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This paper presents a validation of outputs from some GCMs of the CMIP6 project when used to assess climate projection and hydrological flows at a catchment scale for the case study area of the Lombardy region (Northern Italy). The modeling chain consists of (i) a choice of climatic scenarios from 10 GCMs of the CMIP6, (ii) the application of a stochastic downscaling procedure to make projections usable at the local scale, and (iii) the use of a semi-distributed physically based hydrological model Poli-Hydro for the generation of hydrological scenarios. Data on observed precipitation and temperature were collected from automatic weather stations, and the hydrological budget of four target catchments within the study area was assessed using Poli-Hydro. An ex-post (back-casting) analysis was performed upon the control data series from the GCMs by comparing statistics of relevant climate variables and model-simulated discharges against observed counterparts during the historical period 2002–2014. Then, during 2015–2021, the goodness of projections was assessed using confidence intervals. Our results show that the accuracy of GCMs in representing regional climate is not always reflected in a credible evaluation of local hydrology. The validation of climate patterns provides somewhat poor results; thus, the interaction among climate and hydrology needs to be explored carefully to warrant the credibility of hydrological scenarios. Overall, the spatial and temporal consistency of GCM projections, as explored here climatically and hydrologically, provides a clue about their dependability for basin scale management. [ABSTRACT FROM AUTHOR]

This paper studies the Upper Tigris River (UTR) drainage basin in Turkey for climate change impacted runoff estimations. Statistical downscaling method (SDM) is used by taking into consideration spatial dependence function (SDF) for the scenario precipitation projections at a set of available meteorology stations. Temporal adjustment between the climate scenarios and precipitation record time series is achieved by the white Markov (WM) stochastic process. Although various climate research center scenario data are considered, herein, only the general circulation model (GCM) A2 scenario data are adapted from the Hadley Center, England. The precipitation and runoff results are presented in decadal groups starting from 2001 to 2050 as cumulative monthly precipitation (CMP) and cumulative monthly runoff (CMR) graphs. It is observed that after 2021, precipitation decreases at about 12.5% and after 2030, it is 26%. Runoff projections indicate that they may decrease at about 30% especial after 2040. [ABSTRACT FROM AUTHOR]

Climate change and its impacts at global warming of 1.5 °C and 2 °C are major concerns of the international community. Based on the multi-model dynamical downscaling outputs, by using the quantile–quantile (Q-Q) adjustment method to correct the biases for the future periods, projections are made for the variation trend of the extreme precipitation events over the middle and upper Yangtze River in the context of global warming of 1.5 °C and 2 °C. The results show that the Q-Q adjustment method significantly reduces the bias of simulated extreme precipitation. In the dynamical downscaling simulation over the middle and upper Yangtze River, the bias of the number of rainy days for daily precipitation exceeding 25 mm (R25) is reduced from 6 to 1d after correction, and the 95th percentile of precipitation (pq95) is reduced from 40 to 10% over the whole region. At 1.5 °C and 2 °C warming thresholds, precipitation will be more extreme, with increasing frequency and much greater intensity. Under the RCP4.5 scenario, the precipitation intensity (SDII), extreme precipitation contribution rate (pfl95), R25, and pq95 increase more at 2 °C than at 1.5 °C warming level, but the maximum consecutive 1-day and 5-day precipitation (R1d, R5d) increase slightly less. Under the RCP8.5 scenario, SDII, pq95, R1d, and R5d increase more at 2 °C than at 1.5 °C warming target, but pfl95 and R25 increase slightly less. From the perspective of spatial distribution, the future changes of each extreme precipitation indices show significant regional differences. All show a significant increasing trend in the upper Jialing River, the Wu River Basin, and the southern Sichuan Basin, but decreases trend in pq95, pfl95, R1d, and, R5d over the Northern Sichuan Basin. [ABSTRACT FROM AUTHOR]