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Polders are one of the most common artificial hydrological entities in the plain river network regions of China. Due to enclosed dikes, manual drainage and irrigation intake operations, polders have had a significant impact on the hydrological processes of these areas. Distributed hydrological models are effective tools to understand and reproduce...
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... Several scholars have conducted SWAT simulations on national and local scales [21,22]. Relevant scholars also have conducted some applicability simulation in Liyang, confirming the model's effectiveness [23,24]. As the process of generation and transport could be influenced by different landscapes, management practices and other natural conditions and socio-economic activities, NPS pollution loads have represented significant spatial heterogeneity [25]. ...
In the context of rural development and transformation, it is crucial to identify the impact of rural multifunctionality on non-point source (NPS) pollution. This study applies the Soil and Water Assessment Tool (SWAT), geographical detector, and principal component analysis in Liyang, a typical hilly subbasin in China, in order to assess the rural multifunctional development that influences the spatial differentiation of NPS pollution and detect the interactive effects of rural multifunctionality. The R2 and NSE demonstrated that the calibrated SWAT model successfully simulated NPS pollution in Liyang. The village scale was identified as the optimal research scale for examining the rural multifunctional development on NPS pollution distribution. The rural multifunctional indicators, such as the proportion of vegetable farming, sowing area, and grain farming, would influence NPS distribution. The number of family farming cooperatives, the area of pond farming, and the nature reserves area were also significant. The rural multifunctionality in Liyang could be classified into five categories: grain production, mixed agriculture, ecological conservation, leisure tourism, and industry and business function. The superposition of rural multifunctionality has a strengthening effect on NPS pollution, especially when the ecological conservation function is combined with the grain production or modern agriculture function. The study could provide NPS pollution control strategies for policymaking in rural multifunctional development.
... The annual average temperature is 15.5 • C, while the monthly average temperature is 2.7 • C in January and 28.1 • C in July. The annual average sunshine duration is about 1992.5 h, and the prevailing wind direction is eastward (Zhou et al., 2016;Lai et al., 2018). The topography is mainly composed of hills, low mountains, and plains, with elevations ranging from 1 to 535 m. ...
... In the arid region, Farmland (NDVI) had a larger impact on water quality in July and October than in May, which might be related to the time lag effects of LULC (Huang et al., 2019). However, Farmland (NDVI) on NH 3 -N and TP in March and June was greater than in November in the humid region, which may be related to the regional characteristics (Lai et al., 2018). In the arid and humid regions, Forest-grassland (NDVI) have the largest influence on NH 3 -N and TP in spring, which is similar with the findings of Hai et al (2020), who found that vegetation abundance enhanced water quality when abrupt precipitation and temperature rise. ...
Understanding of the buffer scale effect on water quality changes analysis in arid and humid regions is of great significance for exploring the best scale for water quality pollution and scientific planning of land use. The objective of this study is to determine the optimal buffer scales (500, 1000, 1500, 2000, 2500 and 3000 m) of land use that affect ammonia nitrogen (NH3-N) and total phosphorus (TP) in the Jing-Bortala River Watershed (JBRW) (arid region) and the Liyang Region (LR) (humid region) under a site buffer. PLS-SEM was used to fit the pathway relationships between the influencing factors and water quality to investigate the driving factors of NH3-N and TP in different seasons. The results showed that: (1) seasonal fluctuations in NH3-N and TP concentrations were found in both the arid and humid regions, with the arid region having higher values than humid region; the coefficient of variation (CV) of NH3-N and TP was higher in arid and humid region, and the CV of NH3-N and TP were higher in spring and autumn in arid region than that in spring and autumn in humid region; (2) the 2500 m (0.262 ≤ R² ≤ 0.633) buffer is the optimal scale for studying NH3-N and TP in arid region, whereas the 2000 m (0.262 ≤ R² ≤ 0.686) scale works well in humid region; and (3) farmland (NDVI) was the largest influencing factor for NH3-N and TP changes in arid region, accounting for 28.95 % of the average percent total impacts. Furthermore, water area (NDWI) was the predominant influencing factor in humid region, occupying 33.93 % of the average percent total impacts. The influence of land use type (indices) on surface water quality is greater than that of environmental factors. Thus, these new insights into an appropriate buffer scale can be used as a guide for water pollution management in arid and humid environments.
... Modification description [60, 83-85, 180, 186] Incorporated three critical depths for management of water in rice paddies ( Figure 12) [60,84,85,180,186] Ponds can be simulated as real-time irrigation sources (reservoirs simulated like ponds) [60, 8-85, 180, 186] Irrigation simulated as a function of seven different rice growth stages [60, 83-85, 180, 186] ET calculations account for whether paddy fields are in a wet or dry condition [60,84,85,180,186] Revised the land phase structure within the hydrologic cycle [83] Plow layer accounted for in vertical seepage calculations [83] Rice canopy interception module added [83] Dry crop module added to simulate LAI and actual transpiration for winter wheat [83][84][85]180] Canal seepage module added; seepage calculated on the basis of water use efficiency [83][84][85]180] Maximum irrigation amount was allowed to exceed soil field capacity levels [84,85,180] Rising capillary water accounted for; enters root zone and surface water cycle processes [84,85,180] Lateral seepage within paddy fields simulated when soil water exceeds field capacity [84,85,180] Rice [85,180] Multiple irrigation sources supported (rivers, ponds, reservoirs, aquifers, outside sources) [85,180] Irrigation sources can vary between HRUs within a given subwatershed [85,180] Simulates overall irrigation needs from one or more types of irrigations sources [180] Accounts for return flows from rice paddies due to precipitation and/or irrigation inputs [186] New method for calculating IE d and WSP d as a function of the reuse of irrigation return flow [211] Ensure that irrigation water does not overflow paddy impoundments Reference Modification description [178] Hydrologically isolated polder areas accounted for in model structure [178] Accounts for storage and/or drainage from precipitation events in polders [178] Polder pumping systems represented; drain excess water or import irrigation water [178] Rice paddy irrigation simulated as a function of growth stages to supplement irrigation [178] Drainage of excess precipitation water estimated on basis of irrigation schedules [178] Crossed or looped channels are converted to dendritic patterns per SWAT requirements ...
... Modification description [60, 83-85, 180, 186] Incorporated three critical depths for management of water in rice paddies ( Figure 12) [60,84,85,180,186] Ponds can be simulated as real-time irrigation sources (reservoirs simulated like ponds) [60, 8-85, 180, 186] Irrigation simulated as a function of seven different rice growth stages [60, 83-85, 180, 186] ET calculations account for whether paddy fields are in a wet or dry condition [60,84,85,180,186] Revised the land phase structure within the hydrologic cycle [83] Plow layer accounted for in vertical seepage calculations [83] Rice canopy interception module added [83] Dry crop module added to simulate LAI and actual transpiration for winter wheat [83][84][85]180] Canal seepage module added; seepage calculated on the basis of water use efficiency [83][84][85]180] Maximum irrigation amount was allowed to exceed soil field capacity levels [84,85,180] Rising capillary water accounted for; enters root zone and surface water cycle processes [84,85,180] Lateral seepage within paddy fields simulated when soil water exceeds field capacity [84,85,180] Rice [85,180] Multiple irrigation sources supported (rivers, ponds, reservoirs, aquifers, outside sources) [85,180] Irrigation sources can vary between HRUs within a given subwatershed [85,180] Simulates overall irrigation needs from one or more types of irrigations sources [180] Accounts for return flows from rice paddies due to precipitation and/or irrigation inputs [186] New method for calculating IE d and WSP d as a function of the reuse of irrigation return flow [211] Ensure that irrigation water does not overflow paddy impoundments Reference Modification description [178] Hydrologically isolated polder areas accounted for in model structure [178] Accounts for storage and/or drainage from precipitation events in polders [178] Polder pumping systems represented; drain excess water or import irrigation water [178] Rice paddy irrigation simulated as a function of growth stages to supplement irrigation [178] Drainage of excess precipitation water estimated on basis of irrigation schedules [178] Crossed or looped channels are converted to dendritic patterns per SWAT requirements ...
... Modification description [60, 83-85, 180, 186] Incorporated three critical depths for management of water in rice paddies ( Figure 12) [60,84,85,180,186] Ponds can be simulated as real-time irrigation sources (reservoirs simulated like ponds) [60, 8-85, 180, 186] Irrigation simulated as a function of seven different rice growth stages [60, 83-85, 180, 186] ET calculations account for whether paddy fields are in a wet or dry condition [60,84,85,180,186] Revised the land phase structure within the hydrologic cycle [83] Plow layer accounted for in vertical seepage calculations [83] Rice canopy interception module added [83] Dry crop module added to simulate LAI and actual transpiration for winter wheat [83][84][85]180] Canal seepage module added; seepage calculated on the basis of water use efficiency [83][84][85]180] Maximum irrigation amount was allowed to exceed soil field capacity levels [84,85,180] Rising capillary water accounted for; enters root zone and surface water cycle processes [84,85,180] Lateral seepage within paddy fields simulated when soil water exceeds field capacity [84,85,180] Rice [85,180] Multiple irrigation sources supported (rivers, ponds, reservoirs, aquifers, outside sources) [85,180] Irrigation sources can vary between HRUs within a given subwatershed [85,180] Simulates overall irrigation needs from one or more types of irrigations sources [180] Accounts for return flows from rice paddies due to precipitation and/or irrigation inputs [186] New method for calculating IE d and WSP d as a function of the reuse of irrigation return flow [211] Ensure that irrigation water does not overflow paddy impoundments Reference Modification description [178] Hydrologically isolated polder areas accounted for in model structure [178] Accounts for storage and/or drainage from precipitation events in polders [178] Polder pumping systems represented; drain excess water or import irrigation water [178] Rice paddy irrigation simulated as a function of growth stages to supplement irrigation [178] Drainage of excess precipitation water estimated on basis of irrigation schedules [178] Crossed or looped channels are converted to dendritic patterns per SWAT requirements ...
The Soil and Water Assessment Tool (SWAT) is an ecohydrological watershed-scale model which was initially developed in the early 1990s to simulate the impacts of land use, management systems, and climate on hydrology and/or water quality. First adopted in the U.S., the use of the model then spread to Europe and then later to Asia and other regions. The range of applications that SWAT has been applied to have also expanded dramatically, which influenced ongoing model development which has been virtually continuous over the past two decades. A key component of many SWAT applications in Asia is accounting for rice paddy production that is common in some subregions within the continent. However, most of these studies do not provide explicit details of how rice production was simulated in SWAT. Other research has revealed that significant problems occur when trying to represent rice paddy systems in standard versions of SWAT, due to limitations in algorithms based on the runoff curve number approach or the pothole option. In response, key modifications have been made to SWAT in recent studies that have resulted in more accurate representation of rice paddy systems. These developments point to the need for the incorporation of an enhanced rice paddy module within SWAT to better capture rice paddy hydrological and pollutant dynamics, which would support improved use of the model in Asia and other rice production regions. Subtopics related to simulating rice production in SWAT are discussed as follows: 1) an overview of global rice production; 2) history of SWAT development; 3) typical approaches for simulating rice production; 4) problems associated with the typical approaches; 5) recent code modifications to address deficiencies in replicating rice paddy systems; 6) recommendations for developing a standard rice paddy module for future SWAT codes.
... However, efforts to evaluate the relationship between driving factors and hydrological processes and then identify principal factors, especially for lowland polders, are complicated (Odongo et al., 2014) for the following reasons. Firstly, a polder is strongly affected not only by natural factors, but also by human factors (Lai et al., 2018). Normally, Asian polders are situated in subtropical monsoon areas with drastic fluctuations in seasonal precipitation and temperature that challenge the analysis of the relationship between drivers and hydrological processes. ...
Quantifying the relative effect of driving factors on polder hydrological processes and identifying the key factors are prerequisites to adopting effective measures for alleviating flooding, soil loss, and eutrophication. Based on the output results of calibrated Polder Hydrology and Nitrogen modeling System (PHNS) and Polder Hydrology and Phosphorus modeling System (PHPS), the boosted regression tree (BRT) model screened key influential factors and identified the corresponding threshold point values in a typical Chinese agricultural polder. Temporal changes in discharge and soil erosion were most sensitive to weather factors, while those of nitrogen and phosphorus exports were mainly influenced by human management actions. Precipitation was the largest contributor to the temporal variation of discharge (51.5%), soil erosion (69.4%). Vegetation cover and management factor of dryland C2 (34.5%) defined as the ratio of sediment loss from cropland to the corresponding loss from clean-tilled and continuous fallow, and phosphorus fertilization application to paddy field (30.7%) were the two determinants of phosphorus export, whilst surface water level to stop pump drainage (48.2%) had the largest contributions to nitrogen export. Precipitation from 0 to 50 mm/d contributed to a significant increase in discharge, soil erosion and nitrogen and phosphorus exports. Vegetation cover and management factor of paddy field C1 under 0.28 contributed to an elevated risk of nitrogen export, and C2 below 0.34 facilitated soil loss and phosphorus export.
... This study established a model for large and medium-sized reservoirs; however, many small reservoirs and sluices in the watershed were not considered. An alternative approach, namely, a virtual reservoir approach, was adopted to model polders in the watershed based on a SWAT model, although the watershed delineation of this approach considers dikes of each polder and makes each polder match the divided subbasins so that a reservoir was set up for each sub-basin (Li et al. 2013;Lai et al. 2018). However, the difference of the function between a polder and a reservoir is quite large so that it insufficiently represents the mechanism of the drainage and irrigation intake processes of a polder. ...
The construction and operation of sluices and dams inevitably change the natural state of river hydrology and have an impact on river ecosystems. Therefore, simulating the hydrological processes of sluice-controlled rivers is of great significance to river water resource management and ecological restoration. The present study analyzed the complex characteristics of the water cycle of sluice-controlled rivers in the plains area of China including the extraction of the river network’s canal system. The treatment of sluice dams and the simulation of the base flow process of the soil and water assessment tool (SWAT) were improved. A distributed hydrological model of the sluice-controlled rivers in the plains area was constructed. Then, we applied the model to the Shaying River Basin, which has many sluices and dams. The Nash–Sutcliffe efficiency coefficient, percentage deviation, and determination coefficient were used to evaluate the model. The evaluation indices and simulation results of the three hydrological stations in the basin show that the improved SWAT model more accurately identifies the effects of the regulation and storage of the sluices and dams on runoff in the plains area and demonstrates that this model is suitable for simulating the hydrological processes of the sluice-controlled rivers in the plains area.
... To date, researchers worldwide have developed numerous geographic simulation models for different applicable areas, at different spatial and temporal scales, and for different processes (e.g., hydrological [e.g., Liu et al., 2014Liu et al., , 2016Lai et al., 2016Lai et al., , 2018Zhu et al., 2019;Salas et al., 2020], atmospheric [e.g., Zhang et al., 2014;Yan et al., 2016;Ning et al., 2019], geomorphological [e.g., Shobe et al., 2017;Barnhart et al., 2018;Reichenbach et al., 2018;Batista et al., 2019;Rossi et al., 2019;Broeckx et al., 2020]). However, these are typically single-domain and single-scale models, and as such, they have limited capacity for simulating comprehensive geographic phenomena (Lu, 2011;Harpham et al., 2014;Gianni et al., 2018). ...
Integrated geographic modelling and simulation is a computational means to improve understanding of the environment. With the development of Service Oriented Architecture (SOA) and web technologies, it is possible to conduct open, extensible integrated geographic modelling across a network in which resources can be accessed and integrated, and further distributed geographic simulations can be performed. This open web-distributed modelling and simulation approach is likely to enhance the use of existing resources and can attract diverse participants. With this approach, participants from different physical locations or domains of expertise can perform comprehensive modelling and simulation tasks collaboratively. This paper reviews past integrated modelling and simulation systems, highlighting the associated development challenges when moving to an open web-distributed system. A conceptual framework is proposed to introduce a roadmap from a system design perspective, with potential use cases provided. The four components of this conceptual framework - a set of standards, a resource sharing environment, a collaborative integrated modelling environment, and a distributed simulation environment - are also discussed in detail with the goal of advancing this emerging field.
Polder is usually used for flood control in the river delta area. With the rapid development of urbanization, the dikes or pumps cut the original stream network system, and the stream network connectivity (SNC) and the river system pattern have changed. The dikes or pumps generally force up the river's water level, and regional flood formation mechanisms and processes have changed. In order to quantitatively describe the characteristics of polder‐type flood control measure (PFCM) and the change law of SNC, firstly, the streams inside polders were generalized as virtual streams, a hydrological‐hydrodynamic model was constructed by connecting Hydrologic Engineering Center‐Hydrologic Modeling System and MIKE11 model. Secondly, an SNC evaluation model was constructed based on flow resistance and hydrological process. Finally, the SNC under different scenarios was simulated and evaluated to reveal the influence of the PFCM on SNC. And the dominance analysis method obtained the main control factors of SNC changes. The results showed that the pumps as the main drainage facility under the PFCM, SNC after the opening of the pumps were increased by 0.060, 0.103, and 0.311 for 50%, 30%, and 3% frequency flood scales compared with the pumps closed, respectively. However, compared with the natural stream (without the PFCM), the SNC decreased by 0.391, 0.456, and 0.487, respectively, at the same time of the same flood scale. The PFCM negatively impacted the SNC, and the number of pumps was the main control factor of the SNC.
Flooding risk in polders is dictated by not only rainfall, topography, and land use, but also massive pumping. Unfortunately, existing models are inadequate for resolving floods as water transfer due to pumping is insufficiently accounted for. Here an improved hydrological model (MGB-MP) is proposed under the framework of the large-scale hydrological model (MGB) based on the principle of water balance, explicitly incorporating massive pumping within a polder and also out to external rivers. The proposed model is calibrated and validated for the Lannihu basin, a typical polder with an area of 1353 km² and 126 pumping stations in the Dongting Lake District, China and surrounded by Xiangjiang River and Zishui River. The model performs fairly well, with Nash-Sutcliffe efficiencies concerning water levels over 0.76 for the calibration and over 0.73 for the validation. The model is applied to the Lannihu basin under different pumping station settings and rainfall scenarios to unravel how and to what extent massive pumping affects the flood processes as characterized by water levels and discharge hydrographs. It is shown that massive pumping considerably alters the discharge hydrographs and accordingly leads to substantial decrease in the water levels of rivers, which are independent unit-polders, due to water transfer between unit-polders within the basin and out of the basin. The closer the unit-polders are to pumping stations, the more the water levels in unit-polders decrease. The water levels in unit-polders away from a pumping station is affected by the pumping station capacity to a greater extent than the pumping station's threshold water level for initiating pumping.
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A new modelling framework, Polder Hydrology and Nitrogen modeling System (PHNS), was developed to simulate the nitrogen dynamics and processes in polder systems. PHNS is a mass-balance model that simulates water and nitrogen dynamics in soil and surface water systems through integrating the WALRUS-paddy, MUSLE, and INCA models. The model explicitly considers the interactions among surface water, groundwater, and vadose water, as well as irrigation, pumping, and fertilizer application, which are the key processes controlling the nitrogen cycle in polders. The sensitivity analysis, calibration, and validation of the developed model were conducted in a Chinese agricultural polder by using three years of measured hydro-meteorological data. The calibrated and validated results proved that the model has a good performance with an R² of 0.748 and a Nash-Sutcliffe (NS) efficiency coefficient of 0.619 for total nitrogen (TN) concentration during the validation period. The nitrogen budget results (net export of 40.4 kg/ha/yr) revealed that the polder is a nitrogen source for downstream freshwater systems. Reducing the amount of fertilizers, retaining crop residues, and restoring aquatic plants in surface water are effective countermeasures for reducing nitrogen export from polders. This study provides an efficient modelling tool and useful insights into polder management.
