This paper presents an efficient and reliable swarm intelligence-based approach, namely elitist-mutated particle swarm optimization (EMPSO) technique, to derive reservoir operation policies for multipurpose reservoir systems. Particle swarm optimizers are inherently distributed algorithms, in which the solution for a problem emerges from the interactions between many simple individuals called particles. In this study the standard particle swarm optimization (PSO) algorithm is further improved by incorporating a new strategic mechanism called elitist-mutation to improve its performance. The proposed approach is first tested on a hypothetical multireservoir system, used by earlier researchers. EMPSO showed promising results, when compared with other techniques. To show practical utility, EMPSO is then applied to a realistic case study, the Bhadra reservoir system in India, which serves multiple purposes, namely irrigation and hydropower generation. To handle multiple objectives of the problem, a weighted ap...

This review provides a broad overview of the current state of flood research, current challenges, and future directions. Beginning with a discussion of flood-generating mechanisms, the review synthesizes the literature on flood forecasting, multivariate and nonstationary flood frequency analysis, urban flooding, and the remote sensing of floods. Challenges and future flood research directions are outlined and highlight emerging topics where more work is needed to help mitigate flood risks. It is anticipated that the future urban systems will likely have more significant flood risk due to the compounding effects of continued climate change and land-use intensification. The timely prediction of urban floods, quantification of the socioeconomic impacts of flooding, and developing mitigation strategies will continue to be challenging. There is a need to bridge the scales between model capabilities and end-user needs by integrating multiscale models, stakeholder input, and social and citizen science input for flood monitoring, mapping, and dissemination. Although much progress has been made in using remote sensing for flood applications, recent and upcoming Earth Observations provide excellent potential to unlock additional benefits for flood applications. The flood community can benefit from more downscaled, as well as ensemble scenarios that consider climate and land-use changes. Efforts are also needed for data assimilation approaches, especially to ingest local, citizen, and social media data. Also needed are enhanced capabilities to model compound hazards and assess as well as help reduce social vulnerability and impacts. The dynamic and complex interactions between climate, societal change, watershed processes, and human factors often confronted with deep uncertainty highlights the need for transdisciplinary research between science, policymakers, and stakeholders to reduce flood risk and social vulnerability.

This paper presents a methodology to downscale monthly precipitation to river basin scale in Indian context for special report of emission scenarios (SRES) using Support Vector Machine (SVM). In the methodology presented, probable predictor variables are extracted from (1) the National Center for Environmental Prediction (NCEP) reanalysis data set for the period 1971–2000 and (2) the simulations from the third generation Canadian general circulation model (CGCM3) for SRES emission scenarios A1B, A2, B1 and COMMIT for the period 1971–2100. These variables include both the thermodynamic and dynamic parameters and those which have a physically meaningful relationship with the precipitation. The NCEP variables which are realistically simulated by CGCM3 are chosen as potential predictors for seasonal stratification. The seasonal stratification involves identification of (1) the past wet and dry seasons through classification of the NCEP data on potential predictors into two clusters by the use of K-means clustering algorithm and (2) the future wet and dry seasons through classification of the CGCM3 data on potential predictors into two clusters by the use of nearest neighbour rule. Subsequently, a separate downscaling model is developed for each season to capture the relationship between the predictor variables and the predictand. For downscaling precipitation, the predictand is chosen as monthly Thiessen weighted precipitation for the river basin, whereas potential predictors are chosen as the NCEP variables which are correlated to the precipitation and are also realistically simulated by CGCM3. Implementation of the methodology presented is demonstrated by application to Malaprabha reservoir catchment in India which is considered to be a climatically sensitive region. The CGCM3 simulations are run through the calibrated and validated SVM downscaling model to obtain future projections of predictand for each of the four emission scenarios considered. The results show that the precipitation is projected to increase in future for almost all the scenarios considered. The projected increase in precipitation is high for A2 scenario, whereas it is least for COMMIT scenario. Copyright © 2007 Royal Meteorological Society