Enhancing Deep Learning Soil Moisture Forecasting Models by Integrating Physics-based Models

Accurate soil moisture(SM)prediction is critical for understanding hydrological processes.Physics-based(PB)models exhibit large uncertainties in SM predictions arising from uncertain parameterizations and insufficient representation of land-surface processes.In addition to PB models,deep learning(DL)models have been widely used in SM predictions recently.However,few pure DL models have notably high success rates due to lacking physical information.Thus,we developed hybrid models to effectively integrate the outputs of PB models into DL models to improve SM predictions.To this end,we first developed a hybrid model based on the attention mechanism to take advantage of PB models at each forecast time scale(attention model).We further built an ensemble model that combined the advantages of different hybrid schemes(ensemble model).We utilized SM forecasts from the Global Forecast System to enhance the convolutional long short-term memory(ConvLSTM)model for 1–16 days of SM predictions.The performances of the proposed hybrid models were investigated and compared with two existing hybrid models.The results showed that the attention model could leverage benefits of PB models and achieved the best predictability of drought events among the different hybrid models.Moreover,the ensemble model performed best among all hybrid models at all forecast time scales and different soil conditions.It is highlighted that the ensemble model outperformed the pure DL model over 79.5%of in situ stations for 16-day predictions.These findings suggest that our proposed hybrid models can adequately exploit the benefits of PB model outputs to aid DL models in making SM predictions.

Assessment of global meteorological,hydrological and agricultural drought under future warming based on CMIP6

Quantifying the changes and propagation of drought is of great importance for regional eco-environmental safety and water-related disaster management under global warming.In this study,phase 6 of the Coupled Model Intercomparison Project was employed to examine future meteorological(Standardized Precipitation Index,SPI,and Standardized Precipitation-Evapotranspiration Index,SPEI),hydrological(Standardized Runoff Index,SRI),and agricultural(Standardized Soil moisture Index,SSI) drought under two warming scenarios(SSP2-4.5 and SSP5-8.5).The results show that,across the globe,different types of drought events generally exhibit a larger spatial extent,longer duration,and greater severity from 1901 to 2100,with SPEI drought experiencing the greatest increases.Although SRI and SSI drought are expected to be more intensifying than SPI drought,the models show higher consistency in projections of SPI changes.Regions with robust drying trends include the southwestern United States,Amazon Basin,Mediterranean,southern Africa,southern Asia,and Australia.It is also found that meteorological drought shows a higher correlation with hydrological drought than with agricultural drought,especially in warm and humid regions.Additionally,the maximum correlation between meteorological and hydrological drought tends to be achieved at a short time scale.These findings have important implications for drought monitoring and policy interventions for water resource management under a changing climate.

Plant drought tolerance trait is the key parameter in improving the modeling of terrestrial transpiration in arid and semi-arid regions

The prediction of precipitation depends on accurate modeling of terrestrial transpiration.In recent decades,the trait-based plant hydraulic stress scheme has been developed in land surface models,in order to better predict the hydraulic constraint on terrestrial transpiration.However,the role that each plant functional trait plays in the modeling of transpiration remains unknown.The importance of different plant functional traits for modeled transpiration needs to be addressed.Here,the Morris sensitivity analysis method was implemented in the Common Land Model with the plant hydraulic stress scheme(CoLM-P_(50)HS).Traits related to drought tolerance(P_(50);),stomata,and photosynthesis were screened as the most critical from all 17 plant traits.Among 12 FLUXNET sites,the importance of P_(50);,measured by normalized sensitivity scores,increased towards lower precipitation,whereas the importance of stomatal traits and photosynthetic traits decreased towards drier climate conditions.P_(50);was more important than stomatal traits and photosynthetic traits in arid or semi-arid sites,which implies that hydraulic safety strategies are more crucial than plant growth strategies when plants frequently experience drought.Large variation in drought tolerance traits further proved the coexistence of multiple plant strategies of hydraulic safety.Ignoring the variation in drought tolerance traits may potentially bias the modeling of transpiration.More measurements of drought tolerance traits are therefore necessary to help better represent the diversity of plant hydraulic functions.