Interactive effects of climate variability and human activities on blue and green water scarcity in rapidly developing watershed

Liang, Jie; Liu, Qiang; Zhang, Hua; Li, Xiaodong; Zhan, Qian; Lei, Manqin; Li, Xin; Peng, Yuhui; Li, Shuai; Zeng, Guangming

doi:10.1016/j.jclepro.2020.121834

Cited by 66 publications

(28 citation statements)

References 45 publications

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“…R 2 (coefficient of determination) and NSE (Nash-Sutcliffe Efficiency) are used to evaluate accuracy of SWAT model. R 2 is from 0 to 1 and if the value is close to 1, it indicates that the simulated data is close to the observed data and vice versa [42].…”

Section: Swat Modelmentioning

confidence: 81%

Impact of Grain for Green Project on Water Resources and Ecological Water Stress in Yanhe River Basin

Han

Xia

Huang

et al. 2021

Preprint

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Grain for Green project (GGP) initialed by China government since 1999 has achieved substantial achievements accompanied with surface runoff decrease in the Loess Plateau but impacts of large-scale afforestation on regional water resources are uncertain. Hence, the objective of this study is to explore the impact of land use change on generalized water resources and ecological water stress using blue and green water concept taking Yanhe River Basin as a case study. Soil and Water Assessment Tool (SWAT) is applied to quantify summary of green and blue water which is defined as generalized water resources, ecological water requirement of vegetation (forest and grass), agricultural water footprint and virtual water flow are considered as regional water requirements. Land use types of 1980 (scenario?), 2017 (scenario?) are input in SWAT model while keeps other parameters constant in order to isolate the influence of land use changes. Results show that average annual difference of blue, green and generalized water resources is -72.08 million m 3 , 24.34 million m 3 , -47.74 million m 3 respectively when simulation results of scenario? subtracts scenario?and it presents that land use change caused by GGP leads to decrease in blue and generalized water resources whereas increase in green water resources. SURQ in scenario?is more than that in scenario?in all the study period from 1980-2017, green water storage in scenario?is more than that in scenario? in all the study period except in 1998; whereas LATQ in scenario?is less than that in scenario? except in 2000 and 2015, GWQ in 1992, 2000 and 2015, green water flow in 1998. Blue water, green water storage and green water flow in scenario? is less than that in scenario?in the whole basin, 12.89 percent of the basin and 99.21 percent of the basin respectively. Total WF increases from 1995 to 2010 because forest WF increases significantly in this period though agricultural WF and grass WF decreases. Ecological water stress index has no obvious temporal change trend in both land use scenarios but ecological water stress index in scenario? is more than that in scenario?which illustrates that GGP leads to increase of ecological water stress from perspective of generalized water resources

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Section: Swat Modelmentioning

confidence: 81%

Impact of Grain for Green Project on Water Resources and Ecological Water Stress in Yanhe River Basin

Han

Xia

Huang

et al. 2021

Preprint

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“…The climate in the lake area is mild and dry. The average temperature in July is [15][16][17][18][19][20][21][22][23][24][25] • C, and the average temperature in January is −6 • C. The Lake Issyk-Kul drainage basin occupies a surface area of 22,080 km 2 , and there are 118 large and small rivers with a surface runoff of about 36.7 × 10 8 m 3 [62]. The main river flows are Chong-Koi-Suu, Cholpon-Ata, Chong-Ak-Suu, Ak-Suu, Chong-Uryukty, Pzhergalan, Ak-sai, Tossor, Ton, Dzhuuku, Tamga, Karakol, and Chong-Kyzyl-Su [63].…”

Section: Study Area and Data 21 Study Areamentioning

confidence: 99%

Trend Analysis of Annual and Seasonal River Runoff by Using Innovative Trend Analysis with Significant Test

Alifujiang

Abuduwaili

2021

Water

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This study investigated the temporal patterns of annual and seasonal river runoff data at 13 hydrological stations in the Lake Issyk-Kul basin, Central Asia. The temporal trends were analyzed using the innovative trend analysis (ITA) method with significance testing. The ITA method results were compared with the Mann-Kendall (MK) trend test at a 95% confidence level. The comparison results revealed that the ITA method could effectively identify the trends detected by the MK trend test. Specifically, the MK test found that the time series percentage decreased from 46.15% in the north to 25.64% in the south, while the ITA method revealed a similar rate of decrease, from 39.2% to 29.4%. According to the temporal distribution of the MK test, significantly increasing (decreasing) trends were observed in 5 (0), 6 (2), 4 (3), 8 (0), and 8 (1) time series in annual, spring, summer, autumn, and winter river runoff data. At the same time, the ITA method detected significant trends in 7 (1), 9 (3), 6(3), 9 (3), and 8 (2) time series in the study area. As for the ITA method, the “peak” values of 24 time series (26.97%) exhibited increasing patterns, 25 time series (28.09%) displayed increasing patterns for “low” values, and 40 time series (44.94%) showed increasing patterns for “medium” values. According to the “low”, “medium”, and “peak” values, five time series (33.33%), seven time series (46.67%), and three time series (20%) manifested decreasing trends, respectively. These results detailed the patterns of annual and seasonal river runoff data series by evaluating “low”, “medium”, and “peak” values.

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“…Eight synthetic hydrological models representing different I/O burdens and different levels of river network complexity were used to evaluate the efficiency of GP-SWAT. These synthetic models were built based on the Harp Lake and Jinjiang hydrological models (Fu et al, 2014;Zhang et al, 2020;Zhang et al, 2015). The Harp Lake and Jinjiang hydrological models include 38 and 99 subbasins, respectively.…”

Section: Case Studymentioning

confidence: 99%

“…With the enhanced availability of high-resolution remote sensing data and long periods of hydrometeorological data, hydrologists are increasingly building high-fidelity hydrological models to investigate water availability (Liang et al, 2020), water quality (Fang et al, 2020), climate change (Cai et al, 2016), and watershed management options (Jayakody et al, 2014;Qi and Altinakar, 2011;Lee et al, 2010). However, these hydrological models, which contain detailed representations of real-world systems and processes, can demand large computational budgets and require prohibitively high execution times, ranging from minutes to days (Razavi et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

GP-SWAT (v1.0): a two-layer graph-based parallel simulation framework for the SWAT model

Zhang¹,

Lin²,

Wu³

et al. 2021

Preprint

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Abstract. High-fidelity and large-scale hydrological models are increasingly used to investigate the impacts of human activities and climate change on water availability and quality. However, the detailed representations of real-world systems and processes contained in these models inevitably lead to prohibitively high execution times, ranging from minutes to days. This becomes computationally prohibitive or even infeasible when large iterative model simulations are involved. In this study, we propose a generic two-layer model parallelization scheme to reduce the run time of computationally expensive model applications through a combination of model spatial decomposition and the graph-parallel Pregel algorithm. Taking the Soil and Water Assessment Tool (SWAT) as an example, we implemented a generic tool named GP-SWAT, enabling model-level and subbasin-level model parallelization on a Spark computer cluster. We then evaluated GP-SWAT in two sets of experiments to demonstrate the potential of GP-SWAT to accelerate single and iterative model simulations and to run in different environments. In each test set, Spark-SWAT was applied for the parallel simulation of eight synthetic hydrological models with different input/output (I/O) burdens and river network characteristics. The experimental results indicate that GP-SWAT can effectively solve high-computational-demand problems of the SWAT model. In addition, as a scalable and flexible tool, it can be run in diverse environments, from a commodity computer running the Microsoft Windows operating system to a Spark cluster consisting of a large number of computational nodes. Moreover, it is possible to apply this generic scheme to other subbasin-based hydrological models or even acyclic models in other domains to alleviate input/output (I/O) demands and optimize model computational performance.

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Interactive effects of climate variability and human activities on blue and green water scarcity in rapidly developing watershed

Cited by 66 publications

References 45 publications

Impact of Grain for Green Project on Water Resources and Ecological Water Stress in Yanhe River Basin

Impact of Grain for Green Project on Water Resources and Ecological Water Stress in Yanhe River Basin

Trend Analysis of Annual and Seasonal River Runoff by Using Innovative Trend Analysis with Significant Test

GP-SWAT (v1.0): a two-layer graph-based parallel simulation framework for the SWAT model

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