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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. 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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4. 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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5. Evaluating Multiple Canopy‐Snow Unloading Parameterizations in SUMMA With Time‐Lapse Photography Characterized by Citizen Scientists

   https://doi.org/10.1029/2021WR030852  

   Lumbrazo, Cassie ; Bennett, Andrew ; Currier, William Ryan ; Nijssen, Bart ; Lundquist, Jessica ( June 2022 , Water Resources Research)

   Canopy‐snow unloading is the complex physical process of snow unloading from the canopy through meltwater drip, sublimation to the atmosphere, or solid snow unloading to the snowpack below. This process is difficult to parameterize due to limited observations. Time‐lapse photographs of snow in the canopy were characterized by citizen scientists to create a data set of snow interception observations at multiple locations across the western United States. This novel interception data set was used to evaluate three snow unloading parameterizations in the Structure for Unifying Multiple Modeling Alternatives (SUMMA) modular hydrologic modeling framework. SUMMA was modified to include a third snow unloading parameterization, termed Wind‐Temperature (Roesch et al., 2001,https://doi.org/10.1007/s003820100153), which includes wind‐dependent and temperature‐dependent unloading functions. It was compared to a meltwater drip unloading parameterization, termed Melt (Andreadis et al., 2009,https://doi.org/10.1029/2008wr007042), and a time‐dependent unloading parameterization, termed Exponential‐Decay (Hedstrom & Pomeroy, 1998,https://doi.org/10.1002/(SICI)1099-1085(199808/09)12:10/11<1611::AID-HYP684>3.0.CO;2-4). Wind‐Temperature performed well without calibration across sites, specifically in cold climates, where wind dominates unloading and rime accretion is low. At rime prone sites, Wind‐Temperature should be calibrated to account for longer interception events with less sensitivity to wind, otherwise Melt can be used without calibration. The absence of model physics in Exponential‐Decay requires local calibration that can only be transferred to sites with similar unloading patterns. The choice of unloading parameterization can result in 20% difference in SWE on the ground below the canopy and 10% difference in estimated average winter canopy albedo. These novel observations shed light on processes that are often overlooked in hydrology.

    

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