Modelling nutrient transport in Currency Creek,NSW with AnnAGNPS and PEST

The modelling package Annualized Agricultural Nonpoint Source Model (AnnAGNPS) was applied to the prediction of export of nitrogen and phosphorus from Currency Creek, a small experimental catchment within the Hawkesbury–Nepean drainage basin of the Sydney Region. The catchment is 255 ha in area and has experienced extensive soil erosion and losses of nutrients from intensive vegetable cultivation, irrigated dairy pasture and poultry farms. Simulations of nitrogen and phosphorus loads in the Currency Creek catchment were performed at various temporal scales and the degree of calibration was quantified by comparing the simulated data with the monitoring results. In addition, the model independent, nonlinear parameter estimation code PEST, was applied for sensitivity testing to determine and assess the relative importance of the key parameters of the model. Event flows were simulated satisfactorily with AnnAGNPS but only moderate accuracy was achieved for prediction of event-based nitrogen and phosphorus exports. The biggest deviations from the measured data were observed for daily simulations but trends in the generated nutrients matched observed data. Despite achieving good resemblance between measured and predicted phosphorus loads the model showed high level of sensitivity to assigned pH values for topsoil. Increase in pH by one unit resulted in up to 34% increase in model generated particulate phosphorus load.     

Nutrient monitoring,simulation and management within a major lowland UK river system: the Kennet

This paper describes an assessment of the nitrogen and phosphorus dynamics of the River Kennet in the south east of England. The Kennet catchment (1200 km²) is a predominantly groundwater fed river impacted by agricultural and sewage sources of nutrient (nitrogen and phosphorus) pollution. The results from a suite of simulation models are integrated to assess the key spatial and temporal variations in the nitrogen (N) and phosphorus (P) chemistry, and the influence of changes in phosphorous inputs from a Sewage Treatment Works on the macrophyte and epiphyte growth patterns. The models used are the Export Co-efficient model, the Integrated Nitrogen in Catchments model, and a new model of in-stream phosphorus and macrophyte dynamics: the ‘Kennet’ model. The paper concludes with a discussion on the present state of knowledge regarding the water quality functioning, future research needs regarding environmental modelling and the use of models as management tools for large, nutrient impacted riverine systems.     

Development and application of a marine ecosystem dynamic model

A nutrient-phytoplankton-zooplankton-detritus (NPZD) type of marine ecosystem model was developed in this study, and was further coupled to a three-dimensional primitive-equation ocean circulation model with a river discharge model and a solar radiation model to reproduce the dynamics of the low nutrition level in the Bohai Sea (BS). The simulation results were validated by observations and it was shown that the seasonal variation in the phytoplankton biomass could be characterized by the double-peak structure, corresponding to the spring and summer blooms, respectively. It was also found that both nitrogen and phosphate declined to the lowest level after the onset of the summer bloom, since the large amounts of nutrients were exhausted by phytoplankton for photosynthesis, and the concentrations of nutrients could resume in winter after a series of the biogeochemical-physical processes. By calculating the nitrogen/phosphorus (N/P) ratio, it is easy to see that the phytoplankton dynamics is nitrogen-limited as a whole in BS, though the phosphorus limitation may occur in the Yellow River (YR) Estuary where the input of riverine nitrogen is much more than that of phosphate.     

非平稳时间序列的组合模型及其在中长期水文预报中的应用

基于水文序列是一类周期性非平稳时间序列的特点,本文提出了一种适合描述这一特点 的组合模型.这个组合模型的优点在于它能较充分地利用历史数据中蕴含的信息,即它既能 利用以年为采样周期的时间序列的相关信息,又能利用以月为采样周期的时间序列的相关信 息;编制了FORTRAN语言程序,将它应用于河流的中长期水文预报.从文中所列出的汉口和 龙滩水文站的向前1至12个月的水文预报结果可以看出,组合模型的预报效果是较好的.     

基于流水线模式的河系径流预报并发计算研究

在河系径流预报计算中,一方面受单站水文过程计算复杂性影响,另一方面下游站点依赖上游关联节点,现有洪水预报系统在河系预报计算时多采用串联模式进行计算。这在河流系预报节点较多、模型方法略为复杂时,计算效率较低。为突破河系径流预报计算效率瓶颈,本研究引入流水线并行模式,对河系径流预报站点初始化、单元产汇流计算、河道洪水演算、校正分析等模块进行拆解,构建流水线式工作站,将径流预报站点按水力联系连续入站,实现河系节点集径流过程的平行并发计算。选取淮河正阳关以上流域50余断面进行了模拟试验,结果表明：研究构建的并发计算方法计算结果可靠,较串行结构效率提升超3倍,可满足洪水预报实时性要求、尤其适用于B/S模式对系统响应效率的需求。     

Predicting daily streamflow using rainfall forecasts,a simple loss module and unit hydrographs: Two Brazilian catchments

The performance of a simple, spatially-lumped, rainfall–streamflow model is compared with that of a more complex, spatially-distributed model. In terms of two model-fit statistics it is shown that for two catchments in Brazil (about 30,000 km² and 34,000 km²) with different flow regimes, the simpler catchment models, which are unit hydrograph-based and require only rainfall, streamflow and air temperature data for calibration, perform about as well as more complex catchment models that require additional information from satellite images and digitized maps of elevation, land-use and soils. Simple catchment models are applied in forecasting mode, using daily rainfall forecasts from a regional weather forecasting model. The value of the rainfall forecasts, relative to the case where rainfall is known, is assessed for both catchments. The results are discussed in the context of on-going work to compare different modelling approaches for many other Brazilian catchments, and to apply improved forecasting algorithms based on the simple modelling approach to the same, and other, catchments.     

A real-time probabilistic channel flood-forecasting model based on the Bayesian particle filter approach

Reliable real-time probabilistic flood forecasting is critical for effective water management and flood protection all over the world. In this study, we develop a real-time probabilistic channel flood-forecasting model by combining a channel hydraulic model with the Bayesian particle filter approach. The new model is tested in the upstream river reach of Three Gorges Dam (TGD) on the Yangtze River, China. Stage observations at seven hydrological stations are used simultaneously to adjust the Manning's roughness coefficients and to update discharges and stages along the river reach to attain reliable probabilistic flood forecasting. The synthetic experiments are applied to demonstrate the new model's correction and forecasting performances. The real-world experiments show that the new model can make accurate flood forecasting as well as derive reliable intervals for different confidence levels. The new probabilistic flood forecasting model not only outperforms the existing deterministic channel flood-forecasting models in accuracy, but also provides a more robust tool with which to incorporate uncertainty into flood-control efforts.     

Data‐driven model for river flood forecasting based on a Bayesian network approach

Uncertainty analysis of hydrological models often requires a large number of model runs, which can be time consuming and computationally intensive. In order to reduce the number of runs required for uncertainty prediction, Bayesian networks (BNs) are used to graphically represent conditional probability dependence between the set of variables characterizing a flood event. Bayesian networks (BNs) are relevant due to their capacity to handle uncertainty, combine statistical data and expertise and introduce evidences in real‐time flood forecasting. In the present study, a runoff–runoff model is considered. The discharge at a gauging station located is estimated at the outlet of a basin catchment based on discharge measurements at the gauging stations upstream. The BN model shows good performances in estimating the discharges at the basin outlet. Another application of the BN model is to be used as a reverse method. Knowing discharges values at the outlet of the basin, we can propagate back these values through the model to estimate discharges at upstream stations. This turns out to be a practical method to fill the missing data in streamflow records which are critical to the sustainable management of water and the development of hydrological models.     

AFFDEF: A spatially distributed grid based rainfall–runoff model for continuous time simulations of river discharge

This paper aims to present a rainfall-runoff model that was recently released into the public domain via the World Wide Web. The model, AFFDEF, is spatially distributed (grid based) and performs continuous time simulations of river flows at any time step and at any location in the catchment. It does not, however, account for snowmelt. Conceptual and physically based schemes are employed for simulating the rainfall–runoff transformation. AFFDEF's main strength is its computational efficiency, which allows the model to perform long simulation runs (e.g. thousands of years at hourly time step). Furthermore, AFFDEF does not require extensive information in terms of historical hydrological data or geomorphology of the contributing area. It may, therefore, represent a powerful tool for performing hydrological simulation studies. The model code, written in FORTRAN programming language, provides a user friendly and ready to use tool that runs on personal computers, as well as UNIX systems. We believe that AFFDEF may represent an easy model to use and attractive instrument for hydrological applications where long simulation runs of river flows are needed at different locations of the catchment. Of particular interest is the possibility to generate river flows data in ungauged cross-sections of the watershed.     

A simulation model of hydrology and nutrient dynamics in wetlands

A simulation model of the hydrology and nutrient dynamics in wetlands typical of central and north Florida was developed to predict parameters of water depth, recharge, and surface outflow as well as concentrations of total phosphorus, and three nitrogen species. The hydrological portion of the model predicts hydroperiod and depth of inundation for any elevation, and can be used to establish characteristic hydroperiod for vegetation and associations of species. The nutrient portion of the model can be used to predict water quality within, and quality of surface water outflow from natural wetlands, constructed wetlands, and wetlands receiving treated effluent or storm drainage. Programmed in BASIC for simulation on desktop microcomputers, the model is interactive requiring data input such as wetland type, discharge rate, and the concentrations of nutrients in surface water inflow.     

A wavelet-based autoregressive fuzzy model for forecasting algal blooms

This paper proposes fuzzy models for forecasting the complex behavior of algal blooms. The models are developed through the integration of autoregressive models, the Takagi-Sugeno fuzzy model, and discrete wavelet transform algorithms. The premise parts of the proposed models are determined using the subtractive clustering technique and the consequent parts are optimized using weighted least squares. To train and validate the proposed fuzzy models, a large number of data sets were collected from Daecheong reservoir in Geum River in the Republic of Korea. The data include both water quality and hydrological variables. Total nitrogen, total phosphorous, dissolved oxygen, chemical oxygen demand, biochemical oxygen demand, pH, air temperature, water temperature and outflow water were evaluated as input signals while chlorophyll-a was used as an output. It is demonstrated from the simulation that the proposed fuzzy models are effective in forecasting algal blooms.     

基于小波非线性自回归网络的水文预测模型

不同时间尺度上的水文序列预测在水资源调配和防洪减灾决策中起着重要的作用。提出了一种基于小波分解和非线性自回归神经网络相结合的水文时间序列预测模型(WNARN)。运用Daubechies 5(db5)离散小波将水文序列数据分解为低频和高频子序列,作为非线性自回归神经网络模型(NARN)的输入变量,贝叶斯正则化优化算法用来泛化网络,训练模型对各子序列进行模拟预测,预测值经db5小波重构后得到原序列预测值。利用渭河流域三个水文站40多年的月径流量序列对所提出的WNARN模型进行验证和向前48步的预测能力测试,并与单一NARN模型的验证和预测结果进行对比。结果显示在相同的网络结构下所提出的方法能够显著提高水文序列的预测精度、预测周期及对重大水文事件的预测性,具有较高的泛化能力。     

基于ASTER-GDEM的延河流域水系分维特征分析

沟谷水系提取及分析，在描述地形骨架、获取构建分布式水文模型参数及研究地貌发展进程中有重要作用。传统的水系分维计算方法主要有量规法和栅格法， 本文将汇流阈值类比量规，遵照量规法的计算方法，研究流域的不同汇流，值得到不同的水系长度，分析其对数关系以计算水系的分维值。基于ASTER GDEM数据源， 以陕西省延河流域为例进行分析，其分维值为1.058；结合流域侵蚀积分曲线可知，延河流域水系侵蚀强烈。     

基于Matlab与VB混合编程的流域降水预报系统

利用研究区获得的水文观测资料,采用模块开发和系统集成方式,研制了研究区流域降水预报系统。介绍了系统的体系结构、主要功能、运行情况及开发的关键技术。叙述了流域降水预报的各种预报方法,并建立了基于遗传算法的降水预报神经网络模型（GA-BP网络模型）。结果表明,GA-BP网络是一种精度较高的降水预报模型,提高预测精度,增长有效预见期。该系统能根据流域观测数据、高空数据、卫星云图、数值产品等数据,实现不同数据源的信息处理和不同时效的降水预报制作,为洪水预警预报和防洪决策服务。     

Using WEC-C,a distributed,deterministic catchment model,to simulate hydrologic responses to agricultural clearing

The water and environmental consultants — catchment model is a distributed, deterministic, catchment-scale water flow and solute transport model specifically developed to assess the effects of land uses such as agricultural clearing, forest logging and surface mining on the stream flows and salinities of a catchment. This paper describes its application to Lemon catchment, a small catchment in the low rainfall zone of the Darling Range of Western Australia, which had half its area cleared for agriculture. Specific parameterisation to Lemon catchment was not the aim; rather a parameter set consistent with our present understanding of the behaviour of Darling Range catchments and with the available hydrological data for the catchment. The resultant model was termed a mimic by the authors in that its appearance and behaviour are similar to the original in a way that satisfies the observer, but due to the known limitations of distributed, deterministic models it, like all such models, cannot be considered a true construct of the catchment. It was noted however that this particular model did appear to accurately mimic the catchment's hydrology and that this was achieved while: extensively using published and measured field data to parameterise the model; using fewer calibrated parameters than comparable models; and successfully matching history to groundwater level and stream flow, salinity and salt load data. The authors believe that a workable methodology for the application of distributed, deterministic models was presented and that the utility of these models was placed into correct perspective.     

Putting space into modeling landscape and water quality relationships in the Han River basin,South Korea

When examining the relationship between landscape characteristics and water quality, most previous studies did not pay enough attention to the spatial aspects of landscape characteristics and water quality sampling stations. We analyzed the spatial pattern of total nitrogen (TN), total phosphorus (TP), chemical oxygen demand (COD), and suspended solids (SS) in the Han River basin of South Korea to explore the role of different distance considerations and spatial statistical approaches to explaining the variation in water quality. Five-year (2012 through 2016) seasonal averages of those water quality attributes were used in the analysis as the response variables, while explanatory variables like land cover, elevation, slope, and hydrologic soil groups were subjected to different weighting treatments based on distance and flow accumulation. Moran's Eigenvector-based spatial filters were used to consider spatial relations among water quality sampling sites and were used in regression models. Distinct spatial patterns of seasonal water quality exist, with the highest concentrations of TN, TP, COD, and SS in downstream urban areas and the lowest concentrations in upstream forest areas. TN concentrations are higher in dry winter than the wet summer season, while SS concentrations are higher in wet summer than the dry season. Spatial models substantially improved the model fit compared to aspatial models. The flow accumulation-based models performed best when the spatial filters were not used, but all models performed similarly when spatial filters were used. The distance weighting approaches were instrumental in understanding watershed level processes affecting source, mobilization, and delivery of physicochemical parameters that flow into the river water. We conclude that a consideration of the spatial aspects of sampling sites is as important as accounting for different distances and hydrological processes in modeling water quality.     

Modeling the pathways and attenuation of nutrients from domestic wastewater treatment systems at a catchment scale

Domestic Wastewater Treatment Systems (DWTS) are often cited as significant sources of pollution in rural catchments. A mass balance based model has been developed to determine annual nutrient loading from individual DWTS into rivers in Ireland. The transport and attenuation of nitrogen and phosphorus in DWTS effluent to groundwater and surface water has been formulated using the results from field research in Ireland, as well as being informed by other international studies. Conceptually the model splits the transport of nutrients to the river into three pathways: direct to surface water (for areas of inadequate percolation), a near surface (subsoil) pathway, and a groundwater pathway. The model quantifies the net nutrient contribution for each DWTS and has been incorporated into a broader source load apportionment catchment model which includes agricultural inputs, thereby enabling the relative risk of nutrient pollution from DWTS in a catchment to be defined.     

On the use of ocean color remote sensing to measure the transport of dissolved organic carbon by the Mississippi River Plume

We investigated the use of ocean color remote sensing to measure the transport of dissolved organic carbon (DOC) by the Mississippi River to the Gulf of Mexico. From 2000 to 2005 we recorded surface measurements of DOC, colored dissolved organic matter (CDOM), salinity, and water-leaving radiances during five cruises to the Mississippi River Plume. These measurements were used to develop empirical relationships to derive DOC, CDOM, and salinity from monthly composites of SeaWiFS imagery collected from 1998 through 2005. We compared our remote sensing estimates of river flow and DOC transport with data collected by the United States Geological Survey (USGS) from 1998 through 2005. Our remote sensing estimates of river flow and DOC transport correlated well (r² ∼ 0.70) with the USGS data. Our remote sensing estimates and USGS field data showed low variability in DOC concentrations in the river end-member (7-11%), and high seasonal variability in river flow (∼ 50%). Therefore, changes in river flow control the variability in DOC transport, indicating that the remote sensing estimate of river flow is the most critical element of our DOC transport measurement. We concluded that it is possible to use this method to estimate DOC transport by other large rivers if there are data on the relationship between CDOM, DOC, and salinity in the river plume.     

Assessment of the different sources of uncertainty in a SWAT model of the River Senne (Belgium)

Although rainfall input uncertainties are widely identified as being a key factor in hydrological models, the rainfall uncertainty is typically not included in the parameter identification and model output uncertainty analysis of complex distributed models such as SWAT and in maritime climate zones. This paper presents a methodology to assess the uncertainty of semi-distributed hydrological models by including, in addition to a list of model parameters, additional unknown factors in the calibration algorithm to account for the rainfall uncertainty (using multiplication factors for each separately identified rainfall event) and for the heteroscedastic nature of the errors of the stream flow. We used the Differential Evolution Adaptive Metropolis algorithm (DREAM_(zs)) to infer the parameter posterior distributions and the output uncertainties of a SWAT model of the River Senne (Belgium). Explicitly considering heteroscedasticity and rainfall uncertainty leads to more realistic parameter values, better representation of water balance components and prediction uncertainty intervals.     

An enhanced SWAT wetland module to quantify hydraulic interactions between riparian depressional wetlands,rivers and aquifers

This study develops a modified version of the Soil and Water Assessment Tool (SWAT) designed to better represent riparian depressional wetlands (SWATrw). It replaces existing unidirectional hydrological interactions between a wetland and a river/aquifer with a more robust bidirectional approach based on hydraulic principles. SWATrw incorporates a more flexible wetland morphometric formula and a connecting channel concept to model wetland-river interactions. SWAT and SWATrw were tested for the Barak-Kushiyara River Basin (Bangladesh and India). Although the two models showed small differences in simulated stream flow, SWATrw outperformed SWAT in reproducing river stages and the pre-monsoon river-spills into riparian wetlands. SWATrw showed that the observed presence of dry season water in the wetland was due to reduced seepage to the local groundwater table whilst continuous seepage simulated by SWAT resulted in the wetland drying out completely. The new model therefore more closely simulates the hydrological interactions between wetlands, rivers and groundwater.