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Exploring process and scale dependencies on the predictability and variability of drought in the United States

Two major land surface/hydrologic modeling systems currently in U.S. forecast operations are the North American Land Data Assimilation System (NLDAS; Mitchell et al. 2004) at NCEP and the National Water Model (NWM; Gochis et al. 2013) at NWC. Both systems have some commonalities: applications to drought forecasting and monitoring, the use of the Noah-MP land surface model (LSM), and execution over what is effectively a CONUS domain. However, the NWM is based on a single land-hydrology system while NLDAS employs a multi-LSM ensemble approach. The NLDAS operates on a 0.125° (~12km) spatial resolution grid while the NWM runs on a much higher resolution 1km grid.

We hypothesize that although NLDAS and NWM have different missions and end-user communities, leveraging the high-resolution of the NWM with the multi-model ensemble approach of NLDAS adds value to improving US drought prediction. The overarching goal of the proposed work is to enhance coordination and communication between these two modeling systems to advance the monitoring and prediction of drought by (1) determining the spatial scales at which processes such as terrain-influenced snowpack and groundwater are necessary to capture the dominant drought/hydrology signals, (2) quantifying the hydrology prediction uncertainty through an intelligent selection of model process ensemble members, and (3) producing estimates of spatially- and temporally-varying soil and vegetation parameters to be used in the NWM and NLDAS modeling systems.

The project goal will be attained through a series of tasks that utilize the NWM and NLDAS, namely:
1. Creating a hydrologic landscape heterogeneity scale to determine where (and potentially
when) increased spatial heterogeneity is needed to capture drought-relevant processes;
2. Determining physical processes, and hence ensemble members, that are necessary in both
NWM and NLDAS to improve hydrologic prediction;
3. Leveraging parameter estimation efforts that can be shared between systems.

Project deliverables include:
1. Enhanced understanding of spatial scales and physical processes critical for US drought
monitoring and prediction;
2. Improved NLDAS and NWM systems that can share drought information;
3. A development pathway for both NLDAS and NWM to leverage the advantages of both systems.

Climate Risk Area: Water Resources

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