An Approach to Integrate Ground- and Satellite-based Products for Multivariate Hydrometeorological Network Design to Monitor Dry and Wet conditions

In recent decades, the uncertainty associated with characteristics (i.e., frequency, intensity, severity, and duration) of extreme events (e.g., droughts, floods) has increased considerably due to the changing global climatic condition and intensification of anthropogenic activities. Effective in-situ monitoring of the hydrometeorological drivers (e.g., precipitation, temperature) is crucial for precise prediction/forecasting and early warnings to initiate measures for mitigating the adverse effects of these extreme events.  However, due to the increased availability of satellite-based data products and economic constraints, the density of in-situ gauges has reduced drastically over the past few decades. Against this backdrop, this study proposes a multivariate hydrometeorological gauge network design methodology to facilitate integrated monitoring of dry and wet conditions. It harnesses the advantages of multi-objective optimization and fuzzy concepts and involves multi-level clustering and the use of multiple ground- and satellite-based hydrometeorological products.  The multi-level clustering is based on (i) a newly proposed multi-objective Non-dominated Sorting Genetic Algorithm III (NSGA-III) based fuzzy optimization clustering and (ii) fuzzy ensemble clustering. The key stations in the designed network were selected based on the Drought/Wetness Gauge Demand Index (DWGDI), which accounts for the region's drought/wetness characteristics and crop yield.  It also offers scope to consider additional attributes based on the specific purpose of the network design. The potential of the proposed methodology is illustrated through Monte Carlo simulations on a hypothetical region and a case study on Karnataka state (~191,791 km²) in India to arrive at gauge network monitoring three hydrometeorological variables (precipitation, maximum and minimum temperature, and soil moisture). A random forest-based merging procedure is considered to obtain hydrometeorological time-series at ungauged locations using ground-based measurements and multiple gridded/satellite-based products (CRU, CPC, IMD, CHIRPS and IMERG). Overall, the proposed network design methodology appears promising for application to small as well as large data-sparse areas. To the best of our knowledge, this is the first study of its kind, which proposes a multivariate gauge network design procedure for integrated monitoring of dry and wet conditions. The proposed methodology yielded wet/dry condition-specific monitoring networks for the Karnataka state. Additionally, the key stations crucial for monitoring both wet and dry conditions are identified. The counts of precipitation, temperature and soil moisture stations in the network designed for monitoring (i) dry conditions are 1059, 1059, and 552, respectively, (ii) wet conditions are 1144, 1144, and 664, respectively. Stations in the two networks were prioritized by assigning ranks based on DWGDI. The information could be helpful to decision-makers in identifying potential locations for the installation of new gauges accounting for budgetary constraints. The real-time observations from the designed gauge network could be helpful for various purposes, such as better water management to meet irrigation demands, monitoring droughts and floods, and forecasting natural hazards like wildfires, soil erosion, and landslides.