Abstract
Prediction of soil moisture is critical for water resources management. With Global Precipitation Measurement (GPM) and Soil Moisture Active Passive (SMAP) satellites by NASA, spatio-temporal interrelation between rainfall and soil moisture fields (at different extents) will be of great value for satellite product calibration/validation and other hydrologic science investigations. In this study, we explored a non-parametric evolutionary algorithm for prediction of soil moisture from a time series of spatially-distributed rainfall across multiple weather locations under two different hydro-climatic regions. A new genetic algorithm-based hidden Markov model (HMMGA) was developed to estimate long-term soil moisture dynamics at different soil depths using precipitation data as a proxy. Also, we tested transposability of our approach across time under different climatic conditions. To test the new approach, we selected two different soil moisture fields, Oklahoma (130 km × 130 km) and Illinois (300 km × 500 km), during 1995–2009 and 1994–2010, respectively. We found that the newly developed framework performed well in predicting soil moisture dynamics at different spatial extents. Although our approach has limitations in predicting daily values, it estimates well the weekly soil moisture across the spatial and temporal domains with predictable uncertainties. Furthermore, this approach could provide advantages for good transposability under different weather conditions compared to those of physically-based hydrological models. Overall, our suggested approach could predict weekly soil moisture estimates with precipitation and soil moisture histories and showed the potential of transposability under different weather and land surface conditions. Since the proposed algorithm requires only precipitation (and historical soil moisture data) from existing, established weather stations, it can serve an attractive alternative that can forecast soil moisture using climate change scenarios.
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