Stoch Environ Res Risk Assess DOI 10.1007/s00477-016-1333-4

ORIGINAL PAPER

Effects of check on runoff and load in a semi-arid basin of the Yellow River

1,3 1,2 1,2 2 2 Erhui Li • Xingmin Mu • Guangju Zhao • Peng Gao • Wenyi Sun

Ó Springer-Verlag Berlin Heidelberg 2016

Abstract Check has become an efficient measure to contributed 24.8 and 27.7% to the decrease of annual control and soil in the runoff and sediment load during the period of 1990–1999, areas. It plays an important role in soil and whereas it reached up to 65.2% for runoff decline and agricultural production in the Loess Plateau. Due to con- 78.3% for sediment load reduction within 2000–2012. struction of numerous check dams, it is necessary to assess Overall, this study illustrated a case study of the dominant the impact of check dams on runoff and sediment load at role of check dams on variation of runoff and sediment basin scale. This study applied the SWAT model to sim- load in the Huangfuchuan basin. ulate monthly runoff and sediment load in the Huang- fuchuan basin in the middle reaches of the Yellow River. Keywords Runoff Á Sediment load Á SWAT model Á Check Twenty key check dams are coupled to the SWAT model dams simulation in the calibration (1978–1984) and validation period (1985–1989). The determination coefficient (R2) and the Nash–Sutcliffe coefficient (NS) were 0.94 and 0.83 1 Introduction for runoff, and 0.82 and 0.81 for sediment load in the calibration period, respectively. During the validation Check dam is an important engineering measure in period, the R2 and NS were 0.93 and 0.80 for runoff, and retaining floodwater, intercepting sediment, increasing 0.90 and 0.83 for sediment load respectively. The results farmland for agricultural production and reducing sediment showed that the model simulation was acceptable. Subse- transport in downstream. Different from regular , quently, the calibrated model was used to examine the the construction of check dams is mainly for the purpose of effect of check dams on runoff and sediment load between capturing sediment and generating fertile farmland in the 1990 and 2012. It showed that the increasing check dams gully-dominated areas (Fig. 1). In the past decades, a number of studies have focused on the effects of check dams on the hydrological process. & Xingmin Mu Mishra et al. (2007) estimated the effects of check dams on [email protected] sediment transport from the Banha watershed in India, & Guangju Zhao suggested that sediment load could be reduced more than [email protected] 64% by check dams. Abouabdillah et al. (2014) showed

1 that large reservoirs contributed to the retention of State Key Laboratory of Soil Erosion and Dryland Farming approximately 67,000 t/a sediment in the Merguellil basin on the Loess Plateau, Northwest A&F University, Xinong Road 26, Yangling 712100, Shaanxi, China of the central Tunisia. Boix-Fayos et al. (2007) found that sediment yield reduced by 77% due to the check dams in 2 Institute of Soil and Water Conservation, Chinese Academy of Science & Ministry of Water Resources, Xinong Road 26, the Rogativa basin of Spain. Besides, Martı´n-Rosales et al. Yangling 712100, Shaanxi, China (2007) investigated the recharge induced by check dams in 3 State Key Laboratory of Hydroscience and Engineering, a semiarid region of Spain, and indicated the proportion of Tsinghua University, Beijing 100084, China runoff that infiltrates through the check dams varies from 3 123 Stoch Environ Res Risk Assess

Fig. 1 A view of the check dam in the study area (Photo: Guangju Zhao (left), Zhengfeng An (right)) to 50%. Together, these studies gave a detailed view of decreasing. However, the multi-linear regression did not check dam, which is an effective measure in trapping include all land use types, such as grass land and forest. Mu sediment transportation to the river . et al. (2007) applied double mass curve method to assess the The Chinese Loess Plateau is located in the upper and impacts of soil and water conservation on runoff and sedi- middle reaches of the Yellow River basin, and is one of the ment in the Hekou-Longmen region of the Yellow River, most severe soil erosion areas in the world (Mu et al. 2012; and point out that soil conservation measures were expected Xin et al. 2012). The average annual soil loss in this area to be the main driving factors for the sediment decline and ranges from 5000 to 10,000 t/km2, leading to great risk of the relative contribution to annual sediment decrease were land degradation and threatening to sustainability of 55%. However, they investigated the combined effects of ecosystem development (Zhao et al. 2016). To solve the the changes in vegetation, land use and dams on runoff and severe soil erosion problems, many soil and water conser- sediment load reduction. The statistical method uses the vation measures such as afforestation, terracing, and check long-term observation data to establish statistical relation dams, which greatly altered the land use and land cover, and between hydrological variables and their influential factors, further affected the land surface hydrological processes e.g. but it is difficult to distinguish the contributions of different the water infiltration, interception and evaporation (Zhao driving factors and the effects of spatial distribution of et al. 2014). Many studies focused on the impact of land use driving factors can not be assessed. Physically based changes and check dams on hydrological process. The hydrological models can reveal hydrological physical pro- paired catchment, statistical method and hydrological cess and consider sufficiently the spatial and temporal dis- modeling are three common methods that are applied to tribution of climate, land use and topography, which makes assess the hydrological effects of environmental change (Li it easy to assess the effects of different driving factors on et al. 2009). Paired catchment method has been widely used hydrological process. The Soil and Water Assessment Tool as a traditional method to detect impacts of land use change (SWAT) model is a basin scale model and has been widely on hydrological process (Zhao et al. 2010; Ochoa-Tocachi applied in hydrological simulations, evaluation of land use et al. 2016). However, paired catchments are not always management, soil erosion, climate change impact and available because they generally require relevant informa- interfaces of SWAT with other models (Nie et al. 2011; tion about vegetation cover change and measured hydro- Zhang et al. 2012; Shen et al. 2013; Krysanova and Srini- logical data. In addition, it is difficult to find two vasan 2015; Yang et al. 2016). Majority studies applied the suitable large-medium experimental catchments and the SWAT model to identify the impact of land use change on investigations are expensive to conduct (Wang et al. 2013). hydrological process. Li et al. (2009) used the SWAT model Statistical method is a simple and effective approach to to assess the impacts of climate change and land use on quantify the impacts of environmental change on hydro- surface hydrology in the Heihe River on the Loess Plateau, logical process. Li et al. (2014) used the multi-linear and pointed out that climate change and land use decreased regression to detect the relations between annual runoff and runoff by 95.8 and 9.6%, respectively. Zuo et al. (2016) also the influential factors including precipitation, urban area, investigated that land use effect decreased 25.3% of the water area, unused area and agricultural area in the Luanhe water yield and 40.6% of the sediment yield using the River basin, China, and showed that water area decrease SWAT model in the Huangfuchuan watershed on the Loess was the main factor contributing to annual runoff Plateau. Shao et al. (2013) applied the verification SWAT

123 Stoch Environ Res Risk Assess model to assess effect of the water conservancy measures on southern Inner Mongolia, and flows into the Yellow River runoff in the Wei River on the Loess Plateau, and showed in Fugu County of Shaanxi Province. The Huangfuchuan that the runoff reduction rate grew up to 51.6 9 108 m3/y at basin belongs to the region crisscrossed by wind and water Huaxian station and the relative contribution 88% for water erosion. There are complex geomorphological types conservancy measures. including sandstone weathering hilly-gully region, the As one of the effective measures for soil erosion control, loess hilly-gully region and sandy loess hilly-gully region check dam has extracted great attention due to its unique (Zhao et al. 2015). The basin is located in the transitional environmental settings and regional food supply needs belt of warm temperate and mesothermal zones, with (Wang et al. 2011). Check dams are intended to trap sed- average precipitation of 350–450 mm, more than 80% of iment and prevent the undercutting of channel. which occurs between June and September (Tian et al. When check dams fill with sediment, the surface of the 2013). As a result, runoff and sediment are characterized sediment wedge become high productivity farmland. Xu by high peak and short duration, and concen- et al. (2004) showed that there were 113,500 check dams in trated in the flood season from June to September, the Loess Plateau until 2002, which brought about accounting for more than 80% of the annual total (Hu et al. 3200 km2 of high-yield farmland and captured approxi- 2015). The basin suffered from severe soil erosion, with mately 700 million m3 sediment that flows into the Yellow more than 15,000 t/km2/y of sediment yield on average River. In addition, the Ministry of Water Resources of (Ran and Gao 2003). To solve the soil erosion problems, China invested more than 20 billion RMB to build 47,000 numerous soil and water conservation measures were check dams from 2010 to 2015 (Jin et al. 2012). The large implemented in this area, in which check dams became the number of check dams on the Loess Plateau had a great dominant engineering measures. By 2010, the dam-con- influence on hydrological process. Traditional field mea- trolled areas accounted for approximately 70% of the entire surement of the effects of check dam is very time con- basin (Tian et al. 2013). suming and just represent the dam-controlled watershed conditions, which is not easy to transfer to large watershed scale (Abedini et al. 2012). In addition, there are many 3 Data and methods check dams in a medium or large watershed and the effects of all check dams on hydrological progress in the water- 3.1 Dataset shed are difficult to calculate. The SWAT model provide an alternative approach to simulate the effects of all check Meteorological and hydrological data from 1976 to 2012 dams on runoff and sediment load. The SWAT model for the Huangfuchuan basin were obtained from the Data provides four methods for calculating outflow of the Sharing Network of Earth System Science (http://www. reservoirs: observed daily outflow, observed monthly out- geodata.cn/) and the Yellow River Conservancy Commis- flow, annual discharge for uncontrolled , and sion. Daily precipitation data within the Huangfuchuan controlled outflow with target release. The reservoir mod- basin consist of ten rainfall gauging stations (Fig. 2). The ule of SWAT model can simulate the operation of check information at ten rainfall stations were shown in Table 1. dam, which provides good opportunity to quantify the The China Meteorological Data Sharing Service System influence of check dams on runoff and sediment load. (http://data.cma.gov.cn/) provided the daily meteorological The objectives of this study were: (a) to assess the data including rainfall, maximum and minimum tempera- applicability of SWAT model with reservoir module in the tures, wind speeds and relative humidity at Hequ station Huangfuchuan basin on the Loess Plateau, which has (Fig. 2). There were no missing data for the Hequ station. extreme high sediment yield and controlled by numerous The daily records were used to generate climatic data or to check dams; and (b) to quantify the impacts of check dams fill in the gaps of the measured data. The missing data for in different periods on variation of runoff and sediment the ten rainfall stations were substituted by -99, and then load by using available check dams data and hydro-mete- the SWAT model could automatically identify the missing orological data. data and interpolate the missing data by the weather generator. Continuous monthly runoff and sediment load records 2 Study area were available at Huangfu hydrological station. The Huangfu station is located in the downstream gauging The Huangfuchuan basin is located in the middle reaches station with a controlled area of 3199 km2. of the Yellow River at 110°180–111°120E and 39°120– The digital elevation model (DEM) with the horizontal 39°540N, with an area of 3246 km2 (Fig. 2). The Huang- resolution of 90 m in the Huangfuchuan basin was obtained fuchuan River is 137 km long, which originates from the from International Scientific & Technical Data Mirror Site, 123 Stoch Environ Res Risk Assess

Fig. 2 The distribution of hydro-meteorological stations and check dams in the study area

Table 1 Information of rainfall stations in the Huangfuchuan bare land, respectively (Fig. 3). The soil type data were watershed derived from the 1:500,000 soil map of the Loess Plateau, No. Name Data span Missing data which was provided by the Ecological Environment Database of Loess Plateau. Seven soil types were identi- 1 Wulangou 1976–2012 2005 fied: dark loessial soil, castanozems, cultivated loessial 2 Deshengxi 1976–2012 1990 soil, alluvial soil, aeolian soil, skeletol soil, litho soil 3 Shagedu 1976–2012 2005 (Fig. 3a). 4 Gucheng 1976–2012 2007 The Yellow River Conservancy Commission provided 5 Liujiata 1976–2012 1976, 2005 the check dams data in the Huangfuchuan basin. The sta- 6 Haizita 1976–2012 No missing data tistical data showed that there were 277 check dams in the 7 Huangfu 1976–2012 No missing data basin before 1980. Due to severe soil erosion, large num- 8 Nalin 1976–2012 1990 bers of small check dams were filled up by sediment 9 Xiyingzi 1976–2012 1990 in a few years. The operation of retaining 10 Changtan 1976–2012 1977, 1981 floodwater and capturing sediment for check dams did not work. Thus, this study selected twenty large-sized check dams (with total volume greater than 1.0 million m3) Computer Network Information Center, Chinese Academy constructed before 1980 in the study area, which controlled 2 of Sciences (http://www.gscloud.cn/). The DEM was used an area of 336.03 km in the basin, and had a total volume 6 3 to generate river network, flow direction, sub-basins and of 66.22 9 10 m . All the check dams are in regular slope. Land use maps in 1978, 1995 and 2006 were operation during the simulation period. The detailed extracted from satellite images with 30 m spatial resolu- information of the check dams was listed in Table 2 and tion, which generated by Enhanced Thematic Mapper the location of the check dams were plotted in Fig. 2. (ETM) sensor (http://glcfapp.umiacs.umd.edu:8080/esdi/ index.jsp). The images were interpreted by using the 3.2 Methods unsupervised classification method. The land use was divided into seven types, and they are grassland, arable The Soil and Water Assessment Tool (SWAT) was applied land, residential land, forest, sandy land, water body and to simulate hydrologic processes of the Huangfuchuan 123 Stoch Environ Res Risk Assess

Fig. 3 Soil types (a) and land use in 1978 (b), 1995 (c) and 2006 (c) basin in this study. The SWAT model is a distributed The SWAT model provides a reservoir module to eval- watershed based on physical mecha- uate the impact of reservoirs operation on runoff and sedi- nism, which has been widely applied to study runoff and ment load. The water balance for a reservoir is given as: sediment transport at small and large watershed scale V ¼ Vstored þ Vflowin À Vflowout þ Vpcp À Vevap À Vseep ð1Þ (Sakaguchi et al. 2014; Wang et al. 2014).The detailed 3 description about SWAT model can be found in Arnold where V is the reservoir storages at the end of a day, m ; Vstored is 3 and Fohrer (2005). the reservoir storages at the beginning of a day, m ; Vflowin and

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Table 2 Check dams information in the Huangfuchuan basin No. Name Construction time Controlled areas (km2) Total volume (104 m3) Water surface area (ha)a

1 Buerdong 1976 80.06 1135.3 112.6 2 Yinsuita 1978 56.89 1206.4 122.6 3 Nalinyihao 1959 17.75 803.7 69.3 4 Daoge 1975 1.03 138.5 5.9 5 Zhongba 1980 15.4 494.7 35.1 6 Nianfanggou 1980 5.8 141.6 6.0 7 Zhaochenggou 1974 3.73 200.2 9.8 8 Hujiagou 1979 6.85 178.7 8.4 9 Manhonggou 1975 6.78 150.4 6.6 10 Weijiapangou 1975 4.05 114.7 4.5 11 Nalingou 1959 10.65 225.5 11.6 12 Danalin 1971 19.75 241.7 12.8 13 Changgetu 1974 6.93 123.3 5.0 14 Liumujiangou 1969 2.93 278.3 15.6 15 Wusugou 1974 9.98 120.9 4.8 16 Chuanzhanggou 1977 39.87 255 13.8 17 Baolaotugou 1972 8.3 192 9.3 18 Fanpugou 1970 7.22 226 11.7 19 Changhangou 1971 21.2 293 16.8 20 Shilanhuigou 1973 10.86 102 3.8 a The water surface area is calculated by Eq. 6

3 Vflowout are daily inflow volume and outflow volume, m , V ¼ ðÞþV À V q Á 86; 400 if flowout em rel ð5Þ respectively; Vpcp is the daily precipitation falling on the water Vem À Vpr [ qrel Á 86;400 3 surface, m ; Vevap is the daily evaporation from the water body, 3 3 where V is the outflow volume during a day, m3; V is the m ; Vseep is the daily seepage volume, m . flowout 3 There are four options to calculate the outflow from reservoir storages, m ; Vpr is the reservoir storages when filled 3 reservoirs: observed daily outflow, observed monthly out- to the principal spillway, m ; Vem is the reservoir storages 3 flow, average annual discharge for uncontrolled reservoir, when filled to the emergency spillway, m ;andqrel is the 3 controlled outflow with target release (Neitsch et al. 2009). aveage daily principal spillway release rate, m /s. Due to unavailable inflow and outflow data, we estimated The input parameters for the reservoirs module are the outflow by average annual discharge for uncontrolled RES_ESA (reservoir surface area when the reservoir is reservoir to analyze the effect of check dams. filled to the emergency spillway), RES_EVOL (volume of In such case, all the check dams were treated as ‘‘un- water needed to fill the reservoir to the emergency spill- controlled reservoirs’’, and the reservoir releases were way), RES_PSA (reservoir surface area when the reservoir calculated depending on the reservoirs’ volume. If the is filled to the principal spillway), RES_PVOL (volume of water level in the reservoirs lies between the emergency water needed to fill the reservoir to the principal spillway). spillway volume and the principal spillway volume, the Different from common reservoirs, check dams are mainly reservoir releases are calculated as: constructed for intercepting sediment rather than long-term storing water, and usually there are no discharge and sur- V ¼ V À V if V À V \q Á 86;400 ð2Þ flowout pr pr rel face area data. Tian et al. (2013) established a relationship Vflowout ¼ qrel Á 86;400 if V À Vpr [ qrel Á 86;400 ð3Þ between check dam water surface and storage capacity, which obtained a good accuracy compared with traditional If the water volume in the reservoirs exceeds the field survey approach. The empirical equation is: emergency spillway volume, the reservoir releases are calculated as: V ¼ 39:306 Â Area0:712 ð6Þ ÀÁ V ¼ ðÞþV À V V À V if where V is the storage of check dam, 104 m3; Area is the flowout em em pr ð4Þ Vem À Vpr\qrel Á 86;400 check dam water surface area, ha. The surface area for each

123 Stoch Environ Res Risk Assess check dam was estimated based on the total volume and the 4 Results Eq. 6. Table 2 listed the estimated results. Hence, the parameters of RES_EVOL and RES_ESA 4.1 Variation of runoff and sediment load were determined by using the values in Table 2. Consid- in the Huangfuchuan basin ering the function of check dams, the parameters of RES_PVOL and RES_PSA are set at 70% of the RES_E- Figure 4 shows the temporal variation of annual precipi- VOL and RES_ESA. tation, runoff and sediment load from 1976 to 2012. It can Model calibration and validation were carried out by be clearly seen that runoff and sediment load showed a comparing the observed and simulated runoff and sediment decreasing trend, whereas precipitation showed insignifi- load at Huangfu station. There were 37 years (1976–2012) cant changes during the past 37 years. Average annual of continuous monthly runoff and sediment load records at runoff was 0.95 9 108 m3 within 1976–2012, while it was Huangfu station. After the initial warm up period from 1976 0.84 9 108 m3 in the flood season, accounting for to 1977, runoff and sediment load data from 1978 to 1984 approximately 88.4% of the annual total (Table 3). In were used for calibration and 1985–1989 for validation. The contrast, average annual sediment load in flood season was simulated results represent applicability of the model in the 0.29 9 108 t during 2000–2012, almost 100% of the study areas. The determination coefficient (R2) and the annual total. Both annual runoff and sediment load showed Nash–Sutcliffe coefficient (NS) (Nash and Sutcliffe 1970) significant reduction during 2000–2012 (P \ 0.05), which were used to evaluate consistency between simulated and were 21.9 and 17% comparing with those within observed values. The R2 and NS are calculated as: 1976–1989. ÂÃÀÁÀÁ P 2 Oi À O Si À S 4.2 Parameter sensitivity analysis, model R2 ¼ P ÀÁi P ÀÁ ð7Þ 2 2 calibration and validation i Oi À O i Si À S P 2 ðÞO À S There are many parameters in the SWAT model and the NS ¼ 1 À P iÀÁi ð8Þ 2 first simulated results were always unsatisfactory when the i Oi À O initial default parameters were used for simulation. In order where Oi and Si are observed and simulated values in the to quickly search for the sensitive parameters that con- month i, respectively; O and S are the average values of tribute most to the results and improve the efficiency of the observed and simulated data, respectively. According to model calibration, the sensitivity analysis was applied to Bracmort et al. (2006), model performance is accepted as identify parameters that affecting the simulated results. The satisfactory if NS [ 0.5 and R2 [ 0.6 for runoff and sedi- ment load. The runoff and sediment load in the Huangfuchuan basin were mainly affected by climate, land use and check dams. To better understand and quantify the effects of check dams on runoff and sediment load, the time series were divided into three periods according to land use data in 1978, 1995 and 2006. The land use data in 1978, 1995 and 2006 represented the corresponding land use condi- tions in the three periods (i.e. 1978–1989, 1990–1999 and 2000–2012). We used the DEM, soil type data, land use map in 1978, meteorological data between 1976 and 1989 and the twenty large-size check dams data as input for SWAT model calibration and validation. After model verification, the land use map in 1995 and meteorological data from 1990 to 1999 were inputted into the validated SWAT model. If the numbers of check dams did not change, the simulations and observations would be similar. If the differences between simulated and observed values were large, it represented the relative effect of increasing check dams on runoff and sediment load. The effect of check dams on runoff and sediment load in the period Fig. 4 Temporal variation of annual precipitation (a), runoff (b) and 2000–2012 could be calculated in the same way. sediment load (c) 123 Stoch Environ Res Risk Assess

SWAT model provided an automatic sensitivity analysis measured data bracketed by the 95% prediction uncer- module, which used the LH-OAT analysis method (Van tainty. R-factor is the average width of the 95PPU band Griensven et al. 2006). We used the automatic sensitivity divided by the standard deviation of the corresponding analysis module to obtain the ranked sensitive parameters measured variable (Setegn et al. 2010). for runoff in the Huangfuchuan basin. The most sensitive Table 4 listed the adjustment ranges and best fitted parameters for runoff were listed in Table 4. The most values of the parameters. The CN2 was found to be the sensitive parameter for runoff is the CN2 that represented most sensitive parameter affecting . It was the water yield capability. The second sensitive parameter increased by 22% of the default value representing a quick is saturated hydraulic conductivity (SOL_K), which con- runoff yield in the study area. This was because the trolled the water velocity in the soil. Soil evaporation mechanism of surface runoff yield is infiltration excess compensation factor (ESCO) adjusted soil evaporation runoff in the Huangfuchuan basin. The basin was mainly among different soil layers. The parameters for sediment covered by silty loess, sand, and weathered sandstone. load were selected according to the previous studies in the Intense storms, sparse vegetation and weak surface per- Loess Plateau (Qiu et al. 2012; Xu et al. 2013; Zhang et al. meability make land surface easy to generate surface runoff 2014), which included sediment yield parameters in the (Li et al. 2015). SOL_K increased by 14% to increase the modified universal soil loss equation (MUSLE) and sedi- movement of lateral flow in the soil profiles. ESCO was ment transportation parameters (Table 4). adjusted to 0.76 because the model can obtain soil water According to the results of sensitive analysis, twenty from different depth to meet soil evaporation. CANMX has parameters were selected for model calibration by using influence on soil infiltration, surface runoff and evapora- SWAT-CUP. The SWAT-CUP is individual software that tion. The poor vegetation covers in the Huangfuchuan displays the results of sensitive analysis and calibration basin led to the low value of CANMX. The SOL_AWC visually. There are five algorithms for parameter calibra- was reduced by 10% for all soil types to decrease the tion in the SWAT-CUP, we used the sequential uncertainty available water capacity of the soil layer. The ALPHA_BF fitting algorithm (SUFI-2) for parameter calibration. Before and GW_DELAY have a direct influence on baseflow, run the SUFI-2, the twenty parameter ranges were assumed finally on surface runoff. The low value of ALPHA_BF according to the physically meaningful range. Then SUFI-2 reveals a small contribution of the base flow to the river calculated the parameter uncertainties and narrowed the discharge (Gebremicael et al. 2013). The GWQMN affects parameter ranges, which makes the new parameter ranges surface flow response. The water flow between the shallow are smaller than the previous ranges. The new parameter and the unsaturated root zone is controlled by the ranges are updated in this way and are centered around the GW_REVAP. The values of basin parameters (SPCON and best adjustment value. SPEXP) and channel parameters (CH_COV1 and In SUFI-2, uncertainty sources such as driving variables CH_COV2) resulted in increasing sediment transportation (e.g. rainfall), conceptual models, parameters, and mea- and slowing down sediment deposition. The parameters sured data (e.g. observed streamflow, sediment load) are USLE_P, USLE_C and USLE_K in MUSLE were modi- comprised in parameter ranges, which are calibrated to fied 0.99, 0.15 and 0.58, respectively. Two other sensitive bracket most of the measured data in the 95% prediction parameters affecting sediment yield, SLSUBBSN and uncertainty (95PPU) (Zuo et al. 2016). The 95PPU is cal- HRU_SLP, were one time larger than the default values to culated at 2.5 and 97.5% levels of the cumulative distri- obtain better model accuracy. bution of an output variable obtained through Latin The simulated monthly runoff and sediment load by hypercube sampling. There are two indices revealing the SWAT model for the calibration period (1978–1984) and strength of the calibration/uncertainty analysis and they are validation period (1985–1989) showed a good agreement P-factor and R-factor. P-factor is the percentage of with the observed values and the observed runoff values were bracketed by the 95PPU (Fig. 5). Table 5 showed the Table 3 Statistics of runoff and sediment load at the annual and flood evaluation indices for the model simulation performance. season scales during 1976–2012 The 95PPU band brackets 65 and 52% of the observations for runoff and sediment load in the calibration period, Periods Runoff (108 m3) Sediment load (108 t) respectively. The lower P-factor for sediment load can be Average season Average Flood season attributed to the low sediment load during the non-flood 1976–1989 1.60 1.38 0.53 0.53 season. In other words, this is probably because the model 1990–1999 0.90 0.78 0.25 0.25 can not be performed during the dry season. The R-factor is 2000–2012 0.35 0.35 0.09 0.09 higher for the validation period than that for the calibration Mean 0.95 0.84 0.29 0.29 period, which indicated the uncertainty increasing. The NS and R2 are both higher than 0.8 for runoff and sediment 123 Stoch Environ Res Risk Assess

Table 4 Ranked sensitive parameters for runoff and best fitted values for calibration Parameter Description Ranks of Type of Adjustment Best parameters parameter range adjustment alteration* value

CN2.mgt SCS 1 r 35–98 0.22 SOL_K(1).sol Saturated hydraulic conductivity 2 r 0–2000 0.14 ESCO.hru Soil evaporation compensation factor 3 v 0–1 0.76 CANMX.hru Maximum canopy storage 4 v 0–100 0.09 SOL_AWC(1).sol Available water capacity of the soil layer 5 r 0–1 -0.1 SLSUBBSN.hru Average slope length 6 r 10–150 1.08 GW_REVAP.gw Groundwater re-evaporation coefficient 7 v 0.02–0.2 0.14 GWQMN.gw Threshold depth of water in the shallow aquifer required for 8 v 0–5000 0.62 return flow to occur ALPHA_BF.gw Base flow alpha factor 9 v 0–1 0.26 GW_DELAY.gw Groundwater delay 10 v 0–500 447 USLE_P.mgt USLE equation support practice – v 0–1 0.99 USLE_C.crop.dat USLE C factor for land cover – v 0.01–1 0.15 USLE_K(1).sol USLE equation soil erodibility (K) factor – v 0–0.65 0.58 CH_COV1.rte Channel erodibility factor – v -0.05 to 0.6 0.55 CH_COV2.rte Channel cover factor – v -0.001 to 1 0.67 SPCON.bsn Linear parameter for calculating the maximum amount of –v-0.0001 to 0.01 0.007 sediment that can be retrained during channel sediment routing SPEXP.bsn Exponent parameter for calculating sediment retrained in – v 1–1.5 1.49 channel sediment routing CH_S2.rte Average slope of main channel – r -0.001 to 10 -0.07 CH_L2.rte Length of main channel – r -0.05 to 500 0.29 HRU_SLP.hru Average slope steepness – r 0–0.6 1.28 * v means the parameter value would be replaced by the given value; r means the parameter value would be multiplied by 1 the given value load. The model performed relative well for both calibra- tion and validation periods, suggesting that the SWAT model could be practically used for simulating runoff and sediment load in the Huangfuchuan basin.

4.3 Impacts of check dams on runoff and sediment load

After model calibration and validation, the model was applied to simulate the monthly runoff and sediment load in the 1990s (1990–1999) and the 2000s (2000–2012). Figure 6a, b showed the simulated runoff and sediment load and the observations in the 1990s. It can be clearly seen that the model over/under-estimated the runoff compared to the observed values, and the correlation coefficient (R2) and Nash–Sutcliffe coefficient (NS)were 0.64 and 0.61, respectively. The simulated sediment load Fig. 5 Observed and simulated monthly runoff (a) and sediment load exhibited similar variability with runoff, and did not (b) during the calibration and validation period match with the observed values. The R2 (0.52) and NS (0.43) demonstrated that the modeling efficiencies observations in the 2000s (Fig. 6c, d), indicating dis- decreased obviously. However, most of the simulated agreement between simulated and observed values. The runoff and sediment load values were higher than the differences between the simulated and observed values

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Table 5 The simulation results of monthly runoff and sediment load During 1990–1999, the check dams contributed to 24.8 and Period R2 NS P-factor R-factor 27.7% of reduction in annual runoff and sediment load, respectively. In contrast, extremely high deceasing rates Runoff caused by check dams were found in annual runoff and Calibration 0.94 0.83 0.65 0.34 sediment load during 2000–2012, which accounted for 65.2 Validation 0.82 0.81 0.53 0.79 and 78.3% of the total reduction, respectively. The results Sediment load suggested that the check dams played a dominant role for Calibration 0.93 0.8 0.52 0.38 both runoff and sediment load reduction in the Huang- Validation 0.9 0.83 0.57 0.75 fuchuan basin during 2000–2012. may be caused by the model uncertainties and new built check dams in the study area. 5 Discussion The land use data and meteorological data in the 1990s and 2000s were applied for the validated model, respec- The differences between the observed and simulated values tively. Under meteorological data, the corresponding land in the 1990s and 2000s could be attributed to the following use conditions and check dams in the 1990s and 2000s, reasons. Firstly, the spatial and temporal distribution of respectively, the simulated values will correspond with the precipitation is uneven in the study area. The monthly observed values. However, we did not consider the changes sediment load and runoff have seasonal variation with high of check dams in the 1990s and 2000s due to a lack of data values in rainy season and low values in dry season at and rapid construction of the check dams. Thus, the dif- Huangfu station. The rainfall in this region was charac- ferences between the simulated and observed values in the terized by the short duration, small covering area and high period of 1990s and 2000s were mainly caused by the intensity storms. The SWAT model could not preferably effects of check dams. Table 6 showed the relative con- simulate the floods caused by intense rainstorms. The tributions of check dams to the variations of runoff and parameters of empirical equations in the SWAT model are sediment load during 1990–1999 and 2000–2012. It can be regional and not universal in some other areas, which seen that observed runoff and sediment load accounted for might lead to the differences between the observations and more than 80% of the annual total in the flood season. The simulations. Secondly, there was only one meteorological observed runoff had decreased by 0.162 9 108 m3 in the station which could not represent the temperature, wind 1990s and 0.639 9 108 m3 in the 2000s in flood season, speed and solar radiation of the whole basin. The evapo- respectively. In addition, comparing with runoff during ration might vary greatly in such a large river basin. In 1978-1989, the total reduction was 0.623 9 108 m3 in the addition, we only considered twenty large check dams in 1990s and 1.049 9 108 m3 from 2000 to 2012, respec- the SWAT model and there are numerous medium and tively, and approximately 26 and 60.9% reduction attrib- small check dams in the study area, which may trap a uted to check dams in the flood season, respectively. number of runoff and sediment.

Fig. 6 Observed and simulated monthly runoff and sediment load during the 1990–1999 (a, b) and 2000–2012 (c, d)

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Table 6 Statistics of observed and simulated runoff and sediment load during 1990–1999 and 2000–2012 Runoff (108 m3) Sediment (108 t) 1978–1989 1990–1999 2000–2012 1978–1989 1990–1999 2000–2012

Annual values Observation 1.64 0.899 0.354 0.551 0.255 0.091 Simulation 1.39 1.083 1.193 0.44 0.337 0.451 Total reduction -0.741 -1.286 -0.296 -0.46 Reduction of check dam 0.184 0.839 0.082 0.36 Effect of check dam (%) -24.8 -65.2 -27.7 -78.3 Flood season Observation 1.4 0.777 0.351 0.547 0.253 0.091 Simulation 1.28 0.939 0.99 0.43 0.314 0.405 Total reduction -0.623 -1.049 -0.294 -0.456 Reduction of check dam 0.162 0.639 0.061 0.314 Effect of check dam (%) -26.0 -60.9 -20.7 -68.9

The simulation of our study exhibited a great trapping reduced runoff and sediment load. Tian et al. (2013) effect of check dams on runoff and sediment load, which showed that the number of check dams had reached 567 by was similar to the previous studies. Jiao et al. (2004) the end of 2010, and the dam-controlled area was indicated that there were 381 check dams until 1992 in the 2216 km2. As the check dams increasing, the proportion of Huangfuchuan basin, and the total storage capacity and check dam in reduction runoff and sediment is increasing, residual storage capacity was 2.29 9 108 and 0.88 9 which could explain the contribution of check dams to 108 m3, respectively, which represented 1.41 9 108 m3 decrease in annual runoff and sediment load is higher from sediment retained in the check dams. The results of Ran 2000 to 2012 than that from 1990 to 1999. Thus, it can be et al. (2006) showed that approximately 0.097 9 108 t concluded that check dams are the main factor retaining sediment was trapped by check dams during 1990–1996 in runoff and sediment in the Huangfuchuan basin. the Huangfuchuan basin. Our study found that check dams lead to 0.082 9 108 t sediment reduction from 1990 to 1999. There were slight differences between our estimation 6 Conclusion and the results from Ran et al. (2006). According to the field survey data from the Hydrology This study investigated the impacts of check dams on Bureau of the Yellow River Water Resources Commission, runoff and sediment load in the Huangfuchuan basin using there were 502 large/medium-sized check dams in the the SWAT model from 1976 to 2012. The main conclusion Huangfuchuan basin by 2009, with the dam-controlled area can be summarized as follows: 1815 km2, accounting for about two thirds of the entire Twenty key check dams were added to the SWAT basin. The number of check dams is increasing. In the model during the calibration (1978–1984) and validation 1990s, there were 74 newly built large/medium-sized check periods (1985–1989), which were set as ‘‘uncontrolled dams, while 231 large/medium-sized check dams were reservoir’’ in the SWAT model. The simulated monthly constructed from 2000 to 2009, approximately three times runoff and sediment load showed a good agreement with of that in the former period. The dams newly built during the observations. The R2 and NS were 0.94 and 0.83 for 2000–2009 controlled an area of 638 km2, accounting for runoff, 0.82 and 0.81 for sediment load for calibration 19.6% of the basin. The filled storage capacity with sedi- period. For validation period, both R2 and NS were higher ment deposition is 0.17 9 108 m3 by the end of 2009, and than 0.8 for runoff and sediment load, indicating satisfac- approximately 0.25 9 108 t sediment was trapped in the tory results for SWAT model. newly built check dams. Our simulated results showed that Check dams played a critical role in reducing runoff and sediment load reduced by 0.36 9 108 t during 2000–2012, sediment load in the Huangfuchuan basin. In flood season which is larger than the field survey data. Because the field (from June to September), runoff decreased by survey data just calculated the effects of large/medium- 0.162 9 108 and 0.639 9 108 m3 in the 1990s and 2000s, sized check dams, and there are a number of newly built respectively, and the sediment load decreased by small-sized dams in the Huangfuchuan basin, which also 0.061 9 108 and 0.314 9 108 t, respectively. The relative

123 Stoch Environ Res Risk Assess contribution of check dams to annual runoff and sediment Loess Plateau. Trans Chin Soc Agric Eng 19:302–306 (in load decrease were 24.8 and 27.7% from 1990 to 1999, Chinese) Jin Z, Cui BL, Song Y, Shi WY, Wang KB, Wang Y, Liang J (2012) respectively. In contrast, check dams contributed approxi- How many check dams do we need to build on the Loess mately 65.2 and 78.3% of reduction in annual runoff and Plateau? Environ Sci Technol 46:8527–8528 sediment load from 2000 to 2012, respectively. Thus, Krysanova V, Srinivasan R (2015) Assessment of climate and land construction of check dams in recent years is an effective use change impacts with SWAT. Reg Environ Chang 15:431–434 measure for sediment control in the Huangfuchuan basin. Li Z, Liu WZ, Zhang XC, Zheng FL (2009) Impacts of land use This study provided a good application of the distributed change and climate variability on hydrology in an agricultural SWAT model for assessing of check dams impacts on catchment on the Loess Plateau of China. J Hydrol 377:35–42 runoff and sediment load in the Loess Plateau. However, Li JZ, Tan SM, Chen FL, Feng P (2014) Quantitatively analyze the impact of land use/land cover change on annual runoff decrease. the model results were relative worse during the later Nat Hazard 74(2):1191–1207 period with more new dams built. Thus, further study Li EH, Mu XM, Zhao GJ, Gao P (2015) Multifractal detrended should be undertaken to estimate the effects of changes in fluctuation analysis of streamflow in the Yellow River Basin, land use/cover and check dams with more available data. China. Water 7:1670–1686 Martı´n-Rosales W, Gisbert J, Pulido-Bosch A, Vallejos A, Ferna´ndez- Corte´s A (2007) Estimating induced by Acknowledgements The work is financially supported by the engineering systems in a semiarid area (southeastern Spain). National Natural Science Foundation of China (Grant Nos.: Environ Geol 52:985–995 41671279, 41271295, 41201266), the Major Programs of the Chinese Mishra A, Froebrich J, Gassman PW (2007) Evaluation of the SWAT Academy of Sciences (KZZD-EW-04-03), the National Key Scientific model for assessing sediment control structures in a small Research Project (2016YFC0402401), Special-Funds of Scientific watershed in India. Trans ASABE 50:469–477 Research Programs of State Key Laboratory of Soil Erosion and Mu XM, Basang CL, Zhang L, Gao P, Wang F, Zhang XP (2007) Dryland Farming on the Loess Plateau (A314021403-Q2), Beijing Impact of conservation measures on runoff and sediment in Water Science & Technology Institute (Z141100003614052). 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