Hydrologic Prediction through Changes in Soil Moisture and Snowpack: Estimating Natural and Anthropogenic Fluxes

Ben Livneh
University of Colorado / CIRES
  18 July, 10 am, in 2155


Water stored at the Earth surface—in the soil and snow—represents a window into the future and past, controlling the rate of water transfer to the atmosphere and rivers, and providing a basis for forecasting. The first part of this presentation will focus on two applications using remotely sensed soil moisture to estimate evaporation and irrigation. Evaporation is an integral component of the water balance, yet it’s estimation over large areas is limited by observational scarcity and is hence typically estimated using models. An approach for producing spatial estimates of evaporation using changes in soil moisture from NASA’s SMAP satellite will be presented and evaluated at a set of monitoring sites across the U.S.

Next, variations in remotely sensed soil moisture are evaluated as a means to estimate irrigation. Water withdrawals for agriculture represent the single largest consumptive use for many parts of the U.S., bearing a large anthropogenic footprint on the water and energy cycles. However, practical challenges exist in estimating irrigation magnitude and resulting impacts on water supply modeling. A synthetic data assimilation experiment is presented to estimate irrigation, with potential errors sources evaluated using land surface outputs in the place of remote sensing.

The last part of the presentation will focus on how the predictive value of snowpack-based drought indicators—identified as the most useful and reliable drought indicator by western U.S. water stakeholders—are expected change in a warmer world; where projections show more rain versus snow. Across the western U.S., snow-water equivalent (SWE) at key dates during the year (e.g., April 1) is routinely used in water resource planning as it embodies stored water to be released, through melt, during critical periods later in the summer. The robustness of these snowpack-based drought indicators will be assessed under historical and future climate.