Tangnaihai Station Research Articles

Surface Water Resource Accessibility Assessment of Rural Settlements in the Yellow River Basin

Analyzing the spatial relationship between humans and water is crucial for regional development and water allocation schemes, particularly in the face of extreme water scarcity in the Yellow River Basin. A quantitative evaluation model of surface water resource accessibility (SWRA) has been developed, with rural settlements serving as the research unit. This model is built upon three key dimensions: topography, distance, and surface water resources within the Yellow River Basin. The results show that: (1) The SWRA range spans from 0.13 to 0.88, with an average value of 0.47 and a standard deviation of 0.05. Higher SWRA values are concentrated in the eastern and western regions, while lower values are predominantly found in the central area. (2) The gradient of SWRA across the 12 catchments, from low to high, is as follows: Sanmenxia station, Lanzhou station, Shizuishan station, Longmen station, Tongguan station, Toudaoguai station, Xiaolangdi station, Huayuankou station, Lijin station, Gaocun station, Ai Shan station, and Tangnaihai station. (3) At the city scale, the SWRA values are generally higher in the eastern areas of 10 cities, with one exception being higher in the west. Conversely, in the western areas of nine cities, the SWRA values are lower. The remaining cities exhibit SWRA values at a medium level. The correlation coefficient between primary industry gross domestic product (GDP) and SWRA is 0.271 (N = 56, Sig = 0.043, in 0.05 level, the correlation is significant), which confirms that SWRA serves as a factor influencing GDP and is appropriately designed for assessing water accessibility. Consequently, managers can utilize SWRA results to make informed decisions regarding regional development and water allocation.

Evaluation of multisource precipitation input for hydrological modeling in an Alpine basin: a case study from the Yellow River Source Region (China)

Abstract Alpine basins are typically poorly gauged and inaccessible owing to the harsh prevailing environment and complex terrain. In this study, two representative satellite precipitation products (Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42RTV7 and Integrated Multi-Satellite Retrievals for GPM (IMERG) Final Run Version 06) and two reanalysis precipitation products (China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS) and Climate Forecast System Reanalysis (CFSR)) in the Yellow River Source Region (YRSR) were selected for evaluation and hydrological verification against gauge-observed data (GO). Results show that the accuracy of these precipitation products in the warm season is higher than that in the cold season, and IMERG exhibits the best performance, followed by the CMADS, CFSR, and 3B42RTV7. Models that use the GO as input yielded satisfactory performance during 2008–2013, and precipitation products have poor simulation results. Although the model using the IMERG as input yielded unsatisfactory performance during 2014–2016, this did not affect the use of the IMERG as a potential data source for the YRSR. The model driven by the combination of GO and CMADS precipitation performed the best in all scenarios (R2=0.77, Nash–Sutcliffe efficiency (NSE)=0.72 at the Tangnaihai station; R2=0.53, NSE=0.48 at the Jimai station).

Application of Entropy Method to Quantify Future Ecological Flow in the Yellow River Basin.

Due to both anthropogenic and climate change impacts, precipitation and runoff in the Yellow River basin have decreased in the past 50 years, leading to more pressure in sustaining human beings and ecosystem needs. It is essential to evaluate the flow condition in the Yellow River basin and see whether it may satisfy its ecological flow in the future. Therefore, this study applied an entropy-based method to calculate the flow duration curves from both observed and simulated data to evaluate the impact of climate change on ecological flow in the Yellow River basin. The simulated FDCs from H08 and DBH models show good agreement with each other and fit observation well. Results show that the decadal FDC at each station is generally predicted to be higher or stay in the higher range under both RCP 2.6 and 8.5 scenarios, suggesting an increase in water amount in the future. It is found that the high flows increase much faster than the low flows, resulting in larger slopes than the references ones, which is due to the larger entropy and M values in the future. At most of the stations, the future values of Q95 and Q90 will safely exceed the threshold. It is found that at the Lanzhou, Wubao, Longmen, and Huayuankou stations, there will be no or little threat to future ecological flow. Still, at the Toudaoguai and Sanmanxia stations, the ecological requirement is not always satisfied. The water stress at the Tangnaihai station from the upper stream of the Yellow River may be threatened in the future.

Last two millennia of streamflow variability in the headwater catchment of the Yellow River basin reconstructed from tree rings

The headwater catchment of the Yellow River Basin (HCYRB) controls 35% of the streamflow of the Yellow River (YR) which faces increasing water shortages. To better understand streamflow variability in the region we require a better understanding of high and low flow characteristics. This study presents a new annual (Nov-Oct) streamflow reconstruction at the Tangnaihai station in the HCYRB for the last two millennia (159–2016 C.E.) using 12 tree-ring chronologies. The nested principal component regression model combined with the stepwise best subset selection method was proposed to improve the temporal length and model skill of reconstruction. The stepwise best subset selection method was presented to select the best principal components subset, instead of a confidence test, based on k-fold cross-validation error and Akaike’s information criteria (AIC). The model assessment results verify that the proposed model exhibits strong reconstruction skills. Besides, the magnitude and duration of both high and low flow periods were analyzed. The results show that (1) the significant high-flow periods are the early 3rd century, circa 300 C.E., early 13th century, 16th century and circa 1900 C.E., while the low-flow periods are the late 5th century and late 15th century; (2) the durations and magnitudes of low-flow periods are longer and larger than high-flow periods and the severities of high-flow periods are greater than low-flow periods. The reconstruction also suggests that a warm climate is more likely accompanied by a high-flow period and low-flow periods are more likely to occur in cold periods associated with the Asian Summer Monsoon and solar activity.

Assessing the oxidative weathering of pyrite and its role in controlling atmospheric CO2 release in the eastern Qinghai-Tibet Plateau

Carbonate weathering, coupled with sulfuric acid, derived from the oxidative weathering of pyrite (OWP), has been suggested to compensate for the CO2 drawdown by silicate weathering over the Cenozoic. However, the above compensation had not been well documented because of the lack of evidence of OWP and the failure to differentiate between sulfide-derived and gypsum-derived sulfate. In this study, monthly sampling campaigns from July 2012 to June 2014 were conducted at Tangnaihai station in the eastern Qinghai-Tibet Plateau (QTP) to demonstrate the behavior of sulfide-derived sulfate and its effect on the local atmospheric CO2 balance. Sulfur and triple oxygen isotopes of sulfate were employed to discern OWP, and sulfur isotope was used to calculate the precise contributions of sulfide-derived sulfate. The results revealed that fractions of riverine sulfate from OWP ranged from 2% to 99% with a mean value of 61%. The corresponding annual CO2 release flux by carbonate weathering coupled with sulfuric acid (CSW) ranged from 0.005 Tmol /yr to 0.015 Tmol /yr with average values of 0.012 Tmol /yr and 0.007 Tmol /yr in 2012–2013 and 2013–2014, respectively, while the annual CO2 consumption flux by silicate weathering coupled with carbonic acid (SCW) varied from 0.003 Tmol /yr to 0.015 Tmol /yr with average annual values of 0.008 Tmol /yr and 0.010 Tmol /yr in 2012–2013 and 2013–2014, respectively. These results indicated that CO2 release by CSW exceeded CO2 consumption by SCW in 2012–2013, but not in 2013–2014. In the uplifting mountain areas, the pyrite oxidation and produced CO2 release sometimes could not balance the CO2 consumption by SCW, and its roles in long term climate change are uncertain. In future research, the role of sulfide oxidation coupled to carbonate weathering should be reestimated to clarify for present and past global climate change.

Evolution and variation characteristics of the recorded runoff for the major rivers in China during 1956—2018

Surface water is a key natural resources and a restrictive element to maintain regional ecological balance and support development of social economy. River runoff is the main form of surface water resources. Variation of the both river runoff and surface water will directly influence water resources management of a basin. Based on the annual report series of water resources and the recorded runoff of 10 representative hydrometric stations on major rivers in China, the variations of the surface water resources of the ten water resources zones and recorded runoff of major rivers in China during 1956—2018 were investigated. The results show that:① The annual runoff gauged on major rivers during 1956—2018 shows a downward trend with exception of the Datong station on the Yangtze River. ② The recorded annual runoff of the Tangnaihai station on the upper Yellow River presented an insignificant decreasing trend while that gauged at the Huayuankou station on the Yellow River exhibited a significant decreasing trend. During 1980—2000 and 2001—2018, the recorded runoff at the Tangnaihai station changed by 1.8% and-5.9% as comparing to baseline in 1956—1979 while that of the Huayuankou station decreased by 26.7% and 41.0% respectively. ③ Geographically, the Yellow River could be treated as separation boundary of runoff variation trends from the north to the south of China in terms of significance level. Recorded annual runoff presented significant decreasing trends for rivers in the north to the Yellow River while that exhibited insignificant variation trends for rivers in its south. The recorded runoff in 2001—2018 decreased by more than 25% relative to the baseline of 1956—1979 for the northern China rivers, with the highest reduction of over-80% occurring in the Haihe River. ④ The average annual surface water resources of China during 1956—2018 was approximately 2 726.6 billion m3, which is 12.2 billion m3 less than that in the second water resources assessment conduced in 2004. Reduction of surface water in the Haihe River zone, Yellow River zone, and Liaohe River zone aggravated contradiction between water supply and water demand since the early 21st century.

The Capacity of the Hydrological Modeling for Water Resource Assessment under the Changing Environment in Semi-Arid River Basins in China

Conducting water resource assessment and forecasting at a basin scale requires effective and accurate simulation of the hydrological process. However, intensive, complex human activities and environmental changes are constraining and challenging the hydrological modeling development and application by complicating the hydrological cycle within its local contexts. Six sub-catchments of the Yellow River basin, the second-largest river in China, situated in a semi-arid climate zone, have been selected for this study, considering hydrological processes under a natural period (before 1970) and under intensive human disturbance (2000–2013). The study aims to assess the capacity and performance of the hydrological models in simulating the discharge under a changing environment. Four well-documented and applied hydrological models, i.e., the Xin’anjiang (XAJ) model, GR4J model, SIMHYD model, and RCCC-WBM (Water Balance Model developed by Research Center for Climate Change) model, were selected for this assessment. The results show that (1) the annual areal temperature of all sub-catchments presented a significant rising trend, and annual precipitation exhibited insignificant decline trend; (2) as a result of climate change and intensive human activities, the annual runoff series showed a declining trend with abrupt changes mostly occurring in the 1980s with the exception of the Tangnaihai station; (3) the four hydrological models generally performed well for runoff simulation for all sub-catchments under the natural period. In terms of Nash–Sutcliffe efficiency coefficient, the XAJ model worked better in comparison to other hydrological models due to its detailed representations and complicated mechanism in runoff generation and flow-routing scheme; (4) environmental changes have impacted the performance of the four hydrological models under all sub-catchments, in particularly the Pianguan River catchment, which is could be attributed to the various human activities that in turn represent more complexity for the regional hydrological cycle to some extent, and reduce the ability to predict the runoff series; (5) the RCCC-WBM model, well known for its simple structure and principles, is considered to be acceptable for runoff simulation for both natural and human disturbance periods, and is recommended for water resource assessment under changing environments for semi-arid regions.

A Stochastic Simulation Model for Monthly River Flow in Dry Season

Streamflow simulation gives the major information on water systems to water resources planning and management. The monthly river flows in dry season often exhibit high autocorrelation. The headwater catchment of the Yellow River basin monthly flow series in dry season exhibits this clearly. However, existing models usually fail to capture the high-dimensional, nonlinear dependence. To address this issue, a stochastic model is developed using canonical vine copulas in combination with nonlinear correlation coefficients. Kendall’s tau values of different pairs of river flows are calculated to measure the mutual correlations so as to select correlated streamflows for every month. Canonical vine copula is used to capture the temporal dependence of every month with its correlated streamflows. Finally, monthly river flow by the conditional joint distribution functions conditioned upon the corresponding river flow records was generated. The model was applied to the simulation of monthly river flows in dry season at Tangnaihai station, which controls the streamflow of headwater catchment of Yellow River basin in the north of China. The results of the proposed method possess a smaller mean absolute error (MAE) than the widely-used seasonal autoregressive integrated moving average model. The performance test on seasonal distribution further verifies the great capacity of the stochastic-statistical method.

Variation Analysis of Streamflows from 1956 to 2016 Along the Yellow River, China

With the change of climate and the impacts of human activities, the water resources crisis of the Yellow River is becoming increasingly serious. How and why did the streamflows of the Yellow River basin change? Based on observed annual runoff data (1956–2016) of 10 main hydrological stations along the Yellow River, the linear regression method, the Spearman rank correlation method and the Mann-Kendall test method are used to analyze runoff trend. The orderly clustering method, the sliding t test method and the Lee-Heghinian Method are used to identify the abrupt change point. Finally, the wavelet analysis method is used to identify runoff time series period. The results show that: (1) With the exception of the streamflow of Tangnaihai, the streamflows of all examined stations have significantly declining trends. The decrease of the streamflow from the upper to the middle to the lower reaches is becoming more and more obvious; (2) The runoff of the Yellow River has changed greatly. The abrupt change point at Tangnaihai occurred in 1989. The abrupt change points of the other stations took place in 1985; (3) The runoff along the Yellow River presents multi-time scale changes. The streamflows appear to have strongest periods of 25–40 years with a 40-year scale, which indicate the alternate oscillations of the high and the low water periods. The periods of <6 and 7–24 years are not stable and are complicated. The first main period of runoff in the Yellow River is 30 years; (4) The streamflow upstream of Tangnaihai station is mainly affected by the climate. The streamflows downstream of Tangnaihai station are influenced by human activities, especially water extraction and diversion and the operations of the large reservoirs. These research results have important practical guiding significance for hydrological forecasting, evaluation and management of water resources, construction of water conservancy projects and sustainable utilization of water resources in the region.

Impacts of climate change on hydrology in the Yellow River source region, China

Abstract Variations of precipitation, temperature, and runoff in the Yellow River source region were analyzed with the Mann–Kendall and Spearman rank correlation tests over the past 60 years. Based on the seven climate scenarios from CMIP5 climate models under RCP2.6, RCP4.5, and RCP8.5, responses of hydrological process to climate change were simulated using the Variable Infiltration Capacity (VIC) model. Variation analysis results indicated that recorded temperature presented significant increasing trend. Daily minimum temperature presented higher increasing trend than daily maximum temperature. Annual gross precipitation presented minor increasing and annual runoff presented minor decreasing. The VIC model performed well on simulating monthly discharge at Tangnaihai station, with NSE of 0.91 and 0.93 in calibration and validation periods, respectively. The projected annual mean temperature would rise (with 25th and 75th percentiles) 1.07–1.32 °C, 1.76–2.33 °C, 3.45–4.29 °C, annual precipitation is expected to increase 3.43%–11.77%, 8.05%–17.27%, 12.84%–27.89%, and runoff would moderately increase with high variability of 0.82%–14.26%, −3.41%–19.14%, 1.43%–38.26% relative to the baseline of 1961–1990 under each RCP in the 2080s, respectively. The inhomogeneity of runoff may increase in the future. Many more droughts and floods under climate change may threaten social development in this region in the future.

Hybrid Models Combining EMD/EEMD and ARIMA for Long-Term Streamflow Forecasting

Long-term streamflow forecast is of great significance for water resource application and management. However, accurate monthly streamflow forecasting is challenging due to its non-stationarity and uncertainty. Time series analysis methods have been proved to perform well in stationary time series forecasting, which can be derived from decomposition of the non-stationary sequence. As common decomposition methods in time domain, Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD) are selected to decompose the components with different time-scale characteristics in the original hydrological time series in this study. The derived components are proved to be stationary by the stationarity test. Thus, Autoregressive Integrated Moving Average (ARIMA) model, a simple and effective time series analysis method, is used to forecast the components. A hybrid EMD/EEMD-ARIMA model is proposed in this study for long-term streamflow forecasting, which is applied to the upper stream of the Yellow River. The original daily streamflow time series of six years at the Tangnaihai station are firstly decomposed by EMD/EEMD into several stationary or simple non-stationary sub-series to explore detailed data information with different time scales. ARIMA models with appropriate parameters are then established for each subsequence to forecast the stream flow of the next year. Predicted ten-day and monthly stream flow is finally obtained combing the predictions of all the components. The EMD-ARIMA hybrid model performs best in forecasting high and moderate value of streamflow and fits best with the observation compared with EEMD-ARIMA and ARIMA models. The results not only verify the effectiveness of the proposed hybrid EMD/EEMD-ARIMA model in exploiting comprehensive information to improve the prediction but also indicate that the EMD-ARIMA model with end points disposal performs the best and can be used for long-term hydrological forecasting.

Analyses of trends and causes for variations in runoff and sediment load of the Yellow River

Due to the impacts of globe climate change and human activities, dramatic variations in runoff and sediment load were observed for the Yellow River. Analyses of nearly 65 years’ data measured at main hydrologic-stations on the Yellow River from 1950 to 2014 indicated that, except for the Tangnaihai station in the head region, sharp downward trends existed in both the annual runoff and annual sediment load according to the Mann–Kendal trend test; and their abrupt changes occurred in 1986 and in 1980, respectively, according to the rank sum test. Factors affecting the changes in the runoff and sediment load were very complicated. Results indicated that the reducing precipitation and the increasing water consumption were the main causes for the runoff decline, while the impoundment of the Longyangxia Reservoir and its combined operation with the Liujiaxia Reservoir exerted a direct bearing on the abrupt change in the annual runoff. In addition to the sediment load decrease associated with the runoff reduction, the reduced storm intensity, the conducted soil erosion control, and the constructed dam buildings all played an important role in the trends and abrupt changes of sediment load decline.

Application of Support Vector Regression for Mid- and Long-term Runoff Forecasting in “Yellow River Headwater” Region

Accurate mid- and long-term runoff forecasting is of great significance for rational development and utilization of water resources in “Yellow River Headwater” region, where runoff in the headwater region contributes to nearly 35% of the total amount of the Yellow River basin. In this paper, the monthly runoff data of Tangnaihai station in “Yellow River Headwater” region are analyzed as case studies. This paper presents support vector regression model for mid- and long-term runoff forecasting, and analyzes the influence of support vector regression model's parameters on the runoff forecasting accuracy, and finally compared with Auto Regressive model (AR) and Radial basis function neural network (RBFNN). The results indicate that SVR showed the best performance and is proved to be competitive with the AR and RBFNN models in both stations. SVR methods provide a promising reliable methods of mid- and long-term runoff forecasting.

Assessing the impact of climate change on flood in an alpine catchment using multiple hydrological models

A growing number of studies on streamflow projection in the context of global climate change have been widely reported in past years. However, current knowledge on the role of different hydrological models to estimate future climate impact on flood in the Tibet Plateau is still limited so far. In this work, a group of hydrological models in conjunction with statistical downscaling outputs (SDSM and ANN) from the HadCM3 GCM model are used to evaluate the impacts of climate change on floods in the 21st century over the headwater catchment of Yellow River, the Tibet Plateau. The influence of different hydrological models on flood projection and quantile estimation are addressed. The results show that: (1) three hydrological models generate acceptable results of flood magnitude and frequency at Tangnaihai station during the past 50 years; (2) quite similar projections for future floods are obtained by means of different hydrological models, decreasing flood magnitude corresponding to the 2-, 5-, 10- and 50-year return periods is found under most scenarios in the 21st century. Meanwhile, flood frequency is likely to reduce in response to climate change; (3) the uncertainty in projected flood quantile by three different hydrological models increases with recurrence interval in term of relative length of confidence interval (RL). Besides, RL for flood quantile in future climate scenarios is likely to become larger than the baseline period. The results are valuable to improve our current knowledge of climate impact research in the alpine regions.

Regime Shift Identification of Runoff and Sediment Loads in the Yellow River Basin, China

Runoff and sediment loads have exhibited significant changes over the past six decades in the Yellow River Basin, China. The current study evaluates the changing trends and regime shifts in runoff and sediment loads at both the annual and monthly time scales. The associated spatial and temporal variations are analyzed by a sequential t-test analysis of the regime shifts (STARS) approach and the “breaks for additive seasonal and trend” (BFAST) model using hydrological data at eight stations from the 1950s to 2011. Both runoff and sediment loads exhibit significant declines (p < 0.05), except in the upper reaches of the river near the Tangnaihai station. The regime shifts detected by the STARS approach are not completely consistent with the results from the BFAST method. In most cases, the regime shifts occurred in 1969 and 1986, due to the construction of large reservoirs. Climate change and other human activities, such as large-scale soil and water conservation measures, can result in abrupt changes in hydrological series at some stations. The trapping effects of reservoirs not only cause regime shifts of runoff and sediment loads, but also adjust their inter-annual and seasonal distributions. Various soil and water conservation measures are responsible for the significant reduction in runoff and sediment loads in the mid-lower reaches of the Yellow River Basin. In addition, water withdrawals from both river runoff and ground water play a critical role in the changing trends in runoff and indirectly alter the sediment loads. The findings provide a good reference for the effective promotion of climate change adaptation, water resources planning and river basin management.

Complex response of runoff–precipitation ratio to the rising air temperature: the source area of the Yellow River, China

The impact of climate warming on the water cycle and water resources is an important issue in science and for sustainable socioeconomic development. The drainage area above Tangnaihai station is the source area of the Yellow River, which is located in the Qinghai–Tibet Plateau and yields 41.3 % of the river’s total natural runoff. Thus, it is known as the “water tower of the Yellow River.” Based on climate and hydrometric data from 1956 to 2008, the purpose of this study is to deal with the influence of climate warming on the water cycle in this region and thereby to provide some knowledge for sustainable water resources management of the Yellow River. The statistical analysis shows basically no trend in annual precipitation, but a significant rising trend in air temperature. Hence, this area is ideal to study the response of the water cycle system to climate warming. We found nonlinear variations in annual runoff (Qw) and the runoff–precipitation ratio (Rrp): they increased to a peak and then decreased. According to the response of Rrp to rising temperature, we established a three-stage descriptive model for the complex response of runoff generation to the rising air temperature. The mechanism of the complex response is explained based on the evidence of environmental changes previously reported in the study area, driven by rising temperatures.

A Conjunction Method of Wavelet Transform-Particle Swarm Optimization-Support Vector Machine for Streamflow Forecasting

Streamflow forecasting has an important role in water resource management and reservoir operation. Support vector machine (SVM) is an appropriate and suitable method for streamflow prediction due to its best versatility, robustness, and effectiveness. In this study, a wavelet transform particle swarm optimization support vector machine (WT-PSO-SVM) model is proposed and applied for streamflow time series prediction. Firstly, the streamflow time series were decomposed into various details (Ds) and an approximation (A3) at three resolution levels (21-22-23) using Daubechies (db3) discrete wavelet. Correlation coefficients between eachDsubtime series and original monthly streamflow time series are calculated.Dscomponents with high correlation coefficients (D3) are added to the approximation (A3) as the input values of the SVM model. Secondly, the PSO is employed to select the optimal parameters,C,ε, andσ, of the SVM model. Finally, the WT-PSO-SVM models are trained and tested by the monthly streamflow time series of Tangnaihai Station located in Yellow River upper stream from January 1956 to December 2008. The test results indicate that the WT-PSO-SVM approach provide a superior alternative to the single SVM model for forecasting monthly streamflow in situations without formulating models for internal structure of the watershed.

Tree ring based streamflow reconstruction for the Upper Yellow River over the past 1234 years

We established a Juniperus przewalski tree ring width chronology, based on tree ring cores collected from the A’nyemaqen Mountains. Statistical analysis showed that the chronology was highly correlated with instrumental streamflow records from previous August to current July from the Tangnaihai Station in the upper reaches of the Yellow River, with a correlation coefficient of 0.656. Streamflow for the upper reaches of the Yellow River was reconstructed for the past 1234 years. Low flow periods for the 11-year averaged streamflow reconstruction were definite as lower than mean plus 1 standard deviation, and high flow periods were higher than mean minus 1 standard deviation. Over the past 1234 years, high flows occurred 18 times, and low flows occurred 12 times. The main low flow periods were identified as AD 1140–1156, AD 1295–1309, AD 1473–1500, and AD 1820–1847, and the main high flow periods were identified as AD 846–873, and AD 1375–1400. Extremely low streamflow over the reconstruction period was seen during the late 15th century, coinciding with a widespread drought phenomenon, which took place in the northeastern Tibetan Plateau over the same period. Reconstructed streamflow shows significant low-frequency variability, which is in line with drought variability of neighboring regions, as inferred from tree rings and other proxies. Multi-taper spectral analysis suggests the existence of significant periods of 2–5, 22, 35–38, 55–62, and 114–227 years, particularly significant for cyclic variations of years 159 and 36.

Recent changes of water discharge and sediment load in the Yellow River basin, China

The Yellow River basin contributes approximately 6% of the sediment load from all river systems globally, and the annual runoff directly supports 12% of the Chinese population. As a result, describing and understanding recent variations of water discharge and sediment load under global change scenarios are of considerable importance. The present study considers the annual hydrologic series of the water discharge and sediment load of the Yellow River basin obtained from 15 gauging stations (10 mainstream, 5 tributaries). The Mann-Kendall test method was adopted to detect both gradual and abrupt change of hydrological series since the 1950s. With the exception of the area draining to the Upper Tangnaihai station, results indicate that both water discharge and sediment load have decreased significantly (p<0.05). The declining trend is greater with distance downstream, and drainage area has a significant positive effect on the rate of decline. It is suggested that the abrupt change of the water discharge from the late 1980s to the early 1990s arose from human extraction, and that the abrupt change in sediment load was linked to disturbance from reservoir construction.