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Scientists and consultants need to estimate and map properties of the terrestrial environment. These include plant nutrients and parasites in soil, gaseous emissions from soil, pollutant metals and xenobiotics in waste and contaminated land, salt in groundwater and species abundances above ground. The scale varies from small experimental plots to catchments, and the land may be enclosed in fields or be open grassland, forest or desert. Those who sample the variables to obtain the necessary data need guidance on the design and analysis of sampling methods for their conclusions and recommendations to be valid. This book provides that guidance, backed by sound rationale and statistical theory. It concentrates on design-based sampling for estimates of mean values of environmental properties, emphasizing replication and randomization. It starts with simple random sampling and then progresses to more efficient designs, such as spatially stratified random sampling, stratification by classes and cluster sampling. It includes a section on purposive sampling in classical soil survey, which is relevant to other environmental properties such as vegetation. It also describes the effects of bulking on errors and the use of ancillary information and regression to improve estimates. The authors draw the important distinction between design-based sampling for estimating means and model-based methods (geostatistics) for local spatial prediction and mapping, and focus on the latter. They describe designs suitable for computing variograms and prediction by kriging, as well as a staged approach, so that sampling is neither inadequate nor excessive, and designs adapt as knowledge is accumulated. Including numerous worked case studies of sampling in agriculture, ecology and environmental science, the book will be of immediate practical value.

Groundwater is a primary source of drinking water for billions worldwide. It plays a crucial role in irrigation, domestic, and industrial uses, and significantly contributes to drought resilience in various regions. However, excessive groundwater discharge has left many areas vulnerable to potable water shortages. Therefore, assessing groundwater potential zones (GWPZ) is essential for implementing sustainable management practices to ensure the availability of groundwater for present and future generations. This study aims to delineate areas with high groundwater potential in the Bankura district of West Bengal using four machine learning methods: Random Forest (RF), Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), and Voting Ensemble (VE). The models used 161 data points, comprising 70% of the training dataset, to identify significant correlations between the presence and absence of groundwater in the region. Among the methods, Random Forest (RF) and Extreme Gradient Boosting (XGBoost) proved to be the most effective in mapping groundwater potential, suggesting their applicability in other regions with similar hydrogeological conditions. The performance metrics for RF are very good with a precision of 0.919, recall of 0.971, F1-score of 0.944, and accuracy of 0.943. This indicates a strong capability to accurately predict groundwater zones with minimal false positives and negatives. Adaptive Boosting (AdaBoost) demonstrated comparable performance across all metrics (precision: 0.919, recall: 0.971, F1-score: 0.944, accuracy: 0.943), highlighting its effectiveness in predicting groundwater potential areas accurately; whereas, Extreme Gradient Boosting (XGBoost) outperformed the other models slightly, with higher values in all metrics: precision (0.944), recall (0.971), F1-score (0.958), and accuracy (0.957), suggesting a more refined model performance. The Voting Ensemble (VE) approach also showed enhanced performance, mirroring XGBoost's metrics (precision: 0.944, recall: 0.971, F1-score: 0.958, accuracy: 0.957). This indicates that combining the strengths of individual models leads to better predictions. The groundwater potentiality zoning across the Bankura district varied significantly, with areas of very low potentiality accounting for 41.81% and very high potentiality at 24.35%. The uncertainty in predictions ranged from 0.0 to 0.75 across the study area, reflecting the variability in groundwater availability and the need for targeted management strategies. In summary, this study highlights the critical need for assessing and managing groundwater resources effectively using advanced machine learning techniques. The findings provide a foundation for better groundwater management practices, ensuring sustainable use and conservation in Bankura district and beyond.

Water-scarce regions, like the Mediterranean, face worsening conditions due to climate change, intensifying pressure on key economic sectors such as hydropower. Additionally, environmental conservation policies, particularly the implementation of environmental flows, present challenges for hydropower systems. Certainty regarding the impact of these factors on future hydropower production is crucial for informed decision-making in the transition to sustainable energy. This study introduces S  +  HydPower , a tool coupled with SWAT+ to assess climate change and watershed management effects on small hydropower plant (SHP) systems. In this study, we used this tool to investigate the consequences of implementing environmental flows and climate change on run-of-river SHPs in the Catalan River Basin District (CRBD), in Catalonia. The results show that applying environmental flows would lead to a significant 27% reduction in SHP production. However, this reduction would represent only 0.25% of the region’s current energy demand. Furthermore, the study reveals a potential 38% to 73% reduction in SHP production by the end of the twenty-first century due to the combined effects of environmental flows and climate change. This suggests a substantial decline in run-of-river SHP’s contribution to the CRBD’s electricity supply. These findings emphasize the need to explore alternative and sustainable energy sources to ensure the long-term reliability and resilience of the region’s energy supply.

This study highlights drought forecasting for understanding the semi-arid area in India, where drought phenomena play vital role in the irrigation, drinking water supplies, and sustaining the ecological with economic balance for every nation. Therefore, drought forecasting is important for the future drought planning based on the machine learning (ML) models. Hence, The Standardized Precipitation Index (SPI) at 3- and 6-month periods have been selected and used for future drought forecasting scenarios in area. The combinations of ten inputs SPI-1- and SPI-10 were used for predicting modeling for SPI-3 and SPI-6 timescales, that modeling developed based on the historical SPI datasets from 1989 to 2019 years. The SPI-3 and SPI-6 maximum and minimum values are shown SPI-3 (2.03 and -5.522) and SPI-6 (1.94 and -6.93). The SPI is a popular method for estimating the drought analysis and has been used everywhere at global level. The developed models have been compared with each other, with the best combination of input variables selected using subset regression models and sensitivity studies. After that, the active input parameters were used for forecasting of SPI-3 and SPI-6 values to understanding of drought in semi-arid area. The finest input variables combination have been used in the Ml models and established the novel five models such as robust linear regression, bagged trees, boosted trees, support vector regression (SVM-Linear), and Matern Gaussian Process Regression (Matern GPR) models. Such kind of models first time has been applied for the forecasting of future drought conditions. Whole models were fine and improved modeling by using hyperparameters tuning, bagging, and boosting models. Entire ML models’ accuracy was compared using different statistical metrics. Compared with five ML models accuracy, we have found that the Matern GPR model better accuracy than other ML models. The best model accuracy is R 2  = 0.95 and 0.93, RMSE, MSE, MAE, MARE, and NSE values, respectively, for predicting SPI-3 and SPI-6 values in the area. Therefore, the Matern GPR model was identified as the finest ML algorithm for predicting SPI-3 and SPI-6 associated with other algorithms. This research demonstrates the Matern GPR model's efficacy in predicting multiscale SPI-3 and SPI-6 under climate variations. It can be helpful in soil and water resource conservation planning and management and understanding droughts in the entire basin areas of the country India.

Pesticides in surface freshwater primarily originate from their emissions in agricultural lands, potentially leading to violations of surface freshwater quality standards. To aid global regulatory agencies in effectively managing surface freshwater quality by estimating and controlling pesticide emission rates, this study proposes a hierarchical screening approach for countries and regions worldwide to select appropriate modeling tools. Hierarchical indicators are introduced to classify countries globally, considering their spatial distribution areas, pesticide emission conditions, and legislative systems. Consequently, different categories of countries are matched with suitable model groups, such as the standard model group for regulatory scenarios, the general model group for continental scenarios, and the advanced model group with high spatial resolution. Results indicated that a total of 193 countries worldwide were categorized into six country groups, of which 153, 34, and 6 countries were found to fit the standard, general, and advanced model groups, respectively, based on the model assignments for these country groups. Furthermore, 12 commonly used pesticides were selected to demonstrate the back-calculation process, which estimates the pesticide emission rate (input) by pesticide surface freshwater quality standards (output) by standard and general model groups. The Advanced model group was not applied in this process due to its intensive computation. An approximate approach was developed to simplify the calculation of the emission rate factor of pesticides using the PWC and TOXSWA selected in the standard model group as well as SWAT in the general model group, serving as a demonstration. This approach can be applied to control pesticide emission rates from surface freshwater quality standards across countries that fit in the standard and general model groups. The results highlight that pesticide fate models selected through the hierarchical screening approach, can assist global countries in establishing a quantitative relationship between pesticide emission rates and surface freshwater quality standards, which can help global agencies manage pesticide emissions and freshwater quality from a legal perspective. There is a need to update and simplify suitable advanced model for calculation demonstration in future studies to aid in pesticide management. Further research is needed to thoroughly investigate pesticide emissions and freshwater residue concentrations under varying conditions.

The agricultural sector faces challenges in managing water resources efficiently, particularly in arid regions dealing with water scarcity. To overcome water stress, treated wastewater (TWW) is increasingly utilized for irrigation purpose to conserve available freshwater resources. There are several critical aspects affecting the suitability of TWW for irrigation including salinity which can have detrimental effects on crop yield and soil health. Therefore, this study aimed to develop a novel approach for TWW salinity prediction using artificial intelligent (AI) ensembled machine learning approach. In this regard, several water quality parameters of the TWW samples were collected through field investigation from the irrigation zones in Al-Hassa, Saudi Arabia, which were later assessed in the lab. The assessment involved measuring Temperature (T), pH, Oxidation Reduction Potential (ORP), Electrical Conductivity (EC), Total Dissolved Solids (TDS), and Salinity, through an Internet of Things (IoT) based system integrated with a real-time monitoring and a multiprobe device. Based on the descriptive statistics of the data and correlation obtained through the Pearson matrix, the models were formed for predicting salinity by using the Hammerstein-Wiener Model (HWM) and Support Vector Regression (SVR). The models’ performance was evaluated using several statistical indices including correlation coefficient (R), coefficient of determination (R 2 ), mean square error (MSE), and root mean square error (RMSE). The results revealed that the HWM-M3 model with its superior predictive capabilities achieved the best performance, with R 2 values of 82% and 77% in both training and testing stages. This study demonstrates the effectiveness of AI-ensembled machine learning approach for accurate TWW salinity prediction, promoting the safe and efficient utilization of TWW for irrigation in water-stressed regions. The findings contribute to a growing body of research exploring AI applications for sustainable water management.

The release rate of CO 2 gas can be influenced by peatlands’ physical properties, such as water level and soil moisture, and rainfall. To anticipate the unstable condition which is when the peatland emit more carbon, we developed the Generalized Space Time Autoregressive (GSTAR) model in predicting these physical properties for the following weeks. As the innovation in modelling, the spatial weight matrix was based on three-dimensional coordinates with a modification on the height factor. The data we used are real-time data of water level on the peatlands in Pulang Pisau Regency, Central Kalimantan Province from 20 February 2021 to 18 March 2023. We then used Ordinary Kriging interpolation on the prediction results to create contour maps on different dates. There were empty data on several dates, especially from 24 March until 3 August 2022. To fill the empty data, we used linear interpolation and then we added white noise to the interpolation results. From the data, the water level has a downward trend pattern from around November to September and an upward trend pattern from October to November. Furthermore, we found that the best model for water level was GSTAR (2;0.1) with a modified matrix a = 0.1 and b = 1.1 . Based on the predicted water level, there is a risk of changes in the properties of the peatlands in several areas in Pulang Pisau Regency.