A Novel Framework for Homogeneous Climate Regionalisation using Advanced State Space Modeling and Ensemble Fuzzy Clustering

Increasing anthropogenic activities in the post-industrial era, coupled with variability in natural forcings (e.g., solar radiation, volcanic eruption) and changes in geomorphological characteristics make the climate highly non-stationary in nature. This hinders effective climate projections, adaptation and mitigation strategies for extreme weather events, hydraulic structure planning, and irrigation activity. Regionalization, which is the process of demarcating regions of similar hydroclimatic characteristics, is therefore essential for water resources planning and management. However, there are no existing approaches which take into account the non-stationarity inherent in the hydroclimatic variables (e.g., precipitation, temperature, humidity, water level) during the process of regionalization. The most widely used feature based clustering techniques involve identifying key static attributes of the hydroclimatic time series to identify dominant patterns. However, these methods often fail to capture the temporal dynamics and evolving non-stationary characteristics of the climate variables, which is a major concern in the era of climate change. To address this research gap, this study integrates two major objectives - (a) develop a novel model based regionalization procedure that accounts for non-stationarity in the hydroclimatic time series, and (b) evaluate the performance of the proposed methodology against the existing regionalization approaches using a real world case study for the Indian subcontinent. 

By coupling the Latent Gaussian State Space Models (LGSSM) with advanced fuzzy ensemble clustering techniques, the proposed methodology aims to capture this inherent non-stationarity of the hydroclimatic data, yielding better domain informed homogeneous regions. Largely used in the field of data science for future data predictions and grouping; the LGSSM model is a parametric model with sufficient flexibility which can effectively describe the non-stationary climate variables in the Euclidean Space. Further, fuzzy ensemble clustering techniques aggregate results from multiple clustering realizations, mitigating the biases inherent in any single clustering approach and incorporate fuzzy set theory by assigning membership degrees to each study area grid. Cluster validity indices such as the Dunn Index and Davies-Bouldin Index are used to find the optimal number of clusters based on intra cluster compactness and inter cluster separation. 

Hydroclimatic datasets (eg., IMD data, ERA5 reanalysis data) are obtained at 0.25x0.25 degrees spatial and daily temporal frequency for the Indian subcontinent. The methodology identified K=10 and K=6 optimum number of clusters for precipitation and temperature respectively. Final homogeneous regions are delineated by integrating topographical features such as distance from sea, elevation etc. The identified major climate regions are - (a) Northern Cold Himalayan Zone, (b) Thar Desert Area, (c) Indo-Gangetic Plain, (d) Southern Peninsular Region, (e) Western Ghats Area and (f) Dry Semi-Arid Zone. These regions are validated using regional homogeneity tests such as HoskinWallish Test. This study is the first to integrate the advanced state space modeling with fuzzy ensemble clustering for climatic regionalization, making a paradigm shift in hydrology research, from solely relying on basin-scale boundaries to an integrated approach that considers both atmospheric and physiographic boundaries. This proposed methodology provides a ready to use powerful tool for homogeneous regionalization and future projections of complex non-stationary hydroclimatic variables.