Hydrologic Response of Climate Change in the Source Region of the Yangtze River, Based on Water Balance Analysis

Self Cite

This paper developed a multi-space prediction model for seasonal precipitation using a high-resolution grid dataset (0.5 • × 0.5 • ) together with climate indices. The model is based on principal component analyses (PCA) and artificial neural networks (ANN). Trend analyses show that mean annual and seasonal precipitation in the area is increasing depending on spatial location. For this reason, a multi-space model is especially suited for prediction purposes. The PCA-ANN model was examined using a 64-grid mesh over the source region of the Yangtze River (SRYR) and was compared to a traditional multiple regression model with a three-fold cross-validation method. Seasonal precipitation anomalies were converted using PCA into principal components. Hierarchical lag relationships between principal components and each potential predictor were identified by Spearman rank correlation analyses. The performance was compared to observed precipitation and evaluated using mean absolute error, root mean squared error, and correlation coefficient. The proposed PCA-ANN model provides accurate seasonal precipitation prediction that is better than traditional regression techniques. The prediction results displayed good agreement with observations for all seasons with correlation coefficients in excess of 0.6 for all spatial locations.

Section: Discussionsupporting

confidence: 77%

Multi-Space Seasonal Precipitation Prediction Model Applied to the Source Region of the Yangtze River, China

Berndtsson

et al. 2019

Self Cite

“…Climate change and its impacts have been evident in the Qinghai Tibetan Plateau over the last decade [1][2][3][4][5]. The warming rate has been more significant during the last 50 years on the Qinghai Tibetan Plateau compared to other regions in the world with the same altitude [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…Woo, et al [13] and Xu, et al [14] found that summer flows will increase due to more extreme precipitation events, more frequent warm nights, and long heatwaves in the HWYZ. Most of the studies conducted in this region were related to temperature and precipitation trends [15][16][17][18][19][20][21][22] and very few investigated linkages with stream flows [4,23,24]. The wavelet method is one of the most reliable tools for analyzing the variations in the hydrological and climatic components by the discretization of the time series into different periods and scales.…”

Section: Introductionmentioning

confidence: 99%

Climatic Variability and Periodicity for Upstream Sub-Basins of the Yangtze River, China

Ahmed

Wang

Booij

et al. 2020

The headwaters of the Yangtze River are located on the Qinghai Tibetan Plateau, which is affected by climate change. Here, treamflow trends for Tuotuohe and Zhimenda sub-basins and relations to temperature and precipitation trends during 1961–2015 were investigated. The modified Mann–Kendall trend test, Pettitt test, wavelet analysis, and multivariate correlation analysis was deployed for this purpose. The temperature and precipitation significantly increased for each sub-basin, and the temperature increase was more significant in Tuotuohe sub-basin as compared to the Zhimenda sub-basin. A statistically significant periodicity of 2–4 years was observed for both sub-basins in different time spans. Higher flow periodicities for Tuotuohe and Zhimenda sub-basin were found after 1991 and 2004, respectively, which indicates that these are the change years of trends in streamflows. The influence of temperature on streamflow is more substantial in Tuotuohe sub-basin, which will ultimately impact the melting of glaciers and snowmelt runoff in this sub-basin. Precipitation plays a more critical role in the Zhimenda streamflow. Precipitation and temperature changes in the headwaters of the Yangtze River will change the streamflow variability, which will ultimately impact the hydropower supply and water resources of the Yangtze Basin. This study contributes to the understanding of the dynamics of the hydrological cycle and may lead to better hydrologic system modeling for downstream water resource developments.

“…Imbalances in water supply and demand lead to shortages; specifically, global climate change, population growth, rapid urbanization, and agricultural expansion have all caused the water demands of different sectors to increase rapidly [7][8][9][10][11]. Global warming leads to increased evapotranspiration, which aggravates water cycling processes and causes increased rainfall [12][13][14][15]. However, in most arid regions where water is scarce, rainfall is decreasing, leading to an increasing imbalance between global water supply and demand [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Patterns of Crop Irrigation Water Requirements in the Heihe River Basin, China

Liu

Song

Deng

2017

Abstract:Agricultural expansion, population growth, rapid urbanization, and climate change have all significantly impacted global water supply and demand and have led to a number of negative consequences including ecological degradation and decreases in biodiversity, especially in arid and semi-arid areas. The agricultural sector consumes the most water globally; crop irrigation alone uses up more than 80% of available agricultural water. Thus, to maintain sustainable development of the global economy and ecosystems, it is crucial to effectively manage crop irrigation water. We focus on the arid and semi-arid Heihe River Basin (HRB), China, as a case study in this paper, extracting spatiotemporal information on the distribution of crop planting using multi-temporal Thematic Mapper and Enhanced Thematic Mapper Plus (TM/ETM+) remote sensing (RS) images. We estimate the spatiotemporal crop irrigation water requirements (IWR c ) using the Food and Agriculture Organization of the United Nations (FAO) Penman-Monteith method and reveal variations in IWR c . We also analyze the impact of changes in crop planting structure on IWR c and discuss strategies for the rational allocation of irrigation water as well as policies to alleviate imbalance between water supply and demand. The results of this study show that effective rainfall (ER) decreases upstream-to-downstream within the HRB, while crop evapotranspiration under standard conditions (ET c ) increases, leading to increasing spatial variation in IWR c from zero up to 150 mm and between 300 and 450 mm. Data show that between 2007 and 2012, annual mean ER decreased from 139.49 to 106.29 mm, while annual mean ET c increased from 483.87 to 500.38 mm, and annual mean IWR c increased from 339.95 to 370.11 mm. Data show that monthly mean IWR c initially increased before decreasing in concert with crop growth. The largest values for this index were recorded during the month of June; results show that IWR c for May and June decreased by 8.14 and 11.67 mm, respectively, while values for July increased by 5.75 mm between 2007 and 2012. These variations have helped to ease the temporal imbalance between water supply and demand. Mean IWR c values for oilseed rape, corn, barley, and other crops all increased over the study period, from 208. 43, 349.35, 229.26, and 352.85 mm, respectively, in 2007, to 241.81, 393.10, 251.17, and 378.86 mm, respectively, in 2012. At the same time, the mean IWR c of wheat decreased from 281.53 mm in 2007 to 266.69 mm in 2012. Mainly because of changes in planting structure, the total IWR c for the HRB in 2012 reached 2692.58 × 10 6 m 3 , an increase of 332.16 × 10 6 m 3 (14.07%) compared to 2007. Data show that 23.11% (76.77 × 10 6 m 3 ) of this increase is due to crop transfers, while the remaining 76.89% (255.39 × 10 6 m 3 ) is the result of the rapid expansion of cultivated land. Thus, to maintain both the sustainable development and ecological security of the HRB, it is crucial to efficiently manage and utilize agricultural water in light of spat...