Modelling the hydrological processes of a Chinese lowland polder and identifying the key factors using an improved PHPS model

Journal of Hydrology 578 (2019) 124083

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Research papers

Modelling the hydrological processes of a Chinese lowland polder and identifying the key factors using an improved PHPS model

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Renhua Yan, Junfeng Gao , Jiacong Huang Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, PR China

A R T I C LE I N FO

A B S T R A C T

This manuscript was handled by Huaming Guo, Editor-in-Chief, with the assistance of ChongYu, Associate Editor

Improved hydrological processes simulation and identification of determinants are urgently needed in risk management to reduce socio-economic losses resulting from polder water problems. This paper improved the lowland Polder Hydrology and Phosphorus modelling System (called PHPS2.0) by coupling the PHPS, Modified Universal Soil Loss Equation (MUSLE), and Integrated Catchments model of Phosphorus dynamics (INCA-P). Then, based on the PHPS2.0 and data collected from the Jianwei polder in southeastern China, a boosted regression tree (BRT) algorithm was used to explore the key factors determining the temporal variations in polder discharge and phosphorus export. The results showed that PHPS2.0 achieved excellent agreements between the simulated and observed discharge, surface water and phosphorus. The model performance was significantly improved because field-scale phosphorus dynamics were considered. The water and phosphorus balances exhibited substantial seasonal changes, especially in terms of fertilization, crop harvest, and discharge. The temporal change in catchment discharge mainly depended on precipitation and evaporation, which accounted for 63.7% of the discharge variations. In contrast, the temporal variation in phosphorus export was determined by the crop cover and fertilizer application, which accounted for 75.4% of the phosphorus variation. To mitigate the phosphorus loss to downstream freshwater, it is recommended to remain crop residues, reduce the overuse of phosphorus chemical fertilizers, and draft emergency plans. This study expands the knowledge of the relationship between polder environmental factors and hydrological processes and provides guidance for pollution controls.

Keywords: Lowland polder Model improvement Key factor Phosphorus dynamics

1. Introduction Humans have a long history of successfully reclaiming new land from bodies of water, leading to the spread of numerous polders in floodplains, especially in river deltas, seaside, and lakeside zones (Koch et al., 2013; Shi et al., 2018; Warner et al., 2018; Xu and Blussé, 2018). Polders are lowland areas surrounded by dikes that form a controlled hydrologic unit. The polder water exchange with external water occurs mainly through artificial devices, such as pumps and culverts. The increase in global climate change raises the risk of flooding and water pollution in lowland polders, especially those under a monsoon climate system with large spatial and temporal oscillations. Moreover, the low elevations, slight slopes, dense populations, and advanced agriculture make polder vulnerable to heavy socio-economic losses from natural disasters and environmental deterioration. Therefore, improving risk management is necessary to protect polders from many types of deterioration. For alleviating natural and environmental hazards, hydrological

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models are commonly used as an effective tool for risk assessment. These model results provide a higher level of understanding of the relationship between driving forces (e.g., precipitation, evaporation, vegetation management, and fertilizer application) (Bornschein and Pohl, 2018; Sterk et al., 2016; Yu et al., 2018) and hydrological processes (i.e., discharge and nutrient processes), and can be used to identify the crucial factors, which are prerequisites for meeting water-related challenges. However, most of the existing hydrology models such as Soil and Water Assessment Tool (SWAT, Neitsch et al. (2011), Agricultural Nonpoint–Source Pollution (AGNPS, Young et al. (1989)), Xinanjiang (Zhao, 1992), and GR4J (Perrin et al., 2003), were designed mainly for freely draining catchments, thereby making them unsuitable for regulated lowland polders with shallow groundwater tables and pumps. Capillary forces related to shallow groundwater tables in lowlands substantially affect the vertical soil moisture profile, which is replenished by capillary forces during dry periods. Therefore, the vadose zone is closely linked to the groundwater zone, forming a tightly coupled system (Brauer et al., 2014; Hopmans and Van Immerzeel,

Corresponding author at: Nanjing Institute of Geography & Limnology, Chinese Academy of Sciences, 73 East Beijing Road, Nanjing 210008, PR China. E-mail address: [email protected] (J. Gao).

https://doi.org/10.1016/j.jhydrol.2019.124083 Received 3 December 2018; Received in revised form 21 August 2019; Accepted 27 August 2019 Available online 27 August 2019 0022-1694/ © 2019 Elsevier B.V. All rights reserved.

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paper. In case the pond water level exceeds a specific threshold level, the surplus floodwater is exported by a culvert to the surrounding rivers during the non-rice growth period or by a flood pump during the rice growth period. During the non-rice growth period, the culvert is opened to export surplus water to the surrounding river by gravity because the farmland does not need much water. During the rice growth period, the culvert has to be closed because the polder surface water level is lower than the water level of the surrounding river, and rice growth needs substantial water. In case of floodwater, the pumping station starts to pump water from the polder to the surrounding river during this period. In contrast, during the dry period, irrigation is often implemented by pumping water from the surrounding rivers into the polder to meet the rice water requirements in paddy fields. For dryland, no irrigation is supplied to the field because wheat and oilseed rape have low water requirements. Rice-wheat rotation is the cropping system in the paddy fields, wherein rice is grown from June to October and wheat is from November to May. Rice seedlings are transplanted into the paddy field in early June. A nitrogen fertilizer application rate of 450 kg/ha/yr and phosphorus fertilizer application rate of 7.88 kg/ha/yr are usually supplied to farmland for increasing grain yield. Nevertheless, only a portion of the fertilizer is utilized by the crops due to over-fertilization, and some fertilizers are lost to surface water via runoff and soil erosion (Huang and Gao, 2015), thereby increasing the risk of eutrophication. Taihu Basin is an area with serious eutrophication, which is partly due to agricultural non-point source pollution from lowland areas (Guo et al., 2004). Residential areas are another pollution source due to rural living and household scatter breeding, of which the pollutants are exported to the surface water due to residential rainfall-runoff. There is no industrial pollution or a sewage treatment system in this area. The Jianwei polder belongs to a semi-tropical climate zone with an annual mean temperature of 17 °C and an annual pan evaporation of 849 mm. The average annual precipitation is 1082 mm and more than 70% of annual precipitation occurs between April and September. Such a high rainfall frequency during this period is more likely to cause soil erosion. Daily precipitation data were obtained from a rain gauge on site, and daily pan evaporation was obtained from the nearest national weather station called Liyang station (8.5 km away from the polder). The land use data were interpreted from Systeme Probatoired Observation de la Terre (SPOT) imagery. Discharges at the outlet (Qout) consisted of pumping and culvert discharges. Daily pumping drainage was obtained from the pumping capacity and operational time recorded by a local water manager. Hourly culvert drainage was monitored by an H-flume with a water level gauge and stage-discharge relation function. Soil texture data were derived from a particle size analysis of the soil samples at sites G1, G2, and G3 by our research group (Table S1) (Yan, 2016). The vegetation coverage index of aquatic plants was measured monthly at sites V1, V2, V3, V4, and V5. The water quality data (particulate phosphorus PP, dissolved phosphorus DP and total phosphorus TP concentrations) were obtained from water sampling at sites W1, W2, W3, W4, W5, W6, W7, W8, W9, and W10, and sediment sampling was performed at sites S1, S2, and S3. Water samples were collected biweekly at sites W1, W2, and W3, and those at sites W4, W5, W6, W7, W8, W9, and W10 were collected from a rainfall-runoff event on 24 August 2013. The sediment samples were collected once in 2013. Additionally, to test the model’s capability to simulate the surface water level, an automatic water level gauge was placed in the pond site (SL1) to collect the hourly data. The study period spanned Jan. 2015–Dec. 2016. More detailed descriptions of the data collection are provided in Yan et al. (2016, 2017).

1988; Stenitzer et al., 2007). Due to the wide distribution of surface water and shallow groundwater, groundwater discharge and backwater infiltration in lowlands often occur at the groundwater-surface water interface (Brauer et al., 2011). To create favourable water conditions for rice growth, irrigation and drainage are executed to ensure ponding water levels in paddy rice fields that are neither very low nor very high (Yan et al., 2016). These processes are not considered in the models, and errors occur when these models are applied to lowland areas. Faced with these problems, a coupled system called the lowland Polder Hydrology and Phosphorus modelling System (PHPS, Yan et al. (2017)) was proposed based on the studies by Brauer et al. (2014), Huang et al. (2016), and Yan et al. (2016). Compared to previous version of models, the PHPS expands its application to East Asian polders with paddy rice fields and pumping stations, such as the polders in China, Korea, and Japan with Multiple land use types. The PHPS takes into account some processes that are essential for lowland polders with paddy rice fields and pumping stations, including the tightly coupled groundwater-unsaturated zone, surface water-groundwater feedback, frequent paddy irrigation and drainage, discharge controlled by culverts and pumping, and the complicated physical and biochemical cycles of phosphorus in surface water. However, the detailed phosphorus dynamics are not considered at the field scale; thus, the model fails to fully reflect the phosphorus dynamics in response to environmental changes (such as irrigation, drainage, and fertilization) in farmlands. As a result, the model accuracy is reduced and the identification of critical factors for hydrological processes is limited. Based on the abovementioned analysis results, this study aimed to (1) integrate the PHPS with a suitable field-scale phosphorus model and soil erosion model to provide a better description of hydrological and phosphorus-related processes that will overcome the mentioned disadvantages, (2) apply the improved PHPS as well as boosted regression trees (BRT) to identify the key environmental factors that control the culvert and pump discharges at the polder outlet as well as phosphorus loss processes by using data collected from the Jianwei polder. The improved model and BRT analysis results will provide more accurate modelling results and can be used to identify the critical factors for hydrological changes. This study will provide some useful insights into the hydrological processes and then deepen the necessary knowledge to improve polder risk management. 2. Materials and methods 2.1. Study area and data The Jianwei polder was selected here because it has a detailed dataset that includes water quantity and quality data, which has been applied to the PHPS development process (Yan et al., 2017). The Jianwei polder covers an area of 0.106 km2 and is settled in the northwestern part of the Taihu Basin in China (Fig. 1). The percentages of paddy fields, drylands, surface water (ditches and ponds), and residential areas are 50.1%, 21.7%, 19.2%, and 9%, respectively. Thus, the polder is a representative case for Chinese agricultural polders due to the dominance of farmland. The soil in the polder is silt loam. The land gradually descends from the dike to the centre. The polder is inhabited by approximately 100 permanent residents. An enclosed dike separates the polder from the surrounding rivers to prevent surrounding floodwater. Consequently, the water and nutrient exchanges between the polder and the surrounding rivers are controlled by water managers through a culvert and three pumps [culvert and pump P1 (numbered pump) used for flood control during the non-rice growth period and rice growth period, respectively; pumps P2 and P3 used for irrigation]. Rainfall-runoff and nutrients from the various land use areas are first transported through a ditch and then stored in the pond. The water flow from each land use due to rainfall was called rainfall-runoff or runoff; the water flow from the polder outlet through a human-controlled pump and culvert was called discharge in this 2

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Fig. 1. Location of the study area, and its land use and sampling sites (revised from Yan et al. (2017)).

2.2. Development of lowland polder hydrology and phosphorus modelling system 2.0

Table 1 Optimized values of model parameters for the Jianwei polder. Parameter

2.2.1. Lowland polder hydrology and phosphorus modelling system (PHPS) The PHPS is aimed at highly regulated lowland polders with shallow groundwater. This model considers not only the important lowland characteristics (e.g., groundwater-surface water interaction, saturatedunsaturated zone coupling, and phosphorus-related physical and biological processes in the surface water), but also human-controlled processes (frequent artificial irrigation and drainage of paddy field, culvert discharge during the non-rice growth period and pumping discharge during the rice growth period due to floodwater (Yan et al., 2016)). Therefore, this model was chosen in this study to describe the polder hydrological processes. This modelling system consists of four water balance modules (i.e., dryland, paddy field, surface water, and residential area water balance modules) and a phosphorus balance module. The total phosphorus (TP) in this model is separated into particulate phosphorus (PP) and dissolved phosphorus (DP). The PHPS has advantages over other models (e.g., SWAT, GR4J, and AGNPS), such as a simple model structure (only five mass-balance modules), few parameters to be calibrated (only nine water quantity parameters and five water quality parameters; Table 1), and time-saving calculation (one-year daily modelling takes only 1 min, which is shorter than the runtime of other models such as the 10 min required for Xinanjiang). This system was tested in the Jianwei polder, Eastern China (Fig. 1) using a one-year hydro-meteorological dataset and was proven to perform well in discharge and phosphorus modelling (Yan et al., 2016, 2017). However, the PHPS doesn’t consider the phosphorus dynamics in drylands and paddy fields (Yan et al., 2017). Consequently, the observed phosphorus concentrations of runoff from multiple land use types must be provided by the model user in the current model version. This need for observed multi-landuse runoff phosphorus data does not

Water quantity cw1 (mm) cw2 (mm) cv1 (h) cv2 (h) cG1 (mm h) cG2 (mm h) cG3 (mm h) cQ1 (h) cQ2 (h) Water quality Ksetting KresuspensionMax Kmineralization Krelease Kuptake

Meaning

Optimized value

Wetness index parameter for paddy field Wetness index parameter for dryland Vadose zone relaxation time for paddy field Vadose zone relaxation time for dryland Groundwater reservoir constant for paddy field Groundwater reservoir constant for dryland Groundwater exchange constant between paddy field and dryland Quickflow reservoir constant for paddy field Quickflow reservoir constant for dryland

150 100 20 11 75 × 106

Settling rate of particulate phosphorus to sediment Maximum resuspension rate of particulate phosphorus from sediment Mineralization rate of particulate phosphorus Releasing rate of dissolved phosphorus from sediment Phosphorus uptake rate of plant

25 × 106 80 × 106 3 1 2.86 × 10−4 8.29 × 10−5 3.13 × 10−4 1.40 × 10−3 1 × 10−3

facilitate driving the model and limits the further increase in the model precision. Thus, the model needs to be improved by incorporating some modules that describe the field-scale phosphorus dynamics in drylands and paddy fields. 2.2.2. Modified universal soil loss equation (MUSLE) The MUSLE is one of the most widely used soil erosion models around the world and provides a more accurate modelled result than the original version (USLE) (Sadeghi et al., 2014; Williams and Berndt, 3

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2.3. Model calibration and validation methods

1977). This model has been incorporated in SWAT and Environmental Policy Integrated Climate Model (EPIC, Williams and Singh, 1995) models. The MUSLE (Neitsch et al., 2011) can be described as follows:

Sed = 11.8·(0.001·fQS ·qpeak ·A)0.56·K ·C·Pcontrol·LS·CFRG·100

In this model, nine water quantity parameters (cw1, cw2, cv1, cv2, cG1, cG2, cG3, cQ1, and cQ2) and five water quality parameters (K resuspensionMax , K settling , K mineralization , K release, and K uptake ) were adjusted to represent catchment-specific characteristics (Table 1). The meanings of each calibrated parameter can be seen in Table 1. The main input dataset for the model consists of meteorological, land use, discharges at the polder outlet, soil texture, vegetation coverage, and water quality data, as discussed in Section 2.1. The observed daily discharges at the catchment outlet and the daily surface water level for 2015 and 2016 were employed to calibrate and validate the water quantity parameters (Table 1), respectively. Afterwards, based on the optimum values of water quantity parameters, the biweekly TP of the surface water (TPS) at site W1 for the years 2015 and 2016 (Section 2.1) were applied to calibrate and validate the water quantity parameters, respectively. HydroPSO (Zambrano-Bigiarini and Rojas, 2013), which is a modelindependent particle swarm optimisation method for environmental models, was used to automatically calibrate the parameters. This method is not easily influenced by discontinuities in the response surface and more readily obtains the optimum parameter values than other gradient-based techniques. Moreover, we have the experience of this method to calibrate original model (Yan et al., 2017). More information about HydroPSO is provided in the Supplemental material. The Nash–Sutcliffe index [NS, Nash and Sutcliffe (1970)] of the discharges, surface water level and TP concentration of the surface water (TPS) were used as the objective function to calibrate the water quantity and quality parameters, respectively. The NS, coefficient of determination (R2), index of agreement [d, Willmott (1981)], root mean square error (RMSE), and mean error (ME) were used to evaluate the model performance.

(1)

where Sed is the amount of soil erosion for a given land use type (kg/d), fQS is the quickflow (surface runoff, mm/d) of farmland, qpeak is the daily peak runoff rate (m3/s), and A is the catchment area (m2). The values of 0.001 and 100 are the factors for converting miscellaneous units to uniform units. The estimates of fQS and qpeak are based on the simulated results of the PHPS. K, C, Pcontrol, LS, and CFRG denote soil erodibility, vegetation cover and land management, soil erosion control practice, topographic (slope length and slope steepness) and coarse fragment factors, respectively. For the calculation methods of these factors, the reader can refer to the Supplemental material. Based on the daily calculated soil erosion, the PP concentration of runoff water can be computed as follows:

PPQS =

TPPQS fQS ·A

TPPQS = concsedP·Sed·λP:sed

(2) (3)

where PPQS is the PP concentration of the surface runoff (mg/L), TPPQS is the amount of PP loss with sediment to the surface water in surface runoff (kg/d), concsedP is the concentration of phosphorus attached to sediment (kg/kg soil, measured), and λP : sed is the ratio of particle phosphorus transported with the sediment to the concentration of the soil surface layer, which is estimated with a method proposed by Menzel (1980).

2.2.3. Integrated catchments model of phosphorus dynamics (INCA-P) INCA-P was originally developed by Wade et al. (2002) as a processbased and mass-balance model for describing the daily phosphorus dynamics and delivery within catchments. This model has been used in many catchments around the world over the past decade (JacksonBlake et al., 2016). The model accurately describes the phosphorus dynamics in soil systems in terms of land use types, phosphorus forms (such as DP consisting of dissolved organic phosphorus [DOP] and dissolved inorganic phosphorus [DIP]), and phosphorus sources. INCAP was therefore coupled with the PHPS in this study to model DP in the surface runoff. More details on the model equations are given in Wade et al. (2002) and Table S2.

2.4. Boosted regression trees (BRT) analyses The relationships between discharge and phosphorus export and environmental factors were explored by BRT analysis, which is a stateof-the-art machine learning method (Elith et al., 2008; Yang et al., 2016). BRT integrates regression trees with a boosting technique to increase the predictive accuracy, where a host of tree models are combined with a forward and stage-wise procedure. The BRT is applicable to different types of predictor variables and adapts for missing data. Prior data transformation and discarding the outliers are not necessary in this method. BRT automatically handles interaction effects between predictors and has a better predictive performance than most statistical methods. In this study, 10 environmental variables, namely, P, Ir1, hQ,min1, hQ,max1, hQ,flood1, fXG, fXS, hS,min, ETpot, and hSstop pump were selected as the predictor variables to identify the environmental variable that was key to the discharge (Table 2). In addition to these variables, C1, C2, TDIPfert1, TDIPfert2 , and VI were used for phosphorus export. The discharge and phosphorus export data used in BRT were derived from the continuous output results of PHPS2.0 and the above-mentioned environmental variables were from field investigation from 2015 to 2016. Four parameters including loss function (LS), learning rate (LR), tree complexity (TC), and bag fraction (BF) are need to be defined by the user during BRT analysis (Elith et al., 2008). After multiple tests of different parameter combinations, the Gaussian function, 0.0001, 1, and 0.5 were set as the parameter values for LR, TC, and BF, respectively, to obtain the minimum deviation for discharge, whilst the Gaussian function, 0.001, 1, and 0.5 were set for these parameter values to achieve the minimum deviation for phosphorus export. All combinations produced no fewer than 1000 trees based on a 10-fold crossvalidation method. The BRT analysis was performed in R based on the “gbm” package (Ridgeway, 2010) and a supplemental script written by Elith et al. (2008).

2.2.4. Coupling of PHPS, MUSLE, and INCA-P MUSLE and INCA-P were coupled with PHPS on the R language platform to enhance the model’s capacity to describe the PP and DP dynamics within the farmland, respectively. R is an excellent programming language and environment for computing and data analysis (Venables et al., 2009) and has been used to build many hydrological processes models (Brauer et al., 2014; Fuka et al., 2014). Here, the PHPS was used to simulate the rainfall-runoff and phosphorus transport processes. Based on the simulated runoff by the PHPS, the soil erosion owing to rainfall-runoff was calculated with the MUSLE. INCA-P was used to represent the field-scale phosphorus dynamics. The improved version of PHPS is called PHPS 2.0, and its conceptual structure for field phosphorus processes in paddy fields is shown in Fig. 2. Table S2 demonstrates the model equations, variables, and parameters for field phosphorus processes. Detailed descriptions of the PHPS 2.0 processes are available in the Supplemental material. The dryland processes are not displayed here because they are similar to those of paddy fields, except for lacking equations to describe hydrological and phosphorus dynamics caused by artificial irrigation and drainage. 4

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Fig. 2. Conceptual structure of coupling of PHPS, MUSLE, and INCA-P for the simulation of field phosphorus processes in a paddy field. The green variables are the state variables. The subscript 1 represents a variable belonging to the paddy field. Descriptions of the processes in Fig. 2 can be found in the Supplemental material. Descriptions of all variables are listed in Table S2. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

3. Results

3.2. Water and phosphorus budgets

3.1. Calibration and validation results

For the polder, the average annual value of the total input water reached 1732 mm/yr (Fig. 5). Among the input components, the annual precipitation of 1535 mm/yr contributed the largest proportion to the total input water (89%), followed by the irrigation of 195.6 mm/yr (11%). Most of the annual precipitation fell during the summer and autumn, and a large difference in the amount of annual precipitation was observed between 2015 (1445 mm/yr) and 2016 (1627 mm/yr). Irrigation primarily occurred in summer and autumn (June to October), which belonged to the rice growth period. Irrigation is executed to maintain a continuously inundated field for water-intensive rice growth (more information about the mechanism of irrigation, monthly precipitation and temperature is provided in the Supplementary material) and is not done for drought-tolerant wheat, which is grown from November to May. The average annual amount of the total output water was 1722.2 mm/yr. The actual evapotranspiration of 803.7 mm/yr and discharge of 792.1 mm/yr were the two largest output components, accounting for 47% and 46% of the total output water, respectively. Seepage accounted for 7% of the output water. Similar to precipitation, discharge and evapotranspiration mostly occurred in summer and

The model reproduced the discharge (at the polder outlet) and surface water levels (at site SL1 in the pond) well during calibration and validation (Fig. 3a and b). The NS and R2 values for the discharge were larger than 0.82 in the calibration, and those for surface water level were 0.649 and 0.735, respectively (Table 3). The validated results for discharge were as good as the calibrated results, with NS and R2 values of approximately 0.82. Most of the peak and low TP values in surface water were captured well. The NS and R2 values were 0.72 and 0.83 in the calibration period, respectively, and 0.73 and 0.74 in the validation period, respectively (Fig. 4a, b and Table 3). The model also provided excellent agreement between the modelled and observed PP in surface water. Although DP was not modelled better than PP because of its high chemical reactivity compared to PP, the model still achieved acceptable precision in DP modelling in terms of the statistical assessment indices. The optimized values of the model parameters are listed in Table 2.

Table 2 List of the relative importance of each environmental factor for discharge and phosphorus export as depended on the boosted regression trees (BRT) analyses (— denotes the value is 0 or this factor is not related to the corresponding hydrological variable). Environmental factor

Meaning

Discharge (Rank)

Phosphorus export (Rank)

P (mm/d) ETpot (mm/d) Ir1 (mm/d) hQ,min1 (mm) hQ,max1 (mm) hQ,flood1 (mm) fXG (mm/d) fXS (mm/d) hS,min (mm)

hSstop pump (mm)

Daily precipitation Daily pan evaporation Irrigation water into paddy field Lower limit of appropriate water level for the paddy field Upper limit of appropriate water level for the paddy field Maximum submergence-tolerant water level for rice growth Seepage Surface water supply/extration Surface water level to start culvert drainage or pump drainage Surface water level to stop pump drainage

51.5% (1) 12.2% (4) — 0.2% (5) 0.1% (6) 20.9% (2) — — 15.1% (3) —

14.2% (4) 3.7% (6) — — — — — — 5.1% (5) —

C1 C2 TDIPfert1(kg/m2) TDIPfert2 (kg/m2) VI

Vegetation cover and management factor of paddy field Vegetation cover and management factor of dryland Dissolved inorganic phosphorus input to paddy field caused by fertilization Dissolved inorganic phosphorus input to dryland caused by fertilization Vegetation coverage index of aquatic plant in surface water

— — — — —

31.9% (1) 19.0% (3) 24.5% (2) — 2.1% (7)

5

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Fig. 3. Daily simulated and observed discharges (Q) and surface water level (hS), and other model outputs for the calibration period (a) and for the validation period (b) in the Jianwei polder. Qobs and Qmod indicate the observed and modelled discharge, dV represents the storage deficit of the vadose zone, dG represents the groundwater table, and cD represents the channel depth. dV and dG are the area-weighted means of the paddy field and dryland, respectively. The parameters with subscript 1 represent the parameters related to the paddy field, while those with subscript 2 represent the parameters related to dryland.

decreased substantially, attenuating the phosphorus export loads to external rivers. Moreover, the temporary storage of water in the pond increased the retention time for physical and biogeochemical removal processes, thereby significantly diminishing the phosphorus concentration in the surface water and discharge. Fertilization of 5.67 kg/ ha/yr (converted from 7.88 kg/ha/yr for the farmland) and domestic sewage and human excreta of 1.32 kg/ha/yr for the total polder area were the two major contributors to the phosphorus input, being responsible for 64.2% and 15.0%, respectively. Crop harvest of 2.29 kg/ ha/yr and discharge of 1.29 kg/ha/yr constituted most of the phosphorus output (64% and 15%, respectively). Seasonal changes in input components and output components were substantial, especially in fertilization, crop harvest, and discharge. A larger amount of phosphate chemical fertilizer amount (summer: 3.47 kg/ha, autumn: 2.21 kg/ha) is applied by farmers to the polder before planting rice and wheat in summer and autumn than other seasons, leading to increased amounts of phosphorus in the polder during this period. More phosphorus (5.67 kg/ha/yr) is extracted from the polder through wheat harvest in early summer and rice harvest in late autumn than in other seasons.

autumn (63.9% and 79.5% of the annual amount, respectively) due to the higher water availability (much precipitation), higher temperature, and more vigorous transpiration than in other seasons. Furthermore, large differences were found in the annual precipitation and discharge between the two years. Fig. 6 displays the annual and seasonal phosphorus balances of the entire polder. The data for crop harvest, fertilization, domestic sewage and human excreta were derived from interviewing local farmers and administrators and referring to the yearbook. These data have good accuracy in comparison with the studies in nearby sites (Pan et al., 2013; Wang, 2010). The remaining data (actual evapotranspiration, discharge, phosphorus input by irrigation, and phosphorus export) were obtained from the PHPS2.0 modelled results. The average annual amount of the TP input amounted to 8.02 kg/ha/yr, whereas the total output due to crop harvest, discharge and seepage was only 3.74 kg/ha/ yr. As a result, a net retention of 4.28 kg/ha/yr in the polder soil and surface water system was found, implying that the polder could remove phosphorus (Yan et al., 2018). Compared to freely draining catchment, polder discharge intercepted by the pumping station and embankment

Table 3 Statistical assessment of the goodness-of-fit between modeled and observed results for PHP2.0 model. R2

NS

Q hS PPS DPS TPS

d

RMSE

ME

Calibration

Validation

Calibration

Validation

Calibration

Validation

Calibration

Validation

Calibration

Validation

0.877 0.649 0.649 0.593 0.716

0.820 0.424 0.563 0.472 0.731

0.882 0.735 0.711 0.659 0.825

0.822 0.608 0.621 0.492 0.744

0.967 0.918 0.886 0.885 0.904

0.950 0.872 0.875 0.827 0.908

2.766 130.633 0.019 0.017 0.028

4.765 166.363 0.023 0.028 0.030

0.547 −15.433 0.008 0.006 0.014

0.299 −9.151 −0.007 0.003 −0.004

6

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Fig. 4. Simulated and observed phosphorus in the surface water at W1 (Fig. 3) for the calibration period (a) and for the validation period (b). PPs, DPs, and TPs denote the particulate phosphorus, dissolved phosphorus, and total phosphorus in surface water, respectively.

Fig. 5. Composition of annual water budget (left) and seasonal change in the water balance components (right). For the left panel, input is the water entering the polder, and output denotes the water exiting the polder. For the right panel, positive values (upper part) denote the input components, whereas negative values (lower part) are the output components. All values were obtained from the corresponding component divided by the polder area.

Moreover, 1.12 kg/ha/yr of phosphorus output caused by discharge was also substantial in the same period due to the increased discharge.

amount of phosphorus export. Precipitation contributed 14.2% of the variation, illustrating an even smaller effect than illustrated by C2.

3.3. BRT analysis result

4. Discussions

The precipitation and pan evaporation had relative effects of 51.5% and 12.2% on discharge Q variation, respectively (Table 2). Therefore, weather elements contributed to 63.7% of the Q variation. Because the Chinese polders are commonly situated in humid subtropical areas, precipitation and soil moisture condition are the most influential variables for polder discharge. Water management factors explained 36% of the discharge (Q) variation, of which 20.9% was the hQ,flood1 and 15.1% was hS,min. A portion of 31.9%, 24.5%, and 19.0% of the variation in phosphorus export was explained by C1, TDIPfert1, and C2, respectively. C1 represents the vegetation cover and management factor associated with the extent of vegetation cover and prior crop residues, which defended the soil against erosion for the paddy field. Thus, it significantly affected the PP transport related to soil erosion. TDIPfert1 is the amount of inorganic fertilization inputted to paddy fields and thus influences the

4.1. Model performance As shown in Fig. 4a and b, the improved model captured the magnitudes and fluctuations of observed phosphorus better than the original model. Tables 3 and 4 showed that the values of most indices of the improved model were greater than those of the original version. These results implied that the performance of the PHPS model for the phosphorus simulation was significantly improved because field-scale phosphorus dynamics were considered. According to the result of Wellen et al. (2015) through an overview of 257 previous case studies of watershed water quality modelling, the improved PHPS model performed better than most of these previous cases. This performance was remarkably good, especially for lowland polders subjected to the strong human intervention. The results for DP were not as good as those for TP and PP. 7

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Fig. 6. Composition of annual phosphorus budget (left) and seasonal change in the phosphorus balance components (right). For the left panel, input means the phosphorus entering the polder and output denotes the phosphorus exiting from the polder. For the right panel, positive values (upper part) denote the input components, whereas negative values (lower part) are the output components. All values were obtained from the corresponding component divided by the polder area.

4.2. Major influential factors on discharge and phosphorus export

4.3. References from the current study for risk management

The considerable influence of weather variables (63.7%) on the discharge variation indicated that the precipitation and evaporation were the crucial environmental variables that affected the temporal variation in polder discharge (Table 2). Water resource management factors such as hQ,flood1 (20.9%) were also important factors for the discharge (Q) variation. hQ,flood1 denotes the maximum submergencetolerant water level for paddy fields, above which the water level will threaten the rice growth. In general, once the water depth in paddy fields exceeds hQ,flood1 during high-intensity precipitation, manual drainage is implemented to remove redundant floodwater until the water depth decreases to hQ,max1. A larger hQ,flood1 led to the removal of more redundant water (amounts to hQ,flood1 minus hQ,max1) into surface water, thereby increasing the peak value of polder discharge. Unlike discharge, which primarily depended on natural variables, phosphorus export was governed by agricultural practices (C1, TDIPfert1, and C2) (Table 2). C1 was the most important factor (31.9%), followed by TDIPfert1 (24.5%) and C2 (19.0%). A large C1 represented poorer vegetation cover and few available crop residues (Sadeghi et al., 2014), which facilitated an increase in soil erosion, thereby increasing the phosphorus that adsorbed on the soil particles scoured into freshwater. TDIPfert1 is the inorganic fertilization amount. In fact, fertilization is usually the largest source of phosphorus for agricultural polders (Fig. 6). Therefore, the amount of phosphorus exports was highly sensitive to a larger TDIPfert1 because of an increase in phosphorus availability. The importance of precipitation decreased from the first to the fourth order in the phosphorus export relative to that in the discharge.

4.3.1. Increasing cropland cover/residue The BRT analysis results revealed that vegetation cover substantially influenced the phosphorus export processes, indicating that vegetation cover was the most important factor controlling the phosphorus load exported in the discharge. Crop residue is an important method of increasing the vegetation cover and preserving nutrients. On the one hand, protecting crop residue (stalks and stubble left in the farmland after the crops have been harvested) plays a significant role in increasing the vegetation cover, especially during the early crop recovery and tillering period. This increasing cover strengthens the ground roughness, decreases the flow speed, diminishes erosion energy, and mitigates the soil and its attached phosphorus losses (Prasad and Power, 1991). On the other hand, wheat residue retains approximately 25–30% of the phosphorus uptake by wheat, and rice straw contains 0.7–1.2 kg/ton phosphorus based on dry weight (M Duxbury et al., 2000) for the rice-wheat rotation system. If no-burning, no-till or reduced till agriculture practices are implemented, the remaining residue would reduce the risk of the nutrient and phosphorus losses. However, residue burning is commonly practiced in rural zones in China as a fast and convenient way to wipe out the crop residue (1.10–1.58 × 108 ton) (Wang et al., 2016a). Consequently, a portion of the phosphorus attached to residue ash is lost to its surrounding water and less organic material is returned to the soil under subtropical monsoon climate (Miao et al., 2011). Therefore, preventing farmers from burning crop residue is greatly beneficial for promoting yields, managing risk, and protecting the environment.

Table 4 Statistical assessment of the goodness-of-fit between modeled and observed results for PHP1.0 model. R2

NS

PPS DPS TPS

d

RMSE

ME

Calibration

Validation

Calibration

Validation

Calibration

Validation

Calibration

Validation

Calibration

Validation

0.173 0.017 0.299

0.112 0.060 0.584

0.514 0.360 0.438

0.426 0.402 0.566

0.769 0.425 0.749

0.777 0.743 0.888

0.029 0.029 0.043

0.033 0.038 0.037

−0.018 −0.001 −0.019

−0.012 0.021 0.009

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good as that of TP and PP because of the insufficient description of DPrelated biogeochemical processes and conversion between inorganic and organic forms. Many mature empirical formulas (such as SWAT and Environmental Fluid Dynamics Code (EFDC)) that provide a detailed description of DP dynamics can be easily incorporated into the PHPS. Thirdly, this study mainly revealed the influence of environmental factors on the temporal distribution of discharge and phosphorus loss based on a typical polder. Whilst investigating the effects of environmental factors on the spatial variation in hydrological processes based on many lowland polders, may also provide insights into efficient water resource and quality management. In our future work, a dataset including hydro-metrological, land use, population density, and agricultural management from all the polders (2539 polders, covering 10 630. 91 km2) in Taihu Basin will be collected and used to quantify the relationships between environmental factors (e.g., elevation, land use, soil water storage capacity, and slope) and the spatial patterns of hydrological processes at a large basin scale. Both spatial and temporal analyses can provide comprehensive references for water resource management.

4.3.2. Reducing excessive P fertilizer application to the optimized value required by crop growth In this study, both the modelled results of the phosphorus budget and the BRT analysis results proved that the phosphorus chemical fertilizer plays a dominant role in phosphorus export from polders to the surrounding environment. The Jianwei polder produced a TP concentration of 0.11–0.31 mg/L in the discharge at the polder outlet (polder discharge has the same phosphorus concentration as site W1 in the pond) under a 7.88 kg/ha/yr application rate of phosphorus fertilizer (Huang et al., 2016) to farmlands. In comparison, the experiment sites from Changshu and Anzhen in Taihu Basin with the same cropping system generated a TP of 0.75–1.75 mg/L in the discharge at the polder outlet under a phosphorus fertilizer application rate of 50 kg/ha/yr, and 0.91–4.94 mg/L under a phosphorus fertilizer application rate of 230 kg/ha/yr, respectively (Zhang et al., 2003a,b). The results showed that the amount of phosphorus chemical fertilizer determines the phosphorus concentration and load of catchment discharge. Over-application of fertilizer [52.39 kg/ha/yr deduced from (Wang et al., 2012)] is commonly practiced in the other intensive agricultural polders of Taihu Basin (Miao and Zhang, 2011; Wang et al., 2016b). This situation reduced the average coefficient of fertilizer utilization to be only 10%–25% (Lu, 1998; Wei, 2014) and increased the risk of P loss. Experiments have been conducted to find the optimum amount of phosphorus fertilizer, which made agriculture efficient and environmentally friendly (Miao and Zhang, 2011; Wang et al., 2001, 2018, 2016b; Zhang et al., 2003b). For instance, the four-year pot experimental results of Wang et al. (2015, 2018) using 10 paddy soils with varied phosphorus levels (rich, moderate, and low content) showed that a phosphorus fertilizer reduction of approximately 20–50% was feasible to maintain the high crop yield for the rice-wheat rotation in Taihu Basin. Miao et al. (2011) recommended that the sustainable nutrient management mode should accurately match field-to-field and temproal variations in nutrient supply and crop demand. If the over-application of fertilizer is abandoned and the optimal amount of fertilizer is applied, the risks of phosphors loss from farmland and eutrophication of downstream rivers will be significantly reduced.

5. Conclusions This study coupled PHPS, INCA-P, and MUSLE to better account for the hydrological and phosphorous responses to natural and anthropogenic factors. The model was calibrated and validated using data collected from the Jianwei polder, and the results indicated good performance. On the basis of the modelled results of the coupled model, BRT analysis was used to quantify the relationships between environmental factors and hydrological processes. The NS and R2 of the discharge were larger than 0.82 in the calibration, and approximately 0.82 in the validation. The NS and R2 of TP reached 0.72 and 0.83 in the calibration and 0.73 and 0.74 in the validation, respectively. The good consistency between the simulated and the observed daily values for discharge, surface water and different phosphorus forms proved that the model can provide good estimates of polder hydrological processes. The comparison of model accuracy between the improved model and the original model implied that the phosphorus modelling performance was significantly improved in this study. Fertilization of 5.67 kg/ha/yr and domestic sewage and human excreta of 1.32 kg/ha/yr were the two major contributors of phosphorus input to Jianwei polder. Crop harvest of 2.29 kg/ha/yr and discharge of 1.29 kg/ha/yr were the two largest components of phosphorus output. The seasonal changes in phosphorus input and output components were substantial, especially in fertilization, crop harvest, and discharge. The weather elements including precipitation and evaporation, had a total relative effect of 63.7% on the temporal variation in discharge, thereby implying that natural factors played crucial roles in the discharge variation. However, phosphorus export mainly depended on agricultural practices including crop cover and fertilizer application, which contributed 75.4% of the temporal variation in phosphorus export. The recommend strategies included optimizing phosphorus fertilizer amounts, increasing the cropland cover, and drafting an emergency plan, all of which would facilitate the mitigation of phosphorus export and reduction of environmental pollution. Future research will involve uncertainty and sensitivity analyses and explore the key factor influencing the spatial hydrological processes of lowland polders.

4.3.3. Drafting and initiating an emergency plan to protect the polder from a disaster The improved model in combination with BRT analysis provided reliable simulated results and improved quantification of the hydrological response to environmental factors. This information can provide references for risk management and emergency plan formation. Although the effect of environmental factors still needs to be further discussed using additional experimental data from many agricultural polders across Taihu Basin, an emergency plan with multiple measures should be drafted to reduce agricultural loss and environmental problems. 4.4. Limitations and assumptions Compared with the original model, the PHPS coupled with INCA-P and MUSLE can describe the detailed dynamic changes in phosphorus concentration in dryland and paddy fields, thereby supporting the analysis of the phosphorus response to various farmland management techniques (e.g., fertilization, irrigation, and drainage). Our study area is a typical Chinese agriculture polder in terms of land use, weather conditions, agricultural water management, crop planting and management, and water problems. Thus, the results from this case have potential significance for other Chinese agricultural polders and can provide a scientific basis for water resource protection. However, there were still some limitations and assumptions in this study. Firstly, sensitivity and uncertainty analyses of the model were not performed in this study considering the page limit. Both analyses are beneficial for testing the robustness of the model structure and improving the model performance (Lu et al., 2013). Secondly, the simulation of DP was not as

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. 9

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Acknowledgements

application of the MUSLE model worldwide. Hydrol. Sci. J. 59 (2), 365–375. Shi, B., Jiang, J., Sivakumar, B., Zheng, Y., Wang, P., 2018. Quantitative design of emergency monitoring network for river chemical spills based on discrete entropy theory. Water Res. 134, 140–152. https://doi.org/10.1016/j.watres.2018.01.057. Stenitzer, E., Diestel, H., Zenker, T., Schwartengräber, R., 2007. Assessment of capillary rise from shallow groundwater by the simulation model SIMWASER using either estimated pedotransfer functions or measured hydraulic parameters. Water Resour. Manage. 21 (9), 1567–1584. Sterk, A., Schijven, J., de Roda Husman, A.M., de Nijs, T., 2016. Effect of climate change on runoff of campylobacter and cryptosporidium from land to surface water. Water Res. 95, 90–102. https://doi.org/10.1016/j.watres.2016.03.005. Venables, W.N., David, M.S., Team, a.R.D.C., 2009. An introduction to R. Wade, A.J., Whitehead, P.G., Butterfield, D., 2002. The Integrated Catchments model of Phosphorus dynamics (INCA-P), a new approach for multiple source assessment in heterogeneous river systems: model structure and equations. Hydrol. Earth Syst. Sci. Discuss. 6 (3), 583–606. Wang, G., Dobermann, A., Witt, C., Sun, Q., Fu, R., 2001. Performance of site-specific nutrient management for irrigated rice in southeast China. Agron. J. 93 (4), 869–878. Wang, S., Zhao, X., Xing, G., 2012. Phosphorus pool in paddy soil and scientific fertilization in typical areas of Taihu Lake Watershed. Soils 44, 158–162. Wang, W.H.M.T.X., 2010. Estimation of sewage production and discharge coefficients of rural areas in taihu lake basin. J. Ecol. Rural Environ. 26 (6), 616–621. Wang, Y., et al., 2016a. Characterization and influence factors of PM2.5 emitted from crop straw burning. Acta Chim. Sin. 74 (4), 356–362. Wang, Y., et al., 2018. Response of soil microbes to a reduction in phosphorus fertilizer in rice-wheat rotation paddy soils with varying soil P levels. Soil Tillage Res. 181, 127–135. Wang, Y., et al., 2016b. A five-year P fertilization pot trial for wheat only in a rice-wheat rotation of Chinese paddy soil: interaction of P availability and microorganism. Plant Soil 399 (1–2), 305–318. Wang, Y., et al., 2015. The regime and P availability of omitting P fertilizer application for rice in rice/wheat rotation in the Taihu Lake Region of southern China. J. Soils Sediments 15 (4), 844–853. Warner, J.F., van Staveren, M.F., van Tatenhove, J., 2018. Cutting dikes, cutting ties? Reintroducing flood dynamics in coastal polders in Bangladesh and the netherlands. Int. J. Disaster Risk Reduct. https://doi.org/10.1016/j.ijdrr.2018.03.020. Wei, X., 2014. A Novel Framework for Agricultural Non-Point Sources Pollution Management and Control in China. Northwest Agriculture and Forestry University, Yan Ling. Wellen, C., Kamran-Disfani, A.-R., Arhonditsis, G.B., 2015. Evaluation of the current state of distributed watershed nutrient water quality modeling. Environ. Sci. Technol. 49 (6), 3278–3290. https://doi.org/10.1021/es5049557. Williams, J.R., Berndt, H.D., 1977. Sediment yield prediction based on watershed hydrology. Trans. ASAE 20 (6), 1100–1104. https://doi.org/10.13031/2013.35710. Williams, J.R., Singh, V.P., 1995. The EPIC model, 909–1000 pp. Willmott, C.J., 1981. On the validation of models. Phys. Geogr. 2 (2), 184–194. Xu, G., Blussé, L., 2018. Land reclamation in the rhine and yangzi deltas: an explorative comparison, 1600–1800. Fudan J. Human. Soc. Sci. https://doi.org/10.1007/ s40647-018-0223-1. Yan, R.H., 2016. Modeling the Hydrological Processes and Phosphorus Transport of Lowland Polders. University of Chinese Academy of Sciences, Nanjing. Yan, R.H., Gao, J.F., Huang, J.C., 2016. WALRUS-paddy model for simulating the hydrological processes of lowland polders with paddy fields and pumping stations. Agric. Water Manage. 169, 148–161. Yan, R.H., Huang, J.C., Li, L.L., Gao, J.F., 2017. Hydrology and phosphorus transport simulation in a lowland polder by a coupled modeling system. Environ. Pollut. 227, 613–625. Yan, R.H., Li, L., Gao, J., 2018. Modelling the regulation effects of lowland polder with pumping station on hydrological processes and phosphorus loads. Sci. Total Environ. 637, 200–207. Yang, R.M., et al., 2016. Comparison of boosted regression tree and random forest models for mapping topsoil organic carbon concentration in an alpine ecosystem. Ecol. Ind. 60, 870–878. https://doi.org/10.1016/j.ecolind.2015.08.036. Young, R.A., Onstad, C.A., Bosch, D.D., Anderson, W.P., 1989. AGNPS: a non-point-source pollution model for evaluating agricultural watersheds. J. Soil Water Conserv. 44 (2), 168–173. Yu, L., et al., 2018. Groundwater impacts on surface water quality and nutrient loads in lowland polder catchments: monitoring the greater Amsterdam area. Hydrol. Earth Syst. Sci. 22 (1), 487–508. https://doi.org/10.5194/hess-22-487-2018. Zambrano-Bigiarini, M., Rojas, R., 2013. A model-independent particle swarm optimisation software for model calibration. Environ. Modell. Softw. 43, 5–25. Zhang, H., Cao, Z., Wang, G., Zhang, H., Wong, M.H., 2003a. Winter runoff losses of phosphorus from paddy soils in the Taihu Lake Region of South China. Chemosphere 52 (9), 1461–1466. Zhang, H.C., Cao, Z.H., Shen, Q.R., Wong, M.H., 2003b. Effect of phosphate fertilizer application on phosphorus (P) losses from paddy soils in Taihu Lake Region: I. Effect of phosphate fertilizer rate on P losses from paddy soil. Chemosphere 50 (6), 695–701. Zhao, R.J., 1992. The Xinanjiang model applied in China. J. Hydrol. 135 (1), 371–381.

The study was financed by the National Natural Science Foundation of China (41701031), Major Science and Technology Program for Water Pollution Control and Treatment (2017ZX07203002-02), the Water Resources Science and Technology Program of the Jiangsu Water Resources Department (2017002ZB), the Swiss Government Excellence Scholarship and State Scholarship Fund of China Scholarship Council (201804910378), and the Talents-Import Program in the Nanjing Institute of Geography and Limnology (NIGLAS2017QD06). Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.jhydrol.2019.124083. References Bornschein, A., Pohl, R., 2018. Land use influence on flood routing and retention from the viewpoint of hydromechanics. J. Flood Risk Manage. 11 (1), 6–14. https://doi.org/ 10.1111/jfr3.12289. Brauer, C., et al., 2011. Anatomy of extraordinary rainfall and flash flood in a Dutch lowland catchment. Hydrol. Earth Syst. Sci. 15 (6), 1991–2005. Brauer, C.C., Teuling, A.J., Torfs, P.J.J.F., Uijlenhoet, R., 2014. The wageningen lowland runoff simulator (WALRUS): a lumped rainfall-runoff model for catchments with shallow groundwater. Geosci. Model Dev. 7 (5), 2313–2332. Elith, J., Leathwick, J.R., Hastie, T., 2008. A working guide to boosted regression trees. J. Anim. Ecol. 77 (4), 802–813. Fuka, D., Walter, M., Steenhuis, T., Easton, Z., 2014. Package ‘SWATmodel’. Guo, H.Y., Zhu, J.G., Wang, X.R., Wu, Z.H., Zhang, Z., 2004. Case study on nitrogen and phosphorus emissions from paddy field in taihu region. Environ. Geochem. Health 26 (2), 209–219. https://doi.org/10.1023/B:EGAH.0000039583.71423.b4. Hopmans, J.W., Van Immerzeel, C., 1988. Variation in evapotranspiration and capillary rise with changing soil profile characteristics. Agric. Water Manage. 13 (2–4), 297–305. Huang, J., Gao, J., 2015. Phosphorus loss simulation of lowland polder system. J. Lake Sci. 27 (2), 216–226. Huang, J.C., Gao, J.F., Yan, R.H., 2016. A Phosphorus Dynamic model for lowland Polder systems (PDP). Ecol. Eng. 88, 242–255. Jackson-Blake, L.A., et al., 2016. The INtegrated CAtchment model of phosphorus dynamics (INCA-P): description and demonstration of new model structure and equations. Environ. Modell. Softw. 83, 356–386. Koch, S., Bauwe, A., Lennartz, B., 2013. Application of the SWAT model for a tile-drained lowland catchment in North-Eastern Germany on subbasin scale. Water Resour. Manage. 27 (3), 791–805. Lu, H.S., et al., 2013. The streamflow estimation using the Xinanjiang rainfall runoff model and dual state-parameter estimation method. J. Hydrol. 480, 102–114. https://doi.org/10.1016/j.jhydrol.2012.12.011. Lu, R.K., 1998. Soil-plant nutrients principles and fertilization, 153–208pp. M Duxbury, J., Abrol, I., Gupta, R., Bronson, K., 2000. Long-Term Soil Fertility Experiments with Rice-Wheat Rotations in South Asia. Menzel, R., 1980. Enrichment ratios for water quality modeling. CREAMS: A Field-Scale Model for Chemicals, Runoff, and Erosion from Agricultural Management Systems Conservation Research Report Number 26, May, 1980. p 486-492, 1 Fig. 2 Tab, 11 Ref. Miao, Y., Stewart, B.A., Zhang, F., 2011. Long-term experiments for sustainable nutrient management in China A review. Agron. Sustain. Dev. 31 (2), 397–414. https://doi. org/10.1051/agro/2010034. Miao, Y., Zhang, F., 2011. Long-term experiments for sustainable nutrient management in China A review. Agron. Sustain. Dev. 31 (2), 397–414. Nash, J.E., Sutcliffe, J.V., 1970. River flow forecasting through conceptual models part I—A discussion of principles. J. Hydrol. 10 (3), 282–290. Neitsch, S., Arnold, J., Kiniry, J., Williams, J., 2011. Soil & Water Assessment Tool—Theoretical Documentation Version 2009. Texas Water Resour, Inst. Pan, P., Yang, G., Su, W., Zhang, J., 2013. Spatio-temporal structure variation of grain production and the evaluation of grain security in Taihu Basin. J. Nat. Resour. 28 (6), 931–943. Perrin, C., Michel, C., Andréassian, V., 2003. Improvement of a parsimonious model for streamflow simulation. J. Hydrol. 279 (1), 275–289. https://doi.org/10.1016/S00221694(03)00225-7. Prasad, R., Power, J.F., 1991. Crop residue management. In: In: Stewart, B.A. (Ed.), Advances in Soil Science Vol. 15. Springer New York, New York, NY, pp. 205–251 DOI:10.1007/978-1-4612-3030-4_5. Ridgeway, G., 2010. gbm: generalized boosted models: a guide to the gbm package. Sadeghi, S., Gholami, L., Khaledi Darvishan, A., Saeidi, P., 2014. A review of the

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