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1

Effect of landscape restoration on evapotranspiration and water use in the Yellow River Basin, China

Jian Shengqi, Wang Aoxue, Hu Caihong, Yan Denghua

Acta Geophysica

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2024

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Vol. 72, no. 1

341--356

EN

Evapotranspiration (ET) plays a key role in water cycle and energy balance, and the construction of large areas of vegetation in the Yellow River Basin (YRB) will cause an increase in regional ET and changes in water use in the basin. Based on GLEAM evapotranspiration products, meteorological stations and land use data, this study used slope trend analysis, Mann-Kendall (M-K) test and partial correlation analysis to analyse changes in evapotranspiration and water use in the YRB before and after reforestation from 1980 to 2019. The results showed that the annual average ET of the YRB from 1980 to 2019 ranged from 363 to 447 mm with an average change rate of 10.2 mm/10a. The actual evapotranspiration/potential evapotranspiration (ET/PET) growth rate of the YRB decreased after the Grain for Green Project (GGP), and the actual evapotranspiration/precipitation (ET/P) decreased extremely significantly. Water use efficiency in the YRB was significantly reduced, and grassland was subjected to greater water stress. This study provides scientific support for future water balance, landscape restoration, ecological protection and quality development of the YRB.

2

Comparison of different ensemble precipitation forecast system evaluation, integration and hydrological applications

Tang Yehai, Wu Qiang, Soomro Shan-e-hyder, Li Xiang, Sun Yue, Hu Caihong

Acta Geophysica

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2023

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Vol. 71, no. 1

405--421

EN

The examination and integration of numerical forecast products are essential for using and developing numerical forecasts and hydrological forecasts. In this paper, the control forecast products from 2010 to 2014 of four model data (China Meteorological Administration (CMA), the National Centers for Environmental Prediction (NCEP), the European Centre for Medium-Range Weather Forecasts (ECMWF), and the United Kingdom Meteorological Office (UKMO)) from The Interactive Grand Global Ensemble (TIGGE) data center were evaluated comprehensively. On this basis, a study of runoff forecasting based on multi-model (multiple regression (MR), random forest (RF), and convolutional neural network-gradient boosting decision tree (CNN-GBDT)) precipitation integration is carried out. The results show that the CMA model performs the worst, while the other models have their advantages and disadvantages in different evaluation indexes. Compared with the single-index optimal model, CMA model had a higher root-mean-square error (RMSE) of 18.4%, and a lower determination coefficient (R2 ) of 14.7%, respectively. The integration of multiple numerical forecast information is better than that of a single model, and CNN-GBDT method is superior to the multiple regression method and random forest method in improving the precision of rainfall forecast. Compared with the original model, the RMSE decreases by 13.1 ~27.9%, PO decreases to 0.538 at heavy rainfall, and the R2 increases by 4~15.2%, but the degree of improvement decreases gradually with the increase in rainfall order. The method of multi-model ensemble rainfall forecasting based on a machine learning model is feasible and can improve the accuracy of short-term rainfall forecasting. The runoff forecast based on multi-model precipitation integration has been improved, and NSE increases from 0.88 to 0.935, but there is still great uncertainty about food peaks during the food season.

3

Identify runoff generation patterns of check dams and terraces and the efects on runoff: a case study

Chen Youqian, Jian Shengqi, Wu Qiang, Hu Caihong, Soomro Shan-e-hyder

Acta Geophysica

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2022

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Vol. 70, no 2

819--832

EN

Over the past 50 years, China has implemented a series of ecological construction projects in the Loess Plateau that have significantly decreased the runoff and sediment from the Yellow River, and it plays an important role in check dams and terraced fields. In this study, the hydrological characteristics of check dams and terraces are used to distinguish their runoff generation pattern. Combined with different runoff generation patterns, runoff generation models were built, and quantitative analysis was conducted on the runoff reduction situation of check dams and terraced fields. Chenggou River Basin, in the Loess Plateau Zhuli River System's second tributary, was selected as an example for analyzing quantitatively the influence of check dam and terraced fields on the runoff production process. Twenty-nine rainfall-food events from 2013 to 2017 were used to evaluate the effect of the runoff generation model, and the results showed that the built model could well simulate the runoff generation in the basin with many check dams and terraces in which runoff relative error of the model was less than 10%. The effect of check dams and terraces on runoff was studied by setting different scenarios. The results show that the dam system can intercept over 50% of the runoff yield of the basin. Terraced fields can enhance the water storage capacity of the basin and reduce the runoff of the basin, and intercept over 10% of the runoff yield of the basin.

4

Mapping food extend and its impact on land use/land cover and settlements variations: a case study of Layyah District, Punjab, Pakistan

Soomro Shan-e-hyder, Hu Caihong, Boota Muhammad Waseem, Soomro Mairaj Hyder Alias Aamir, Jian Shengqi, Zafar Zeeshan, Li Xiaoping

Acta Geophysica

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2021

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Vol. 69, no. 6

2291--2304

EN

Floods are the most frequent and most distractive natural disaster around the globe. Pakistan is facing frequent flooding since 1929 and foods in the Indus river basin cost more than 7000 lives and caused mighty changes in land use and land covers (LULC) since 1947. District Layyah hit by food on August 1, 2010. Landsat ETM+ with 30 m spatial resolution was utilized to investigate the LULC changes in district Layyah for the 2010 food. It was revealed water area increased 8.05% from July 3 (379.13 km2 ) to August 20 (656.02 km2 ) in district Layyah. Vegetation cover increased from 1149.62 km2 on July 3 to 1842.23 km2 on August 20 in district Layyah and showed a 20.13% increment. Barren/built-up area showed a decrement of 28.18% from 1911.72 km2 in pre-food analysis to 941.90 km2 in the post-food analysis. Total 15 union councils (UC) of district Layyah were affected by food from which 10 lies in tehsil Layyah and 5 belongs to tehsil Karor Lal Esan. Flood affects 177 settlements in district Layyah from which 156 belong to tehsil Layyah and 21 were from tehsil Karor Lal Esan. These results suggest that the impacts of the food on LULC need more attention to cope with the challenge of frequent flooding and impacts in Pakistan.