Climate Change Impacts on Extreme Flows Under IPCC RCP Scenarios in the Mountainous Kaidu Watershed, Tarim River Basin

Effects of climate change and anthropogenic activities on lake environmental dynamics: A case study in Lake Bosten Catchment, NW China

Arid and semiarid regions account for ∼ 40% of the world's land area. Rivers and lakes in these regions provide sparse, but valuable, water resources for the fragile environments, and play a vital role in the development and sustainability of local societies. During the late 1980s, the climate of arid and semiarid northwest China dramatically changed from "warm-dry" to "warm-wet". Understanding how these environmental changes and anthropogenic activities affect water quantity and quality is critically important for protecting aquatic ecosystems and determining the best use of freshwater resources. Lake Bosten is the largest inland freshwater lake in NW China and has experienced inter-conversion between freshwater and brackish status. Herein, we explored the long-term water level and salinity trends in Lake Bosten from 1958 to 2019. During the past 62 years, the water level and salinity of Lake Bosten exhibited inverse "W-shaped" and "M-shaped" patterns, respectively. Partial least squares path modeling (PLS-PM) suggested that the decreasing water level and salinization during 1958-1986 were mainly caused by anthropogenic activities, while the variations in water level and salinity during 1987-2019 were mainly affected by climate change. The transformation of anthropogenic activities and climate change is beneficial for sustainable freshwater management in the Lake Bosten Catchment. Our findings highlight the benefit of monitoring aquatic environmental changes in arid and semi-arid regions over the long-term for the purpose of fostering a balance between socioeconomic development and ecological protection of the lake environment.

Case Study: Development of the CNN Model Considering Teleconnection for Spatial Downscaling of Precipitation in a Climate Change Scenario

Global climate models (GCMs) are used to analyze future climate change. However, the observed data of a specified region may differ significantly from the model since the GCM data are simulated on a global scale. To solve this problem, previous studies have used downscaling methods such as quantile mapping (QM) to correct bias in GCM precipitation. However, this method cannot be considered when certain variables affect the observation data. Therefore, the aim of this study is to propose a novel method that uses a convolution neural network (CNN) considering teleconnection. This new method considers how the global climate phenomena affect the precipitation data of a target area. In addition, various meteorological variables related to precipitation were used as explanatory variables for the CNN model. In this study, QM and the CNN models were applied to calibrate the spatial bias of GCM data for three precipitation stations in Korea (Incheon, Seoul, and Suwon), and the results were compared. According to the results, the QM method effectively corrected the range of precipitation, but the pattern of precipitation was the same at the three stations. Meanwhile, for the CNN model, the range and pattern of precipitation were corrected better than the QM method. The quantitative evaluation selected the optimal downscaling model, and the CNN model had the best performance (correlation coefficient (CC): 69% on average, root mean squared error (RMSE): 117 mm on average). Therefore, the new method suggested in this study is expected to have high utility in forecasting climate change. Finally, as a result of forecasting for future precipitation in 2100 via the CNN model, the average annual rainfall increased by 17% on average compared to the reference data.

Urban Flood Risk and Economic Viability Analyses of a Smart Sustainable Drainage System

Urban drainage systems are in transition from functioning simply as a transport system to becoming an important element of urban flood protection measures providing considerable influence on urban infrastructure sustainability. Rapid urbanization combined with the implications of climate change is one of the major emerging challenges. The increased concerns with water security and the ageing of existing drainage infrastructure are new challenges in improving urban water management. This study carried out in the Seixal area in Portugal examines flood risk analyses and mitigation techniques performed by computational modelling using MIKE SHE from the Danish Hydraulic Institute (DHI). Several scenarios were compared regarding flood risk and sustainable urban drainage systems (SuDS) efficiency. To obtain a more accurate analysis, the economic viability of each technique was analyzed as well through (i) life cost analysis and (ii) taking into account the damages caused by a certain type of flood. The results present that the best scenario is the one that will minimize the effects of great urbanization and consequently the flood risk, which combines two different measures: permeable pavement and detention basin. This alternative allows us to fully explore the mitigation capacity of each viable technique, demonstrating a very important improvement in the flood mitigation system in Seixal.

Assessment of Surface Water Availability under Climate Change Using Coupled SWAT-WEAP in Hongshui River Basin, China

Climate change adversely affects the hydrological cycle at the basin level. This study integrated two models, the Soil and Water Assessment Tool (SWAT) for future climate prediction, and Water Evaluation and Planning (WEAP) for the simulation of water quantity in the Hongshui River Basin (HRB), to evaluate the impacts of climate change, which plays a significant role in the lives of inhabitants downstream of the basin. Downscaled monthly rainfalls and temperatures under four Representative Concentration Pathways (RCPs) emission scenarios from five global circulation models (GCMs) were used to generate streamflow using the SWAT model. Streamflow data (1991–2001) were used to calibrate and validate, with the period of 1991–1997 used for calibration and that of 1998–2001 used for validation. Six scenarios were established to evaluate the response of the basin under socio-economic scenarios. The simulated results show that precipitation and streamflow would likely undergo a slight increase. The available water resources would be sufficient to meet the existing needs until 2050. The results indicated that no water shortages exist under socio-economic, low, and medium climate change emission scenarios, however the basin will experience a water shortage under the high climate change emission scenario (RCP-8.5). The study proposed that, to ensure the sustainability of water resources, better long-term management policies are required to be implemented in the basin and to meet future downstream water needs.

Assessment of the Future Climate Change Projections on Streamflow Hydrology and Water Availability over Upper Xijiang River Basin, China

Hydrological models are widely applied for simulating complex watershed processes and directly linking meteorological, topographical, land-use, and geological conditions. In this study, the Soil and Water Assessment Tool (SWAT) was calibrated at two monitoring stations, which improved model performance and increased the reliability of flow predictions in the Upper Xijiang River Basin. This study evaluated the potential impacts of climate change on the streamflow and water yield of the Upper Xijiang River Basin using Arc-SWAT. The model was calibrated (1991–1997) and validated (1998–2001) using the Sequential Uncertainty Fitting Algorithm (SUFI-2). Model calibration and validation suggest a good match between the measured and simulated monthly streamflow, indicating the applicability of the model for future daily streamflow predictions. Large negative changes of low flows are projected under future climate scenarios, exhibiting a 10% and 30% decrease in water yield over the watershed on a monthly scale. Overall, findings generally indicated that winter flows are expected to be affected the most, with a maximum impact during the January–April period, followed by the wet monsoon season in the May–September period. Water balance components of the Upper Xijiang River Basin are expected to change significantly due to the projected climate change that, in turn, will seriously affect the water resources and streamflow patterns in the future. Thus, critical problems, such as ground water shortages, drops in agricultural crop yield, and increases in domestic water demand are expected at the Xijiang River Basin.