We present a framework for forecasting water storage requirements in the agricultural sector and an application of this framework to water risk assessment in India. Our framework involves defining a crop-specific water stress index and applying a particular statistical forecasting model to predict seasonal water stress for the crop of interest. The application focused on forecasting crop water stress for potatoes grown during the monsoon season in the Satara district of Maharashtra.

A global assessment of flood events using the Dartmouth Flood Observatory (DFO) database is performed here to explore the planetary nature of the trends in the frequency and duration of floods (short, moderate, and long). This comprehensive study is the very first global study of actual flood events which identifies temporal changes in frequencies and characteristics of probability distribution of flood durations to understand the changing organization of the local to global dynamical systems.

We develop a hierarchical Bayesian (HB) model to reduce uncertainties in flood frequency analysis and explore suitable mapping function linking GEV parameters with drainage area. The results of HB model are compared with ordinary GEV model and index flood method.The application shows HB model provides more adequate confidence intervals than other used methods. This model also provides a better way for using fragmented data with varying lengths for analyzing spatial distribution of flood frequency.

Floods are the main natural disaster in Brazil, causing substantial economic damage and loss of life. Here we seek to better understand the flood-generating mechanisms in the flood-prone Paraná River basin, including large-scale patterns of the ocean and atmospheric circulation. This study provides new insights for understanding causes of floods in the region and around the world and is a step forward to improve flood risk management, statistical assessments, and short-term flood forecasts.

While water-risk management plans for economic assets are the norm on a local basis, it is possible that there is correlation in the climate induced portfolio water risk across operational sites. Therefore, from an investor's perspective, a need exists for a water risk index that allows for an exploration of the possible space and/or time clustering in exposure across many sites. This paper represents an attempt to develop such an index using long daily global modelled rainfall data sets.

We examined the potential to use the basin-scale rainfall to directly model the streamflow and evaluate the effect of dam operations on summer flow risk over Yangtze River Basin. The result show that floods and droughts experienced in the post dam period were amplified, driven, or alleviated. The approach demonstrated here enabled one to develop and test both the rainfall induced variations and changes due to human activities on a river.

This paper studies the nexus of air moisture, heavy rain and floods in the Northeast USA, with key research questions of how the moisture from the tropics move to the region, how they are related to heavy rain and floods in the area for different seasons, and what are the roles of atmosphere and climate. The study demonstrates the utility of statistics to understand physical system. It provides a statistically and physically based framework that similar studies can be conducted for other areas.