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Climate change and anthropogenic influences amplify drought complexity, entangle non‐stationarity (NS) and further challenge drought comprehension. This study aims to understand the dynamic evolution of drought propagation patterns due to climatic and anthropogenic pressures by assessing the non‐stationary linkages between hydrological variables and drought characteristics. It employs four standardized drought indicators to comprehensively examine the spatio‐temporal evolution of meteorological (MD) and hydrological (HD) drought characteristics. Data from 29 semi‐arid catchments from six river basins in Peninsular India, are analyzed to uncover distinct drought propagation patterns. This study utilizes a novel Non‐overlapping Block‐stratified Random Sampling (NBRS) approach to detect NS in drought characteristics and hydrological variables, shedding light on the underlying drivers of this dynamic behavior. The results indicate similarities in drought behavior for the Sabarmati, Mahi and Tapi (SMT) basins compared with the Godavari, Krishna and Pennar (GKP) basins, with shorter (longer) propagation times noted for SMT (GKP) basins. While HD severity decreases over time in SMT basins, it intensifies in GKP basins, which are linked to intensive anthropogenic interventions such as river regulation and reservoir operations, thus resulting in prolonged and intensified droughts. Rainfall primarily exhibits time‐invariance, while significant NS is observed in potential evapotranspiration (particularly in the Krishna and Pennar basins), streamflow and baseflow across all basins. The study also identified three distinct drought propagation patterns in these basins, highlighting cases where MD did not transition to HD, instances of HD occurring without preceding MD and synchronous propagation of MD to HD. The study outcomes provide profound insights into the evolution of drought dynamics under climatic and anthropogenic pressures, which will aid policymakers and stakeholders in formulating strategies for drought preparedness and response.
Climate change and anthropogenic influences amplify drought complexity, entangle non‐stationarity (NS) and further challenge drought comprehension. This study aims to understand the dynamic evolution of drought propagation patterns due to climatic and anthropogenic pressures by assessing the non‐stationary linkages between hydrological variables and drought characteristics. It employs four standardized drought indicators to comprehensively examine the spatio‐temporal evolution of meteorological (MD) and hydrological (HD) drought characteristics. Data from 29 semi‐arid catchments from six river basins in Peninsular India, are analyzed to uncover distinct drought propagation patterns. This study utilizes a novel Non‐overlapping Block‐stratified Random Sampling (NBRS) approach to detect NS in drought characteristics and hydrological variables, shedding light on the underlying drivers of this dynamic behavior. The results indicate similarities in drought behavior for the Sabarmati, Mahi and Tapi (SMT) basins compared with the Godavari, Krishna and Pennar (GKP) basins, with shorter (longer) propagation times noted for SMT (GKP) basins. While HD severity decreases over time in SMT basins, it intensifies in GKP basins, which are linked to intensive anthropogenic interventions such as river regulation and reservoir operations, thus resulting in prolonged and intensified droughts. Rainfall primarily exhibits time‐invariance, while significant NS is observed in potential evapotranspiration (particularly in the Krishna and Pennar basins), streamflow and baseflow across all basins. The study also identified three distinct drought propagation patterns in these basins, highlighting cases where MD did not transition to HD, instances of HD occurring without preceding MD and synchronous propagation of MD to HD. The study outcomes provide profound insights into the evolution of drought dynamics under climatic and anthropogenic pressures, which will aid policymakers and stakeholders in formulating strategies for drought preparedness and response.
Heavy rainfall during flood seasons has become more concentrated, whereas rainfall during dry seasons has decreased, owing to the impacts of climate change. Despite similar average annual precipitation, this phenomenon has led to the occurrence of stronger and more localized droughts. To address this issue, the Korea Water Resources Corporation (K-water) adopted the K-water disaggregation method (KDM) to manage dam operations efficiently by considering regional characteristics. KDM calculates the monthly inflow for the upcoming year using the correlation between the annual and monthly inflow frequency analysis results. However, the current implementation of KDM, which provides a single scenario, often exhibits significant discrepancies from the observed inflow. To overcome this limitation, this study proposes incorporating uncertainty through a disaggregation model to enhance the accuracy of inflow estimation. Thus, a wider range of inflow scenarios can be considered, thereby enhancing the strategy for dam operations. This study compared the inflow scenarios generated by two different methods and assessed the corresponding drought coping capacities expected from operating dams under these scenarios. The drought coping capacity assessment included calculating the Supply-day (S-day) and dam storage performance measures. The results indicated that KDM generally showed a lower S-day during the water supply seasons and exhibited a lower dam storage performance than the disaggregation method inflow scenarios. Consequently, the single scenario provided by KDM may distort the potential inflow scenarios for the target dam. Considering a range of monthly scenarios for an annual drought quantile proved advantageous for assessing drought coping capacity. This study issues a broader warning, not only for Korea but also for other countries, about the risks of relying on a single scenario for determining next-year drought inflows, which may increase the likelihood of encountering more severe droughts than anticipated.
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