Research on the conversion relationships between the river and groundwater in the Yellow River drainage area

Huang

Hydrological Sciences Journal

et al. 2016

Under the combined influences of climate changes and human activities, the hydrological regimes of the Wei River show remarkable variations which have caused many issues in the Wei River in recent decades such as a lack of freshwater, water pollution, flood disaster, and channel sedimentation. Hence hydrologic regime changes and potential human-induced impacts have been drawing increasing attention from local government and hydrologists. This study investigates hydrological regime changes in the natural and measured runoff series of four hydrologic stations on the main Wei River and quantifies features of their long-term change by analysing their historical annual and seasonal runoff data using several approaches: continuous wavelet transform, cross-wavelet, wavelet coherence, trend-free pre-whitening Mann-Kendall test, and detrended fluctuation analysis. By contrasting two different analysis results between natural and measured river runoff series, the impacts of human activities on the long-term hydrological regime in terms of spatio-temporal distribution in dominant periods, the trends and long-range memory of river runoff were investigated. The results show the following: (1) Periodic properties of the streamflow changes are the result of climate changes and precipitation changes in particular. Human activities play a minor role in the changes in periodic properties. (2) A significant decreasing trend can be observed in the natural streamflow series along the entire main stream of the Wei River, which should be attributed to human-induced influences in recent decades. (3) Decreasing streamflow in the Wei River may trigger serious shortages of freshwater, and the sustainable utilization of water resources in the Wei River basin may face a challenging situation. This study has important implications for the scientific management of water resources and enhancement of human mitigation of climate changes and human-induced water shortage in the Wei River basin.

Section: Introductionmentioning

confidence: 99%

Changes in the long-term hydrological regimes and the impacts of human activities in the main Wei River, China

Huang

Hydrological Sciences Journal

et al. 2016

“…The annual average sediment discharge from the Weihe River can reach up to 0.458 billion tons [30]. The average annual natural runoff in the Weihe River has decreased by 45% since the 1980s due to the complex impact of natural and human activities [34,35]. The annual runoff measured at the Huaxian gauging stations from 1991 to 2000 decreased by 50.3% compared with that from 1981 to 1990 [35].…”

Section: Measurement Of Streambed Vertical Hydraulic Conductivitymentioning

confidence: 99%

“…The average annual natural runoff in the Weihe River has decreased by 45% since the 1980s due to the complex impact of natural and human activities [34,35]. The annual runoff measured at the Huaxian gauging stations from 1991 to 2000 decreased by 50.3% compared with that from 1981 to 1990 [35]. The significant decrease of runoff has reduced the sediment scouring capacity of the river channel especially in the lower Weihe River [33].…”

Section: Measurement Of Streambed Vertical Hydraulic Conductivitymentioning

confidence: 99%

Spatial and Temporal Variations of Streambed Vertical Hydraulic Conductivity in the Weihe River, China

Wang

Song

et al. 2016

Water

This study demonstrates the spatial and temporal variations of streambed vertical hydraulic conductivity K v from October 2011 to November 2014 along the Weihe River, the largest tributary of the Yellow River. The streambed K v values of a total number of 385 locations from five test sites were estimated on the basis of in situ falling-head standpipe permeameter tests. The difference of K v values for all test locations reaches five orders of magnitude with a range from 5.87E-04 to 61.3 m/d and a median value of 1.62E-01 m/d. The streambed K v values are neither normally nor log-normally distributed, but display significant spatial variability among the five test sites. The highest K v values occur at the site with mainly sandy sediment, while the K v values at the other four sites with mainly silt-clay sediment are relatively close and have less variability than those at the sandy sediment site. The median K v values from all of the sites exhibit no statistically significant temporal trends. However, the median K v values indeed show temporal variations that might be influenced by changes in silt-clay content of the sediment, especially for the sandy sediment site and the combined data from all sites. Weak evidence demonstrates that streambed K v values decrease with depth.

Journal of Hydroinformatics

“…However, since the 1980s, the river has suffered many problems, including greatly reduced annual river runoff, geologic hazards caused by water projects, high concentrations of sediment and consequential heavy flooding, as well as heavy water pollution Wei et al 2012). Since fhe late 1990s, many parts of fhe river have lost ecosystem functionality, which have restricted fhe sustainable development of the region (Zhao 2003; Wang et al 2004). In this context, if is of significant interest to have an advanced technique fo model fhe river's discharge with improved prediction accuracy.…”

Section: Study Area and Materialsmentioning

confidence: 99%

Improving prediction accuracy of river discharge time series using a Wavelet-NAR artificial neural network

Wei

Zuo

Song

2012

This study developed a wavelet transformation and nonlinear autoregressive (NAR) artificial neural network (ANN) hybrid modeling approach to improve the prediction accuracy of river discharge time series. Daubechies 5 discrete wavelet was employed to decompose the time series data into subseries with low and high frequency, and these subseries were then used instead of the original data series as the input vectors for the designed NAR network (NARN) with the Bayesian regularization (BR) optimization algorithm. The proposed hybrid approach was applied to make multi-step-ahead predictions of monthly river discharge series in the Weihe River in China. The prediction results of this hybrid model were compared with those of signal NARNs and the traditional Wavelet-Artificial Neural Network hybrid approach (WNN). The comparison results revealed that the proposed hybrid model could significantly increase the prediction accuracy and prediction period of the river discharge time series in the current case study.