![]() ![]() Results show that graph coarsening achieves comparable prediction skillsĪt only a fraction of training cost, thus providing important insights Procedure is introduced and is demonstrated over a much larger basin. To improve scalability, a graph-coarsening Graph-based data fusion further reduces mismatch between the GNN modelĪnd observations, with as much as 50 % KGE improvement over Kling–Gupta efficiency (KGE) greater than 0.97. Results show that the trained GNN model can effectively serve as a surrogate of the process-based model with high accuracy, with median A series of experiments are performed to test different training and imputation strategies. Over a snow-dominated watershed in the western United States. The GNN-based framework is first demonstrated We further apply a graph-based, data-fusion step toĬorrect prediction biases. Model we then fine-tune the pretrained GNN model with streamflow To approximate outputs of a high-resolution vector-based river network This work presents a multistage, physics-guided, graph neural network (GNN) approach for basin-scale river network learningĪnd streamflow forecasting. Large river basins at increasingly fine resolutions, but are computationally demanding. Years, vector-based river network models have enabled modeling of To quantify and manage the river states in a timely manner is criticalįor protecting the public safety and natural resources. Hardiness Zone Maps, climate data for areas outside the continental U.S., and mapĪnalyze and download time-series data for a single location.Rivers and river habitats around the world are under sustained pressureįrom human activities and the changing global environment. Prepared for outside agencies but now released for public use. Using monthly modeling are available for the years 1895-1990. Prior to 1981 are based on less extensive observations. Posted in this section, along with annual values computed at the end of each year. At that point the time series datasets are Recent Years: Daily and monthly observationsīecome stabilized after 6 months. Results based on both monthly and daily data are available for the 6 most recently This Month: Although still very preliminary, resultsīased on daily data readings are available for the month-in-progress. The current set of 30-year normals covers the period 1991-2020.Ĭomparisons: Maps showing how observed values have been deviating from long-term conditions (also known as anomalies) - includes the new Drought Indicator tool. Values for temperature and precipitation are computed over the preceding 30 years. Help improve PRISM data as a citizen scientistģ0-Year Normals: At the end of each decade, average.Whenever possible, we offer these datasets to the public,Įither free of charge or for a fee (depending on dataset size/complexity and The resulting datasets incorporate a variety of modeling techniques and areĪvailable at multiple spatial/temporal resolutions, covering the period from 1895 Spatial climate datasets to reveal short- and long-term climate patterns. Monitoring networks, applies sophisticated quality control measures, and develops The PRISM Climate Group gathers climate observations from a wide range of The tool also provides the option of including an animation (MP4 format) of the generated map graphics. The graphics are always generated from the most up-to-date PRISM datasets. ![]() ![]() This tool allows users to generate up to a month's worth of daily map graphics at a time, on-the-fly, for any PRISM climate variable from 1981 to present. Our new web-based Daily Map Graphics Generator has been released. Website Update: Daily Map Graphics Generator ![]()
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