Multivariate remotely sensed and in-situ data assimilation for enhancing community WRF-Hydro model forecasting. (November 2020)

• Record Type: Journal Article
• Authors: Abbaszadeh, Peyman; Gavahi, Keyhan; Moradkhani, Hamid
• Is Part Of: Advances in water resources. Volume 145(2020)
• Publication Date: 2020-11
• Subjects: Flood prediction -- WRF-Hydro -- Data assimilation -- SMAP soil moisture; Hydrology -- Periodicals; Hydrodynamics -- Periodicals; Hydraulic engineering -- Periodicals; 551.48
• DOI: 10.1016/j.advwatres.2020.103721
• Languages: English
• ISSNs: 0309-1708

Abstract

Highlights: The WRF-Hydro model performance with and without DA for the period of hurricane Harvey. The benefit of multivariate and univariate assimilation of streamflow and soil moisture. Joint assimilation of streamflow and soil moisture resulted in better flood prediction. Abstract: Flood is one of the most catastrophic natural disasters in the United States, particularly in the Southeast states where hurricanes and tropical storms are most prevalent, causing billions of dollars in damage annually and significant losses of life and property. The Weather Research and Forecasting Hydrological model (WRF-Hydro) is a community-based hydrologic model designed to improve the skill of hydrometeorological forecasts, such as river discharge, through simulating hydrologic prognostic (e.g., soil moisture) and diagnostic (e.g., energy fluxes) variables. These quantities are potentially biased or erroneous due to the uncertainties involved in all layers of hydrologic predictions. In this study, we use an ensemble based Data Assimilation (DA) approach to explore the benefit of independently and jointly assimilating remotely sensed SMAP (Soil Moisture Active Passive) soil moisture (at different spatial resolutions) and USGS streamflow observations to improve the accuracy and reliability of WRF-Hydro model predictions while accounting for uncertainties. This study is conducted over a large region near to Houston, Texas where heavy rainfall from hurricane Harvey caused flooding in 2017. Before implementing DA, we first calibrated the WRF-Hydro model parameters using four United States Geological Survey (USGS) stream gauges installed within the watershed. In this step, we identified the most dominant model parameters, which were used later in the development of joint state-parameter DA. The findings of this study showed that the multivariate assimilation of soil moisture and streamflow observations results in improved prediction of streamflow as compared to univariate assimilation configurations and regardless of the watershed's streamflow regime. The results also revealed that, during the normal streamflow condition, assimilation of downscaled SMAP soil moisture at 1 km spatial resolution, would improve the accuracy of streamflow simulation more than the assimilation of coarse resolution products (i.e., the native SMAP at 36 km spatial resolution and its interpolated version at 9 km spatial resolution). However, during the period of hurricane Harvey, the soil moisture observations at different resolutions showed a similar impact on improving the streamflow prediction.