A hydrological simulation dataset of the Upper Colorado River Basin from 1983 to 2019

Abstract

This article presents a hydrological reconstruction of the Upper Colorado River Basin with an hourly temporal resolution, and 1-km spatial resolution from October 1982 to September 2019. The validated dataset includes a suite of hydrologic variables including streamflow, water table depth, snow water equivalent (SWE) and evapotranspiration (ET) simulated by an integrated hydrological model, ParFlow-CLM. The dataset was validated over the period with a combination of point observations and remotely sensed products. These datasets provide a long-term, natural-flow, simulation for one of the most over-allocated basins in the world.

Climate lags and genetics determine phenology in quaking aspen (Populus tremuloides)

“…We obtained simulations of subsurface water transport at 1 km spatial resolution and hourly temporal resolution from 2013 to 2019 over the Upper Gunnison River Basin using the Parflow ‐Community Land Model (CLM) model (Tran et al ., 2022). Parflow ‐CLM solves three‐dimensional variably saturated flow in the subsurface, integrated with physical‐based overland flow (Kuffour et al ., 2020).…”

“…We obtained simulations of subsurface water transport at 1 km spatial resolution and hourly temporal resolution from 2013 to 2019 over the Upper Gunnison River Basin using the PARFLOW-Community Land Model (CLM) model (Tran et al, 2022). solves the coupled water-energy balance at the land surface including snow accumulation and melt, infiltration, root water uptake, plant transpiration, interception, and bare soil evaporation.…”

The Budyko shape parameter as a descriptive index for streamflow loss

“…We analyzed the hydrological cycle of the UCRB from a 37-year simulation from October 1982 to September 2019 with an hourly temporal resolution and 1-km spatial resolution. The UCRB hydrological dataset encompasses a wide range of hydrologic variables including streamflow, water table depth (WTD), Potential Evapotranspiration (ET P ) and evapotranspiration (ET) from an integrated hydrological model, ParFlow-CLM (Tran et al, 2022). Dynamic atmospheric forcing for the model is from the NLDAS project.…”

“…NLDAS inputs include eight variables, namely, precipitation, air temperature, short and long-wave radiation, east-west and south-north wind speed, atmospheric pressure and specific humidity (Cosgrove et al, 2003;Mitchell et al, 2004). When comparing the dataset with a wide range of products and observations, the average monthly relative bias (measuring differences in the simulated and observed variables volume) for streamflow, WTD and ET are 0.043, 0.356, and 0.001, respectively (Tran et al, 2022). The corresponding average monthly Spearman's Rho (measuring differences in the simulated and observed variables timing) for streamflow, WTD and ET are 0.460, 0.432, and 0.854, respectively (Tran et al, 2022).…”

“…The dataset has been validated with a whole range of remotely sensed products and point observations and proven its fidelity. More details can be found in Tran et al (2022). Budyko (1963Budyko ( , 1974 framework divides long-term mean precipitation in a specific watershed into streamflow and evapotranspiration based on the watershed water balance.…”