Long Term Quantification of Climate and Land Cover Change Impacts on Streamflow in an Alpine River Catchment, Northwestern China

Yin, Zhenliang; Feng, Qi; Yang, Linshan; Wen, Xin; Si, Jing; Zou, Songbing

doi:10.3390/su9071278

Cited by 20 publications

(15 citation statements)

References 50 publications

(77 reference statements)

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“…The climate is characterized by cold and dry conditions in winter and warm and humid conditions in summer with large spatial and temporal heterogeneity. The multi-year average air temperature ranges from −5 to 4 • C. The air temperature increased by 0.37 • C/10yr in 1980-2010, which is faster than the rate over the last 60 years by 0.30 • C/10yr [29]. The multi-year average annual precipitation exceeds 400 mm, increasing by 15.5-16.4 mm for every 100 m rise in altitude.…”

Section: Study Areamentioning

confidence: 93%

Hydrological Responses to the Future Climate Change in a Data Scarce Region, Northwest China: Application of Machine Learning Models

Zhu

Yang

Liu

et al. 2019

Water

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Forecasting the potential hydrological response to future climate change is an effective way of assessing the adverse effects of future climate change on water resources. Data-driven models based on machine learning algorithms have great application prospects for hydrological response forecasting as they require less developmental time, minimal input, and are relatively simple compared to dynamic or physical models, especially for data scarce regions. In this study, we employed an ensemble of eight General Circulation Models (GCMs) and two artificial intelligence-based methods (Support Vector Regression, SVR, and Extreme Learning Machine, ELM) to establish the historical streamflow response to climate change and to forecast the future response under Representative Concentration Pathway (RCP) scenarios 4.5 and 8.5 in a mountainous watershed in northwest China. We found that the artificial-intelligence-based SVR and ELM methods showed very good performances in the projection of future hydrological responses. The ensemble of GCM outputs derived very close historical hydrological hindcasts but had great uncertainty in future hydrological projections. Using the variables of GCM outputs as inputs to SVR can reduce intermediate downscaling links between variables and decrease the cumulative effect of bias in projecting future hydrological responses. Future precipitation in the study area will increase in the future under both scenarios, and this increasing trend is more significant under RCP 8.5 than under scenario 4.5. The results also indicate the streamflow change will be more sensitive to temperature (precipitation) under the RCP 8.5 (4.5) scenario. The findings and approach have important implications for hydrological response studies and the evaluation of impacts on localized regions similar to the mountainous watershed in this study.

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Section: Study Areamentioning

confidence: 93%

Hydrological Responses to the Future Climate Change in a Data Scarce Region, Northwest China: Application of Machine Learning Models

Zhu

Yang

Liu

et al. 2019

Water

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“…The magnitudes of the estimated changes in the trend of temperature and precipitation were estimated by using a simple non-parametric procedure that Sen developed. The Sen [30] method alleviates the consequences of anomalous trends by using the median of the series of slopes as the judgmental foundation [13]. The Q i is given by the following:…”

Section: The Precipitation and Temperature Trend Analysismentioning

confidence: 99%

“…From the hydrological models available, we chose the Soil and Water Assessment Tool (SWAT) model due to the fact that this model has been used worldwide to simulate the hydrological cycle and has been used extensively to evaluate the impacts of LULC changes on watershed hydrology [11]. Likewise, a multitude of studies have addressed separating the influences of human activities and climate variability on water resources using the SWAT model [3,[11][12][13]. However, only two references have been found on the application of the SWAT model to assess the impacts of LULC changes in Spain.…”

Section: Introductionmentioning

confidence: 99%

Assessing Impacts of Climate Variability and Reforestation Activities on Water Resources in the Headwaters of the Segura River Basin (SE Spain)

et al. 2018

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Climate change and the land-use and land-cover changes (LULC) resulting from anthropic activity are important factors in the degradation of an ecosystem and in the availability of a basin’s water resources. To know how these activities affect the quantity of the water resources of basins, such as the Segura River Basin, is of vital importance. In this work, the Soil and Water Assessment Tool (SWAT) was used for the study of the abovementioned impacts. The model was validated by obtaining a Nash–Sutcliffe efficiency (NSE) of 0.88 and a percent bias (PBIAS) of 17.23%, indicating that SWAT accurately replicated monthly streamflow. Next, land-use maps for the years of 1956 and 2007 were used to establish a series of scenarios that allowed us to evaluate the effects of these activities on both joint and individual water resources. A reforestation plan applied in the basin during the 1970s caused that the forest area had almost doubled, whereas the agricultural areas and shrubland had been reduced by one-third. These modifications, together with the effect of climate change, have led to a decrease of 26.3% in the quantity of generated water resources, not only due to climate change but also due to the increase in forest area.

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“…As a result, it is a widely used tool for the study of the hydrological effects of environmental change [18][19][20]. Yin [21] applied the SWAT model to quantify the impact of climate variability/land use change on the streamflow over a long historical time period. Kim [22] used SWAT model to evaluate the separated and combined impacts of future changes and land use changes on the streamflow.…”

Section: Introductionmentioning

confidence: 99%

The Impacts of Climate Variability and Land Use Change on Streamflow in the Hailiutu River Basin

Guang-hua

Guan

Zhang

et al. 2018

Water

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Abstract:The Hailiutu River basin is a typical semi-arid wind sandy grass shoal watershed in northwest China. Climate and land use have changed significantly during the period 1970-2014. These changes are expected to impact hydrological processes in the basin. The Mann-Kendall (MK) test and sequential t-test analysis of the regime shift method were used to detect the trend and shifts of the hydrometeorological time series. Based on the analyzed results, seven scenarios were developed by combining different land use and/or climate situations. The Soil Water Assessment Tool (SWAT) model was applied to analyze the impacts of climate variability and land use change on the values of the hydrological components. The China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS) was applied to enhance the spatial expressiveness of precipitation data in the study area during the period 2008-2014. Rather than solely using observed precipitation or CMADS precipitation, the precipitation values of CMADS and the observed precipitation values were combined to drive the SWAT model for better simulation results. From the trend analysis, the annual streamflow and wind speed showed a significant downward trend. No significant trend was found for the annual precipitation series; however, the temperature series showed upward trends. With the change point analysis, the whole study period was divided into three sub-periods (1970-1985, 1986-2000, and 2001-2014). The annual precipitation, mean wind speed, and average temperature values were 316 mm, 2.62 m/s, and 7.9 • C, respectively, for the sub-period [1970][1971][1972][1973][1974][1975][1976][1977][1978][1979][1980][1981][1982][1983][1984][1985] 272 were the increased area of shrub and grass land and decreased area of sandy land. In the simulation it was shown that these changes caused the mean annual streamflow to decrease by 0.23 mm/year and 0.68 mm/year during the periods 1986-2000 and 2001-2014, respectively. Thus, the impact of climate variability on the streamflow was more profound than that of land use change. Under the impact of coupled climate variability and land use change, the mean annual streamflow decreased by 2.45 mm/year during the period 1986-2000, and the contribution of this variation to the decrease in observed streamflow was 27.8%. For the period 2001-2014, the combined climate variability and land use change resulted in an increase of 0.84 mm/year in annual streamflow. The results obtained in this study could provide guidance for water resource management and planning in the Erdos plateau.

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Long Term Quantification of Climate and Land Cover Change Impacts on Streamflow in an Alpine River Catchment, Northwestern China

Cited by 20 publications

References 50 publications

Hydrological Responses to the Future Climate Change in a Data Scarce Region, Northwest China: Application of Machine Learning Models

Hydrological Responses to the Future Climate Change in a Data Scarce Region, Northwest China: Application of Machine Learning Models

Assessing Impacts of Climate Variability and Reforestation Activities on Water Resources in the Headwaters of the Segura River Basin (SE Spain)

The Impacts of Climate Variability and Land Use Change on Streamflow in the Hailiutu River Basin

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