There is growing concern with evidence that droughts have been intensified due to climate variation and with ongoing land development driven by population growth. This has correspondingly aggravated water scarcity, which threatens the long-term sustainability of water resources. To mitigate the drought vulnerability, an effective drought monitoring system is critical for decision makers. Estimation of key environmental variables such as soil moisture, and evapotranspiration are of paramount importance due to their strong influence on many water resources applications including agricultural production which control the behavior of the climate system. Reliable simulation of land surface properties is highly dependent on the initial and boundary conditions, quality of forcing data, accuracy of measured or calibrated model parameters, and proper estimation of prognostic and diagnostic variables. These can be addressed through Data Assimilation (DA) as a means of merging observations (satellite or in-situ) with model outputs. DA methods based on sequential Bayesian estimation seem better able to take advantage of the temporal organization and structure ofinformation, so that better compliance of the model outputs with observations can be achieved. This proposal responds to competition 1: Advancing Earth System Data Assimilation-objective 2 by proposing a 3-year collaborative project conducted by investigators from Portland State University and NOAA-NESDIS Center for Satellite Applications and Research. The proposed project aligns well with the NOAA’s long‐term climate goal as described in NOAA CPO’s strategy to “address challenges in the area of Weather and climate extremes, and effectively coordinate across these components through the development and deployment of end‐to-end research‐based integrated information systems that address needs of high societal relevance”. The proposed investigation will be conducted by employing Noah- Multiparameterization (Noah-MP) land surface model and the ensemble data assimilation is implemented by means of a novel sequential Bayesian method, the evolutionary particle filter (PF). An optimal assimilation method is needed to maximize information content from observations and model simulations. PF data assimilation has shown to have strong theoretical foundation providing full probabilistic characterization of prognostic variables which are robust and not prone to violation of mass conservation, and Gaussian assumption of noises in the observations. In this proposal, we plan to utilize available global satellite data products of soil moisture (i.e. JPSS/GCOM-W1/AMSR2 and other products available from Soil Moisture Operational Product System (SMOPS)). In addition, other land surface variables (i.e. JPSS surface type, land surface temperature, albedo, and vegetation indices) will be used as needed during the assimilation process. We determine seasonal and regional systematic and random errors in model forcing using the best available resources and reduce systematic errors in the proposed assimilation framework to enhance monitoring capability. The study enhances the use of remotely sensed satellite soil moisture, evapotranspiration data as complementary sources of observation to improve prediction of prognostic and diagnostic hydrologic variables. In this project, we will use the USDM, the USDA’s disaster declaration, and the drought economic loss as references to assess the open loop and DA drought monitoring skill. In addition, drought characteristics such as mean duration, areal extent, total magnitude, intensity, and severity– area–duration curves will be quantified and verified for the recorded droughts across the CONUS.
Climate Risk Area: Water Resources