Comparison of Remotely Sensed Precipitation and Evapotranspiration Products for a Statewide Water Assessment of New Mexico
Abstract:Precipitation and evapotranspiration (ET) are the major components of the water balance in New Mexico. Therefore, it is critical to acquire accurate precipitation and ET data as input into a statewide water balance. Since existing meteorological stations in New Mexico don’t cover the entire state and leave many areas without accurate information, we propose to evaluate the accuracy of existing nationwide remotely sensed databases for precipitation and ET to quantify the spatial and temporal distributions of those components in a statewide water balance.
In this study we compare five precipitation products and three ET products: the CHIRPS (Climate Hazard Group InfraRed Precipitation with Station data) model, the National Weather Service Advanced Hydrologic Prediction Service product, the PERSIANN-GCCS (Precipitation Estimation from Remote Sensed Information using Artificial Neural Network – Global Cloud Classification System) model, the PRISM (Parameter-elevation Relationships on Independent Slopes) model the TRMM (Tropical Rainfall Measuring Mission, the ALExI (Atmosphere-Land Exchange Inverse) model, the MOD 16 ( MODIS Global Evapotranspiration Product) model of NASA, and the SSEB (Simplified Surface Energy Balance) model produced by the USGS.
Early results show a strong relationship between all precipitation products across the state of New Mexico from 2000 to 2013 with an average depth of 315 mm, except for the PERSIANN model which has a rainfall depth approximately 53% higher (673mm) than the average of the other models. Additionally there is a strong relationship between the ALExI and SSEB ET models yet these models exceed the precipitation in the state by approximately 35%. The MOD 16 ET model has an average ET depth approximately 42% less than the average of the precipitation models and about 60% less than the ALExI and SSEB ET models.
Future work includes validation of precipitation and ET models using high density rain gauge networks, as well as METRIC (Mapping EvapoTranspiration at high spatial Resolution with Internalized Calibration) and eddy-covariance towers across the state. Additionally the ET models will be systematically compared with land cover and precipitation in effort to identify areas with greater errors and biases in those models.