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Groundwater recharge feeds aquifers supplying fresh-water to a population over 80 million in Iran—a global hotspot for groundwater depletion. Using an extended database comprising abstractions from over one million groundwater wells, springs, and qanats, from 2002 to 2017, here we show a significant decline of around −3.8 mm/yr in the nationwide groundwater recharge. This decline is primarily attributed to unsustainable water and environmental resources management, exacerbated by decadal changes in climatic conditions. However, it is important to note that the former’s contribution outweighs the latter. Our results show the average annual amount of nationwide groundwater recharge (i.e., ~40 mm/yr) is more than the reported average annual runoff in Iran (i.e., ~32 mm/yr), suggesting the surface water is the main contributor to groundwater recharge. Such a decline in groundwater recharge could further exacerbate the already dire aquifer depletion situation in Iran, with devastating consequences for the country’s natural environment and socio-economic development. Groundwater recharge feeds aquifers supplying fresh-water to a population over 80 million in Iran. The authors here show a significant decline of around −3.8 mm/yr in the nationwide groundwater recharge.

Discharge of pollution loads into natural water systems remains a global challenge that threatens water and food supply, as well as endangering ecosystem services. Natural rehabilitation of contaminated streams is mainly influenced by the longitudinal dispersion coefficient, or the rate of longitudinal dispersion ( D x ), a key parameter with large spatiotemporal fluctuations that characterizes pollution transport. The large uncertainty in estimation of D x in streams limits the water quality assessment in natural streams and design of water quality enhancement strategies. This study develops an artificial intelligence-based predictive model, coupling granular computing and neural network models (GrC-ANN) to provide robust estimation of D x and its uncertainty for a range of flow-geometric conditions with high spatiotemporal variability. Uncertainty analysis of D x estimated from the proposed GrC-ANN model was performed by alteration of the training data used to tune the model. Modified bootstrap method was employed to generate different training patterns through resampling from a global database of tracer experiments in streams with 503 datapoints. Comparison between the D x values estimated by GrC-ANN to those determined from tracer measurements shows the appropriateness and robustness of the proposed method in determining the rate of longitudinal dispersion. The GrC-ANN model with the narrowest bandwidth of estimated uncertainty (bandwidth- factor  = 0.56) that brackets the highest percentage of true D x data (i.e., 100%) is the best model to compute D x in streams. Considering the significant inherent uncertainty reported in the previous D x models, the GrC-ANN model developed in this study is shown to have a robust performance for evaluating pollutant mixing ( D x ) in turbulent environmental flow systems.

Applying data mining techniques for rainfall modeling because of a lack of sufficient memory components may increase uncertainty in rainfall forecasting. To solve this issue, in this research, a deep-learning-based long short-term memory (LSTM) model is developed for the first time for forecasting monthly rainfall data, and its capability is compared with a random forest (RF) data-driven model. To this end, monthly rainfall data for a period of 41 years (1980-2020) from two meteorological stations in Turkey, namely Rize and Konya, with different climatic conditions, are used. The analysis is carried out using optimum window sizes for determining the optimum lag times of rainfall time series. The performance of the models is evaluated using five statistical measures, namely root mean square error (RMSE), RMSE-observations standard deviation ratio (RSR), Legate and McCabe's index (LMI), correlation coefficient (R) and Nash-Sutcliffe efficiency (NSE), and also using two visual means, namely Taylor and violin diagrams. The results reveal that the LSTM model, as a more efficient tool, outperforms the RF model in forecasting rainfall at both stations, with improved RMSE of 12.2-14.9%, RSR of 12.3-14.8%, R of 9.4-13.5% and NSE of 32.9-33.2%. The LSTM-based approach proposed herein could be adopted over any global climatic conditions to forecast the monthly rainfall with reasonable accuracy.

The accurate forecast of air temperature plays an important role in water resources management, land–atmosphere interaction, and agriculture. However, it is difficult to accurately predict air temperature due to its non-linear and chaotic nature. Several deep learning techniques have been proposed over the last few decades to forecast air temperature. This study provides a comprehensive review of artificial neural network (ANN)-based approaches (such as recurrent neural network (RNN), long short-term memory (LSTM), etc.), which were used to forecast air temperature. The focus is on the works during 2005–2020. The review shows that the neural network models can be employed as promising tools to forecast air temperature. Although the ANN-based approaches have been utilized widely to predict air temperature due to their fast computing speed and ability to deal with complex problems, no consensus yet exists on the best existing method. Additionally, it is found that the ANN methods are mainly viable for short-term air temperature forecasting. Finally, some future directions and recommendations are presented.

Soil moisture (SM) and land surface temperature (LST) are entangled, and the retrieval of one of them requires a priori specification of the other one. Due to insufficient observational information, retrieval of LST and SM from passive microwave remote sensing data is often ill-posed, and the retrieval accuracy needs to be improved. In this study, a novel fully-coupled paradigm is developed to robustly retrieve SM and LST from passive microwave data, which integrates deep learning, physical methods, and statistical methods. The key condition of the general paradigm proposed by us is that the output parameters of deep learning can be uniquely determined by the input parameters theoretically through a certain mathematical equation. Firstly, the physical method is deduced based on the energy radiation balance equation. The nine unknowns require the brightness temperatures of nine channels to construct nine equations, and the solutions of the physical method equations are obtained by model simulation. Based on the derivation of the physical method, the solution of the statistical method is constructed using multi-source data. Secondly, the solutions of physical and statistical methods constitute the training and test data of deep learning, which is used to obtain the solution curve of physical and statistical methods. The retrieval accuracy of LST and SM is greatly improved by smartly utilizing the mutual prior knowledge of SM and LST and cross iterative optimization calculations. Finally, validation indicates that the mean absolute error of the retrieved SM and LST data are 0.027 m3/m3 and 1.38 K, respectively, at an incidence angle of 0–65°. A model-data-knowledge-driven and deep learning method can overcome the shortcomings of traditional methods and provide a paradigm for retrieval of other geophysical variables. The proposed paradigm not only has physical meaning, but also makes deep learning physically interpretable, which is a milestone in the retrieval of geophysical remote sensing parameters based on artificial intelligence technology.

Electrical conductivity (EC) is widely recognized as one of the most essential water quality metrics for predicting salinity and mineralization. In the current research, the EC of two Australian rivers (Albert River and Barratta Creek) was forecasted for up to 10 days using a novel deep learning algorithm (Convolutional Neural Network combined with Long Short-Term Memory Model, CNN-LSTM). The Boruta-XGBoost feature selection method was used to determine the significant inputs (time series lagged data) to the model. To compare the performance of Boruta-XGB-CNN-LSTM models, three machine learning approaches—multi-layer perceptron neural network (MLP), K-nearest neighbour (KNN), and extreme gradient boosting (XGBoost) were used. Different statistical metrics, such as correlation coefficient (R), root mean square error (RMSE), and mean absolute percentage error, were used to assess the models' performance. From 10 years of data in both rivers, 7 years (2012–2018) were used as a training set, and 3 years (2019–2021) were used for testing the models. Application of the Boruta-XGB-CNN-LSTM model in forecasting one day ahead of EC showed that in both stations, Boruta-XGB-CNN-LSTM can forecast the EC parameter better than other machine learning models for the test dataset (R = 0.9429, RMSE = 45.6896, MAPE = 5.9749 for Albert River, and R = 0.9215, RMSE = 43.8315, MAPE = 7.6029 for Barratta Creek). Considering the better performance of the Boruta-XGB-CNN-LSTM model in both rivers, this model was used to forecast 3–10 days ahead of EC. The results showed that the Boruta-XGB-CNN-LSTM model is very capable of forecasting the EC for the next 10 days. The results showed that by increasing the forecasting horizon from 3 to 10 days, the performance of the Boruta-XGB-CNN-LSTM model slightly decreased. The results of this study show that the Boruta-XGB-CNN-LSTM model can be used as a good soft computing method for accurately predicting how the EC will change in rivers.

Scouring around bridge piers poses a critical threat to structural integrity, leading to costly damage and safety risks. Traditional equations often fail to accurately predict equilibrium scour depth ( S eq ) due to the complexity and nonlinearity of the underlying hydraulic processes. This study proposes two approaches for estimating S eq : (1) optimizing a Categorical Boosting (CatBoost) machine learning model using five metaheuristic algorithms—Harris Hawk Optimization (HHO), Moth–Flame Optimization (MFO), Whale Optimization Algorithm (WOA), Pelican Optimization Algorithm (POA), and Fox Optimization Algorithm (FOX); and (2) using the abovementioned optimization methods (i.e., HHO, MFO, WOA, POA, and FOX) to derive explicit equations. Among the hybrid models, HHO–CatBoost achieved the highest accuracy, with a root-mean-square error (RMSE) of 0.0286 m, mean absolute error (MAE) of 0.0178 m, and coefficient of determination (R 2 ) of 0.9670 during testing. Among the explicit formulations, the HHO-based model excluding the Reynolds number outperformed 18 existing equations, achieving an RMSE of 0.066 m, an MAE of 0.043 m, and an R 2 of 0.828. SHapley Additive exPlanations (SHAP) analysis identified pier diameter as the most influential factor and critical velocity as the least, while sensitivity analysis highlighted the ratio of pier diameter to flow depth as most important and Reynolds number as the least significant under turbulent flow conditions.

Near-surface air temperature (Ta) is an important physical parameter that reflects climate change. Many methods are used to obtain the daily maximum (Tmax⁡), minimum (Tmin⁡), and average (Tavg) temperature, but are affected by multiple factors. To obtain daily Ta data (Tmax⁡, Tmin⁡, and Tavg) with high spatio-temporal resolution in China, we fully analyzed the advantages and disadvantages of various existing data. Different Ta reconstruction models were constructed for different weather conditions, and the data accuracy was improved by building correction equations for different regions. Finally, a dataset of daily temperature (Tmax⁡, Tmin⁡, and Tavg) in China from 1979 to 2018 was obtained with a spatial resolution of 0.1∘. For Tmax⁡, validation using in situ data shows that the root mean square error (RMSE) ranges from 0.86 to 1.78∘, the mean absolute error (MAE) varies from 0.63 to 1.40∘, and the Pearson coefficient (R2) ranges from 0.96 to 0.99. For Tmin⁡, the RMSE ranges from 0.78 to 2.09∘, the MAE varies from 0.58 to 1.61∘, and the R2 ranges from 0.95 to 0.99. For Tavg, the RMSE ranges from 0.35 to 1.00∘, the MAE varies from 0.27 to 0.68 ∘, and the R2 ranges from 0.99 to 1.00. Furthermore, various evaluation indicators were used to analyze the temporal and spatial variation trends of Ta, and the Tavg increase was more than 0.03 ∘C yr−1, which is consistent with the general global warming trend. In summary, this dataset has high spatial resolution and high accuracy, which compensates for the temperature values (Tmax⁡, Tmin⁡, and Tavg) previously missing at high spatial resolution and provides key parameters for the study of climate change, especially high-temperature drought and low-temperature chilling damage. The dataset is publicly available at https://doi.org/10.5281/zenodo.5502275 (Fang et al., 2021a).

Soil moisture (SM) is an important parameter in water cycle research. Rapid and accurate monitoring of SM is critical for hydrological and agricultural applications, such as flood detection and drought characterization. The Global Navigation Satellite System (GNSS) uses L-band microwave signals as carriers, which are particularly sensitive to SM and suitable for monitoring it. In recent years, with the development of Global Navigation Satellite System–Reflectometry (GNSS-R) technology and data analysis methods, many studies have been conducted on GNSS-R SM monitoring, which has further enriched the research content. However, current GNSS-R SM inversion methods mainly rely on auxiliary data to reduce the impact of non-target parameters on the accuracy of inversion results, which limits the practical application and widespread promotion of GNSS-R SM monitoring. In order to promote further development in GNSS-R SM inversion research, this paper aims to comprehensively review the current status and principles of GNSS-R SM inversion methods. It also aims to identify the problems and future research directions of existing research, providing a reference for researchers. Firstly, it introduces the characteristics, usage scenarios, and research status of different GNSS-R SM observation platforms. Then, it explains the mechanisms and modeling methods of various GNSS-R SM inversion research methods. Finally, it highlights the shortcomings of existing research and proposes future research directions, including the introduction of transfer learning (TL), construction of small models based on spatiotemporal analysis and spatial feature fusion, and further promoting downscaling research.

Retrieving soil moisture (SM) and land surface temperature (LST) provides crucial environmental data for smart agriculture, enabling precise irrigation, crop health monitoring, and yield optimization. The rapid advancement of Artificial intelligence (AI) hardware offers new opportunities to overcome the limitations of traditional geophysical parameter retrieval methods. We propose a nested large–small model method that uses AI techniques for the joint iterative retrieval of passive microwave SM and LST. This method retains the strengths of traditional physical and statistical methods while incorporating spatiotemporal factors influencing surface emissivity for multi-hierarchical classification. The method preserves the physical significance and interpretability of traditional methods while significantly improving the accuracy of passive microwave SM and LST retrieval. With the use of the terrestrial area of China as a case, multi-hierarchical classification was applied to verify the feasibility of the method. Experimental data show a significant improvement in retrieval accuracy after hierarchical classification. In ground-based validation, the ascending and descending orbit SM retrieval models 5 achieved MAEs of 0.026 m3/m3 and 0.030 m3/m3, respectively, improving by 0.015 m3/m3 and 0.012 m3/m3 over the large model, and 0.032 m3/m3 and 0.028 m3/m3 over AMSR2 SM products. The ascending and descending orbit LST retrieval models 5 achieved MAEs of 1.67 K and 1.72 K, respectively, with improvements of 0.67 K and 0.49 K over the large model, and 0.57 K and 0.56 K over the MODIS LST products. The retrieval model can theoretically be enhanced to the pixel level, potentially maximizing retrieval accuracy, which provides a theoretical and technical basis for the parameter retrieval of AI passive microwave large models.