Abstract

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.