Quantifying the Impact of Future Climate Change on Runoff in the Amur River Basin Using a Distributed Hydrological Model and CMIP6 GCM Projections

Wen, Ke; Gao, Bing; Li, Mingliang

doi:10.3390/atmos12121560

Cited by 17 publications

(5 citation statements)

References 38 publications

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“…One is to determine the high and low flows by the high and low percentiles based on the flow frequency distribution curve, and is widely used to validate the performance of hydrological models and can reflect the most common situations of high and low flows [32,79]. The other method of description is the annual maximum or minimum of specific moving window mean discharges, and is commonly applied to reflect flood or drought risks [27,80]. Since the changes in future hydrological hazards are focused more in this study, we selected the second method to reflect high and low flows.…”

Section: Statistical Approaches For Analyzing the Changes In High And Low Flowsmentioning

confidence: 99%

Future Changes in High and Low Flows under the Impacts of Climate and Land Use Changes in the Jiulong River Basin of Southeast China

Yang

Zhao

et al. 2022

Atmosphere

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Climate change and human activities have profoundly affected the world with extreme precipitation, heat waves, water scarcity, frequent floods and intense droughts. It is acknowledged that climate change will persist and perhaps intensify in the future, and it is thus meaningful to explore the quantitative impacts of these changes on hydrological regimes. The Jiulong River basin serves as an important watershed on the southeast coast of China. However, future hydrological changes under the combined impacts of climate change and land use change have been barely investigated. In this study, the climate outputs from five general circulation models (GCMs) under the Coupled Model Intercomparison Project Phase 6 (CMIP6) were corrected and spatially downscaled by a statistical downscaling method combining quantile mapping and machine learning. The future high-resolution land use maps were projected by the CA–Markov model with land use changes from the Land-Use Harmonization 2 (LUH2) as constraints. The future dynamic vegetation process was projected by the Biome-GBC model, and then, the future hydrological process under four representative concentration pathways and shared socioeconomic pathways (RCP–SSP) combined scenarios was simulated by a distributed hydrological model. Based on the copula method, the flood frequency and corresponding return periods were derived. The results demonstrated that future precipitation and air temperature would continue to rise, and future land use changes would have different developing pathways determined by the designs in various SSP–RCPs. Under the combined impacts of climate and land use change, the total available water resources will increase due to increasing precipitation, and the high flow and low flow will both increase at three stations under the four SSP–RCPs. The annual 1-day maximum discharge is projected to increase by 67–133% in the last decade of the 21st century, and the annual 7-day minimum discharge is projected to increase by 19–39%. The flood frequency analysis showed that the Jiulong River basin would face more frequent floods in the future. By the end of the 21st century, the station-average frequency of a historical 100-year flood will increase by 122% under the most optimistic scenario (SSP126) and increase by 213% under the scenario of greatest regional rivalry (SSP370). We demonstrated that climate change would be the major cause for the increase in future high flows and that land use change would dominate future changes in low flows. Finally, we recommend integrated and sustainable water management systems to tackle future challenges in this coastal basin.

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Section: Statistical Approaches For Analyzing the Changes In High And Low Flowsmentioning

confidence: 99%

Future Changes in High and Low Flows under the Impacts of Climate and Land Use Changes in the Jiulong River Basin of Southeast China

Yang

Zhao

et al. 2022

Atmosphere

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“…The Amur River Basin, spanning Russia, China, and Mongolia, ranks among the ten largest river basins in the world [14]. The Chinese part of the basin constitutes 48% of the entire basin and hosts the majority of freshwater lakes in Northeast China.…”

Section: Introductionmentioning

confidence: 99%

Accurate and Rapid Extraction of Aquatic Vegetation in the China Side of the Amur River Basin Based on Landsat Imagery

Chen,

Zhang,

Jia

et al. 2024

Remote Sensing

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Since the early 1950s, the development of human settlements and over-exploitation of agriculture in the China side of the Amur River Basin (CARB) have had a major impact on the water environment of the surrounding lakes, resulting in a decrease of aquatic vegetation. According to the United Nations Sustainable Development Goals, a comprehensive understanding of the extent and variability of aquatic vegetation is crucial for preserving the structure and functionality of stable aquatic ecosystems. Currently, there is a deficiency in the CARB long-sequence dataset of aquatic vegetation distribution in China. This shortage hampers effective support for actual management. Therefore, the development of a fast, robust, and automatic method for accurate extraction of aquatic vegetation becomes crucial for large-scale applications. Our objective is to gather information on the spatial and temporal distribution as well as changes in aquatic vegetation within the CARB. Utilizing a hybrid approach that combines the maximum spectral index composite and Otsu algorithm, along with the integration of convolutional neural networks (CNN) and random forest, we applied this methodology to obtain an annual dataset of aquatic vegetation spanning from 1985 to 2020 using Landsat series imagery. The accuracy of this method was validated through both field investigations and Google Images. Upon assessing the confusion matrix spanning from 1985 to 2020, the producer accuracy for aquatic vegetation classification consistently exceeded 87%. Further quantitative analysis unveiled a discernible decreasing trend in both the water and vegetation areas of lakes larger than 20 km2 within the CARB over the past 36 years. Specifically, the total water area decreased from 3575 km2 to 3412 km2, while the vegetation area decreased from 745 km2 to 687 km2. These changes may be attributed to a combination of climate change and human activities. These quantitative data hold significant practical implications for establishing a scientific restoration path for lake aquatic vegetation. They are particularly valuable for constructing the historical background and reference indices of aquatic vegetation.

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“…It is crucial to assess the potential climate change impact on the Jajrood River's flow in the future. For decades, the climate crisis and its negative impacts on the hydrological cycle of watersheds have been studied by scientists around the world [6][7][8][9][10]. Regarding the Tibetan Plateau, which is the highest plateau on earth, Tian et al (2019) [11] reported an increase in temperature, due to the impacts of climate change, on the Lhasa River basin, that will lead to a significant reduction in future flow in the middle of the century (2045-2055) and at the end of the century (2090-2100).…”

Section: Introductionmentioning

confidence: 99%

Assessment of Climate Change’s Impact on Flow Quantity of the Mountainous Watershed of the Jajrood River in Iran Using Hydroclimatic Models

Najimi,

Aminnejad,

Nourani

2023

Sustainability

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Rivers are the main source of fresh water in mountainous and downstream areas. It is crucial to investigate the possible threats of climate change and understand their impact on river watersheds. In this research, climate change’s impact on the mountainous watershed of the Jajrood River, upstream of Latyan Dam in Iran, was assessed by using a multivariate recursive quantile-matching nesting bias correction (MRQNBC) and the soil and water assessment tool (SWAT). Also, this study considered ten global circulation models (GCMs) from the coupled model intercomparison project phase VI (CMIP6). With a higher correlation coefficient, the MIROC6 model was selected among other models. For the future period of 2031–2060, the large-scale outputs of the MIROC6 model, corresponding to the observational data were extracted under four common socioeconomic path scenarios (SSPs 1–2.6, 2–4.5, 3–7.0, 5–8.5). The bias was corrected and downscaled by the MRQNBC method. The downscale outputs were given to the hydrological model to predict future flow. The results show that, in the period 2031–2060, the flow will be increased significantly compared to the base period (2005–2019). This increase can be seen in all scenarios. In general, changes in future flow are caused by an increase in precipitation intensity, as a result of an increase in temperature. The findings indicate that, although the results show an increase in the risk of flooding, considering the combined effects of three components, i.e., increased precipitation concentration, temperature, and reduced precipitation, climate change is intensifying the problem of water scarcity.

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Quantifying the Impact of Future Climate Change on Runoff in the Amur River Basin Using a Distributed Hydrological Model and CMIP6 GCM Projections

Cited by 17 publications

References 38 publications

Future Changes in High and Low Flows under the Impacts of Climate and Land Use Changes in the Jiulong River Basin of Southeast China

Future Changes in High and Low Flows under the Impacts of Climate and Land Use Changes in the Jiulong River Basin of Southeast China

Accurate and Rapid Extraction of Aquatic Vegetation in the China Side of the Amur River Basin Based on Landsat Imagery

Assessment of Climate Change’s Impact on Flow Quantity of the Mountainous Watershed of the Jajrood River in Iran Using Hydroclimatic Models

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