scholarly journals Irrigation, damming, and streamflow fluctuations of the Yellow River

2020 ◽  
Author(s):  
Zun Yin ◽  
Catherine Ottlé ◽  
Philippe Ciais ◽  
Feng Zhou ◽  
Xuhui Wang ◽  
...  
Keyword(s):  
Land Surface ◽  
Flood Control ◽  
Yellow River ◽  
Dominant Factor ◽  
Anthropogenic Factors ◽  
The Yellow River ◽  
Surface Model ◽  
Operation Rules ◽  
Monthly Streamflow ◽  
Artificial Reservoirs

Abstract. The streamflow of the Yellow River is strongly affected by human activities of irrigation and dam regulation. Many attribution studies focused on the long-term trend of discharge, yet the contributions of these anthropogenic factors to streamflow fluctuations have not been well quantified. This study aims to quantify the roles of irrigation and artificial reservoirs in monthly streamflow fluctuations of the Yellow River from 1982 to 2014 by using the global land surface model ORCHIDEE with a new developed irrigation module, and a separate offline dam operation model. Validation with obsevations demonstrates the ability of our model in simulating the main hydrological processes under human disturbances in the Yellow River basin. Irrigation is found to be the dominant factor leading to 63.7 % reduction of the annual discharges. It might lead to discharge increase in the summer if irrigation is widely applied during a dry spring. After illustrating dam regulation as the primary driver affecting streamflow seasonality, we simulated the changes of water storages in several large artificial reservoirs by a new developed dam model, which does not require any prior knowledge from observations but only implements two simple operation rules based on their inherent regulation capacities: reducing peak flows for flood control and securing base flows during the dry season. Inclusion of dams with this simplified model substantially improved the simulated discharge by at least 42 %. Moreover, simulated water storage changes of the LongYangXia and LiuJiaXia dams coincide well with observations with a high correlation value of about 0.9. We also found that the artificial reservoirs can affect the inter-annual fluctuations of the streamflows, which however was not reproduced faithfully by our dam model due to lack of annual operation rules. From the mismatches between simulations and observations, we inferred the potential impacts of multiple medium reservoirs and five large irrigation districts (e.g., the Hetao Plateau), which were ignored in most previous hydrological studies.

scholarly journals Irrigation, damming, and streamflow fluctuations of the Yellow River

2021 ◽  
Vol 25 (3) ◽  
pp. 1133-1150
Author(s):  
Zun Yin ◽  
Catherine Ottlé ◽  
Philippe Ciais ◽  
Feng Zhou ◽  
Xuhui Wang ◽  
...  
Keyword(s):  
Human Activities ◽  
Land Surface ◽  
Census Data ◽  
Yellow River ◽  
Land Surface Model ◽  
Anthropogenic Factors ◽  
The Yellow River ◽  
Surface Model ◽  
Monthly Streamflow ◽  
Dam Operation

Abstract. The streamflow of the Yellow River (YR) is strongly affected by human activities like irrigation and dam operation. Many attribution studies have focused on the long-term trends of streamflows, yet the contributions of these anthropogenic factors to streamflow fluctuations have not been well quantified with fully mechanistic models. This study aims to (1) demonstrate whether the mechanistic global land surface model ORCHIDEE (ORganizing Carbon and Hydrology in Dynamic EcosystEms) is able to simulate the streamflows of this complex rivers with human activities using a generic parameterization for human activities and (2) preliminarily quantify the roles of irrigation and dam operation in monthly streamflow fluctuations of the YR from 1982 to 2014 with a newly developed irrigation module and an offline dam operation model. Validations with observed streamflows near the outlet of the YR demonstrated that model performances improved notably with incrementally considering irrigation (mean square error (MSE) decreased by 56.9 %) and dam operation (MSE decreased by another 30.5 %). Irrigation withdrawals were found to substantially reduce the river streamflows by approximately 242.8±27.8×108 m3 yr−1 in line with independent census data (231.4±31.6×108 m3 yr−1). Dam operation does not change the mean streamflows in our model, but it impacts streamflow seasonality, more than the seasonal change of precipitation. By only considering generic operation schemes, our dam model is able to reproduce the water storage changes of the two large reservoirs, LongYangXia and LiuJiaXia (correlation coefficient of ∼ 0.9). Moreover, other commonly neglected factors, such as the large operation contribution from multiple medium/small reservoirs, the dominance of large irrigation districts for streamflows (e.g., the Hetao Plateau), and special management policies during extreme years, are highlighted in this study. Related processes should be integrated into models to better project future YR water resources under climate change and optimize adaption strategies.

scholarly journals Evaluation of Evapotranspiration Estimates in the Yellow River Basin against the Water Balance Method

Water ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 1884 ◽  
Author(s):  
Guojie Wang ◽  
Jian Pan ◽  
Chengcheng Shen ◽  
Shijie Li ◽  
Jiao Lu ◽  
...  
Keyword(s):  
Water Balance ◽  
River Basin ◽  
Land Surface ◽  
Yellow River ◽  
Yellow River Basin ◽  
Balance Method ◽  
The Yellow River ◽  
Water Balance Method ◽  
Global Land ◽  
The Yellow River Basin

Evapotranspiration (ET), a critical process in global climate change, is very difficult to estimate at regional and basin scales. In this study, we evaluated five ET products: the Global Land Surface Evaporation with the Amsterdam Methodology (GLEAM, the EartH2Observe ensemble (E2O)), the Global Land Data Assimilation System with Noah Land Surface Model-2 (GLDAS), a global ET product at 8 km resolution from Zhang (ZHANG) and a supplemental land surface product of the Modern-ERA Retrospective analysis for Research and Applications (MERRA_land), using the water balance method in the Yellow River Basin, China, including twelve catchments, during the period of 1982–2000. The results showed that these ET products have obvious different performances, in terms of either their magnitude or temporal variations. From the viewpoint of multiple-year averages, the MERRA_land product shows a fairly similar magnitude to the ETw derived from the water balance method, while the E2O product shows significant underestimations. The GLEAM product shows the highest correlation coefficient. From the viewpoint of interannual variations, the ZHANG product performs best in terms of magnitude, while the E2O still shows significant underestimations. However, the E2O product best describes the interannual variations among the five ET products. Further study has indicated that the discrepancies between the ET products in the Yellow River Basin are mainly due to the quality of precipitation forcing data. In addition, most ET products seem to not be sensitive to the downward shortwave radiation.

scholarly journals Evaluation of Environmental and Ecological Impacts of the Leading Large-Scale Reservoir on the Upper Reaches of the Yellow River

2019 ◽  
Vol 11 (14) ◽  
pp. 3818
Author(s):  
Jun Qiu ◽  
Tie-Jian Li ◽  
Fang-Fang Li
Keyword(s):  
Flood Control ◽  
Large Scale ◽  
Yellow River ◽  
Yellow River Basin ◽  
Water Area ◽  
Economic Benefits ◽  
Control Area ◽  
Ecological Impacts ◽  
The Yellow River ◽  
Water Demands

Large-scale reservoirs have played a significant role in meeting various water demands and socio-economic development, while they also lead to undeniable impacts on the environment and ecology. The Longyangxia reservoir located on the Yellow River is the first large-scale reservoir on the upper Yellow River with a control area of 18% of the entire Yellow River Basin. Since it was put into operation in 1987, it has made great contributions to the national economy for over 30 years. In this study, the socio-economic benefits of the Longyangxia reservoir in power generation, water supply, flood control, and ice prevention are investigated. More importantly, its impacts on the ecology and environment are also presented and analyzed, such as the impacts on river morphology, flow regimes, peak flow, fish, phytoplankton, and zooplankton. It can be concluded that the construction of the Longyangxia reservoir contributes greatly to socio-economic benefits, the water area nearby has formed a new ecological environment, and the trophic level of the aquatic environment has probably increased.

scholarly journals Evaluation of ORCHIDEE-MICT-simulated soil moisture over China and impacts of different atmospheric forcing data

2018 ◽  
Vol 22 (10) ◽  
pp. 5463-5484 ◽  
Author(s):  
Zun Yin ◽  
Catherine Ottlé ◽  
Philippe Ciais ◽  
Matthieu Guimberteau ◽  
Xuhui Wang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Yellow River ◽  
Climatic Research Unit ◽  
Surface Model ◽  
Atmospheric Forcing ◽  
Meteorological Forcing ◽  
Soil Moisture Dynamics ◽  
Moisture Variation ◽  
Moisture Dynamics

Abstract. Soil moisture is a key variable of land surface hydrology, and its correct representation in land surface models is crucial for local to global climate predictions. The errors may come from the model itself (structure and parameterization) but also from the meteorological forcing used. In order to separate the two source of errors, four atmospheric forcing datasets, GSWP3 (Global Soil Wetness Project Phase 3), PGF (Princeton Global meteorological Forcing), CRU-NCEP (Climatic Research Unit-National Center for Environmental Prediction), and WFDEI (WATCH Forcing Data methodology applied to ERA-Interim reanalysis data), were used to drive simulations in China by the land surface model ORCHIDEE-MICT(ORganizing Carbon and Hydrology in Dynamic EcosystEms: aMeliorated Interactions between Carbon and Temperature). Simulated soil moisture was compared with in situ and satellite datasets at different spatial and temporal scales in order to (1) estimate the ability of ORCHIDEE-MICT to represent soil moisture dynamics in China; (2) demonstrate the most suitable forcing dataset for further hydrological studies in Yangtze and Yellow River basins; and (3) understand the discrepancies of simulated soil moisture among simulations. Results showed that ORCHIDEE-MICT can simulate reasonable soil moisture dynamics in China, but the quality varies with forcing data. Simulated soil moisture driven by GSWP3 and WFDEI shows the best performance according to the root mean square error (RMSE) and correlation coefficient, respectively, suggesting that both GSWP3 and WFDEI are good choices for further hydrological studies in the two catchments. The mismatch between simulated and observed soil moisture is mainly explained by the bias of magnitude, suggesting that the parameterization in ORCHIDEE-MICT should be revised for further simulations in China. Underestimated soil moisture in the North China Plain demonstrates possible significant impacts of human activities like irrigation on soil moisture variation, which was not considered in our simulations. Finally, the discrepancies of meteorological variables and simulated soil moisture among the four simulations are analyzed. The result shows that the discrepancy of soil moisture is mainly explained by differences in precipitation frequency and air humidity rather than differences in precipitation amount.

scholarly journals El Niño-Southern Oscillation and water resources in Headwaters Region of the Yellow River: links and potential for forecasting

2010 ◽  
Vol 7 (5) ◽  
pp. 8521-8543 ◽  
Author(s):  
A. Lü ◽  
S. Jia ◽  
H. Yan ◽  
S. Wang
Keyword(s):  
Water Resources ◽  
El Niño ◽  
Yellow River ◽  
El Nino ◽  
Southern Oscillation ◽  
El Niño Southern Oscillation ◽  
El Nino Southern Oscillation ◽  
The Yellow River ◽  
Enso Events ◽  
Monthly Streamflow

Abstract. Many studies have examined that El Niño-Southern Oscillation (ENSO) could result in the variation of rainfall and runoff of different rivers across the world. In this paper, we will look specifically at the Headwaters Region of the Yellow River (HRYR) to explore the rainfall-ENSO and runoff-ENSO relationships and discuss the potential for water resources forecasting using these relationships. Cross-correlation analyses were performed to determine the significant correlation between rainfall, runoff and ENSO indicators (e.g. SOI, Niño 1.2, Niño 3, Niño 4, and Niño 3.4) and the lag period for each relationship. Main result include: (1) there are significant correlation at 95% confidence level during three periods, i.e. January and March, from September to November; (2) there were significant correlations between monthly streamflow and monthly ENSO indictors during three periods, i.e. JFM, June, and OND, with lag periods between one and twelve months. As ENSO events can be accurately predicted one to two years in advances using physical model of coupled ocean-atmosphere system, the lead time for forecasting runoff using ENSO indicator in the HRYR can be extent to one to thirty-six months. Therefore, ENSO may have potential as a powerful forecast tool for water resource in headwater regions of Yellow River.

scholarly journals Drought Monitoring over Yellow River Basin from 2003–2019 Using Reconstructed MODIS Land Surface Temperature in Google Earth Engine

2021 ◽  
Vol 13 (18) ◽  
pp. 3748
Author(s):  
Xiaoyang Zhao ◽  
Haoming Xia ◽  
Li Pan ◽  
Hongquan Song ◽  
Wenhui Niu ◽  
...  
Keyword(s):  
River Basin ◽  
Land Surface ◽  
Drought Index ◽  
Yellow River ◽  
Yellow River Basin ◽  
Condition Index ◽  
Drought Indices ◽  
The Yellow River ◽  
Drought Conditions ◽  
The Yellow River Basin

Drought is one of the most complex and least-understood environmental disasters that can trigger environmental, societal, and economic problems. To accurately assess the drought conditions in the Yellow River Basin, this study reconstructed the Land Surface Temperature (LST) using the Annual Temperature Cycle (ATC) model and the Normalized Difference Vegetation Index (NDVI). The Temperature Condition Index (TCI), Vegetation Condition Index (VCI), Vegetation Health Index (VHI), and Temperature-Vegetation Drought Index (TVDI), which are four typical remote sensing drought indices, were calculated. Then, the air temperature, precipitation, and soil moisture data were used to evaluate the applicability of each drought index to different land types. Finally, this study characterized the spatial and temporal patterns of drought in the Yellow River Basin from 2003 to 2019. The results show that: (1) Using the LST reconstructed by the ATC model to calculate the drought index can effectively improve the accuracy of drought monitoring. In most areas, the reconstructed TCI, VHI, and TVDI are more reliable for monitoring drought conditions than the unreconstructed VCI. (2) The four drought indices (TCI, VCI, VH, TVDI) represent the same temporal and spatial patterns throughout the study area. However, in some small areas, the temporal and spatial patterns represented by different drought indices are different. (3) In the Yellow River Basin, the drought level is highest in the northwest and lowest in the southwest and southeast. The dry conditions in the Yellow River Basin were stable from 2003 to 2019. The results in this paper provide a basis for better understanding and evaluating the drought conditions in the Yellow River Basin and can guide water resources management, agricultural production, and ecological protection of this area.

scholarly journals Investigating basin-scale water budget dynamics in 18 rivers across Tibetan Plateau through multiple datasets

2017 ◽  
Author(s):  
Wenbin Liu ◽  
Fubao Sun ◽  
Yanzhong Li ◽  
Guoqing Zhang ◽  
Yan-Fang Sang ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Land Surface ◽  
Water Budget ◽  
Water Resource Management ◽  
Management Practices ◽  
Yellow River ◽  
Aridity Index ◽  
Surface Model ◽  
The Tibetan Plateau ◽  
Basin Scale

Abstract. The dynamics of basin-scale water budgets are not well understood nowadays over the Tibetan Plateau (TP) due to the lack of hydro-climatic observations. In this study, we investigate seasonal cycles and trends of water budget components (e.g., precipitation-P, evapotranspiration-ET and runoff-Q) in eighteen TP river basins during the period 1982–2011 through the use of multi-source datasets (e.g., in situ observations, satellite retrievals, reanalysis outputs and land surface model simulations). A water balance-based two-step procedure, which considers the changes in basin-scale water storage at the annual scale, is also adopted to calculate actual ET. The results indicated that precipitation (mainly snowfall from mid-autumn to next spring), which mainly concentrated during June–October (varied among different monsoons-impacted basins), was the major contributor to the runoff in TP basins. Increased P, ET and Q were found in most TP basins during the past 30 years except for the upper Yellow River basin and some sub-basins of Yalong River, which were mainly affected by the weakening East Asian Monsoon. Moreover, the aridity index (PET/P) and runoff coefficient (Q/P) decreased in most basins, which were in agreement with the warming and moistening climate in the Tibetan Plateau. The results obtained demonstrated the usefulness of integrating multi-source datasets to hydrological applications in the data-sparse regions. More generally, such approach might offer helpful insights towards understanding the water and energy budgets and sustainability of water resource management practices of data-sparse regions in a changing environment.

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