Warming inhibits increases in vegetation net primary productivity despite greening in India.

India is the second-highest contributor to the post-2000 global greening. However, with satellite data, here we show that this 18.51% increase in Leaf Area Index (LAI) during 2001-2019 fails to translate into increased carbon uptake due to warming constraints. Our analysis further shows 6.19% decrease in Net Primary Productivity (NPP) during 2001-2019 over the temporally consistent forests in India despite 6.75% increase in LAI. We identify hotspots of statistically significant decreasing trends in NPP over the key forested regions of Northeast India, Peninsular India, and the Western Ghats. Together, these areas contribute to more than 31% of the NPP of India (1274.8 TgC.year-1). These three regions are also the warming hotspots in India. Granger Causality analysis confirms that temperature causes the changes in net-photosynthesis of vegetation. Decreasing photosynthesis and stable respiration, above a threshold temperature, over these regions, as seen in observations, are the key reasons behind the declining NPP. Our analysis shows that warming has already started affecting carbon uptake in Indian forests and calls for improved climate resilient forest management practices in a warming world.

Thermal illusions for thermal displays: a review.

Thermal illusions, a subset of haptic illusions, have historically faced technical challenges and limited exploration. They have been underutilized in prior studies related to thermal displays. This review paper primarily aims to comprehensively categorize thermal illusions, offering insights for diverse applications in thermal display design. Recent advancements in the field have spurred a fresh perspective on thermal and pain perception, specifically through the lens of thermal illusions.

Framework for global sensitivity analysis in a complex 1D-2D coupled hydrodynamic model: Highlighting its importance on flood management over large data-scarce regions.

Sensitivity analysis determines how perturbation or variation in the values of an independent variable affects a particular dependent variable. The present study attempts to comprehend the sensitivity of the static input parameters on the accuracy of the outputs in a hydrodynamic flood model, which subsequently improves the model accuracy. Hydrodynamic flood modeling is computationally strenuous and data-intensive. Moreover, the accuracy of the flood model outputs is extremely sensitive to the quality of hydrologic and hydraulic inputs, along with a set of static parameters that are traditionally assumed and primarily used for calibration. Therefore, we focus on developing a framework for global sensitivity analysis (GSA) of static input parameters in a 1D-2D coupled hydrodynamic flood modeling system. A set of numerical experiments is conducted by perturbing various combinations of input parameters from their standard (or observed) values to generate flow hydrographs. Nonparametric probability density functions (PDFs) of the river discharge at different locations are compared to calculate the Kullback-Leibler (KL) entropy or KL-divergence, which is used to quantify the sensitivity of the input parameters. We demonstrated the proposed framework on a highly flood-prone rural catchment of the Shilabati River in West Bengal, India, and infer that the sensitivity of the static input parameters is highly dynamic, and their importance varies spatially from the upstream to the downstream of the river. However, Manning's n values of the channel and the banks are significantly sensitive irrespective of the location in the river reach. We suggest that any flood modeling exercise should accompany a GSA, which sets a guideline for the modelers to prioritize the set of sensitive static input parameters during data monitoring, collection, and retrieval. This study is the first attempt at a GSA in a 1D-2D coupled hydrodynamic flood modeling system, whose importance cannot be over-emphasized in any flood modeling platform. The proposed novel framework is generic and can be implemented prior to flood risk analyses for any floodplain management exercise. All free and commercially-available flood models can incorporate the proposed framework for a GSA as an extension toolbox.

Rainwater harvesting and water balance simulation-optimization scheme to plan sustainable second crop in small rain-fed systems.

Environmental degradation in the form of water shortage and uncertainty has severely affected the food systems across the globe. Especially in India, which is dominated by rain-fed farmers, the need for sustainable water resource and its management at farm level is imperative for farming livelihoods and food security of the country. Rainwater harvesting in on-farm reservoirs (OFR) can enable crop diversification, year round cropping and seasonal vegetable cultivation in rain-fed farming systems in India. However appropriate sizing of OFR remains a serious concern especially for small and marginal farmers with limited land holdings. In this study, a novel and comprehensive simulation-optimization model was developed to determine the optimal size and utilization of OFR. The simulation consisted of water balance of soil and OFR using hydrological analysis for last 28 years, through which supplement irrigation needs and, rainwater harvesting potential was estimated. Optimal use of available water in OFR was designed using a multi-stage process wherein the model generated, compared and screened appropriate vegetable plans for Rabi cultivation. The model was simulated for different OFR sizes and the optimal size was chosen based on its economic feasibility. To demonstrate the model, a case study was simulated wherein high supplement irrigation was estimated, indicating a severe limitation in rain-fed farming. A minimum OFR size of 9.9% of the total land was required. With an increase in OFR sizes, the profits increased however, the growth rate declined as the cropping area was reduced. An OFR size of 15.5% of total land was found to be optimal which gave benefit-cost ratio and payback period of 2.4 and 6.8 years respectively. Trends in cultivation plans for different sizes of OFR was observed wherein for small OFR sizes, the model generated fewer options of cultivation plans and preferred crops with high water productivity over crops with high profitability. The proposed model is generic and applicable at multiple scales and scenarios. The model could be used by environmental decision makers, farm managers, policy makers and researchers to determine the feasibility of any water resource intervention using an ecosystem centric approach when multiple scenarios of cultivation are possible.

A novel flood risk mapping approach with machine learning considering geomorphic and socio-economic vulnerability dimensions.

Quantifying flood hazards by employing hydraulic/hydrodynamic models for flood risk mapping is a widely implemented non-structural flood management strategy. However, the unavailability of multi-domain and multi-dimensional input data and expensive computational resources limit its application in resource-constrained regions. The fifth and sixth IPCC assessment reports recommend including vulnerability and exposure components along with hazards for capturing risk on human-environment systems from natural and anthropogenic sources. In this context, the present study showcases a novel flood risk mapping approach that considers a combination of geomorphic flood descriptor (GFD)-based flood susceptibility and often neglected socio-economic vulnerability components. Three popular Machine Learning (ML) models, namely Decision Tree (DT), Random Forest (RF), and Gradient-boosted Decision Trees (GBDT), are evaluated for their abilities to combine digital terrain model-derived GFDs for quantifying flood susceptibility in a flood-prone district, Jagatsinghpur, located in the lower Mahanadi River basin, India. The area under receiver operating characteristics curve (AUC) along with Cohen's kappa are used to identify the best ML model. It is observed that the RF model performs better compared to the other two models on both training and testing datasets, with AUC score of 0.88 on each. The socio-economic vulnerability assessment follows an indicator-based approach by employing the Charnes-Cooper-Rhodes (CCR) model of Data Envelopment Analysis (DEA), an efficient non-parametric ranking method. It combines the district's relevant socio-economic sensitivity and adaptive capacity indicators. The flood risk classes at the most refined administrative scale, i.e., village level, are determined with the Jenks natural breaks algorithm using flood susceptibility and socio-economic vulnerability scores estimated by the RF and CCR-DEA models, respectively. It was observed that >40 % of the villages spread over Jagatsinghpur face high and very high flood risk. The proposed novel framework is generic and can be used to derive a wide variety of flood susceptibility, vulnerability, and subsequently risk maps under a data-constrained scenario. Furthermore, since this approach is relatively data and computationally parsimonious, it can be easily implemented over large regions. The exhaustive flood maps will facilitate effective flood control and floodplain planning.

Risk mapping of Indian coastal districts using IPCC-AR5 framework and multi-attribute decision-making approach.

Strategic location of coastal areas across the world causes them to be prone to disaster risks. In the global south, the Indian coast is one of the most susceptible to oceanic extreme events, such as cyclones, storm surge and high tides. This study provides an understanding of the risk experienced (currently as well as back in 2001) by the districts along the Indian coastline by developing a quantitative risk index. In the process, it attempts to make a novel contribution to the risk literature by following the definition of risk as a function of hazard, exposure and vulnerability as stated in the most recent (Fifth) assessment report of the Intergovernmental Panel on Climate Change (IPCC). Indicators of bio-physical hazards (such as cyclones, storm surge, tides and precipitation), and socio-economic contributors of vulnerability (such as infrastructure, technology, finance and social nets) and exposure (space), are combined to develop an overall risk index at a fine administrative scale of district-level over the entire coastline. Further, the study employs a multi-attribute decision-making (MADM) method, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), to combine the contributing indicators and generate indices on hazard, exposure and vulnerability. The product of these three components is thereafter defined as risk. The results suggest that most districts of the eastern coast have higher risk indices compared to those in the west, and the risk has increased since 2001. The higher risk can be attributed to the higher hazard indices in the eastern districts which are aggravated by their higher vulnerability index values. This study is the first effort made to map risk for the entire coastline of India - which in turn has resulted in a new cartographic product at a district-scale. Such assessments and maps have implications for environmental and risk-managers as they can help identify the regions needing adaptive interventions.

An integrated approach for modeling uncertainty in human health risk assessment.

A human health risk assessment (HHRA) will not remain simple and straightforward when it involves multiple uncertain input variables. Uncertainties in HHRA result from the unavailability and subjectivity of input variables. Though several studies have performed HHRA, the quantification of uncertainty in HHRA under a situation of data scarcity and the simultaneous application of random and non-random input variables have rarely been reported. The present study proposes an integrated hybrid health risk modeling framework involving the concurrent treatment of random and non-random input variables and estimating the uncertainties linked to the input variables in HHRA. The proposed framework presents the flexibility to classify the input variables into fuzzy and probabilistic categories, based on their data availability and provenience nature. The framework is demonstrated over the Turbhe sanitary landfill in Navi Mumbai, India, where the fate and transport of heavy metals in leachate are investigated through LandSim modeling. The present study considers the LandSim-simulated heavy metal concentration and body weight as a random variable and water intake, exposure duration, frequency, bioavailability, and average time as fuzzy variables. Further, the uncertainties in the non-carcinogenic human health risk have been quantified using Monte Carlo simulations, followed by a comprehensive multivariate sensitivity analysis of the proposed framework. High health risk at Turbhe is estimated for the male and female population. This study presents the first effort to quantify the non-carcinogenic human health risks from leachate-contaminated groundwater considering the health risk input variables as non-deterministic. The proposed framework is generic and applicable to any landfill site and will remain unaltered when integrated health risk assessment and uncertainty assessment are performed for the landfill.

WebFRIS: An efficient web-based decision support tool to disseminate end-to-end risk information for flood management.

The present study describes the development of a web-based flood risk information system 'WebFRIS' for Jagatsinghpur district, a severely flood-prone region in Eastern India. The WebFRIS is designed by using various readily available open-source web tools and packages such as Google Map, PHP, MySQL, and JSON. Special emphasis is directed towards designing the layout and architecture, to be easily accessible by any end-user irrespective of any technical know-how. The WebFRIS illustrates spatial maps of flood hazard, socio-economic vulnerability, and flood risk at the village level for two-time scenarios. While analyzing a set of graphical statistics depicting the changes in flood risk components, a significant increase in high and very-high categories of both flood hazard (~140%) and socio-economically vulnerable villages (~68%) is noticed during Scenario-I. The number of villages facing compound risk (contributed equally by flood hazard and socio-economic vulnerability) nearly doubled in Scenario-I. A spatial analysis of diametric changes in flood risk shows that a large proportion of villages in Balikuda, Ersama, and Tirtol tehsils have undergone radical changes. Following these observations, a set of possible engineering, social, and policy measures are proposed, whose implementation in the near future is expected to reinforce flood management in the study area. The WebFRIS architecture is flexible, easy-to-use; it is expected to provide crucial lessons to the local bodies, town-planners, water professionals, flood experts, and also the citizens, a precious knowledge on flood risk management. The WebFRIS may be considered as a precious cartographic product for environmental management. The proposed web platform is generic, as it can be applied to study other inter-related systems such as environmental protection, land-use planning, coastal habitat restoration, and community resilience building.

Rationalization of water quality monitoring locations under spatiotemporal heterogeneity of diffuse pollution using seasonal export coefficient.

Water quality is continuously changing because of anthropogenic origin of point and diffuses (non-point) pollution sources. Most of the time diffuse sources are not considered for rationalization of sampling sites as their accurate estimation is tedious and data intensive. The estimation of diffuse pollution is conventionally carried out using observed water quality data. These conventional approaches are data intensive and demands detailed information for a considerably long-time horizon and hence becomes challenging to implement in data-scarce regions. Also, diffuse pollution sources are characterized by spatio-temporal heterogeneity as they depend upon seasonal behavior of precipitation. The present study proposes an innovative semi-empirical approach of Seasonal Export Coefficients (SECs) for estimation of diffuse pollution loads, especially for tropical countries like India. This approach takes into account the effect of seasonality on the estimation of diffuse pollution loads, by considering seasonal heterogeneity of terrain and precipitation impact factors and land use applications. This seasonal heterogeneity is then tested for its possible impact on rationalization of water quality monitoring locations for Kali River basin in India. The SECs are estimated for available water quality dataset of 1999-2000 and are further used for simulation of nutrient loading for experimental years 2004-2005, 2009-2010, and 2014-2015. The resulting SECs for Kali river basin are: 2.03 (agricultural), 1.44 (fallow), and 0.92 (settlement) for monsoonal nitrate; while for non-monsoonal nitrate, SECs are 0.51 (agricultural), 0.23 (fallow), and 0.10 (settlement). The monsoonal phosphate SECs for land use classes - agricultural, fallow and settlement are 1.01, 0.68, and 0.25, while non-monsoonal phosphate SECs are 0.27, 0.14 and, 0.03 respectively. The seasonal variation of diffuse pollution sources is effectively captured by SECs. The proposed approach, by considering both point and diffuse pollution, is found efficient in determining optimum locations and number of monitoring sites where seasonal variations are found evident during experimental years.

A two-stage multi-attribute decision-making model for selecting appropriate locations of waste transfer stations in urban centers.

Selecting appropriate locations for municipal solid waste (MSW) management facilities, such as transfer stations, is an important issue in rapidly developing regions. Multiple alternatives and evaluation attributes need to be analyzed for finalizing the locations of these facilities. Multi-attribute decision-making (MADM) approaches are found to be very effective for ranking several potential locations and hence selecting the best among them based on the identified attributes. However, conventional MADM approaches fail to find the rankings of alternatives derived from all possible combinations of these potential locations. Therefore, this study presents a two-stage MADM model that also accounts for all possible combinations of locations. This study evaluates economical, environmental, social and technical attributes based on realistic conditions of the study area, i.e., Nashik city (India). The results provide the ranks of all possible combinations along with their probabilities of rank reversibility. The mean and standard deviation of the relative closeness are further evaluated for the top-ranking locations under distinct schemes. The present study will help stakeholders in finding suitable locations for MSW management facilities while considering economic, environmental, social and technical attributes.

A new bivariate risk classifier for flood management considering hazard and socio-economic dimensions.

Identification of flood-risk dynamics is pivotal for refurbishing the existing and future flood-management options. The present study quantifies the marginal and compound contributions of hazard and vulnerability to flood-risk through an innovative concept of Risk-classifier, designed in the form of a 5 × 5 choropleth. The proposed framework is demonstrated at the finest administrative scale of village-level over Jagatsinghpur district in Mahanadi River basin, Odisha (India) for two-time frames: Scenario-I (1970-2011) and Scenario-II (1970-2001). An increase in high and very high hazard and vulnerable villages is noticed in Scenario-I, the majority of them lying in the coastal stretches (S-E region) and adjoining flood plains of Mahanadi River (N-W region). Scenario-I is characterized by the majority of hazard-driven and compound (both hazard and vulnerability) risk villages, while Scenario II is characterized by a majority of vulnerability driven-risk villages. For the vulnerability-driven risk villages, rigorous enforcement of policies and mitigation schemes are recommended, while for hazard-driven risk villages, enhancement of structural measures and flood-plain zoning should be exercised. Such exhaustive flood-risk information may serve as a valuable cartographic product for the civic authorities and stakeholders and help in prioritizing flood mitigation actions for improved environmental planning and management.

Large-scale dynamics have greater role than thermodynamics in driving precipitation extremes over India.

The changing characteristics of precipitation extremes under global warming have recently received tremendous attention, yet the mechanisms are still insufficiently understood. The present study attempts to understand these processes over India by separating the 'dynamic' and 'thermodynamic' components of precipitation extremes using a suite of observed and reanalysis datasets. The former is mainly due to changes in atmospheric motion, while the latter is driven mainly by the changes associated with atmospheric moisture content. Limited studies have attributed dynamic and thermodynamic contributions to precipitation extremes, and their primary focus has been on the horizontal atmospheric motion component of the water budget. Our study, on the other hand, implements the decomposition of vertical atmospheric motion, based on the framework proposed by Oueslati et al. (Sci Rep 9: 2859, 2019), which has often been overlooked, especially for India. With the focus on two major and recent extreme events in the Kerala and Uttarakhand regions of India, we show that the vertical atmospheric motion has a more significant contribution to the events than the horizontal atmospheric motion. Further, decomposition of the vertical atmospheric motion shows that the dynamic component overwhelms the thermodynamic component's contribution to these extreme events, which is found to be negligible. Using a threshold method to define extreme rainfall, we further extended our work to all India, and the results were consistent with those of the two considered events. Finally, we evaluate the contributions from the recently made available CMIP6 climate models, and the results are interestingly in alignment with the observations. The outcomes of this study will play a critical role in the proper prediction of rainfall extremes, whose value to climate adaptation can hardly be overemphasised.

Increased Spatial Variability and Intensification of Extreme Monsoon Rainfall due to Urbanization.

While satellite data provides a strong robust signature of urban feedback on extreme precipitation; urbanization signal is often not so prominent with station level data. To investigate this, we select the case study of Mumbai, India and perform a high resolution (1 km) numerical study with Weather Research and Forecasting (WRF) model for eight extreme rainfall days during 2014-2015. The WRF model is coupled with two different urban schemes, the Single Layer Urban Canopy Model (WRF-SUCM), Multi-Layer Urban Canopy Model (WRF-MUCM). The differences between the WRF-MUCM and WRF-SUCM indicate the importance of the structure and characteristics of urban canopy on modifications in precipitation. The WRF-MUCM simulations resemble the observed distributed rainfall. WRF-MUCM also produces intensified rainfall as compared to the WRF-SUCM and WRF-NoUCM (without UCM). The intensification in rainfall is however prominent at few pockets of urban regions, that is seen in increased spatial variability. We find that the correlation of precipitation across stations within the city falls below statistical significance at a distance greater than 10 km. Urban signature on extreme precipitation will be reflected on station rainfall only when the stations are located inside the urban pockets having intensified precipitation, which needs to be considered in future analysis.

A long-term comparative assessment of human health risk to leachate-contaminated groundwater from heavy metal with different liner systems.

The handling and management of municipal solid waste (MSW) are major challenges for solid waste management in developing countries. Open dumping is still the most common waste disposal method in India. However, landfilling also causes various environmental, social, and human health impacts. The generation of heavily polluted leachate is a major concern to public health. Engineered barrier systems (EBSs) are commonly used to restrict potentially harmful wastes by preventing the leachate percolation to groundwater and overflow to surface water bodies. The EBSs are made of natural (e.g., soil, clay) and/or synthetic materials such as polymeric materials (e.g., geomembranes, geosynthetic clay liners) by arranging them in layers. Various studies have estimated the human health risk from leachate-contaminated groundwater. However, no studies have been reported to compare the human health risks, particularly due to the leachate contamination with different liner systems. The present study endeavors to quantify the human health risk to contamination from MSW landfill leachate using multiple simulations for various EBSs. To quantify the variation in health risks to groundwater consumption to the child and adult populations, the Turbhe landfill of Navi Mumbai in India has been selected. The leachate and groundwater samples were collected continuously throughout January-September in 2015 from the landfill site, and heavy metal concentrations were analyzed using an inductively coupled plasma system. The LandSim 2.5 Model, a landfill simulator, was used to simulate the landfill activities for various time slices, and non-carcinogenic human health risk was determined for selected heavy metals. Further, the uncertainties associated with multiple input parameters in the health risk model were quantified under a Monte Carlo simulation framework.

A facility location model for municipal solid waste management system under uncertain environment.

In municipal solid waste management system, decision makers have to develop an insight into the processes namely, waste generation, collection, transportation, processing, and disposal methods. Many parameters (e.g., waste generation rate, functioning costs of facilities, transportation cost, and revenues) in this system are associated with uncertainties. Often, these uncertainties of parameters need to be modeled under a situation of data scarcity for generating probability distribution function or membership function for stochastic mathematical programming or fuzzy mathematical programming respectively, with only information of extreme variations. Moreover, if uncertainties are ignored, then the problems like insufficient capacities of waste management facilities or improper utilization of available funds may be raised. To tackle uncertainties of these parameters in a more efficient manner an algorithm, based on interval analysis, has been developed. This algorithm is applied to find optimal solutions for a facility location model, which is formulated to select economically best locations of transfer stations in a hypothetical urban center. Transfer stations are an integral part of contemporary municipal solid waste management systems, and economic siting of transfer stations ensures financial sustainability of this system. The model is written in a mathematical programming language AMPL with KNITRO as a solver. The developed model selects five economically best locations out of ten potential locations with an optimum overall cost of [394,836, 757,440] Rs.1 /day ([5906, 11,331] USD/day) approximately. Further, the requirement of uncertainty modeling is explained based on the results of sensitivity analysis.

A Framework for Assessing Uncertainty Associated with Human Health Risks from MSW Landfill Leachate Contamination.

Landfilling is a cost-effective method, which makes it a widely used practice around the world, especially in developing countries. However, because of the improper management of landfills, high leachate leakage can have adverse impacts on soils, plants, groundwater, aquatic organisms, and, subsequently, human health. A comprehensive survey of the literature finds that the probabilistic quantification of uncertainty based on estimations of the human health risks due to landfill leachate contamination has rarely been reported. Hence, in the present study, the uncertainty about the human health risks from municipal solid waste landfill leachate contamination to children and adults was quantified to investigate its long-term risks by using a Monte Carlo simulation framework for selected heavy metals. The Turbhe sanitary landfill of Navi Mumbai, India, which was commissioned in the recent past, was selected to understand the fate and transport of heavy metals in leachate. A large residential area is located near the site, which makes the risk assessment problem both crucial and challenging. In this article, an integral approach in the form of a framework has been proposed to quantify the uncertainty that is intrinsic to human health risk estimation. A set of nonparametric cubic splines was fitted to identify the nonlinear seasonal trend in leachate quality parameters. LandSim 2.5, a landfill simulator, was used to simulate the landfill activities for various time slices, and further uncertainty in noncarcinogenic human health risk was estimated using a Monte Carlo simulation followed by univariate and multivariate sensitivity analyses.

Lack of Dependence of Indian Summer Monsoon Rainfall Extremes on Temperature: An Observational Evidence.

The intensification of precipitation extremes in a warming world has been reported on a global scale and is traditionally explained with the Clausius-Clapeyron (C-C) relation. The relationship is observed to be valid in mid-latitudes; however, the debate persists in tropical monsoon regions, with the extremes of the Indian Summer Monsoon Rainfall (ISMR) being a prime example. Here, we present a comprehensive study on the dependence of ISMR extremes on both the 2 m surface air temperature over India and on the sea surface temperature over the tropical Indian Ocean. Remarkably, the ISMR extremes exhibit no significant association with temperature at either spatial scale: neither aggregated over the entire India/Tropical Indian Ocean area nor at the grid levels. We find that the theoretical C-C relation overestimates the positive changes in precipitation extremes, which is also reflected in the Coupled Model Intercomparison Project 5 (CMIP5) simulations. We emphasize that the changing patterns of extremes over the Indian subcontinent need a scientific re-evaluation, which is possible due to availability of the unique long-term in-situ data. This can aid bias correction of model projections of extremes whose value for climate adaptation can hardly be overemphasized, especially for the developing tropical countries.

Indian Summer Monsoon Rainfall: Implications of Contrasting Trends in the Spatial Variability of Means and Extremes.

India's agricultural output, economy, and societal well-being are strappingly dependent on the stability of summer monsoon rainfall, its variability and extremes. Spatial aggregate of intensity and frequency of extreme rainfall events over Central India are significantly increasing, while at local scale they are spatially non-uniform with increasing spatial variability. The reasons behind such increase in spatial variability of extremes are poorly understood and the trends in mean monsoon rainfall have been greatly overlooked. Here, by using multi-decadal gridded daily rainfall data over entire India, we show that the trend in spatial variability of mean monsoon rainfall is decreasing as exactly opposite to that of extremes. The spatial variability of extremes is attributed to the spatial variability of the convective rainfall component. Contrarily, the decrease in spatial variability of the mean rainfall over India poses a pertinent research question on the applicability of large scale inter-basin water transfer by river inter-linking to address the spatial variability of available water in India. We found a significant decrease in the monsoon rainfall over major water surplus river basins in India. Hydrological simulations using a Variable Infiltration Capacity (VIC) model also revealed that the water yield in surplus river basins is decreasing but it is increasing in deficit basins. These findings contradict the traditional notion of dry areas becoming drier and wet areas becoming wetter in response to climate change in India. This result also calls for a re-evaluation of planning for river inter-linking to supply water from surplus to deficit river basins.

A framework for assessment and characterisation of municipal solid waste landfill leachate: an application to the Turbhe landfill, Navi Mumbai, India.

Rapid industrialisation, growing population and changing lifestyles are the root causes for the generation of huge amounts of solid waste in developing countries. In India, disposal of municipal solid waste (MSW) through open dumping is the most common waste disposal method. Unfortunately, leachate generation from landfill is high due to the prolonged and prominent monsoon season in India. As leachate generation rate is high in most of the tropical countries, long-term and extensive monitoring efforts are expected to evaluate actual environmental pollution potential due to leachate contamination. However, the leachate characterisation involves a comprehensive process, which has numerous shortcomings and uncertainties possibly due to the complex nature of landfilling process, heterogeneous waste characteristics, widely varying hydrologic conditions and selection of analytes. In order to develop a sustainable MSW management strategy for protecting the surface and ground water resources, particularly from MSW landfill leachate contamination, assessment and characterisation of leachate are necessary. Numerous studies have been conducted in the past to characterise leachate quality from various municipal landfills; unfortunately, none of these propose a framework or protocol. The present study proposes a generic framework for municipal landfill leachate assessment and characterisation. The proposed framework can be applied to design any type of landfill leachate quality monitoring programme and also to facilitate improved leachate treatment activities. A landfill site located at Turbhe, Navi Mumbai, India, which had not been investigated earlier, has been selected as a case study. The proposed framework has been demonstrated on the Turbhe landfill site which is a comparatively new and the only sanitary landfill in Navi Mumbai.

Seasonal rationalization of river water quality sampling locations: a comparative study of the modified Sanders and multivariate statistical approaches.

The design of surface water quality sampling location is a crucial decision-making process for rationalization of monitoring network. The quantity, quality, and types of available dataset (watershed characteristics and water quality data) may affect the selection of appropriate design methodology. The modified Sanders approach and multivariate statistical techniques [particularly factor analysis (FA)/principal component analysis (PCA)] are well-accepted and widely used techniques for design of sampling locations. However, their performance may vary significantly with quantity, quality, and types of available dataset. In this paper, an attempt has been made to evaluate performance of these techniques by accounting the effect of seasonal variation, under a situation of limited water quality data but extensive watershed characteristics information, as continuous and consistent river water quality data is usually difficult to obtain, whereas watershed information may be made available through application of geospatial techniques. A case study of Kali River, Western Uttar Pradesh, India, is selected for the analysis. The monitoring was carried out at 16 sampling locations. The discrete and diffuse pollution loads at different sampling sites were estimated and accounted using modified Sanders approach, whereas the monitored physical and chemical water quality parameters were utilized as inputs for FA/PCA. The designed optimum number of sampling locations for monsoon and non-monsoon seasons by modified Sanders approach are eight and seven while that for FA/PCA are eleven and nine, respectively. Less variation in the number and locations of designed sampling sites were obtained by both techniques, which shows stability of results. A geospatial analysis has also been carried out to check the significance of designed sampling location with respect to river basin characteristics and land use of the study area. Both methods are equally efficient; however, modified Sanders approach outperforms FA/PCA when limited water quality and extensive watershed information is available. The available water quality dataset is limited and FA/PCA-based approach fails to identify monitoring locations with higher variation, as these multivariate statistical approaches are data-driven. The priority/hierarchy and number of sampling sites designed by modified Sanders approach are well justified by the land use practices and observed river basin characteristics of the study area.