Direct and indirect simulating and projecting hydrological drought using a supervised machine learning method.

There is a trend in using Artificial Intelligence methods as simulation tools in different aspects of hydrology, including river discharge simulations, drought predictions, and crop yield simulations. The motivation of this work was to assess two various concepts in applying these methods in simulations and projections of hydrological drought. In this study, Standardized Runoff Index (SRI) was simulated and projected using Artificial Neural Networks (ANNs). Maximum and minimum temperature, precipitation, and meteorological drought indicators (the Standardized Precipitation Index (SPI)) were selected as predictors. A direct approach (directly simulating and projecting SRI) and an indirect approach (simulating and projecting river discharge, then calculating SRI) were assessed. Our results show that the indirect approach performs better than the direct approach in simulations of SRI in four discharge stations in the Odra River Basin (a transboundary river basin in Central Europe) from 2000 to 2019. Moreover, a considerable difference between these two approaches was detected in projections of hydrological drought under the RCP8.5 emission scenario for two horizons (near future: 2021-2040, and far future: 2041-2060). Based on the run theory, both approaches show somewhat similar drought conditions for future projections.

Satellite-based soil moisture enhances the reliability of agro-hydrological modeling in large transboundary river basins.

Satellite-based observations of soil moisture, leaf area index, precipitation, and evapotranspiration facilitate agro-hydrological modeling thanks to the spatially distributed information. In this study, the Climate Change Initiative Soil Moisture dataset (CCI SM, a product of the European Space Agency (ESA)) adjusted based on Soil Water Index (SWI) was used as an additional (in relation to discharge) observed dataset in agro-hydrological modeling over a large-scale transboundary river basin (Odra River Basin) in the Baltic Sea region. This basin is located in Central Europe within Poland, Czech Republic, and Germany and drains into the Baltic Sea. The Soil and Water Assessment Tool+ (SWAT+) model was selected for agro-hydrological modeling, and measured data from 26 river discharge stations and soil moisture from CCI SM (for topsoil and entire soil profile) over 1476 sub-basins were used in model calibration for the period 1997-2019. Kling-Gupta efficiency (KGE) and SPAtial EFficiency (SPAEF) indices were chosen as objective functions for runoff and soil moisture calibration, respectively. Two calibration strategies were compared: one involving only river discharge data (single-objective - SO), and the second one involving river discharge and satellite-based soil moisture (multi-objective - MO). In the SO approach, the average KGE for discharge was above 0.60, whereas in the MO approach, it increased to 0.67. The SPAEF values showed that SWAT+ has acceptable accuracy in soil moisture simulations. Moreover, crop yield assessments showed that MO calibration also increases the crop yield simulation accuracy. The results show that in this transboundary river basin, adding satellite-based soil moisture into the calibration process could improve the accuracy and consistency of agro-hydrological modeling.

Detecting characteristics of extreme precipitation events using regional and satellite-based precipitation gridded datasets over a region in Central Europe.

Perception of the spatio-temporal events of extreme precipitation and their variations is essential for diminishing the natural hazards linked with extreme events. In this research, a satellite-based precipitation dataset derived from remotely sensed soil moisture (SM2RAIN-ASCAT, obtained from ASCAT satellite soil moisture data through the Soil Moisture to Rain algorithm) was selected to evaluate the accuracy of daily precipitation and extreme events estimations against a regional gridded weather dataset by employing various performance indicators, and ETCCDI indicators (CDD, and CWD, SDII, R10mm, R20mm, R95p, R99p, Rx1day, and Rx5day). The study area includes entire Poland as well as small parts of Ukraine, Belarus, Slovakia, the Czech Republic, Russia, and Germany. According to PBIAS (~ -3.9 %) and coefficient of correlation (~0.74), SM2RAIN-ASCAT has good accuracy in the study area. Assessments reveal that, in general, over southern, mountainous part SM2RAIN-ASCAT does not have accurate estimations. According to the reference dataset, during the 2007-2019 period, on average, the length of dry days was ~22 days, while SM2RAIN-ASCAT shows ~19.6 consecutive dry days. In contrast, SM2RAIN-ASCAT overestimated (16 days/year) the consecutive wet days compared to the reference dataset (~8.7 days/year). SM2RAIN-ASCAT underestimated the number of heavy precipitation days index (R10mm) over the northern part of the region, close to the Baltic Sea), but the accuracy increased in the southern parts. SM2RAIN-ASCAT underestimated the maximum 1-day rainfall total and relative max 5-day precipitation amount indices. The total precipitation divided by the amount of wet days index shows that SM2RAIN-ASCAT has relatively acceptable accuracy in the center and south of the study area. Our results show that SM2RAIN-ASCAT should be improved for relatively higher extreme indicators.

Model-based water accounting for integrated assessment of water resources systems at the basin scale.

Agricultural activities in the concept of integrated water resources management play a vital role. Especially in dry and semi-dry regions, agricultural activities have the largest share of water consumption. By employing a model-based approach using modified Soil and Water Assessment Tool (SWAT agro-hydrological model), this study has prepared Water Accounting Plus (WA+) framework requirements to investigate different conditions of supply and demand in wet (1985-2000) and dry (2001-2015) periods in a semi-dry basin (Karkheh River Basin) in Iran. Our assessments based on WA+ show decreasing 10% (21.65 to 19.29 Billion Cubic Meters (BCM)/year) of precipitation in the dry period caused a 4% (0.13 BCM/year) decline in natural evapotranspiration. However, the basin experienced a 24% increment in evapotranspiration from agricultural activities at the same period, and runoff was approximately halved (2.45 BCM/year). Therefore, especially in downstream parts, surface water withdrawal has decreased by 18%. These new conditions have put pressure on groundwater resources. The aquifer extraction and total withdrawal for irrigation have grown by about 17% and 4%, respectively. Finally, it is evident that the manageable water has diminished due to climate change; not only the managed water consumption in the basin has not reduced, but it has also highly risen. The current study results help water authorities arrange new hydrological and climatic conditions strategies.