Quantifying the climate and human impacts on the hydrology of the Yalong River Basin using two approaches

Zhao, Yong; Dong, Ningpeng; Wang, Hao

doi:10.1002/rra.3782

Cited by 12 publications

(5 citation statements)

References 44 publications

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“…In terms of space, the annual average precipitation increases from northwest to southeast and varies greatly from upstream to downstream; it is 600-800 mm upstream, 1000-1400 mm in the middle and 900-1300 mm downstream [55]. In terms of time, precipitation is mainly concentrated from June to September, and heavy precipitation is prone to occur in July and August [1,56]. However, it can be seen from Figure 1 that the precipitation stations in the Yalong River are relatively scarce and mainly concentrated in low-altitude areas due to the constraints of terrain conditions and the economic level.…”

Section: Study Areamentioning

confidence: 99%

A Method for Spatiotemporally Merging Multi-Source Precipitation Based on Deep Learning

Fang,

Qin,

Liu

et al. 2023

Remote Sensing

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Reliable precipitation data are essential for studying water cycle patterns and climate change. However, there are always temporal or spatial errors in precipitation data from various sources. Most precipitation fusion methods are influenced by high-dimensional input features and do not make good use of the spatial correlation between precipitation and environmental variables. Thus, this study proposed a novel multi-source precipitation spatiotemporal fusion method for improving the spatiotemporal accuracy of precipitation. Specifically, the attention mechanism was used to first select critical input information to dimensionalize the inputs, and the Convolutional long-short-term memory network (ConvLSTM) was used to merge precipitation products and environmental variables spatiotemporally. The Yalong River in the southeastern part of the Tibetan Plateau was used as the case study area. The results show that: (1) Compared with the original precipitation products (IMERG, ERA5 and CHIRPS), the proposed method has optimal accuracy and good robustness, and its correlation coefficient (CC) reaches 0.853, its root mean square coefficient (RMSE) decreases to 3.53 mm/d and its mean absolute error (MAE) decreases to 1.33 mm/d. (2) The proposed method can reduce errors under different precipitation intensities and greatly improve the detection capability for strong precipitation. (3) The merged precipitation generated by the proposed method can be used to describe the rainfall–runoff relationship and has good applicability. The proposed method may greatly improve the spatiotemporal accuracy of precipitation in complex terrain areas, which is important for scientific management and the allocation of water resources.

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Section: Study Areamentioning

confidence: 99%

A Method for Spatiotemporally Merging Multi-Source Precipitation Based on Deep Learning

Fang,

Qin,

Liu

et al. 2023

Remote Sensing

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“…However, under the influence of towns and roads, the rural settlements, as essential elements of the Grand Canal, are mainly driven by agricultural production and road networks, indicating that rural settlement has changed from natural to sudden growth (C. B. Wen, 2019;Zhao, Dong, & Wang, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Villages along the canal are distributed in a regular and skewed matrix with natural distribution (M. Zhang & Tang, 2018), mainly influenced by natural geographical location, socioeconomic policies, and canal functions (C. Li, 2007; C. Zhang, 2019a; Y. Y. Zhang, 2019b). However, under the influence of towns and roads, the rural settlements, as essential elements of the Grand Canal, are mainly driven by agricultural production and road networks, indicating that rural settlement has changed from natural to sudden growth (C. B. Wen, 2019; Zhao, Dong, & Wang, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

An analysis of the spatial evolution and influencing factors of rural settlements along the Shandong section of the Grand Canal of China

Huo

Tang

et al. 2021

River Research & Apps

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Since 1980s, the weakening of the transportation function of the Grand Canal and rapid urbanisation has generated significant changes in the spatial pattern of rural settlements along the Grand Canal. Analysing the changing characteristics of this spatial pattern, and exploring the natural, social, and economic factors influencing these changes are key to understanding the regional spatial structure and development law, and grasping the degree of influence of these factors.This study selected approximately 13,000 rural settlements in 27 county-level units in the Shandong section of the Grand Canal, from 1980 to 2018, as the research object. The Grand Canal's distance buffer zone is divided into canal-side, near-canal, far-canal, and further-canal settlements. The correlation model with the canal is constructed through the controlled experiment. The distribution, scale and form of the settlement are analysed quantitatively by applying the change of gravity centre model (CGC), average nearest neighbour analysis (ANN), landscape metrics (LM), and other methods. The quantitative analysis of geographic detectors in spatial pattern differentiation factors shows the relative importance and interaction within them. This study indicates that the spatial distribution shows 'large dispersion and small concentration' and 'small-scale agglomeration' characteristics. The larger the scale of the Page 9 of 35 http://mc.manuscriptcentral.com/rra River Research and Applicationssettlement expands, the more the number of settlements decreases. The closer the settlement nears the river, the more complex the shape clusters. The total output value of agriculture, forestry, animal husbandry, fisheries, the total power of agricultural machinery, and the effective irrigation area are the main influencing factors. The grain area and the per capita disposable income of rural residents are the main auxiliary influencing factors.

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“…The most obvious impacts of climate change have resulted in the rise in global temperatures and altered precipitation patterns [4,5]. Due to the inseparable linkage between the climatic and hydrological cycle, global warming is reported to accelerate water circulation, which will lead to spatio-temporal redistribution of water resources at global and regional scales [6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Variability in Hydroclimate over the Upper Yangtze River Basin, China

Yang

Bing

2022

Atmosphere

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As global warming produces dramatic climate changes, water management is facing increasingly serious challenges. Given to the process of climate change and its complex effects on watershed hydrology, this paper investigates the spatial and temporal variation characteristics of major climatic factors (i.e., precipitation and temperature) over the upper Yangtze River basin (UYRB), China. The statistical analyses are based on annual and seasonal scales during 1951–2020 with a recorded period of seven decades. The Mann–Kendall nonparametric test and R/S analysis are used to record the temporal trends (past and future) of climate variables; the Pettitt test, standard normal homogeneity test and Buishand test are used to detect the homogeneity in climate series. The sensitivities of the streamflow to climatic parameters are assessed at the watershed scale, especially considering the Three Gorges Dam’s (TGD) effect on changing runoff. The results of the study indicate that the annual precipitation of 29 out of 34 series indicate homogeneity, while 31 out of 34 annual mean temperature series show heterogeneity, with jump points around 1997 in the mean temperature of 20 sites. Detectable changes in precipitation were not observed during 1951–2020; however, the temperature increased significantly in the whole basin on annual and seasonal scales, except for several stations in the eastern part. The magnitude of increase in air temperature in high altitudes (Tibet Plateau) is higher than that in low altitudes (Sichuan Plain) over the last seven decades, and future temperatures continue to sharply increase in high altitudes. The TGD plays an important role in explaining the seasonal variations in streamflow at Yichang station, with streamflow experiencing a sharp increase in winter and spring (dry season) and a decrease in summer and autumn (rainy season) compared to the pre-TGD period. The streamflow variation at an annual scale is mainly regulated by climate fluctuation (variation in precipitation). During the last seven decades, increasing air temperature and decreases in rainfall and runoff signify reduced water resources availability, and the climate tends to be warmer and drier over the basin. The sensitivity of the streamflow to watershed precipitation is higher than that to temperature, with variation in annual rainfall explaining 71% of annual runoff variability.

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Quantifying the climate and human impacts on the hydrology of the Yalong River Basin using two approaches

Cited by 12 publications

References 44 publications

A Method for Spatiotemporally Merging Multi-Source Precipitation Based on Deep Learning

A Method for Spatiotemporally Merging Multi-Source Precipitation Based on Deep Learning

An analysis of the spatial evolution and influencing factors of rural settlements along the Shandong section of the Grand Canal of China

Spatio-Temporal Variability in Hydroclimate over the Upper Yangtze River Basin, China

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