Watershed model parameter estimation in low data environments

Garna, Roja; Fuka, Daniel R.; Faulkner, Joshua W.; Collick, Amy S.; Easton, Zachary M.

doi:10.1016/j.ejrh.2022.101306

Cited by 4 publications

(4 citation statements)

References 71 publications

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“…However, many water catchments globally are ungauged, and a lesser proportion of those have corresponding water quality data to inform model customisation. Techniques to overcome such data voids in ungauged areas are necessary 4 , 5 . Methods to simulate flows in ungauged areas are well researched 6 , 7 , however, refinement of methods that simulate nutrients in ungauged areas remained unresolved.…”

Section: Introductionmentioning

confidence: 99%

Explainable AI approach with original vegetation data classifies spatio-temporal nitrogen in flows from ungauged catchments to the Great Barrier Reef

O’Sullivan,

Deo,

Ghahramani

2023

Sci Rep

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Transfer of processed data and parameters to ungauged catchments from the most similar gauged counterpart is a common technique in water quality modelling. But catchment similarities for Dissolved Inorganic Nitrogen (DIN) are ill posed, which affects the predictive capability of models reliant on such methods for simulating DIN. Spatial data proxies to classify catchments for most similar DIN responses are a demonstrated solution, yet their applicability to ungauged catchments is unexplored. We adopted a neural network pattern recognition model (ANN-PR) and explainable artificial intelligence approach (SHAP-XAI) to match all ungauged catchments that flow to the Great Barrier Reef to gauged ones based on proxy spatial data. Catchment match suitability was verified using a neural network water quality (ANN-WQ) simulator trained on gauged catchment datasets, tested by simulating DIN for matched catchments in unsupervised learning scenarios. We show that discriminating training data to DIN regime benefits ANN-WQ simulation performance in unsupervised scenarios ( p< 0.05). This phenomenon demonstrates that proxy spatial data is a useful tool to classify catchments with similar DIN regimes. Catchments lacking similarity with gauged ones are identified as priority monitoring areas to gain observed data for all DIN regimes in catchments that flow to the Great Barrier Reef, Australia.

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Section: Introductionmentioning

confidence: 99%

Explainable AI approach with original vegetation data classifies spatio-temporal nitrogen in flows from ungauged catchments to the Great Barrier Reef

O’Sullivan,

Deo,

Ghahramani

2023

Sci Rep

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“…The effect of different DEM scenarios on the SWAT model annual output is shown in Figure 7. Surface runoff, lateral flow, groundwater flow, percolation flow, evapotranspiration (ET), and water yield are the most common interest outputs for water balance components in SWAT model studies [71][72][73][74]. Comparison of the RDs in the modeled outputs indicated that coarser DEM resolutions (10 m and 30 m) affect the model output uncertainties.…”

Section: Impact Of Dem Resolutions On the Swat Model Outputmentioning

confidence: 99%

SWAT Model Performance Using Spatially Distributed Saturated Hydraulic Conductivity (Ksat) and Varying-Resolution DEMs

Jin,

Higgins,

Thompson

et al. 2024

Water

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Saturated hydraulic conductivity (Ksat) is a hydrologic flux parameter commonly used to determine water movement through the saturated soil zone. Understanding the influences of land-use-specific Ksat on the model estimation error of water balance components is necessary to advance model predictive certainties and land management practices. An exploratory modeling approach was developed in the physically based Soil and Water Assessment Tool (SWAT) framework to investigate the effects of spatially distributed observed Ksat on local water balance components using three digital elevation model (DEM) resolution scenarios (30 m, 10 m, and 1 m). All three DEM scenarios showed satisfactory model performance during calibration (R2 > 0.74, NSE > 0.72, and PBIAS ≤ ±13%) and validation (R2 > 0.71, NSE > 0.70, and PBIAS ≤ ±6%). Results showed that the 1 m DEM scenario provided more realistic streamflow results (0.315 m3/s) relative to the observed streamflow (0.292 m3/s). Uncertainty analysis indicated that observed Ksat forcings and DEM resolution significantly influence predictions of lateral flow, groundwater flow, and percolation flow. Specifically, the observed Ksat has a more significant impact on model predictive confidence than DEM resolution. Results emphasize the potential uncertainty of using observed Ksat for hydrological modeling and demonstrate the importance of finer-resolution spatial data (i.e., 1 m DEM) applied in smaller watersheds.

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“…The open library, source and data to repeat the findings of this study are openly available at Garna et al, (2023).…”

Section: Data Availability Statementmentioning

confidence: 99%

“…To test this, we use the SWAT model for 21 United States Department of Agriculture (USDA)‐Agricultural Research Service (ARS)‐NRCS‐Conservation Effects Assessment Project (CEAP) watersheds (Sadler et al, 2008, 2015) across five Köppen climate classification (Peel et al, 2007) regions in the United States and compare model predicted streamflow to observed data. To perform the evaluation, we developed a new modelling interface, ‘FillMissWX’ (Garna et al, 2023), to automatically download GHCN data (precipitation, maximum temperature and minimum temperature) from all monitors that are located in a specific radius from the target location and then estimate and interpolate missing data based on three estimation methods; Closest Station, IDW and IDEW. Then, complete weather data time series from multiple GHCN monitors generated by FillMissWX estimation methods are used to force SWAT models and simulate streamflow.…”

Section: Introductionmentioning

confidence: 99%

Employing higher density lower reliability weather data from the Global Historical Climatology Network monitors to generate serially complete weather data for watershed modelling

Garna,

Easton,

Faulkner

et al. 2023

Hydrological Processes

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Hydrological models require complete and accurate weather data time series to represent watershed‐scale responses adequately. The Global Historical Climatology Network (GHCN) is the most comprehensive weather database used in hydrological modelling studies globally. Since higher‐density, lower‐reliability precipitation measurements from private citizens collected by the Community Collaborative Rain, Hail, and Snow (CoCoRaHS) network data were integrated into the GHCN, hydrological modellers in the United States have access to a much greater amount of weather data. However, the benefit of using CoCoRaHS data has not been assessed. The objectives of this work were to develop a method for generating a complete weather data time series based on the combination of data from multiple GHCN monitors and to assess several methods for the estimation of missing weather data. Weather data from GHCN monitors located within a specific radius of a watershed were obtained and interpolated using three estimation methods (Inverse Distance Weighting (IDW), Inverse Distance and Elevation Weighting (IDEW) and Closest Station), creating a seamless time series of weather observations. To evaluate the performance of the methodologies, weather data obtained from each estimation method was used to force the Soil and Water Assessment Tool (SWAT) and Thornthwaite‐Mather models for 21 US Department of Agriculture‐Conservation Effects Assessment Project watersheds in different climate regions to simulate daily streamflow for 2010–2021. Except for three watersheds, all of the SWAT models had Nash‐Sutcliffe Efficiency above 0.5, the ratio of the root mean square error to the standard deviation of observations below 0.7, and percent bias from −25% to 25% with a satisfactory performance rating. IDEW and IDW performed similarly, and the Closest Station method resulted in the poorest streamflow simulation. A comparison with published SWAT model results further corroborated improved model performance using novel combined GHCN data with all Closest Station, IDW and IDEW methods.

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Watershed model parameter estimation in low data environments

Cited by 4 publications

References 71 publications

Explainable AI approach with original vegetation data classifies spatio-temporal nitrogen in flows from ungauged catchments to the Great Barrier Reef

Explainable AI approach with original vegetation data classifies spatio-temporal nitrogen in flows from ungauged catchments to the Great Barrier Reef

SWAT Model Performance Using Spatially Distributed Saturated Hydraulic Conductivity (Ksat) and Varying-Resolution DEMs

Employing higher density lower reliability weather data from the Global Historical Climatology Network monitors to generate serially complete weather data for watershed modelling

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