Technical Note: Evaluation of the skill in monthly-to-seasonal soil moisture forecasting based on SMAP satellite observations over the southeastern US

Abstract

Abstract. Providing accurate soil moisture (SM) conditions is a critical step in model initialization in weather forecasting, agricultural planning, and water resources management. This study develops monthly-to-seasonal (M2S) top layer SM forecasts by forcing 1- to 3-month-ahead precipitation forecasts with Noah3.2 Land Surface Model. The SM forecasts are developed over the southeastern US (SEUS), and the SM forecasting skill is evaluated in comparison with the remotely sensed SM observations collected by the Soil Moisture Active Passive (SMAP) satellite. Our results indicate potential in developing real-time SM forecasts. The retrospective 18-month (April 2015–September 2016) comparison between SM forecasts and the SMAP observations shows statistically significant correlations of 0.62, 0.57, and 0.58 over 1-, 2-, and 3-month lead times respectively.

Improved National‐Scale Above‐Normal Flow Prediction for Gauged and Ungauged Basins Using a Spatio‐Temporal Hierarchical Model

“…Uncertainty in flood predictions arises from multiple sources that include input uncertainty from precipitation predictions, hydrologic model uncertainty, and uncertainty in quantifying the initial conditions (Mazrooei et al., 2021; Mendoza et al., 2012). Efforts have focused on reducing these uncertainties ranging from multi‐model combination (Devineni et al., 2008) on precipitation predictions, on hydrologic models (Singh & Sankarasubramanian, 2014), and through correcting initial conditions through data assimilation (Mazrooei et al., 2020). However, most of these uncertainty reduction techniques have focused primarily on gauged basins with limited/no evaluation of these techniques for ungauged basins.…”

Improved National-Scale Flood Prediction for Gauged and Ungauged Basins using a Spatio-temporal Hierarchical Model

“…Uncertainty in flood predictions arises from multiple sources that include input uncertainty from precipitation predictions, hydrologic model uncertainty, and uncertainty in quantifying the initial conditions (Mazrooei et al, 2021;Mendoza et al, 2012). Efforts have focused on reducing these uncertainties ranging from multi-model combination (Devineni et al, 2008) on precipitation predictions, on hydrologic models (Singh & Sankarasubramanian, 2014), and through correcting initial conditions through data assimilation (Mazrooei et al, 2020). However, most of these uncertainty reduction techniques have focused primarily on gauged basins with limited/no evaluation of these techniques for ungauged basins.…”