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Climate model, a complex numerical representation of the global climate system, has been developed to simulate current climate and used to project future climatic conditions. Simulated climatic variables from climate models often exhibit significant deviations from observations. In climate projections, different approaches were introduced to deal with systematic deviations and random model errors. This paper demonstrates the intercomparison of four bias correction approaches (linear scaling, delta change correction, distribution mapping, and variance scaling) underlying the assumptions of stationary output from climate models. Mean monthly temperatures derived from five global climate models were corrected by four bias correction approaches for five states of southern India. The suitability of correction approaches depends upon the climate models and regional framework. The applied approaches improve the mean values and other statistical properties. The results show that all four bias techniques significantly improved the simulated data, but distribution mapping and variance scaling were more effective in removing systematic model biases. [ABSTRACT FROM AUTHOR]

Climate change (precipitation and temperature) has significantly affected the hydrological regimes and future climate projection. Integration of climate model with physical based model is crucial for quantitative measurement of changes in surface water regime. For accurate estimation, modelling framework need finer scale resolution of climate model output. In this study, we examined the bias corrected, statistically downscale models drawn from the NASA, Earth Exchange Global Daily Downscaled Projections–Coupled Model Intercomparison Project Phase 5 (NEX-GDDP-CMIP5) over the study region. The rainfall and temperature projection output from the INMCM-4, MRI-CGCM3 and their ensemble mean performed well over the Mahi River basin (MRB), India. In this study, the climate data integrated with the SWAT model to analyse the potential impact of climate change on the discharge of MRB. The finding indicates that in the near future (2011–2040) projection of annual average streamflow increases by 76.74% based on the INMCM-4 outputs, 25% based on the MRI-CGCM3 outputs, and 24.53% based on the ensemble mean in comparison to the baseline period (1981–2010). Further, the modelling results of mean monthly streamflow in rainy season indicated that the lowest and highest streamflow changes will be ranging from about 631.07–2718.42 m3/s as observed by INMCM-4, 491.71–2938 m3/s observed by MRI-CGCM3, 513.02–2270.18 m3/s observed by ensemble mean, in the near future. Similarly, in the summer season, the lowest level of stream flow is found to be 158.27 m3/s observed by MRI-CGCM3, 193.38 m3/s (ensemble mean) and 258.53 m3/s (INMCM-4), respectively. Additionally, the streamflow trend was assessed by Mann–Kendall and Sen's slope method at the monthly, seasonal and annual scales. The future streamflow projection represented the ascending trend observed in south west and winter monsoon, while the descending trend was observed in pre-monsoon and post-monsoon under the INMCM-4, MRI-CGCM3, and ensemble mean. Results on projected precipitation, temperature and streamflow accretion would help to develop effective adaptation measures for reducing the impacts of climate change and to work out long-term water resource management plans in the river basin. [ABSTRACT FROM AUTHOR]

We investigate the extent to which climate change will result in insurable and uninsurable losses for farmers in India. Changes in temperature and precipitation patterns may increase the volatility of farmers' yields, leading to rising but insurable risk. These changes may also reduce the expected yield in an 'average year', causing uninsurable reductions in the returns to farming. We use a multi-run climate model to predict the future distribution of potential yields at the district level for sixteen major crops. For the average district, we project a sharp decline in expected agricultural revenue, but small shifts in volatility. This is because weather draws resulting in extremely low agricultural returns—what had once been 1-in-100-year events—are predicted to become the norm by the end of the century. Our projections therefore imply substantial uninsurable losses from the changing climate for Indian farmers. [ABSTRACT FROM AUTHOR]

Projections of future streamflow extremes are subjected to various sources of uncertainties. Uncertainties arising from different stages in the modelling process of extremes contribute to the overall uncertainty in the final estimates. In this study, some important sources of uncertainties that occur in the modelling chain for projection of future extremes are analysed. The contribution of climate models, hydrological models, representative concentration pathways (RCPs) and the parameters of the extreme value distribution to the overall uncertainty in the streamflow return levels of the Chaliyar river basin in Kerala, India, was quantified using analysis of variance. 5, 10, 25, and 50-year return levels for the near (2021–2050) and far (2070–2099) future periods were computed. Results of the study indicate that the median values of return levels for the near (0.6–13.5%) and far (21.5–37.3%) future periods are higher than the corresponding values for the observed data. Also the uncertainty ranges of the return levels in the near (2–2.4 times) and far (3–3.7 times) future for different return periods are higher compared to that of the observed. The contribution to uncertainties in the return levels from climate models, parameters of the extreme value distribution employed and hydrological models is higher than that from the RCPs. The contribution of parameter uncertainty increases with the return period and that of the climate models reduces with the return period for the two future periods considered in this study, indicating that parameter uncertainty is an important source of uncertainty and has to be considered while predicting the future extremes. The contribution to uncertainty by the hydrological models and by the RCPs does not exhibit much variation for different return periods in both the future time periods considered. Understanding the uncertainties in future extremes would help to devise proper adaptation strategies to mitigate the probable impacts of climate change. [ABSTRACT FROM AUTHOR]

Intensification of hydrologic cycle, and consequence rise of intense short-term precipitation, are considered as the manifestations of climate change. This may lead to an alteration in Intensity–Duration–Frequency (IDF) relationship that may change other hydrological processes as well. The IDF relationship also serves as a crucial information for the design of any water infrastructure. This study investigates the spatiotemporal changes in IDF relationship involving hourly precipitation events between past and future climate at various return periods across India that spans over a wide range of climatology. Contrast between historical (1979–2014), using two reanalysis data, and future periods (immediate future: 2015–2039, near-future: 2040–2059 and far-future: 2060–2100) is explored along with its spatial (re-) distribution. The future simulations of precipitation are derived from three climate models, participating in 6th phase of Coupled Model Intercomparison Project (CMIP6), for three shared socio-economic pathways (SSPs), i.e., SSP126, SSP245 and SSP585. The results show that almost entire Indian mainland will experience an increase (~41–44%) in the hourly precipitation intensity under the worst climate change scenario (SSP585) with a return period as low as 2 years (almost a regular incidence). Furthermore, even under a moderate climate change scenario (SSP245), almost entire Indian mainland (~82–99% of spatial extent) will be affected from a significant increase (on an average 19%) in the hourly precipitation intensity. It is true for higher return periods as well. Findings of the study are alarming for many water infrastructures. This study develops new set of IDF curves across India considering a changing climate that will be useful to set a revised design criteria for water infrastructure. [ABSTRACT FROM AUTHOR]