Climate and land-use changes can alter the dynamics of hydro-climatic extremes by modifying the flow regimes. Here, we have attempted to disentangle the relationship between changing environmental conditions and hydro-climatic extremes considering associated uncertainties for the Subarnarekha, a flood prone-basin of India. A comprehensive, integrated modelling system was developed that incorporates a spatially explicit land-use model, a hydrological model, and an ensemble of regional climate models (RCMs). MIKE SHE/MIKE HYDRO RIVER was used to simulate the hydrological processes. The uncertainties associated with model parameters, model inputs, and model structures are analysed collectively using ‘quantile regression.’ A transferable framework was developed for the analysis of hydro-climatic extremes that deal with numerous aspects like sensitivity, occurrences, severity, and persistence for four-time horizons: baseline (1976–2005) and early (2020s), mid (2050s), end-centuries (2080s). ANOVA is used for partitioning uncertainty due to different sources. The results obtained from numerous analysis of the developed framework suggests that low, high, and medium flows will probably increase in the future (20%-85% increase), indicating a higher risk of floods, especially in the 2050s and 2080s. Partitioning of uncertainty suggests RCMs contribute 40%-62% to the uncertainty in streamflow projections. The developed modelling systems incorporates a flexible framework so update any other water sustainability issue in the future. These findings will help better meet the challenges associated with the possible risk of increasing high flows in the future by ceding references to the decision-makers for framing better prevention measures associated with land-use and climate changes.

气候和土地利用的变化可以通过改变水流状态来改变极端水文气候的动力。在这里，我们考虑到印度洪水多发地区Subarnarekha的相关不确定性，试图解决不断变化的环境条件和极端水文气候之间的关系。开发了一个综合的综合建模系统，该系统整合了空间明确的土地利用模型，水文模型和区域气候模型（RCM）的集合。MIKE SHE / MIKE HYDRO RIVER被用来模拟水文过程。使用“分位数回归”共同分析与模型参数，模型输入和模型结构相关的不确定性。'开发了一个可转移的框架用于分析极端时期的水文气候，涉及灵敏度，发生率，严重性和持续性四个方面的多个方面：基线（1976-2005）和早期（2020s），中期（2050s） ，末个世纪（2080年代）。ANOVA用于划分由于来源不同而导致的不确定性。通过对已开发框架进行的大量分析得出的结果表明，未来低，高和中等流量可能会增加（增加20％-85％），这表明发生洪水的风险更高，尤其是在2050年代和2080年代。不确定性的划分表明，RCM对流量预测的不确定性贡献了40％-62％。开发的建模系统采用了灵活的框架，因此将来会更新任何其他水可持续性问题。