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1. Spatiotemporal distribution of seasonal snow water equivalent in High Mountain Asia from an 18-year Landsat–MODIS era snow reanalysis dataset

   https://doi.org/10.5194/tc-15-5261-2021  

   Liu, Yufei; Fang, Yiwen; Margulis, Steven A. (January 2021, The Cryosphere)

   Abstract. Seasonal snowpack is an essential component in the hydrological cycle and plays a significant role in supplying water resources to downstream users. Yet the snow water equivalent (SWE) in seasonal snowpacks, and its space–time variation, remains highly uncertain, especially over mountainous areas with complex terrain and sparse observations, such as in High Mountain Asia (HMA). In this work, we assessed the spatiotemporal distribution of seasonal SWE, obtained from a new 18-year HMA Snow Reanalysis (HMASR) dataset, as part of the recent NASA High Mountain Asia Team (HiMAT) effort. A Bayesian snow reanalysis scheme previously developed to assimilate satellite-derived fractional snow-covered area (fSCA) products from Landsat and MODIS platforms has been applied to develop the HMASR dataset (at a spatial resolution of 16 arcsec (∼500 m) and daily temporal resolution) over the joint Landsat–MODIS period covering water years (WYs) 2000–2017. Based on the results, the HMA-wide total SWE volume is found to be around163 km3 on average and ranges from 114 km3 (WY2001) to 227 km3 (WY2005) when assessed over 18 WYs. The most abundant snowpacks are found in the northwestern basins (e.g., Indus, Syr Darya and Amu Darya) that are mainly affected by the westerlies, accounting for around 66 % of total seasonal SWE volume. Seasonal snowpack in HMA is depicted by snow accumulating through October to March and April, typically peaking around April and depleting in July–October, with variations across basins and WYs. When examining the elevational distribution over the HMA domain, seasonal SWE volume peaks at mid-elevations (around 3500 m), with over 50 % of the volume stored above 3500 m. Above-average amounts of precipitation causes significant overall increase in SWE volumes across all elevations, while an increase in air temperature (∼1.5 K) from cooler to normal conditions leads to an redistribution in snow storage from lower elevations to mid-elevations. This work brings new insight into understanding the climatology and variability of seasonal snowpack over HMA, with the regional snow reanalysis constrained by remote-sensing data, providing a new reference dataset for future studies of seasonal snow and how it contributes to the water cycle and climate over the HMA region. 

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2. 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)

   Abstract 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 ( P 15 , 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. Significance Statement 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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3. 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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4. California’s 2023 snow deluge: Contextualizing an extreme snow year against future climate change

   https://doi.org/10.1073/pnas.2320600121  

   Marshall, Adrienne M.; Abatzoglou, John T.; Rahimi, Stefan; Lettenmaier, Dennis P.; Hall, Alex (May 2024, Proceedings of the National Academy of Sciences)

   The increasing prevalence of low snow conditions in a warming climate has attracted substantial attention in recent years, but a focus exclusively on low snow leaves high snow years relatively underexplored. However, these large snow years are hydrologically and economically important in regions where snow is critical for water resources. Here, we introduce the term “snow deluge” and use anomalously high snowpack in California’s Sierra Nevada during the 2023 water year as a case study. Snow monitoring sites across the state had a median 41 y return interval for April 1 snow water equivalent (SWE). Similarly, a process-based snow model showed a 54 y return interval for statewide April 1 SWE (90% CI: 38 to 109 y). While snow droughts can result from either warm or dry conditions, snow deluges require both cool and wet conditions. Relative to the last century, cool-season temperature and precipitation during California’s 2023 snow deluge were both moderately anomalous, while temperature was highly anomalous relative to recent climatology. Downscaled climate models in the Shared Socioeconomic Pathway-370 scenario indicate that California snow deluges—which we define as the 20 y April 1 SWE event—are projected to decline with climate change (58% decline by late century), although less so than median snow years (73% decline by late century). This pattern occurs across the western United States. Changes to snow deluge, and discrepancies between snow deluge and median snow year changes, could impact water resources and ecosystems. Understanding these changes is therefore critical to appropriate climate adaptation. 

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5. Sources of seasonal water supply forecast uncertainty during snow drought in the Sierra Nevada

   https://doi.org/10.1111/1752-1688.13221  

   Boardman, Elijah N; Renshaw, Carl E; Shriver, Robert K; Walters, Reggie; McGurk, Bruce; Painter, Thomas H; Deems, Jeffrey S; Bormann, Kat J; Lewis, Gabriel M; Dethier, Evan N; et al (July 2024, JAWRA Journal of the American Water Resources Association)

   Abstract Uncertainty attribution in water supply forecasting is crucial to improve forecast skill and increase confidence in seasonal water management planning. We develop a framework to quantify fractional forecast uncertainty and partition it between (1) snowpack quantification methods, (2) variability in post‐forecast precipitation, and (3) runoff model errors. We demonstrate the uncertainty framework with statistical runoff models in the upper Tuolumne and Merced River basins (California, USA) using snow observations at two endmember spatial resolutions: a simple snow pillow index and full‐catchment snow water equivalent (SWE) maps at 50 m resolution from the Airborne Snow Observatories. Bayesian forecast simulations demonstrate a nonlinear decrease in the skill of statistical water supply forecasts during warm snow droughts, when a low fraction of winter precipitation remains as SWE. Forecast skill similarly decreases during dry snow droughts, when winter precipitation is low. During a shift away from snow‐dominance, the uncertainty of forecasts using snow pillow data increases about 1.9 times faster than analogous forecasts using full‐catchment SWE maps in the study area. Replacing the snow pillow index with full‐catchment SWE data reduces statistical forecast uncertainty by 39% on average across all tested climate conditions. Attributing water supply forecast uncertainty to reducible error sources reveals opportunities to improve forecast reliability in a warmer future climate. 

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