Field data-based implementation of land management and terraces on the catchment scale for an eco-hydrological modelling approach in the Three Gorges Region, China

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Field data-based implementation of land management and terraces on the catchment scale for an eco-hydrological modelling approach in the Three Gorges Region, China Alexander Strehmel ∗ , Amy Jewett, Ronja Schuldt, Britta Schmalz, Nicola Fohrer University of Kiel, Institute for Natural Resource Conservation, Department of Hydrology and Water Resources Management, Kiel, Germany

a r t i c l e

i n f o

Article history: Received 17 July 2015 Received in revised form 4 October 2015 Accepted 10 October 2015 Available online xxx Keywords: Land management Terraces Eco-hydrological modelling Best Management Practices Three Gorges Region

a b s t r a c t In this study, an innovative method to generate spatially-distributed data sets on land management and terraces based on sparse field data for a steep-sloping catchment in the Three Gorges Region in China is introduced and tested using the eco-hydrological model SWAT. The generation of such data sets is necessary for the development and evaluation of Best Management Practices (BMP) towards a reduction of high inputs of sediment and nutrients in water bodies. It is hypothesized that the inclusion of land management as well as terraces in the eco-hydrological modelling approach are individually as well as combined able to increase the model efficiency regarding streamflow and sediment. The results of the study show that the field data sets on land management and terraces can be used to generate useful SWAT input data sets to represent management and conservation practices and the model results are plausible. The effect of land management and terraces on streamflow is identified to be rather small. At the same time a strong effect of the inclusion of the terrace dataset on sediment yields can be observed, which can be seen as an improvement of the process representation within the model. By introducing the new method the study contributes to an improved representation of land management and terraces in datascarce study regions in eco-hydrological models. At the same time the study confirms the importance of the consideration of BMPs in eco-hydrological modelling, especially towards the representation of the dynamics of sediment and sediment-bound substances. © 2015 Elsevier B.V. All rights reserved.

1. Introduction & Motivation The damming of the Yangtze River by the Three Gorges Dam in Hubei Province in central China is causing a rapid land use change in the Three Gorges Reservoir Region (TGR) (Ye et al., 2009; Zhang et al., 2009; Seeber et al., 2010). These land use changes encompass the land reclamation for new agricultural areas, which is connected to a loss in natural forest and shrubland, as well as the construction of infrastructure and settlements for the relocated population (McDonald et al., 2008; Zhang et al., 2009). While the steep-sloping topography of the area as well as its shallow and highly erodible soils already cause high erosion rates in the region (SchönbrodtStitt et al., 2012), the fast land use change is a major trigger for even higher soil losses installing new agricultural areas on steep

∗ Corresponding author at: University of Kiel, Institute for Natural Resource Conservation, Department of Hydrology and Water Resources Management, Kiel, Germany, Olshausenstraße 75, Kiel, Germany. E-mail address: [email protected] (A. Strehmel).

hillsides, causing high inputs of sediment and particle-bound agrochemicals into rivers, from where they are transported to the Three Gorges Reservoir (Seeber et al., 2010; Bieger et al., 2015a). This has severe negative effects on the water quality and ecology of the reservoir (Wang et al., 2010; Bergmann et al., 2012; Holbach et al., 2012), while at the same time fostering its sedimentation (Lu and Higgitt, 2001; Xu and Milliman, 2009). The development of suitable management strategies to mitigate high inputs of nutrients and sediment into the reservoir can be based on a sound eco-hydrological modelling approach, which has to consider the specific conditions of the region (Gassman et al., 2007; Bieger et al., 2015a). The model SWAT (Soil and Water Assessment Tool; Arnold et al., 1998) is a semi-distributed eco-hydrological model designed to assess the impacts of land use and climatic changes on water, sediment and nutrient dynamics in large, complex catchments (Neitsch et al., 2011). It relies on the concept of Hydrological Response Units (HRUs), subunits of uniform land use, soil type and slope class, which are aggregated for every subbasin of the catchment. Due to the possibility to implement agricultural management as well as

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soil and water conservation strategies, such as terraces, the SWAT model has emerged as a suitable and widely applied tool to evaluate Best Management Practices (BMP) for catchments (Arabi et al., 2008; Lam et al., 2012; Kaini et al., 2012). It has been used for the assessment of land use changes and management strategies in a wide variety of different environments (Douglas-Mankin et al., 2010; Memarian et al., 2013; Gassman et al., 2014)., e.g. in a tropical monsoon-influenced mountain catchment in India (Wagner et al., 2013), in hot arid regions of Tunisia (Ouessar et al., 2009), but also under subarctic conditions in Northern Mongolia (Hülsmann et al., 2014) and Canada (Abbaspour et al., 2010). A study by Vache et al. (2002) in the US Corn Belt showed that the SWAT model is a suitable tool to evaluate the effects of different management practices on discharge as well as sediment and nutrient loads in catchments. Behera and Panda (2006) used the SWAT model in a small agricultural catchment in Western India for the identification of critical source areas of sediment and nutrients, and the subsequent testing of BMPs for the affected regions to reduce high diffuse loads into the streams. Ullrich and Volk (2009) point out that SWAT model predictions can be very sensitive to changes in management practices, especially concerning crop rotations, the duration of the vegetation period, and soil cover characteristics. All these studies show, that the SWAT model can be a suitable tool for the assessment of BMPs to reduce diffuse matter inputs on the catchment level. For the development of efficient management strategies in the TGR, suitable datasets of land management as well as of soil conservation measures have to be generated. In the TGR, especially farming on bench terraces is a widely applied soil conservation measure (Shi et al., 2012; Schönbrodt-Stitt et al., 2013). The effect of bench terraces on the reduction of soil losses is usually substantial (Hammad et al., 2004; Li and Nguyen, 2008; Shi et al., 2012), and therefore has to be regarded in an eco-hydrological modelling framework. Shao et al. (2013) developed a terrace module for the SWAT model, which divides every HRU in a terraced and nonterraced portion, and calculates hydrological as well as sediment and nutrient processes separately for both parts. This process-based terrace module of Shao et al. (2013), which accounts for the relevant process changes on the terrace tread like additional water storage and associated higher infiltration amounts as well as sediment and nutrient accumulation, is currently not part of the original SWAT code. The module produced good validation results on the hillslope scale, but has not been tested in a large scale catchment yet, and hence was not used in this study. A more simple approach to simulate terraces by reducing the slope length, the curve number as well as the P-factor of the Modified Universal Soil loss Equation for terraced HRUs was proposed by Arabi et al. (2008). However, also for such a parameter-based representation of terraces, a suitable dataset on the terrace inventory in the research area is necessary. For the assessment of BMPs, this terrace dataset has to be complemented by a dataset of land management procedures in the catchment. The creation of spatially-distributed datasets for soil and water conservation practices and land management procedures, however, is a major challenge in areas, where suitable cadastral or statistical data cannot be obtained. Remote sensing data are able to provide multi-temporal high-resolution data of land cover and landforms (Lillesand et al., 2008), but lack the ability to identify erosion forms on fields, tillage or fertilization practices, but also conditions of terraces sufficiently well. Therefore, relevant data on the land management procedures as well as soil conservation measures are best surveyed in the field. In this study a method to survey, spatially extrapolate and prepare such field survey data on land management and terraces to generate suitable datasets for use in the SWAT model is developed and applied. Based on these datasets, the objective of our study is to assess the effect of the implementation of land management and terrace information in the SWAT model. The datasets will be implemented

in the model successively to evaluate effects on the model performance by land management and terraces individually, as well as their combined effect. We hypothesize that both, the information on land management and terraces in the SWAT model are able to improve the model prediction for streamflow and sediment substantially. Furthermore, it is expected that the model efficiency is highest, when both land management and terrace information are contained in the SWAT model framework. 2. Material & Methods 2.1. Study Area The catchment, which serves as basis for the assessment of the effect of land management and terraces on model efficiency, is the catchment of the Xiangxi River in Hubei Province, China. The Xiangxi River is a Northern tributary of the impounded Yangtze River about 30 km upstream of the Three Gorges Dam (Fig. 1). The backwater area of the reservoir stretches about 35 km into the Xiangxi River Valley. The catchment has a total area of 3205 km2 and is characterized by a mountainous and steep-sloping topography with an average slope angle of 24◦ . Almost half of the catchment’s area exhibits slopes above 45◦ . The elevation gradient reaches from 175 m.a.s.l at the outlet of the Xiangxi River into the Three Gorges Reservoir up to 3106 m.a.s.l. at Mt. Shennongjia in the Northwest of the catchment. The climate is humid subtropical with dry winters. The unimodal rainfall regime is governed by the East Asian monsoon (Fu et al., 2008), causing strong rainfall events in summer, when about 70% of the average annual precipitation of about 1000 mm are measured. The average annual temperature in the catchment is 16.4 ◦ C. The prevailing soil types are Cambisols, Avisols and Luvisols (FAO/IIASA/ISRIC/ISSCAS/JRC, 2012). About 75% of the catchment are covered by evergreen and mixed forests (Fig. 2; RapidEye, 2012; Strehmel et al., 2015), while only about 10% of the catchment’s area are used as agricultural land. Farmed fields as well as orange plantations are predominantly found on the slopes of the river valleys and are mostly terraced (Schönbrodt-Stitt et al., 2013; Bieger et al., 2015a). Larger areas with intensive cultivation of oranges are found along the reservoir banks in the South of the catchment (Seeber et al., 2010). Due to the steep terrain, only few agricultural areas, consisting mainly of tea plantations, can be found in the upper parts of the catchment. 2.2. The SWAT Model The model SWAT is a semi-distributed conceptual model, dividing a watershed in HRUs, which are defined by the same land use, soil type and slope class, and whose areas are lumped within the subbasins of the catchment. All hydrological components, sediment and nutrient yields as well as plant growth processes are modelled on the level of each HRU on a daily time step. The different flow components are then summed up for all HRUs within every subbasin and for every time step, and routed through the river network to the catchment’s outlet. The calculation of surface runoff in the model is based on the SCS curve number method (SCS, 1972; Rallison and Miller, 1981), which partitions the effective rainfall into a runoff component and an infiltration component. It is calculated as:

 2 Rday − 0.2 · S  Qsurf =  Rday + 0.8 · S

with S = 25.4

 1000 CN2

(1)



− 10

(2)

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Fig. 1. Map of the Xiangxi catchment in relation to the Three Gorges Dam and the reservoir with its main reaches as well as the climate and gauging stations.

where Rday is the precipitation amount, S is the retention parameter and CN2 is the curve number for moisture condition II (average soil moisture) for the day, which varies with land use and land management, the soil type as well as the current soil moisture content. According to Arabi et al. (2008), curve number values should be increased on terraced agricultural land due to the higher water retention potential on the terrace tread, which leads to higher infiltration amounts. Whenever the soil water content on a given day exceeds the field capacity, lateral subsurface flow is generated on sloped HRUs, which contributes to streamflow. The lateral flow Qlat is calculated separately for every soil layer as: Qlat = 0.024 ·

 2 · SW

excess

· Ksat · slp

d · Lhill



(3)

where, SWexcess is the amount of soil water exceeding field capacity, Ksat is the saturated hydraulic conductivity, slp represents the slope of the HRU, d is the drainable porosity of the soil layer and Lhill is the slope length of the HRU. Following this equation, high lateral flow amounts can be expected for very steep-sloping catchments due to its direct relation to the slope of an HRU. On terraces, the slope length can be assumed to be reduced due to the partitioning of the fields into terraces, which prevents an undisturbed water flow down the hillslope (Arabi et al., 2008) The sediment amount in SWAT is calculated for every time step and on an HRU level using the Modified Universal Soil Loss Equation (MUSLE; Williams, 1975), which is based on the Universal Soil Loss Equation (USLE; Wischmeier and Smith, 1978). It is calculated as: sed = 11.8 · Qsurf · Qpeak · areahru 0.56 · KUSLE · CUSLE · PUSLE · LSUSLE · CFRG (4)

where sed is the sediment amount in the HRU in tons, Qsurf is the amount of surface runoff, Qpeak is the peak runoff rate, areahru is the HRU area, KUSLE is the USLE K-factor, CUSLE is the USLE C-factor, PUSLE is the USLE P-factor, LSUSLE is the USLE LS-factor and CFRG is a coarse fragment factor. The MUSLE relies on a runoff-erosivity factor instead of a rainfall erosivity factor, as it is used in the USLE. The other factors correspond to the respective USLE factors, and are discussed in detail in Wischmeier and Smith (1978). In the USLE, the P-factor represents the presence of soil conservation measures, lowering soil erosion. Hence, this factor has to be regarded for the implementation of terraces on agricultural land (Wischmeier and Smith, 1978; Arabi et al., 2008). A major advantage of the model SWAT is its capability to represent plant growth and the possibility to implement agricultural management practices. Agricultural management practices influence plant growth and alter the characteristics of topsoil, affecting the water balance and the hydrological processes on an HRU. The plant growth in SWAT is regulated by heat units (Boswell, 1926; Barnard, 1948), which are accumulated over the growth cycle of a plant (Neitsch et al., 2011). A heat unit is defined as the difference between the base temperature for growth to occur for a plant and the actual temperature at any given day. With the base temperature differing among different plant types, a higher temperature value on the given day contributes to the accumulation of heat units. A plant reaches maturity, when the accumulated heat units reach a certain value of potential heat units, which also differs among different plant types. At maturity, the plant does not gain any further biomass and requires no additional water or nutrients until harvested (Neitsch et al., 2011). The fraction between the accumulated

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Fig. 2. Land use map of the Xiangxi catchment with the different crop rotations on agricultural areas based on the field data (RapidEye, 2012; Strehmel et al., 2015).

heat units and the potential heat units determines the development of the Leaf Area Index (LAI) over time. The reciprocal of the LAI is proportional to the magnitude of the canopy resistance term in the Penman–Monteith equation (Penman, 1956; Monteith, 1965) for the calculation of evapotranspiration (Neitsch et al., 2011). Hence, higher LAI values allow for higher plant transpiration. Management operations in the SWAT model include tillage, planting, fertilization, irrigation, tile drainage, pesticide application and harvesting operations. Crop rotations can be implemented by consecutively scheduling several planting and harvesting operations for different plants. These rotation periods can stretch over several years, and are repeated successively over the whole modelling period. For a detailed description of the management operations it is referred to Neitsch et al. (2011). Modification of the curve number for surface runoff calculation with the slope gradient The standard curve number values in SWAT for the moisture condition II (CN2 ) are based on a slope of 5%. A curve number correc-

tion algorithm has been developed by Williams (1995) to account for an increase of surface runoff with steeper slope angles. However, this approach increases the curve number – and hence surface runoff – only efficiently up to slope gradients of 25◦ (Bieger et al., 2015b). At the same time several empirical studies (e.g, Huang et al., 2006; El Kateb et al., 2013), observed surface runoff increases with increasing slope gradients up to 45◦ . Due to an average slope gradient of 24◦ in the study area, an efficient increase of surface runoff for steeper slopes than provided by the Williams (1995) is required to maintain a sufficient process representation on steep slopes. Hence, a new slope correction algorithm for the runoff curve number was developed, which is able to increase the curve number for slopes up to 45◦ . This correction approach was implemented in the SWAT source code and used throughout this study. The correction equation is given as:

CN2s = a · slp + CN2 − 0.05 · a

(5)

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with a = 158 · exp (−0.035 · CN2 )

(6)

where CN2s is the slope-corrected curve number value, CN2 is the original curve number for soil moisture condition II and slp is the tangent of the slope angle. 2.3. Geodatabase The SWAT model requires datasets on the land use, soils and the topography of a catchment to define HRUs and derive flow paths, which connect the different subbasins. As topography input, the SRTM-DEM (Jarvis et al., 2008), which was resampled to a resolution of 30 × 30 m by cubic convolution, was used. Soil data for the Xiangxi catchment originate from available soil maps and profile descriptions based on the Second National Soil Survey in China that was conducted from 1979 to 1994 (Shi et al., 2010). These data were digitized into a vector map to set up the SWAT model, while the soil profile information were used to parameterize the soils in the SWAT geodatabase. A spatially-distributed land use dataset was generated by a maximum-likelihood classification of available RapidEye data (RapidEye, 2012; Strehmel et al., 2015) for the year 2012 with an overall accuracy of 68%. This land use map distinguishes agricultural areas in farmed dryland, orange plantations, paddy fields as well as tea plantations. In the SWAT model, the main driver of all hydrological processes is the climate, which encompasses precipitation, minimum and maximum temperature, wind speed, relative humidity and solar radiation. Weather data on a daily time step were available from three stations for the period from 1960 to 2009 (CMA, 2012), of which one lies within the catchment, one outside of the catchment in its North, and one outside its Southern border (Fig. 1). For the calibration and validation of the model, discharge and sediment yield data on a daily basis for the period from 2002 to 2008 with missing data for the year 2006 were provided by the China Meteorological Administration (CMA, 2014) for the gauging station Xingshan (31◦ 15 N, 110◦ 44 E). 2.4. Field Methodology Two field campaigns have been carried out in the Xiangxi catchment in autumn 2012 and spring 2013 to assess crop rotations, land management patterns as well as the abundance and quality of bench terraces. The two main tasks during this field campaign were: – Large-scale mapping of land use and terraces with the aim to cover a large catchment area to be able to describe the spatial variability within the catchment. – Interviewing of local farmers regarding their cropping techniques and crop rotations. The large-scale mapping of land use and terraces was carried out mostly from a vehicle, while driving through the accessible parts of the catchment. The mapping covered the slopes along the main river valleys as well as some side valleys and plateaus on higher altitudes (Fig. 3), where the land use map had indicated agricultural use. The use of a camera with GPS functionality and automatic geo-tagging of images during the field-mapping resulted in the acquisition of a total number of 2775 geo-tagged photos. The photos were used together with the hand-written notes from the field sampling to derive mapping units for land management, which were defined by their specific pattern of crop (rotation) frequency (Fig. 3). Regarding terraces, the focus was laid on surveying the terrace walls, as its condition is regarded to be the decisive factor for the terrace’s protective effect against soil erosion (Schönbrodt-Stitt et al., 2013). While, only a small number of terraces was directly

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surveyed in the field, most terraces were classified later from the field photos under consideration of representing a wide variety of locations in the catchment. At the same time, only terrace photos which allowed for a sufficiently precise categorization of the terrace condition were considered. This procedure allowed for the consideration of a total of 422 terraces in the catchment (Fig. 3). To keep the terrace sampling as representative as possible, it was avoided to consider adjacent terraces or several terraces belonging to the same field unit for analysis. Hence, the 422 terraces are counted as individual terraces at differing locations. Detailed information on cropping systems and techniques, crop yields, fertilizer types and amounts as well as common crop rotations were inquired from local farmers in a non-standardized interviewing scheme. In total, twelve interviews were conducted in different parts of the catchment (Fig. 3), aiming at acquiring information for all commonly found field crops in the catchment. The evaluation of the interviews was conducted timely after their acquisition in the field and under consideration of the characteristics of region, where the interview was conducted. 2.5. Processing of the Field Data 2.5.1. Spatial extrapolation of land management data The land management data, which were taken during the field campaigns, were used to define mapping units of the Xiangxi catchment, which are categorized by a unique pattern of crops and crop rotation frequencies. Based on a Nearest Neighbor extrapolation, these mapping units were then extended over the whole catchment area. Within each mapping unit, the agricultural areas of the land use map were then further subdivided in classes, which represent the observed crop rotations. This subdivision had to consider the frequency distribution of crop rotations within the mapping unit. To ensure the equal spatial distribution of crop rotation classes over the mapping units, a GIS approach was used, which assigned the crop rotation classes randomly to the respective agricultural areas (farmed dryland and paddy fields) of the land use classification map, while maintaining the frequency ratios between crop rotation classes for the respective mapping unit. This procedure resulted in a land use map with a total of 19 classes, which served as the basis for the land management setup in SWAT (Fig. 2). 2.5.2. Classification and spatial extrapolation of terrace data The condition of each of the 422 evaluated terraces was classified according to a method developed by Schönbrodt-Stitt et al. (2013), distinguishing four different condition classes, which range from ‘well maintained’ (1) over ‘poorly maintained’ (2) and ‘partially collapsed’ (3) to completely collapsed (4). The aggregation of terrace conditions on all agricultural areas of the catchment was carried out on the subbasin level. A more detailed approach linking terrace conditions to land use or topographic features on the field level could not be established due to the relatively low spatial accuracy of the terrace locations. In the field, the georeferenced photos of terraces were often taken between 50 and 100 meters away from the location of a terrace, which would have led to spatial mismatches for a field level approach using supplementary GIS data on land use and topography. Hence, the uncertainty of such an approach using the available field data was considered as too high, and the subbasin level was chosen for terrace aggregation and extrapolation. In a first step, average terrace conditions were calculated for all subbasins with existing terrace field data. An approach based on Schönbrodt-Stitt et al. (2013) was selected to extrapolate the observed terrace conditions onto the whole catchment area. The approach predicts terrace conditions with the help of explanatory co-variables based on landscape features. Under the assumption of an interval-scale for the terrace condition values between 1 (well maintained) and 4 (completely collapsed), average terrace

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Fig. 3. Map of field work features, including the location of mapping un its for the land management evaluation, the farmer interview locations as well as all 422 examined terraces. Table 1 Parameters for the multiple regression approach for the prediction of average terrace conditions per subbasin.

Intercept % Dryland % Orange orchard Elevation Overall performance

Coefficients

Coefficient of determination (R2 )

p-value

3.60683 −0.00520 0.00254 −0.00092 – ––

–– 0.28 0.34 0.34 0.44

0.02 0.64 0.82 0.13 0.04

conditions per subbasin were calculated and correlated with topographic as well as land-use-related co-variables on the subbasin level (Fig. 4). The analysis showed that the average subbasin elevation as well as the share of cropped dryland and of orange orchards on the total agricultural area of a subbasin show significant correlations with the average terrace condition of a subbasin. Therefore, these three variables were used as independent variables for the development of a linear multiple regression model to predict the

average terrace condition for the subbasins without any field data points. Based on the parameters of the multiple regression model (Table 1), the average terrace conditions for these subbasins were calculated using the following equation:

TCsub = 3.60683 − 0.00520 · PercDrysub + 0.00254 · PercOransub − 0.00092 · Elevsub

(8)

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Fig. 4. Correlation of topographic and land-use related variables with the average terrace condition per subbasin, which were regarded for the development of the multiple regression approach.

where, TCsub is the average terrace condition of a subbasin, PercDrysub is the percentage of cropped dryland on the whole agriculturally used area of that subbasin, PercOransub is the percentage of orange orchards on the whole agricultural area of that subbasin and Elevsub is the mean subbasin elevation. With an overall coefficient of determination of 0.44, almost half the variance of average terrace conditions among subbasins can be explained by the three co-variables. While this value still imposes a high uncertainty, the regression model is only used for the subbasins without any field data on terrace conditions. These 27 subbasins are mostly in upstream areas with very little agricultural use, hence, mainly imposing uncertainty on these regions. Therefore, the overall influence of the uncertainty, which is imposed by this regression approach is seen as acceptable. The resulting map of the extrapolation of average terrace conditions onto the whole catchment is shown in Fig. 5. 2.6. SWAT Model Setup & Calibration For comparison purposes, in total four different SWAT models of the Xiangxi catchment were set up: A base model without a parameterization to account for either land management or terraces, one model with land management parameterization, one model with terrace parameterization and one model combining the parameterizations for land management and terraces. The revision 622 of the SWAT model, which was modified to include the correction approach for curve number values with HRU slope, was used for the calibration and validation as well as all consecutive analysis steps,

2.6.1. Base model The watershed delineation, which was carried out using the ArcSWAT interface (Olivera et al., 2006), using the resampled SRTM-DEM resulted in 45 subbasins for the entire catchment. Since the year 1999, a large reservoir exists in the Gufu River, just a few kilometers north of its confluence with the Xiangxi River. This reservoir was included in the model setup, because it strongly affects the discharge regime at the gauge Xingshan and is, hence, necessary to represent the hydrological processes in the model accurately. The management of the reservoir was derived using a simple approach comparing monthly ratios of precipitation and discharge before and after the start of the operation of the reservoir in 1999. This management scheme was then implemented in SWAT using relative monthly target storages for the reservoir. The land use map with 19 land use classes representing individual crop rotations (Fig. 2) was used for HRU definition, ensuring that the number and distribution of HRUs were the same for all model setups The base model aims at simulating a setup, where detailed information on cultivated crop types, management operations and soil conservation measures in the watershed are not present, e.g. because land use information are only obtained by a first-level land use classification from remote sensing data, and any field survey information are not available. Hence, for the base model setup, all agriculturally used HRUs were parameterized with the standard parameterization for generic agricultural land (AGRL) as provided by the ArcSWAT land use database. The default heat unit scheduling for plant growth was maintained as standard operation schedule. For orange plantations, the default parameterization for orchards (ORCD), as provided in the land use database was used. Also for all

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Fig. 5. Map of average terrace conditions per subbasin for the Xiangxi catchment based on the multiple regression approach. Terrace condition categories range from 1 (well maintained) over 2 (poorly maintained) and 3 (partially collapsed) to 4 (completely collapsed).

other land use classes, default parameters and operation schemes, as provided by the ArcSWAT database, were used. Due to the highly variable topography of the catchment, five slope classes were chosen for HRU definition. The slope classes were defined as ranges from 0–15◦ , 15–25◦ ,25–35◦ , 35–45◦ and above 45◦ . The overlay of land use, slope and soil maps resulted in a total of 4485 HRUs. Due to the low coverage of the catchment area with climate stations, the spatial pattern of precipitation and temperature was diversified by implementing elevation bands in the base model setup with a precipitation lapse rate of 320 mm/km as well as a temperature lapse rate of -6 ◦ C/km following the setup for the Xiangxi catchment of Bieger et al. (2014).

2.6.2. Implementation of land management For the land management setup, the land use classes, for which the field interviews had provided sufficient and conclusive data, were parameterized with their specific management scheme

(Table 2). For all other crop classes, the standard parameterization from the ArcSWAT database was used, and no additional management was implemented. Whenever the auto-irrigation operation was used in the management setup, it was parameterized in a way to retrieve water from the shallow aquifer of the subbasin, where the HRU is located.

2.6.3. Implementation of terraces To include information on terrace conditions in the model, the values for the SWAT model parameters curve number (CN2), slope length (SLSUBBSN) and the USLE p-factor (USLE P) were modified following the recommendations given in Arabi et al. (2008). It was assumed, that the parameter modifications of Arabi et al. (2008) refer to ideal terrace conditions, represented by the terrace category ‘well maintained’. Hence, a decrease in terrace conditions was implemented by adjusting the magnitude of the value changes for the three parameters according to Table 3. With this,

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Fig. 6. Graphs of observed and modelled (a) streamflow and (b) sediment for the calibration period, including the 95PPU for the setup with land management and terraces. For streamflow (a) only the hydrograph of the setup with land management and terraces is shown. The hydrographs of the other setups widely overlap with this graph.

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Table 2 Frequency of crop rotations in the Xiangxi catchment and their implementation in SWAT for the land management setup. Crop (rotation)

Areal percen-tage (%)

Date(Year)

Operation

Comment

Corn - rapeseed

40.29

15th March(1) 16th March(1) 1st June(1) 1st July(1) 1st September(1) 1st October(1) 2nd October(1) 2nd October(1) 1st March(2) 1st April 2nd April 2nd April 25th April 1st September 1st March(1) 2nd March(1) 1st April(1) 14th July(1) 15th July(1) 31st October(1) 1st November(1) 28th February(2) 15th April(1) 1st December(1) 1st April(2) 15th July(2) 1st October(2) 1st March(3) 15th April(3) 1st December(3) 1st April(4) 1st September(4) 1st October(4) 1st March(5) 15th April 16th April 16th April 1st December 1st April(1) 1st April(1) 2nd April(1) 25th April(1) 1st September(1) 1st October(1) 2nd October(1) 1st March(2) 31st July 31st December 31st January 1st May

Planting Fertilization Fertilization Fertilization Harvest & kill Planting Auto fertilization init. Auto irrigation init. Harvest & kill Planting Auto-irrigation init. Fertilization Fertilization Harvest & kill Planting Fertilization Fertilization Harvest & kill Planting Harvest & kill Planting Harvest & kill Planting Harvest & kill Planting Harvest & kill Planting Harvest & kill Planting Harvest & kill Planting Harvest & kill Planting Harvest & kill Planting Auto irrigation init. Fertilization Harvest & kill Planting Fertilization Auto irrigation init Fertilization Harvest & kill Planting Auto fertilization init. Harvest & Kill Fertilization Harvest Fertilization Harvest

Corn NPK 15-15-15 Elemental N Elemental N

Rice

Potato–sweet potato–cabbage

Tobacco- WatermelonRapeseed-Corn

Tobacco

Rice-rapeseed

13.10

6.27

0.22

0.11

0.02

Orange

35.42

Tea

4.57

Rapeseed NPK 15-15-15

Rice Elemental N Elemental N Potato NPK 15-15-15 Elemental N Sweet Potato Cabbage Tobacco Watermelon Rapeseed Tobacco Corn Rapeseed Tobacco NPK 15-15-15 Rice NPK 15-15-15& Swine Manure ANH-NH3 Rapeseed NPK 15-15-15 NPK 15-15-15 Swine manure Every two years

Table 3 Changes in SWAT parameters for different terrace condition classes. Terrace condition Well maintained Fairly maintained Partially collapsed Completely collapsed Not terraced** * **

CN2 (add) −6 −5 −4 −2 0

P USLE (absolute)

Slope length (relative)

Slope length (steep* ) (relative)

0.2 0.4 0.6 0.8 1.0

−40% −30% −20% −10% −0%

−20% −15% −10% −5% −0%

Steeper 50%. Only agricultural land steeper than 20% is terraced.

the parameter values reflect a decreasing erosion protection of terraces with decreasing average terrace condition. The values for the three parameters were adjusted for all agricultural areas with a slope above 20%, based on the average terrace condition of the subbasin they belong to. Agricultural HRUS with a slope angle below 20% were assumed to not be terraced. The changed parameter values were interpolated, whenever the average terrace condition for a subbasin was between two terrace categories. Methodologically, the parameter changes were achieved by first exchanging

the parameter values for all relevant HRUs in the ArcSWAT project database, and afterwards rewriting the respective input files of the SWAT setup (*.hru and *.mgt). 2.6.4. Model calibration and validation The calibration for streamflow and sediment amount was carried out for all four model setups at the gauge Xingshan using the SUFI2 routine of SWAT-CUP version 5.1.5 (Abbaspour, 2007). The location of the gauge just downstream of the confluence of

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Table 4 Calibration parameters, parameter ranges and best parameter values for the streamflow and sediment calibrations for all model setups. For the sediment prediction, the highest model efficiencies were reached using different parameter sets for the model setups with and without terrace implementation. Streamflow: Parameter

File

ESCO SURLAG SLSUBBSN CN2 SOL AWC(1) SOL K(1) SOL Z(1) ALPHA BF GW DELAY GW REVAP GWQMN ALPHA BNK CH K2 CH N2

Type of value change

.bsn .bsn .hru .mgt .sol .sol .sol .gw .gw .gw .gw .rte .rte .rte

Replace value Replace value Percent change Percent change Percent change Percent change Percent change Replace value Replace value Replace value Replace value Replace value Replace value Replace value

Parameter Range Lower

Upper

Fitted value

0.5 0.05 −50% −20% −10% −20% −10% 0 0 0 0 0.05 0 0

1 1 +50% +20% +10% +20% +30% 0.1 40 0.3 1500 0.2 30 0.1

0.61 0.11 −40.7% −14.5% −8.0% -13.8% +27.9% 0.08 29.7 0.20 1291.25 0.18 29.00 0.07

Sediment: Parameter

USLE P USLE K(1) SPCON SPEXP LAT SED

File

.mgt .sol .bsn .bsn .hru

Type of value change

Percent change Percent change Replace value Replace value Replace value

Parameter range

Fitted value

Lower

Upper

Without terraces

With terraces

−50% −50% 0.0001 1 0

+50% +50% 0.01 1.5 250

−48.7% +33.4% 0.0075 1.23 13.04

−13.4% +19.5% 0.0016 1.05 154.5

the Xiangxi River with the Gufu River restricts the area that was regarded for calibration and validation to 1857 km2 (58% of the catchment area). Based on the availability of discharge and sediment data at the gauging station, the calibration period was defined from 2002 to 2005. The models were validated for the years 2007 and 2008. The streamflow calibration was carried out first, and the calibration for sediment was carried out afterwards on the basis of the best simulation for streamflow. For the streamflow calibration on a daily basis, in total 14 parameters were used (Table 4). As objective function for the calibration the Nash–Sutcliffe efficiency (Nash and Sutcliffe, 1970) was used. A total of 2000 different parameter value sets, which had been generated by Latin Hypercube Sampling within the SUFI2 framework, were evaluated in one iteration. To be able to compare the calibrations of the four different model setups directly, the same 2000 parameter value sets were used for the calibration of all four model setups. The highest model efficiency for streamflow was reached for all four models using the parameter value set given in Table 4. Following the flow calibration, the sediment calibration was carried out on a monthly time step using the five parameters shown in Table 4. Also for the sediment calibration, the Nash–Sutcliffe efficiency was used as objective function and the same 2000 parameter value sets were evaluated for all four model setups in one iteration. 3. Results & Discussion 3.1. Calibration and Validation Results The evaluation statistics for the streamflow calibration and validation based on the best parameterization (Table 4) for the four different model setups are shown in Table 5. The statistics show that this parameterization yields very similar model efficiencies for all setups, and that a significant improvement of the SWAT model for the Xiangxi catchment regarding streamflow cannot be achieved by including detailed land management and terrace information. The observed and calibrated streamflow hydrographs are shown in Fig. 6a. Because the courses of the hydrographs for all

four model setups overlap, only the hydrograph for the model setup with land management and terraces is shown in the figure. The hydrographs are shown together with the 95% prediction uncertainty (95PPU; Abbaspour et al., 2004; Abbaspour, 2007). The fraction of the observed streamflow data, which is bracketed by the 95PPU (p-factor), is at 58%, while the r-factor (the ratio between the average thickness of the 95PPU band and the standard deviation of the observed data series) has a value of 0.48 for streamflow. The comparison of the modelled and observed hydrographs shows that the amount of baseflow is generally met well, while high discharge peaks are often underestimated, and moderate and low peaks are met well, and are sometimes even overestimated. The implications of the p- and r-factors values for model uncertainty as well as the possible causes for this uncertainty are discussed in Section 3.4. In contrast to the flow optimization, the best sediment simulations of the four SWAT model setups could not be realized using a single calibration parameter value set. Instead, the highest model efficiencies for the four setups as shown in Table 6 are a result of two different parameterizations for the five calibration parameters (Table 4). However, the evaluation statistics show significantly lower model efficiencies, if the same parameterization as for the models containing terraces is applied for the base model as well as the model containing only land management (Table 7). Under consideration of Table 6, it is evident that the effect of terraces on sediment yield can be compensated largely by choosing a different set of the calibration parameters. Distributed hydrological models often exhibit such equifinality effects, especially if the number of model parameters is very high (e.g., Beven and Binley, 1992; Wagener and Kollat, 2007). The occurrence of equifinality effects during SWAT modelling in the TGR was previously already observed in a study by Shen et al. (2012). They recommend evaluating the physical basis and the associated plausibility of parameter values for an assessment of process representation under the given parameter set. Following this procedure, the comparison of the best parameter value sets for the non-terraced and terraced conditions indicates that the parameterization for the models containing terraces can be seen as better: The correction factors for the MUSLE P-

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Table 5 Evaluation statistics of the calibrated model setups for streamflow. Streamflow (daily)calibration/validation

Base model

Model with land management only

Model with terraces only

Model with manage-ment and terraces

R2 Nash–Sutcliffe PBIAS RSR

0.69/0.70 0.69/0.70 −0.01/-1.25 0.56/0.55

0.69/0.70 0.69/0.70 0.07/-1.16 0.56/0.55

0.69/0.70 0.69/0.70 −0.43/−1.59 0.56/0.55

0.69/0.70 0.69/0.70 −0.36/−1.48 0.56/0.55

Table 6 Evaluation statistics of the calibrated model setups for sediment under consideration of the two different best parameter sets for model setups with and without terrace implementation. Sediment (monthly)calibration/validation 2

R Nash–Sutcliffe PBIAS RSR

Base model

Model with land management only

Model with terraces only

Model with manage-ment and terraces

0.79/0.45 0.79/0.32 −0.90/−42.47 0.46/0.83

0.81/0.50 0.81/0.42 −0.24/−40.23 0.44/0.76

0.84/0.48 0.82/0.45 5.33/−26.87 0.42/0.74

0.83/0.53 0.81/0.51 5.54/−22.25 0.44/0.70

Table 7 Evaluation statistics of the model setups without terraces, when using the parameter set for the highest model efficiency of the model setups with terrace implementation. Sediment (monthly)calibration/validation

Base model

Model with land management only

R2 Nash–Sutcliffe PBIAS RSR

0.82/0.45 0.53/−0.60 −59.55/−129.14 0.69/1.27

0.81/0.51 0.58/−0.32 -57.41/−118.88 0.65/1.15

and K-factor are in a much lower range for the models with terraces, compared to the parameterization, which yields highest model efficiencies for the model setups without terraces. Furthermore, the parameters SPCON and SPEXP, which control the re-entrainment of sediment in the channel sediment routing, are also closer to their standard parameterization (0.0001 for SPCON and 1.0 for SPEXP), when using the best parameter set for the models containing terraces. Only the parameter LAT SED, which defines the sediment concentration in the lateral flow, exhibits a higher value for the best parameterization for the terrace models than for the setups without terraces. The parameter, however, is not very sensitive regarding the sediment prediction. A sensitivity test of reducing the parameter value to 13.04 for the model setup with management and terraces results in a decrease of the Nash–Sutcliffe efficiency to a value of 0.80. Therefore, the sediment amount in lateral flow has only minor regulatory functions for the sediment amount at the gauge. At the same time, the USLE P- and K-factors exhibited the highest sensitivities during the sediment calibration. With this in mind, the overall comparison of the best parameter sets indicates that the process representation is met better for the models containing terraces. The observed and modelled sediment graphs for all model setups are shown in Fig. 6b. In the figure, the graphs for all model setups are based on the parameterization for the models with terraces. The 95PPU in this graph represents the uncertainty for the setup with land management and terraces. The p-factor for this setup is at 73%, while the r-factor has a value of 0.92. The figure shows that the courses for the different setups are very similar during periods of low sediment amounts. Furthermore, it can be seen that the better performance of the setups with terraces is mainly induced by lower sediment amounts during peak times, which comes closer to the observed sediment loads. At the same time, the comparison of calibration and validation for the sediment amount shows higher model efficiencies for the calibration period than for the validation period. These differences in model evaluation statistics indicate an overfitting of the parameter values onto the calibration period, which often occurs in spatially-distributed and parameter-rich hydrological models (e.g., Beven, 2006; Schoups et al., 2008). Such an overfitting is usually connected with high uncertainties in the model prediction (van der

Perk, 1997; Pande et al., 2009), which is discussed in more detail in Section 3.4. 3.2. Effects of Land Management and Terraces on Streamflow and Flow Components The similarity in the model efficiencies among the four different model setup is also expressed in the water balance and the flow components (Table 8), which were averaged over the years 1999 to 2008. The measured evapotranspiration in the Xiangxi catchment is estimated to be about 600 mm (Bieger et al., 2014), and therefore slightly underestimated in all four models. The differences in flow components are marginal between the different model setups. The lateral flow constitutes the main runoff component due to the steep-sloping topography of the Xiangxi catchment and the direct relation of the lateral flow amount to the slope of an HRU. The surface runoff depends on the slope gradient only indirectly by adjusting the runoff curve number. Its reduction in the models with terraces is caused by the lower curve numbers on terraced HRUs, while the increase of lateral flow amounts can be attributed to the shortened slope length on terraced HRUs. This increase in lateral flow amounts leaves less water available for percolation to the aquifer, and hence for groundwater flow. The low magnitude of all changes between model setups can be attributed to the low percentage of agricultural areas in the catchment, which are affected by land management changes and the implementation of terraces. The small changes in the water balance components are responsible for the similar model efficiencies of the streamflow calibration and the negligible differences in the streamflow hydrograph at the gauge between the four model setups. These small changes in streamflow and water balance among model setups are likely to be a result of the low percentage of agricultural land of only about 10% in the total catchment area. Therefore, changes induced by the land management and the terrace implementation affect only a small area portion and cause only very small changes on the catchment scale. Hence, in a second analysis step only HRUs affected by land management changes and the implementation of terraces were evaluated regarding changes of flow components compared to the base model (Fig. 7a–d). The values shown are based on averages of all HRUs with the respective

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Fig. 7. Differences in (a) evapotranspiration, (b) lateral flow, (c) surface runoff and (d) water yield of the model setups with land management and terrace implementation in comparison to the base model for all HRUs with the respective crop rotation and terracing in the catchment. The values are averaged for the years 1999–2008.

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Table 8 Water balance and flow components for the base model setup and percent changes of the components for the other model setups. The values are averages for the years 1999 to 2008. [mm/% change]

Base model

Model with land management only

Model with terraces only

Model with management & terraces

Precipitation Evapotranspiration Water yield Surface runoff Lateral flow Groundwater flow

1211.7 567.8 599.0 50.9 516.1 32.04

+0.00% +0.35% −0.20% −0.22% −0.13% −1.25%

+0.00% −0.30% +0.43% −3.83% +0.93% −0.72%

+0.00% +0.11% +0.26% −4.03% +0.83% −2.06%

crop rotation in the catchment. Such an assessment helps to identify, whether the land management and terrace implementation is plausible regarding changes in flow components and water balance on the HRU level. The analysis shows that the implementation of management leads to an increase in evapotranspiration for all crop rotations (Fig. 7a), which is caused by the increase of the period with vegetation cover during every year and hence a higher average LAI than for the base model. The longer vegetation cover is induced by the crop rotation scheduling by date, compared to the heat unit scheduling in the base model setup. An assessment of biomass production on agricultural areas for the base model in the course of one year revealed that the heat unit fractions for harvesting are often already reached in autumn and afterwards plant growth is not observed until the next year. Hence, the vegetation cover during autumn winter, and early spring is lower in the base model setup than in the setups with land management implementation. The implementation of terraces, however, causes a decrease in evapotranspiration, which can be attributed to lower water availability in the soil due to increased amounts of lateral flow by the reduced slope length (Fig. 7b). For the model setup with land management and terraces, the effect on evapotranspiration can be roughly seen as the sum of the effects of the land management and terrace setups. Hence, balancing or feedback effects between land management and terraces are rather small, and their detailed assessment can be neglected. It can be seen, however, that for most crop rotations the land management has a tendency to exhibit a stronger effect on changes in evapotranspiration than the terrace implementation, because also for the setup with land management and terraces increasing evapotranspiration amounts are observed. Increased evapotranspiration is linked to higher LAI values with higher plant transpiration, which make water transport through the plant from the root zone to the leaves more efficient. This causes lower average soil water contents, affecting the runoff curve number for the next model time step to be lower, which decreases the average amount of surface runoff under the land management parameterization (Fig. 7c). Increased infiltration amounts due to lower curve number values are not able to compensate the lower soil water contents, before the water is transported away by lateral flow (especially on steep slopes) or seeping to the shallow aquifer. Whenever the surface runoff amounts are higher than for the base model run, the specific CN2 value for one or more of the crop types of the rotation is higher than for generic agricultural land (e.g. for corn; refer to Fig. 7c). In the model parameterizations with terraces, the reduced curve number values are mainly responsible for the strong decrease in surface runoff among all crop rotations. Lateral flow amounts are dominated by soil characteristics, and hence there is no clear tendency for decreasing or increasing amounts with the management parameterization. However, the lateral flow amounts increase due to the reduction of the slope length for the model setup with terraces (c.f. Section 2.2). This effect is also dominant for the model with land management and terraces. Because lateral flow is the dominant runoff process in the catchment (c.f. Table 8), also the variation of water yields of the different crop rotations among model parameterizations mainly reflects the pattern of changes for the lateral flow (Fig. 7d). The changes in

water yield are, however, mainly within the range of 5% increase or decrease for the different parameterizations compared to the base model. Therefore it can be assumed that also a higher share of agricultural areas on the whole catchment area would not result in strong alterations in streamflow. 3.3. Effects of Land Management and Terraces on Sediment In contrast to the flow calibration, the sediment calibration results in strongly differing model efficiencies for the different model setups using the same parameter set, which is also reflected in stronger differences of sediment yields among setups. Fig. 8 shows the average monthly modeled temporal distribution of sediment releases (soil erosion) from all HRUs in the catchment for the period between 1999 and 2008 for all model setups. The sediment releases generally are lower in winter and higher during the summer months. At the same time, the releases of the model setups with terraces exhibit substantially lower amounts of sediment compared to the other setups during the spring and summer months, which are rich in rainfall, while the differences between all model setups are rather small in winter. Hence, the terrace implementation has a stronger effect on sediment yields than the implementation of land management. At the same time it can be observed that the sediment amounts for the model setups with land management are slightly lower in spring, while being slightly higher during autumn. This can mainly be explained by the implemented cropping pattern, where most crops are already being planted in March or early April (c.f. Table 2). At the same time, corn and rice, which are the two most frequent summer crops, are harvested at the beginning of September, leaving many fields fallow during autumn, until the winter crops are planted. The generic agricultural land, which is implemented in the models without land management, however, still has biomass on the fields during that time, and therefore exhibits lower erosion rates due to lower C-factor values. This observed discrepancy of the effect of land management on soil erosion and sediment yield shows the importance of a proper land management implementation regarding sediment loads in SWAT in order to avoid artificially low sediment loads during times when many fields are fallow. In reality not all fields are harvested at the same date, but rather within a time period of several days or weeks. Furthermore, planting and harvesting dates differ with elevation, which makes the heat unit approach for the implementation of management practices more feasible. However, data on the heat unit accumulation until maturity for the conditions and plants in the Three Gorges Region were insufficient and too sparse to justify the use of the heat unit method for the scheduling of management operations in this study. A comparison of the reduction of sediment yields by crop rotation (Fig. 9) shows that except for the rotations involving rice as well as for the perennial orange orchards and tea plantations the terracing is clearly the decisive factor for the reduction of sediment yields. While the effect of terraces and management for rotations containing rice is relatively balanced, land management is clearly the driving factor for a reduction in sediment amounts for perennial plants. The reason for this behavior lies in a base

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Fig. 8. Average monthly comparison of sediment releases (soil erosion amounts) for all four model setups between the years 1999 and 2008 for the entire Xiangxi catchment.

Fig. 9. Differences in sediment yield of the model setups with land management and terrace implementation in comparison to the base model.

parameterization for both, tea plantation and orange orchards, which includes a ‘harvest and kill’ operation after reaching a certain amount of accumulated heat units. After the application of this operation, biomass is removed from the respective HRUs, which affects the crop cover (C-factor) of the MUSLE. Only the altered land management parameterization was able to simulate a perennial, and therefore realistic, development of biomass. Hence, it is recommended to parameterize perennial plants in SWAT carefully and to check the plausibility of the biomass development on an HRU, especially for studies involving the analysis of sediment dynamics of a catchment. A similar issue regarding an implausible development of biomass and the LAI for forested HRUs in tropical areas was already identified and corrected by Wagner et al. (2011) and Strauch and Volk (2013), but has not found its way into the released SWAT code (Revision 622) so far.

3.4. Uncertainty, sensitivity and model performance The datasets on both, land management and terraces are based on only few observations, compared to the size of the whole Xiangxi catchment. Even though it was attempted to record the different parts and landscape units in the catchment, this was not always possible due to inaccessibility and due to time constraints during the field campaign. Therefore, the datasets are likely to exhibit a considerable amount of uncertainty. Furthermore, the aggregation of terrace conditions to the subbasin level and the random distribution of land management schemes on the agricultural areas of a mapping unit induces additional uncertainty. Nevertheless, according to the evaluation criteria of Moriasi et al. (2007) the use of these datasets is able to generate mostly good and very good model efficiencies for streamflow and sediment yield after model calibration. At the same time, the calibration results indicate that there is still considerable uncertainty in the model prediction, due to (1) the

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p-factor and r-factor values of the best simulations, and (2) due to the discrepancy between calibration and validation model efficiencies for the sediment prediction. The r-factor is a measure to assess whether the 95PPU band for the simulated data is in a meaningful range compared to the variability of the observed data (Abbaspour et al., 2004). This range can be seen as satisfactory, as long as the r-factor lies below the value of 1. The r-factors of 0.48 for streamflow and 0.92 for sediment are both below that value, and hence a meaningful interpretation of p-factor values regarding prediction uncertainties is possible (Abbaspour et al., 2004). The p-factor is a measure of the fraction of parameter uncertainty contributing to the total prediction uncertainty of the model. Its value of 58% for streamflow indicates that more than 40% of the prediction uncertainty originate from other sources than the parameter uncertainty. The remaining uncertainty is likely to be connected to the low availability of climate data for the model setup, which is limited to only three climate station. Given the topography of the catchment, this sparse coverage is not sufficient to capture local characteristics of the climate, like small-scale orographic rainfall events in the high altitudes of the catchments or in side valleys. The implementation of elevation bands can only partially counteract this source of uncertainty. Another factor of uncertainty is the implementation of the reservoir in the Gufu River with monthly target storages. As daily release rates from the reservoir were not available, the monthly target storage approach can only be an approximation of the reservoir management and cannot consider higher or lower releases on a daily basis. However, such releases can also have a high influence on the streamflow hydrograph on certain days. The monthly target storage approach cannot consider such daily variations or abnormalities in reservoir management, which induces a certain degree of prediction uncertainty. At last, the interviews with local farmers indicated a dependence of management operation timings with altitude, which was not regarded in the model setup. Due to differences in the timing of management operations in the model setup with the actual timings, which slightly vary with altitude, but also with annual date changes imposed by the use of the Chinese lunar calendar by many farmers, uncertainty is brought into the land management setup. Comparing the model setups with and without land management implementation, however, this uncertainty can be assumed to be not really relevant regarding the streamflow simulation, but could impose some uncertainty on the prediction of sediment. The p-factor for the sediment calibration has a value of 73%. With this, only about 25% of prediction uncertainty are assumed to stem from other sources besides the parameter uncertainty. However, this value is not directly comparable to the p-factor value for streamflow, as sediment was calibrated on a monthly time step. With a monthly calibration, also the uncertainty imposed by the reservoir management and its implementation by monthly target storages can be assumed as lower, as daily deviations in reservoir management do not carry as much weight for a monthly prediction. However, the uncertainty imposed by the elevation and calendric dependence of management operation timings as well as the accuracy of the climate input persists. The uncertainty of the climate input, however, is likely to be partially compensated by adjusting the USLE P- and K-factor values, which then allows for high model efficiencies during the calibration period. The discrepancy between the model evaluation statistics for calibration and validation indicates an overfitting of calibration parameters on the observed time series. This indicates a high degree of prediction uncertainty, which is not induced by parameter uncertainty, but rather an uncertainty in the process representation (van der Perk, 1997; Beven, 2006). In the present study, such uncertainty can be induced by the simplifications performed for the terrace extrapolation and the aggregation of terrace conditions on the subbasin level.

At the same time, this study lacks the implementation of tillage as management practice, which is usually also associated with changes in the runoff curve number. However, the study by Ullrich and Volk (2009) concluded, that SWAT water balance and sediment results behave less sensitive on tillage practices than on the choice of crop type as well as the planting and harvesting dates. Therefore, the lack of tillage operation implementation in this study can be seen as acceptable. 4. Conclusion The present study assessed the effects of the implementation of land management and terraces on streamflow, flow components and sediment yields in the SWAT model of the Xiangxi catchment in the TGR based on sparse field data. The results of the comparison of flow components and sediment yield for the different model setup shows that both, the implementation of land management and terraces have an effect on the water balance and the sediment amounts of agricultural areas. For streamflow predictions on the catchment scale, however, these effects do not reflect on model efficiencies considerably due to the small portion of agricultural land in the Xiangxi catchment. The land use wise comparison of flow components revealed that water yields of agricultural land are affected by the implementation of land management and terraces only modestly. The results show, that the terrace implementation has a stronger effect on water yields than the land management, which is connected to changed curve number values and slope lengths in the terrace implementation. The differences in sediment yields from agricultural land are more pronounced between the different model setups, yielding higher model efficiencies for the model setups including terraces. However, these differences in model efficiencies can be largely compensated by calibration and the assignment of a different parameter set for the models without terraces. However, the process representation of this alternative parameter set was found to be inferior compared to the parameter set of the setups with terraces. Generally, the implementation of terraces leads to stronger changes in the flow components and sediment yields than the implementation of land management. However, the hypotheses about land management and terraces having a positive effect on overall model efficiencies cannot be met sufficiently in this study. The overall performance for streamflow could not be altered strongly by implementing land management and terraces. Regarding sediment, an improvement in process representation could be met by implementing terraces in the model, while the implementation of land management showed only marginal effects in reducing sediment yields on the catchment level. Nevertheless, the results corroborate the importance of the implementation of soil conservation measures for BMP studies, even in catchments with a low share of agricultural land. At the same time the methodology, which was developed for the processing of the land management and terrace datasets, could be transferred to and tested in regions with similar landscape characteristics, and further adjusted and improved for other landscapes, where cadastral and statistical data are not sufficiently available. The introduction of this new method to consider land management and terrace information based on sparse field data is therefore an important step for the improvement of eco-hydrological modelling efforts towards BMP assessment in regions without high data availability. Furthermore, the proposed field method is a suitable alternative to excessive analyses of statistical data or remote-sensing-based assessments for the extrapolation of management and soil conservation data for large

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Please cite this article in press as: Strehmel, A., et al., Field data-based implementation of land management and terraces on the catchment scale for an eco-hydrological modelling approach in the Three Gorges Region, China. Agric. Water Manage. (2015), http://dx.doi.org/10.1016/j.agwat.2015.10.007