Assessing the feasibility of integrating ecosystem-based with engineered water resource governance and management for water security in semi-arid landscapes: A case study in the Banas catchment, Rajasthan, India

Much of the developing world and areas of the developed world suffer water vulnerability. Engineering solutions enable technically efficient extraction and diversion of water towards areas of demand but, without rebalancing resource regeneration, can generate multiple adverse ecological and human consequences. The Banas River, Rajasthan (India), has been extensively developed for water diversion, particularly from the Bisalpur Dam from which water is appropriated by powerful urban constituencies dispossessing local people. Coincidentally, abandonment of traditional management, including groundwater recharge practices, is leading to increasingly receding and contaminated groundwater. This creates linked vulnerabilities for rural communities, irrigation schemes, urban users, dependent ecosystems and the multiple ecosystem services that they provide, compounded by climate change and population growth. This paper addresses vulnerabilities created by fragmented policy measures between rural development, urban and irrigation water supply and downstream consequences for people and wildlife. Perpetuating narrowly technocentric approaches to resource exploitation is likely only to compound emerging problems. Alternatively, restoration or innovation of groundwater recharge practices, particularly in the upper catchment, can represent a proven, ecosystem-based approach to resource regeneration with linked beneficial socio-ecological benefits. Hybridising an ecosystem-based approach with engineered methods can simultaneously increase the security of rural livelihoods, piped urban and irrigation supplies, and the vitality of river ecosystems and their services to beneficiaries. A renewed policy focus on local-scale water recharge practices balancing water extraction technologies is consistent with emerging Rajasthani policies, particularly Jal Swavlamban Abhiyan (‘water self-reliance mission’). Policy reform emphasising recharge can contribute to water security and yield socio-economic outcomes through a systemic understanding of how the water system functions, and by connecting goals and budgets across multiple, currently fragmented policy areas. The underpinning principles of this necessary paradigm shift are proven and have wider geographic relevance, though context-specific research is required to underpin robust policy and practical implementation

Mapping Prospects for Artificial Groundwater Recharge Utilizing Remote Sensing and GIS Methods

The indiscriminate use of groundwater and its overexploitation has led to a significant decline in groundwater resources in India, making it essential to identify potential recharge zones for aquifer recharge. A study was conducted to determine such potential recharge zones in the Nandhour-Kailash River watershed. The study area included 1481 streams divided into 12 sub-basins (SWS). The results show that the downstream Saraunj sub-basins (SWS-11) and Odra sub-basins (SWS-12) were high priority and required immediate soil and water conservation attention. Sub catchments Lobchla West (SWS-4), Deotar (SWS-5), Balot South (SWS-8), Nandhour (SWS-9), and Nakoliy (SWS-10) had medium priority and were designated for moderate soil erosion and degradation. In contrast, sub-catchments Aligad (SWS-1), Kundal (SWS-2), Lowarnala North (SWS-3), Bhalseni (SWS-6), and Uparla Gauniyarao (SWS-7) had low priority, indicating a low risk of soil erosion and degradation. Using the existing groundwater level data, the potential map of groundwater was validated to confirm its validity. According to the guidelines provided by the Integrated Mission for Sustainable Development (IMSD), the results of the groundwater potential zones for good to very good zones have been integrated at the slope and stream order. In a 120.94 km2 area with a slope of 0–5% in first-order streams, 36 ponds were proposed, and in a 218.03 km2 area with a slope of 15% in first- to fourth-order streams, 105 retention dams were proposed and recognized as possible sites for artificial groundwater recharge. The proposed water harvesting structure may aid in continuously recharging these zones and benefit water resource managers and planners. Thus, various governmental organizations can use the results to identify possible future recharge areas

A rare case of a peripheral Ewing sarcoma primitive neuroectodermal tumor of pelvic origin

Primitive neuroectodermal tumors (PNET) represent malignant neuroectodermal tumors composed of small round cells. They can be differentiated between originating from the peripheral nervous system or the central nervous system. Peripheral PNET (pPNET) can be further subclassified as one of the Ewing family tumors (EFT). Although rare, EFT can originate in the female genital tract and pelvic region. Here, we present a case of a middle-aged female with PNET masses in her uterus, abdomen, and hepatic lobes. We discuss the diagnostic modalities, including immunohistochemistry, histopathology, and imaging findings associated with this rare malignancy

Multi-ahead electrical conductivity forecasting of surface water based on machine learning algorithms

Abstract The present research work focused on predicting the electrical conductivity (EC) of surface water in the Upper Ganga basin using four machine learning algorithms: multilayer perceptron (MLP), co-adaptive neuro-fuzzy inference system (CANFIS), random forest (RF), and decision tree (DT). The study also utilized the gamma test for selecting appropriate input and output combinations. The results of the gamma test revealed that total hardness (TH), magnesium (Mg), and chloride (Cl) parameters were suitable input variables for EC prediction. The performance of the models was evaluated using statistical indices such as Percent Bias (PBIAS), correlation coefficient (R), Willmott’s index of agreement (WI), Index of Agreement (PI), root mean square error (RMSE) and Legate-McCabe Index (LMI). Comparing the results of the EC models using these statistical indices, it was observed that the RF model outperformed the other algorithms. During the training period, the RF algorithm has a small positive bias (PBIAS = 0.11) and achieves a high correlation with the observed values (R = 0.956). Additionally, it shows a low RMSE value (360.42), a relatively good coefficient of efficiency (CE = 0.932), PI (0.083), WI (0.908) and LMI (0.083). However, during the testing period, the algorithm’s performance shows a small negative bias (PBIAS = − 0.46) and a good correlation (R = 0.929). The RMSE value decreases significantly (26.57), indicating better accuracy, the coefficient of efficiency remains high (CE = 0.915), PI (0.033), WI (0.965) and LMI (− 0.028). Similarly, the performance of the RF algorithm during the training and testing periods in Prayagraj. During the training period, the RF algorithm shows a PBIAS of 0.50, indicating a small positive bias. It achieves an RMSE of 368.3, R of 0.909, CE of 0.872, PI of 0.015, WI of 0.921, and LMI of 0.083. During the testing period, the RF algorithm demonstrates a slight negative bias with a PBIAS of  − 0.06. The RMSE reduces significantly to 24.1, indicating improved accuracy. The algorithm maintains a high correlation (R = 0.903) and a good coefficient of efficiency (CE = 0.878). The index of agreement (PI) increases to 0.035, suggesting a better fit. The WI is 0.960, indicating high accuracy compared to the mean value, while the LMI decreases slightly to − 0.038. Based on the comparative results of the machine learning algorithms, it was concluded that RF performed better than DT, CANFIS, and MLP. The study recommended using the current month’s total hardness (TH), magnesium (Mg), and chloride (Cl) parameters as input variables for multi-ahead forecasting of electrical conductivity (ECt+1, ECt+2, and ECt+3) in future studies in the Upper Ganga basin. The findings also indicated that RF and DT models had superior performance compared to MLP and CANFIS models. These models can be applied for multi-ahead forecasting of monthly electrical conductivity at both Varanasi and Prayagraj stations in the Upper Ganga basin

Metal/Metalloid-Based Nanomaterials for Plant Abiotic Stress Tolerance: An Overview of the Mechanisms

In agriculture, abiotic stress is one of the critical issues impacting the crop productivity and yield. Such stress factors lead to the generation of reactive oxygen species, membrane damage, and other plant metabolic activities. To neutralize the harmful effects of abiotic stress, several strategies have been employed that include the utilization of nanomaterials. Nanomaterials are now gaining attention worldwide to protect plant growth against abiotic stresses such as drought, salinity, heavy metals, extreme temperatures, flooding, etc. However, their behavior is significantly impacted by the dose in which they are being used in agriculture. Furthermore, the action of nanomaterials in plants under various stresses still require understanding. Hence, with this background, the present review envisages to highlight beneficial role of nanomaterials in plants, their mode of action, and their mechanism in overcoming various abiotic stresses. It also emphasizes upon antioxidant activities of different nanomaterials and their dose-dependent variability in plants’ growth under stress. Nevertheless, limitations of using nanomaterials in agriculture are also presented in this review