<i>In‐Situ</i> Quantification and Prediction of Water Yield From Southern US Pine Forests

Acharya, Subodh; Kaplan, D. A.; McLaughlin, Daniel L.; Cohen, Matthew J.

doi:10.1029/2021wr031020

Cited by 8 publications

(4 citation statements)

References 104 publications

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“…Local variation in upland ET impacts water table elevations (Le Maitre et al., 1999) and by extension wetland hydrological function (Jones, McLaughlin, et al., 2018). We quantified upland ET based on measured LAI (Acharya et al., 2022) in the adjacent upland forest surrounding each wetland, but while this variable varied widely, it did not significantly influence wetland groundwater export in any of the wetlandscapes (Table S2 in Supporting Information ). We note that groundwater flows are nighttime measurements, so one explanation for this absent association is that daytime ET losses do not directly impact groundwater recession.…”

Section: Discussionmentioning

confidence: 99%

Patterns of Wetland Hydrologic Connectivity Across Coastal‐Plain Wetlandscapes

Lee

Epstein

Cohen

2023

Water Resources Research

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Depressional wetlands influence the functions of wetlandscapes by storing and releasing water, providing critical habitat, amplifying carbon and nutrient cycling, and influencing microclimate. Despite persistent subsurface connectivity, depressional wetlands are surrounded by uplands so only sporadically connect via surface pathways. However, the frequency, duration, and relative importance of surface connectivity in depressional wetlands remains poorly understood, limiting quantification of their landscape functions. Using multiple years of stage variation in 67 depressional wetlands across four contrasting wetlandscapes, we observed wetland spill elevation is exceeded 10%–40% of the time, with substantial variation within and between wetlandscapes. Moreover, surface connectivity increased water loss rates by 200%–350% on a depth basis and 350%–850% on a volumetric basis compared with subsurface water loss rates. This temporally disproportionate water export suggests that short‐lived surface connectivity is crucial for aggregate landscape export of water‐borne materials and numerous hydrologic and habitat services. Contrasting water loss rates above and below spill thresholds create homeostatic feedback that stabilizes water levels near the thresholds. We explored geomorphic, climatic, and vegetative influences on hydrologic connectivity, quantified as nighttime recession rates below the spill threshold for groundwater connectivity and percent time above thresholds for surface connectivity. Groundwater connectivity was consistently greater in deeper wetlands and wetlandscape identity was the primary factor explaining variation in surface and subsurface connectivity. Our results highlight the critical role of surface connectivity in coastal plain wetlands, illustrate the heterogeneity of those wetland functions within and across wetlandscapes, and provide hydrologic benchmarks for evaluating restoration of aggregate landscape functions.

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Section: Discussionmentioning

confidence: 99%

Patterns of Wetland Hydrologic Connectivity Across Coastal‐Plain Wetlandscapes

Lee

Epstein

Cohen

2023

Water Resources Research

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“…For natural hazards and geomorphology, both soil moisture and streamflow can influence the likelihood of flooding (Koster et al 2010;Massari et al 2014) and debris flows (Coe et al 2008;Kean et al 2013;Tang et al 2019), while soil moisture has also been used to predict drought (Xu et al 2020) and shallow landslides (Gasmo et al 2000;Handwerger et al 2019;Johnson and Sitar 1990;Ray and Jacobs 2007;Sweeney and Robertson 1979). In water supply management, soil moisture influences forest water yield and streamflow controls suspended sediment transport and water quality (Acharya et al 2022;Colby 1956). Water in soil and river channels also drives the productivity and sustainability of terrestrial ecosystems, especially in arid and semi-arid regions (Legates et al 2011), influencing crop yields and other aspects of agriculture (Berg and Sheffield 2018;Carrão et al 2016;Kang et al 2009).…”

Section: Introductionmentioning

confidence: 99%

Toward improved regional hydrological model performance using a novel soil data-informed calibration method

Li¹,

Wang

et al. 2023

Preprint

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Accurate soil moisture and streamflow data are an aspirational need of many hydrologically-relevant fields. Model simulated soil moisture and streamflow hold promise but numerical models require calibration prior to application to ensure sufficient model performance. Manual or automated calibration methods require iterative model runs and hence are computationally expensive. In this study, we leverage the Soil Survey Geographic (SSURGO) database and the probability mapping of SSURGO (POLARIS) to help constrain soil parameter uncertainties in the Weather Research and Forecasting Hydrological modeling system (WRF-Hydro) over a central California domain. After calibration, WRF-Hydro soil moisture exhibits increased correlation coefficients (r), reduced biases, and increased Kling-Gupta Efficiencies (KGEs) across seven in-situ soil moisture observing stations. Compared to four well-established soil moisture datasets including Soil Moisture Active Passive Level 4 data and three Phase 2 North American Land Data Assimilation System land surface models, our POLARIS-calibrated WRF-Hydro produces the highest mean KGE (0.67) across the seven stations. More importantly, WRF-Hydro streamflow fidelity also increases especially in the case where the model domain is set up with an SSURGO-informed total soil thickness. Both the magnitude and timing of peak flow events are better captured, r increases across nine United States Geological Survey stream gages, and the mean Nash-Sutcliffe Efficiency across seven of the nine gages increases from 0.19 in default WRF-Hydro to 0.63 after calibration. Our soil data-informed calibration approach, which is transferable to other spatially-distributed hydrological models, uses open-access data and non-iterative steps to improve model performance and is thus operationally and computationally attractive.

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“…For natural hazards, soil moisture is an important proxy to predict agricultural drought (Cook et al., 2018; Xu et al., 2020) and shallow landslides (Gasmo et al., 2000; Handwerger et al., 2019; Johnson & Sitar, 1990; Ray & Jacobs, 2007; Sweeney and Robertson, 1979), while streamflow is used in the prediction of hydrological drought (Cook et al., 2018), flooding (Koster et al., 2010; Massari et al., 2014), and debris flows (Coe et al., 2008; Kean et al., 2013; Li et al., 2022; Tang et al., 2019). In water supply management, soil moisture influences forest water yield while streamflow discharge controls suspended sediment transport and water quality (Acharya et al., 2022; Colby, 1956). Water in soil and river channels also drives the productivity and sustainability of terrestrial ecosystems, especially in arid and semi‐arid regions (Legates et al., 2011), influencing crop yields and other aspects of agriculture (Berg & Sheffield, 2018; Carrão et al., 2016; Kang et al., 2009).…”

Section: Introductionmentioning

confidence: 99%

“…. In water supply management, soil moisture influences forest water yield while streamflow discharge controls suspended sediment transport and water quality (Acharya et al, 2022;Colby, 1956). Water in soil and river channels also drives the productivity and sustainability of terrestrial ecosystems, especially in arid and semi-arid regions (Legates et al, 2011), influencing crop yields and other aspects of agriculture (Berg & Sheffield, 2018;Carrão et al, 2016;Kang et al, 2009).…”

mentioning

confidence: 99%

Toward Improved Regional Hydrological Model Performance Using State‐Of‐The‐Science Data‐Informed Soil Parameters

Wang

et al. 2023

Water Resources Research

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Accurate soil moisture and streamflow data are an aspirational need of many hydrologically‐relevant fields. Model simulated soil moisture and streamflow hold promise but models require validation prior to application. Calibration methods are commonly used to improve model fidelity but misrepresentation of the true dynamics remains a challenge. In this study, we leverage soil parameter estimates from the Soil Survey Geographic (SSURGO) database and the probability mapping of SSURGO (POLARIS) to improve the representation of hydrologic processes in the Weather Research and Forecasting Hydrological modeling system (WRF‐Hydro) over a central California domain. Our results show WRF‐Hydro soil moisture exhibits increased correlation coefficients (r), reduced biases, and increased Kling‐Gupta Efficiencies (KGEs) across seven in‐situ soil moisture observing stations after updating the model’s soil parameters according to POLARIS. Compared to four well‐established soil moisture datasets including Soil Moisture Active Passive data and three Phase 2 North American Land Data Assimilation System land surface models, our POLARIS‐adjusted WRF‐Hydro simulations produce the highest mean KGE (0.69) across the seven stations. More importantly, WRF‐Hydro streamflow fidelity also increases, especially in the case where the model domain is set up with SSURGO‐informed total soil thickness. The magnitude and timing of peak flow events are better captured, r increases across nine United States Geological Survey stream gages, and the mean KGE across seven of the nine gages increases from 0.12 to 0.66. Our pre‐calibration parameter estimate approach, which is transferable to other spatially‐distributed hydrological models, can substantially improve a model’s performance, helping reduce calibration efforts and computational costs.

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In‐Situ Quantification and Prediction of Water Yield From Southern US Pine Forests

Cited by 8 publications

References 104 publications

Patterns of Wetland Hydrologic Connectivity Across Coastal‐Plain Wetlandscapes

Patterns of Wetland Hydrologic Connectivity Across Coastal‐Plain Wetlandscapes

Toward improved regional hydrological model performance using a novel soil data-informed calibration method

Toward Improved Regional Hydrological Model Performance Using State‐Of‐The‐Science Data‐Informed Soil Parameters

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