Evaluation of meteorological drought effects on underground water level fluctuations using data mining methods (case study: semi-deep wells of Golestan province).

In most arid and semi-arid environments, groundwater is one of the precious resources threatened by water table decline and desiccation, thus it must be constantly monitored. Identifying the causes influencing the variations of the subsurface water level, such as meteorological drought, is one approach for monitoring these fluctuations. In the present study, the effect of two meteorological drought indices SPI and SPEI on the fluctuations of the underground water level was evaluated, as was their relationship with the drought index of the subsurface water level (SWI) using multivariate linear regression and M5 decision tree regression. After calculating climatic and hydrological drought indices in a 6-month time window for a long-term statistical period (1989-2018), the semi-deep aquifers of Golestan province, which is located in northern Iran, were considered as a research location for this purpose. The results demonstrated that the effect of meteorological drought does not immediately manifest in the changes of the subsurface water table and the hydrological drought index. By adding the meteorological drought index with a 6-month lag step, the average air temperature, and the total rainfall from the previous 6 months as new variables, the correlation with the SWI index increases, so that in the best-case scenario, the M5 decision tree model provides the best result in predicting the SWI index. The second half of the year yielded a coefficient of determination of 0.92 and an error value of RMSE = 0.27 for the SPEI index. Among the meteorological drought indices, the SPEI index, which is based on precipitation and evapotranspiration, created a stronger link with the SWI index, which highlights the significance of potential evapotranspiration. It is a warning that, as a result of global warming, subsurface water tables in this region may fall in the future.

Evaluation of the effect of scenarios in the 6th report of IPCC on the prediction groundwater level using the non-linear model of the input-output time series.

Due to the increase in greenhouse gases, water and climate crises, increasing population, and decreasing water resources, accurately predicting the changes in the GWL is essential for the management of water resources. For this purpose, in this research, the MIROCES2L model was used to predict the climatic parameters of Birjand Plain under three scenarios of the sixth climate change report: SSP1-2.6, SSP2-4.5, and SSP5-8.5. The minimum temperature, maximum temperature, and precipitation parameters from these three scenarios were measured using the CMhyd model. The results indicated that the minimum and maximum temperature would generally increase in the future under the influence of climate change, but precipitation has a sinusoidal behavior and has a decreasing trend in the summer and spring seasons and an increasing trend in the winter and autumn seasons. Then, three ANN, NIO, and MLR models were employed to simulate groundwater depletion. The results indicated that the evaluation of the performance criteria of the NIO model is superior to the other two models, and it was chosen as the model for predicting groundwater depletion in the future period under the influence of climate change based on all three mentioned scenarios. The final results of this research indicated that the GWL of Birjand Plain in the future period (2024-2041) under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios would respectively decrease to 5.58m, 5.13m, and 5.38. The results of this research indicate that the need for sustainable management to conserve groundwater resources is also very important in the study area.