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

Abstract

Snowpack provides the majority of predictive information for water supply forecasts (WSFs) in snow-dominated basins across the western US. 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 gages 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, i.e., 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, i.e., below-median years (P15, P57.5]), 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 NRCS procedure in nine large 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 upto 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.

Climate change induced Melamchi extreme flood and environment implication in central Himalaya of Nepal

“…Furthermore, the SCS curve number of the HEC HMS Model was applied to verify the peak discharge. This study analyzed the relationship between temperature and snow water equivalent (SWE) of the Melamchi catchment using regional equation (DHM, 2006) and then SWE was converted in to stream ow (Modi et al, 2022).…”

“…In the Himalaya, snowmelt also has signi cant contribution to total runoff of the river (Li et al 2017;ICIMOD, 2011). During summer, snowmelt water i.e amount of snow water equivalent (SWE) has positively correlated with stream ow in the rivers of Himalayas (Modi et al, 2022).…”

Is Snow Drought a Messenger for the Upcoming Severe Drought Period? A Case Study in the Upper Mississippi River Basin

“…Some studies have shown that the lack of or limited snow accumulation in winters plays an important role in the following period of streamflow (Dierauer et al, 2018) and its predictability during drought periods (Modi et al, 2022), and groundwater recharge and its monitoring (Sit et al, 2021). Understanding snow accumulation is an important factor for flood forecasting (Krajewski et al, 2021;Muste et al, 2022) and mapping to support mitigation (Alabbad et al, 2022) and agricultural planning decisions (Yildirim and Demir, 2022).…”

Six Consecutive Seasons of High‐Resolution Mountain Snow Depth Maps From Satellite Stereo Imagery

“…In mountainous, snow‐dominant watersheds, these estimates are needed for streamflow forecast modeling and reservoir management. Knowledge of initial hydrologic conditions (i.e., snow depth at the beginning of the melt season) drives forecasting skill (Anghileri et al., 2016), so timely and spatially distributed observations of snowpack state are needed for accurate forecasting and management, especially during anomalous years (Modi et al., 2022) and in a changing climate (e.g., Gordon et al., 2022; Marshall et al., 2019).…”

“…In mountainous, snow-dominant watersheds, these estimates are needed for streamflow forecast modeling and reservoir management. Knowledge of initial hydrologic conditions (i.e., snow depth at the beginning of the melt season) drives forecasting skill (Anghileri et al, 2016), so timely and spatially distributed observations of snowpack state are needed for accurate forecasting and management, especially during anomalous years (Modi et al, 2022) and in a changing climate (e.g., Gordon et al, 2022;Marshall et al, 2019).Stereo photogrammetry with very-high-resolution (VHR, <1-2 m resolution) optical satellite images provides digital elevation models (DEMs) that can precisely measure snow surface elevation. These "snow-on" DEMs offer fine-scale snow depth mapping when differenced with a snow-free DEM.…”