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

Xiaoxiang Guan 1,2, Jianyun Zhang 1,2,3,*, Amgad Elmahdi 4 , Xuemei Li 5, Jing Liu 2,3, Yue Liu 1,2, Junliang Jin 2,3, Yanli Liu 2,3 , Zhenxin Bao 2,3, Cuishan Liu 2,3, Ruimin He 2,3 and Guoqing Wang 1,2,3,6,*

1 Institute of Hydrology and Water Resources, Hohai University, Nanjing 210098, China 2 Research Center for Climate Change, Ministry of Water Resources, Nanjing 210029, China 3 State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China 4 International Water Management Institute-IWMI, Head of MENA Region, 3000 Cairo, Egypt 5 Hydrology Bureau, Yellow River Conservancy Commission, Ministry of Water Resources, Zhengzhou 450003, China 6 School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China * Correspondence: [email protected] (J.Z.); [email protected] (G.W.); Tel.: +86-25-85828007 (J.Z.); +86-25-8582-8531 (G.W.)


Received: 6 May 2019; Accepted: 24 June 2019; Published: 27 June 2019


Abstract: 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.

Water 2019, 11, 1328; doi:10.3390/w11071328 www.mdpi.com/journal/water Water 2019, 11, 1328 2 of 20

Keywords: Yellow River basin; climate change; hydro-meteorological variation; hydrological modeling; water resource assessment

1. Introduction Hydrological models are essential for river flow forecasting, regional water resource assessment, and climate change impact analysis, owing to their consistent simulation capacity of hydrological series [1,2]. However, with rapid socio-economic development, most areas or river basins have been highly regulated with intensive human activities, such as large-scale soil and water conservation measures in arid or semi-arid climatic zones, water conservancy projects (e.g., reservoirs, water intake projects, etc.), and urbanization, etc. These human activities can influence or change the regional hydrological cycle and, more importantly, change the natural relationship between rainfall and runoff [3]. Society and hydrology have been main themes of “Panta Rhei—Everything Flows” and the new Scientific Decade 2013–2022 of IAHS (International Association of Hydrological Sciences) [4], and hydrological modeling under a changing environment has been an international leading scientific topic of the current WMO-IHP (International Hydrological Programme of World Meteorological Organization) [5]. The consistency of the hydrological series was usually changed due to large-scale human activities [6–10]. Numerous statistical methods, e.g., rank-sum test [11], Mann–Kendall rank correlation test [12], rescaled-range (R/S) analysis [13], Brown–Forsythe test [14] and Bayesian methods [15] have widely been applied around the world to detect the characteristics and variation features of the hydrological series. Guan et al. [16] analyzed the characteristics and applicability of different statistical methods for testing the consistency of hydrological series and indicated that the Mann–Kendall test method is optimal due to its clear principle and intuitive result. Hou et al. [17] analyzed the evolution of runoff time series by using the Kendall rank correlation coefficient, R/S analysis, and precipitation-runoff double cumulative curve method in the Weihe River (an important tributary of the Yellow River), and found that the human activities in the late 1980s led to an abrupt drop in the annual runoff series, which is consistent with the conclusion drew by Guan et al. [18]. In general, the hydrological models have been considered a powerful tool for revealing hydrological cycle mechanisms, flow forecasting, and regional water resource assessment at basin level [19]. Most of the recent work on hydrological models and their applications focus on model improvement and parameterization [20], runoff change attribution [21–23], flood forecasting, etc. Hydrological models could provide a technical theoretical basis for water resource management and river basin management based on an understanding of the impact of human activities and runoff sensitivity analysis [24–29]. For a human-regulated river basin, models were usually applied in two stages separately, as a non-interference period and human interference period [23]. Xu and Cheng [30] reviewed the application prospects of the distributed hydrological model and pointed out that the distributed hydrological models have shown their advantages in hydrologic process knowledge in the upward (bottom-up) approach to predict overall catchment response. However, distributed hydrological models are confronting some other problems in the application due to the amount of data that are required. Meanwhile, the error and uncertainty in multiple input data and many parameters often would enlarge and propagate uncertainty in simulations, and the accumulated error induced by many uncertainties can also not be ignored. The downward approach attempts to build a conceptual or lumped model directly at the level of interest to capture the whole system behavior [31]. Guo and Ma [32] applied the conceptual SIMHYD model to catchments in Australia and the Yellow River basin and found that this model is acceptable for monthly runoff prediction. Wang, et al. [20] established a monthly scale water balance model and successfully applied to many catchments in different climatic zones. The previous studies on model applications mainly focus on simulation of discharge series under natural period [33,34]. However, due to intensive human activities, it is very Water 2019, 11, 1328 3 of 20 difficult to find a consistent runoff series with a long-time span, particularly during dry years [35]. Zhang, et al., [36] analyzed the possible drivers of runoff reduction in the Middle of the Yellow River and found that the annual runoff from 1980–2000 at Huayuankou station decreased by more than 30% in relation to the previous period of time, and that human activities, such as land cover change, water conservation projects, etc., which played a dominant role in runoff decrease, accounted for about 62% of the total reduction. The mechanism of runoff yield in arid areas is quite a complex process and hydrological modeling is therefore a challenge in hydrological sciences [37,38]. Numerous studies have indicated that almost all hydrological models perform well for humid catchments; only a few models could acceptably simulate the hydrological process for arid catchments [39,40]. In addition, human activities alter the relationship between rainfall and runoff by changing land use and water consumption styles, and thus add more complexity to the hydrological processes in runoff generation in arid catchments [41]. Therefore, it is necessary to study hydrological modeling for river basins in dry regions under a changing environment, to strongly support flood control and water resource management decisions for complex dry river basins. The Yellow River is an important water source in north China, with 15% arable land and 12% of the population in China, and represents only 2.6% of China’s national water resources. Being highly influenced by the Asia Monsoon climate, the Yellow River basin is currently affected by a decline of water resource, due to the decrease of precipitation and intensive evapotranspiration. With rapid development of agriculture and industry and population growth, water resources in the Yellow River basin have been under extremely increasing pressure. Sustainable water resource use of the Yellow River urgently needs effective flow forecasting based on strong hydrological modeling that could represent the runoff series in terms of environmental changes. The main objectives of this paper are as follows: (1) to understand climate change in the study areas and variation of observed runoff as a result of environment changes; (2) to test the performance of four hydrological models that are selected for this study and find the most suitable to support hydrological forecasting; (3) to investigate the impact of environment change on hydrological modeling.

2. Data Source and Methods

2.1. Study Areas and Data Sources The Yellow River is the second-largest river basin in China, with a drainage area of about 752,443 km2 and total length of 5464 km in China. Due to the significant Asian Monson influence, the climate in the Yellow River basin could be characterized as hot and wet in summer, and cold and dry in winter. The annual precipitation over the basin is about 480mm with uneven distribution in time and space. Precipitation tends to decrease from southeast to northwest. More than 70% of the precipitation comes during flood season, from July to September. The total water amount of the Yellow River basin is only about 2.6% of China’s national water availability, with which it supports more than 13% of China’s farmland, food production, and about 25% of China’s coal and oil production. Due to dry climate conditions and heavy rainfall in wet season, the ecological system in the Yellow River is very vulnerable [42]. The basin has been experiencing intensive human activities aimed at improving the local environment since the 1970s with large-scale soil and water conservation measures [43]. As a result, the observed runoff has dramatically decreased, particularly for tributaries on the Loess Plateau in the middle reaches. Some tributaries even have zero values of river discharge during the dry season [44]. To investigate the influence of environmental change on the hydrological modeling, six hydrometric stations were selected, among which two stations are on the main stream of the Yellow River, and the other four stations are on tributaries. Hydrological data were collected from the Hydrological Yearbook issued by the Ministry of Water Resources, China. Within or near to the Yellow River basin, there are 65 meteorological stations with available data. Daily precipitation, daily average, and maximum and minimum air temperature over 1951–2013 were collected from the Water 2019, 11, 1328 4 of 20

Water 2019, 11, x FOR PEER REVIEW 4 of 21 Chinese Meteorology Administration (CMA, http://cdc.cma.gov.cn) to drive the hydrological models thein this hydrological study. River models system in andthis thestudy. locations River syst of theem 4 and tributary the locations catchments of the and 4 tributary two key maincatchments stream andhydrometric two key stations main stream are shown hydrometric in Figure1 ,stations and Table are1 providesshown in information Figure 1, andand dataTable regarding 1 provides the informationstations and and tributary data regarding catchments. the stations and tributary catchments.

Figure 1. River system and location of meteorological and hydrometric stations in the Yellow River basin. Figure 1. River system and location of meteorological and hydrometric stations in the Yellow River basin.Table 1. Information of 4 tributary catchments and 2 key stations of the Yellow River basin.

Drainage Discharge Precipitation/ Runoff/ Annual Average River Stations 2 Table 1. Information of 4 tributaryArea/km catchmentsSeries and 2 key stationsmm of themm Yellow RiverTemperature basin. /◦C Upper Yellow Tangnaihai 121,973Drainage 1956–2013Discharge Precipitation/ 507.9 166.9Runoff/ Annual4.9 Average RiverRiver Stations − 2 Tao River Hongqi 24,973Area/km 1955–2015Series 547.2mm 180.6mm Temperature/°C 0.2 UpperPianguan Yellow River Pianguan 1896 1951–2013 425.3 14.7 4.3 Tangnaihai 121,973 1956–2013 507.9 166.9 −4.9 SanchuanRiver River Houdacheng 4105 1957–2015 487.1 51.1 7.0 Yellow River Huayuankou 730,036 1951–2013 437.8 56.7 5.1 DawenTao River River Daicunba Hongqi 8264 24,973 1958–2015 1955–2015 870.9 547.2 73.9 180.6 13.9 0.2 Pianguan River Pianguan 1896 1951–2013 425.3 14.7 4.3 Sanchuan River Houdacheng 4105 1957–2015 487.1 51.1 7.0 The Upper Yellow River gauged by the Tangnaihai station located on the Tibetan Plateau, Gelic Yellow River Huayuankou 730,036 1951–2013 437.8 56.7 5.1 Leptosols, is the predominant soil type, which covers 60.1% of the catchment. The Tao River is in the Dawen River Daicunba 8264 1958–2015 870.9 73.9 13.9 transitional area from the Loess Plateau to the Tibetan Plateau; the catchment is covered with sparse grass.The The Upper Pianguan Yellow River River and thegauged Sanchuan by the River Tangnaih both areai loess station hilly located catchments on the on theTibetan Loess Plateau, Plateau, Gelicin which Leptosols, silt and is sandy the predominant loam are the soil main type, soil which texture covers of the 60.1% catchments. of the catchment. The Dawen The River Tao River is in the is inlower the transitional reaches of the area Yellow from River;the Loess the catchmentPlateau to the is geographically Tibetan Plateau; characterized the catchment as a is rocky covered and with hilly sparsewatershed, grass. in The which Pianguan sandy clay River loam and is the the predominantSanchuan River soil both texture. are Permafrostloess hilly catchments change induced on the by Loessglobal Plateau, warming in haswhich changed silt and regional sandy loam hydrology are the for main the Uppersoil texture Yellow of the River catchments. basin, while The the Dawen other Riverriver catchmentsis in the lower have reaches been highly of the regulated Yellow River; by intensive the catchment human activitiesis geographically for the purposes characterized of soil as and a rockywater and conservation hilly watershed, and agricultural in which development. sandy clay loam is the predominant soil texture. Permafrost changeTable induced1 shows by that global the selected warming six catchmentshas changed have regional a wide hydrology range of drainage for the area Upper and hydro-climateYellow River basin,characteristics. while the Theother drainage river catchments area spans have from been 1896 highly km2 (Pianguan regulated River) by intensive to 730,036 human km2 (Yellowactivities River for thegauged purposes at Huayuankou of soil and station).water conser Precipitationvation and varies agricultural from 425 mmdevelopment. to 870 mm and temperature changes fromTable4.9 ◦1C inshows the Upperthat the Yellow selected River six to 13.9catchments◦C in the have Dawen a Riverwide locatedrange of in thedrainage lower reaches.area and − hydro-climate characteristics. The drainage area spans from 1896 km2 (Pianguan River) to 730,036 km2 (Yellow River gauged at Huayuankou station). Precipitation varies from 425 mm to 870 mm and temperature changes from −4.9 °C in the Upper Yellow River to 13.9 °C in the Dawen River located in the lower reaches.

Water 2019, 11, 1328 5 of 20

2.2. Mann–Kendall Rank Test The Mann–Kendall rank test is a non-parametric, distribution-free method to detect trends of time series with minimal assumptions [45]. The method has been widely applied to test trends in hydrological and meteorological series all over the world. The Mann–Kendall rank trend test statistic index U (so-called M–K value) follows the standard normal distribution. A positive value of U represents an upward trend, while a negative one indicates a downward trend. The statistic U is under the null hypothesis of no trend when the number of series increases. The null hypothesis is rejected at the significance level of α, if U U , U is the critical value of the standard normal distribution | | ≥ α/2 α/2 with a probability exceeding α/2. If U < U , the null hypothesis is accepted, and the trend was not significant. For a significance | | α/2 level of α = 5%, then Uα/2 = 1.96.

2.3. Average-Difference T-Test The average-difference T-test is one of many statistically based approaches available in the literature to diagnose an abrupt change in time series [46,47]. If the variance is the same, statistic t is defined as:

X X P t = 1P − 2 (1) 1/2 SP(1/M1 + 1/M2)

2 2 (M1 1)S + (M2 1)S S2 = − 1 − 2 (2) P M + M 2 2 1 − where X1P and S1 are, separately, the average value and standard deviation of the data before base year, whose number is M1, X2P and S2 are average value and standard deviation of the data after base 2 year, the number of which is M2. SP is the joint sample variance. Time series of t are calculated by moving the base year continuously. The statistic t follows the t-distribution with the degree of freedom that is M + M 2, when the statistic value t > tα is at the significance level of α or the calculated 1 2 − P-value is less than α, then the average values of two sequences before and after the base year share a significant difference between each other, and the base year is an abrupt change point. When a first abrupt change point is detected, the two sequences can be tested with the same procedure respectively for the second or third abrupt change point if the null hypothesis is rejected.

2.4. Hydrological Models Considering model structure, physical interpretation, application performance, and accessibility, four conceptual hydrological models of Xin’anjiang model, SIMHYD rainfall–runoff model, GR4J model, and RCCC-WBM model are selected to conduct the study and assess their capacity in generation of runoff series in dry regions with environmental changes. The brief introduction of each model is given below:

2.4.1. Xin’anjiang Model Xin’anjiang (XAJ) model is a conceptual rainfall–runoff hydrological model developed by Hohai University, which has been widely used in humid and semi-humid catchments globally [48]. The schematic diagram of the model is shown in Figure2 and the notations and physical meanings of the model parameters (italicized in Figure2) and state variables can be found in references [ 49,50] for detail. The key hypothesis of the model is that the runoff is not generated until the soil moisture content of the aeration zone reaches field capacity [51]. The model is mainly composed of four parts, namely evapotranspiration being represented by a model of three soil layers, runoff yield component which separates runoff into surface flow, interflow and ground water, and a flow-routing component. The model has 16 parameters, mainly reflecting the properties of the soil layer, such as hydraulic Water 2019, 11, x FOR PEER REVIEW 6 of 21

content of the aeration zone reaches field capacity [51]. The model is mainly composed of four parts, namely evapotranspiration being represented by a model of three soil layers, runoff yield Watercomponent2019, 11, 1328 which separates runoff into surface flow, interflow and ground water, 6 ofand 20 a flow-routing component. The model has 16 parameters, mainly reflecting the properties of the soil conductivitylayer, such andas hydraulic moisture conductivity capacity of tensionand moisture water andcapacity free of water, tension which water greatly and free influences water, which the behaviorgreatly ofinfluences runoff yield the andbehavior flow of routing. runoff Dailyyield an areald flow average routing. precipitation Daily areal (sum average of rainfall precipitation and snowfall)(sum of and rainfall potential and snowfall) evapotranspiration and potential data evapotra series arenspiration required data to drive series the are model required and calculateto drive the themodel discharge. and calculate the discharge.

Figure 2. Flowchart of Xin’anjiang model. Figure 2. Flowchart of Xin’anjiang model. 2.4.2. SIMHYD Model 2.4.2. SIMHYD Model The SIMHYD model is a simplified lumped rainfall–runoff model that has been applied successfullyThe inSIMHYD many semi-arid model is or a semi-humid simplified basinslumped located rainfall–runoff in the United model States, that Australia, has been and otherapplied countriessuccessfully [52,53 ]in. Componentsmany semi-arid of runo or semi-humidff simulated basins with the located SIMHYD in the model United consist States, of surfaceAustralia, flow and (SRUN),other countries interflow [52,53]. (INF), andComponents base flow of (BAS). runoff The simulated first component with the SIMHYD is an infiltration model consist excess of runo surfaceff, interflowflow (SRUN), is based interflow on a saturation (INF), excessand base mechanism, flow (BAS). and The base first flow component is simulated is as an a linearinfiltration recession excess functionrunoff, of interflow groundwater is based store on (GW). a saturation Infiltration excess is a keymechanism, component and of base this modelflow is andsimulated is simulated as a linear as a negativerecession exponential function of function groundwater of soil wetnessstore (GW). (SM). Infiltration Model structure is a key and component mathematical of this formulas model and are is shownsimulated in Figure as3 .a negative exponential function of soil wetness (SM). Model structure and mathematical formulas are shown in Figure 3. 2.4.3. GR4J Model 2.4.3.The GR4J GR4J Model model is an empirical and lumped, reservoir-based model. It was developed by the researchThe group GR4J at CEMAGREFmodel is an empirical (now IRSTEA) and lumped, [54]. It was reservoir-based conceived for model. water resourceIt was developed management by the andresearch spring floodgroup prediction at CEMAGREF for hydrologic (now IRSTEA) applications. [54]. It was Initially, conceived this model for water was resource parsimonious management with onlyand 4 parameters,spring flood withprediction most secondary for hydrologic processes applications. being represented Initially, this by model empirical was constants, parsimonious which with hasonly been 4 parameters, widely used with in Europe most secondary and Australia processes [55,56 being]. The represented first operation by empirical is the subtraction constants, ofwhichE fromhasP beento determine widely used either in Europe a net rainfall and Australia (Pn) or a[55,56]. net evapotranspiration The first operation capacity is the subtractionEn. A part ofPs E fromof Pn,P determined to determine as either a function a net of rainfall water ( contentPn) or a ( Snet), fills evapotrans the productionpiration storecapacity and EnEs.will A part evaporate Ps of Pn, fromdetermined the store. as A a percolation function of leakage water contentPerc from (S), the fills store the isproduction then calculated store and as a Es power will functionevaporate of fromS andthe added store. to APn percolation-Ps as effective leakage rainfall Perc Prfrom. Then, the storePr is is divided then calculated into two parts,as a power 90% of function which (ofQ9 S in and Figureadded3) infiltrates to Pn-Ps as into effective the routing rainfall store Pr. and Then, forms Pr is the divided slow flow into (twoQr) parts, with a90% unit of hydrograph, which (Q9 in while Figure the3) remaining infiltrates 10%into ofthe the routing components store and (Q 1)forms forms the the slow fast flow flow (Qr (Qd) ).with Figure a unit3 describes hydrograph, the modelwhile the structure and detailed calculation process and the formula can be found in reference [56].

Water 2019, 11, x FOR PEER REVIEW 7 of 21

remaining 10% of the components (Q1) forms the fast flow (Qd). Figure 3 describes the model structure and detailed calculation process and the formula can be found in reference [56]. Water 2019, 11, 1328 7 of 20 2.4.4. RCCC-WBM Model 2.4.4.The RCCC-WBM RCCC-WBM Model (Water Balance Model, WBM) model is a simplified large-scale hydrological modelThe with RCCC-WBM a strong (Waterphysical Balance interpretation Model, WBM) of the model runoff is yield a simplified mechanism, large-scale developed hydrological by the modelResearch with Center a strong for physical Climate interpretationChange, Ministry of the of runo Waterff yield Resources mechanism, of China developed [20]. The by model the Research has the Centerability forto Climateestimate Change, monthly Ministry stream of Waterflow from Resources monthly of China rainfall, [20]. Thetemperature, model has and the abilitypotential to estimateevaporation monthly data, stream where flow catchment from monthly evaporation rainfall, is temperature,estimated with and a potentialone-layer evaporation soil evaporation data, whereformula, catchment and monthly evaporation precipitation is estimated is divided with into a one-layer rainfall (PR soil in evaporation Figure 3) and formula, snowfall and (PSN) monthly with precipitationpreset upper is and divided lower into temperature rainfall (PR (TH in Figureand TL)3) andcriteria snowfall by linear (PSN) partitioning with preset method upper andto calculate lower temperaturesurface flow (TH(GS) and and TL) snow-melt-driven criteria by linear flow partitioning (QSN), while method the surface to calculate flow surfaceis only proportional flow (GS) and to snow-melt-drivensoil moisture (S) and flow rainfall, (QSN), and while the the snow-melt surface flowrate is onlyan exponential proportional function to soil of moisture air temperature (S) and rainfall,and is proportional and the snow-melt to snow rate accumulation is an exponential (SN), and function lastly the of air base temperature flow (QG) andis simulated is proportional as a linear to snowrecession accumulation function of (SN), soil andmoisture. lastly theThe base model flow has (QG) only is four simulated parameters as a linear and recessionhas been successfully function of soilapplied moisture. in semi-arid The model and has humid only fourcatchments parameters located and in has China been successfully[2]. Compared applied to other in semi-arid hydrological and humidmodels, catchments the RCCC-WBM located model in China has [ 2the]. Compared advantages to of other a simpler hydrological structure, models, fewer the parameters, RCCC-WBM and modelflexibility has thein use. advantages The model of a simplerstructure structure, and descri fewerptions parameters, are shown and in flexibility Figure 3. in For use. more The modeldetails, structureplease refer and to descriptions reference [20]. are shown in Figure3. For more details, please refer to reference [20].

Figure 3. Model structures of SIMHYD, GR4J, and RCCC-WBM. Figure 3. Model structures of SIMHYD, GR4J, and RCCC-WBM. 2.5. Model Calibration and Objective Functions 2.5. Model Calibration and Objective Functions Firstly, the default value ranges of model parameters are estimated, then different parameter sets areFirstly, selected the todefault drive thevalue model ranges and of calculate model para the objectivemeters are functions estimated, which then represent different the parameter quality ofsets simulation are selected results to drive and the is used model for and later calculate parameter the modificationobjective functions until optimumwhich represent is obtained. the quality This calibrationof simulation method results is and partially is used reliant for later on experience parameter andmodification expertise. until Therefore, optimum objective is obtained. functions This shouldcalibration be well method selected is topartially evaluate reliant the performance on experience of hydrological and expertise. models Therefore, in stream objective flow simulation functions andshould assist be model well calibration.selected to evaluate Root mean the squared performance error (RMSE),of hydrological mean absolute models percentage in stream errorflow (MAPE),simulation and and Nash–Sutcli assist modelffe effi ciencycalibration. (NSE) Root are widelymean usedsquared by hydrologistserror (RMSE), to evaluatemean absolute model performancepercentage error in many (MAPE), previous and studies, Nash–Sutcliffe among which efficiency NSEis (NSE) a normalized are widely statistic used [57 by]. hydrologists Normalization to facilitates the easier comparison of hydrological model performance for disparate catchments. Water 2019, 11, x FOR PEER REVIEW 8 of 21 evaluateWater 2019 ,model11, 1328 performance in many previous studies, among which NSE is a normalized statistic 8 of 20 [57]. Normalization facilitates the easier comparison of hydrological model performance for disparate catchments. ItIt is is desirable desirable to to have have a good a good fit fitbetween between the the observed observed and and simulated simulated runoff runo timeff time series, series, but also but also toto minimize minimize overall overall bias bias in in the the simulation. simulation. Theref Therefore,ore, the theNSE NSE and andthe relative the relative error error (RE) (RE)between between simulatedsimulated and and observed observed runoff runoff werewere both both employed employed as as objective objective functions functions in in calibrating calibrating the the models models [2]. [2].Normally, Normally, a good a good simulation simulation result result will will have have NSEs NSEs approaching approaching toto 11 andand REsREs close to 0. 0.

3.3. Results Results and and Discussion Discussion

3.1.3.1. Variability Variability and and Trends Trends ofof Prec Precipitationipitation and and Temp Temperatureerature in 1951–2013 in 1951–2013 PrecipitationPrecipitation and and temperature temperature are are the the most most important important meteorological meteorological elements elements that thatmodulate modulate thethe riverriver regime.regime. Long-termLong-term variations variations of of annual annu arealal areal average average precipitation precipitation (calculated (calculated by arithmetic by arithmeticaveraging averaging method) and method) temperature and temperature are shown are in Figure shown4. in Trends Figure test 4. ofTrends precipitation test of precipitation and temperature andof the temperature six catchments of the are six summarized catchments are in Tablesummarize2, in whichd in Table the slope 2, in coewhichfficient the slope (S) of coefficient the regression (S) line ofillustrates the regression the magnitude line illustrates of the the upward magnitude or downward of the upward trend or of downward a time series. trendU of(or a M–K)time series. value U shows (orsignificance M–K) value of shows a series significance trend. of a series trend.

900 Temperature 1000 Temperature Tao River Sanchuan Temperature Dawen River Precipitation Precipitation 2 Precipitation 15.0 River 1500 800 800 8 0 700 600 1000 12.5 600 -2 4 400 Temperature (℃) Temperature (℃) Temperature (℃)

Precipitation(mm) Precipitation(mm) 500 Precipitation(mm) -4 500 10.0 400 200 1960 1980 2000 2020 1960 1980 2000 2020 1960 1980 2000 2020 800 Temperature Pianguan Temperature 800 Upper Yellow -2 Yellow River 800 Temperature Precipitation River Precipitation 6 River Precipitation 700 700 -4 6 600 600 4 600 -6

500 4 400 2 500 -8 Temperature (℃) Temperature (℃) Temperature (℃)

Precipitation(mm) 400 Precipitation(mm) Precipitation(mm) 400 -10 200 300 2 0 1960 1980 2000 2020 1960 1980 2000 2020 1960 1980 2000 2020

FigureFigure 4. 4. Long-termLong-term variations variations of ofannual annual precipitation precipitation (unit: (unit: mm, mm, dark dark blue bluelines) lines) and temperature and temperature (unit:(unit: °C,◦C, red red lines) lines) ofof the the selected selected basins basins during during 1951–2013. 1951–2013. Table 2. Trend test of annual precipitation and temperature of selected typical basins at annual scale Table 2. Trend test of annual precipitation and temperature of selected typical basins at annual scale from 1951–2013. from 1951–2013. Annual Precipitation Annual Temperature Annual Precipitation Annual Temperature River/Basins Hydrometric S S HydrometricStation Trend U S Trend U River/Basins (mm/year) Tren ( C/year) Tren Station S (mm/year) U (°C/year◦ U Dawen River Daicunba 1.031 d 0.88 0.017 d 4.18 − ↓ − ) ↑ Tao River Hongqi 0.226 0.05 0.019 4.15 Dawen River Daicunba − −1.031 ↓↓ −−0.88 0.017 ↑↑ 4.18 Sanchuan River Houdacheng 0.462 0.41 0.029 4.74 Tao River Hongqi − −0.226 ↓↓ −−0.05 0.019 ↑↑ 4.15 Yellow River Huayuankou 0.483 0.72 0.022 4.78 − ↓↓ − ↑↑ SanchuanPianguan River River Houdacheng Pianguan 0.433−0.462 0.23−0.41 0.029 0.022 4.74 3.7 − ↓ − ↑ YellowUpper YellowRiver Huayuankou −0.483 ↓ −0.72 0.022 ↑ 4.78 Tangnaihai 0.703 1.8 0.013 3.36 PianguanRiver River Pianguan −0.433 ↑ ↓ −0.23 0.022 ↑↑ 3.7 Upper Yellow Tangnaihai 0.703 ↑ 1.8 0.013 ↑ 3.36 River Figure4 shows that all catchments are characterized by similar increases in temperature series during the period 1951–2013. There were no significant upward or downward trends observed in the precipitation series, which fluctuate around the mean value without great abrupt change over the period. Table2 shows that temperatures of the six catchments exhibit significant rising trends, with linear rising rate of >0.13 ◦C/10 year. The highest rise occurred in the Sanchuan River basin, and the Upper Yellow River basin shows the lowest rise in temperature. Insignificant variation trends were Water 2019, 11, x FOR PEER REVIEW 9 of 21

Figure 4 shows that all catchments are characterized by similar increases in temperature series during the period 1951–2013. There were no significant upward or downward trends observed in the precipitation series, which fluctuate around the mean value without great abrupt change over the period. Table 2 shows that temperatures of the six catchments exhibit significant rising trends, withWater linear2019, 11 rising, 1328 rate of >0.13 °C/10 year. The highest rise occurred in the Sanchuan River basin,9 and of 20 the Upper Yellow River basin shows the lowest rise in temperature. Insignificant variation trends weredetected detected in the in precipitation the precipitation series for series all six for catchments. all six catchments. Annual precipitation Annual precipitation series of five series catchments of five catchmentspresent slight present decreasing slight trends decreasing (from trends10.3 mm (from/10 year−10.3 to mm/102.3 mm year/10 to year) −2.3 with mm/10 the exceptionyear) with of the the − − exceptionUpper Yellow of the River, Upper which Yellow exhibits River, an which increasing exhibits trend an increasing (7.03 mm/10 trend year). (7.03 mm/10 year).

3.2.3.2. Inter-Annual Inter-Annual Variations Variations and and Abrupt Abrupt Change Change of of Observed Observed Runoff Runoff FigureFigure 5 shows the long-term variations of the ob observedserved runoff runo ff atat the the six six hydrometric hydrometric stations. stations. VariationVariation trends inin annualannual discharge discharge series series were were investigated investigated using using the Mann–Kendallthe Mann–Kendall test andtest linear and linearregression. regression. Abrupt Abrupt change change was detected was detected by applying by applying average-di average-differencefference T-test (inT-test Table (in3 Table). 3).

150 800 Daicunba Runoff Hongqi Runoff Houdacheng Runoff 5-yr moving 400 5-yr moving 5-yr moving Linear regression Linear regression Linear regression 600 100 300 y= -1.378(t-1951)+226.7 400 y= -1.465(t-1951)+166.8 200

Runoff(mm) Runoff(mm) Runoff(mm) 50 200

100 y= -0.887(t-1951)+82.2 0 0 1950 1960 1970 1980 1990 2000 2010 2020 1950 1960 1970 1980 1990 2000 2010 2020 1950 1960 1970 1980 1990 2000 2010 2020

150 Huayuankou Runoff 80 Pianguan Runoff 350 Tangnaihai Runoff 5-yr moving 5-yr moving 5-yr moving 300 Linear regression 60 Linear regression Linear regression 100 y= -0.667(t-1951)+71.5 y= -0.608(t-1951)+36.3 250 40 200

Runoff(mm) 50 Runoff(mm) 20 Runoff(mm) 150

100 0 y= -0.229(t-1951)+171.8 0 50 1950 1960 1970 1980 1990 2000 2010 2020 1950 1960 1970 1980 1990 2000 2010 2020 1950 1960 1970 1980 1990 2000 2010 2020 FigureFigure 5. Variation ofof annualannual runo runoffff (unit: (unit: mm, mm, black black dotted dotted line) line) at hydrometric at hydrometric stations stations in the in Yellow the YellowRiver basin River and basin their and 5-year their moving5-year moving average average (green solid(green line) solid and line) linear and trend linear line trend (red line dash (red line). dash line).Table 3. Results of trends detection and abrupt change test for runoff series gauged at the six hydrometric stations. Table 3. Results of trends detection and abrupt change test for runoff series gauged at the six hydrometric stations. Abrupt Previous Studies River Stations U Statistics P-Value Change Year Change Year References 2 Previous Studies Dawen River Daicunba 1.19Abrupt 1975 1.866 7.78 10− 1975 [58] − × 3 River Tao RiverStations Hongqi U3.02 1986 3.168Statistics3.50 10−P-Value1989 Change [59 ] − Change Year × 5 References Sanchuan River Houdacheng 3.60 1997 17.18 1.74 10− 1995Year [58] − − × 3 Yellow River Huayuankou 5.57 1986 3.505 5.02 10− 1986 [58] − × −2 Dawen RiverPianguan River Daicunba Pianguan −1.197.41 1983 1975 6.166 1.8661.30 10 7.787 × 101984 1975 [60] [58] − × − Upper Yellow River Tangnaihai 1.13 1990 3.432 7.62 10 3 1990−3 [61] Tao River Hongqi −3.02− 1986 − 3.168 × 3.50− × 10 1989 [59] Sanchuan River Houdacheng −3.60 1997 −17.18 1.74 × 10−5 1995 [58] Yellow RiverFigure 5 shows Huayuankou that (1) observed −5.57 runo ff at Daicunba 1986 station, 3.505 Hongqi station, 5.02 × 10 and−3 Tangnaihai1986 station [58] Pianguanshows River higher variability Pianguan with greater−7.41 annual runo 1983ff fluctuation 6.166in comparison 1.30 to× 10 the−7 other three1984 stations. [60] Upper Yellow(2) Runo Riverff at all the Tangnaihai six stations shows−1.13 declining 1990 trends with linear−3.432 decreasing 7.62 rates× 10− of3 1.4651990 (Daicunba [61] − station), 1.378 (Hongqi station), 0.887 (Houdacheng station), 0.667 (Huayuankou station), 0.608 Figure− 5 shows that (1) observed− runoff at Daicunba station,− Hongqi station, and Tangnaihai− (Pianguan station), and 0.229 mm/year (Tangnaihai station), respectively. (3) Significant declining station shows higher variability− with greater annual runoff fluctuation in comparison to the other threetrends stations. are detectable (2) Runoff except at all for the runo sixff stationsgauged show at Tangnaihais declining station trends and with Daicunba linear decreasing station (based rates on ofU −values1.465 in(Daicunba Table3). station), −1.378 (Hongqi station), −0.887 (Houdacheng station), −0.667 Table3 presents the abrupt change year detected by using the T-test. The earliest abrupt change year for all six catchments occurred in 1975, and the latest abrupt change occurred in 1997; this agrees with the results detected by using Mann–Kendall test published in previous studies [58–62]. Water 2019, 11, x FOR PEER REVIEW 10 of 21

(Huayuankou station), −0.608 (Pianguan station), and −0.229 mm/year (Tangnaihai station), respectively. (3) Significant declining trends are detectable except for runoff gauged at Tangnaihai station and Daicunba station (based on U values in Table 3). Table 3 presents the abrupt change year detected by using the T-test. The earliest abrupt change year for all six catchments occurred in 1975, and the latest abrupt change occurred in 1997; Waterthis agrees2019, 11 ,with 1328 the results detected by using Mann–Kendall test published in previous studies10 [58– of 20 62]. 3.3. Relationships between Runoff and Precipitation in a Changing Environment 3.3. Relationships between Runoff and Precipitation in a Changing Environment The observed annual rainfall and runoff series were divided into three periods [before 1975, The observed annual rainfall and runoff series were divided into three periods [before 1975, 1976–1999, and 2000–2013] (Figure6) to explore their phased relationships and abrupt change analysis. 1976–1999, and 2000–2013] (Figure 6) to explore their phased relationships and abrupt change Figure5 indicates that: (1) runo ff is highly correlated with precipitation before 1975 (black hollow analysis. Figure 5 indicates that: (1) runoff is highly correlated with precipitation before 1975 (black points and black regression line) for every catchment with natural conditions; (2) with the exception hollow points and black regression line) for every catchment with natural conditions; (2) with the of the Upper Yellow River basin, the points of runoff against precipitation after 1975 fall towards the exception of the Upper Yellow River basin, the points of runoff against precipitation after 1975 fall lower phase, especially in 2000–2013, which shows that runoff was much reduced in terms of similar towards the lower phase, especially in 2000–2013, which shows that runoff was much reduced in magnitude of precipitation at the annual scale. terms of similar magnitude of precipitation at the annual scale.

Before 1975 1976-1999 2000-2013 Linear fit of Before 1975 Linear fit of 1976-1999 Linear fit of 2000-2013 800 400 Dawen River Tao River 120 Sanchuan River 600 300 100 400 80 200 60 200 40

Annual runoff(mm) Annual runoff(mm) Annual 100 runoff(mm) Annual 0 20 500 1000 1500 400 500 600 700 800 200 400 600 800 Annual precipitation (mm) Annual precipitation (mm) Annual precipitation (mm) 150 300 Yellow River 60 Pianguan River Upper Yellow River 250 100 40 200

50 20 150

Annual runoff(mm) Annual runoff(mm) Annual 0 runoff(mm) Annual 100 0 300 400 500 600 200 400 600 800 400 450 500 550 600 650 Annual precipitation (mm) Annual precipitation (mm) Annual precipitation (mm) Figure 6.6.Relationship Relationship of of annual annual runo runoffff (mm) (mm) and areal and average areal average precipitation precipitation (mm) for the(mm) six catchments.for the six catchments. 3.4. Hydrological Simulation for Stream Flow

3.4.3.4.1. Hydrological Hydrological Simu Modelinglation for underStream Natural Flow Period

3.4.1.To Hydrological evaluate the Modeling models’ performanceunder Natural for Period simulating the discharge under the natural condition without severe impacts of human activities, the data series before abrupt changing years was divided To evaluate the models’ performance for simulating the discharge under the natural condition into calibration and verification periods for the six selected hydrometric stations, respectively. The XAJ, without severe impacts of human activities, the data series before abrupt changing years was GR4J, and SIMHYD models simulated the daily discharge and results were aggregated to monthly divided into calibration and verification periods for the six selected hydrometric stations, discharge volumes at the hydrometric stations, while monthly rainfall, temperature, and potential respectively. The XAJ, GR4J, and SIMHYD models simulated the daily discharge and results were evaporation data series were necessary as model inputs to estimate monthly stream flow by the aggregated to monthly discharge volumes at the hydrometric stations, while monthly rainfall, RCCC-WBM model. The performance of the four models over the six sub-basins is presented in temperature, and potential evaporation data series were necessary as model inputs to estimate Tables4 and5. The monthly observed and simulated discharge at the Hongqi and Pianguan station monthly stream flow by the RCCC-WBM model. The performance of the four models over the six are taken as typical examples and shown in Figure7. sub-basins is presented in Tables 4 and 5. The monthly observed and simulated discharge at the Hongqi and Pianguan station are taken as typical examples and shown in Figure 7.

Table 4. Simulated monthly discharge by the four hydrological models for the calibration period ion results for monthly discharge in the Yellow River basin by using four hydrological models.

Water 2019, 11, x FOR PEER REVIEW 11 of 21

Calibration NSE 1 RE (%) River Water 2019Station, 11, 1328 11 of 20 Period XAJ GR4J SIMHYD WBM XAJ GR4J SIMHYD WBM Dawen Daicunba 1958–1968 0.827 0.870 0.709 0.703 −0.68 −0.36 −10.2 −6.73 River Table 4. Simulated monthly discharge by the four hydrological models for the calibration period ion Tao River results Hongqi for monthly 1955–1965 discharge in the 0.849 Yellow River 0.852 basin by 0.779 using four 0.732 hydrological −5.48 models. −5.46 −9.26 5.15

Sanchuan 1 Calibration NSE RE (%) HoudachengRiver Station 1957–1967 0.814 0.754 0.747 0.728 −4.58 −9.63 1.64 −11.9 River Period XAJ GR4J SIMHYD WBM XAJ GR4J SIMHYD WBM Yellow Dawen HuayuankouDaicunba 1958–1968 1958–1968 0.665 0.827 0.560 0.870 0.7090.647 0.7030.752 0.68−7.570.36 −7.6010.2 −9.356.73 0.10 River River − − − − Pianguan Tao River Hongqi 1955–1965 0.849 0.852 0.779 0.732 5.48 5.46 9.26 5.15 Pianguan 1957–1967 0.838 0.729 0.799 0.812 − −4.11− −9.27− −2.13 −10.9 River Sanchuan Houdacheng 1957–1967 0.814 0.754 0.747 0.728 4.58 9.63 1.64 11.9 Upper River − − − Yellow River Huayuankou 1958–1968 0.665 0.560 0.647 0.752 7.57 7.60 9.35 0.10 Yellow Tangnaihai 1956–1966 0.834 0.725 0.738 0.735 − 4.80− −0.55− −6.95 −2.02 Pianguan River Pianguan 1957–1967 0.838 0.729 0.799 0.812 4.11 9.27 2.13 10.9 River − − − − 1 NSEs are calculated by monthly observed and simulated discharges, so are the NSEs in the following tables. Upper Tangnaihai 1956–1966 0.834 0.725 0.738 0.735 4.80 0.55 6.95 2.02 Yellow River − − − Table 5. Simulated monthly discharge by the four hydrological models for the verification period. 1 NSEs are calculated by monthly observed and simulated discharges, so are the NSEs in the following tables. Verification NSE RE (%) River Station Table 5. SimulatedPeriod monthly dischargeXAJ byGR4J the four SIMHYD hydrological modelsWBM forXAJ the verification GR4J period.SIMHYD WBM Dawen Daicunba 1969–1975Verification 0.784 0.750 NSE 0.638 0.696 9.00 9.17 RE (%) −13.9 15.4 River River Station Period XAJ GR4J SIMHYD WBM XAJ GR4J SIMHYD WBM Tao River Hongqi 1966–1975 0.915 0.883 0.828 0.781 4.72 11.6 5.17 11.0 Dawen Sanchuan Daicunba 1969–1975 0.784 0.750 0.638 0.696 9.00 9.17 13.9 15.4 HoudachengRiver 1968–1975 0.816 0.782 0.764 0.714 7.47 −14.9− 6.79 −10.9 River Tao River Hongqi 1966–1975 0.915 0.883 0.828 0.781 4.72 11.6 5.17 11.0 Yellow Sanchuan HuayuankouHoudacheng 1969–19751968–1975 0.555 0.816 0.599 0.782 0.529 0.764 0.714 0.686 7.47 16.9 14.9 8.54 6.79 13.110.9 23.3 River River − − Pianguan Yellow River Huayuankou 1969–1975 0.555 0.599 0.529 0.686 16.9 8.54 13.1 23.3 Pianguan 1968–1975 0.815 0.788 0.713 0.834 −8.48 −16.1 15.6 −16.7 River Pianguan Pianguan 1968–1975 0.815 0.788 0.713 0.834 8.48 16.1 15.6 16.7 Upper River − − − Upper Yellow TangnaihaiTangnaihai 1967–1975 1967–1975 0.781 0.781 0.718 0.718 0.705 0.705 0.739 0.739 8.30 8.30 0.33 0.33 8.81 8.812.45 −2.45 Yellow River − River

Figure 7. Monthly simulated and observed discharge (a) for 1965–1975 at Hongqi station of the Tao Figure 7. Monthly simulated and observed discharge (a) for 1965–1975 at Hongqi station of the Tao River catchment and (b) for 1957–1975 at Pianguan station of the Pianguan River catchment. River catchment and (b) for 1957–1975 at Pianguan station of the Pianguan River catchment. Tables4 and5 show that: (1) the four hydrological models not only perform well for the monthly discharge of tributary catchments but also for Tangnaihai and Huayuankou stations on the main Water 2019, 11, x FOR PEER REVIEW 12 of 21

Water 2019Tables, 11, 13284 and 5 show that: (1) the four hydrological models not only perform well for12 ofthe 20 monthly discharge of tributary catchments but also for Tangnaihai and Huayuankou stations on the main stream of the Yellow River. For the tributary catchments (excluding Huayuankou station), stream of the Yellow River. For the tributary catchments (excluding Huayuankou station), NSEs in NSEs in the calibration period vary from 0.814 to 0.849 for XAJ model, from 0.729 to 0.870 for GR4J the calibration period vary from 0.814 to 0.849 for XAJ model, from 0.729 to 0.870 for GR4J model, model, from 0.709 to 0.799 for SIMHYD model and from 0.703 to 0.812 for RCCC-WBM model. In from 0.709 to 0.799 for SIMHYD model and from 0.703 to 0.812 for RCCC-WBM model. In addition, addition, the REs were all between −15% and 15%. NSEs in the verification period were all greater the REs were all between 15% and 15%. NSEs in the verification period were all greater than 0.7 than 0.7 with the absolute− value of REs less than 15%. (2) The XAJ model has the best discharge with the absolute value of REs less than 15%. (2) The XAJ model has the best discharge simulation simulation performance in the Yellow River basin with the highest NSEs and lowest REs for both performance in the Yellow River basin with the highest NSEs and lowest REs for both calibration and calibration and verification period among the four hydrological models, followed by GR4J. The verification period among the four hydrological models, followed by GR4J. The adequate performance adequate performance of the simulations indicates that the hydrological models may be useful for of the simulations indicates that the hydrological models may be useful for investigating the stream investigating the stream flow in the Yellow River basin. flow in the Yellow River basin. The calibrated XAJ model was then forced by meteorological data for the entire period of The calibrated XAJ model was then forced by meteorological data for the entire period of record, record, using the constant set of parameters to generate runoff estimates (Figure 8). The simulated using the constant set of parameters to generate runoff estimates (Figure8). The simulated runo ff series runoff series reflect the benchmark situation without major human influences throughout the basin. reflect the benchmark situation without major human influences throughout the basin. Figure8 shows Figure 8 shows the following: (1) the simulated and observed annual runoff at the Daicunba and the following: (1) the simulated and observed annual runoff at the Daicunba and Hongqi stations Hongqi stations match well, not only for the first model calibration and verification periods, but also match well, not only for the first model calibration and verification periods, but also for several years for several years after these periods; (2) as the parameters were kept constant, variability of the after these periods; (2) as the parameters were kept constant, variability of the simulated runoff reflects simulated runoff reflects the changes in climatic variables, such as the variability in precipitation and the changes in climatic variables, such as the variability in precipitation and trend in temperature; trend in temperature; (3) the simulation errors (the difference between observed and simulated (3) the simulation errors (the difference between observed and simulated runoff) become ever larger runoff) become ever larger (overestimated) after 1980, which show large value after 2000 for the (overestimated) after 1980, which show large value after 2000 for the other 4 hydrometric stations, other 4 hydrometric stations, suggesting substantial human activities and influence were observed. suggesting substantial human activities and influence were observed. The starting year of abrupt The starting year of abrupt change in the runoff series indicated in Figure 7 was similar to the T-test change in the runoff series indicated in Figure7 was similar to the T-test results in Table4. results in Table 4.

800 150 Daicunba Simulated Hongqi Simulated Houdacheng Simulated Observed 400 Observed Observed 600 100 300 400

200 200 50 Annual runoff(mm) Annual runoff(mm) Annual runoff(mm) 0 100 1960 1980 2000 2020 1960 1980 2000 2020 1960 1980 2000 2020 300 120 Huayuankou Simulated Pianguan Simulated Tangnaihai Simulated Observed 60 Observed Observed 100 250 40 80 200 60 20 150 40 Annual runoff(mm) Annual runoff(mm) Annual runoff(mm) Annual 20 0 100 1960 1980 2000 2020 1960 1980 2000 2020 1960 1980 2000 2020 Figure 8. Annual simulated (by using XAJ model) and observed runoff (mm) at selected hydrometric Figure 8. Annual simulated (by using XAJ model) and observed runoff (mm) at selected hydrometric stations in Yellow River basin. stations in Yellow River basin. 3.4.2. Hydrological Modeling under a Changing Environment 3.4.2. Hydrological Modeling under a Changing Environment Under the changing environments where impacts of human activities such as land use change and artificialUnder the water changing intake environments are expected where to have impacts a great of influence human activities on hydrological such as land regime, use namelychange theand responseartificial water of runo intakeff to are rainfall; expected therefore, to have the a great hydrological influence model on hydrolog performance,ical regime, reliability, namely and the applicabilityresponse of requirerunoff reassessment.to rainfall; therefore, Based on the this, hy thedrological input data model series inperformance, 2000–2013 that reliability, were used and to driveapplicability the 4 hydrological require reassessment. models and Based the monthly on this, the simulation input data results series for in calibration 2000–2013 and that verification were used periodsto drive are the presented 4 hydrological in Tables 6models and7. As and the rainfall–runothe monthly ffsimulationrelationship results is the simplestfor calibration hydrological and model,verification scatters periods between are monthlypresented precipitation in Tables 6 and and runo 7. ffAsand the linear rainfall–runoff regression withrelationship R-squared is (Rthe2) calculatedsimplest hydrological were drew in model, Figure9 .scatters The monthly between observed monthly and simulatedprecipitation discharge and runoff from 2000–2013 and linear at theregression Hongqi with and PianguanR-squared stations (R2) calculated are taken aswere typical drew examples, in Figure and 9. shown The monthly in Figure 10observed. and

Water 2019, 11, 1328 13 of 20

Table 6. Simulated monthly discharge by the four hydrological models for the calibration period under changing environments.

NSE RE (%) River Station Calibration Period XAJ GR4J SIMHYD WBM XAJ GR4J SIMHYD WBM Dawen River Daicunba 2000–2010 0.748 0.726 0.544 0.486 3.42 16.8 35.1 8.06 − Tao River Hongqi 2000–2010 0.661 0.652 0.705 0.409 11.8 6.61 1.26 4.16 − − − Sanchuan River Houdacheng 2000–2010 0.341 0.199 0.416 0.490 14.6 28.7 3.40 4.25 − − − Yellow River Huayuankou 2000–2010 0.065 0.885 0.099 0.217 3.15 28.4 0.35 0.46 − − − Pianguan River Pianguan 2000–2010 0.293 0.271 0.352 0.265 4.16 12.2 19.6 25.9 − − Upper Yellow River Tangnaihai 2000–2010 0.824 0.818 0.761 0.776 10.9 6.70 0.27 0.87 − − −

Table 7. Simulated monthly discharge by the four hydrological models for the verification period under changing environments.

NSE RE (%) River Station Verification Period XAJ GR4J SIMHYD WBM XAJ GR4J SIMHYD WBM Dawen River Daicunba 2011–2013 0.701 0.719 0.465 0.558 10.5 1.57 27.9 11.0 − − − Tao River Hongqi 2011–2013 0.794 0.740 0.695 0.495 11.3 8.42 17.9 16.4 − − − Sanchuan River Houdacheng 2011–2013 0.549 0.560 0.005 0.640 34.2 18.3 59.8 9.25 − − − − Yellow River Huayuankou 2011–2013 0.359 1.99 -0.270 0.170 29.5 9.52 31.3 22.1 − − − − − − Pianguan River Pianguan 2011–2013 v3.27 v1.32 1.89 0.264 296 157 213 193 − Upper Yellow River Tangnaihai 2011–2013 0.791 0.832 0.778 0.769 18.8 13.3 15.0 11.2 − − − Water 2019, 11, 1328 14 of 20

Before 1975 2000-2013 Linear fit of P-R before 1975 Linear fit of P-R in 2000-2013

300 Before 1975 2000-2013100 Linear fit of P-R before 1975 Linear fit of P-R in 2000-2013 Dawen River Tao River60 Sanchuan River 300 10080 2 Dawen River Tao RiverR =0.551 Sanchuan River 200 2 60 2 R =0.530 80 2 R =0.616 60 R =0.551 40 200 R2=0.530 R2=0.616 60 40 100 40 20 100 4020 20 Monthly runoff (mm) Monthly runoff (mm) Monthly runoff (mm) Monthly runoff 0 2 2 2 R =0.325 20 0 R =0.376 0 R =0.455 Monthly runoff (mm) Monthly runoff (mm) Monthly runoff (mm) Monthly runoff 0 2 2 2 0 200R 400=0.325 600 0 0 50 100R =0.376 150 200 0 0 50 100 150 200R =0.455 250 300 350 Monthly precipitation (mm) Monthly precipitation (mm) Monthly precipitation (mm) 0 200 400 600 0 50 100 150 200 0 50 100 150 200 250 300 350 Monthly precipitation (mm) 60 Monthly precipitation (mm) 60 Monthly precipitation (mm) Yellow River Pianguan River Upper Yellow 2 60 60 River 20Yellow RiverR =0.408 Pianguan River Upper Yellow 40 2 40 20 R2=0.408 R =0.651 RiverR2=0.617 40 40 10 R2=0.651 R2=0.617 20 20 10 20 20

Monthly (mm) runoff Monthly 2 (mm) runoff Monthly 2 (mm) runoff Monthly 2 0 R =0.163 0 R =0.278 0 R =0.624

Monthly (mm) runoff Monthly 2 (mm) runoff Monthly 2 (mm) runoff Monthly 2 0 R =0.163 0 R =0.278 0 R =0.624 0 60 120 180 0 60 120 180 240 300 360 0 40 80 120 160 200 Monthly precipitation (mm) Monthly precipitation (mm) Monthly precipitation (mm) 0 60 120 180 0 60 120 180 240 300 360 0 40 80 120 160 200 Monthly precipitation (mm) Monthly precipitation (mm) Monthly precipitation (mm) Figure 9. Relationship of monthly runoff (mm) against areal average precipitation (mm) for the six FigureFigurecatchments. 9. 9. RelationshipRelationship of of monthly monthly runoff runo ff(mm)(mm) against against areal areal average average precipitation precipitation (mm) (mm) for forthe thesix catchments.six catchments.

Figure 10. Monthly simulated and observed discharge (a) at Hongqi station of the Tao River catchment andFigure (b) Pianguan10. Monthly station simulated of the Pianguan and observed River catchment discharge for (a 2000–2013.) at Hongqi station of the Tao River Figurecatchment 10. Monthlyand (b) Pianguan simulated station and ofobserved the Pian dischargeguan River ( acatchment) at Hongqi for 2000–2013.station of the Tao River catchmentThe runo ffandsimulation (b) Pianguan results station show of the that: Pian (1)guan the River 4 hydrological catchment modelsfor 2000–2013. performed comparatively well inThe Dawen runoff River, simulation Tao River, results and Upper show Yellow that: River (1) basinthe especially4 hydrological for XAJ models and GR4J performed models, withcomparativelyThe NSEs runoff over 0.6well simulation and in absoluteDawen resultsRiver, value Tao ofshow REs River, less that: an thand Upper(1) 20% the for Yellow both4 hydrological calibration River basi andn especiallymodels verification performedfor periods.XAJ and comparativelyForGR4J the models, Upper well Yellowwith in NSEs Dawen River overbasin, River, 0.6 and NSEsTao absoluteRiver, calculated an valued Upper by of 4 modelsREsYellow less wereRiver than allbasi20% aboven for especially both 0.7, whichcalibration for XAJ were and and in GR4Jgreatverification agreementmodels, periods. with with NSEs thatFor overthe shown Uppe 0.6 in andr Figure Yellow absolute9, whereRiver value basin, points of NSEsREs of annual less calculated than runo 20%ff byagainst for 4 models both precipitation calibration were all above inand all verificationthe0.7, three which periods periods.were fallin Forgreat into the theagreement Uppe samer Yellow or similarwith River that domain showbasin, meaningn NSEs in Figure calculated the 9, relationship where by 4 pointsmodels between of were annual runo all aboveffrunoffand 0.7,precipitationagainst which precipitation were did in not great change in agreementall significantly;the three with periods that (2) the showfall performance inton in theFigure same of 9, the orwhere foursimilar conceptualpoints domain of annual hydrologicalmeaning runoff the againstmodelsrelationship forprecipitation runo betweenff simulation in runoff all atthe and Houdacheng, three precipitation periods Huayuankou, fall did into not thechange and same Pianguan significantly; or similar stations domain(2) were the notperformance meaning satisfactory the of relationshipwhere the NSEs between were runoff no greater and precipitation than 0.4 for the did calibration not change period significantly; and even (2) less the than performance zero for theof Water 2019, 11, x; doi: FOR PEER REVIEW www.mdpi.com/journal/water Water 2019, 11, x; doi: FOR PEER REVIEW www.mdpi.com/journal/water Water 2019, 11, 1328 15 of 20 verification period for Huayuankou and Pianguan stations. Furthermore, the REs for the three hydrometric stations fluctuated with large amplitude and even exceeded 100% at Pianguan station where discharge was gauged to be not zero only in summer (in Figure9, the runo ff mostly approaches zero during 2000–2013, meaning weak influence on runoff from the rainfall exerted) making the correlation between precipitation and runoff so weak and in turn the conceptual hydrological models could not depict the law of runoff generation and flow routing any more under a changing environment; (3) the four models’ simulation results agree with that expressed by the rainfall–runoff relationship at both yearly and monthly scale. In other words, conceptual or lumped hydrological models lose their advantage in simulating the discharge under a changing environment, which poses great challenges to watershed water resource assessment and management.

3.5. Discussion Among all the six hydrometric stations, the earliest abrupt change occurred in 1975 for the Dawen River, and the latest abrupt change was detected in 1997 for the Sanchuan River (in Table3). Previous studies indicated that human activities are the main drivers of runoff’s change for the middle Yellow River [60]. Also, the variation of rainfall dominates the fluctuation of runoff while temperature could influence runoff by changing evaporation. In addition, large-scale soil and water conservation measures have been implemented along the Yellow River since the 1970s [43]. By comparing the modeling capacity in simulation of the stream flow for the four hydrological models during the natural period, the average NSE of the four models for the calibration period for Tangnaihai and Huayuankou stations were 0.758 and 0.656, respectively, while the absolute value of REs during the verification period ranges from 0.33 to 8.81 and from 8.54 to 23.34 for the same two stations. Therefore, it can be concluded that the four hydrological models are performing better at Tangnaihai station than Huayuankou station. This conclusion can be attributed to a lower anthropic impact on the hydrological regime in the Upper Yellow River basin even though the upper basin of Huayuankou station encompasses complex and variable climate zones and underlying conditions, whose area occupies over 97% of the whole Yellow River basin. Figure 11 shows the comparison results of the four models’ performances at the 6 typical sub-catchments under both natural (before 1975) and changing environment periods (2000–2013), where shapes of the points stand for hydrometric stations and colors of the points distinguish the 4 hydrological models. The 4 points representing Tangnaihai station are above or on the 1:1 line (solid black line), implying good performance of the 4 models in the Upper Yellow River basin. The other points represent the other 5 stations all located below the 1:1 line, which implies low performance. In addition, the NSE values for the 2000–2013 period are below 0 at Huayuankou and Pianguan stations except for the WBM model, meaning that the ability of stream flow simulation by the conceptual models are weakened at a catchment scale under a changing environment, especially for Pianguan and Huayuankou stations because the rainfall–runoff relationship is greatly disturbed by human production practice. Water 2019, 11, 1328 16 of 20 Water 2019, 11, x FOR PEER REVIEW 3 of 21

Figure 11. Comparison of NSEs calculated by monthly observed and simulated discharge before 1975 Figure 11. Comparison of NSEs calculated by monthly observed and simulated discharge before and 2000–2013. 1975 and 2000–2013. 4. Conclusions 4. Conclusions Trends of forecasting and hydro-meteorological elements and stream flow simulation under changingTrends environments of forecasting have and played hydro-meteorological significant roles in elements watershed and water stream resource flow simulation assessment under and management,changing environments and have becomehave played a major significant challenge roles in modernin watershed water water science resource research. assessment Therefore, and variationmanagement, trends and in annual have become and seasonal a major hydro-climatic challenge in variables modern (e.g.,water discharge, science research. precipitation, Therefore, and temperature)variation trends over in six annual selected and catchments seasonal hydro-climatic of the Yellow Rivervariables basin (e.g., were discharge, investigated precipitation, to assess theand hydrologicaltemperature) model over six capacity selected (four catchments models XAJ, of the SIMHYD, Yellow River GR4J, basin and RCCC-WBM were investigated were selectedto assess for the thishydrological analysis) in model simulating capacity the streamflow(four models discharge XAJ, SIMHYD, under natural GR4J, and changingRCCC-WBM environments were selected in the for drythis region. analysis) Temperatures in simulating over the the streamflow Yellow River discharge basin andunder sub-catchments natural and changing have tended environments to increase in withthe significantdry region. rising Temperatures trends exhibited over the by Mann–KendallYellow River basin rank testand probably sub-catchm dueents to global have warming,tended to whileincrease there with are significant no significant rising upward trends or exhibited downward by Mann–Kendall trends established rank in test the probably annual precipitation due to global series,warming, which while fluctuate there around are no the significant mean value upward without or greatdownward abruption trends over established all the six catchments. in the annual precipitationThe annual series, runo whichff series fluctuate are relatively around stationary the mean until value approximately without great the abruption 1980s and over mostly all the the six high-runocatchments.ff at the hydrometric stations occurred before the 2000s and continuously and unprecedentedly declineThe from annual 2000, runoff which series is in accordance are relatively to thestationary abrupt changeuntil approximately years in annual thethe 1980s runo andff series mostly that the mostlyhigh-runoff occurred at betweenthe hydrometric 1980 and 2000stations and occurred was detected before by usingthe 2000s the average-di and continuouslyfference T-test and analysis.unprecedentedly This is can decline be attributed from 2000, to changing which is environments in accordance such to the as regulationabrupt change of the years system, in annual land use the changes,runoff series conservation that mostly infrastructure, occurred etc. between 1980 and 2000 and was detected by using the average-differenceAppropriate periods T-test of analysis. data were This applied is can to calibrate be attributed and validate to changing the four environments selected hydrological such as models.regulation The of study the system, found land that theuse fourchange hydrologicals, conservation models infrastructure, performed etc. well for runoff simulation in theAppropriate Yellow River basin.periods In termsof data of thewere Nash–Sutcli applied fftoe ecalibratefficiency coeandffi cient,validate owing the to four the detailedselected andhydrological complicated models. mechanism The study of runo foundff generation that the four and hydrological flow routing models being well performed represented well infor model runoff structure,simulation the in XAJ the model Yellow worked River betterbasin. thanIn terms SIMHYD, of the WBM, Nash–Sutcliffe and GR4J. efficiency However, coefficient, under the changing owing to environmentthe detailed (2000–2013), and complicated the discharge mechanism at Houdacheng, of runoff Huanyuankou,generation and and flow Pianguan routing stations being were well notrepresented simulated in appropriately model structure, because the theXAJ rainfall–runo model workedff relationship better than in SIMHYD, the three WBM, sub-catchments and GR4J. haveHowever, changed under a lot duethe tochanging the intensive environment human activities (2000 during–2013), thatthe period,discharge where at the Houdacheng, conceptual hydrologicalHuanyuankou, models and Pianguan lose their stations ascendancy were andnot simulated capacity. appropriately However, the because RCCC-WBM the rainfall–runoff model, in comparisonrelationship to in the the other three models, sub-catchments could be considered have changed to be acceptable,a lot due to and the has intensive the capacity human to simulateactivities during that period, where the conceptual hydrological models lose their ascendancy and capacity. However, the RCCC-WBM model, in comparison to the other models, could be considered to be

Water 2019, 11, 1328 17 of 20 the runoff under natural and changing environmental periods, which is can be attributed to its simple structure and easy-to-understand principle, and it can be recommended for water resource assessment under changing environments. The Yellow River basin has a range of climatic zones with distinguishing rainfall–runoff mechanisms and different geomorphological features, to which hydrological models are required to have plasticity and adaptation as well as sufficient precision in rapid assessment of water resources under changing environments to allow the simulation of the runoff under human activities. These will require a fair presentation of the regulation regime.

Author Contributions: J.Z. and G.W. designed the study and improved the manuscript; X.G. performed model application and drafted the paper; X.L. and J.L. collected hydrological data and meteorological data; Y.L. (Yue Liu) and J.J. analyzed variation of hydro-meteorological data series; Y.L. (Yanli Liu) and Z.B. investigated relationships between runoff and precipitation for various periods; C.L. and R.H. conducted comparison of model performance for hydrological modeling; A.E. reviewed the designed study, results, analysis in addition to the document and edited the paper. Funding: This study has been financially supported by the National Key Research and Development Programs of China (Grants: 2016YFA0601501), the National Natural Science Foundation of China (Grants: 41830863, 51879162, 41601025, 51879164, 41330854, 51779146), and State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering (Grant No. 2017490211). Acknowledgments: We appreciate Tiesheng Guan from Nanjing Hydraulic Research Institute and Qinli Yang from University of Electronic Science and Technology of China for their comments on the study, Thanks also to the anonymous reviewers and editors for their valuable comments. Conflicts of Interest: The authors declare no conflict of interest.

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