Impacts of recent climate change on the hydrology in the source region of the Yellow River basin

Meng, Fanchong; Su, Fengge; Yang, Daqing; Tong, Kai; Hao, Zhenchun

doi:10.1016/j.ejrh.2016.03.003

Cited by 72 publications

(97 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Hence the tolerable limits of runoff variations can be arbitrarily within 10% and 20% and at the most between 10% and 25%. It was shown that streamflow elasticity analysis [10,47] was carried out easily when bivariate or multivariate linear regression models were used [49,[52][53][54]. These models have been reported to be suitable for streamflow elasticity because they are cable of approximating observed climate and streamflow data and they are simple from their theoretical expressions [50,52].…”

Section: Elasticity and Mean Annual Runoffmentioning

confidence: 99%

“…In addition, they do not require model assumptions in the vicinity of the mean for regression parameter determination, hence streamflow elasticity [50]. Statistical analysis streamflow elasticity is relatively simple [52], e.g., the performance of regression models using statistical metrics [47], correlation analysis to assess the relationship between the hydrological and meteorological variables [49], computation of confidence interval, standard error of the mean of elasticity are easily performed [48,50]. Streamflow elasticity can be regarded as a simple tool to calculate the contribution of climate change to runoff [51] and to support policy and planning decisions, in the situation of increasing uncertainty regarding water resources due to change of climatic conditions [47].…”

Section: Elasticity and Mean Annual Runoffmentioning

confidence: 99%

“…The linear equation (model) was the most dominant relationship [46]. Hence MAR elasticity was approached within the context of linear regression models [49,50,[52][53][54]56]. For the kth TC, the simple linear model can be given in the form of Equation (7) to correlate hydrological response (i.e., MAR) and its entropy:…”

Section: Elasticity and Mean Annual Runoffmentioning

confidence: 99%

“…Streamflow elasticity can be regarded as a simple tool to calculate the contribution of climate change to runoff [51] and to support policy and planning decisions, in the situation of increasing uncertainty regarding water resources due to change of climatic conditions [47]. The climate parameters considered are precipitation and temperature in most streamflow elasticity [49,[52][53][54]56], but other parameters such as change in groundwater storage [55], land use and water use [50] have been considered recently. Andressian et al [47] reviewed different categories of elasticity and stressed the non-dimensional aspect of precipitation and streamflow (runoff), when calculating rainfall elasticity of streamflow to make a relatively fair comparison between catchments.…”

Section: Elasticity and Mean Annual Runoffmentioning

confidence: 99%

“…This justifies the prefix pseudo-elasticity. That is from streamflow elasticity [49,50,[52][53][54]56], the regression coefficient (ε k ) is generally interpreted as MAR elasticity of entropy associated with MAR.…”

Section: Elasticity and Mean Annual Runoffmentioning

confidence: 99%

See 4 more Smart Citations

Assessing Catchment Resilience Using Entropy Associated with Mean Annual Runoff for the Upper Vaal Catchment in South Africa

Ilunga

2017

Entropy

View full text Add to dashboard Cite

Abstract:The importance of the mean annual runoff (MAR)-hydrological variable is paramount for catchment planning, development and management. MAR depicts the amount of uncertainty or chaos (implicitly information content) of the catchment. The uncertainty associated with MAR of quaternary catchments (QCs) in the Upper Vaal catchment of South Africa has been quantified through Shannon entropy. As a result of chaos over a period of time, the hydrological catchment behavior/response in terms of MAR could be characterized by its resilience. Uncertainty (chaos) in QCs was used as a surrogate measure of catchment resilience. MAR data on surface water resources (WR) of South Africa of 1990 (i.e., WR90), 2005 (WR2005) and 2012 (W2012) were used in this study. A linear zoning for catchment resilience in terms of water resources sustainability was defined. Regression models (with high correlation) between the relative changes/variations in MAR data sets and relative changes in entropy were established, for WR2005 and WR2012. These models were compared with similar relationships for WR90 and WR2005, previously reported. The MAR pseudo-elasticity of the uncertainty associated with MAR was derived from regression models to characterize the resilience state of QCs. The MAR pseudo-elasticity values were relatively small to have an acceptable level of catchment resilience in the Upper Vaal catchment. Within the resilience zone, it was also shown that the effect of mean annual evaporation (MAE) was negatively significant on MAR pseudo-elasticity, compared to the effect of mean annual precipitation (MAP), which was positively insignificant.

show abstract

Section: Elasticity and Mean Annual Runoffmentioning

confidence: 99%

Section: Elasticity and Mean Annual Runoffmentioning

confidence: 99%

Section: Elasticity and Mean Annual Runoffmentioning

confidence: 99%

Section: Elasticity and Mean Annual Runoffmentioning

confidence: 99%

Section: Elasticity and Mean Annual Runoffmentioning

confidence: 99%

See 3 more Smart Citations

Assessing Catchment Resilience Using Entropy Associated with Mean Annual Runoff for the Upper Vaal Catchment in South Africa

Ilunga

2017

Entropy

View full text Add to dashboard Cite

show abstract

Using stable isotopes of surface water and groundwater to quantify moisture sources across the Yellow River source region

Kuang

Luo

Jiao

et al. 2019

Hydrological Processes

View full text Add to dashboard Cite

Characterization of stable isotope compositions (δ2H and δ18O) of surface water and groundwater in a catchment is critical for refining moisture sources and establishing modern isotope–elevation relationships for paleoelevation reconstructions. There is no consensus on the moisture sources of precipitation in the Yellow River source region during summer season. This study presents δ2H and δ18O data from 111 water samples collected from tributaries, mainstream, lakes, and groundwater across the Yellow River source region during summertime. Measured δ18O values of the tributaries range from −13.5‰ to −5.8‰ with an average of −11.0‰. Measured δ18O values of the groundwater samples range from −12.7‰ to −10.5‰ with an average of −11.9‰. The δ18O data of tributary waters display a northward increase of 1.66‰ per degree latitude. The δ18O data and d‐excess values imply that moisture sources of the Yellow River source region during summertime are mainly from the mixing of the Indian Summer Monsoon and the Westerlies, local water recycling, and subcloud evaporation. Analysis of tributary δ18O data from the Yellow River source region and streamwater and precipitation δ18O data from its surrounding areas leads to a best‐fit second‐order polynomial relationship between δ18O and elevation over a 4,600 m elevation range. A δ18O elevation gradient of −1.6‰/km is also established using these data, and the gradient is in consistence with the δ18O elevation gradient of north and eastern plateau. Such relationships can be used for paleoelevation reconstructions in the Yellow River source region.

show abstract

Response of runoff components to climate change in the source‐region of the Yellow River on the Tibetan plateau

Zhang

2022

Hydrological Processes

View full text Add to dashboard Cite

Climate change will likely increase the total streamflow in most headwaters on the Tibetan Plateau in the next decades, yet the response of runoff components to climate change and permafrost thaw remain largely uncertain. Here, we investigate the changes in runoff components under a changing climate, based on a high-resolution cryosphere-hydrology model (Spatial Processes in Hydrology model, SPHY) and multi-decadal streamflow observations at the upstream (Jimai) and downstream stations (Maqu and Tangnaihai) in the source-region of the Yellow River (SYR). We find that rainfall flow dominates the runoff regime in SYR (contributions of 48%-56%), followed by snowmelt flow (contributions of 26%/23% at Maqu/Tangnaihai). Baseflow is more important at Jimai (32%) than at the the downstream stations (21%-23%). Glacier meltwater from the Anyê Maqên and Bayankala Mountains contributes negligibly to the downstream total runoff. With increasing temperature and precipitation, the increase in total runoff is smaller in the warm and wet downstream stations than in the cold and dry upstream station. This is because of a higher increase in evapotranspiration and a larger reduction in snowmelt flow in the downstream region in response to a warming climate. With temperature increase, there is less increase in rainfall flow in the downstream region due to increased water loss through evapotranspiration. Meanwhile, the decline in snowmelt flow is larger further downstream, which can negatively impact the spring irrigation for the whole Yellow River basin that supports the livelihoods of 140 million people. Importantly, we find that baseflow plays an increasingly important role in the permafrost-dominated upstream region with atmospheric warming and permafrost thaw, accompanied by decreased surface flow. These findings improve our current understanding of how different hydrological processes respond to climate change and provide insights for optimizing hydropower and irrigation systems in the entire Yellow River basin under a rapidly changing climate.

show abstract

Impacts of recent climate change on the hydrology in the source region of the Yellow River basin

Cited by 72 publications

References 54 publications

Assessing Catchment Resilience Using Entropy Associated with Mean Annual Runoff for the Upper Vaal Catchment in South Africa

Assessing Catchment Resilience Using Entropy Associated with Mean Annual Runoff for the Upper Vaal Catchment in South Africa

Using stable isotopes of surface water and groundwater to quantify moisture sources across the Yellow River source region

Response of runoff components to climate change in the source‐region of the Yellow River on the Tibetan plateau

Contact Info

Product

Resources

About