On the importance of soil moisture in calibration of rainfall–runoff models: two case studies

Shahrban, Mahshid; Walker, Jeffrey P.; Wang, QJ

doi:10.1080/02626667.2018.1487560

Cited by 19 publications

(9 citation statements)

References 47 publications

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“…In turn, snow persistence has been found to produce more rapid snowmelt to induce soil saturation (Trujillo & Molotch, 2014), after which water can move vertically as recharge or laterally through the soil zone as interflow to promote more streamflow (Barnhard et al, 2020; Barnhart et al, 2016). Lateral movement of water is largely a function of soil storage and water holding capacity (Xiao et al, 2019) and initial soil moisture content, such that soil moisture observations can improve streamflow forecasts (Crow et al, 2018; Mahanama et al, 2012; Shahrban et al, 2018). Use of LiDAR snow observations informs where snow ends up, not necessarily where it fell.…”

Section: Discussionmentioning

confidence: 99%

Efficiency of the Summer Monsoon in Generating Streamflow Within a Snow‐Dominated Headwater Basin of the Colorado River

Carroll

Gochis

Williams

2020

Geophysical Research Letters

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Section: Discussionmentioning

confidence: 99%

Efficiency of the Summer Monsoon in Generating Streamflow Within a Snow‐Dominated Headwater Basin of the Colorado River

Carroll

Gochis

Williams

2020

Geophysical Research Letters

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“…Previous studies used ground‐based or alternatively remote sensing products or their combination as such additional information on hydrologic processes. Soil moisture (Kundu et al, 2017; Kunnath‐Poovakka et al, 2016; Parajka et al, 2006; Rajib et al, 2016; Shahrban et al, 2018), evapotranspiration (Gui et al, 2019; Herman et al, 2018; Immerzeel & Droogers, 2008; Kunnath‐Poovakka et al, 2016), and groundwater level data (Demirel et al, 2019; Seibert, 2000) were often used for model calibration to improve the model's internal consistency. These studies showed the added value of different observations besides runoff, for example, for soil moisture, evapotranspiration, and groundwater levels.…”

Section: Introductionmentioning

confidence: 99%

The Added Value of Different Data Types for Calibrating and Testing a Hydrologic Model in a Small Catchment

Széles

Parajka

Hogan

et al. 2020

Water Resources Research

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This study investigated the added value of different data for calibrating a runoff model for small basins. The analysis was performed in the 66 ha Hydrological Open Air Laboratory, in Austria. An Hydrologiska Byråns Vattenbalansavdelning (HBV) type, spatially lumped hydrologic model was parameterized following two approaches. First, the model was calibrated using only runoff data. Second, a step-by-step approach was followed, where the modules of the model (snow, soil moisture, and runoff generation) were calibrated using measurements of runoff and model state variables and output fluxes. These measurements comprised laser-based measurements of precipitation, satellite and camera observations of snow, ultrasonic measurements of snow depth, eddy covariance measurements of evapotranspiration, time domain transmissometry-based soil moisture measurements, time-lapse photography of overland flow, and groundwater level measurements by piezometers. The two model parameterizations were evaluated on annual, seasonal, and daily time scales, in terms of how well they simulated snow, soil moisture, evapotranspiration, overland flow, storage change in the saturated zone, and runoff. Using the proposed step-by-step approach, the relative runoff volume errors in the calibration and validation periods were 0.00 and −0.01, the monthly Pearson correlation coefficients were 0.92 and 0.82, and the daily logarithmic Nash Sutcliffe efficiencies were 0.59 and 0.18, respectively. By using different sources of data besides runoff, the overall process consistency improved, compared to the case when only runoff was used for calibration. Soil moisture and evapotranspiration observations had the largest influence on simulated runoff, while the parameterization of the snow and runoff generation modules had a smaller influence.

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“…This may be partially due to the use of a simplified soil moisture conceptual model in SWAT, such as a bucket [40]. Shahrban et al [66] also found that a hydrologic model using a bucket concept for soil moisture poorly simulated low-flow conditions relative to a model that used a continuous distribution of soil moisture according to vertical depth. Based on this FDC result, we postulate that RSWAT may have a greater capacity to replicate water partitioning processes than SWAT.…”

Section: Streamflow and Et Predictions At The Watershed Levelmentioning

confidence: 99%

Utility of Remotely Sensed Evapotranspiration Products to Assess an Improved Model Structure

Lee

Kim

et al. 2021

Sustainability

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There is a certain level of predictive uncertainty when hydrologic models are applied for operational purposes. Whether structural improvements address uncertainty has not well been evaluated due to the lack of observational data. This study investigated the utility of remotely sensed evapotranspiration (RS-ET) products to quantitatively represent improvements in model predictions owing to structural improvements. Two versions of the Soil and Water Assessment Tool (SWAT), representative of original and improved versions, were calibrated against streamflow and RS-ET. The latter version contains a new soil moisture module, referred to as RSWAT. We compared outputs from these two versions with the best performance metrics (Kling–Gupta Efficiency [KGE], Nash-Sutcliffe Efficiency [NSE] and Percent-bias [P-bias]). Comparisons were conducted at two spatial scales by partitioning the RS-ET into two scales, while streamflow comparisons were only conducted at one scale. At the watershed level, SWAT and RSWAT produced similar metrics for daily streamflow (NSE of 0.29 and 0.37, P-bias of 1.7 and 15.9, and KGE of 0.47 and 0.49, respectively) and ET (KGE of 0.48 and 0.52, respectively). At the subwatershed level, the KGE of RSWAT (0.53) for daily ET was greater than that of SWAT (0.47). These findings demonstrated that RS-ET has the potential to increase prediction accuracy from model structural improvements and highlighted the utility of remotely sensed data in hydrologic modeling.

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On the importance of soil moisture in calibration of rainfall–runoff models: two case studies

Cited by 19 publications

References 47 publications

Efficiency of the Summer Monsoon in Generating Streamflow Within a Snow‐Dominated Headwater Basin of the Colorado River

Efficiency of the Summer Monsoon in Generating Streamflow Within a Snow‐Dominated Headwater Basin of the Colorado River

The Added Value of Different Data Types for Calibrating and Testing a Hydrologic Model in a Small Catchment

Utility of Remotely Sensed Evapotranspiration Products to Assess an Improved Model Structure

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