Physically Based,Hydrologic Model Results Based on Three Precipitation Products1

Abstract: The main objective of the study is to examine the accuracy of and differences among simulated streamflows driven by rainfall estimates from a network of 22 rain gauges spread over a 2,170 km² watershed, NEXRAD Stage III radar data, and Tropical Rainfall Measuring Mission (TRMM) 3B42 satellite data. The Gridded Surface Subsurface Hydrologic Analysis (GSSHA), a physically based, distributed parameter, grid‐structured, hydrologic model, was used to simulate the June‐2002 flooding event in the Upper Guadalupe River watershed in south central Texas. There were significant differences between the rainfall fields estimated by the three types of measurement technologies. These differences resulted in even larger differences in the simulated hydrologic response of the watershed. In general, simulations driven by radar rainfall yielded better results than those driven by satellite or rain‐gauge estimates. This study also presents an overview of effects of land cover changes on runoff and stream discharge. The results demonstrate that, for major rainfall events similar to the 2002 event, the effect of urbanization on the watershed in the past two decades would not have made any significant effect on the hydrologic response. The effect of urbanization on the hydrologic response increases as the size of the rainfall event decreases.     

Comparison and Evaluation of Gridded Precipitation Datasets in a Kansas Agricultural Watershed Using SWAT

Gridded precipitation datasets are becoming a convenient substitute for gauge measurements in hydrological modeling; however, these data have not been fully evaluated across a range of conditions. We compared four gridded datasets (Daily Surface Weather and Climatological Summaries [DAYMET], North American Land Data Assimilation System [NLDAS], Global Land Data Assimilation System [GLDAS], and Parameter‐elevation Regressions on Independent Slopes Model [PRISM]) as precipitation data sources and evaluated how they affected hydrologic model performance when compared with a gauged dataset, Global Historical Climatology Network‐Daily (GHCN‐D). Analyses were performed for the Delaware Watershed at Perry Lake in eastern Kansas. Precipitation indices for DAYMET and PRISM precipitation closely matched GHCN‐D, whereas NLDAS and GLDAS showed weaker correlations. We also used these precipitation data as input to the Soil and Water Assessment Tool (SWAT) model that confirmed similar trends in streamflow simulation. For stations with complete data, GHCN‐D based SWAT‐simulated streamflow variability better than gridded precipitation data. During low flow periods we found PRISM performed better, whereas both DAYMET and NLDAS performed better in high flow years. Our results demonstrate that combining gridded precipitation sources with gauge‐based measurements can improve hydrologic model performance, especially for extreme events.     

Hydrologic Modeling Uncertainty Resulting From Land Cover Misclassification1

Abstract: A stochastic, spatially explicit method for assessing the impact of land cover classification error on distributed hydrologic modeling is presented. One‐hundred land cover realizations were created by systematically altering the North American Landscape Characterization land cover data according to the dataset’s misclassification matrix. The matrix indicates the probability of errors of omission in land cover classes and is used to assess the uncertainty in hydrologic runoff simulation resulting from parameter estimation based on land cover. These land cover realizations were used in the GIS‐based Automated Geospatial Watershed Assessment tool in conjunction with topography and soils data to generate input to the physically‐based Kinematic Runoff and Erosion model. Uncertainties in modeled runoff volumes resulting from these land cover realizations were evaluated in the Upper San Pedro River basin for 40 watersheds ranging in size from 10 to 100 km² under two rainfall events of differing magnitudes and intensities. Simulation results show that model sensitivity to classification error varies directly with respect to watershed scale, inversely to rainfall magnitude and are mitigated or magnified by landscape variability depending on landscape composition.     

SWMHMS - SMALL WATERSHED MONTHLY HYDROLOGIC MODELING SYSTEM1

ABSTRACT: SWMHMS is a conceptual computer modeling program developed to simulate monthly runoff from a small nonurban watershed. The input needed to run model simulations include daily precipitation, monthly data for evapotranspiration determination (average temperature, crop consumptive coefficients, and percent daylight hours), and six watershed parameter values. Evapotranspiration was calculated with the Blaney-Criddle equation while surface runoff was determined using the Soil Conservation Service curve number procedure. For watershed parameter evaluation, SWMHMS provides options for both optimization and sensitivity analysis. Observed runoff data are required along with the model input previously mentioned in order to conduct parameter optimization. SWMEIMS was tested with data from six watersheds located in different regions of the United States. Model accuracy was generally found to be very good except on watersheds having substantial snowfall accumulation. In having only six watershed parameters, SWMHMS is less complex to use than many other computer programs that calculate monthly runoff. Consequently, SWMHMS may find its greatest application as an educational tool for students learning principles of hydrologic modeling, such as parameter evaluation procedures and the impacts of input data uncertainty on model results.     

Using SWAT to Model Streamflow in Two River Basins With Ground and Satellite Precipitation Data1

Abstract: Both ground rain gauge and remotely sensed precipitation (Next Generation Weather Radar – NEXRAD Stage III) data have been used to support spatially distributed hydrological modeling. This study is unique in that it utilizes and compares the performance of National Weather Service (NWS) rain gauge, NEXRAD Stage III, and Tropical Rainfall Measurement Mission (TRMM) 3B42 (Version 6) data for the hydrological modeling of the Middle Nueces River Watershed in South Texas and Middle Rio Grande Watershed in South Texas and northern Mexico. The hydrologic model chosen for this study is the Soil and Water Assessment Tool (SWAT), which is a comprehensive, physical‐based tool that models watershed hydrology and water quality within stream reaches. Minor adjustments to selected model parameters were applied to make parameter values more realistic based on results from previous studies. In both watersheds, NEXRAD Stage III data yields results with low mass balance error between simulated and actual streamflow (±13%) and high monthly Nash‐Sutcliffe efficiency coefficients (NS > 0.60) for both calibration (July 1, 2003 to December 31, 2006) and validation (2007) periods. In the Middle Rio Grande Watershed NEXRAD Stage III data also yield robust daily results (time averaged over a three‐day period) with NS values of (0.60‐0.88). TRMM 3B42 data generate simulations for the Middle Rio Grande Watershed of variable qualtiy (MBE = +13 to ?16%; NS = 0.38‐0.94; RMSE = 0.07‐0.65), but greatly overestimates streamflow during the calibration period in the Middle Nueces Watershed. During the calibration period use of NWS rain gauge data does not generate acceptable simulations in both watersheds. Significantly, our study is the first to successfully demonstrate the utility of satellite‐estimated precipitation (TRMM 3B42) in supporting hydrologic modeling with SWAT; thereby, potentially extending the realm (between 50°N and 50°S) where remotely sensed precipitation data can support hydrologic modeling outside of regions that have modern, ground‐based radar networks (i.e., much of the third world).     

Modeling Hydrology in a Small Rocky Mountain Watershed Serving Large Urban Populations1

Abstract: This article describes the development of a calibrated hydrologic model for the Blue River watershed (867 km²) in Summit County, Colorado. This watershed provides drinking water to over a third of Colorado’s population. However, more research on model calibration and development for small mountain watersheds is needed. This work required integration of subsurface and surface hydrology using GIS data, and included aspects unique to mountain watersheds such as snow hydrology, high ground‐water gradients, and large differences in climate between the headwaters and outlet. Given the importance of this particular watershed as a major urban drinking‐water source, the rapid development occurring in small mountain watersheds, and the importance of Rocky Mountain water in the arid and semiarid West, it is useful to describe calibrated watershed modeling efforts in this watershed. The model used was Soil and Water Assessment Tool (SWAT). An accurate model of the hydrologic cycle required incorporation of mountain hydrology‐specific processes. Snowmelt and snow formation parameters, as well as several ground‐water parameters, were the most important calibration factors. Comparison of simulated and observed streamflow hydrographs at two U.S. Geological Survey gaging stations resulted in good fits to average monthly values (0.71 Nash‐Sutcliffe coefficient). With this capability, future assessments of point‐source and nonpoint‐source pollutant transport are possible.     

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.     

EFFECTS OF SIMULATED CLOUD SEEDING ON STREAMFLOW OF SELECTED WATERSHEDS IN PENNSYLVANIA1

A mathematical model was developed to simulate the hydrologic behavior of five small watersheds in central Pennsylvania. Continuous hydrographs for the 6-month period, April to September 1964, were simulated. Synthesized rainfall cycles consisting of increasing rainfall by 10, 20, and 30 percent to simulate the effects of cloud seeding were processed through the watershed model to determine the effects on low flow augmentation. Other rainfall cycles used consisted of increasing every third storm by 30 percent and of developing a rainfall cycle by processing daily radiosonde data through a mathematical cumulus cloud model to obtain a prediction of rainfall following seeding. A comparison of actual and predicted hydrographs indicated that simulated cloud seeding resulted in significant monthly and seasonal water yields. In general, the results of the study appear to indicate that on a theoretical basis cloud seeding would be a feasible method of augmenting low stream-flow during the summer months on watersheds in the northern Appalachian region.     

Heterogeneity of Hydrologic Response in Urban Watersheds1

Meierdiercks, Katherine L., James A. Smith, Mary Lynn Baeck, and Andrew J. Miller, 2010. Heterogeneity of Hydrologic Response in Urban Watersheds. Journal of the American Water Resources Association (JAWRA) 46(6):1221–1237. DOI: 10.1111/j.1752-1688.2010.00487.x Abstract: The changing patterns of streamflow associated with urbanization are examined through analyses of discharge and rainfall records for the study watersheds of the Baltimore Ecosystem Study (BES). Analyses utilize a decade (1999-2008) of observations from a dense U.S. Geological Survey stream gaging network and Hydro-NEXRAD radar rainfall fields. The principal study watershed of the BES is Gwynns Falls (171 km²). Focus is given to two Gwynns Falls basins with contrasting patterns and histories of development, Dead Run and Upper Gwynns Falls. The sharp contrasts in streamflow properties between the basins reflect the differences in urban development prior to implementation of stormwater management regulations (much of Dead Run) and development for which stormwater management is an integral part of the hydrologic system (Upper Gwynns Falls). The mean annual runoff in Dead Run (558 mm) is 35% larger than that of Upper Gwynns Falls; larger contrasts in runoff properties typify the “warm season” and are linked to storm event hydrologic response. Spatial heterogeneities in storm event response are reflected in seasonal and diurnal properties of streamflow. Analyses of storm event response are presented for June 2006, during which monthly rainfall over the BES region ranged from less than 150 to more than 500 mm. Baisman Run, the BES forest reference watershed, and Moores Run, a highly urbanized watershed in Baltimore City, provide “end-member” representations of urban impacts on streamflow.     

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.     

Topographic Effects on Soil Organic Carbon in Louisiana Watersheds

Terrestrial carbon storage is influenced by a number of environmental factors, among which topographic and geomorphological features are of special significance. This study was designed to examine the relationships of soil organic carbon (SOC) density to various terrain parameters and watershed characteristics across Louisiana, USA. A polygon data set of 484 watersheds and 12 river drainage basins for Louisiana was used to form the landscape units. SOC densities were calculated for each soil map unit using the State Soil Geographic (STATSGO) database. Average drainage densities and average slopes at watershed and basin scales were quantified with the 1:24 K Digital Elevation Models (DEM) data, and the Louisiana hydrographic water features. Correlation and regression analyses were performed to determine relationships among drainage density, slope, elevation, and SOC. The study found an average watershed drainage density of 1.6 km/km² and an average watershed slope of 2.9 degrees in Louisiana. The results revealed that SOC density at both watershed and basin scales was closely related to drainage density, slope, and elevation. SOC density was positively correlated with watershed drainage density, but negatively correlated with watershed slope gradient and elevation. Regression models were developed for predicting SOC density at watershed and basin scales, obtaining regression coefficients (r ²) ranging from 0.43 to 0.83. The study showed that estimation of SOC at watershed and drainage basin scales combining DEM data can be a feasible approach to improve the understanding of the relationships among SOC, topographic, and geomorphological features.     

Satellite Rainfall Estimates Over South America – Possible Applicability to the Water Management of Large Watersheds1

Pereira Filho, Augusto J., Richard E. Carbone, John E. Janowiak, Phillip Arkin, Robert Joyce, Ricardo Hallak, and Camila G.M. Ramos, 2010. Satellite Rainfall Estimates Over South America – Possible Applicability to the Water Management of Large Watersheds. Journal of the American Water Resources Association (JAWRA) 46(2):344-360. DOI: 10.1111/j.1752-1688.2009.00406.x Abstract: This work analyzes high-resolution precipitation data from satellite-derived rainfall estimates over South America, especially over the Amazon Basin. The goal is to examine whether satellite-derived precipitation estimates can be used in hydrology and in the management of larger watersheds of South America. High spatial-temporal resolution precipitation estimates obtained with the CMORPH method serve this purpose while providing an additional hydrometeorological perspective on the convective regime over South America and its predictability. CMORPH rainfall estimates at 8-km spatial resolution for 2003 and 2004 were compared with available rain gauge measurements at daily, monthly, and yearly accumulation time scales. The results show the correlation between satellite-derived and gauge-measured precipitation increases with accumulation period from daily to monthly, especially during the rainy season. Time-longitude diagrams of CMORPH hourly rainfall show the genesis, strength, longevity, and phase speed of convective systems. Hourly rainfall analyses indicate that convection over the Amazon region is often more organized than previously thought, thus inferring that basin scale predictions of rainfall for hydrological and water management purposes have the potential to become more skillful. Flow estimates based on CMORPH and the rain gauge network are compared to long-term observed average flow. The results suggest this satellite-based rainfall estimation technique has considerable utility. Other statistics for monthly accumulations also suggest CMORPH can be an important source of rainfall information at smaller spatial scales where in situ observations are lacking.     

MODELING STREAMFLOW FROM ARTIFICIALLY DRAINED AGRICULTURAL WATERSHEDS IN ILLINOIS1

ABSTRACT: Evaluation of the applicability and validity of hydrologic simulation models for various cropping systems in different hydrogeologic and soil conditions is needed for a range of spatial scales. We calibrated and tested the ADAPT model for simulating streamflow from 552 to 1,985 km² watersheds in central Illinois, where more than 79 percent of the land is used for maize‐soybean production and tile drainage is common. Model calibration was performed with a seven year period (1987 through1993) of measured streamflow from one of the watersheds, and model testing was done using independent weather and measured streamflow data from the two neighboring watersheds for the same seven year period. Simulations of annual streamflow were accurate with a coefficient of determination and Willmott's index of agreement of 0.98 and 0.99, respectively. For simulation of monthly streamflow, Willmott's index of agreement ranged from 0.93 to 0.95. For simulation of daily streamflow, Willmott's index of agreement ranged from 0.84 to 0.85. The daily simulations challenged the temporal and spatial resolution of our measured precipitation data. Discrepancies between simulated and measured data may result from the model's inability to effectively address frozen soils and snowmelt runoff processes and in accurately representing evapotranspiration.     

Evaluation of Satellite‐Derived Rainfall Data for Multiple Physio‐Climatic Regions in the Santiago River Basin,Mexico

Assessment of water resources requires reliable rainfall data, and rain gauge networks may not provide adequate spatial representation due to limited point measurements. The Tropical Rainfall Measuring Mission (TRMM) provides rainfall data at global scale, and has been used with good results. However, TRMM data are an indirect measurement of rainfall, and therefore must be validated for its proper use. In this work, a validation scheme was designed and implemented to compare the TRMM Version 7 (V7) monthly rainfall product at different time frames with data measured in two hydrologic subregions of the Santiago River Basin (SRB) in Mexico: Río Alto Santiago and Río Bajo Santiago (RBS). Additionally, three physio‐climatic regions provide an assessment of the interplay of topography, distance from coastal regions, and seasonal weather patterns on the correspondence between both datasets. The TRMM V7 rainfall product exhibited good agreement with the rain gauge data particularly for the RBS and for the whole SRB during wettest summer and autumn seasons. However, strong regional dependence was observed due to differences in climate and topography. Overall, in spite of some noted underestimations, the monthly TRMM V7 rainfall product was found to provide useful information that can be used to complement limited monitoring as is the case of RBS. An improved combined rainfall product could be generated and thus gaining the most benefits from both data sources.     

APPLICATION OF THE RHESSys MODEL TO A CALIFORNIA SEMIARID SHRUBLAND WATERSHED1

ABSTRACT: Distributed hydrologic models which link seasonal streamflow and soil moisture patterns with spatial patterns of vegetation are important tools for understanding the sensitivity of Mediterranean type ecosystems to future climate and land use change. RHESSys (Regional Hydro‐Ecologic Simulation System) is a coupled spatially distributed hydroecological model that is designed to be able to represent these feedbacks between hydrologic and vegetation carbon and nutrient cycling processes. However, RHESSys has not previously been applied to semiarid shrubland watersheds. In this study, the hydrologic submodel of RHESSys is evaluated by comparing model predictions of monthly and annual streamflow to stream gage data and by comparing RHESSys behavior to that of another hydrologic model of similar complexity, MIKESHE, for a 34 km² watershed near Santa Barbara, California. In model intercomparison, the differences in predictions of temporal patterns in streamflow, sensitivity of model predictions to calibration parameters and landscape representation, and differences in model estimates of soil moisture patterns are explored. Results from this study show that both models adequately predict seasonal patterns of streamflow response relative to observed data, but differ significantly in terms of estimates of soil moisture patterns and sensitivity of those patterns to the scale of landscape tessellation used to derive spatially distributed elements. This sensitivity has implications for implementing RHESSys as a tool to investigate interactions between hydrology and ecosystem processes.     

Modeling Discharge Rates Using a Coupled Modeled Approach for the Merced River in Yosemite National Park

This study describes the application of the NASA version of the Carnegie‐Ames‐Stanford Approach (CASA) ecosystem model coupled with a surface hydrologic routing scheme previously called the Hydrological Routing Algorithm (HYDRA) to model monthly discharge rates from 2000 to 2007 on the Merced River drainage in Yosemite National Park, California. To assess CASA‐HYDRA's capability to estimate actual water flows in extreme precipitation years, the focus of this study is the 2007 water year, which was very dry, and the 2005 water year, which was a moderately wet year in the historical record. Prior to comparisons to gauge records, CASA‐HYDRA snowmelt algorithms were modified with equations from the U.S. Department of Agriculture Snowmelt‐Runoff Model (SRM), which has been designed to predict daily streamflow in mountain basins where snowmelt is a major runoff factor. Results show that model predictions closely matched monthly flow rates at the Pohono Bridge gauge station (USGS#11266500), with R² = 0.67 and Nash‐Sutcliffe (E) = 0.65. By subdividing the upper Merced River basin into subbasins with high spatial resolution in the gridded modeling approach, we were able to determine which biophysical characteristics in the Sierra differed to the largest degree in extreme low‐flow and high‐flow years. Average elevation and snowpack accumulation were found to be the most important explanatory variables to understand subbasin contributions to monthly discharge rates.     

Evaluation of the MIKE SHE Model for Application in the Loess Plateau,China1

Abstract: Quantifying the hydrologic responses to land use/land cover change and climate variability is essential for integrated sustainable watershed management in water limited regions such as the Loess Plateau in Northwestern China where an adaptive watershed management approach is being implemented. Traditional empirical modeling approach to quantifying the accumulated hydrologic effects of watershed management is limited due to its complex nature of soil and water conservation practices (e.g., biological, structural, and agricultural measures) in the region. Therefore, the objective of this study was to evaluate the ability of the distributed hydrologic model, MIKE SHE to simulate basin runoff. Streamflow data measured from an overland flow‐dominant watershed (12 km²) in northwestern China were used for model evaluation. Model calibration and validation suggested that the model could capture the dominant runoff process of the small watershed. We found that the physically based model required calibration at appropriate scales and estimated model parameters were influenced by both temporal and spatial scales of input data. We concluded that the model was useful for understanding the rainfall‐runoff mechanisms. However, more measured data with higher temporal resolution are needed to further test the model for regional applications.     

REGIONAL RUNOFF SIMULATION FOR SOUTHEAST MICHIGAN1

ABSTRACT: The feasibility of simulating monthly runoff for southeast Michigan, which comprises four major river basins, was evaluated with the Streamflow Synthesis and Reservoir Regulation watershed model. The evaluation covered a 13-year period (1961–73), which encompassed a complete runoff cycle. Results indicate it is feasible to simulate monthly runoff volumes on a regional scale with a single equivalent watershed by using daily precipitation and temperature data. Simulation of regional flows appears particularly attractive for the Great Lakes basin, since the basin consists of many relatively small watersheds. This method also appears promising for development of monthly runoff forecasts by employing average monthly meteorological data distributed on a daily basis. Tests of six-month runoff forecasts show relatively small deterioration with time and offer considerable improvement over climatology.     

BASE FLOW RECESSION RATES,LOW FLOWS,AND HYDROLOGIC FEATURES OF SMALL WATERSHEDS IN PENNSYLVANIA,USA1

This paper examines the relationships between measurable watershed hydrologic features, base flow recession rates, and the Q_7,10 low flow statistic (the annual minimum seven‐day average streamflow occurring once every 10 years on average). Base flow recession constants were determined by analyzing hydrograph recession data from 24 small (>130 km²), unregulated watersheds across five major physiographic provinces of Pennsylvania, providing a highly variable dataset. Geomorphic, hydrogeologic, and land use parameters were determined for each watershed. The base flow recession constant was found to be most strongly correlated to drainage density, geologic index, and ruggedness number (watershed slope); however, these three parameters are intercorrelated. Multiple regression models were developed for predicting the recession rate, and it was found that only two parameters, drainage density and hydrologic soil group, were required to obtain good estimates of the recession constant. Equations were also developed to relate the recession rates to Q_7,10 per unit area, and to the Q_7,10/Q₅₀ ratio. Using these equations, estimates of base flow recession rates, Q_7,10, and streamflow reduction under drought conditions can be made for small, ungaged basins across a wide range of physiography.     

The Great Lakes Hydrography Dataset: Consistent,Binational Watersheds for the Laurentian Great Lakes Basin

Ecosystem‐based management of the Laurentian Great Lakes, which spans both the United States and Canada, is hampered by the lack of consistent binational watersheds for the entire Basin. Using comparable data sources and consistent methods, we developed spatially equivalent watershed boundaries for the binational extent of the Basin to create the Great Lakes Hydrography Dataset (GLHD). The GLHD consists of 5,589 watersheds for the entire Basin, covering a total area of approximately 547,967 km², or about twice the 247,003 km² surface water area of the Great Lakes. The GLHD improves upon existing watershed efforts by delineating watersheds for the entire Basin using consistent methods; enhancing the precision of watershed delineation using recently developed flow direction grids that have been hydrologically enforced and vetted by provincial and federal water resource agencies; and increasing the accuracy of watershed boundaries by enforcing embayments, delineating watersheds on islands, and delineating watersheds for all tributaries draining to connecting channels. In addition, the GLHD is packaged in a publically available geodatabase that includes synthetic stream networks, reach catchments, watershed boundaries, a broad set of attribute data for each tributary, and metadata documenting methodology. The GLHD provides a common set of watersheds and associated hydrography data for the Basin that will enhance binational efforts to protect and restore the Great Lakes.