On the value of satellite remote sensing to reduce uncertainties of regional simulations of the Colorado River

As the major water resource in the southwestern United States, the Colorado River is experiencing decreases in naturalized streamflow and is predicted to face severe challenges under future climate scenarios. To better quantify these hydroclimatic changes, it is crucial that the scientific community establishes a reasonably accurate understanding of the spatial patterns associated with the basin hydrologic response. In this study, we employed remotely sensed land surface temperature (LST) and snow cover fraction (SCF) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to assess a regional hydrological model applied over the Colorado River Basin between 2003 and 2018. Based on the comparison between simulated and observed LST and SCF spatiotemporal patterns, a stepwise strategy was implemented to enhance the model performance. Specifically, we corrected the forcing temperature data, updated the time-varying vegetation parameters, and upgraded the snow-related process physics. Simulated nighttime LST errors were mainly controlled by the forcing temperature, while updated vegetation parameters reduced errors in daytime LST. Snow-related changes produced a good spatial representation of SCF that was consistent with MODIS but degraded the overall streamflow performance. This effort highlights the value of Earth observing satellites and provides a roadmap for building confidence in the spatiotemporal simulations from regional models for assessing the sensitivity of the Colorado River to climate change.