Ensemble long-term soil moisture forecast using hydrological modeling

Abstract

Long-term soil moisture forecasting allows for better planning in sectors as agriculture. However, there are still few studies dedicated to estimate soil moisture for long lead times, which reflects the difficulties associated with this topic. An approach that could help improving these forecasts performance is to use ensemble predictions. In this study, a soil moisture forecast for lead times of one, three and six months in the Ijuí River Basin (Brazil) was developed using ensemble precipitation forecasts and hydrologic simulation. All ensemble members from three climatologic models were used to run the MGB hydrological model, generating 207 soil moisture forecasts, organized in groups: (A) for each model, the most frequent soil moisture interval predicted among the forecasts made with each ensemble member, (B) using each model’s mean precipitation, (C) considering a super-ensemble, and (D) the mean soil moisture interval predicted among group B forecasts. The results show that long-term soil moisture based on precipitation forecasts can be useful for identifying periods drier or wetter than the average for the studied region. Nevertheless, estimation of exact soil moisture values remains limited. Forecasts groups B and D performed similarly to groups A and C, and require less data management and computing time.

Evaluation of Earth Observations and In Situ Data Assimilation for Seasonal Hydrological Forecasting

“…Current ensembles are commonly obtained by applying different perturbations to the control (unperturbed) forecast, and should preferably be based on multiple models (Sahai et al., 2021). Ensemble forecasting is used to predict flooding (Alfieri et al., 2014; Ramos et al., 2007), droughts (Colossi & Tucci, 2020); and in hydro‐climate services, for example, for water resources management applications (Macian‐Sorribes et al., 2020; Pechlivanidis et al., 2020), agriculture (Villani et al., 2021) and hydropower (Contreras et al., 2020).…”