SWAT modeling with uncertainty and cluster analyses of tillage impacts on hydrological processes

Wang, Gangsheng; Barber, Michael E.; Chen, Shulin; Wu, Ji

doi:10.1007/s00477-013-0743-9

Cited by 20 publications

(5 citation statements)

References 55 publications

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“…where J opt denotes the minimum objective function value (achieved for each calibration), n is the number of observations, p is the number of calibrated parameters, and F α,p,n−p denotes the value of the F-distribution given α = 0.05 and the degree of freedom, p and n-p 58,59 .…”

Section: Methodsmentioning

confidence: 99%

“…If P < 0.05, it indicated that the distribution of the short-term parameter estimate was significantly different from that of the long-term parameter. where J opt denotes the minimum objective function value (achieved for each calibration), n is the number of observations, p is the number of calibrated parameters, and F α,p,n−p denotes the value of the F-distribution given α = 0.05 and the degree of freedom, p and n – p 58 , 59 .…”

Section: Methodsmentioning

confidence: 99%

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Multi-year incubation experiments boost confidence in model projections of long-term soil carbon dynamics

Jian

Wang

et al. 2020

Nat Commun

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Global soil organic carbon (SOC) stocks may decline with a warmer climate. However, model projections of changes in SOC due to climate warming depend on microbially-driven processes that are usually parameterized based on laboratory incubations. To assess how lab-scale incubation datasets inform model projections over decades, we optimized five microbially-relevant parameters in the Microbial-ENzyme Decomposition (MEND) model using 16 short-term glucose (6-day), 16 short-term cellulose (30-day) and 16 long-term cellulose (729-day) incubation datasets with soils from forests and grasslands across contrasting soil types. Our analysis identified consistently higher parameter estimates given the short-term versus long-term datasets. Implementing the short-term and long-term parameters, respectively, resulted in SOC loss (–8.2 ± 5.1% or –3.9 ± 2.8%), and minor SOC gain (1.8 ± 1.0%) in response to 5 °C warming, while only the latter is consistent with a meta-analysis of 149 field warming observations (1.6 ± 4.0%). Comparing multiple subsets of cellulose incubations (i.e., 6, 30, 90, 180, 360, 480 and 729-day) revealed comparable projections to the observed long-term SOC changes under warming only on 480- and 729-day. Integrating multi-year datasets of soil incubations (e.g., > 1.5 years) with microbial models can thus achieve more reasonable parameterization of key microbial processes and subsequently boost the accuracy and confidence of long-term SOC projections.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Multi-year incubation experiments boost confidence in model projections of long-term soil carbon dynamics

Jian

Wang

et al. 2020

Nat Commun

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“…Using SWATopt, we estimated 39 parameters (Supporting Information Table S3) governing the hydrologic and water quality processes in SWAT. These parameters were selected based on the sensitivity analyses in previous studies (Abbaspour et al, 2007;Baskaran et al, 2010;Bekele & Nicklow, 2007;Santhi et al, 2001;Wang, Barber, Chen, & Wu, 2014;Wu & Liu, 2012). Generally, these parameters were calibrated step by step.…”

Section: Swatopt Model Calibration and Evaluationmentioning

confidence: 99%

Hydrologic and water quality responses to biomass production in the Tennessee river basin

et al. 2018

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Reducing dependence on fossil-based energy has raised interest in biofuels as a potential energy source, but concerns have been raised about potential implications for water quality. These effects may vary regionally depending on the biomass feedstocks and changes in land management. Here, we focused on the Tennessee River Basin (TRB), USA. According to the recent 2016 Billion-Ton Report (BT16) by the US Department of Energy, under two future scenarios (base-case and high-yield), three perennial feedstocks show high potential for growing profitably in the TRB: switchgrass (Panicum virgatum), miscanthus (Miscanthus × giganteus), and willow (Salix spp.). We used the Soil & Water Assessment Tool (SWAT) to compare hydrology and water quality for a current landscape with those simulated for two future BT16 landscapes. We combined publicly available temporal and geospatial datasets with local land and water management information to realistically represent physical characteristics of the watershed. We developed a new autocalibration tool (SWATopt) to calibrate and evaluate SWAT in the TRB with reservoir operations, including comparison against synthetic and intermediate response variables derived from gage measurements. Our spatiotemporal evaluation enables to more realistically simulate the current situation, which gives us more confidence to project the effects of land-use changes on water quality. Under both future BT16 scenarios, simulated nitrate and total nitrogen loadings and concentrations were greatly reduced relative to the current landscape, whereas runoff, sediment, and phosphorus showed only small changes. Difference between simulated water results for the two future scenarios was small. The influence of biomass production on water quantity and quality depended on the crop, area planted, and management practices, as well as on site-specific characteristics. These results offer hope that bioenergy production in the TRB could help to protect the

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“…Prediction of yield or nitrate leaching at a larger scale has to rely on some successful mathematical formulas (Qiu et al, 2011). Some models based on hydrology are commonly used to predict nutrient loadings in watershed scale, such as SWAT (Wang et al, 2014a) and MIKE SHE (Vansteenkiste et al, 2013). There are also several nutrient-loading prediction models, which include components to simulate not only hydrological processes but also nutrient transport and transformation, such as the Denitrification-Decomposition (DNDC) model (Deng et al, 2011) and STICS soil-crop model (Jego et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Identifying critical nitrogen application rate for maize yield and nitrate leaching in a Haplic Luvisol soil using the DNDC model

Zhang

Wang

Liu

et al. 2015

Science of The Total Environment

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SWAT modeling with uncertainty and cluster analyses of tillage impacts on hydrological processes

Cited by 20 publications

References 55 publications

Multi-year incubation experiments boost confidence in model projections of long-term soil carbon dynamics

Multi-year incubation experiments boost confidence in model projections of long-term soil carbon dynamics

Hydrologic and water quality responses to biomass production in the Tennessee river basin

Identifying critical nitrogen application rate for maize yield and nitrate leaching in a Haplic Luvisol soil using the DNDC model

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