The HYPE model is a hydrological model for small-scale and large-scale assessments of water resources and water quality, developed at the Swedish Meteorological and Hydrological Institute during 2005-2007. In the model, the landscape is divided into classes according to soil type, land use and altitude. In agricultural lands the soil is divided into three layers, each with individual computations of soil wetness and nutrient processes. The model simulates water flow and transport and turnover of nitrogen and phosphorus. Nutrients follow the same pathways as water in the model: surface runoff, macropore flow, tile drainage and outflow from individual soil layers. Rivers and lakes are described separately with routines for turnover of nutrients in each environment. Model parameters are global, or coupled to soil type or land use. The model was evaluated both by local calibrations to internal variables from different test basins and to data on discharge and nutrients from a large number of small basins. In addition, the estimated parameters were transferred to two larger basins in southern Sweden: River Ronnea and River Vindan. The resulting simulations were generally in good agreement with observations.

Development and testing of the HYPE (Hydrological Predictions for the Environment) water quality model for different spatial scales

There is an increasing need for environmental management advice that is wide-scoped, covering various interlinked policies, and realistic about the uncertainties related to the possible management actions. To achieve this, efficient decision support integrates the results of pre-existing models. Many environmental models are deterministic, but the uncertainty of their outcomes needs to be estimated when they are utilized for decision support. We review various methods that have been or could be applied to evaluate the uncertainty related to deterministic models' outputs. We cover expert judgement, model emulation, sensitivity analysis, temporal and spatial variability in the model outputs, the use of multiple models, and statistical approaches, and evaluate when these methods are appropriate and what must be taken into account when utilizing them. The best way to evaluate the uncertainty depends on the definitions of the source models and the amount and quality of information available to the modeller. We review different types of uncertainty present in environmental modelling.We review methods to evaluate uncertainty related to model results.Best way to evaluate uncertainty depends on the models and available information.

An overview of methods to evaluate uncertainty of deterministic models in decision support

Modelling mitigation options to reduce diffuse nitrogen water pollution from agriculture

There is an increasing pressure for development of integrated water quality models that effectively couple catchment and in-stream biogeochemical processes. This need stems from increasing legislative requirements and emerging demands related to contemporary climate and land use changes. Modelling water quality and nutrient transport is challenging due a number of serious constraints associated with the input data as well as existing knowledge gaps related to the mathematical description of landscape and in-stream biogeochemical processes. The present paper summarizes the discussions held during the workshop on ‘Integrated water quality modelling: future demands and perspectives’ (Magdeburg, Germany, 23–24 June 2008). Our primary focus is placed on the current limitations and future challenges in water quality modelling. In particular, we evaluate the current state of integrated water quality modelling, we highlight major research needs to assess and reduce model uncertainties, and we examine opportunities to enhance model predictive capacity. To better account for the need of upscaling process knowledge, we advocate the adoption of combined process-oriented field and modelling studies at representative sites. In-stream nutrient metabolism investigations at the entire range of stream and river scales will enable the improvement of the mathematical representation of these processes and therefore the articulation level of coupled watershed-receiving waterbody models. Keeping the complexity of integrated water quality models in mind, the development of novel uncertainty analysis techniques for rigorous assessing parameter identification and model credibility is essential. In this regard, we recommend the use of Bayesian calibration frameworks that explicitly accommodate measurement errors, parameter uncertainties, and model structure errors. The Bayesian inference can be used to quantify the information the data contain about model inputs, to offer insights into the covariance structure among parameter estimates, to obtain predictions along with credible intervals for model outputs, and to effectively address the ‘change of support’ problems

New challenges in integrated water quality modelling

A pesticide is any substance or mixture of substances intended to repel, destroy or control any pest or prevent plant growth Pesticides may include chemical and biological ingredients and may be further characterized as insecticides, herbicides, fungicides, bactericides, rodenticides, and plant growth regulators, as well as public health insecticides. The use of pesticides can protect crops and prevent post-harvest losses, thus contributing to food security. Pesticides also help to avoid the spread of pests and diseases in global trade and in stored agricultural commodities.

More people, more food, worse water? a global review of water pollution from agriculture

An ensemble of nutrient models was applied in 17 European catchments to analyse the variation that appears after simulation of net nutrient loads and partitioning of nutrient loads at catchment scale. Eight models for N and five models for P were applied in three core catchments covering European-wide gradients in climate, topography, soil types and land use (Vansjo-Hobol (Norway), Ouse (Yorkshire, UK) and Enza (Italy)). Moreover, each of the models was applied in 3-14 other EUROHARP catchments in order to inter-compare the outcome of the nutrient load partitioning at a wider European scale. The results of the nutrient load partitioning show a variation in the computed average annual nitrogen and phosphorus loss from agricultural land within the 17 catchments between 19.1-34.6 kg N ha(-1) and 0.12-1.67 kg P ha(-1). All the applied nutrient models show that the catchment specific variation (range and standard deviation) in the model results is lowest when simulating the net nutrient load and becomes increasingly higher for simulation of the gross nutrient loss from agricultural land and highest for the simulations of the gross nutrient loss from other diffuse sources in the core catchments. The average coefficient of variation for the model simulations of gross P loss from agricultural land is nearly twice as high (67%) as for the model simulations of gross N loss from agricultural land (40%). The variation involved in model simulations of net nutrient load and gross nutrient losses in European catchments was due to regional factors and the presence or absence of large lakes within the catchment.

S.A. Borgvang

Papers

Ensemble modelling of nutrient loads and nutrient load partitioning in 17 European catchments