSE, whereas PBIAS was not affected. Reducing spatial resolution from 1 to 50 km resulted in increases in R² and PBIAS, whereas RMSE was decreased. Increasing spatial aggregation scale from 1 to 50 km resulted in an R² increase of only 0.08. Similarly, improvement in R² was only modest (0.17) compared to an eightfold reduction in temporal resolution (from 15 min to two hours). While aggregating data at coarser temporal resolutions resolved some of the under/overestimation issues of the MRMS system, it was apparent even at coarser spatial and temporal resolutions the MRMS system inherently overestimated smaller precipitation events while underestimated bigger events.     

Spatial and Temporal Evaluation of Hydrological Response to Climate and Land Use Change in Three South Dakota Watersheds

This study analyzed changes in hydrology between two recent decades (1980s and 2010s) with the Soil and Water Assessment Tool (SWAT) in three representative watersheds in South Dakota: Bad River, Skunk Creek, and Upper Big Sioux River watersheds. Two SWAT models were created over two discrete time periods (1981‐1990 and 2005‐2014) for each watershed. National Land Cover Datasets 1992 and 2011 were, respectively, ingested into 1981‐1990 and 2005‐2014 models, along with corresponding weather data, to enable comparison of annual and seasonal runoff, soil water content, evapotranspiration (ET), water yield, and percolation between these two decades. Simulation results based on the calibrated models showed that surface runoff, soil water content, water yield, and percolation increased in all three watersheds. Elevated ET was also apparent, except in Skunk Creek watershed. Differences in annual water balance components appeared to follow changes in land use more closely than variation in precipitation amounts, although seasonal variation in precipitation was reflected in seasonal surface runoff. Subbasin‐scale spatial analyses revealed noticeable increases in water balance components mostly in downstream parts of Bad River and Skunk Creek watersheds, and the western part of Upper Big Sioux River watershed. Results presented in this study provide some insight into recent changes in hydrological processes in South Dakota watersheds. 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.     

Dry Weather Water Quality Loadings in Arid,Urban Watersheds of the Los Angeles Basin,California, USA1

Abstract: Dry weather runoff in arid, urban watersheds may consist entirely of treated wastewater effluent and/or urban nonpoint source runoff, which can be a source of bacteria, nutrients, and metals to receiving waters. Most studies of urban runoff focus on stormwater, and few have evaluated the relative contribution and sources of dry weather pollutant loading for a range of constituents across multiple watersheds. This study assessed dry weather loading of nutrients, metals, and bacteria in six urban watersheds in the Los Angeles region of southern California to estimate relative sources of each constituent class and the proportion of total annual load that can be attributed to dry weather discharge. In each watershed, flow and water quality were sampled from storm drain and treated wastewater inputs, as well as from in‐stream locations during at least two time periods. Data were used to calculate mean concentrations and loads for various sources. Dry weather loads were compared with modeled wet weather loads under a range of annual rainfall volumes to estimate the relative contribution of dry weather load. Mean storm drain flows were comparable between all watersheds, and in all cases, approximately 20% of the flowing storm drains accounted for 80% of the daily volume. Wastewater reclamation plants (WRP) were the main source of nutrients, storm drains accounted for almost all the bacteria, and metals sources varied by constituent. In‐stream concentrations reflected major sources, for example nutrient concentrations were highest downstream of WRP discharges, while in‐stream metals concentrations were highest downstream of the storm drains with high metals loads. Comparison of wet vs. dry weather loading indicates that dry weather loading can be a significant source of metals, ranging from less than 20% during wet years to greater than 50% during dry years.     

EFFECTS OF WATERSHED REPRESENTATION ON RUNOFF AND SEDIMENT YIELD MODELING1

ABSTRACT: This paper evaluates the effects of watershed geometric representation (i.e., plane and channel representation) on runoff and sediment yield simulations in a semiarid rangeland watershed. A process based, spatially distributed runoff erosion model (KINEROS2) was used to explore four spatial representations of a 4.4 ha experimental watershed. The most complex representation included all 96 channel elements identifiable in the field. The least complex representation contained only five channel elements. It was concluded that oversimplified watershed representations greatly influence runoff and sediment yield simulations by inducing excessive infiltration on hillslopes and distorting runoff patterns and sediment fluxes. Runoff and sediment yield decrease systematically with decreasing complexity in watershed representation. However, less complex representations had less impact on runoff and sediment‐yield simulations for small rainfall events. This study concludes that the selection of the appropriate level of watershed representation can have important theoretical and practical implications on runoff and sediment yield modeling in semiarid environments.     

EVALUATING HSPF IN AN ARID,URBANIZED WATERSHED1

ABSTRACT: The Hydrologic Simulation Program‐FORTRAN (HSPF) is a powerful time variable hydrologic model that has rarely been applied in arid environments. Here, the performance of HSPF in southern California was assessed, testing its ability to predict annual volume, daily average flow, and hourly flow. The model was parameterized with eight land use categories and physical watershed characteristics. It was calibrated using rainfall and measured flow over a five‐year period in a predominantly undeveloped watershed and it was validated using a subsequent 4‐year period. The process was repeated in a separate, predominantly urbanized watershed over the same time span. Annual volume predictions correlated well with measured flow in both the undeveloped and developed watersheds. Daily flow predictions correlated well with measured flow following rain events, but predictions were poor during extended dry weather periods in the developed watershed. This modeling difficulty during dry‐weather periods reflects the large influence of, and the poor accounting in the model for, artificially introduced water from human activities, such as landscape overwatering, that can be important sources of water in urbanized arid environments. Hourly flow predictions mistimed peak flows, reflecting spatial and temporal heterogeneity of rainfall within the watershed. Model correlation increased considerably when predictions were averaged over longer time periods, reaching an asymptote after an 11‐hour averaging window.     

A New Open‐Access HUC‐8 Based Downscaled CMIP‐5 Climate Model Forecast Dataset for the Conterminous United States

Watershed‐scale hydrologic simulation models generally require climate data inputs including precipitation and temperature. These climate inputs can be derived from downscaled global climate simulations which have the potential to drive runoff forecasts at the scale of local watersheds. While a simulation designed to drive a local watershed model would ideally be constructed at an appropriate scale, global climate simulations are, by definition, arbitrarily determined large rectangular spatial grids. This paper addresses the technical challenge of making climate simulation model results readily available in the form of downscaled datasets that can be used for watershed scale models. Specifically, we present the development and deployment of a new Coupled Model Intercomparison Project phase 5 (CMIP5) based database which has been prepared through a scaling and weighted averaging process for use at the level of U.S. Geological Survey (USGS) Hydrologic Unit Code (HUC)‐8 watersheds. The resulting dataset includes 2,106 virtual observation sites (watershed centroids) each with 698 associated time series datasets representing average monthly temperature and precipitation between 1950 and 2099 based on 234 unique climate model simulations. The new dataset is deployed on a HydroServer and distributed using WaterOneFlow web services in the WaterML format. These methods can be adapted for downscaled General Circulation Model (GCM) results for specific drainage areas smaller than HUC‐8. Two example use cases for the dataset also are presented.     

WATERSHED OPTIMIZATION OF AGRICULTURAL BEST MANAGEMENT PRACTICES: CONTINUOUS SIMULATION VERSUS DESIGN STORMS1

ABSTRACT: Nonpoint source (NPS) models and expert opinions are often used to prescribe best management practices (BMPs) for controlling NPS pollution. An optimization algorithm (e.g., a genetic algorithm, or GA) linked with a NPS model (e.g., Annualized AGricultural Nonpoint Source pollution model, or AnnAGNPS), can be used to more objectively prescribe BMPs and to optimize NPS pollution control measures by maximizing pollutant reduction and net monetary return from a watershed. Pollutant loads from design storms and annual loads from a continuous simulation can both be used for optimizing BMP schemes. However, which strategy results in a better solution (in terms of providing water quality protection) for a watershed is not clear. The specific objective of the study was to determine the differences in watershed pollutant loads, in an experimental watershed in Pennsylvania, resulting from optimization analyses performed using pollutant loads from a series of five 2‐yr 24‐hr storm events, a series of five 5‐yr 24‐hr storm events, and cumulative pollutant loads from a continuous simulation of five years of weather data. For each of these three different event alternatives, 100 near optimal solutions (BMP schemes) were generated. Sediment (Sed), sediment nitrogen (SedN), dissolved N (SolN), sediment organic carbon (SedOC), and sediment phosphorus (SedP) loads from a different five‐year period (an evaluation period) suggest that the optimal BMP schemes resulting from the use of annual cumulative pollutant loads from a continuous simulation of five years of weather data provide smaller cumulative NPS pollutant loads at the watershed outlet.     

SWATmodel: A Multi‐Operating System,Multi‐Platform SWAT Model Package in R

The Soil and Water Assessment Tool (SWAT) model (Arnold et al., 1998) is a popular watershed management tool. Currently, the SWAT model, actively supported by the U.S. Department of Agriculture and Texas A&M, operates only on Microsoft^® Windows, which hinders modelers that use other operating systems (OS). This technical note introduces the Comprehensive R Archive Network (CRAN) distributed “SWATmodel” package which allows SWAT 2005 and 2012 to be widely distributed and run as a linear model‐like function on multiple OS and processor platforms. This allows researchers anywhere in the world using virtually any OS to run SWAT. In addition to simplifying the use of SWAT across computational platforms, the SWATmodel package allows SWAT modelers to utilize the analytical capabilities, statistical libraries, modeling tools, and programming flexibility inherent to R. The software allows watershed modelers to develop a simple hydrological watershed model conceptualization of the SWAT model and to obtain a first approximation of the minimum expected results a more complicated model should deliver. As a proof of concept, we test the SWAT model by initializing and calibrating 314 U.S. Geological Survey stream gages in the Chesapeake Bay watershed and present the results.     

Calibration and Verification of SWMM for Low Impact Development

The Storm Water Management Model was used to simulate runoff and nutrient export from a low impact development (LID) watershed and a watershed using traditional runoff controls. Predictions were compared to observed values. Uncalibrated simulations underpredicted weekly runoff volume and average peak flow rates from the multiple subcatchment LID watershed by over 80%; the single subcatchment traditional watershed had better predictions. Saturated hydraulic conductivity, Manning's n for swales, and initial soil moisture deficit were sensitive parameters. After calibration, prediction of total weekly runoff volume for the LID and traditional watersheds improved to within 12 and 5% of observed values, respectively. For the validation period, predicted total weekly runoff volumes for the LID and traditional watersheds were within 6 and 2% of observed values, respectively. Water quality simulation was less successful, Nash–Sutcliffe coefficients >0.5 for both calibration and validation periods were only achieved for prediction of total nitrogen export from the LID watershed. Simulation of a 100‐year, 24‐h storm resulted in a runoff coefficient of 0.46 for the LID watershed and 0.59 for the traditional watershed. Results suggest either calibration is needed to improve predictions for LID watersheds or expanded look‐up tables for Green–Ampt infiltration parameter values that account for compaction of urban soil and antecedent conditions are needed.