Multi-resolution meteorological forcing evaluation

Knowledge graph centered on Multi-resolution meteorological forcing evaluation with 49 nodes and 145 connections. Top connected: snowpack persistence, groundwater storage dynamics, leaf water content, evapotranspiration, air temperature.

Method synopsis

Systematic comparison of watershed model performance using meteorological forcing datasets at different spatial and temporal resolutions. Evaluates how forcing resolution affects simulated hydrologic variables through statistical performance metrics.

Synthesized from method descriptions across 1 paper using this protocol.

Procedure as described in the canonical source

Steps below were extracted from the paper that introduces this protocol — The effects of spatial and temporal resolution of gridded meteorological forcing on watershed hydrological responses (2022), Hydrology and Earth System Sciences. Implementations in other papers (listed below) may differ.

1. Meteorological forcing preparation
   Three GMF products compared: PRISM (daily, 800m native resolution), Daymet (daily, 1km), and NLDAS-2 (hourly, 12km). Spatial downscaling performed using data-driven downscaling with random forests and bilinear interpolation. Temporal disaggregation performed by applying NLDAS temporal patterns to daily PRISM data.
   Quantities: 800 m PRISM resolution, 1 km Daymet resolution, 12 km NLDAS resolutionDuration: 2015-2019 data periodConditions: Various spatial and temporal resolutions tested
   Equipment: Random forests downscaling, Bilinear interpolation
2. Model evaluation against observations
   Model outputs compared against USGS hourly streamflow (station 09111250), GHCN-D weather stations, ASO snow surveys (4 flight dates), and MODIS 8-day composite evapotranspiration. Statistical metrics calculated including KGE, NSE, correlation coefficient, bias ratio, and variability ratio.
   Quantities: 15 min streamflow aggregated to hourly, 8-day composite ET, 4 ASO survey datesDuration: 2017-2019 evaluation period for most metricsConditions: Comparison with multiple independent observation datasets
   Equipment: USGS stream gage, GHCN-D stations, Airborne Snow Observatory, MODIS satellite

Typical Equipment

• PRISM Climate Group datasets
• Daymet v4
• NLDAS-2
• statistical analysis software

Output Measurements

• correlation coefficients
• Nash-Sutcliffe efficiency
• Kling-Gupta efficiency
• bias metrics

Papers Using This Protocol (1)

Places Where Used