Does soil data resolution matter? State Soil Geographic database versus Soil Survey Geographic database in rainfall-runoff modeling across Wisconsin

Mednick, Adam C.

doi:10.2489/jswc.65.3.190

Cited by 23 publications

(14 citation statements)

References 27 publications

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“…Uncertainty in soil texture measurements can also introduce errors into soil moisture simulations. Although gSSURGO is arguably the best available soil database across the United States (Zhong & Xu, ) the accuracy of this database varies across regions and depends on the spatial scale over which it is implemented (Drohan et al, ; Mednick, ). A cross‐validation of the gSSURGO data set by Ramcharan et al () showed that RMSE for percent sand and clay are about 17.8% and 12%, respectively.…”

Section: Discussionmentioning

confidence: 99%

The Sensitivity of North American Terrestrial Carbon Fluxes to Spatial and Temporal Variation in Soil Moisture: An Analysis Using Radar‐Derived Estimates of Root‐Zone Soil Moisture

et al. 2019

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This study examines the impact of variation in root-zone soil moisture (RZSM), a key component of the Earth's hydrologic cycle and climate system, on regional carbon fluxes across seven North American ecosystems. P-band synthetic aperture radar-derived RZSM estimates were incorporated into the ecosystem demography (ED2) terrestrial biosphere model through a model-data blending approach. Analysis shows that the model qualitatively captures inter-daily and seasonal variability of observed RZSM at seven flux tower sites (r = 0.59 ± 0.26 and r = 0.70 ± 0.22 for 0-10 and 10-40 cm of soil layers, respectively; P < 0.001). Incorporating the remotely sensed RSZM estimates increases the accuracy (root-mean-square deviations decrease from 0.10 ± 0.07 and 0.09 ± 0.06 m 3 ·m −3 to 0.08 ± 0.05 and 0.07 ± 0.03 m 3 ·m −3 for 0-10 and 10-40 cm of soil layers, respectively) of the model's RZSM predictions. The regional carbon fluxes predicted by the native and RZSM-constrained model were used to quantify sensitivities of gross primary productivity, autotrophic respiration (R a ), heterotrophic respiration (R h ), and net ecosystem exchange to variation in RZSM. Gross primary productivity exhibited the largest sensitivity (6.6 ± 10.7 kg·cm −2 ·year·θ −1 ) followed by R a (2.9 ± 7.3 kg·cm −2 ·year −1 ·θ −1 ), R h (2.6 ± 3.1 kg·cm −2 ·year −1 ·θ −1 ), and net ecosystem exchange (−1.7 ± 7.8 kg·cm −2 ·year −1 ·θ −1 ). Analysis shows that these carbon flux sensitivities varied considerably across regions, reflecting influences of canopy structure, soil properties, and the ecophysiological properties of different plant functional types. This study highlights (1) the importance of improved terrestrial biosphere model predictions of RZSM to improve predictions of terrestrial carbon fluxes, (2) a need for improved pedotransfer functions, and (3) improved understanding of how soil characteristics, climate, and vegetation composition interact to govern the responses of different ecosystems to changing hydrological conditions.

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Section: Discussionmentioning

confidence: 99%

The Sensitivity of North American Terrestrial Carbon Fluxes to Spatial and Temporal Variation in Soil Moisture: An Analysis Using Radar‐Derived Estimates of Root‐Zone Soil Moisture

et al. 2019

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“…The finer-scale soils data from the Soil Survey Geographic Database (SSURGO) was used in this study [51]. Based on studies by Mednick [52] and Zhang et al [53], the higher resolution soil data provides greater accuracy in predicting hydrologic fluxes. In order to accurately simulate runoff potential, four slope classes were considered in this study: ≤1, 1-3, 3-5, and >5 %.…”

Section: Apex Model Setupmentioning

confidence: 99%

Spatial Variability of Biofuel Production Potential and Hydrologic Fluxes of Land Use Change from Cotton (Gossypium hirsutum L.) to Alamo Switchgrass (Panicum virgatum L.) in the Texas High Plains

2016

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Bioenergy crop production has the potential to protect marginal crop lands that generate high surface runoff and produce poor crop yields. Long-term evaluation of the impacts of such land use change on hydrologic fluxes and biofuel production potential is necessary before adopting such strategies on a large scale. In this study, the hydrologic impacts of replacing cotton (Gossypium hirsutum L.) on marginal lands in an intensive agricultural watershed in the Texas High Plains with Alamo switchgrass (Panicum virgatum L.) as a bioenergy crop were evaluated using the Agricultural Policy/ Environmental eXtender (APEX) model. The surface runoff to cotton yield ratio was used as a criterion to identify marginal cotton subareas (homogenous spatial units delineated by APEX) in the study watershed, and three replacement scenarios (low (9 %), medium (33 %), and high (57 %) extents of cotton acreage replaced by switchgrass) were implemented in the scenario analysis. The average (1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)) annual surface runoff decreased by about 84 and 66 %, and the percolation increased by 106 and 57 % in the irrigated and dryland subareas, respectively, when cotton was replaced by switchgrass under the high replacement scenario. Spatial analysis showed that switchgrass was a feasible bioenergy crop for replacing cotton, especially in the western part of the study watershed, due to its higher water use efficiency and better water conservation effects compared to cotton. It is estimated that 193 and 381 million liters of ethanol could be produced from the dryland and irrigated subareas of the study watershed, respectively, under the high replacement scenario.

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“…Models and interfaces utilize regional or national DEM, soils, and crop databases in order to provide default model input values for a given study area. Many studies present the advantages, disadvantages, and best methods of using databases in modeling [14] [15] [25] [26]. Some of these databases, such as soils and crop, may be modified by the model and interface developers based on the structure and possible quality assurance/quality control procedures.…”

Section: Introductionmentioning

confidence: 99%

Comparing the Effects of Inputs for NTT and ArcAPEX Interfaces on Model Outputs and Simulation Performance

Nelson¹,

Moriasi²,

Talebizadeh³

et al. 2019

JWARP

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The Agricultural Policy/Environmental eXtender (APEX) model has five different interfaces used to process and build simulation projects. These interfaces utilize different input databases that lead to different model default values. These values can result in different hydrologic, crop growth, and nutrient flow model outputs. This study compared structural and input value differences of the ArcAPEX and Nutrient Tracking Tool (NTT) interfaces. Long-term, water quality data from the Rock Creek watershed, located in Ohio were used to determine the impact of the differences on computation time, parameter sensitivity, and streamflow, total nitrogen (TN), and total phosphorus (TP) simulation performance. The input structures were the same for both interfaces for all files except soils, where NTT assigns three soil files per field, rather than a single one in ArcAPEX. As a result, computation times were three times as long for NTT as for ArcAPEX. There were twelve sensitive parameters in both cases, but the order of sensitivity was different. Both interfaces simulated streamflow well, but ARCAPEX simulated evapotranspiration, TN, and TP better than NTT, while NTT simulated crop yields better than ArcA-PEX. However, none of the models met all of the performance criteria for either interface. Therefore, more work is needed to ensure models are properly calibrated before being used for scenario analysis. While it is acceptable for the values to be different from the SSURGO database, there is no documentation explaining the rationale for the modifications from the original source. This is one of the examples that highlights lack of detailed documentation that would be useful to model users. Overall, the results indicate that different interfaces lead to different model simulation results and, therefore, the authors recommend users specify the interface used and any modifications made to the associated databases when reporting model results.

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Does soil data resolution matter? State Soil Geographic database versus Soil Survey Geographic database in rainfall-runoff modeling across Wisconsin

Cited by 23 publications

References 27 publications

The Sensitivity of North American Terrestrial Carbon Fluxes to Spatial and Temporal Variation in Soil Moisture: An Analysis Using Radar‐Derived Estimates of Root‐Zone Soil Moisture

The Sensitivity of North American Terrestrial Carbon Fluxes to Spatial and Temporal Variation in Soil Moisture: An Analysis Using Radar‐Derived Estimates of Root‐Zone Soil Moisture

Spatial Variability of Biofuel Production Potential and Hydrologic Fluxes of Land Use Change from Cotton (Gossypium hirsutum L.) to Alamo Switchgrass (Panicum virgatum L.) in the Texas High Plains

Comparing the Effects of Inputs for NTT and ArcAPEX Interfaces on Model Outputs and Simulation Performance

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