English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

. Hydrological modelling in the Canadian sub-Arctic is hindered by sparsemeteorological and snowpack data. The snow water equivalent (SWE) of the wintersnowpack is a key predictor and driver of spring flow, but the use of SWEdata in hydrological applications is limited due to high uncertainty. Globalre-analysis datasets that provide gridded meteorological and SWE data may bewell suited to improve hydrological assessment and snowpack simulation. Toinvestigate representation of hydrological processes and SWE for applicationin hydropower operations, global re-analysis datasets covering 1979–2014from the European Union FP7 eartH2Observe project are applied to global andlocal conceptual hydrological models. The recently developed Multi-SourceWeighted-Ensemble Precipitation (MSWEP) and the WATCH Forcing Data applied toERA-Interim data (WFDEI) are used to simulate snowpack accumulation, springsnowmelt volume and annual streamflow. The GlobSnow-2 SWE product funded bythe European Space Agency with daily coverage from 1979 to 2014 is evaluatedagainst in situ SWE measurement over the local watershed. Results demonstratethe successful application of global datasets for streamflow prediction,snowpack accumulation and snowmelt timing in a snowmelt-driven sub-Arcticwatershed. The study was unable to demonstrate statistically significantcorrelations ( p  ) among the measured snowpack, globalhydrological model and GlobSnow-2 SWE compared to snowmelt runoff volume or peakdischarge. The GlobSnow-2 product is found to under-predict late-seasonsnowpacks over the study area and shows a premature decline of SWE prior tothe true onset of the snowmelt. Of the datasets tested, the MSWEPprecipitation results in annual SWE estimates that are better predictors ofsnowmelt volume and peak discharge than the WFDEI or GlobSnow-2. This studydemonstrates the operational and scientific utility of the global re-analysisdatasets in the sub-Arctic, although knowledge gaps remain in globalsatellite-based datasets for snowpack representation, for example therelationship between passive-microwave-measured SWE to snowmelt runoffvolume.

Global re-analysis datasets to improve hydrological assessment and snow water equivalent estimation in a sub-Arctic watershed