• Energy Research
• environmental engineering close
• Chinese Academy of Sciences close

AbstractThe identification of projected changes in temperature caused by global warming at a fine-scale spatial resolution is of great importance for the high-altitude glacier and snow covered Upper Indus Basin. This study used a multimodel ensemble mean bias-correction technique which uses the ensemble empirical mode decomposition method to correct the bias of ensemble mean of seven CMIP6 GCMs outputs with reference to the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5). The bias-corrected data have a nonlinear trend of seven GCMs but interannual variance and mean climate of ERA5 dataset. The dataset spans from 1985 to 2100 for historical and future climate scenarios (SSP126, SSP245, SSP370, and SSP585) at daily time intervals with a 1 km grid resolution. The result of different scenarios indicates that the increase in maximum (Tmax) and minimum temperature (Tmin) ranging from 1.5 to 5.4 °C and 1.8 to 6.8 °C from 2015 to 2100, respectively. Similarly, elevation-dependent warming is identified in Tmin from 1.7 to 7.0 °C at elevations <2,000 to 6,000 m asl, while the contrary relationship in Tmax is projected under different scenarios from 2015 to 2100. This study provides an insight into how to improve the GCMs projections and can be helpful for further climate change impact studies.

The Nanwenghe Wetlands Reserve in the Yile'huli Mountains is a representative region of the Xing'an permafrost. The response of permafrost to climate change remains unclear due to limited field investigations. Thus, longer-term responses of the ground thermal state to climate change since 2011 have been monitored at four sites with varied surface characteristics: Carex tato wetland (P1) and shrub-C. tato wetland (P2) with a multi-year average temperatures at the depth of zero annual amplitude (TZAA) of −0.52 and −1.19 °C, respectively; Betula platyphylla-Larix gmelinii (Rupr.) Kuzen mixed forest (P3) with TZAA of 0.17 °C, and; the forest of L. gmelinii (Rupr.) Kuzen (P4) with TZAA of 1.65 °C. Continuous observations demonstrate that the ecosystem-protected Xing'an permafrost experienced a cooling under a warming climate. The temperature at the top of permafrost (TTOP) rose (1.8 °C per decade) but the TZAA declined (−0.14 °C per decade), while the active layer thickness (ALT) thinned from 0.9 m in 2012 to 0.8 m in 2014 at P1. Both the TTOP and TZAA increased (0.89 and 0.06 °C per decade, respectively), but the ALT thinned from 1.4 m in 2012 to 0.7 m in 2016 at P2. Vertically detached permafrost at P3 disappeared in summer 2012, with warming rates of +0.42 and + 0.17 °C per decade for TTOP and TZAA, respectively. However, up to date, the ground thermal state has remained stable at P4. We conclude that the thermal offset is crucial for the preservation and persistence of the Xing'an permafrost at the southern fringe.

Study Region Mekong River Basin and surrounding areas. Study Focus: This study investigated the impacts of climate change on future meteorological and hydrological droughts in the Mekong River Basin and its surrounding areas. Our work is based on the output of five global climate models (GCMs) and simulations using the geomorphology-based hydrological model (GBHM) for the historical (1975–2004), near future (2010–2039), middle future (2040–2069), and far future (2070–2099) periods. The meteorological droughts in the study area were measured using SPI and SPEI, while the hydrological droughts were measured using SSI. New Hydrological Insights for the Region: The results suggest that droughts will generally reduce in the future over most of the study area, but will be more unevenly distributed with an eastward migration as compared to the historical period. Both meteorological and hydrological droughts will intensify in the near future, but will then reduce in intensity. Meteorological droughts will increase in the northeastern areas in the near future, followed by migration towards the south. Hydrological droughts showed similar aggravation followed by reduction, with upstream areas showing greater variability. In the general context of drought alleviation, southwestern China and the Mekong River estuary may suffer from a continuously increasing drought intensity in the future. This finding is based on 100-year extreme drought events.

There are many models presented that assess water quality. However, the applications of the models are limited due to the difficulty of preparing input data and interpreting model output. In this paper, we developed a Web-based platform to assist researchers in analyzing water quality. The data from sensors can be automatically imported to the platform according to the configured information of data structures. The platform also provides conventional methods and big data methods for the users to analyze water quality. Moreover, the users can choose the water quality parameters according to the water usage. The presented platform can show the model output in a text format and a graphic format, which allows for the analysis to be better understood by the user. The platform integrates the input, analysis, and output together well and brings great convenience to the research on water quality.

Abstract China is highly susceptible to landslides and debris flow disasters as it is a mountainous country with unique topography and monsoon climate. In this study, an efficient statistical model is used to predict the landslide risk in China under the Representative Concentration Pathway 8.5 by 2050, with the precipitation data from global climate models (GCMs) as the driving field. Additionally, for the first time, the impact of future changes in land use types on landslide risk is explored. By distinguishing between landslide susceptibility and landslide risk, the results indicate that the landslide susceptibility in China will change in the near future. The occurrence of high-frequency landslide risks is concentrated in southwestern and southeastern China, with an overall increase in landslide frequency. Although different GCMs differ in projecting the future spatio-temporal distribution of precipitation, there is a consensus that the increased landslide risk in China’s future is largely attributed to the increase in extremely heavy precipitation. Moreover, alterations in land use have an impact on landslide risk. In the Huang-Huai-Hai Plain, Qinghai Tibet Plateau, and Loess Plateau, changes in land types can mitigate landslide risks. Conversely, in other areas, such changes may increase the risk of landslides. This study aims to facilitate informed decision-making and preparedness measures to protect lives and assets in response to the changing climate conditions.

Southwest China (SWC), characterized by complex climate, undulating topography, intertwined mountains and basins, and diverse ecosystem, is a global hotspot in biodiversity. SWC also is sensitive to climate change, the effects of which can be expressed through alterations in bio-climatology indicators. In this study, we investigated the trends of the key bio-climatology indicators, including mean temperature of the warmest month (TWM), mean temperature of the coldest month (TCM), accumulated temperature above 5 °C (AT5) and 10 °C (AT10), number of days with daily mean temperature above 5 °C (DT5) and 10 °C (DT10), annual precipitation (P), precipitation days (DP), and moisture index (MI). The 105 meteorological stations data from 1961 to 2015 were selected to examine the trend of these indexes in SWC. The results suggested that TWM and TCM both experienced a significant upward trend, with the more pronounced increase in TCM than that in TWM. TWM increased by 0.011 °C year-1 and TCM increased by 0.025 °C year-1. AT5, AT10, DT5, and DT10 also exhibited increasing trend, with AT10 > AT5 and DT10 > DT5, and the trend in DT was found to be less significant than that in AT. The increment of AT5, AT10, DT5, and DT10 were 6.452 °C year-1, 7.158 °C year-1, 0.164 days year-1, and 0.263 days year-1, respectively. P, DP, and MI showed a downward trend, among which DP experienced a significant decrease with - 1.018 days year-1. In general, SWC tends to be drier and warmer, which may alter the structure and function of the local ecosystem, further then affect the role as a global diversity hotspot.

Water quality safety has attracted global attention and is closely related to the development of the social economy and human health. It is widely recognized that climate change and human activities significantly affect water quality changes. Therefore, quantifying the contributions of factors that drive long-term water quality changes is crucial for effective water quality management. Here, we built a climate-water quality assessment framework (CWQAF) based on climate-water quality response coefficients and trend analysis methods, to achieve this goal. Our results showed that the water quality improved significantly by 4.45%-20.54% from 2011 to 2020 in the Minjiang River basin (MRB). Human activities (including the construction of ecological projects, stricter discharge measures, etc.) were the main driving factors contributing 65%-77% of the improvement effect. Notably, there were differences in the contributions of human activities to water quality parameter changes, such as DO (increase (I): 0.12 mg/L, human contribution (HC): 66.8%), CODMn (decrease (D): 0.71 mg/L, HC: 67.2%), BOD5 (D: 1.10 mg/L, HC: 77.7%), CODCr (D: 4.20 mg/L, HC: 81.2%), TP (D: 0.13 mg/L,HC: 72.8%) and NH3-N (D: 0.40 mg/L, HC: 63.0%). Climate change explained 23%-35% of the variation in water quality. The water quality response to climate change was relatively significant with precipitation. For example, the downstream region was more susceptible to climate change than was the upstream region, as the downstream movement of precipitation centers strengthened the process of climatic factors affecting water quality changes in the MRB. Generally, although human activities were the main driving factor of water quality changes at the basin scale, the contribution of climate change could not be ignored. This study provided a manageable framework for the quantitative analysis of the influence of human activities and climate change on water quality to enable more precise and effective water quality management.

Abstract The emergence of new-generation geostationary satellites (such as Himawari-8 or FenYun-4) provides us an opportunity to obtain more accurate global horizontal irradiance (GHI) data. In this study, instantaneous GHI estimates (with a spatio-temporal resolution of 10 min and 5 km) are produced using the cloud products from the new-generation geostationary satellite Himawari-8 with a physically based algorithm. The hourly, daily, and monthly GHI estimates are aggregated using “snapshots” (or instantaneous estimates) at two different sampling frequencies (i.e., 1 h and 10 min), and validated against surface radiometry measurements collected in China. The root mean square errors (RMSE) for hourly, daily and monthly GHI estimates calculated using 1-h snapshots were 106.6, 27.9, and 17.7 W m−2, respectively, whereas those for hourly, daily and, monthly GHI calculated using 10-min snapshots decreased to 94.0, 24.0, and 16.6 W m−2, respectively. This result demonstrates that the accuracies of hourly, daily, and monthly GHI estimates are improved by increasing the frequency of satellite observations from 1 h (frequency of the previous-generation geostationary satellites) to 10 min (frequency of the new-generation geostationary satellites).