Eric A. Sproles

Assimilation of GRACE Terrestrial Water Storage Observations into a Land Surface Model for the Assessment of Regional Flood Potential

We evaluate performance of the Catchment Land Surface Model (CLSM) under flood conditions after the assimilation of observations of the terrestrial water storage anomaly (TWSA) from NASA’s Gravity Recovery and Climate Experiment (GRACE). Assimilation offers three key benefits for the viability of GRACE observations to operational applications: (1) near-real time analysis; (2) a downscaling of GRACE’s coarse spatial resolution; and (3) state disaggregation of the vertically-integrated TWSA. We select the 2011 flood event in the Missouri river basin as a case study, and find that assimilation generally made the model wetter in the months preceding flood. We compare model outputs with observations from 14 USGS groundwater wells to assess improvements after assimilation. Finally, we examine disaggregated water storage information to improve the mechanistic understanding of event generation. Validation establishes that assimilation improved the model skill substantially, increasing regional groundwater anomaly correlation from 0.58 to 0.86. For the 2011 flood event in the Missouri river basin, results show that groundwater and snow water equivalent were contributors to pre-event flood potential, providing spatially-distributed early warning information.

Developing a Snowmelt Forecast Model in the Absence of Field Data

In data poor regions predicting water availability is a considerable challenge for water resource managers. In snow-dominated watersheds with minimal in situ measurements, satellite imagery can supplement sparse data networks to predict future water availability. This technical note presents the first phase of an operational forecast model in the data poor Elqui River watershed located in northern Central Chile (30°S). The approach applies remotely-sensed snow cover products from the Moderate Resolution Imaging Spectrometer (MODIS) instrument as the first order hydrologic input for a modified Snowmelt Runoff Model. In the semi-arid Elqui River, snow and glacier melt are the dominant hydrologic inputs but precipitation is limited to up to six winter events annually. Unfortunately winter access to the Andean Cordillera where snow accumulates is incredibly challenging, and thus measurements of snowpack are extremely sparse. While a high elevation snow monitoring network is under development, management decisions regarding water resources cannot wait as the region is in its eighth consecutive year of drought. Our model applies a Monte Carlo approach on monthly data to determine relationships between lagged changes in snow covered area and previous streamflow to predict subsequent streamflow. Despite the limited data inputs the model performs well with a Nash-Sutcliffe Efficiency and R2 of 0.830 and 0.833 respectively. This model is not watershed specific and is applicable in other regions where snow dominates hydrologic inputs, but measurements are minimal.

SnowCloudHydro—A New Framework for Forecasting Streamflow in Snowy, Data-Scarce Regions

This research was funded by an incubator grant from the Earth Science Information Partners (ESIP) and by NASA grant No. NNX16AG35G.