Improving the Drought Monitoring Capabilities of Land Surface Models by Integrating Bias-Corrected, Gridded Precipitation Estimates

Increasingly, the spending of drought relief money has become more reliant on high-resolution drought assessment, so it is vital we can accurately depict drought on fine spatial scales. An important advancement in our drought assessment capability is the integration of high-resolution drought information into land surface models (LSMs) such as those comprising the North American Land Data Assimilation System (NLDAS). Improvement in the depiction of land use, soil type, and vegetation allows estimation of drought-informative parameters such as soil moisture, evapotranspiration, and streamflow at fine spatial resolutions. However, the accuracy and representativeness of the precipitation data in NLDAS is lagging behind the other information.

We plan on improving precipitation forcing by integrating a radar-based quantitative precipitation estimate (QPE) product in place of the currently operational dataset that uses daily gauge analysis from by the Climate Prediction Center (CPC). Relative to the CPC analysis, gridded QPEs have complete spatial coverage at high resolution using data from radars, satellites, and gauges. A drawback to using the gridded QPEs is the presence of biases related to errors in radar returns. However, we have developed methods for correcting for beam blockage and also mean-field, range-dependent, and two-dimensional biases in a extensively tested three-step algorithm.

We will integrate bias-corrected, gridded QPEs into the NLDAS precipitation forcing dataset to improve the modeling of drought informative variables in LSMs. Testing will optimize the integration bias-corrected NWS QPEs into NLDAS dataset. We will also develop more reliable historical probability distribution functions (PDFs) to improve drought assessment capabilities. We will validate that using bias-corrected, gridded QPEs leads to more accurate estimates of drought-informative variables. The Noah LSM and the NASA Short-term Prediction Research and Transition (SPoRT) Center’s model outputs of soil moisture, water and energy budget variables will be compared to reliable observation-based datasets to validate expected improvements.

This project “Improving the Drought Monitoring Capabilities of Land Surface Models by Integrating Bias-Corrected, Gridded Precipitation Estimates” directly addresses the Modeling, Analysis, Predictions, and Projections (MAPP) competition initiative of advancing drought understanding, monitoring and prediction. The project specifically targets an improvement in drought monitoring by integrating reliable, high-resolution precipitation information into the NLDAS framework. Integration of an improved precipitation dataset into land surface models will advance our ability to predict drought. The project addresses NOAA’s long-term climate goal of aiding “mitigation and adaptation efforts” by providing “sustained, reliable, and timely climate service” and will directly benefit NIDIS, the NOAA Drought Task Force, NLDAS, and the U.S. Drought Monitor.

Climate Risk Area: Water Resources