Assessment of Precipitation Simulations in Central Asia by CMIP5 Climate Models

A multi-scenario ensemble approach incorporating stepwise cluster analysis to reduce uncertainty in large-scale watershed precipitation projections: a case study of Pearl River Basin, South China

Assessing and selecting climate models with lower uncertainty is necessary to predict future climate and hydrological risks at the watershed scale. In this study, we integrated stepwise cluster analysis (SCA) to propose a multi-model ensemble downscaling framework aimed at reducing the uncertainty of GCM-based precipitation projections in large-scale watersheds. The Pearl River Basin (PRB) in southern China was selected as the study area to validate the reliability of this framework. Spatially, we investigated the features of terrain-related spatial heterogeneity in precipitation simulation of different climate models using a stepwise cluster zoning approach. The spatial performance of most CMIP6 models was effective in capturing the annual mean precipitation from the source region to the downstream of the PRB. To further evaluate the model's skill in simulating precipitation patterns, we conducted a seasonal analysis for different periods throughout the year. However, the seasonal precipitation cycle exhibited a wet bias during cold seasons, and the most significant deviation of precipitation percentage intervals occurred during winter. The TSS ranking of CMIP6 models was used to select the top-performing models to construct an improved multi-model ensemble mean (MEM5), resulting in a more accurate precipitation simulation for PRB. Results showed consistent precipitation increases (p < 0.05) for all scenarios in the PRB, with the middle and lower reaches being the most sensitive to changes in precipitation. The improved MEM5 can serve as a valuable reference for accurately simulating hydrological regimes and extreme weather events in the PRB. The proposed multi-model ensemble downscaling framework, which incorporates SCA, offers a new approach for high-resolution and low-uncertainty climate simulations in other large-scale watersheds.

Spatiotemporal analysis of transition probabilities of wet and dry days under SSPs scenarios in the semi-arid Susurluk Basin, Türkiye

Precipitation, especially in regions dominated by the Mediterranean climate, is one of the most critical parameters of the hydrological cycle and the environment affected by climate change. One the one hand, the transition probabilities of wet and dry days in precipitation occurrence are a relatively new topic, on the other hand these are essential in defining the regional climate. For the first time, spatiotemporal variations of transition probabilities of wet and dry days in the Susurluk Basin, northwestern Türkiye, dominated by a semi-arid Mediterranean climate and also having a mountain climate, were analyzed based on the observation (1979-2014) and future terms (2030-2059 as short and 2070-2099 as long), under four Shared Socioeconomic Pathways (SSPs) scenarios. To do this, statistical downscaling was performed for 14 general circulation models (GCMs) from the CMIP6. By applying an ensemble of four high-performing GCMs, four indices for transition probabilities of wet and dry, i.e., a dry day following a dry day (FDD), a wet day following a dry day (FDW), a dry day following a wet day (FWD), and a wet day following a wet day (FWW), were calculated, and their changes were determined statistically. Monotonic and partial trends of the indices were also analyzed. According to the results, the FDD will increase in water year and wet period and autumn in the future, especially for the long term, in the basin dominated by the FDD (75 % in water year). The risks are higher in the western part of the basin, where human activities are intense, as the FDD is higher in this part than other parts especially in summer (90-100 %) in SSP3-7.0 and SSP5-8.5 scenarios for the long term. So, the length of consecutive dry days in the wet period and water year will increase in the basin, thus increasing the likelihood of droughts. As for the intra-term trends, the FDD increases and the FWW decreases in the water year and seasons in SSP3-7.0 and SSP5-8.5, contrary to the observation term.

Spatiotemporal monitoring of drought using satellite precipitation products: The case of Borena agro-pastoralists and pastoralists regions, South Ethiopia

Drought is increasingly affecting farmers in agro-pastoralist and pastoralists region. It is one of the most harmful natural disasters that significantly affects rain-fed agriculture in developing countries. Drought assessment is an important component of drought risk management. This study used CHIRPS rainfall data to monitor the characteristics of drought in Borena Zone in southern Ethiopia. The standardized precipitation index (SPI) is used to calculate the magnitude, intensity, and severity of drought during the rainy season. Results show that severe and extreme droughts were detected in the first rainy season (March to May) and second wet season (September to November). Severe and extreme droughts were detected in the first rainy/wet season in 1992, 1994, 1999, 2000, 2002-2004, 2008,2009, 2011, 2019-2021. The spatial and temporal variability of drought is highly influenced by El Nino-Southern Oscillation (ENSO) in Ethiopia. Results revealed that most of the first rainy season was dry. 2011 was the driest year during the first wet season. Drought risk events in the first wet season were greater than in the second wet season. Results show that drought more frequently occurred in the northern and southern part in the first wet season. In the second rainy season extreme drought was detected in 1990, 1992, 1993, 1994, 1996, and 1997. The results of this study will promote the importance of early warning measures, drought risk management, and food security management in the study area.

Reconstruction of Hydrometeorological Data Using Dendrochronology and Machine Learning Approaches to Bias-Correct Climate Models in Northern Tien Shan, Kyrgyzstan

Tree-ring-width chronologies for 33 samples of Picea abies (L.) Karst. were developed, and a relationship between tree growth and hydrometeorological features was established and analyzed. Precipitation, temperature, and discharge records were extrapolated to understand past climate trends to evaluate the accuracy of global climate models (GCMs). Using Machine Learning (ML) approaches, hydrometeorological records were reconstructed/extrapolated back to 1886. An increase in the mean annual temperature (Tmeana) increased the mean annual discharge (Dmeana) via glacier melting; however, no temporal trends in annual precipitation were detected. For these reconstructed climate data, root-mean-square error (RMSE), Taylor diagrams, and Kling–Gupta efficiency (KGE) were used to evaluate and assess the robustness of GCMs. The CORDEX REMO models indicated the best performance for simulating precipitation and temperature over northern Tien Shan; these models replicated historical Tmena and Pa quite well (KGE = 0.24 and KGE = 0.24, respectively). Moreover, the multi-model ensembles with selected GCMs and bias correction can significantly increase the performance of climate models, especially for mountains region where small-scale orographic effects abound.

Comparison of Projections of Precipitation over Yangtze River Basin of China by Different Climate Models

Based on the observational dataset CN05.1 and the Coupled Model Intercomparison Project (CMIP), this study assesses the performance of CMIP5 and CMIP6 projects in projecting mean precipitation at annual and seasonal timescales in the Yangtze River Basin of China over the period 2015–2020 under medium emission scenarios (RCP4.5/SSP2-4.5). Results indicate that the multi-model ensemble (MME) of CMIP6 overall has lower relative bias and root-mean square error of both annual and seasonal mean than that of CMIP5, except for winter, but both of the two ensembles show the best projected accuracy in winter. Generally, CMIP6 outperformed CMIP5 in capturing spatial and temporal pattern over the YRB, especially in the midstream and downstream areas, which have high precipitation. Further analyses suggest that the CMIP6 GCMs have lower median normalized root-mean square error than CMIP5 GCMs. Based on the Taylor skill (TS) score, both CMIP6 and CMIP5 GCMs are ranked to evaluate relative model performance. CMIP6 GCMs have higher ranks than CMIP5 GCMs, with an average TS score of 0.68 (0.55) for CMIP6 (CMIP5), and three out of the five highest scored GCMs are CMIP6 GCMs. However, the CMIP6 precipitation projections are still quite uncertain, thus requiring further assessment and correction.

Comparison of CMIP5 and CMIP6 Multi-Model Ensemble for Precipitation Downscaling Results and Observational Data: The Case of Hanjiang River Basin

Evaluating global climate model (GCM) outputs is essential for accurately simulating future hydrological cycles using hydrological models. The GCM multi-model ensemble (MME) precipitation simulations of the Climate Model Intercomparison Project Phases 5 and 6 (CMIP5 and CMIP6, respectively) were spatially and temporally downscaled according to a multi-site statistical downscaling method for the Hanjiang River Basin (HRB), China. Downscaled precipitation accuracy was assessed using data collected from 14 meteorological stations in the HRB. The spatial performances, temporal performances, and seasonal variations of the downscaled CMIP5-MME and CMIP6-MME were evaluated and compared with observed data from 1970–2005. We found that the multi-site downscaling method accurately downscaled the CMIP5-MME and CMIP6-MME precipitation simulations. The downscaled precipitation of CMIP5-MME and CMIP6-MME captured the spatial pattern, temporal pattern, and seasonal variations; however, precipitation was slightly overestimated in the western and central HRB and precipitation was underestimated in the eastern HRB. The precipitation simulation ability of the downscaled CMIP6-MME relative to the downscaled CMIP5-MME improved because of reduced biases. The downscaled CMIP6-MME better simulated precipitation for most stations compared to the downscaled CMIP5-MME in all seasons except for summer. Both the downscaled CMIP5-MME and CMIP6-MME exhibit poor performance in simulating rainy days in the HRB.

Evaluation of CMIP5 Climate Models Using Historical Surface Air Temperatures in Central Asia

Using historical data compiled by the Climate Research Unit, spatial and temporal analysis, trend analysis, empirical orthogonal function (EOF) analysis, and Taylor diagram analysis were applied to test the ability of 24 Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models to accurately simulate the annual mean surface air temperature in central Asia from the perspective of the average climate state and climate variability. Results show that each model can reasonably capture the spatial distribution characteristics of the surface air temperature in central Asia but cannot accurately describe the regional details of climate change impacts. Some of the studied models, including CNRM-CM5, GFDL-CM3, and GISS-E2-H, could better simulate the high- and low-value centers and the contour distribution of the surface air temperature. Taylor diagram analysis showed that the root mean square errors of all models were less than 3, the standard deviations were between 8.36 and 13.45, and the spatial correlation coefficients were greater than 0.96. EOF analysis showed that the multi-model ensemble can accurately reproduce the surface air temperature characteristics in central Asia from 1901 to 2005, including the rising periods and the fluctuations of the north and south inversion phases. Overall, this study provides a valuable reference for future climate prediction studies in central Asia.

The Future of Water Management in Central Asia

Central Asia is an increasingly important strategic geopolitical region. During the latest decades, the region has often been identified as close to potential conflict regarding water usage. This includes the sharing of water from the Syr Darya and the Amu Darya in the Aral Sea Basin. The Aral Sea disaster has exposed a complex picture of water needs and potential political conflict. Rapid population increase together with climate change impacts are likely to further aggravate the short- and long-term future precarious situation for water management in the region. This Special Issue focuses on present and future water management issues in Central Asia in view of future climate changes and how these will affect socioeconomic development. Central Asia is, in general, water rich; however, exercising efficient and fair water management will be important in view of future population increase and climate change. At the same time, water and natural resource development is a cornerstone in all the Central Asian republics. Especially, water resources are, to a great extent, shared between all five republics. A common ground for water-sharing is, therefore, of utmost importance.

Simulation of the Potential Impacts of Projected Climate Change on Streamflow in the Vakhsh River Basin in Central Asia under CMIP5 RCP Scenarios

Millions of people in Uzbekistan, Turkmenistan, Tajikistan, and Kyrgyzstan are dependent on the freshwater supply of the Vakhsh River system. Sustainable management of the water resources of the Vakhsh River Basin (VRB) requires comprehensive assessment regarding future climate change and its implications for streamflow. In this study, we assessed the potential impacts of projected climate change scenarios on the streamflow in the VRB for two future periods (2022–2060 and 2061–2099). The probable changes in the regional climate system were assessed using the outputs of five global climate models (GCMs) under two representative concentration pathways (RCPs), RCP4.5 and RCP8.5. The probable streamflow was simulated using a semi-distributed hydrological model, namely the Soil and Water Assessment Tool (SWAT). Evidence of a significant increase in the annual average temperature by the end of the 21st century was found, ranging from 2.25 to 4.40 °C under RCP4.5 and from 4.40 to 6.60 °C under RCP8.5. The results of three GCMs indicated a decreasing tendency of annual average precipitation (from −1.7% to −16.0% under RCP4.5 and from −3.4% to −29.8% under RCP8.5). Under RCP8.5, two GCMs indicated an increase (from 2.3% to 5.3%) in the average annual precipitation by the end of 2099. The simulated results of the hydrological model reported an increasing tendency of average annual streamflow, from 17.5% to 52.3% under both RCPs, by the end of 2099. A shift in the peak flow month was also found, i.e., from July to June, under both RCPs. It is expected that in the future, median and high flows might increase, whereas low flow might decrease by the end of 2099. It is concluded that the future seasonal streamflow in the VRB are highly uncertain due to the probable alterations in temperature and precipitation. The findings of the present study could be useful for understanding the future hydrological behavior of the Vakhsh River, for the planning of sustainable regional irrigation systems in the downstream countries, i.e., Uzbekistan and Turkmenistan, and for the construction of hydropower plants in the upstream countries.