Groundwater is a vital source of freshwater, supporting the livelihood of over two billion people worldwide. The quantitative assessment of groundwater resources is critical for sustainable management of this strained resource, particularly as climate warming, population growth, and socioeconomic development further press the water resources. Rapid growth in the availability of a plethora of in-situ and remotely sensed data alongside advancements in data-driven methods and machine learning offer immense opportunities for an improved assessment of groundwater resources at the local to global levels. This systematic review documents the advancements in this field and evaluates the accuracy of various models, following the protocol developed by the Center for Evidence-Based Conservation. A total of 197 original peer-reviewed articles from 2010–2020 and from 28 countries that employ regression machine learning algorithms for groundwater monitoring or prediction are analyzed and their results are aggregated through a meta-analysis. Our analysis points to the capability of machine learning models to monitor/predict different characteristics of groundwater resources effectively and efficiently. Modeling the groundwater level is the most popular application of machine learning models, and the groundwater level in previous time steps is the most employed input data. The feed-forward artificial neural network is the most employed and accurate model, although the model performance does not exhibit a striking dependence on the model choice, but rather the information content of the input variables. Around 10–12 years of data are required to develop an acceptable machine learning model with a monthly temporal resolution. Finally, advances in machine and deep learning algorithms and computational advancements to merge them with physics-based models offer unprecedented opportunities to employ new information, e.g., InSAR data, for increased spatiotemporal resolution and accuracy of groundwater monitoring and prediction.

Drought is a natural phenomenon that can occur in almost any region. Vulnerability and resiliency are two important concepts in studying any natural hazard, including drought. This study investigates the application of both of these concepts in drought management in East Azerbaijan province in Iran. A vulnerability map of the region was developed by considering the spatial characteristics of some important parameters in drought in a geographic information system environment. A general framework is also presented for quantifying system’s resiliency to drought in the Aharchay watershed, which contains the important Sattarkhan Dam Reservoir. This watershed is one of the important watersheds in the case study. To develop this framework, the characteristics related to four resiliency components—rapidity, robustness, resourcefulness, and redundancy—are determined. The relative importance of these components is determined using multicriteria decision making (MCDM). By combining resiliency and vulnerabili...

In this study, a hybrid index called ADI (aggregated drought index) is developed for drought assessment integrating various meteorological, hydrological, and agricultural features of the region. To this end, precipitation, reservoir storage, discharge, temperature, potential evapotranspiration, and the degree of vegetation based on remote sensing images are utilized to quantify ADI. Then, the performance of two models, ARIMA and ANFIS, are investigated in predicting the developed drought index. The proposed framework is applied to the Aharchay watershed located in East Azarbaijan Province, Iran. The results demonstrate that the region has experienced normal and mild drought conditions for the time period. ADI is also applied in another watershed (Ajichay) showing ADI capability in different climatic settings. Regarding the predictive capability, the ANFIS model outperforms ARIMA in drought prediction, particularly in severe weather conditions. The ADI benefits planning for drought mitigation and preparedness by incorporating several different aspects of the region.