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1. Enhancing Soil and Water Assessment Tool Snow Prediction Reliability with Remote-Sensing-Based Snow Water Equivalent Reconstruction Product for Upland Watersheds in a Multi-Objective Calibration Process

   https://doi.org/10.3390/w12113190  

   Liu, Zhu ; Yin, Jina ; E. Dahlke, Helen ( November 2020 , Water)

   null (Ed.)

   Precipitation occurs in two basic forms defined as liquid state and solid state. Different from rain-fed watershed, modeling snow processes is of vital importance in snow-dominated watersheds. The seasonal snowpack is a natural water reservoir, which stores snow water in winter and releases it in spring and summer. The warmer climate in recent decades has led to earlier snowmelt, a decline in snowpack, and change in the seasonality of river flows. The Soil and Water Assessment Tool (SWAT) could be applied in the snow-influenced watershed because of its ability to simultaneously predict the streamflow generated from rainfall and from the melting of snow. The choice of parameters, reference data, and calibration strategy could significantly affect the SWAT model calibration outcome and further affect the prediction accuracy. In this study, SWAT models are implemented in four upland watersheds in the Tulare Lake Basin (TLB) located across the Southern Sierra Nevada Mountains. Three calibration scenarios considering different calibration parameters and reference datasets are applied to investigate the impact of the Parallel Energy Balance Model (ParBal) snow reconstruction data and snow parameters on the streamflow and snow water-equivalent (SWE) prediction accuracy. In addition, the watershed parameters and lapse rate parameters-led equifinality is also evaluated. The results indicate that calibration of the SWAT model with respect to both streamflow and SWE reference data could improve the model SWE prediction reliability in general. Comparatively, the streamflow predictions are not significantly affected by differently lumped calibration schemes. The default snow parameter values capture the extreme high flows better than the other two calibration scenarios, whereas there is no remarkable difference among the three calibration schemes for capturing the extreme low flows. The watershed and lapse rate parameters-induced equifinality affects the flow prediction more (Nash-Sutcliffe Efficiency (NSE) varies between 0.2–0.3) than the SWE prediction (NSE varies less than 0.1). This study points out the remote-sensing-based SWE reconstruction product as a promising alternative choice for model calibration in ungauged snow-influenced watersheds. The streamflow-reconstructed SWE bi-objective calibrated model could improve the prediction reliability of surface water supply change for the downstream agricultural region under the changing climate. 

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2. Investigating model performance and parameter sensitivity for runoff simulation across multiple events for a large green roof

   https://doi.org/10.1002/hyp.14387  

   Worthen, Lucie ; Kelleher, Christa ; Davidson, Cliff ( October 2021 , Hydrological Processes)

   Green roofs are a form of green infrastructure aimed at retaining or slowing the movement of precipitation as stormwater runoff to sewer systems. To determine total runoff versus retention from green roofs, researchers and practitioners alike employ hydrologic models that are calibrated to one or more observed events. However, questions still remain regarding how event size may impact parameter sensitivity, how best to constrain initial soil moisture (ISM), and whether limited observations (i.e., a single event) can be used within a calibration‐validation framework. We explored these questions by applying the storm water management model to simulate a large green roof located in Syracuse, NY. We found that model performance was very high (e.g., Nash Sutcliffe efficiency index > 0.8 and Kling‐Gupta efficiency index > 0.8) for many events. We initially compared model performance across two parameterizations of ISM. For some events, we found similar performance when ISM was varied versus set to zero; for others, varying ISM yielded higher performance as well as greater water balance closure. Within a calibration‐validation framework, we found that calibrating to larger events tended to produce moderate to high performance for other non‐calibration events. However, very small storms were notoriously difficult to simulate, regardless of calibration event size, as these events are likely fully retained on the roof. Using regional sensitivity analysis, we confirmed that only a subset of model parameters was sensitive across 16 events. Interestingly, many parameters were sensitive regardless of event size, though some parameters were more sensitive when simulating smaller events. This emphasizes that storm size likely influences parameter sensitivity. Overall, we show that while calibrating to a single event can achieve high performance, exploring simulations across multiple events can yield important insight regarding the hydrologic performance of green roofs that can be used to guide the gathering of in situ properties and observations for refining model frameworks.

    

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3. Water balance response of permafrost-affected watersheds to changes in air temperatures

   https://doi.org/10.1088/1748-9326/ac12f3  

   Debolskiy, Matvey V ; Alexeev, Vladimir A ; Hock, Regine ; Lammers, Richard B ; Shiklomanov, Alexander ; Schulla, Joerg ; Nicolsky, Dmitry ; Romanovsky, Vladimir E ; Prusevich, Alexander ( August 2021 , Environmental Research Letters)

   Abstract Observations show increases in river discharge to the Arctic Ocean especially in winter over the last decades but the physical mechanisms driving these changes are not yet fully understood. We hypothesize that even in the absence of a precipitation increase, permafrost degradation alone can lead to increased annual river runoff. To test this hypothesis we perform 12 millennium-long simulations over an idealized hypothetical watershed (1 km 2 ) using a distributed, physically based water balance model (Water flow and Balance Simulation Model, WaSiM). The model is forced by both a hypothetical warming defined by an air temperature increase of 7.5 ∘ C over 100 years, and a corresponding cooling scenario. To assess model sensitivity we vary soil saturated hydraulic conductivity and lateral subsurface flow configuration. Under the warming scenario, changes in subsurface water transport due to ground temperature changes result in a 7%–14% increase in annual runoff accompanied by a 6%–20% decrease in evapotranspiration. The increase in runoff is most pronounced in winter. Hence, the simulations demonstrate that changes in permafrost characteristics due to climate warming and associated changes in evapotranspiration provide a plausible mechanism for the observed runoff increases in Arctic watersheds. In addition, our experiments show that when lateral subsurface moisture transport is not included, as commonly done in global-scale Earth System Models, the equilibrium water balance in response to the warming or cooling is similar to the water balance in simulations where lateral subsurface transport is included. However, the transient changes in water balance components prior to reaching equilibrium differ greatly between the two. For example, for high saturated hydraulic conductivity only when lateral subsurface transport is considered, a period of decreased runoff occurs immediately after the warming. This period is characterized by a positive change in soil moisture storage caused by the soil moisture deficit developed during prior cooling. 

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4. Improving Alpine Summertime Streamflow Simulations by the Incorporation of Evapotranspiration Data

   https://doi.org/10.3390/w11010112  

   Tobin, Kenneth ; Bennett, Marvin ( January 2019 , Water)

   Over the last decade, autocalibration routines have become commonplace in watershed modeling. This approach is most often used to simulate a streamflow at a basin’s outlet. In alpine settings, spring/early summer snowmelt is by far the dominant signal in this system. Therefore, there is great potential for a modeled watershed to underperform during other times of the year. This tendency has been noted in many prior studies. In this work, the Soil and Water Assessment Tool (SWAT) model was auto-calibrated with the SUFI-2 routine. A mountainous watershed from Idaho was examined (Upper North Fork). In this study, this basin was calibrated using three estimates of evapotranspiration (ET): Moderate Resolution Imagining Spectrometer (MODIS), Simplified Surface Energy Balance, and Global Land Evaporation: the Amsterdam Model. The MODIS product in particular, had the greatest utility in helping to constrain SWAT parameters that have a high sensitivity to ET. Streamflow simulations that utilize these ET parameter values have improved recessional and summertime streamflow performances during calibration (2007 to 2011) and validation (2012 to 2014) periods. Streamflow performance was monitored with standard objective metrics (Bias and Nash Sutcliffe coefficients) that quantified overall, recessional, and summertime peak flows. This approach yielded dramatic enhancements for all three observations. These results demonstrate the utility of this approach for improving watershed modeling fidelity outside the main snowmelt season. 

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5. Exploring Random Forest Machine Learning and Remote Sensing Data for Streamflow Prediction: An Alternative Approach to a Process-Based Hydrologic Modeling in a Snowmelt-Driven Watershed

   https://doi.org/10.3390/rs15163999  

   Islam, Khandaker Iftekharul ; Elias, Emile ; Carroll, Kenneth C. ; Brown, Christopher ( August 2023 , Remote Sensing)

   Thenkabail, Prasad S. (Ed.)

   Physically based hydrologic models require significant effort and extensive information for development, calibration, and validation. The study explored the use of the random forest regression (RFR), a supervised machine learning (ML) model, as an alternative to the physically based Soil and Water Assessment Tool (SWAT) for predicting streamflow in the Rio Grande Headwaters near Del Norte, a snowmelt-dominated mountainous watershed of the Upper Rio Grande Basin. Remotely sensed data were used for the random forest machine learning analysis (RFML) and RStudio for data processing and synthesizing. The RFML model outperformed the SWAT model in accuracy and demonstrated its capability in predicting streamflow in this region. We implemented a customized approach to the RFR model to assess the model’s performance for three training periods, across 1991–2010, 1996–2010, and 2001–2010; the results indicated that the model’s accuracy improved with longer training periods, implying that the model trained on a more extended period is better able to capture the parameters’ variability and reproduce streamflow data more accurately. The variable importance (i.e., IncNodePurity) measure of the RFML model revealed that the snow depth and the minimum temperature were consistently the top two predictors across all training periods. The paper also evaluated how well the SWAT model performs in reproducing streamflow data of the watershed with a conventional approach. The SWAT model needed more time and data to set up and calibrate, delivering acceptable performance in annual mean streamflow simulation, with satisfactory index of agreement (d), coefficient of determination (R2), and percent bias (PBIAS) values, but monthly simulation warrants further exploration and model adjustments. The study recommends exploring snowmelt runoff hydrologic processes, dust-driven sublimation effects, and more detailed topographic input parameters to update the SWAT snowmelt routine for better monthly flow estimation. The results provide a critical analysis for enhancing streamflow prediction, which is valuable for further research and water resource management, including snowmelt-driven semi-arid regions.

    

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