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Rainfall, which is one of the most important hydrologic processes, is influenced by many meteorological factors like climatic change, atmospheric temperature, and atmospheric pressure. Even though there are several stochastic and data driven hydrologic models, accurate forecasting of rainfall, especially smaller time step rainfall forecasting, still remains a challenging task. Effective modelling of rainfall is puzzling due to its inherent erratic nature. This calls for an efficient model for accurately forecasting daily rainfall. Singular Spectrum Analysis (SSA) is a time series analysis tool, which is found to be a very successful data pre-processing algorithm. SSA decomposes a given time series into a finite number of simpler and decipherable components. This study proposes integration of Singular Spectrum Analysis (SSA), Auto Regressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) into a hybrid model (SSA-ARIMA-ANN), which can yield reliable daily rainfall forecasts in a river catchment. In the present study, spatially averaged daily rainfall data over Koyna catchment, Maharashtra has been used. In this study SSA is proposed as a data pre-processing tool to separate stationary and non-stationary components from the rainfall data. Correlogram and Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test has been used to validate the stationary and non-stationary components. In the developed hybrid model, the stationary components of rainfall data are modelled using ARIMA method and non-stationary components are modelled using ANN. The study of statistical performance of the model shows that the hybrid SSA-ARIMA-ANN model could forecast the daily rainfall of the catchment with reliable accuracy.

According to the World Meteorological Organization, since 2000, there has been an increase in global flood-related disasters by 134 percent compared to the previous decades. Efficient flood risk management strategies necessitate a holistic approach to evaluating flood vulnerabilities and risks. Catastrophic losses can occur when the peak flow values in the rivers in a basin coincide. Therefore, estimating the joint flood risks in a region is vital, especially when frequent occurrences of extreme events are experienced. This study focuses on estimating the joint flood risks due to river flow extremes in the Grand River watershed in Canada. For this purpose, the study uses copula analysis to investigate the joint occurrence of extreme river flow events in the Speed and Grand Rivers in the Grand River Watershed in Ontario, Canada. By estimating the joint return period for extreme flows in both rivers, we demonstrate the interdependence of the two river flows and how this interdependence influences the behavior of river flow extreme patterns. Our findings suggest that the interdependence between the two river flows leads to changes in the river flow extreme pattern. Determining the interdependence of floods at multiple locations using state-of-the-art tools will benefit various stakeholders, such as the insurance industry, the disaster management sector, and most importantly, the public.

A thorough understanding of the impact of climatic factors on agricultural production is crucial for improving crop models and enhancing predictability of crop prices and yields. Fluctuations in crop yield and price can have significant implications for the market sector and farming community. Given the projected increase in frequency and intensity of extreme events, reliable modelling of cropping patterns becomes essential. Temperature anomalies are expected to play a prominent role in future extreme events, emphasizing the need to comprehend their influence on crop yield. Forecasting extreme yield, which encompasses both the highest and lowest levels of agricultural production within a given time period, along with peak crop prices representing the highest market values, poses greater challenges in forecasting compared to other values. Probability-based predictions, accounting for uncertainty and variability, offer a more accurate approach for extreme value estimation and risk assessment. In this study, we employ a multivariate analysis based on vine copula to explore the interdependencies between temperature anomalies and daily strawberry yield in Santa Maria, California. By considering the maximum and minimum daily yields each month, we observe an increased probability of yield loss with rising temperature anomalies. While we do not explicitly consider the specific impacts of temperature anomalies under individual Representative Concentration Pathway (RCP) scenarios, our analysis is conducted within the broader context of the current global warming scenario. This allows us to capture the overall anticipated effects of regional temperature anomalies on agriculture. The findings of this study have potential impacts and consequences for understanding the vulnerability of agricultural systems and improving crop model predictions. By enhancing our understanding of the relationships between temperature anomalies and crop yield, we can inform decision-making processes related to the impact of climate change on agriculture. This research contributes to the ongoing efforts in improving agricultural sustainability and resilience in the face of changing climatic conditions.

This study employs the Graph Model for Conflict Resolution (GMCR) to systematically analyze and evaluate potential solutions to disputes arising from the Parambikulam-Aliyar Project (PAP) agreement in India. By incorporating hydrological analysis in the study, the research assesses the potential impacts of proposed solutions on water demand. The GMCR methodology is applied through a comprehensive decision support system (GMCR II), involving the identification of decision-makers, options, and preferences, followed by the development of a conflict resolution model. The analysis is based on a thorough literature review of previous studies on GMCR and PAP systems. The strategic analysis using GMCR II reveals nine stable states, representing feasible resolution scenarios. The study evaluates the real-world implications of various resolution scenarios by assessing their hydrological consequences on demand sites using Water Evaluation and Planning (WEAP). The results provide valuable insights into both conflict resolution and environmental considerations, evaluating various resolution scenarios and their feasibility. The study discusses the practical applicability and long-term effectiveness of the proposed solutions, addressing potential challenges and impacts. For instance, this study examines the potential impacts of new constructions in the PAP system, based on hypothetical data assumptions regarding water divergence and reservoir capacity. The study indicates that such a solution involving new construction can reduce the overall unmet water demand by up to 39%, with a notable decrease of up to 50% in unmet demand for irrigation in Tamil Nadu. However, the study also reveals potential challenges, including a 14% increase in unmet demand for irrigation in Kerala. This study contributes to the existing literature by providing a novel application of GMCR to a complex water management conflict, highlighting its potential to support policymakers in mitigating conflicts and promoting cooperation in the context of transboundary water management. While offering valuable insights into the strategic dynamics of the PAP agreement, the analysis is constrained by limited data availability, such as long-term hydrologic data and real-time water usage data. Future research addressing data scarcity can leverage this study’s framework to develop more robust and actionable management strategies.

Accurate forecasting of rainfall, especially daily time-step rainfall, remains a challenging task for hydrologists' invariance with the existence of several deterministic, stochastic and data-driven models. Several researchers have fine-tuned the hydrological models by using pre-processed input data but improvement rate in prediction of daily time-step rainfall data is not up to the expected level. There are still chances to improve the accuracy of rainfall predictions with an efficient data pre-processing algorithm. Singular spectrum analysis (SSA) is one such technique found to be a very successful data pre-processing algorithm. In the past, the artificial neural network (ANN) model emerged as one of the most successful data-driven techniques in hydrology because of its ability to capture non-linearity and a wide variety of algorithms. This study aims at assessing the advantage of using SSA as a pre-processing algorithm in ANN models. It also compares the performance of a simple ANN model with SSA-ANN model in forecasting single time-step as well as multi-time-step (3-day and 7-day) ahead daily rainfall time series pertaining to Koyna watershed, India. The model performance measures show that data pre-processing using SSA has enhanced the performance of ANN models both in single as well as multi-time-step ahead daily rainfall prediction.

Conceptual development in the theory of externalities have opened up several policy options for their internalization including payment towards environmental services. Hence as externalities are social costs, accountability is crucial in increasing environmental awareness and for collective action through education and extension more so in developing countries. Here a modest attempt has been made to estimate externalities in water, forests and environment with field data from peninsular India to reflect on the economic perception of externalities by farmers and users of environment for the consideration of policy makers to devise institutions for payment towards environmental services. The methodology largely used here in estimation / valuation of externalities is by considering 'with - without' situations (including 'before - after' in some cases) akin to 'project valuation'. Studies cover empirical estimation of externalities inter alia due to over extraction of groundwater , sand mining, watershed development, conservation of forests, sacred groves, cultivation of organic coffee, use of medicinal plants as alternate medicines and the annual values presented are in 2008 prices. The negative externality due to sand mining 24 [euro] per acre, that due to distillery effluent pollution is 34 [euro] per acre. The positive externality due to watershed program is around 51 [euro] per acre, and that due to rehabilitation of irrigation tanks is 26 [euro] per acre. The positive externality due to cultivation of shade coffee is 9 [euro] per acre and that due to forest conservation 27 [euro] per acre. The positive externality due to sacred grove conservation was 12 [euro] per family. The impact of forest conservation on Non timber forest products was 88 [euro] / per tribal household. The positive externality due to use of medicinal plants as alternate medicine is equal to 35 [euro] per patient suffering from osteo-arthritis and 19 [euro] per patient suffering from peptic-ulcer. While these estimates are not sacro sanct as t(This abstract was borrowed from another version of this item.)

Conceptual development in the theory of externalities have opened up several policy options for their internalization including payment towards environmental services. Hence as externalities are social costs, accountability is crucial in increasing environmental awareness and for collective action through education and extension more so in developing countries. Here a modest attempt has been made to estimate externalities in water, forests and environment with field data from peninsular India to reflect on the economic perception of externalities by farmers and users of environment for the consideration of policy makers to devise institutions for payment towards environmental services. The methodology largely used here in estimation / valuation of externalities is by considering 'with - without' situations (including 'before - after' in some cases) akin to 'project valuation'. Studies cover empirical estimation of externalities inter alia due to over extraction of groundwater , sand mining, watershed development, conservation of forests, sacred groves, cultivation of organic coffee, use of medicinal plants as alternate medicines and the annual values presented are in 2008 prices. The negative externality due to sand mining 24 [euro] per acre, that due to distillery effluent pollution is 34 [euro] per acre. The positive externality due to watershed program is around 51 [euro] per acre, and that due to rehabilitation of irrigation tanks is 26 [euro] per acre. The positive externality due to cultivation of shade coffee is 9 [euro] per acre and that due to forest conservation 27 [euro] per acre. The positive externality due to sacred grove conservation was 12 [euro] per family. The impact of forest conservation on Non timber forest products was 88 [euro] / per tribal household. The positive externality due to use of medicinal plants as alternate medicine is equal to 35 [euro] per patient suffering from osteo-arthritis and 19 [euro] per patient suffering from peptic-ulcer. While these estimates are not sacro sanct as the methodologies for valuation of externalities are subject to further review and improvement, they however serve as initial indicators of spillovers. And they signal possibilities for consideration of policy makers for devising alternate institutions for potential payment towards environmental services.