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1. Investigating the Role of Snow Water Equivalent on Streamflow Predictability during Drought

   https://doi.org/10.1175/JHM-D-21-0229.1  

   Modi, Parthkumar A. ; Small, Eric E. ; Kasprzyk, Joseph ; Livneh, Ben ( October 2022 , Journal of Hydrometeorology)

   Snowpack provides the majority of predictive information for water supply forecasts (WSFs) in snow-dominated basins across the western United States. Drought conditions typically accompany decreased snowpack and lowered runoff efficiency, negatively impacting WSFs. Here, we investigate the relationship between snow water equivalent (SWE) and April–July streamflow volume (AMJJ-V) during drought in small headwater catchments, using observations from 31 USGS streamflow gauges and 54 SNOTEL stations. A linear regression approach is used to evaluate forecast skill under different historical climatologies used for model fitting, as well as with different forecast dates. Experiments are constructed in which extreme hydrological drought years are withheld from model training, that is, years with AMJJ-V below the 15th percentile. Subsets of the remaining years are used for model fitting to understand how the climatology of different training subsets impacts forecasts of extreme drought years. We generally report overprediction in drought years. However, training the forecast model on drier years, that is, below-median years (P₁₅,P_57.5], minimizes residuals by an average of 10% in drought year forecasts, relative to a baseline case, with the highest median skill obtained in mid- to late April for colder regions. We report similar findings using a modified National Resources Conservation Service (NRCS) procedure in nine large Upper Colorado River basin (UCRB) basins, highlighting the importance of the snowpack–streamflow relationship in streamflow predictability. We propose an “adaptive sampling” approach of dynamically selecting training years based on antecedent SWE conditions, showing error reductions of up to 20% in historical drought years relative to the period of record. These alternate training protocols provide opportunities for addressing the challenges of future drought risk to water supply planning.

   Seasonal water supply forecasts based on the relationship between peak snowpack and water supply exhibit unique errors in drought years due to low snow and streamflow variability, presenting a major challenge for water supply prediction. Here, we assess the reliability of snow-based streamflow predictability in drought years using a fixed forecast date or fixed model training period. We critically evaluate different training protocols that evaluate predictive performance and identify sources of error during historical drought years. We also propose and test an “adaptive sampling” application that dynamically selects training years based on antecedent SWE conditions providing to overcome persistent errors and provide new insights and strategies for snow-guided forecasts.

    

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2. LSTM-Based Data Integration to Improve Snow Water Equivalent Prediction and Diagnose Error Sources

   https://doi.org/10.1175/JHM-D-22-0220.1  

   Song, Yalan ; Tsai, Wen-Ping ; Gluck, Jonah ; Rhoades, Alan ; Zarzycki, Colin ; McCrary, Rachel ; Lawson, Kathryn ; Shen, Chaopeng ( January 2024 , Journal of Hydrometeorology)

   Accurate prediction of snow water equivalent (SWE) can be valuable for water resource managers. Recently, deep learning methods such as long short-term memory (LSTM) have exhibited high accuracy in simulating hydrologic variables and can integrate lagged observations to improve prediction, but their benefits were not clear for SWE simulations. Here we tested an LSTM network with data integration (DI) for SWE in the western United States to integrate 30-day-lagged or 7-day-lagged observations of either SWE or satellite-observed snow cover fraction (SCF) to improve future predictions. SCF proved beneficial only for shallow-snow sites during snowmelt, while lagged SWE integration significantly improved prediction accuracy for both shallow- and deep-snow sites. The median Nash–Sutcliffe model efficiency coefficient (NSE) in temporal testing improved from 0.92 to 0.97 with 30-day-lagged SWE integration, and root-mean-square error (RMSE) and the difference between estimated and observed peak SWE valuesd_maxwere reduced by 41% and 57%, respectively. DI effectively mitigated accumulated model and forcing errors that would otherwise be persistent. Moreover, by applying DI to different observations (30-day-lagged, 7-day-lagged), we revealed the spatial distribution of errors with different persistent lengths. For example, integrating 30-day-lagged SWE was ineffective for ephemeral snow sites in the southwestern United States, but significantly reduced monthly-scale biases for regions with stable seasonal snowpack such as high-elevation sites in California. These biases are likely attributable to large interannual variability in snowfall or site-specific snow redistribution patterns that can accumulate to impactful levels over time for nonephemeral sites. These results set up benchmark levels and provide guidance for future model improvement strategies.

    

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3. Addressing data challenges in riverine nutrient load modeling of an intensively managed agro‐industrial watershed

   https://doi.org/10.1111/1752-1688.13097  

   Niroula, Sundar ; Wallington, Kevin ; Cai, Ximing ( April 2023 , JAWRA Journal of the American Water Resources Association)

   Data limitations often challenge the reliability of water quality models, especially in intensively managed watersheds. While numerous studies report successful hydrological model setup and calibration, few have addressed in detail the data challenges for multisite and multivariable model calibration to an intensively managed watershed. In this study, we address some of these challenges based on our reflective experience calibrating the Soil and Water Assessment Tool (SWAT) to the Upper Sangamon River Watershed in central Illinois based on daily flow, annual crop yield, and monthly sediment, nitrate, and total phosphorus loads. We highlight some challenges in SWAT calibration processes due to data errors and inconsistencies, and insufficient precipitation and water quality observations. Following, we demonstrate the merits of additional weather and water quality observations that could help reduce input uncertainties, and we provide suggestions for selecting appropriate observations for the model calibration. After dealing with the data issues, we show that the SWAT model could be calibrated with acceptable results for the case study watershed.

    

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4. Snow Water Equivalent Measurements in Remote Arctic Alaska Watersheds

   https://doi.org/10.1029/2019WR025621  

   Stuefer, S. L. ; Kane, D. L. ; Dean, K. M. ( April 2020 , Water Resources Research)

   Snow surveys in two Arctic watersheds located in Alaska, USA, provide 32 years of spatially distributed snow water equivalent (SWE) and snow depth observations. Annual snow surveys from the Imnavait Creek (20,036 measurements from 1985 to 2017) and Upper Kuparuk River (5,804 measurements from 1997 to 2017) watersheds were conducted to capture end‐of‐winter snow accumulation. The average end‐of‐winter SWE in the Upper Kuparuk River watershed (102 ± 29 mm) is consistently less than the Imnavait Creek watershed (130 ± 34 mm) during the common period of record (1997–2017). The average end‐of‐winter SWE in both watersheds indicates a positive trend. Comparison of SWE records with cumulative solid precipitation measured at the Imnaviat [sic] SNOTEL site highlights the undercatch of gauge precipitation and difference in long‐term trends. In this paper, we present a historic overview of data collection, discuss data accuracy, and point out advantages and limitations associated with ground‐based snow measurements in remote Arctic locations. As new methods and techniques of measuring SWE and solid precipitation become available, the presented data set will provide a historic perspective for new observations and will quantitatively relate current or future snow conditions to those that have occurred since the late twentieth century.

    

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5. Hybrid Physically Based and Deep Learning Modeling of a Snow Dominated, Mountainous, Karst Watershed

   https://doi.org/10.1029/2021WR030993  

   Xu, Tianfang ; Longyang, Qianqiu ; Tyson, Conor ; Zeng, Ruijie ; Neilson, Bethany T. ( March 2022 , Water Resources Research)

   Snow dominated mountainous karst watersheds are the primary source of water supply in many areas in the western U.S. and worldwide. These watersheds are typically characterized by complex terrain, spatiotemporally varying snow accumulation and melt processes, and duality of flow and storage dynamics because of the juxtaposition of matrix (micropores and small fissures) and karst conduits. As a result, predicting streamflow from meteorological inputs has been challenging due to the inability of physically based or conceptual hydrologic models to represent these unique characteristics. We present a hybrid modeling approach that integrates a physically based, spatially distributed, snow model with a deep learning karst model. More specifically, the high‐resolution snow model captures spatiotemporal variability in snowmelt, and the deep learning model simulates the corresponding response of streamflow as influenced by complex surface and subsurface properties. The deep learning model is based on the Convolutional Long Short‐Term Memory (ConvLSTM) architecture capable of handling spatiotemporal recharge patterns and watershed storage dynamics. The hybrid modeling approach is tested on a watershed in northern Utah with seasonal snow cover and variably karstified carbonate bedrock. The hybrid models were able to simulate streamflow at the watershed outlet with high accuracy. The spatial and temporal recharge and discharge patterns learned by the ConvLSTM model were then examined and compared with known hydrogeologic information. Results suggest that ConvLSTM simulates streamflow with higher accuracy than reference models for the study area and provides insight into spatially influenced hydrologic responses that are unavailable within lumped modeling approaches.

    

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