An accurate assessment of hydrometeorological variables/ observations over an urban area is crucial to policy-makers and civic bodies to address an extensive range of water resources and environmental problems for informed decision-making related to the water distribution system and drainage networks. This necessitates the establishment of hydrometeorological monitoring networks that can efficiently obtain consistent and reliable information about the spatiotemporal variability of multiple hydrometeorological observations while being economically sustainable. However, the urban catchments especially in underdeveloped and developing countries are often subjected to spatial, environmental as well as monetary limitations which hinders the application of conventional approaches followed to set up the hydrometeorological networks. With this context, we propose a novel rationalization framework to record numerous hydrometeorological variables and acquire maximum information at an optimal cost. We have attempted to combine a multivariate statistical technique, Principal Component Analysis (PCA) with a multi-attribute decision-making method, Technique for Order of Preference by Similarity to Ideal Solutions (TOPSIS) to rank the significant hydrometeorological stations of an existing Automatic Weather Stations (AWS) network. It is observed that the set of rationalized AWS network obtained from this framework can capture the spatiotemporal information of the hydrometeorological variables considered in this study as efficiently as the entire AWS network. Additionally, the comparison of flood inundation and hazard maps derived from a 3-way coupled hydrodynamic flood modeling framework for the rationalized and original network also reflects its credibility to capture the flooding characteristics for the catchment. This proposed framework has been applied over Mumbai city, India, a major flood-prone area, and is characterized by high spatiotemporal variability of hydro-meteorological observations and space constraints due to dense population. This framework is generic and can be employed to reevaluate the prevailing hydro-meteorological networks in other catchments and help in the reduction of the maintenance cost while efficiently capturing the variability of observations.

With the rapid rise in flooding events induced by climate change across the globe, effective flood management strategies through modelling have garnered attention over the years. In the present study, we propose a holistic hydrodynamic flood modelling framework to derive the flooding extent. Various hydraulic scenarios are integrated into the framework which consider different combinations of cross-section and lining material options along the river channel for this purpose. A 3-way coupled flood model has been developed in MIKE FLOOD platform, over Mithi river catchment an extremely flood-prone area in Mumbai, the financial capital of India. Flood influencers such as precipitation, flows through the channel, overland, storm-water drains, and tidal influences are considered to generate flood inundation and hazard maps for the scenarios. The dearth of data in the model is met by implementing alternate robust procedures to compute the design values of the influencers. Subsequently, the maps are derived for different return periods of design precipitation, tidal elevation and streamflow values to identify the most desirable scenario. The proposed framework efficiently determines that the scenario having trapezoidal river cross-section with concrete lining material maximizes the decrease in spatial extent of flood in comparison to the other scenarios. This user-friendly generic approach can be potentially executed as an effective flood mitigation option in thickly populated and socially vulnerable regions where lack of space limit the implementation of structural measures for flood management. The framework can prove instrumental particularly for the developing and under-developed countries where application of these strategies is hindered by inadequacy of data.

Hydrodynamic flood modeling is computationally complex and data-intensive. The accuracy of the flood model outputs is extremely sensitive towards the quality of input parameters. These input parameters are static (mostly geomorphic) and dynamic (mostly hydrometeorological). Sensitivity analysis helps to identify the importance of each input and subsequently improves model accuracy. In various past studies, the sensitivity of only dynamic input parameters was highlighted. Moreover, the sensitivity analysis was limited to flooding of the channel (1D) or floodplain (2D) but never coupled. The present study focuses on developing a framework for global sensitivity analysis of static input parameters in a 1D-2D coupled hydrodynamic flood model, based on HEC-RAS, an open-source flood modeler developed by the U.S. Army Corps of Engineers. A set of numerical experiments was conducted in the model by perturbing the static input parameters from their standard or surveyed values to generate flow hydrographs. The Kullback-Leibler entropy was used as a metric to quantify sensitivity and was calculated by comparing non-parametric probability density functions (PDFs) of the river discharge at different locations. A Gaussian kernel PDF is found most appropriate in a goodness of fit test than other distributions. A highly flood-prone and densely populated river catchment of the Ganges basin in India, which suffers economic and life losses every monsoon, was selected to demonstrate the proposed framework. This study is the first attempt at a global sensitivity analysis in a 1D-2D coupled flood modeling system, concluding that the sensitivity of static input parameters is highly dynamic, and their importance varies spatially from u/s to d/s of the river. However, the channel roughness and land use classes were found significantly sensitive throughout the river. It is suggested that a flood modeling exercise should accompany a global sensitivity analysis, which will guide flood modelers to identify the sensitive input parameters that one should emphasize during data collection and application. Such effort ensures improved accuracy of the static input parameters resulting in better accuracy of the outputs. The proposed framework is generic and can be implemented for any river catchment prior to flood hazard and risk analyses.