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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]

Characterizing the response of temperature variables to agricultural irrigation is expected to be an important challenge for understanding the full impact of water management on regional climate change. In this paper, the trend analysis and abrupt change test were applied to detect the global warming effect. Then, the quantitative irrigation-induced cooling effects on temperature variables between April and August from 1970 to 2010 in the Lhasa River basin were estimated using historical time series of gridded meteorological records and a map of the area equipped for irrigation. Trends in the maximum temperature (Tmax) were statistically positive, and a significant increasing trend for the minimum temperature (Tmin) was detected at the 0.01 and 0.05 confidence levels. No abrupt changing point of warming was detected in the time series for Tmax. The abrupt changes in Tmin in the irrigation concentration period took place in 1995, 5 years later than the corresponding change in April. Affected by global warming, the increase in temperature was the largest in July and August, when the irrigation-induced cooling effect was also the most significant. The irrigation-induced cooling effect for Tmax and Tmin in April–August (except for June) ranged from − 0.017 to − 0.009 °C/decade and from − 0.011 to − 0.001 °C/decade, respectively, and the cooling effect for diurnal temperature range (DTR) ranged from − 0.011 to 0 °C/decade. The cooling effect on temperature reached above 0.01 °C in July and August, but for the growing seasons, the effect was weak, only 0.001 °C. The Tmax and Tmin trends during the whole growing seasons decreased by both 0.002 °C/decade, respectively, with a 10% increase in irrigation land proportion. Even in July and August, the trends were expected to decrease by about 0.005 °C/decade with a 10% increase in irrigation land proportion. The irrigation-induced cooling effect could partially slow global warming. [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]