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Globally, agriculture irrigation accounts for 70% of water use and is facing extensive and increasing water constraints. Well-designed irrigation scheduling can help determine the appropriate timing and water requirement for crop development and consequently improve water use efficiency. This research aims to assess the probability of irrigation needed for agricultural operations, considering soil moisture, evaporation, and leaf area index as indicators of crop water requirement. The decision on irrigation scheduling is taken based on a three-step methodology. First, relevant variables for each indicator are identified using a Random Forest regressor, followed by the development of a Long Short-Term Memory (LSTM) model to predict the three indicators. Second, errors in the simulation of each indicator are calculated by comparing the predicted values against the actual values, which are then used to calculate the error weights (normalized) of the three indicators for each month (to capture the seasonal variations). Third, the empirical distribution of each indicator is obtained for each month using the estimated error values, which are then adjusted based on the error weights calculated in the previous step. The probabilities of three threshold values (for each indicator) are considered, which correspond to three levels of irrigation requirement, i.e., low, medium, and high. The proposed approach provides a probabilistic framework for irrigation scheduling, which can significantly benefit farmers and policymakers in more informed decision-making related to irrigation scheduling.

GOSSYM, a mechanistic, process-level cotton crop simulation model, has a two-dimensional (2D) gridded soil model called Rhizos that simulates the below-ground processes daily. Water movement is based on gradients of water content and not hydraulic heads. In GOSSYM, photosynthesis is calculated using a daily empirical light response function that requires calibration for response to elevated carbon dioxide (CO2). This report discusses improvements made to the GOSSYM model for soil, photosynthesis, and transpiration processes. GOSSYM’s predictions of below-ground processes using Rhizos are improved by replacing it with 2DSOIL, a mechanistic 2D finite element soil process model. The photosynthesis and transpiration model in GOSSYM is replaced with a Farquhar biochemical model and Ball-Berry leaf energy balance model. The newly developed model (modified GOSSYM) is evaluated using field-scale and experimental data from SPAR (soil–plant–atmosphere-research) chambers. Modified GOSSYM better predicted net photosynthesis (root mean square error (RMSE) 25.5 versus 45.2 g CO2 m−2 day−1; index of agreement (IA) 0.89 versus 0.76) and transpiration (RMSE 3.3 versus 13.7 L m−2 day−1; IA 0.92 versus 0.14) and improved the yield prediction by 6.0%. Modified GOSSYM improved the simulation of soil, photosynthesis, and transpiration processes, thereby improving the predictive ability of cotton crop growth and development.

GOSSYM, a mechanistic, process-level cotton crop simulation model, has a two-dimensional (2D) gridded soil model called Rhizos that simulates the below-ground processes daily. Water movement is based on gradients of water content and not hydraulic heads. In GOSSYM, photosynthesis is calculated using a daily empirical light response function that requires calibration for response to elevated carbon dioxide (CO2). This report discusses improvements made to the GOSSYM model for soil, photosynthesis, and transpiration processes. GOSSYM's predictions of below-ground processes using Rhizos are improved by replacing it with 2DSOIL, a mechanistic 2D finite element soil process model. The photosynthesis and transpiration model in GOSSYM is replaced with a Farquhar biochemical model and Ball-Berry leaf energy balance model. The newly developed model (modified GOSSYM) is evaluated using field-scale and experimental data from SPAR (soil-plant-atmosphere-research) chambers. Modified GOSSYM better predicted net photosynthesis (root mean square error (RMSE) 25.5 versus 45.2 g CO2 m− 2 day− 1; index of agreement (IA) 0.89 versus 0.76) and transpiration (RMSE 3.3 versus 13.7 liters m− 2 day− 1; IA 0.92 versus 0.14) and improved the yield prediction by 6.0%. Modified GOSSYM improved the simulation of soil, photosynthesis, and transpiration processes, thereby improving the predictive ability of cotton crop growth and development.

This study was conducted to investigate the impact of intermittent feeding on performance, clogging, and gaseous emission on macrophyte assisted vermifiltration (MAVF) based treatment system. Synthetic slaughterhouse wastewater was applied to two different integrated vertical flow based MAVFs. Triplicates were used throughout the study. Eisenia fetida earthworms were added to MAVFs, and Carex muskingmenis plants were planted. Wastewater was applied to the reactors on 1) intermittent (8 h/day) (IMAVF) and 2) continuous (24 h/day) (CMAVF) basis. The average chemical oxygen demand, total nitrogen, and total phosphorous removals achieved by the IMAVF were 80.2 ± 1.6%, 53.9 ± 1.3% and 66.5 ± 1% respectively, and 68.3 ± 1.3%, 61.2 ± 1.4%, and 60.5 ± 1.4% by the CMAVF, respectively. The diffusion of air to the bedding of IMAVFs during no-flow conditions facilitated higher organics oxidation, adsorption of phosphorous, nitrification, and ammonification. At the end of the study, hydraulic conductivity of IMAVF and CMAVF were found to be 0.036 cm/s and 0.037 cm/s, respectively. CO2, CH4 and N2O emissions from IMAVF were 245.5 ± 38.0 mg C/m2, 5.0 ± 4.6 mg C/m2 and 2513.5 ± 2629.9 μg N/m2 respectively, while CO2, CH4 and N2O emissions from CMAVF were 123.3 ± 14.5 mg C/m2, 74.8±45.2 mg C/m2 and 328.4 ± 93.4 μg N/m2, respectively. Intermittent application of influent could be considered for improving the performance and lifespan of MAVFs, causing lower environmental footprints.

During early 2019, a series of events set the stage for devastating floods in eastern Nebraska, western Iowa, and southeastern South Dakota. When the floodwaters hit, dams and levees failed, cutting off towns while destroying roads, bridges, and rail lines, further exacerbating the crisis. Lives were lost and thousands of cattle were stranded. Estimates indicate that the cost of the flooding has topped $3 billion as of August 2019, with this number expected to rise. After a warm and wet start to winter, eastern Nebraska, western Iowa, and southeastern South Dakota endured anomalously low temperatures and record-breaking snowfall. By March 2019, rivers were frozen, frost depths were 60–90 cm, and the water equivalent of the snowpack was 30–100 mm. With these conditions in place, a record-breaking surface cyclone rapidly developed in Colorado and moved eastward, producing heavy rain toward the east and blizzard conditions toward the west. In areas of eastern Nebraska, western Iowa, and southeastern South Dakota, rapid melting of the snowpack due to this rain-on-snow event quickly led to excessive runoff that overwhelmed rivers and streams. These conditions brought the region to a standstill. In this paper, we provide an analysis of the antecedent conditions in eastern Nebraska, western Iowa, and southeastern South Dakota and the development of the surface cyclone that triggered the historic flooding, along with a look into the forecast and communication of flood impacts prior to the flood. The study used multiple datasets, including in situ observations and reanalysis data. Understanding the events that led to the flooding could aid in future forecasting efforts.

Groundwater overuse in different domains will eventually lead to global freshwater scarcity. To meet the anticipated demands, many governments worldwide are employing innovative and traditional techniques for forecasting groundwater availability by conducting research and studies. One challenging step for this type of study is collecting groundwater data from different sites and securely sending it to the nearby edges without exposure to hacking and data tampering. In the current paper, we send raw data formats from the Internet of Things to the Distributed Data Storage (DDS) and Blockchain (BC) edges. We use a distributed and decentralized architecture to store the statistics, perform double hashing, and implement access control through smart contracts. This work demonstrates a modern and innovative approach combining DDS and BC technologies to overcome traditional data sharing, and centralized storage, while addressing blockchain limitations. We have shown performance improvements with increased data quality and integrity.

Improvement of nutrient use efficiency and limiting trace elements such as arsenic and uranium bioavailability is critical for sustainable agriculture and food safety. Arsenic and uranium possess different properties and mobility in soils, which complicates the effort to reduce their uptake by plants. Here, we postulate that unsaturated soil amended with ferrihydrite nanominerals leads to improved nutrient retention and helps reduce uptake of these geogenic contaminants. Unsaturated soil is primarily oxic and can provide a stable environment for ferrihydrite nanominerals. To demonstrate the utility of ferrihydrite soil amendment, maize was grown in an unsaturated agricultural soil that is known to contain geogenic arsenic and uranium. The soil was maintained at a gravimetric moisture content of 15.1 ± 2.5%, typical of periodically irrigated soils of the US Corn Belt. Synthetic 2-line ferrihydrite was used in low doses as a soil amendment at three levels (0.00% w/w (control), 0.05% w/w and 0.10% w/w). Further, the irrigation water was fortified (~50 μg L-1 each) with elevated arsenic and uranium levels. Plant dry biomass at maturity was ~13.5% higher than that grown in soil not receiving ferrihydrite, indicating positive impact of ferrihydrite on plant growth. Arsenic and uranium concentrations in maize crops (root, shoot and grain combined) were ~ 20% lower in amended soils than that in control soils. Our findings suggest that the addition of low doses of iron nanomineral soil amendment can positively influence rhizosphere geochemical processes, enhancing nutrient plant availability and reduce trace contaminants plant uptake in sprinkler irrigated agroecosystem, which is 55% of total irrigated area in the United States.

Several areas in Hawaii have background concentrations of unexploded ordnance (UXO) as a consequence of past and current military training. The presence of UXO and the release of their che...

Redox driven mobilization and plant uptake of contaminants under transiently saturated soil conditions need to be clarified to ensure food and water quality across different irrigation systems. We postulate that solid-phase iron reduction in anoxic microsites present in the rhizosphere of unsaturated soil is a key driver for mobilization and bioavailability of contaminants under nonflooded irrigation. To clarify this, two major crops, corn and soybean differing in iron uptake strategies, were grown in irrigated synthetic soil under semiarid conditions with gravimetric moisture content ∼12.5 ± 2.4%. 2-line ferrihydrite, which was coprecipitated with uranium and arsenic, served as the only iron source in soil. Irrespective of crop type, reduced iron was detected in pore water and postexperiment rhizosphere soil confirming ferrihydrite reduction. These results support the presence of localized anoxic microsites in the otherwise aerobic porous bulk soil causing reduction of ferrihydrite and concomitant increase in plant uptake of comobilized contaminants. Our findings indicate that reactive iron minerals undergo reductive dissolution inside anoxic microsites of primarily unsaturated soil, which may have implications on the mobility of trace element contaminants such as arsenic and uranium in irrigated unsaturated soils, accounting for 55% of the irrigated area in the US.

To meet global health and sanitation goals, there is a continued need for sustainable wastewater treatment alternatives that require minimal energy and investment. Vermifiltration, a technology gaining relevance in Africa and Asia, may be an alternative to traditional wastewater treatment systems due to its cost-effectiveness, ease of application and maintenance, and sustainability. However, nitrogen removal in vermifiltration is not well understood since most of the prior research focuses on organics removal. Thus, a state of the art review is necessary to separately focus on the mechanisms associated with nitrogen removal in vermifiltration, along with the factors affecting nitrogen removal. For the first time, this review attempts to present the types of vermifilter based on their flow pattern. The review further discusses the current status of the application of vermifiltration, along with the benefits and limitations associated with the adoption of this technology. It also explores possible strategies that could be adopted to maximize the nitrogen removal potential of vermifilters as optimizing nitrogen removal is critical for improving the performance of vermifiltration based treatment systems.

The impact of commonly-used livestock antibiotics on soil nitrogen transformations under varying redox conditions is largely unknown. Soil column incubations were conducted using three livestock antibiotics (monensin, lincomycin and sulfamethazine) to better understand the fate of the antibiotics, their effect on nitrogen transformation, and their impact on soil microbial communities under aerobic, anoxic, and denitrifying conditions. While monensin was not recovered in the effluent, lincomycin and sulfamethazine concentrations decreased slightly during transport through the columns. Sorption, and to a limited extent degradation, are likely to be the primary processes leading to antibiotic attenuation during leaching. Antibiotics also affected microbial respiration and clearly impacted nitrogen transformation. The occurrence of the three antibiotics as a mixture, as well as the occurrence of lincomycin alone affected, by inhibiting any nitrite reduction, the denitrification process. Discontinuing antibiotics additions restored microbial denitrification. Metagenomic analysis indicated that Proteobacteria, Bacteroidetes, Actinobacteria, and Chloroflexi were the predominant phyla observed throughout the study. Results suggested that episodic occurrence of antibiotics led to a temporal change in microbial community composition in the upper portion of the columns while only transient changes occurred in the lower portion. Thus, the occurrence of high concentrations of veterinary antibiotic residues could impact nitrogen cycling in soils receiving wastewater runoff or manure applications with potential longer-term microbial community changes possible at higher antibiotic concentrations.

A four-decade dataset (1974-2013) of 107,823 nitrate samples in 25,993 wells from western and eastern parts of Nebraska was used to assess long-term trends of groundwater nitrate concentration and decadal changes in the extent of groundwater nitrate-contaminated areas (NO3-N ≥ 10 mg N/L) over the entire state. Spatial statistics and regressions were used to investigate the relationships between groundwater nitrate concentrations and several potential natural and anthropogenic factors, including soil drainage capacities, vadose zone characteristics, crop production areas, and irrigation systems. The results of this study show that there is no statistically significant trend in groundwater nitrate concentrations in western Nebraska, in contrast with the increasing trend (p < .05) to the east. The spatial extent and nitrate concentrations in contaminated groundwater in center pivot-irrigated areas was less than in gravity-irrigated areas. Areas with a thicker vadose zone and larger saturated thickness of the aquifer have relatively lower nitrate concentrations. The results of a classification and regression tree (CART) model indicate the difference in the influence of physical factors on groundwater nitrate concentrations between western and eastern Nebraska, namely that groundwater nitrate concentrations correspond with vadose zone thickness, effective hydraulic conductivity, and saturated thickness in the west, while in eastern Nebraska, concentrations are correlated with average percent sand in the topsoil (0-150 cm), well depth, and effective hydraulic conductivity.

Water is the basis of life. And, unlike various forms of energy generation, water cannot simply be created where it is needed. Freshwater is remarkably scarce, comprising just 2.5% of the global wa...

Runoff and baseflow estimates provide critical information on surface and groundwater flow dynamics, water resource availability, and water usage patterns for effective water resource planning and management. Developing a physical-based model at large spatial scales such as continental/global scales require extensive datasets, complex parameterization, and higher computational capacity. Recent advances in satellite datasets related to water cycle process modeling, on the other hand, involve less parameterization and more effective computation over larger spatial and finer temporal scales. Evapotranspiration (ET) is a critical flux in the water cycle that can be estimated with reasonable accuracy from satellite-based information by using complementary relationship (CR) concepts. Similarly, the Gravity Recovery and Climate Experiment (GRACE) mission satellite datasets provide useful estimates of total water storage change over large spatial extents. GRACE datasets have been extensively used in groundwater storage change and groundwater depletion studies. However, the application of GRACE data products for estimating runoff and baseflow is limited. Therefore, in this study, we used a CR approach to estimate ET and a water balance method for estimating runoff and baseflow over the Conterminous United States (CONUS). We evaluated the CR-based water balance model (CR-GRACE) with United States Geological Survey (USGS) runoff datasets based on hydrologic unit code level-2 (HUC-2) basin boundaries. The CR-GRACE model estimated annual runoff values agreed with USGS runoff values with root mean square error (RMSE) and correlation coefficient values of 14.7 mm/month and 0.81, respectively. We also evaluated the CR-GRACE model with two well-known Land Surface Models (LSM), Noah-LSM and Variable Infiltration Capacity LSM (VIC-LSM), from the Global Land Data Assimilation System (GLDAS) database. The comparison results for baseflow estimates from the CR-GRACE model have improved over Noah-LSM and VIC-LSM model estimates with RMSE values of 62.16, 101.00, and 207.32 mm/year, respectively. Furthermore, this study demonstrates the feasibility of using the CR-GRACE model to estimate runoff and baseflows with limited parameterization and datasets.

Root zone soil moisture (RZSM) is an important state variable for understanding various land surface and ecohydrological processes. Due to the lack of field measurements, different methods have been proposed to estimate RZSM, including the use of exponential filters to predict RZSM from remotely sensed near surface soil moisture data. However, inconsistent findings about the controls on the optimal characteristic time length T opt , which is used in the exponential filter method, have been reported in the literature. To reconcile these inconsistent findings and further improve the use of the method, the impacts of soil properties and climatic conditions on T opt were assessed in this study using observed and modelled soil moisture datasets. Daily soil moisture data, daily meteorological records, and soil properties were retrieved from the Automated Weather Data Network (AWDN) and the Soil Climate Analysis Network (SCAN) within the continental United States. Data from the AWDN stations showed that T opt was mostly controlled by soil texture (e.g., a negative correlation with the sand fraction and a positive one with the clay fraction) as compared to climatic conditions. However, at SCAN stations, T opt was mostly affected by precipitation ( P ), and no significant correlation was found between T opt and soil texture. The difference in controlling factors between ADWN and SCAN stations can be largely attributed to the higher spatial variability in P across the SCAN stations, which overrode the impacts of soil properties on T opt . A 1-D vadose zone model was also utilized to simulate soil moisture at selected SCAN sites using a generated soil hydraulic parameter dataset. The simulation results further demonstrated the negative relationship between T opt and P observed for the SCAN stations. Moreover, the simulation results revealed that T opt was larger under vegetated conditions than under bare surface conditions. Under the same climatic conditions at each simulated site, which could be deemed as reduced variability in P , significant correlations existed between T opt and van Genuchten parameters. In particular, T opt was shown to be smaller for coarser soils, which was consistent with the results observed from AWDN stations. The findings of this study offer additional insights into the use of the exponential filter method for estimating RZSM from near surface soil moisture measurements.

Soil moisture is an important state variable in terrestrial water cycles; however, only few studies are available on regional soil moisture spatial variability (SMSV), which yielded inconsistent findings about regional controls on SMSV. Here, long‐term soil moisture data were obtained from the Automated Weather Data Network and Soil Climate Analysis Network in three regions with different climate regimes across the continental U.S. Comprehensive data sets were compiled to examine regional controls on SMSV using the method of Empirical Orthogonal Function. One dominant spatial structure (EOF1) of soil moisture was found in the study regions, which explained over 75%, 67%, and 86% of the spatial variance in soil moisture in Nebraska, Utah, and the Southeast U.S., respectively. Despite the significant spatial variability in precipitation and potential evapotranspiration in all the study regions, the results showed that meteorological forcings had limited effects on regional SMSV in those regions with different climatic conditions, which differed from the traditional notion that SMSV is mainly controlled by meteorological forcings at the scale from 50 to 400 km. Instead, local factors related to soil (e.g., sand and clay fractions) were found to have significant correlations with EOF1, although the effects of other local factors (e.g., topography and vegetation) were generally negligible. This study provides strong field evidence that soil can exert much stronger impacts on regional SMSV than previously thought, which can override the effects of meteorological forcings. Future studies are still needed to elaborate on the relative roles of climate and soil in affecting regional SMSV.

Contamination from pesticides and nitrate in groundwater is a significant threat to water quality in general and agriculturally intensive regions in particular. Three widely used machine learning models, namely, artificial neural networks (ANN), support vector machines (SVM), and extreme gradient boosting (XGB), were evaluated for their efficacy in predicting contamination levels using sparse data with non-linear relationships. The predictive ability of the models was assessed using a dataset consisting of 303 wells across 12 Midwestern states in the USA. Multiple hydrogeologic, water quality, and land use features were chosen as the independent variables, and classes were based on measured concentration ranges of nitrate and pesticide. This study evaluates the classification performance of the models for two, three, and four class scenarios and compares them with the corresponding regression models. The study also examines the issue of class imbalance and tests the efficacy of three class imbalance mitigation techniques: oversampling, weighting, and oversampling and weighting, for all the scenarios. The models' performance is reported using multiple metrics, both insensitive to class imbalance (accuracy) and sensitive to class imbalance (F1 score and MCC). Finally, the study assesses the importance of features using game-theoretic Shapley values to rank features consistently and offer model interpretability.

The growing global food security crisis is complicated by the need for increased crop production with less arable land and limited water resources. Reuse of treated wastewater for agricultu...

In Table 2, the unit for nitrogen, phosphate, and potash should have been Mg instead of g (Figure Presented).

The complementary relationship areal evapotranspiration (CRAE) model estimates actual evapotranspiration (ET) based on potential and wet environment evapotranspiration rates. The existing C...

Cryptosporidia are opportunistic pathogens, some of which infect humans when ingested in water or food. Filtration through granular media is the primary mechanism to remove them from water....

Nitrification of chloraminated water in distribution systems, particularly in summer time, is a serious problem for some utilities because nitrifying bacteria deplete residual chloramine, a...

Nanomaterials (NMs), both natural and synthetic, are produced, transformed, and exported into our environment daily. Natural NMs annual flux to the environment is around 97% of the total and is significantly higher than synthetic NMs. However, synthetic NMs are considered to have a detrimental effect on the environment. The extensive usage of synthetic NMs in different fields, including chemical, engineering, electronics, and medicine, makes them susceptible to be discharged into the atmosphere, various water sources, soil, and landfill waste. As ever-larger quantities of NMs end up in our environment and start interacting with the biota, it is crucial to understand their behavior under various environmental conditions, their exposure pathway, and their health effects on human beings. This review paper comprises a large portion of the latest research on NMs and the environment. The article describes the natural and synthetic NMs, covering both incidental and engineered NMs and their behavior in the natural environment. The review includes a brief discussion on sampling strategies and various analytical tools to study NMs in complex environmental matrices. The interaction of NMs in natural environments and their pathway to human exposure has been summarized. The potential of NMs to impact human health has been elaborated. The nanotoxicological effect of NMs based on their inherent properties concerning to human health is also reviewed. The knowledge gaps and future research needs on NMs are reported. The findings in this paper will be a resource for researchers working on NMs all over the world to understand better the challenges associated with NMs in the natural environment and their human health effects.

The objective of this study was to treat feedlot runoff by developing an ecologically sustainable, affordable, and resilient treatment system having a relatively long life span. Three horizontal flow soil biofilters were utilized in this study: 1) without earthworms and plants (Biofilter (BF)), 2) with earthworms only (Vermifilter (VF)), and 3) with earthworms and plants (Macrophyte Assisted Vermifilter (MAVF)). The experiments were conducted with a hydraulic retention time of four days using Lumbricus terestrris earthworms and Carex frankii wetland plants. The average COD removal from the BF, VF, and MAVF were 23.2–30.4%, 61.4–69.1%, and 68.3–78.1%, respectively. Average TN removal efficiencies for BF, VF, and MAVF were 15.5–21.4%, 34.4–38.8%, and 39.1–44.0%, respectively. Additionally, average TP removals for BF, VF, and MAVF were 31.9–40.8%, 48.0–54.0%, and 51.1–58.3%, respectively. Comparison of results with literature indicate that the developed system can facilitate more nitrogen removal. Plant roots, along with earthworms, create an aerobic ecosystem within the treatment filter, leading to high organics oxidation and nitrification efficiency among BF, VF, and MAVF. Observational analysis indicates the system with earthworms is prone to clogging while the system with earthworms and plants was less prone to clogging. Thus, it can be concluded that if modularized, the application of MAVF systems can treat feedlot runoffs with higher removal efficiency and expanded life span.

For millennia, the Ganges River, holy to Hindus, has provided livelihoods, food, and water for Nepal, India, and Bangladesh. Last month, one of India's leading environmental activists died after a 111-day hunger strike, failing to evoke changes to save India's most revered river (known as Ganga). After years of unrelenting abuse, Ganga is now one of the world's worst polluted rivers. India's Prime Minister Narendra Modi vowed in 2014 to clean Ganga by 2019, but despite increased funding and much lip service, the river is more polluted than before. Mr. Modi needs a new strategy. Pressure on Ganga has been building for decades. With a tripling of human population since 1950 and rapid urbanization, 50 cities along Ganga daily release 6 billion liters of untreated sewage into the river, by far the largest source of pollution. Untreated industrial effluents compound the problem, together with run-off of chemical fertilizers and pesticides. Unfettered disposal of human and animal corpses into Ganga makes it unfit even for ritual bathing. Barrages and hydroelectric projects on the main stem of Ganga and its tributaries divert 60% of their waters, leaving little mainstream flow, which further concentrates pollutants in the river. ![Figure][1] Women worship on polluted bank of the Ganges, in Allahabad, India. PHOTO: RITESH SHUKLA/NURPHOTO/GETTY IMAGES > “…despite increased funding and much lip service, the river is more polluted than before.” Earlier governments tried cleaning Ganga, investing some U.S.$14 billion, mostly in plants that treat sewage to an acceptable level of pollutants before discharge into waterways. Mr. Modi brought a new sense of urgency to the task, allocating U.S.$27 billion up to 2019. But money alone will not clean the river. Complex long-standing issues must be addressed, including increasing involvement of stakeholders, reducing corruption in pollution control agencies, increasing accountability and rule enforcement, and inciting behavioral change among citizens. This requires tackling three interrelated challenges, each involving different stakeholders. Municipalities must curtail discharge of untreated sewage and rapidly build sewerage infrastructures. Pollution control authorities must ensure treatment of municipal and industrial effluents to the needed standards. And water managers must enhance river flow so that secondary treated waste can be safely discharged into Ganga. Since 1855, a profusion of barrages and dams has diverted water from Ganga and its tributaries for hydropower and canal irrigation. But over the past five decades, farmers have increasingly turned away from canal irrigation to shallow tube wells. Herein lies a big opportunity for cleaning Ganga. During the 1970s, modelers recommended cranking up the “Ganges water machine” to relieve flooding in the eastern parts of the river basin by promoting intensive groundwater-based irrigation during winter and summer months. The strategy was to draw down basin water tables in the copious alluvial aquifers. Shallow aquifers would then absorb monsoon floods and snowmelt, and protect Bihar, North Bengal, and Bangladesh from annual floods. Monsoons would replenish aquifers that shallow tube wells could then tap into for irrigation. This concept did not catch on back then, but today, the water machine is alive and kicking thanks to easy access to credit and cheaper drilling technology. With over 6.5 million shallow tube wells in Nepal, India, and Bangladesh, the Ganga basin is one of the most densely plumbed aquifer systems in the world. More than 80% of farmers depend on these wells. The water machine would be revved up even more but for the high cost of diesel that farmers must use to pump groundwater. But affordable electricity or solar pumps could wean farmers from canal irrigation, leaving more water to flow in Ganga and its tributaries, without adversely affecting hydropower. The quickest, cheapest, and most effective way for Mr. Modi to show a less polluted Ganga by 2019 would be operating dams and barrages in the Ganga basin with the sole objective of augmenting river flows. This would be a start to controlling discharge of untreated sewage and industrial waste, which will take a long time. [1]: pending:yes

Riverbank filtration (RBF) is a simple yet effective treatment technology that uses the natural soil and aquifer media to remove various pollutants from river water during induced infiltrat...

To assess the groundwater vulnerability due to leaching of chemicals, the groundwater system in the unsaturated zone is characterized by conceptual models that are further extended and refined with more detailed mathematical models to understand the governing physical processes in detail. However, due to lack of data and uncertainty level, an intermediate transition through index based models is researched. The attenuation factor (AF) approach, which works under the assumption that the chemicals degrade following a first-order kinetics and determines the fraction of the chemicals that goes to groundwater table, is one of the index based models that has been widely used due to its simplicity. Therefore, the objective of this paper is to review the research works done using the AF approach, to outline the future research needs. Furthermore, the mathematical implementation of the AF approach and the associated uncertainty levels is explained through an example and MATLAB source code.

The need to allocate the existing water in a sustainable manner, even with the projected population growth, has made to assess the consumptive use or evapotranspiration (ET), which determines the irrigation demand. As underscored in the literature, Penman-Monteith method which is a combination of aerodynamic and energy balance method is widely used and accepted as the method of estimation of ET. However, the application of Penman-Monteith relies on many climate parameters such as relative humidity, solar radiation, temperature, and wind speed. Therefore, there exists a need to determine the parameters that are most sensitive and correlated with dependent variable (i.e., ET), to strengthen the knowledge base. However, the sensitivity of ET using Penman-Monteith is oftentimes estimated using meteorological data from climate stations. Such estimation of sensitivity may vary spatially and thus there exists a need to estimate sensitivity of ET spatially. Thus, in this paper, based on One-AT-A-Time (OAT) method, a spatial sensitivity tool that can geographically encompass all the best available climate datasets to produce ET and its sensitivity at different spatial scales is developed. The spatial tool is developed as a Python toolbox in ArcGIS using Python, an open source programming language, and the ArcPy site-package of ArcGIS. The developed spatial tool is demonstrated using the meteorological data from Automated Weather Data Network in Nebraska in 2010. To summarize the outcome of the sensitivity analysis using OAT method, sensitivity indices are developed for each raster cell. The demonstration of the tool shows that, among the considered parameters, the computed ET using Penman-Monteith is highly sensitive to solar radiation followed by temperature for the state of Nebraska, as depicted by the sensitivity index. The computed sensitivity index of wind speed and the relative humidity are not that significant compared to the sensitivity index of solar radiation and temperature.

The drastic decline in groundwater table and many other detrimental effects in meeting irrigation demand, and the projected population growth have force to evaluate consumptive use or evapo-transpiration (ET), the rate of liquid water transformation to vapor from open water, bare soil, and vegetation, which determines the irrigation demand. As underscored in the literature, Pen-man-Monteith method which is based on aerodynamic and energy balance method is widely used and accepted as the method of estimation of ET. However, the estimation of ET is oftentimes carried out using meteorological data from climate stations. Therefore, such estimation of ET may vary spatially and thus there exists a need to estimate ET spatially at different spatial or grid scales/resolutions. Thus, in this paper, a spatial tool that can geographically encompass all the best available climate datasets to produce ET at different spatial scales is developed. The spatial tool is developed as a Python toolbox in ArcGIS using Python, an open source programming language, and the ArcPy site-package of ArcGIS. The developed spatial tool is demonstrated using the meteorological data from Automated Weather Data Network in Nebraska in 2010.