Identifying climate change impacts on water resources in Xinjiang, China

Luo, Min; Li, Shipeng; Meng, Fanhao; Duan, Yongchao; Bao, Anming; Xing, Wei; Feng, Xiahong; Maeyer, Philippe De; Frankl, Amaury

doi:10.1016/j.scitotenv.2019.04.297

Cited by 83 publications

(63 citation statements)

References 56 publications

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“…One of the factors contributing to such a tendency is the locally intense convective activity of JJA precipitation across most of the Central-American Caribbean coast [27,109], which in this case most GCM-RCM models fail to capture and for instance, BC methods render less effective. In general terms, the diminished R and PBIAS performances for these two regions are most likely related to GCM-RCMs difficulties at representing predominantly convective precipitation during JJA; a situation that has largely been identified and investigated [21,58,70,80]. On the other hand, GCM-RCM pairs can also suffer from considerable systematic biases of the precipitation field in orographic terrain such as Costa Rica due to the windward and leeward effects imposed by the northwest-southeast trending cordilleras, producing overestimation on the windward side and underestimation on the lee side [57,59].…”

Section: Jjamentioning

confidence: 99%

“…Even though BC methods are capable of reducing biases from RCM outputs [55], their performances are most likely regionally dependent and should be evaluated and validated over a recent-past period prior to any climate change application [21]. Bias correction of precipitation is equally sensitive to the selection of a particular time period, so a good temporal structure of original observations is imperative [38,70]. Located in the Central America region, this study focuses on Costa Rica where most of the territory has experienced some degree of precipitation change during the period 1961-1990, showing increases on the north-western Caribbean side and decreases on the Pacific side [12,71].…”

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confidence: 99%

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Performance Evaluation of Bias Correction Methods for Climate Change Monthly Precipitation Projections over Costa Rica

Mendez

Maathuis

Hein-Griggs

et al. 2020

Water

127

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Six bias correction (BC) methods; delta-method (DT), linear scaling (LS), power transformation of precipitation (PTR), empirical quantile mapping (EQM), gamma quantile mapping (GQM) and gamma-pareto quantile mapping (GPQM) were applied to adjust the biases of historical monthly precipitation outputs from five General Circulation Models (GCMs) dynamically downscaled by two Regional Climate Models (RCMs) for a total of seven different GCM-RCM pairs over Costa Rica. High-resolution gridded precipitation observations were used for the control period 1951-1980 and validated over the period 1981-1995. Results show that considerable biases exist between uncorrected GCM-RCM outputs and observations, which largely depend on GCM-RCM pair, seasonality, climatic region and spatial resolution. After the application of bias correction, substantial biases reductions and comparable performances among most BC methods were observed for most GCM-RCM pairs; with EQM and DT marginally outperforming the remaining methods. Consequently, EQM and DT were selectively applied to correct the biases of precipitation projections from each individual GCM-RCM pair for a near-future (2011-2040), mid-future (2041-2070) and far-future (2071-2100) period under Representative Concentration Pathways (RCPs) 2.6, 4.5 and 8.5 using the control period . Results from the bias-corrected future ensemble-mean anticipate a marked decreasing trend in precipitation from near to far-future periods during the dry season (December, January, February (DJF) and March, April, May (MAM)) for RCP4.5 and 8.5; with pronounced drier conditions for those climatic regions draining towards the Pacific Ocean. In contrast, mostly wetter conditions are expected during the dry season under RCP2.6, particularly for the Caribbean region. In most of the country, the greatest decrease in precipitation is projected at the beginning of the rainy season (June, July, August (JJA)) for the far-future period under RCP8.5, except for the Caribbean region where mostly wetter conditions are anticipated. Regardless of future period, slight increases in precipitation with higher radiative forcing are expected for SON excluding the Caribbean region, where precipitation is likely to increase with increasing radiative forcing and future period. This study demonstrates that bias correction should be considered before direct application of GCM-RCM precipitation projections over complex territories such as Costa Rica.

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Section: Jjamentioning

confidence: 99%

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confidence: 99%

Performance Evaluation of Bias Correction Methods for Climate Change Monthly Precipitation Projections over Costa Rica

Mendez

Maathuis

Hein-Griggs

et al. 2020

Water

127

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“…The annual precipitation of North Xinjiang is 100 to 500 mm, while that of South Xinjiang is 20 to 100 mm. The annual average temperature in North Xinjiang ranges from 4 • C to 8 • C, and it ranges from 10 • C to 13 • C in South Xinjiang (Luo et al, 2019). There are large areas of forest and grassland vegetation on the Tianshan Mountains, Altai Mountains and Kunlun Mountains.…”

Section: Study Areamentioning

confidence: 99%

Analysis and prediction of vegetation dynamics under the background of climate change in Xinjiang, China

Zhuang

Feng

et al. 2020

PeerJ

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Background Vegetation dynamics is defined as a significant indictor in regulating terrestrial carbon balance and climate change, and this issue is important for the evaluation of climate change. Though much work has been done concerning the correlations among vegetation dynamics, precipitation and temperature, the related questions about relationships between vegetation dynamics and other climatic factors (e.g., specific humidity, net radiation, soil moisture) have not been thoroughly considered. Understanding these questions is of primary importance in developing policies to address climate change. Methods In this study, the least squares regression analysis method was used to simulate the trend of vegetation dynamics based on the normalized difference vegetation index (NDVI) from 1981 to 2018. A partial correlation analysis method was used to explore the relationship between vegetation dynamics and climate change; and further,the revised greyscale model was applied to predict the future growth trend of natural vegetation. Results The Mann-Kendall test results showed that th e air temperature rose sharply in 1997 and had been in a state of high fluctuations since then. Strong changes in hydrothermal conditions had major impact on vegetation dynamics in the area. Specifically, the NDVI value of natural vegetation showed an increasing trend from 1981 to 2018, and the same changes occurred in the precipitation. From 1981 to 1997, the values of natural vegetation increased at a rate of 0.0016 per year. From 1999 to 2009, the NDVI value decreased by an average rate of 0.0025 per year. From 2010 to 2018, the values began an increasing trend and reached a peak in 2017, with an average annual rate of 0.0033. The high vegetation dynamics areas were mainly concentrated in the north and south slopes of the Tianshan Mountains, the Ili River Valley and the Altay area. The greyscale prediction results showed that the annual average NDVI values of natural vegetation may present a fluctuating increasing trend. The NDVI value in 2030 is 0.0196 higher than that in 2018, with an increase of 6.18%. Conclusions Our results indicate that: (i) the variations of climatic factors have caused a huge change in the hydrothermal conditions in Xinjiang; (ii) the vegetation dynamics in Xinjiang showed obvious volatility, and then in the end stage of the study were higher than the initial stage the vegetation dynamics in Xinjiang showed a staged increasing trend; (iii) the vegetation dynamics were affected by many factors,of which precipitation was the main reason; (iv) in the next decade, the vegetation dynamics in Xinjiang will show an increasing trend.

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“…Global climate change and its impact on hydrology and water resources have received special attention due to its effects on land use and development [1]. The hydrologic cycle in watersheds is changing greatly under the influence of global climate change.…”

Section: Introductionmentioning

confidence: 99%

“…Several simulated GCMs have been used as an important input of Soil and Water Assessment Tool (SWAT) models to assess the hydrological responses to climate change in many watersheds [3,4,11,12]. However, the direct use of GCM outputs in studies of hydrological impacts still remains a challenge as the GCM output usually shows errors and uncertainties with observed data [1,13]. Thus, GCM output should be either downscaled to match with the basin scale [14] or corrected to decrease the systematic bias between simulated and observed data to increase model precision and accuracy before being used in any climate and hydrological analysis.…”

Section: Introductionmentioning

confidence: 99%

Selection of Effective GCM Bias Correction Methods and Evaluation of Hydrological Response under Future Climate Scenarios

Tan

Guzmán

Dong

et al. 2020

Climate

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Global climate change is presenting a variety of challenges to hydrology and water resources because it strongly affects the hydrologic cycle, runoff, and water supply and demand. In this study, we assessed the effects of climate change scenarios on hydrological variables (i.e., evapotranspiration and runoff) by linking the outputs from the global climate model (GCM) with the Soil and Water Assessment Tool (SWAT) for a case study in the Lijiang River Basin, China. We selected a variety of bias correction methods and their combinations to correct the lower resolution GCM outputs of both precipitation and temperature. Then, the SWAT model was calibrated and validated using the observed flow data and corrected historical GCM with the optimal correction method selected. Hydrological variables were simulated using the SWAT model under emission scenarios RCP2.6, RCP4.5, and RCP8.5. The results demonstrated that correcting methods have a positive effect on both daily precipitation and temperature, and a hybrid method of bias correction contributes to increased performance in most cases and scenarios. Based on the bias corrected scenarios, precipitation annual average, temperature, and evapotranspiration will increase. In the case of precipitation and runoff, projection scenarios show an increase compared with the historical trends, and the monthly distribution of precipitation, evapotranspiration, and runoff shows an uneven distribution compared with baseline. This study provides an insight on how to choose a proper GCM and bias correction method and a helpful guide for local water resources management.

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Identifying climate change impacts on water resources in Xinjiang, China

Cited by 83 publications

References 56 publications

Performance Evaluation of Bias Correction Methods for Climate Change Monthly Precipitation Projections over Costa Rica

Performance Evaluation of Bias Correction Methods for Climate Change Monthly Precipitation Projections over Costa Rica

Analysis and prediction of vegetation dynamics under the background of climate change in Xinjiang, China

Selection of Effective GCM Bias Correction Methods and Evaluation of Hydrological Response under Future Climate Scenarios

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