REVIEW OF HYDROCLIMATIC TELECONNECTION BETWEEN HYDROLOGIC VARIABLES AND LARGE-SCALE ATMOSPHERIC CIRCULATION PATTERNS WITH INDIAN PERSPECTIVE

Understanding non-stationarity of hydroclimatic extremes and resilience in Peninsular catchments, India

Climate change significantly impacts the global hydrological cycle, leading to pronounced shifts in hydroclimatic extremes such as increased duration, occurrence, and intensity. Despite these significant changes, our understanding of hydroclimatic risks and hydrological resilience remains limited, particularly at the catchment scale in peninsular India. This study aims to address this gap by examining hydroclimatic extremes and resilience in 54 peninsular catchments from 1988 to 2011. We initially assess extreme precipitation and discharge indices and estimate design return levels using non-stationary Generalized Extreme Value (GEV) models that use global climate modes (ENSO, IOD, and AMO) as covariates. Further, hydrological resilience is evaluated using a convex model that inputs simulated discharge from the best hydrological model among SVM, RVM, random forest, and a conceptual model (abcd). Our analysis shows that the spatial patterns of mean extreme precipitation indices (R1 and R5) mostly resemble with extreme discharge indices (Q1 and Q5). Additionally, all extreme indices, including R1, Q1, R5, and Q5, demonstrate non-stationary behavior, indicating the substantial influence of global climate modes on extreme precipitation and flooding across the catchments. Our results indicate that the random forest model outperforms the others. Furthermore, we find that 68.52% of the catchments exhibit low to moderate hydrological resilience. Our findings emphasize the importance of understanding hydroclimatic risks and catchment resilience for accurate climate change impact predictions and effective adaptation strategies.

Revealing alarming changes in spatial coverage of joint hot and wet extremes across India

Compared to any single hydroclimatic variable, joint extremes of multiple variables impact more heavily on the society and ecosystem. In this study, we developed new joint extreme indices (JEIs) using temperature and precipitation, and investigated its spatio-temporal variation with observed records across Indian mainland. Analysis shows an alarming rate of change in the spatial extent of some of the joint extreme phenomena, tending to remain above normal. For example, above normal hot nights and wet days events expands at a rate of 0.61% per year considering entire Indian mainland. If the historical trend continues at the same rate, consecutive cold and wet day events will drop below the threshold of mean value observed in the base line period (1981-2010) everywhere in the country by the end of the twenty-first century. In contrast, the entire country will be covered by hot nights and wet days events only (frequency of occurrence will cross the threshold of mean value observed in the base line period). This observation is also supported by the CMIP6 climate model outputs. It is further revealed that extremes of any single variable, i.e. either precipitation or temperature (e.g., Extreme Wet Days, Consecutive Wet Days, Hot Nights, and Cold Spell Duration Index), do not manifest such an alarming spatial expansion/contraction. This indicates that the consideration of the joint indices of hydroclimatic variables is more informative for the climate change impact analysis.

Variability, trends, and teleconnections of stream flows with large-scale climate signals in the Omo-Ghibe River Basin, Ethiopia

This study assesses variability, trends, and teleconnections of stream flow with large-scale climate signals (global sea surface temperatures (SSTs)) for the Omo-Ghibe River Basin of Ethiopia. Fourteen hydrological indices of variability and extremes were defined from daily stream flow data series and analyzed for two common periods, which are 1972-2006 for 5 stations and 1982-2006 for 15 stations. The Mann-Kendall's test was used to detect trends at 0.05 significance level, and simple correlation analysis was applied to evaluate associations between the selected stream flow indices and SSTs. We found weak and mixed (upward and downward) trend signals for annual and wet (Kiremt) season flows. Indices generated for high-flow (flood) magnitudes showed the same weak trend signals. However, trend tests for flood frequencies and low-flow magnitudes showed little evidences of increasing change. It was also found that El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) are the major anomalies affecting stream flow variability in the Omo-Ghibe Basin. The strongest associations are observed between ENSO/Niño3.4 and the stream flow in August and September, mean Kiremt flow (July-September), and flood frequency (peak over threshold on average three peaks per year (POT3_Fre)). The findings of this study provide a general overview on the long-term stream flow variability and predictability of stream flows for the Omo-Ghibe River Basin.