Widespread floods affecting multiple subbasins in a river basin are more disastrous than localized flooding. Understanding the mechanisms, drivers and probability of widespread flooding is pertinent for devising suitable policy measures. Here, we investigate the occurrence and drivers of widespread flooding in seven Indian sub-continental river basins during the observed climate (1959-2020). We use a novel methodology for determining widespread floods and a non-stationary extreme value distribution to identify the mechanisms of widespread flooding. We find that the peninsular river basins have a high probability of widespread flooding, while the transboundary basins of Ganga and Brahmaputra have a low probability. In addition to wet antecedent conditions, the relative rareness of high flows across different subbasins is crucial in explaining the variability of widespread flood probability across different river basins. Our results show that favourable antecedent baseflow and soil moisture conditions, uniform precipitation distribution, and streamflow seasonality determine the seasonality and probability of widespread floods. Further, widespread floods are associated with large atmospheric circulations, resulting in near-uniform precipitation within a river basin. Moreover, we found no significant relation between widespread floods and oceanic circulations. Our findings highlight the prominent drivers and mechanisms of widespread floods with implications for flood mitigation in India.

The results of probabilistic event attribution studies depend on the choice of the extreme value statistics used in the analysis, particularly with the arbitrariness in the selection of appropriate thresholds to define extremes. We bypass this issue by using the Extended Generalized Pareto Distribution (ExtGPD), which jointly models low precipitation with a generalized Pareto distribution and extremes with a different Pareto tail, to conduct daily precipitation attribution across the contiguous United States (CONUS). We apply the ExtGPD to 12 general circulation models from the Coupled Model Intercomparison Project Phase 6 and compare counterfactual scenarios with and without anthropogenic emissions. Observed precipitation by the Climate Prediction Centre is used for evaluating the GCMs. We find that greenhouse gases rather than natural variability can explain the observed magnitude of extreme daily precipitation, especially in the temperate regions. Our results highlight an unambiguous linkage of anthropogenic emissions to daily precipitation extremes across CONUS.

Skillful forecasts of daily streamflow on river networks are crucial for flood mitigation, especially in rainfall-driven river basins. This is of acute importance on the Narmada River Basin in Central India, which is driven by the summer monsoon rainfall, and floods lead to heavy loss of life and infrastructure. Physical hydrologic models based on the land surface model – Variable Infiltration Capacity (VIC), have been developed in an experimental mode to model and forecast the hydrologic system, which includes – storage, soil moisture and runoff – by incorporating rainfall data from the India Meteorological Department (IMD). To enhance the forecast skill by model-combination, we propose a coupled physical-statistical modeling framework. In this, we couple the VIC model with a novel Bayesian Hierarchical Network Model (BHNM) for daily streamflow forecasts that uses the network topology to capture the spatial dependence. The daily streamflow at each station is modelled as Gamma distribution with time-varying parameters. The distribution parameters for each day are modeled as a linear function of covariates, which include antecedent streamflow from upstream gauges and, daily 2-day, or 3-day precipitation from the upstream contributing areas - that reflect the antecedent land conditions. The posterior distribution of the model parameters and, consequently, the predictive posterior distribution of the daily streamflow at each station and for each day are obtained. To the BHNM model, we will couple the VIC model by including the hydrologic forecasts – especially soil moisture and storage – as additional covariates. The coupled model will be demonstrated by its application to daily summer (July-August) streamflow at 4 gauges in the Narmada basin network for the period 1978 – 2014. The skill of the probabilistic forecast will be assessed using rank histograms and skill scores such as CRPS and RPS. The model skill will also be tested on five high flooding events on both the timing and magnitude. These model combinations will enable to combine the strengths of the individual models in capturing the hydrologic processes, biases and nonstationary relationships, to provide skillful daily streamflow forecasts. This will be of immense help in flood mitigation.

The risk of floods has increased in South Asia due to high vulnerability and exposure. The August 2022 Pakistan flood shows a glimpse of the enormity and devastation that can further rise under the warming climate. The deluge caused by the Pakistan floods in 2022, which badly hit the country’s southern provinces, is incomparable to any recent events in terms of the vast spatial and temporal scale. The 2022 Pakistan flood ranked third in human mortality, while this was the top event that displaced about 32 million people. Using observations and climate projections, we examine the causes and implications of the August 2022 flood in Pakistan. Multiday (~ 15 days) extreme precipitation on wet antecedent soil moisture conditions was the primary driver of the flood in August 2022. The extreme precipitation in August was caused by two atmospheric rivers that passed over southern Pakistan. Streamflow simulations from the multiple hydrological models show that extreme precipitation was the primary driver of floods as several stations in the flood-affected regions experienced anomalously higher flow than the stations located upstream. The frequency of similar multi-day extreme precipitation events is projected to rise four-fold under the high emission scenario. The 2022 Pakistan flood highlights the adaptation challenges that South Asia is facing along with the substantial need for climate mitigation to reduce the risk of such events in the future.

A novel Bayesian Hierarchical Network Model (BHNM) for ensemble forecasts of daily streamflow that uses the spatial dependence induced by the river network topology and hydrometeorological variables from the upstream contributing area between station gauges is presented. Model parameters are allowed to vary with time as functions of selected covariates for each day. Using the network structure to incorporate flow information from upstream gauges and precipitation from the immediate contributing area as covariates allows one to model the spatial correlation of flows simultaneously and parsimoniously. An application to daily monsoon period (July-August) streamflow at four gauges in the Narmada basin in central India for the period 1978 – 2014 is presented. The covariates include daily streamflow from upstream gauges or from the gauge above of the upstream gauges depending on travel times and daily, 2-day, or 3-day precipitation from the area between two stations. The model validation indicates that the model is highly skillful relative to climatology and relative to a null-model of linear regression. We applied the BHNM out of sample to two high flooding years. High skill in both the timing and magnitude of the events is demonstrated.

We developed a novel Bayesian Hierarchical Network Model (BHNM) for daily streamflow, which uses the spatial dependence induced by the river network topology, and average daily precipitation from the upstream contributing area between station gauges. In this, daily streamflow at each station is assumed to be distributed as Gamma distribution with temporal non-stationary parameters. The mean and standard deviation of the Gamma distribution for each day are modeled as a linear function of suitable covariates. The covariates include daily streamflow from upstream gauges or from the gauge above of the upstream gauges depending on the travel times, and daily, 2-day, or 3-day precipitation from the area between two stations that attempts to reflect the antecedent land conditions. Intercepts and slopes of the mean and standard deviation parameters are modeled as a Multivariate Normal distribution (MVN) to capture their dependence structure. To ensure that the covariance matrix of MVN is positive definite, it is model as an Inverse Wishart distribution. Non-informative priors for each parameter were considered. Using the network structure in incorporating flow information from upstream gauges and precipitation from the immediate contributing area as covariates, enables to capture the spatial correlation of flows simultaneously and parsimoniously. The posterior distribution of the model parameters and, consequently, the predictive posterior Gamma distribution of the daily streamflow at each station and for each day are obtained. The model is demonstrated by its application to daily summer (July-August) streamflow at 4 gauges in the Narmada basin network in central India for the period 1978 – 2014. The skill of the probabilistic forecast is carried out by rank histograms and the Continuous Ranked Probability Score (CRPS). The model validation indicates that the model is highly skillful relative to climatology and relative to a null-model of linear regression. The forecasts present an adequate spread of uncertainty and non-bias. Since flooding is of major concern in this basin, we applied the BHNM in a cross-validated mode on two high flooding years – in that, the model was fitted on other years, and forecasts were made for the dropped-out high flooding year. The skill of the model in forecasting the high flood events was very good across the network – in both the timing and magnitude of the events. The model will be of immense help to policy makers in risk-based flood mitigation. The BHNM framework is general in nature and can be applied to any river network with other covariates as appropriate.