scholarly journals Catchment attributes and meteorology for large sample study in contiguous China

2021 ◽  
Author(s):  
Zhen Hao ◽  
Jin Jin ◽  
Runliang Xia ◽  
Shimin Tian ◽  
Wushuang Yang ◽  
...  
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Yellow River ◽  
Potential Evapotranspiration ◽  
Sunshine Duration ◽  
Ground Surface ◽  
Yellow River Basin ◽  
Catchment Characteristics ◽  
The Difference ◽  
The Yellow River Basin

Abstract. We introduce the first large-scale catchment attributes and meteorological time series dataset of contiguous China. To develop the dataset, we compiled diverse data sources to generate basin-oriented features describing the characteristics of the catchment related to hydrological processes. The proposed dataset consists of catchment characteristics including soil, land cover, climate, topography, geology, and 29-year meteorological time series (from 1990 to 2018). The meteorological variables include precipitation, temperature, evapotranspiration, wind speed, ground surface temperature, pressure, humidity and sunshine duration. We also derived a daily potential evapotranspiration time series based on a modified Penman’s equation. The studied catchments are 4875 catchments within contiguous China derived from digital elevation models. The spatial variations of catchment characteristics are analysed and organized into a series of maps; the correlation analysis between attributes was conducted. Compared to the previously proposed datasets, we derived more catchment characteristics resulting in a total of 127 attributes, providing a complete description of the catchments. Besides, we propose Normal-Camels-YR, a hydrological dataset covering 102 basins of the Yellow River basin with normalized streamflow observations. The proposed dataset provides numerous opportunities for comparative hydrological research, such as examining the difference in hydrological behaviours across different catchments and building general rainfall-runoff modelling frameworks for many catchments instead of limited to a few. The dataset is freely available via http://doi.org/10.5281/zenodo.4704017 for community use. We will open-source the complement code for generating the dataset such that the user can generate meteorological series and catchment attributes for any watershedwithin contiguous China.

scholarly journals CCAM: China Catchment Attributes and Meteorology dataset

2021 ◽  
Vol 13 (12) ◽  
pp. 5591-5616
Author(s):  
Zhen Hao ◽  
Jin Jin ◽  
Runliang Xia ◽  
Shimin Tian ◽  
Wushuang Yang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
River Basin ◽  
Large Scale ◽  
Yellow River ◽  
Potential Evapotranspiration ◽  
Yellow River Basin ◽  
The Yellow River ◽  
Catchment Characteristics ◽  
The Yellow River Basin

Abstract. The absence of a compiled large-scale catchment characteristics dataset is a key obstacle limiting the development of large-sample hydrology research in China. We introduce the first large-scale catchment attribute dataset in China. We compiled diverse data sources, including soil, land cover, climate, topography, and geology, to develop the dataset. The dataset also includes catchment-scale 31-year meteorological time series from 1990 to 2020 for each basin. Potential evapotranspiration time series based on Penman's equation are derived for each basin. The 4911 catchments included in the dataset cover all of China. We introduced several new indicators that describe the catchment geography and the underlying surface differently from previously proposed datasets. The resulting dataset has a total of 125 catchment attributes and includes a separate HydroMLYR (hydrology dataset for machine learning in the Yellow River Basin) dataset containing standardized weekly averaged streamflow for 102 basins in the Yellow River Basin. The standardized streamflow data should be able to support machine learning hydrology research in the Yellow River Basin. The dataset is freely available at https://doi.org/10.5281/zenodo.5729444 (Zhen et al., 2021). In addition, the accompanying code used to generate the dataset is freely available at https://github.com/haozhen315/CCAM-China-Catchment-Attributes-and-Meteorology-dataset (last access: 26 November 2021) and supports the generation of catchment characteristics for any custom basin boundaries. Compiled data for the 4911 basins covering all of China and the open-source code should be able to support the study of any selected basins rather than being limited to only a few basins.

scholarly journals Water Conservation Estimation Based on Time Series NDVI in the Yellow River Basin

2021 ◽  
Vol 13 (6) ◽  
pp. 1105
Author(s):  
Yangchengsi Zhang ◽  
Jiaqiang Du ◽  
Long Guo ◽  
Zhilu Sheng ◽  
Jinhua Wu ◽  
...  
Keyword(s):  
Time Series ◽  
Water Conservation ◽  
Large Scale ◽  
Yellow River ◽  
Yellow River Basin ◽  
Support Vector ◽  
The Yellow River ◽  
Grassland Ecosystem ◽  
Line Planning ◽  
The Yellow River Basin

Accurate estimation of the water conservation is of great significance for ecological red line planning. The water conservation of the Yellow River Basin has a vital influence on the development of the environment and the supply of ecological services in China. However, the existing methods used to estimate water conservation have many disadvantages, such as requiring numerous parameters, a complex calculation model, and using data that is often difficult acquire. It is often hard to provide sufficiently precise parameters and data, resulting in a large amount of calculation time and the difficulties in the study of large scale and long time series. In this study, a time series of the Normalized Difference Vegetation Index (NDVI) was applied to estimate water conservation in two aspects using the idea of wholeness and stratification, respectively. The overall fitting results can explain nearly 30% of the water conservation by partial least squares regression and nearly 50% of it by a support vector machine. However, the results of a stratified simulation showed that water conservation and the NDVI have a certain stratified heterogeneity among different ecosystem types. The optimal fitting result was achieved in a water/wetland ecosystem with the highest coefficient of determination (R2P) of 0.768 by the stratified support vector machine (SVM) model, followed by the forest and grassland ecosystem (both R2P of 0.698). The spatial mapping results showed that this method was most suitable for grassland ecosystem, followed by forest ecosystem. According to the results generated using the NDVI time series data, it is feasible to complete a spatial simulation of water conservation. This research can provide a reference for calculating regional or large-scale water conservation and in ecological red line planning.

scholarly journals Drought Evaluation with CMORPH Satellite Precipitation Data in the Yellow River Basin by Using Gridded Standardized Precipitation Evapotranspiration Index

2019 ◽  
Vol 11 (5) ◽  
pp. 485 ◽  
Author(s):  
Fei Wang ◽  
Haibo Yang ◽  
Zongmin Wang ◽  
Zezhong Zhang ◽  
Zhenhong Li
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Large Scale ◽  
Drought Index ◽  
Yellow River ◽  
Yellow River Basin ◽  
The Yellow River ◽  
Precipitation Data ◽  
Standardized Precipitation Evapotranspiration Index ◽  
The Yellow River Basin

The traditional station-based drought index is vulnerable because of the inadequate spatial distribution of the station, and also, it does not fully reflect large-scale, dynamic drought information. Thus, large-scale drought monitoring has been widely implemented by using remote sensing precipitation products. Compared with station data, remote sensing precipitation products have the advantages of wide coverage and dynamic, continuous data, which can effectively compensate for the deficiency in the spatial distribution of the ground stations and provide a new data source for the calculation of a drought index. In this study, the Gridded Standardized Precipitation Evapotranspiration Index (GSPEI) was proposed based on a remote sensing dataset produced by the Climate Prediction Center morphing technique (CMORPH), in order to evaluate the gridded drought characteristics in the Yellow River basin (YRB) from 1998 to 2016. The optimal Ordinary Kriging interpolation method was selected to interpolate meteorological station data to the same spatial resolution as CMORPH data (8 km), in order to compare the ground-based meteorological parameters to remote sensing-based data. Additionally, the gridded drought trends were identified based on the Modified Mann–Kendall (MMK) trend test method. The results indicated that: (1) the GSPEI was suitable for drought evaluation in the YRB using CMORPH precipitation data, which were consistent with ground-based meteorological data; (2) the positive correlation between GSPEI and SPEI was high, and all the correlation coefficients (CCs) passed the significance test of α = 0.05, which indicated that the GSPEI could better reflect the gridded drought characteristics of the YRB; (3) the drought severity in each season of the YRB was highest in summer, followed by spring, autumn, and winter, with an average GSPEI of −1.51, −0.09, 0.30, and 1.33, respectively; and (4) the drought showed an increasing trend on the monthly scale in March, May, August, and October, and a decreasing trend on the seasonal and annual scale.

scholarly journals The Trend and Influencing Factors of Potential Evapotranspiration in the Yellow River

2018 ◽  
Vol 246 ◽  
pp. 01020
Author(s):  
Yanyu Dai ◽  
Fan Lu ◽  
Kui Zhu ◽  
Xinyi Song ◽  
Yiran Xu
Keyword(s):  
River Basin ◽  
Yellow River ◽  
Potential Evapotranspiration ◽  
Great Influence ◽  
Yellow River Basin ◽  
Test Method ◽  
Trend Test ◽  
The Yellow River ◽  
Significant Downward Trend ◽  
The Yellow River Basin

Based on the Penman-Monteith formula recommended by the World Food and Agriculture Organization (FAO) and the Mann-Kendall trend test method, the variation trend of potential evapotranspiration in the Yellow River Basin from 1952 to 2014 is analyzed. The results showed that the potential evapotranspiration of 43.3% of the 90 meteorological stations in the Yellow River Basin showed a significant upward trend. 30% of the stations showed a significant downward trend, and 26.7% of the stations had no obvious trend of change. In all the secondary areas of water resources, the stations above Longyangxia are basically marked upward. The Longyangxia to the northern part of Lanzhou, the Longmen to the east of Sanmenxia and the Sanmenxia to Huayuankou are all significant descending sites. The change trend of the sites below the Huayuankou is not obvious. In other partition three kinds of sites are distributed. Through the correlation analysis, it is found that the increase of temperature has a great influence on the stations where the potential evapotranspiration is significantly increased, and the decrease of wind speed is the main reason for the significant decrease of potential evapotranspiration in some stations.

scholarly journals Using a Gaussian Function to Describe the Seasonal Courses of Monthly Precipitation and Potential Evapotranspiration across the Yellow River Basin, China

2019 ◽  
Vol 20 (11) ◽  
pp. 2185-2201 ◽  
Author(s):  
Yaqin Wang ◽  
Yi Luo ◽  
Muhammad Shafeeque
Keyword(s):  
Water Supply ◽  
Yellow River ◽  
Potential Evapotranspiration ◽  
Seasonal Pattern ◽  
Gaussian Function ◽  
Yellow River Basin ◽  
Energy Availability ◽  
The Yellow River Basin ◽  
Regional Water ◽  
Insight Into

Abstract Seasonal variations in precipitation (P) and potential evapotranspiration (ET0) are critical for regional hydrometeorological studies and water resource management. The sinusoidal function is widely used to describe the seasonal pattern of P and ET0. However, high errors occur either in the arid places or in places with hyperseasonal precipitation. These limitations are intrinsic properties of the sinusoidal climate descriptor and remain a barrier to provide insight into regional water–energy partitions and hydrologic similarity and predictability. In this study, we used a Gaussian framework as an alternative to describe seasonal variations in P and ET0 regimes in the Yellow River basin (YRB). The results show that the Gaussian framework provides a good approximation to the seasonal pattern of P and has a strong regional applicability for reproducing the monthly P and ET0. This allows us to assess the climate seasonality characterizing the regional balance between water supply and energy availability using δP, δET0, and aridity index. The climate seasonality indicates that the balance between water supply and energy availability has a switch in about 32% of the grid cells during the seasonal cycle from 1982 to 2015. These grid cells are mostly located in regions with average annual precipitation above 550 mm. In the northwest region of the YRB, which has a dry climate, the amount of potential evapotranspiration always exceeds the precipitation. We argue that the Gaussian function provides a quantitative conceptual framework for accurate assessment of regional water supply and energy availability and offers a penetrating insight into hydrometeorology.

scholarly journals Multifractal Characteristics Analysis Based on Slope Distribution Probability in the Yellow River Basin, China

2021 ◽  
Vol 10 (5) ◽  
pp. 337
Author(s):  
Zilong Qin ◽  
Jinxin Wang ◽  
Yan Lu
Keyword(s):  
River Basin ◽  
Large Scale ◽  
Yellow River ◽  
Multifractal Spectrum ◽  
Yellow River Basin ◽  
The Yellow River ◽  
Distribution Probability ◽  
Multifractal Theory ◽  
The Yellow River Basin ◽  
Geomorphological Features

Multifractal theory provides a reliable method for the scientific quantification of the geomorphological features of basins. However, most of the existing research has investigated small and medium-sized basins rather than complex and large basins. In this study, the Yellow River Basin and its sub-basins were selected as the research areas, and the generalized fractal dimension and multifractal spectrum were computed and analyzed with a multifractal technique based on the slope distribution probability. The results showed that the Yellow River Basin and its sub-basins exhibit clear multifractal characteristics, which indicates that the multifractal theory can be applied well to the analysis of large-scale basin geomorphological features. We also concluded that the region with the most uneven terrain is the Yellow River Downstream Basin with the “overhanging river”, followed by the Weihe River Basin, the Yellow River Mainstream Basin, and the Fenhe River Basin. Multifractal analysis can reflect the geomorphological feature information of the basins comprehensively with the generalized fractal dimension and the multifractal spectrum. There is a strong correlation between some common topographic parameters and multifractal parameters, and the correlation coefficients between them are greater than 0.8. The results provide a scientific basis for analyzing the geomorphic characteristics of large-scale basins and for the further research of the morphogenesis of the forms.

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