Uncertainty Assessment on the Nutrient Concentration in the Regge Catchment, Vecht River Basin, the Netherlands November 2006

Institute of Inland Water Management and Waste Water Treatment (RIZA), Lelystad, The Netherlands, Alterra, The Netherlands

Uncertainty assessment on the nutrient concentration in the Regge catchment, Vecht River basin, the Netherlands November 2006

Harmonised techniques and representative river basin data for assessment and use of uncertainty information in integrated water management Contract EVK1-CT-2002-00109

Authors Rianne Bijlsma (Institute of Inland Water Management and waste water Treatment (RIZA), Lelystad, The Netherlands), Piet Groenendijk (Alterra), P. Boers (RIZA) and Michiel Blind (RIZA)

Uncertainty assessment on the nutrient concentration in the Regge catchment, Vecht River basin, the Netherlands November 2006

Harmonised techniques and representative river basin data for assessment and use of uncertainty information in integrated water management Contract EVK1-CT-2002-00109

This report is a publicly accessible deliverable D7.4 of the HarmoniRiB project. This R&D project is partly financed within the European Commission´s “Energy, Environment and Sustainable Development” programme, Key Action 1 “ Sustainable Management and Quality of Water”, 1.1 Integrated management and sustainable use of water resources at catchment river basin or sub-basin scale, 1.1.1 Strategic planning and integrated management methodologies and tools at catchment / river scale under contract EVK1-CT 2002-00109. This report may be downloaded from the internet and copied, provided that it is not changed and provided that proper reference to it is made:

Rianne Bijlsma, Piet Groenendijk, P. Boers and Michiel Blind. Uncertainty assessment on the nutrient concentration in the Regge catchment, Vecht River basin, the Netherlands, Institute of Inland Water Management and Waste Water Treatment (RIZA), Lelystad, The Netherlands and Alterra, Wageningen, The Netherlands, November 2006 (www.harmonirib.com) or (http://workplace.wur.nl/QuickPlace/harmonirib/Main.nsf/h_Toc/38da1522d3c0e520c12571 e3002f512c/?OpenDocument).

A HarmoniRiB case study: Vecht River Basin

Uncertainty assessment on the nutrient concentration in the Regge catchment, Vecht River basin, the Netherlands

Bijlsma, R.1* Groenendijk, P.2 Boers, P. 1 Blind, M. 1

Deliverable NO D7.4 Lelystad, November 2006

1 Institute for Inland Water Management and Waste Water Treatment (RIZA), P.O. Box 17, 8200AA Lelystad. * Corresponding author. Email: [email protected] 2 Alterra, Wageningen University and Research Centre, P.O.Box 47, 6700 AA, Wageningen.

Table of contents

Acknowledgement...... iii Summary ...... v 1 Introduction...... 1 1.1 General ...... 1 1.1.1 The Water Framework Directive ...... 1 1.1.2 The HarmoniRiB project ...... 2 1.2 The Vecht case study – Scope and objective...... 2 1.3 Reading guide...... 3 2 The Vecht basin...... 5 2.1 Physical and socio economic description of case study basin ...... 5 2.1.1 Physical characterisation ...... 5 2.1.2 Socio-economic characterisation...... 7 2.2 Identification of water management problems addressed in case study ...... 8 2.2.1 Good status reached or failed? Framing the decision making problem...... 8 2.2.2 Specification of “Good Status”, list of evaluation criteria...... 9 2.2.3 Measures considered ...... 10 2.2.4 Summary of decision problem: set-up of the decision matrix...... 11 3 Physical impact analysis ...... 12 3.1 Introduction: Model selection...... 12 3.2 Data availability ...... 12 3.3 Description of models...... 13 3.4 Assessment of uncertainties...... 21 3.4.1 Uncertainty aspects of oxygen diffusion in soil...... 21 3.4.2 Uncertainty aspects of phosphorus background concentration ...... 22 3.4.3 Uncertainty aspects of fertilisation ...... 22 3.4.4 Uncertainty aspects of iron and aluminium content in the upper soil ...... 23 3.4.5 Uncertainty aspects of storage capacity of detailed surface water system ...... 24 3.5 Integrated uncertainty analysis ...... 27 3.6 Summary of results ...... 37 4 Analysis socio-economic impacts ...... 47 4.1 Costs of measures...... 47 4.2 Other impacts ...... 48 5 Evaluation and Comparison of Measures...... 51 5.1 Summary of Results from Impact Analysis ...... 51 5.2 Comparison of measures ...... 52 6 Conclusions ...... 54 Annex 1: NL-CAT ...... 57 Annex 2: Full uncertainty analyses ...... 58 2.1 Uncertainty aspects of oxygen diffusion in soil...... 58 2.1.1 Summary...... 58 2.1.2 Introduction...... 59 2.1.3 Uncertainty analysis ...... 60 2.2 Uncertainty aspects of phosphorus background concentration ...... 64 2.2.1 Summary...... 64 2.2.2 Introduction...... 64 2.2.3 Uncertainty analysis ...... 65 2.3 Uncertainty aspects of fertilisation ...... 69 2.3.1 Summary...... 69 2.3.2 Introduction...... 70 2.3.3 Uncertainty analysis ...... 70

i 2.3.4 Background: procedure followed to obtain fertilisation input data...... 75 2.4 Uncertainty aspects of iron and aluminium content in the upper soil ...... 77 2.4.1 Summary...... 77 2.4.2 Introduction...... 77 2.4.3 Uncertainty analysis ...... 77 6.1.1 Results ...... 81 2.5 Uncertainty aspects of storage capacity of detailed surface water system ...... 85 2.5.1 Summary...... 85 2.5.2 Introduction...... 85 2.5.3 Uncertainty analysis ...... 88 2.5.4 Background: the schematisation of small watercourses in the model ...... 90 Annex 3: Nutrient concentrations in the upper groundwater ...... 93 Annex 4: Cost calculations ...... 95

ii Acknowledgement

We would like to thank Alterra for the good and pleasant cooperation during this study. We especially would like to thank Christian Siderius, Leo Renaud, Henk Oosterom and Dennis Walvoort, for executing the model calculations, contributing to the discussion on uncertainty assessment and contributing to the search for data and information for this assessment. Furthermore, we would like to thank Gerard Heuvelink, Folkert de Vries and Tom Hoogland for carrying out the geostatistical analysis for iron and aluminium quantity in the upper soil. We are very grateful for the contribution to the discussion on uncertainty assessment by Paul Boers (RIZA) and his support during the carrying out and reporting of the case study. Finally, the data suppliers for the HarmoniRiB project case study are gratefully acknowledged, with special thanks to Hans Reijnders from RIVM.

iii

iv Summary The Vecht case study’s objective has been to assess the influence of data and parameter uncertainty on the effects of alternative measures aimed at reducing the nutrient concentration in the surface water. The study is limited to the Regge catchment, which is a subcatchment of the Vecht. The model selected is NL-CAT. The influence of the uncertainties is assessed for the summer averaged phosphorus and nitrogen concentration for the outflow point of the Regge catchment. In addition, the economic costs of these measures, their side effect, public support and acceptance by Brussels are assessed, including corresponding uncertainties.

The selected measures are: (i) reduction of fertilisation use in agriculture, (ii) broadening of small watercourses and (iii) reduction of nutrient discharge by Waste Water Treatment Plants (WWTP’s). The effects of the measures, combinations of these measures, and the baseline scenario are calculated from 2006-2032. Uncertainty is assigned to five selected inputs/ parameters and propagated through the model by Monte Carlo simulation. The selected sources of uncertainties are: • Oxygen diffusion parameters in soil • Phosphorus background concentration • Fertilisation application load • Iron and aluminium quantity in the upper soil • Storage capacity of the detailed surface water system

The effect of these uncertainties on the results is not very high and decreases with decreasing nutrient concentration. The effect is not exploding as sometimes mentioned as a reason not to do uncertainty analysis. Some of the uncertainties had a smaller effect than expected. Given the results of the analysis, the best solution would be to combine a strong reduction of the WWTP discharge with a less strong reduction of fertilization. This would likely lead to meeting the target for nitrogen with the smallest uncertainty on effects and socio-economic aspects. The target for phosphorus is likely not being met for either of the alternatives.

The study is to be seen as a theoretical exercise rather than a policy advice. The most important simplifications made are that the extent of the measures considered is not realistic, not all important uncertainties are considered and the socio-economic analysis is only carried out on a rough level. Furthermore, some strong assumptions needed to be made in assessing the uncertainties due to lack of knowledge.

Learning points from this study are: • Uncertainty analysis adds extra valuable information on the basis of which you can assess the alternatives. • The specification of the output location has a large influence on the results of both the nutrient concentrations and the magnitude of the uncertainty. • The type of output (e.g. which indicator on which scale) should be the guiding for which uncertainties to assess and how to assess them. Each scale (indicator is more trivial) is influenced by different types of uncertainties. • Uncertainty assessment is always subjective. Assumptions and choices are necessary due to lack of knowledge, so there is a need for a good documentation. • Uncertainty analysis is complex and time consuming for large models. There is a need for better guidance on how to make smart choices.

v vi

1 Introduction

1.1 General

1.1.1 The Water Framework Directive In 2000 the European Parliament and Council passed the ambitious directive 2000/60/EC establishing a framework for Community action in the field of water policy, known as the Water Framework Directive (WFD). The key objective of the directive is to achieve ‘good ecological status of Europe’s water resources by 2015’, including groundwater (article 1-d, purpose of the directive: “ensures the progressive reduction of pollution of groundwater and prevents its further pollution”). To achieve this objective a number of activities need to be carried out, leading to an Integrated River Basin Management Plan (RBMP) in 2009 (figure below). The river basin management and planning process prescribed in the WFD focuses on integrated management, involving all physical domains in water management, sectors of water use, socio-economics and stakeholder participation. As such, the WFD poses new challenges to water resources managers. The traditional physical domain specific and sectoral approaches need to be combined and extended to fulfil the WFD requirements. A major part of such a RBMP will be the programme of measures that needs to be implemented to achieve the objectives. Though the WFD’s focus lies on achieving ‘good ecological status’ of Europe’s water resources, it more broadly aims at sustainable water use, covering issues such as droughts and floods (article 1-e, purpose of the directive: “contributes to mitigating the effects of floods and droughts”). Two other relevant directives are currently in preparation: The Groundwater (WFD-Daughter) Directive and the Flood-directive. In addition to the measures required under the Water Framework Directive, the proposed Groundwater Directive

Submit interim report on Update Revised the implementation to the Assess current overview of RBMP EC (Art. 15) status, analyse Set up significant water preliminary gaps environmental issues (Art. 5-8) objectives (Art. 4)

Evaluate the first and prepare the second 2004 Establish period. monitoring programmes (Art. 8) Implement the 2013 2015 programme of measures for RBD 2012 2006

Public Participation (Art. 14)

2009

Gap analysis

Develop River Basin Set up the programme Management Plan of measures for RDB (RBMP) (Art.13-25, (Art. 11) App.VII) Visualisation of the time line of the WFD and its required activities and deliverables (CIS, 2003).

introduces measures for protecting groundwater from indirect pollution (discharges of pollutants into groundwater after percolation through the ground or subsoil). In practise, the implementation of the WFD depends very much on our interpretation and knowledge of e.g. ‘good ecological status’, processes, cause-effect relationships, costs, etc. This knowledge is general is incomplete and uncertain: Hence, when making decisions this need to be acknowledged and taken into account. Developing new methods and tools to handle uncertainties in data and models is the primary objective of the HarmoniRiB project.

1.1.2 The HarmoniRiB project HarmoniRiB is a Research and Development project carried out under, and sponsored by, the European Commission’s “Energy, Environment and Sustainable Development” programme, Key Action 1 “Sustainable Management and Quality of Water”, 1.1 Integrated management and sustainable use of water resources at catchment river basin or sub-basin scale, 1.1.1 Strategic planning and integrated management methodologies and tools at catchment/river basin scale. As can be concluded from chapter 1.1.1 there is a clear and urgent need for developing new methodologies and tools that can be used to assist in implementing the WFD. The HarmoniRiB project aims to deliver some of these new tools, focussing on issues of uncertainties. The overall goal of HarmoniRiB is to develop methodologies for quantifying uncertainty and its propagation from the raw data to concise management information. The four specific project objectives are: • To establish a practical methodology and a set of tools for assessing and describing uncertainty originating from data and models used in decision-making processes for the production of integrated water management plans. It will include a methodology for integrating uncertainties on basic data and models and socio-economic uncertainties into a decision support concept applicable for implementation of the WFD; • To provide a conceptual model for data management that can handle uncertain data and implement it for a network of representative river basins. • To provide well-documented datasets, suitable for studying the influence of uncertainty on management decisions for a network of representative river basins and to provide examples of their use in the development of integrated water management plans. • To disseminate intermediate and final results among researchers and end-users across Europe and obtain and incorporate feedback on the methodologies, tools and the datasets. Thus, the HarmoniRiB project aims to support the WFD implementation, by addressing issues of uncertainty in data en modelling, and by developing a ‘virtual laboratory for modelling studies’. This virtual laboratory will comprise of a set of river basins, of which data relevant to modelling and the WFD, are readily available for the scientific community. The data can be used for comparison and demonstration of methodologies and models relevant to the WFD.

1.2 The Vecht case study – Scope and objective

This report focuses on HarmoniRiB’s Vecht basin case study. The Vecht basin is situated in the east of the Netherlands and the middle- west of Germany. One of the main concerns for reaching good status in this basin is the water quality and one of the key pollutants is nutrients. The study has been limited to the Regge catchment, which is a subcatchment of the Vecht.

Over the past decades many data have been collected in the Vecht basin, most of them in the Dutch part. A considerable selection of these data has been collected and uploaded to the HarmoniRiB database, making it available for use in future case studies. Uncertainty has been assessed for part of these data.

The objective of this case study is to assess the influence of data and parameter uncertainty on the effects of a number of alternative measures, calculated by the model NL-CAT, aimed at reducing the concentration of nutrients in the surface water. Furthermore, the economic costs of these measures, their side effect, public support and acceptance by Brussels are assessed, including corresponding uncertainties.

Three measures that have a positive effect on the reduction of nutrient concentration and reaching good ecological status were selected: reduction of fertilisation use in agriculture, broadening of small watercourses and reduction of nutrient discharge by WWTP’s. For these measures and for the baseline scenario, uncertainty information is assigned to five selected inputs/ parameters and propagated through the model. Furthermore, their costs and related uncertainties have been determined. By doing this, the uncertainty assessment methodology and tools of HarmoniRiB are tested and assessed, contributing to the assignment of how to deal with uncertainty in the decision-making process set for the WFD.

1.3 Reading guide Chapter 2 “The Vecht Basin” describes the Vecht basin in general, and identifies the key management problems. Chapter 2.1 provides a general description of the physical and socio- economic characterisation of the basin. Chapter 2.2 starts with a global identification of the status with respect to achieving the objectives of the WFD. This is based on the Article 5 report of the WFD “Characterisation of the river basin district, review of the environmental impact of human activity and economic analysis of water use”, complemented with specific information from a variety of sources. Next the specific management problems are identified. The paragraph frames the decision making problem, concentrating on a limited number of problems. It thus tests the initial steps of the methodology prepared within HarmoniRiB’s task “Integration of hydrology, ecology and socio-economy into a concept for supporting decision making under uncertainty”. It leads to the targeted objective of the case study decision problem, and provides a number of measures to achieve this objective. Chapter 3 “Physical impact analysis” consists of two major parts. The first part is rather descriptive and gives insight in the models (3.1) and data (3.2) available. The second part contains a summary of what has been carried out exactly during the physical impact analysis, testing results from HarmoniRiB’s tasks “Development of a methodology for assessing uncertainty in river basin data”, “Development of a methodology for assessing uncertainty in river basin models” and “Development of tools for tracking the propagation of uncertainty in integrated modelling systems”. This part starts with a description of the model and an argumentation of the uncertainties considered for the model (3.3). It continues with a summary on the assessment of these uncertainties (3.4). Next the analysis strategy and the results of the integrated uncertainty analysis are presented (3.5). The chapter closes with conclusions (3.6). Chapter 4 “Analysis socio economic impacts” provides insight in the socio-economic impacts of the selected measures, and as such tests the guidelines prepared in HarmoniRiB’s task “Development of a concept for supporting decision making under uncertainty that integrates hydrological, ecological and economic assessments “. First the costs of the different measures are elaborated in chapter 4.1, including their uncertainty. Next the other impacts of the measures are elaborated in chapter 4.2.

Chapter 5 “Evaluation and comparison of measures” provides a summary of the results presented in the earlier chapters in chapter 5.1. The measures will be compared in chapter 5.2. Finally the chapter 6 “Conclusions” discusses the case study from the perspective of the case study itself and the usefulness of all tools and methodologies tested. It closes with recommendations.

2 The Vecht basin

2.1 Physical and socio economic description of case study basin This chapter provides general descriptions of the physical and the socio-economic characterisation of the total Vecht basin. The further study has been limited to the Regge catchment, a subcatchment of the Vecht that covers approximately one fourth of the total basin (figure 2.1).

2.1.1 Physical characterisation

The Vecht is a middle size rain river that originates in Germany and ends in the central north of the Netherlands, where it flows into Lake IJssel. The total area of the Vecht catchment is 3700 km2, which is almost equally divided over the Netherlands and Germany (figure 2.1). The total length of the river itself is 167 km, of which 60 km is situated in the Netherlands (Schoumans, Groenendijk et al. 2005). The Vecht basin is part of the Rhine Basin. For the Dutch part, the Rhine basin has been split into four parts, one being Rhine- East. The Vecht basin covers two third of this Rhine- East basin. The complete river network of the Vecht catchment is shown in figure 2.2.

Figure 2.1: The Vecht Catchment location and division over Germany and the Netherlands. Source (Schoumans, Groenendijk et al. 2005)

Figure 2.2: Complete river network of the Vecht catchment. Source (Schoumans, Groenendijk et al. 2005)

The elevation in the Vecht catchment ranges from 0 m to 163 m above mean sea level (m.s.l.) in the south (figure 2.3). When the Vecht enters The Netherlands it has already declined to 10 m above m.s.l.. The average rainfall in the catchment is 730 mm and ranges from 550 mm in dry years to 1100 mm in wet years. 35-40% of the precipitation runs off. The mean discharge at the mouth of the Vecht is 50 m3/s. At low water it is only 5 m3/s and under conditions of high water it is about 300 m3/s (Schoumans, Groenendijk et al. 2005). This means that flooding and drought problems are both occurring.

Figure 2.3: Elevation map of the Vecht catchment. Source (Schoumans, Groenendijk et al. 2005)

The soil type is mainly sand. Furthermore there are some peat and clay soils, of which most of them are situated in the northwest. Especially in the north of the catchment there are some forests and in the west there are some large lakes. Land use in the southern Dutch part is predominantly intensive animal husbandry, with growing of grass and maize. In the northern Dutch part as well as in Germany there is more arable land, with mainly potato growing. (De Straat Milieu- adviseurs, 2004). The Dutch part is used more intensively than the German part.

The Dutch groundwater system is divided into two major groundwater bodies. Furthermore 81 groundwater bodies for human consumption specified and for good transition across the German border, nine administrative groundwater bodies are specified here. The permeability in most parts is good, ranging from a few hundreds m2/ day in the east to a few thousands m2/ day in the west and north. In the north some impermeable areas can be found (Arcadis 2004). In Germany no separate groundwater bodies for human consumption have been specified. De groundwater bodies defined are smaller than the Dutch major groundwater bodies, but larger than the groundwater bodies for human consumption.

2.1.2 Socio-economic characterisation

The total population in the German part of the catchment is about 54300. In the Dutch part of the catchment about 800000 people live (Schoumans, Groenendijk et al. 2005) The population density is about 300/ km2. The impervious area covers about 12% of the catchment. There are several cities with more than 50,000 inhabitants, like Enschede (150,000), Hengelo (77,000), and Almelo (65,000) in the south of the catchment, Emmen (56,000) in the north, and Zwolle (110,000) in the west. A summary of characteristics has been given in table 2.1. For the Regge catchment, the percentage urban area is higher than for the total Vecht catchment; approximately 20% of the land cover is urban area.

Land use in the southern part is predominantly intensive animal husbandry, with growing of grass and maize. In the northern part there is more arable land, growing mainly potatoes. The main purposes of water use are: agriculture, drinking water, and recreation. The human pressure on the aquatic environment is high, both from cities and from intensive agriculture. Discharges from many of the sewage treatment plants are into relatively small waters.

Most of the waters in the catchment have been strongly regulated by normalisation and dams. In large parts of the area water inlet from outside the catchment plays an important role for agriculture in the summer. In relatively small areas of about 10-100 km2 there are many interests, as agriculture, urban area, nature, landscape, and drinking water, which may pose different demands to water supply and water quality. Pictures of some tributaries to the Vecht are shown in figure 2.4.

Figure 2.4: Tributaries to the river Vecht: natural situation (left) and normalized river.

Table 2.1: General catchment information. Source (Schoumans, Groenendijk et al. 2005) Dutch part German Part Catchment Area 2400 km2 1300 km2 Elevation Range 0-83 m 10-163 m Rainfall 782 mm Soils 16% peat 30% sand 54% loamy sand Land cover 41% arable land 35% grassland 12% forest and nature 11% urban 1% open water Cities with over Enschede (147 910) Lingen (52 500) 50 000 inhabitants Hengelo (77 480) Nordhorn (52 000) Almelo (65 170) Emmen (56 025)

2.2 Identification of water management problems addressed in case study

This chapter starts with a global identification of the status of the Vecht basin with respect to achieving the objectives of the WFD. This is based on the Article 5 report of the WFD “Characterisation of the river basin district, review of the environmental impact of human activity and economic analysis of water use” (Arcadis, 2004), complemented with specific information from a variety of sources. Next the specific management problems are identified. The chapter frames the decision making problem, concentrating on a limited number of problems. It those tests the initial steps of the methodology prepared within HarmoniRiB’s task “Integration of hydrology, ecology and socio-economy into a concept for supporting decision making under uncertainty”. It leads to the targeted objective of the case study decision problem, and provides a number of measures to achieve this objective.

2.2.1 Good status reached or failed? Framing the decision making problem

The WFD demands to reach the good status simultaneously in all respects. Good status for surface water is defined as two equally important elements, good ecological status and good chemical status. The former is defined based on biological quality elements, together with supporting hydromorphological (hydrological and morphological), physico-chemical (e.g. temperature, salinity) and chemical elements. Groundwater status is defined in terms of water balance and chemical status.

In the WFD article five report for Rhine East an inventory has been made of the most important problems for the Rhine east area (Arcadis 2004). For the Vecht a similar study has been executed just before the WFD took effect (De Straat Milieu- adviseurs 2004). This study has been used to check whether the conclusions of the former report hold for Vecht catchment scale. In the following text a subdivision is made for surface water and groundwater.

Surface water Good chemical status of the surface water is not being met in half of the water bodies of Rhine East. Good ecological status as defined for natural waters is not being met in almost all water bodies. On the biological quality elements, almost all water systems are evaluated as moderate or bad. Based on the economic and autonomous developments of the current policy, it is expected that in 2015 there will be no substantial improvement or declination of this situation. The most important pollutants are copper, zinc, nickel, phosphorus and nitrogen.

Groundwater Most groundwater bodies for human consumption will not reach good status in 2015. Nitrogen, pesticides and influences on terrestic ecosystems are the most important problems for groundwater. At the countryside, diffuse pressures are most important, with nitrogen being the most important pressure. In the urban area point sources are most important.

Concentration on one problem field This case study will focus on one problem field of the above problem outline: nutrients in the surface water system. The tools and methodologies developed in HarmoniRiB are most promising for quantifiable problems and uncertainties. One of the largest problem fields in the study area, that is also reasonably quantifiable, is nutrient pollution. Focussing the case study on nutrient pollution of the surface water will give the possibility of thorough testing of

the HarmoniRiB deliverables and contributing to the WFD assignment for the Vecht Catchment.

The specific focus of the study will be on the nutrient concentration of the surface water, since the formal criteria are set for nutrient concentration.

In the Regge catchment, both diffuse pressures and point sources are important; agricultural lands as well as urban areas are important land covers. Diffuse pressures are in general most important, however, in times of low precipitation rates and according discharges, point sources gain importance. The pressure of nitrogen and phosphorus is relatively high in the upstream system compared to the downstream system.

Identifying the decision maker(s) The responsibilities with respect to the water quantity and water quality in the area are divided by many organizations. Rijkswaterstaat, which is part of the national government, is responsible for the national water policy, including flood protection, water supply, water quality, shipping routes, and acts on a strategic level. Two Provinces, Drenthe and Overijssel, are active in the catchment. They take care of the regional coordination of large projects (e.g. WFD) and the management of strategic groundwater supplies.

For the management and maintenance of the river Vecht itself, two waterboards are responsible: Groot-Salland and Velt en Vecht. In total there are four water boards in the catchment. The water boards are responsible for the (regional) operational water management, discharge of water, surface water levels, and surface water quality. Finally, the municipalities in the catchment are responsible for the drainage of public areas and the sewage water and the drinking water companies take care of the supply of drinking water. Table 2.2 supplies an overview of the most important responsibilities in the Vecht catchment for the WFD and this case study.

Water manager Groundwater Groundwater Surface water River Vecht (formal) (actual) (general) (specific) Province Overijssel x Province Drenthe x Waterboard Regge en Dinkel x x Waterboard Velt en Vecht x x x Waterboard Reest en Wieden x x Waterboard Groot Salland x x x Table 2.2: Segmentation most important responsibilities in the Vecht catchment. Source: (Arcadis 2004) (adapted)

2.2.2 Specification of “Good Status”, list of evaluation criteria

In order to reach good status, the concentration of nutrients in the Vecht has to be reduced. The responsible authorities, the waterboards, have to make a good assessment of the interests when developing measures to reach this reduction. In this study, three different categories of criteria are distinguished for this assessment:

1. Criteria for obtaining good chemical status: - Concentration of nitrogen of 2.2 mg/l before 2027 - Concentration of phosphorus of 0.15 mg/l before 2027

Awaiting further notification of the criteria for good chemical status to be set for the WFD, these concentrations are generally held in the Netherlands. The concentrations are summer- averaged values, from 1 April till 30 September, which is the critical period for eutrofication in stagnant waters. The first check for meeting the criteria is 2015, but the possibility exists to postpone reaching the targets until 2027. Meeting the criteria of good chemical status will be indicated by the actual bandwidth for the nitrogen and phosphorus concentrations that follow from the physical impact analysis.

2. Costs - Costs for the execution of the measures

An indication of the costs of the measures will be indicated by means of a bandwidth.

3. Further criteria - side- effects of measures - public support of the measure - acceptance by Brussels

The side- effects of a measure will be indicated by very negative (--) to very positive (++). The public support of a measure will be indicated by very low (--) to very high (++). The acceptance by Brussels will be indicated by very low (--) to acceptance (0)

2.2.3 Measures considered

Measures for reduction of the nutrient concentration that will be considered in this study are: - Reduction of fertilisation usage by 50%: this can be partly be implemented by technical measures: reduction of nutrient concentration in manure or a more precise application of manure and fertilizer. For the rest of the reduction, the total application dose to the crops needs to be decreased. In total, this measure has to lead to a 50% smaller application of nutrients to the soil by agricultural practice. - Reduction of WWTP nutrient discharge by 50%: by implementation of quaternary treatment, the total nutrient load in the effluent of the WWTP will be reduced by 50% for the total catchment on average. - Increasing the volume of the small watercourses by a factor 2: all small watercourses will be excavated. In order to increase their volume by 2, there width needs to be enlarged by a factor 2.3. This measure will increase the hydraulic retention and thus will stimulate the degradation of nutrients in the surface water.

All measures are implemented at once.

Measures have been selected on the criteria that they: - have a considerable effect; - are of interest to the scientific community; - can be implemented readily in the model.

Measures that are out of the scope of the currently considered measures are preferred to prevent conflict with interest of the water boards.

The goals set for the concentration of nitrogen and phosphorus are very ambitious. The measures need to be quite heavy in order to be able to reach the goals by taking few measures. An advantage of taking considerably large measures is that the effects of the measures and the uncertainty can be visualised more clearly. Roughly, agricultural fertilisation is responsible for 2/3 of the nutrient pollution in the Vecht catchment and WWTP effluent for 1/3 of the nutrient pollution. The first two measures focus on the pollution sources. The last measure focuses on a mitigation of the effects by increasing the hydraulic retention and thus increasing the level of degradation of nutrients. The extent of the measures moves beyond the scope of what is currently thought of.

The following combination of measures will be considered: 1 Baseline scenario, no measures 2 Reduction of the fertilization level by 50% 3 Increase of the storage capacity of the detailed surface water system by a factor 2 4 Reduction of the WWTP discharges by 50% 5 Combination of reduction of fertilization level and WWTP discharges, both by 50% 6 Combination of all measures.

2.2.4 Summary of decision problem: set-up of the decision matrix

The decision problem can be summarised by the matrix shown below. The matrix gives an overview of the alternative measures to consider, the criteria for measuring the contribution of the alternative to the goal and the criteria for measuring the consequences (costs, side effects and public support) of the alternative. In the next chapter, the effects of the measures on the criteria nitrogen concentration and phosphorus concentration will be elaborated. In chapter four the costs, side effects and public support will be elaborated.

Criteria Nitrogen Phosphorus Costs Side effects Public Alternative conc. conc. support 1. Baseline scenario 2. Reduction of fertilization 3. Increase of storage cap. 4. Reduction of WWTP discharges 5. Reduction of fertilization and WWTP discharges 6. Combination of all measures

3 Physical impact analysis

This chapter consists of two major parts. The first part is rather descriptive and gives insight in the models (3.1) and data (3.2) available. The second part contains a summary of what has been carried out during the physical impact analysis, testing results from HarmoniRiB’s tasks “Development of a methodology for assessing uncertainty in river basin data”, “Development of a methodology for assessing uncertainty in river basin models” and “Development of tools for tracking the propagation of uncertainty in integrated modelling systems”. This part starts with a description of the model and a discussion of the uncertainties considered for the model (3.3). It continues with a summary on the assessment of these uncertainties (3.4). Next the analysis strategy and the results of the integrated uncertainty analysis are presented (3.5). The chapter closes with conclusions (3.6).

3.1 Introduction: Model selection

The Vecht catchment has already been subject to modelling studies in the past. For the Vecht catchment as a whole, two modelling studies have been carried out recently. These are Water Quality Vecht/ Zwarte Water (De Straat Milieu- adviseurs 2004) and Euroharp Vecht case study (Groenendijk, Siderius et al. 2005). It has been decided to benefit from the models and knowledge from one of these existing studies. A short description will be given of both model studies and next a motivation for the choice for the Euroharp Vecht case study model NL-CAT.

In the study Water Quality Vecht/ Zwarte Water an overview of the status of the Vecht basin with respect to nutrients is provided. In the study is made use of spreadsheet calculations (balance calculations), based on available data. Autonomous developments of already planned measures and the effects of a list of new measures are included.

The model NL-CAT is a complex model containing both the groundwater and surface water system and both water quantity and water quality processes. In the project Euroharp the model NL-CAT is used to help characterise the relative importance of point and diffuse nutrient pollution in surface freshwater systems. One of the case studies in this project was the Vecht catchment. As a consequence relevant data is collected, the total Vecht catchment is schematised and much experience has been gained during the project.

The NL-CAT model has been chosen because the model allows a more fundamental research for data and parameter uncertainty. Real processes have been modelled in contrast to the simpler spreadsheet model of the Water Quality Vecht/ Zwarte Water study.

3.2 Data availability

In above-mentioned projects numerous data on hydrology, the water system, land use, emissions, retention in surface waters, ecology and costs and effects of measures have been collected. These data will be used in this study. An overview of the data in the Vecht is difficult to obtain, because many different parties own these data. The spatial and temporal resolution of these data range from a gridsize from 25 to 500 m, polygons from 20 km2 to 500 km2, and time intervals from a day to a year. Information on uncertainties is scarce.

Furthermore, several general databases exist in the Netherlands. The limnological database (STOWA) contains biological and physical- chemical data. The Donar database (Rijkswaterstaat) consists of biological, physical- chemical and hydrological data. The DINO database (TNO, dinoloket.tno.nl) contains geohydrological data. Digital landuse information (Alterra) stored in so called LGN maps are freely obtainable in aggregated versions. Again, uncertainty information is scarce.

A selection of available data has been uploaded to the HarmoniRiB databse. An overview of all uploaded data can be found in the Vecht Basin Data Report (…).

Insight on available German data is not easy to obtain. For the Euroharp project, some data were collected. The resolution however is coarser than for the Dutch data. German data from the Euroharp project have been used in this study.

3.3 Description of models

The NL-Cat modelling system comprises of five sub models and a number of modules for spatial discretisation, data processing, process simulation and scripts for model execution (figure 3.1). The sub models are: - The soil water flow module SWAP - The soil nutrient cycle and leaching module ANIMO - The surface water quantity model module SWQN - The surface water quality model module Nuswalite - The erosion module P-USLE

The functions of these sub models are described below. Next, the spatial schematisation procedure for the Vecht catchment is elaborated. More information on scripts and databases can be found in Annex 1.

dbSwan database EuroHarp datasheets, maps and time series R-scripts

SWAP input ANIMO input data data

ANIMO SWAP soil and groundwater hydrological module quality module

Discretization to land key between land P-USLE plots, water sheds and plots and surface surface water nodes water nodes P-transport by erosion

SWQL SWQN point sources surface water quality hydraulic module module

Loads, concentrations, retention in soil, retention in surface water

Figure 3.1 Model components of the quantification tool NL-CAT

Soil water flow module Water discharge to groundwater and surface water is simulated utilising a schematised pseudo-two-dimensional flow concept in a vertical soil column with unit surface. Hydrological information for each of the distinct soil layers is simulated by the field plot model SWAP (Van Dam 2000; Kroes and Van Dam, 2004). In the SWAP version that has been used for the Vecht catchment a simple snow melt and soil frost module has been applied.

Soil nutrient cycle and leaching module The ANIMO model aims to quantify the relation between fertilisation level, soil management and the leaching of nutrients to groundwater and surface water systems for a wide range of soil types and different hydrological conditions. Transport routes from agricultural land are related to surface runoff, leaching to groundwater and leaching to surface water systems. The ANIMO model focuses on the following processes: • Inputs or additions to the soil system (fertiliser, manure, crop residues, atmospheric deposition), • mineralization of nutrient compounds in relation to formation and decomposition of different types of organic matter as organic fertilisers, root residues, yield losses and native soil organic matter; • volatilisation (CO2, NH3, N2, N2O), • nitrification of NH4 and denitrification of NO3; • sorption onto and diffusion within soil particles, described by a combination of instantaneous and time dependent sorption and chemical precipitation of phosphoruss (Schoumans and Groenendijk, 2000); • uptake by the vegetation; • transport of dissolved organic and inorganic nutrients with water flow to deeper soil layers and to adjacent surface water systems; and

• overland flow of particulate and dissolved organic phosphorous and inorganic phosphorus with water flow to adjacent fields (runoff and erosion) ANIMO comprises a description of the organic matter cycle, the nitrogen cycle and the phosphorus cycle, since these cycles are interrelated in most of the modern farming systems and in soil bio-chemistry.

Surface water quantity module Computing water levels and flows in very large schemes of open water courses requires a robust and relatively fast algorithm. To meet such requirements the SWQN model has been developed in which watercourses are schematised into a network of nodes linked by segments. The module is pseudo-dynamical in time, based on the assumption that steady- state conditions prevail during a time step. A connector can be specified as an open watercourse or an artefact like a weir, underflow, pump, etc. It is assumed that the flow between 2 nodes is linear dependent on the difference in water level, the wetted profile and a given resistance. Each artefact, on the other hand, has its own specific stage-discharge relation and is linearised using a number of intervals.

Surface water quality module The surface Water Quality Model SWQL (Jeuken et al., 2005) indirectly calculates the nutrient retention in a surface water system by process oriented model descriptions. The model describes the dissolved organic and mineral fractions of nitrogen and phosphorus concentrations in a network of nodes. Also two fractions of living biomass are considered: a floating fraction, which can be transported with water flow, and an immovable fraction having roots in the sediment. Biomass is considered to have a fixed nutrient ratio, so no separate pools of nitrogen and phosphorus in biomass are defined. Besides inflow, outflow (not for immobile biomass) and loading (not for biomass), the following processes are taken into account • Growth of biomass with linked uptake of nutrients and limited by solar radiation and nutrient availability • Death of biomass which adds to the organic nutrient pools • Degradation of organic nutrients to their mineral forms • Denitrification of inorganic nitrogen • Linear sorption of mineral nutrients to the sediment • Sedimentation of inorganic phosphorus

Input consists of a network layout and a water balance (as could be provided by SWQN or any other hydraulic model), nutrient loading from various sources (e.g. leaching as calculated by ANIMO or point sources), environmental conditions (e.g. temperature and global radiation), initial conditions and parameter settings.

Erosion module (P-USLE) To quantify the amounts of P (and optionally N) added to the surface water system via surface erosion, the NL-Cat model has been extended with a simple erosion module, i.e., P- USLE. This module is based on the modified and revised Universal Soil Loss Equations (respectively MUSLE and RUSLE) and implemented in a GIS-environment. P-USLE quantifies the amounts of P entering the surface water system by surface erosion in two steps. First, the amount of sediment generation E for each grid cell is computed as the product of: • the rainfall and run-off factor R; • the soil erodibility factor K; • the soil cover factor C; • the slope length and steepness factors LS;

• the erosion control practice factor P; • and the coarse fragment factor r. During the second step, the amount of particulate P entering the surface water system is computed by combining E and the amount of phosphorus in the top soil. The latter is computed by ANIMO.

Spatial schematization For the Vecht catchment, the AVSwat (Di Luzio et al, 2002) has been applied for the delineation of the catchment but due to small differences in elevation and the artificial nature of a large part of the water infrastructure results have been corrected by hand on the basis of the actual information of the stream network. The digital watershed map was combined with other maps (Land cover, Land management, Soil, Hydrological boundaries, Meteorology) to arrive at a number of unique calculation units for each watershed. No further definition of geo-referencing was done, so a plot can consist of grid cells scattered over multiple locations in the catchment.

The surface water network is described by a system of surface water nodes connected by surface water sections. The network is an explicit description of the main watercourses on the user-defined scale. The minor watercourses are lumped into a so-called 'added storage' node representing the volume of this part of the system. For each watershed only one node is connected to the added storage volume.

The conversion of output of the soil models to input to the surface water, the link between soil plots and surface water nodes, is defined in two key-files: 1. Link between the soil plot to the sub-catchment it is situated in 2. Link between the sub-catchment and the surface water node which receives the nutrient inputs which contains the information with respect to the area of each plot discharging to a certain surface water node.

Calibration NL-CAT has been calibrated for the total Vecht catchment in the Euroharp project. One of the calibration points has been the Regge catchment. Graphics of the results of the calibration of the Regge outflow point for nitrogen and phosphorus concentrations are shown in figure 3.2. The uncertainty in the measurement data is estimated to be 10% for nitrogen and 12% for phosphorus (Refsgaard et al, manuscript in submission) and is shown in the figures. For nitrogen, the model results follow the dynamics of the measurements quite well. In general, the model results show a slight overestimation of the peaks in the nitrogen concentration. These peaks don’t fall within the uncertainty bandwidth of the measurement data. For phosphorus, the model results fail to follow the dynamics of the measurement data very well. This can be explained by the fact that not all processes for P are present in the model and the data availability for the model is quite low. The general trend of the concentration in phosphorus is followed by the model results.

To be able to perform calculations for the Regge catchment on its own, the surface water schematisation had to be isolated for this catchment with respect to the Euroharp schematisation. The adaptations in the schematisation have been described in the model description earlier this paragraph. The new model results have been shown in the calibration graphics of figure 3.2 and differ only slightly from the original model results. Therefore the changes in schematisation are expected not to influence the model results.

During the assessment of the specified uncertainties later on in this chapter, it turned out that the original Euroharp input data/ parameters for the Fe/Al content in the soil, the phosphorus background concentration and the denitrification parameters were not the median values found in the uncertainty analysis. The Monte Carlo analysis has been run with the bandwidths found in the analysis. This has some implications for the quality of the calibration, which has been performed for the Euroharp input data/ parameters. However, it is beyond the scope of this project to recalibrate the model. In addition, the denitrification parameters have been included in the uncertainty analysis, but have also been used for the calibration of the model. How to deal with calibration parameters and calibration in a Monte Carlo uncertainty analysis is conceptual conflict that needs attention in further uncertainty studies.

In the Euroharp study, the NL-CAT model has not been formally validated for application in the Vecht catchment.

Calibration for nitrate 25 measured model_original 20 model_new

10 Conc. N(mg/l) 5

0 1991 1992 1993 1994 1995 1996 1997 1998 1999 Date

Calibration for phosphate

3 measured

2.5 model_original model_new 2 1.5

Conc. (mg/l) P 0.5

1991 1992 1993 1994 1995 1996 1997 1998 1999 Date

Figure 3.2: Nitrogen (above) and phosphorus (below) calibration graphics at the outflow point of the Regge catchment for the years 1991- 2000. The measurement data have been represented in blue, the original calibration in the Euroharp project have been represented in purple and the new fit of the model results after isolation of the Regge area have been shown in yellow.

Uncertainties considered (data and parameters) With respect to uncertainty in the results of the nutrient concentration, this study specialises on data and parameter uncertainty. Other types of uncertainty are beyond the scope of this study. First an overview will be given of the selection process of the uncertainties. Next a brief overview will be given of most important uncertainties that have not been included.

Uncertainties included The selection of the uncertainties has been based on expert knowledge regarding the importance of uncertainties, curiosity, relation to measures considered and practicability. The expert knowledge has been based on a sensitivity analysis of an earlier version of the SWAP and ANIMO model (Wesseling et al., 1998; Groenenberg et al., 1999 ) and expert judgment. In a first selection step, few uncertainties have been selected for each module of NL-CAT This selection included: • For the soil water flow module SWAP: - Lower boundary flux - Tile drain presence - Evapotranspiration • For the soil nutrient cycle and leaching module ANIMO - Iron and aluminium quantity in the soil - Historical fertilisation surplus and landuse - Phosphorus background concentration - Denitrification (oxygen diffusion coefficients) • The surface water quantity model module SWQN - Storage capacity of the detailed surface water system • The surface water quality model module Nuswalite - Nitrogen and phosphorus loss factors (e.g. denitrification, sedimentation) • The erosion module P-USLE -

The next selection has been made on practicability. In the time available, not all uncertainties can be handled. We have decided to limit our efforts to the uncertainties in ANIMO and Nuswalite. This means that uncertainty in water quantity will not be included. This has been based on the following considerations: • The calculation time of SWAP is very large. Including uncertainties in this module would expand calculation time to a large extend. • In the sensitivity analysis of SWAP it has been found that the largest uncertainties for the SWAP module relate to the boundary conditions. However, these boundary conditions have been deducted from calculation by other models. Including uncertainties would imply an analysis of these models and possibly new calculations. This would be too time consuming for the current study. • The nitrogen and phosphorus loss factors in Nuswalite are modeled in lumped parameters. An uncertainty analysis for these lumped parameters would be difficult.

The selected uncertainties for consideration in this study are: • Oxygen diffusion in soil (ANIMO) • Phosphorus background concentration (ANIMO) • Fertilisation (ANIMO) • Iron and aluminium quantity in the upper soil (ANIMO) • Storage capacity of the detailed surface water system (Nuswalite)

The elaboration of these uncertainties has been documented in the next paragraph. Uncertainty due to meteorological variance has been included indirectly by including a range of 15 different meteorological years in the model calculations. The influence of uncertainty due to meteorological variance will be analysed for the results.

Uncertainty in the results of the measures reduction of fertilization and increase in storage capacity is reflected by the input uncertainties fertilization and storage capacity of the detailed surface water system respectively, that are included in the uncertainty analysis. For the uncertainties of the measures, it has been chosen to apply the same uncertainty for the model input after the implementation of the measure as before. The uncertainty estimated in paragraph 3.4 can already be seen as a rough estimate. We found it not justified to provide for another rough estimate.

Uncertainties not included To start with, not all important data and parameter uncertainties have been included, but a selection has been made (see former paragraph). Furthermore, other important uncertainties that are not directly related to data/ parameters of the model have not been included. Developments and uncertainties that have been left outside the scope of this research but are also very likely to have an important influence on the results are scenario uncertainties, uncertainties related to the measures and uncertainties related to the framing of the study.

The most important scenario uncertainties are: • Climate developments: climate change directly influences temperature, precipitation and evapotranspiration and thus influences the water balance in the catchment. Until 2015 the effects of climate change are assumed to be small. The developments are however expected to have an effect on the results of 2031. Neither climate change nor its uncertainty have been included in the analysis. • Population developments: population growth will influence the amount of domestic and industrial wastewater and the RWZI effluent. The central estimate of the growth estimated for the Netherlands for the period 2000-2030 is 7.3 percent having a considerable bandwidth. (source: Statistics Netherlands (CBS)). However, neither the growth nor its uncertainty have not been included in this study.

Uncertainties related to measures: • Developments due to measures already decided for and their related uncertainties have not been included in this study. The baseline scenario is purely an extension of the current situation. The measures abruptly take effect in 2006. Because of this simplification, this study is just a theoretical study, which cannot be used for direct policy advice. • Uncertainty in the measures themselves: o No specific uncertainty has been included for implementation of the measures. The simplification is made that measures can be implemented without uncertainty. This simplification will probably not hold in real life. o No specific uncertainties have been considered for the measure ‘reduction of the WWTP nutrient discharges’, since uncertainty in the WWTP load is not included as an input uncertainty. Uncertainties in the data will not be very large, due to precise measurement possibilities. However, the relative size of this pollution source is large, which may cause this uncertainty to still have a substantial influence.

Uncertainties related to the framing of the study:

Uncertainties related to the problem framing, selection of measures and selection of criteria have not been included in this study. Because of this simplification, this study is just a theoretical study, which cannot be used for direct policy advice.

3.4 Assessment of uncertainties This chapter described how the uncertainties of data and model parameters selected in chapter 3.3 have been assessed / estimated. A detailed description of the analysis of the uncertainties can be found in Annex 2, together with an explanation about the use of the data or parameter in the model. An overview of the uncertainties, the main characteristics of their analysis and their location in the model can be found in figure 3.3. An overview of the ranges resulting from the uncertainty analysis for each input/ parameter can be found in table 3.1. For the assessment of the uncertainties has been made use of the document “Guidelines for assessing uncertainties in river basin management studies” (Van Loon and Refsgaard, 2005), developed in HarmoniRiB task D2.1 and the DUE software (www.harmonirib.com) developed in HarmoniRiB task D2.4

3.4.1 Uncertainty aspects of oxygen diffusion in soil An uncertainty analysis has been carried out for the parameters p1 and p2 in the oxygen diffusion relationship in soil:

D p2 = p1 ε Do

Where: D Oxygen diffusion coefficient in soil (L2 T-1) Do Oxygen diffusion coefficient in an air medium (L2 T-1) ε Soil air content (-) p1; p2 Parameters to be assessed experimentally (-)

The uncertainty in the parameter values has been assessed for podzolic, medium textured sandy soils for the subsoil zone. The parameters as well as the uncertainty in these parameters are assumed constant throughout the catchment and in time. Experimental data for the diffusion relationship have been used to assess the parameters p1 and p2 simultaneously in a multi-variate analysis. Bootstrapping (Efron & Tibshirani, 1994) has been performed to estimate the joint uncertainty distribution of the parameters. For each bootstrap sample, the parameters p1 and p2 were estimated by means of non-linear least squares regression.

The main assumptions/ limitations of this analysis are: - Analysis is limited for the subsoil zone and podzolic, medium textured sandy soils. - Assumption: no spatial variation in the oxygen diffusion parameters - Assumption: no temporal variation in the oxygen diffusion parameters - Assumption: the experimental data are representative - Uncertainty due to bias in the data itself is not included

average value for the parameters on catchment scale, the variations are smaller and spatial homogeneity can be assumed. When inspecting the results of the NL-Cat model on a more detailed level than the whole Regge catchment one should keep in mind that this aggregated uncertainty model will probably not be correct on this smaller scale. Regarding the representativeness of the data: the data are judged to be representative for the Regge soil characteristics. The amount of data however, is limited. Finally, this uncertainty analysis does not include the uncertainty in the data itself. Uncertainty due to random errors will balance itself out in the aggregation process. However, uncertainty due to any possible bias in the data is not included.

3.4.2 Uncertainty aspects of phosphorus background concentration An uncertainty analysis has been carried out for the variation of the mean phosphorus background concentration in the Regge catchment. In this analysis, the concentration as well as the uncertainty in the concentration are assumed constant throughout the catchment and in time. Point measurement concentration data have been aggregated to a mean concentration on catchment scale. Statistical analysis of the mean concentration resulted in a normal distribution, having a mean of 0.00016 kg/ m3 and a standard deviation of 0.000011 kg/ m3.

Main assumptions/ limitations: - Assumption: The estimated mean and standard deviation are reliable estimates of the true mean and standard deviation - Assumption: Temporal homogeneity for the average concentration on catchment scale. - Assumption: Spatial homogeneity for the average concentration on catchment scale. - Uncertainty due to bias in the data itself is not included

The first two assumptions are assumed to be reasonably fair. The assumption of representativeness of the distribution is supported by the evenly distribution of measurement locations over the area and the considerable number of measurements. Regarding the temporal variation, data analysis has shown that the variations in time are relatively small, certainly in comparison to the variations in space. Regarding the spatial variation, the point measurements show a large variation so the phosphorus background concentration on point measurement scale is not spatial homogene. The autocorrelation length scale of the point measurements is much less than catchment scale so they can be assumed independent. Spatial homogeneity can be assumed when the point measurements are aggregated to an average value for the phosphorus background concentration on catchment scale. This aggregation reduces much of the variability. When inspecting the results of the NL-Cat model on a more detailed level than the whole Regge catchment one should keep in mind that this aggregated uncertainty model will probably not be correct on this smaller scale, due to inhomogeneity of this smaller scale. Finally, this uncertainty analysis does not include the uncertainty due to bias in the data itself. Uncertainty due to random errors will balance itself out in the aggregation process. However, uncertainty due to any possible bias in the data is not included.

3.4.3 Uncertainty aspects of fertilisation An uncertainty analysis is carried out for the amount of fertiliser application in the Regge catchment. The uncertainty in the amount of fertiliser is determined in a two-step approach, first uncertainties relevant on catchment scale and next additional uncertainty on plot scale. On catchment scale, uncertainty caused by bias of the data is assessed. A uniform distribution, with upper and lower bounds ranging 75-125% from the original Euroharp

input, has been set to reflect the variation in the total mean fertilisation application in the area (based on expert judgement). Next, variation in the amount of application within the area has been determined. The four original fertilisation areas from the Euroharp project have been maintained and the total mean application from the former step has been allocated to these regions by ratio. Next, statistical variation on plot scale from the so acquired mean has been determined. A statistical distribution has been obtained from field scale data that have been aggregated to plot scale (by dividing the standard deviation by the number of independent fields in a plot). The assumed autocorrelation of application on field scale is 3 (maize) or 6 (grass) fields. A distinction is made between the application to grass and maize land.

Main assumptions/ limitations: - Analysis is only performed for the main soil type sand and main land uses grass and maize. - Assumption: uncertainty in the mean fertilisation application in the catchment can be expressed by the defined uniform distribution (established by expert judgement) - Assumption: on field level, the standard deviations of the normal distributions found in literature are representative for the year 2000. - Assumption: the autocorrelation for a field application is assumed to be 3 ha for maize and 6 ha for grass. - Assumption: Ratios for division of fertilisation over four fertilisation districts and over types of fertilisation of the Euroharp project are treated as certain. - Assumption: phosphorus application can be deducted from nitrogen application - Variation of the magnitude of the relative uncertainty in time is not taken into account

By only including grass and maize land in this analysis, a small part of the catchment has not been covered. Overall, data to trace back uncertainties in the construction of the input data were difficult to find. Data on uncertainty on field level have been found and aggregated to plot level. For this an assumption needed to be made about the autocorrelation of a field, based on maps and expert judgement. For the uncertainty of assumptions and rules of thumb that influence all data on catchment level has been relied on expert judgement. To do justice to extensive and intensive fertilisation areas, ratios between fertilisation districts specified in the Euroharp project have been applied. Uncertainty in these ratios has not been expressed. Original Euroharp ratios have also been applied for detailed allocation of nitrogen and phosphorus content to types of fertilisation. Uncertainties in these ratios are expected to have a minor influence on the results, the total sums of nitrogen and phosphorus content are more important. Uncertainty in phosphorus content has been directly related to uncertainty in nitrogen content, which will only be approximately true. Variation of uncertainty in time has not been taken into account in this analysis. In summary, many simplification and assumptions were needed in this analysis. Considerable uncertainty bandwidths have been taken because exact magnitude of the uncertainty cannot be determined.

3.4.4 Uncertainty aspects of iron and aluminium content in the upper soil

An uncertainty analysis is carried out for the iron aluminium concentration in the topsoil (1- 120 cm). For this purpose a geostatistical analysis has been applied: conditional sequential Gaussian (block) simulation. Uncertain model input has been created for each plot. The mean and standard deviation have been determined for each soil type and next the autocorrelation length scale for iron and aluminium concentration. The geostatistical analysis has been applied to a 250x250m grid scale, and was then aggregated to plot scale. Direct

analysis on plot scale is not possible because of the nature of the plots, which are scattered over multiple locations in the area. Each realisation for the MC simulation is composed from (aggregated) samples for each plot.

The main assumption and limitations of the analysis were: - The iron aluminium level is assumed constant in time. - The measurements used are assumed to be representative for the area and to provide information that is detailed enough for the analysis. - The analysis has only been executed for the horizontal spatial variations of iron and aluminium. To account for vertical variation, the outcome of the analysis (average vertical concentration) has been subdivided over the soil horizons based on the original ratio’s between horizons.

The first two assumptions are assumed to be reasonably fair. Temporal homogeneity is justified because the composition of the soil does not vary much in time. The measurement data are assumed to be quite representative because the sampling locations are chosen a- select and the data were able to produce satisfactory variograms. Because of the restriction of the analysis to the horizontal dimension, the vertical dimension of the Fe/Al concentration will still contain some unexpressed uncertainty. One needs to be aware of this.

3.4.5 Uncertainty aspects of storage capacity of detailed surface water system An uncertainty analysis has been carried out for the storage capacity of the detailed surface water system. To this respect, uncertainty introduced by the water levels has been analysed. An analysis has been performed per subcatchment for summer as well as winter water levels. Point measurement data on the water levels in the small watercourses have been analysed. For the summer period the exponential distribution has been found representative, for the winter period mostly the lognormal distribution. To aggregate the point measurements to subcatchment scale the distribution of the measurements scale has been used to sample a number of realisations n, with n based on the size of the subcatchment. The average of these n realisations forms one realisation for use in the model.

Main assumptions/ limitations: - Uncertainty in the watercourse dimensions is not included. - Assumption: water levels can be considered constant over the subcatchment scale - Assumption: water levels can be considered constant over the winter and summer period. - Assumption: the data and applied distributions are representative for the subcatchment water volumes. - Bias in measurement data is not included.

The watercourse dimensions are according to a standard profile. On subcatchment scale, deviations in this standard profile will balance each other out, resulting in a minor importance of this uncertainty compared to the water levels. The assumption of homogeneity of water levels in a subcatchment is justified to a large extent by assuming that all surface water in a subcatchment is interconnected. Water levels are not constant over summer and winter. The realisations are an estimate of the summer and winter mean water levels. The output with respect to this uncertainty factor can therefore best be considered for either the summer or the winter period. The data and applied distributions of the smaller catchments are based on few measurements and the representativeness can be doubted. For the larger catchments the analysis results will be more representative. Bias in the measurement data is not included, for example due to assumptions made. Only random errors are included. These

will balance each other out for larger catchments, bias will not. However, information about bias is not available.

Table 3.1 an overview of the ranges resulting from the uncertainty analysis for each input/ parameter.

Uncertain input/ 90% CI – lower 90% CI- upper Median parameter bound bound Fertilisation 442 kg N 643 kg N effective/ 538 kg N effective/ effective/ ha1 ha1 ha1 Phosphorus 0.00015 kg/ m3 0.00018 kg/ m3 0.00016 kg/ m3 background conc. Denitrification P1: 0.34 P1: 1.59 P1: 0.59 parameters P2: 1.95 P2: 2.94 P2: 2.28 Fe/Al in upper soil 54.4 mmol/kg2 62.4 mmol/kg2 59.38 mmol/kg2 Storage capacity of 0.30 m3 0.36 m3 0.32 m3 detailed SW system

1 The presented 90% confidence interval in this table is for the average nitrogen application rate in the total Regge catchment. In the actual realisations of the uncertainty analysis, uncertain input has been specified on plot level and distinction has been made between the land uses grass and maize. An overview of the specific standard deviations on plot level can be found in Annex 2. The presented confidence interval in this table, gives a good overview of the overall effect of the uncertainty applied in the analysis on the level of the total catchment. 2 The presented 90% confidence interval in this table is for the average iron and aluminium content for the total Regge catchment. In the actual realisations of the uncertainty analysis, uncertain input has been specified on plot level and for iron and aluminium separately. An overview of the composition of the input on plot level can be found in Annex 2. The presented confidence interval in this table, gives a good overview of the overall effect of the uncertainty applied in the analysis on the level of the total catchment. 3 The presented 90% confidence interval in this table is for the average waterlevel in the ditches for the total Regge catchment. In the actual realisations of the uncertainty analysis, uncertain input has been specified on subcatchment level and for the summer and winter separately. An overview of the composition of the input on subcatchment level can be found in Annex 2. The presented confidence interval in this table gives a good overview of the overall effect of the uncertainty applied in the analysis on the level of the total catchment.

Uncertain inputs Sub models Output

Fertilizer Input spatial scale: Per plot, only for sand + grass/maize plots

Input temporal scale: Per year

Method of estimation: 1. Sampling a mean fertilisation application for the catchment from a uniform distribution 2. Sampling the regional deviation from this mean from a normal distribution SWAP Key (violated) assumptions: -Uniform and normal distribution are representative -No change of magnitude of relative uncertainty in time -Phosphorus application can be deducted from nitrogen -Ratios for division of fertilisation over four districts and types of fertilisation are valid

Phosphorus background Input spatial scale: Regge catchment

Input temporal scale: Constant Animo Method of estimation: Sampling from normal distribution for average conc.

Key (violated) assumption: Spatial homogeneity Representative data/ no bias in data

Denitrification parameters Input spatial scale: Regge catchment, only for sandy sub soils SWQN

Input temporal scale: Constant

Method of estimation: Bootstrapping from experimental established values for the parameters

Key (violated) assumption: - Spatial homogeneity - Representative data/ no bias in data

Fe/ Al upper soil Input spatial scale: Plot Input temporal scale: N and P concentration per Constant Nuswalite node per season Method of estimation: Geostatistical model

Key (violated) assumption: - No uncertainty in vertical distribution over soil profile

Storage capacity

Input spatial scale: Subcatchment

Input temporal scale: Constant summer and winter value

Method of estimation: Average of n samples of exponential (summer) or lognormal (winter) distribution, n dependent on size subcatchment

Key (violated) assumption: - No uncertainty in watercourse dimensions - Temporal homogeneity in winter/ summer - Spatial homogeneity in (sub)catchment - Representative data/ no bias in data Figure 3.3: overview of uncertainties, the main characteristics of their analyses and their location in the model

3.5 Integrated uncertainty analysis

This chapter describes the effects of the different alternatives on the nitrogen and phosphorus concentration of the surface water and the influence of the specified uncertainties on these effects. The chapter starts with an elaboration of the analysis strategy to describe how the modelling study has been performed. It describes how the NL-CAT model has been used in this study, how the specified uncertainties have been run through the model and which outputs have been selected. Before analysing the results of the alternatives, the influence of each selected uncertain input/ parameter on the modelling results has been analysed individually. This is relevant information to interpret the influence of the uncertainty for the alternatives. Then the effects of the different alternatives on the nitrogen and phosphorus concentration and their related uncertainty are analysed. The chapter finalizes with a summary of the results and conclusions on the effects of uncertainties.

Analysis strategy The effects of the alternative measures on the nitrogen and phosphorus summer averaged concentration for the surface water in the Regge catchment have been analyzed with the NL- CAT model. It has been decided to analyze the results for the outflow point. This point has been chosen because it tells something about the total catchment. The outflow point will most likely not have the largest nutrient concentration or the highest uncertainty. To get an impression of concentrations and uncertainty throughout the catchment at a more local level, three intermediate analysis points have been selected. These points each represent a different part of the subcatchment. The analysis of the intermediate points has been done for the baseline scenario only.

Figure 3.4: overview of the time frame of the model calculations 1941 1990 2000 2006 2032

Initialisation soil modules and Implementation measures surface water modules

Extension input 2000, except fertilisation and meteo

Extension fertilisation data 2006

Figure 3.4 gives an overview of the time frame of the model calculations, which is from 1941 to 2032. The first period, from 1941 to 2000, is used to initialize the model. The soil modules are initialized from 1941 onwards, the surface water modules from 1990 onwards. This initialization period is used to simulate the build up of nutrients supply in the soil and the surface water system. In 1941 the supply is assumed equal to the background concentration and this is adapted in time reflecting the nutrient input. Especially for the phosphorus concentration this long initialization process is relevant, since buffering concentrations take a long time to establish themselves. For the surface water system, 10 years of initialization is sufficient. The model is provided with actual data until the year 2000.

From 2000 on a simulation run is started. In this simulation run the input data for the year 2000 are extended until the year 2032, except for the fertilization and meteorological data. The amount of fertilization has decreased quite a lot from 2000 until 2006. Therefore, actual

input data have been included until 2006. From that point on, the fertilization is extended until 2032. The meteorological characteristics have a large influence on the model results. Due to year-to-year variability, it is possible that for one year the target for nitrogen and phosphorus is met and the next year it is not. It has been decided to include this variability by repetition of the meteorological period 1985-2000, once from 2001-2016 and once from 2017-2032. This results in a repetition of the meteorological circumstances every 16 years, for which corresponding meteorological years are directly comparable, for example 1999, 2015 and 2031. By including this year-to-year meteorological variability, uncertainty in meteorological circumstances is introduced. This uncertainty will be compared to the effects and the earlier specified uncertainty.

The measures have been applied from 2006 onwards. The specified measures take effect immediately in 2006 and are extended until 2032 to see their effect on the long term.

Uncertainties for the five inputs/ parameters as described in the former paragraph, are introduced from the beginning of the initialization period (1941 or 1990, depending for which sub model the uncertainty is defined). 50 Monte Carlo simulation runs have been created with varying realizations of the uncertain inputs/ parameters. This means that the supplies and thus the resulting buffering capacity in the soil and the surface water have established themselves differently for each run. With this approach, the uncertainties in the input data and the parameters are able to evolve completely and have their full effect on the implementation of the measures.

N mean P mean 8 0.4

7 0.35 6 0.3 5 0.25 4 0.2 (mg/l) 3 0.15(mg/l) 2 0.1

N mean concentration mean concentration N 1 concentration mean P 0.05 0 0 0 102030405060 0 102030405060 number of realisations number of realisations

Figure 3.5: the development of the mean concentration averaged over the number of realizations for nitrogen (left) and phosphorus (right).

N STD P STD 2.5 0.07

0.06 2 0.05 1.5 0.04 (mg/l) 1 (mg/l) 0.03 0.02 0.5 N standardN deviation deviation standard P 0.01 0 0 0 102030405060 0 102030405060 number of realisations number of realisations Figure 3.6: the development of the standard deviation averaged over the number of realizations for nitrogen (left) and phosphorus (right).

To see if running the 50 Monte Carlo simulations is sufficient, the results at the outflow point for the last season of 1999 have been analyzed. The bandwidth in the results should be stable with these 50 simulations that means that the mean and the standard deviation of the

results shouldn’t change when a new simulation is performed. Figure 3.5 shows the mean averaged over the number of realizations and figure 3.6 shows the standard deviation averaged over the number of realizations. It can be seen that for both nitrogen and for phosphorus, the mean and standard deviation have stabilized to a large extend. The phosphorus standard deviation still shows a small downward trend, which could be solved by calculating more realizations. This would also slightly increase the performance in the other graphics. However, the trend is only small and calculation of more realization would cost quite some time. Therefore, it has been decided to use 50 MC realizations.

The median run is defined as the simulation for which the inputs are closest to the median values for each specified uncertain input/ parameter. This is an approximation, because there was no simulation for which every input was exactly on its median, but most were very close. The most influencing uncertainty is expected to be fertilization, which is on its median. Due to lack of time, we couldn’t calculate the median run itself.

For the analysis of the results of the alternatives, some representative years have been selected with respect to the discharge from the land to the surface water: an average, an extremely dry and an extremely wet year. An overview of the discharges per year is shown in figure 3.7. For nitrogen, the years 1999, 1997 and 1993 have been chosen respectively. Results are shown for these years and the meteorological identical years. For phosphorus these years are 1999, 2013 and 1994. Again, results are shown for these years and the meteorological identical years. The explanation for the difference wet years for nitrogen and phosphorus is given in the analysis of the results of the baseline scenario. 1996 turned out to be a dry year for which the results barely have an uncertainty bandwidth. Especially for phosphorus, the results have some deviating characteristics compared to the rest of the results. Therefore, the results of 1996 are shown as well, along with their meteorological identical years. The final conclusions on meeting the criteria have been drawn for the average year 2015. While analyzing the results in the coming paragraph, this average year has been given special attention and is especially compared to the nearest dry year 2013 and the nearest representative wet year 2026.

Discharge (mm/yr) 600

500

400

300

200

100

0 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030

Discharge (mm/yr)

700

600

500

400

300

200

100

0 1993 1998 1994 1988 1995 2000 1987 1999 1985 1986 1990 1992 1989 1991 1997 1996

Figure 3.7 upper figure: the discharge from the soil to the surface water in subsequent years from 1985-2032. The dashed lines show the repetition period of the meteorological years. Lower figure: the years 1985-2000 ordered by discharge magnitude.

Influence individual uncertainties Before analysing the results of the alternatives, the influence of each selected uncertain input/ parameter on the modelling results has been analysed individually. This information can be used in the further analysis. The influence of the individual uncertainties has been analyzed by calculating the 90% confidence interval for the year 1999 on the outflow point. In figure 3.7 this 90% confidence interval for the inputs/ parameters has been shown together with the median values. Due to time constraints, the input for Fe/Al and fertilization could not be changed and recalculated for this purpose. The other three parameters, which have an easier input structure, have been. Due to these same constraints, the median values are not completely correct. As mentioned, a median run has not been calculated. The original Euroharp run, intended to provide for the median, was not suitable; for some inputs/ parameters the uncertainty analysis provided for new median values. One of the inputs with new median values is Fe/Al. As mentioned, the input couldn’t be changed in the time available. Because of that, the 90% confidence intervals values and the median are calculated with the original Euroharp Fe/Al values and absolute values are not correct. The results are presented as relative intervals. The expectation is that this will only shift the absolute values of the presented intervals value and that the relative values will not or only slightly change when changing the Fe/Al median.

90% confidence interval for N per input/parameter 90% confidence interval for P per input/parameter

3 90%-CI 2 90%-CI 2 median run 1.5 median run 1 1 0 0.5 -1

-2 0

-3 -0.5

-4 -1 Deviation of the median value (%)

PBC Denitrification SC (%) value median the of Deviation PBC Denitrification SC

Figure 3.7: 90% confidence interval for the phosphorus background concentration (PBC), denitrification and storage capacity of the detailed surface water system (SC) shown together with the results of the median run, for the summer concentrations in 1999.

The 90% confidence interval has been calculated by taking the 0.05-quantile and 0.95- quantile of the 50 realisations. The results are shown for the outflow point of the total catchment. For the confidence intervals, the most extreme simulations have been sought for that specific input/ parameter. For the inputs that are on plot or subcatchment scale, the most extreme simulation is when the averaged value on catchment scale is highest/ lowest.

For nitrogen, uncertainties in the inputs phosphorus background concentration and storage capacity of the detailed surface water system don’t have a large effect on the results. The small effect of phosphorus background concentration was to be expected, since this input effects phosphorus. For the storage capacity we had expected a larger effect. Denitrification has an effect of about 3%, which is smaller than we had expected but considerable. It is expected that uncertainty in fertilization will have a large effect on the results. The effects in the results for nitrogen of uncertainty in Fe/Al will be very small; this input effects phosphorus.

For phosphorus, uncertainty in the phosphorus background concentration has an effect on the results of 1-1.5%. This is smaller than we had expected. Uncertainties in denitrification and storage capacity of the detailed surface water system have a small effect on the results. Denitrification effects nitrogen and therefore the small effect on phosphorus was expected. The small effect of the uncertainty in the storage capacity on the results is however surprising, as it is for nitrogen. It is expected that uncertainty in fertilization will have a large effect on the results. The effects in the results of uncertainty in Fe/Al are expected to be considerable.

For phosphorus background concentration, part of the explanation is the small variation in input that has been found in the uncertainty analysis. On catchment scale, the phosphorus background concentration turned out not to have a very large uncertainty. On a more local scale, uncertainty is much larger. For the storage capacity, part of the explanation could be that the uncertainty in input has balanced itself out on catchment scale.

Results of the alternatives

In this paragraph, first the results of the baseline scenario at the outflow point are analyzed for all years from 1990-2032. Subsequently, the developments at the outflow point have been compared to the results at intermediate points in the catchment to see the effects of aggregating and disaggregating of results. Then the effects of the alternative measures have been analyzed for a selection of years. The figures showing the results are represented at the end of this paragraph.

1. Baseline scenario

Results for nitrogen at the outflow point The nitrogen concentrations at the outflow point for the baseline scenario are shown in figure 3.9. The difference in concentration and uncertainty between subsequent years directly attracts attention. This can for a large part be explained by meteorological variance. Comparison of concentrations can therefore best be done for meteorologically equal years; the meteorological period is 16 years. Analyzing the nitrogen concentration in time, a decreasing trend can be seen for the first years (compare for example 1990 and 2006). This can be explained by the reduction of point and diffuse sources until 2000 and 2006 respectively. From 2006 on, no extra policy is initiated. The concentration of nitrogen reacts quickly to the changes and starts to stabilize from about 2006.

It can be observed that the magnitude of the uncertainty is very much dependent on the magnitude of the nitrogen concentration. An explanation is that when there is more wash-off of nutrients, this process will be more uncertain. This means that in general wetter years have more uncertainty. A part of the explanation may also come from the uncertain inputs/ parameters chosen in the analysis. First and foremost, the uncertainty in fertilization input is set relative to the amount of fertilization. Less fertilization leads to less N and P added to the soil and to a smaller uncertainty bandwidth. Next, when fewer nutrients from fertilization will infiltrate the ground having a smaller bandwidth, the uncertain processes in the ground, denitrification and fixation to iron and aluminium, will also have a smaller bandwidth. For the same reason, the process of hydraulic retention in the smaller watercourses has a smaller bandwidth. The uncertain input phosphorus background concentration operates independent from the nutrient concentration in the upper soil. The way in which the uncertainty bandwidth for inputs/ parameters has been specified can explain the fact that the uncertainty bandwidth is larger before 2006 than after 2006. This observation implies that reduction of nitrogen concentration due to measures will reduce uncertainty as well.

Years that attract attention are 1993, 2009 and 2025, because of the high concentrations of nitrogen and the high uncertainty. These are wet years with an extremely high discharge from the soil into the surface water. The next years that attract attention are 1996, 2012 and 2028, because of the low concentration and the extreme low uncertainty. These are dry years with an extremely low discharge from the soil into the surface water. The second driest years with respect to discharge from the soil to the surface water are 1997, 2013 and 2017. These years have a slightly higher nitrogen concentration, but substantially more uncertainty. Finally, the years 2006 and 2012 attract attention because of the large jump in concentration with respect to 2005 and 2011. This is because the summer of 2005 and 2011 has been extremely wet, not because 2006 has been particularly dry.

For the further analysis of the alternatives will be focused on the concentration development for the wet years 1997, 2013 and 2017, for the average years 1999, 2015 and 2031 and for dry year 1997, 2013 and 2017. The more dry years 1996, 2012 and 2028 appear to show some deviating characteristics, especially for phosphorus (see below). Therefore, these years have not been chosen to represent the dry years in the analysis. However, their results are presented to see what exactly happens when alternative measures are implemented. Figure 3.13a shows the selected years for the baseline scenario. A selection of characteristics: • The concentration for the average year 2015 ranges between 3.08-4.51 mg/l, with median 3.51 mg/l. This implies that the target of 2.2 mg/l will not be met for 2015. • The dry year 2013 ranges from 3.08-3.81 mg/l with median 3.34 mg/l. For this dry year, the uncertainty bandwidth and the median have decreased. • The wet year 2025 ranges from 3.98-7.21, with median 4.94 mg/l. For this wet year, the uncertainty bandwidth and median have increased. • For the years 1996, 2012 and 2028 the average doesn’t change over time and uncertainty is very small.

Results for phosphorus at the outflow point The phosphorus concentrations at the outflow point for the baseline scenario are shown in figure 3.9. For the concentration of phosphorus a small decreasing trend might be seen in the early nineties. An increasing trend can be detected for the long term. An explanation for this trend may be that the buffering capacity is fully exploited on the long run for the simulation runs with a high phosphorus input.

As for nitrogen, a large part of the variation can be explained by the meteorological differences. However, phosphorus seems to react differently on this meteorological difference then nitrogen. When one looks at meteorological identical years, the phosphorus concentration fluctuates (for example for the years 1999, 2015 and 2031). The bandwidth for phosphorus seems to be more dependent on the meteorological year than on the magnitude of the concentration. An explanation is that phosphorus concentration is mostly influenced by the subsequent delivery from supply in the soil.

As observed, phosphorus seems the react differently to meteorological circumstances then nitrogen. The wet years 1993, 2009 and 2025 that show a high nitrogen concentration, don’t show a particularly high phosphorus concentration. The years 1994, 2010 and 2026 are slightly less wet, but these years do show a high phosphorus concentration. It turns out that for phosphorus some other factors play an important role as well in establishing the phosphorus concentration. The driest years 1996, 2012 and 2028 show some unexpected characteristics. Uncertainty is still low, as it is for nitrogen, but the concentrations in 2012 and 2028 are unexpectedly higher than in 1996. The second driest years, 1997, 2013 and 2029 don’t show this characteristic. The uncertainty bandwidths of equal meteorological years show a slight increase.

For the further analysis of the alternatives for phosphorus, it has been decided to focus on the wet years 1993, 2009 and 2025. The other years of the analysis for phosphorus will be the same as for nitrogen. Figure 3.14a shows the selected years for the baseline scenario. A selection of characteristics: • The concentration for the average year 2015 ranges between 0.44-0.59 mg/l. The target of 0.15 mg/l is not met. • The dry year 2013 ranges from 0.41-0.48 mg/l. For this dry year, the uncertainty bandwidth and the median have decreased. • The wet year 2026 ranges from 0.48-0.78. For this wet year, the uncertainty bandwidth and median have increased. • For the years 1996, 2012 and 2028 the average concentration increases a lot between 1996 and 2012. Between 2012 and 2028 it stays constant. Uncertainty is very small for all years.

When in the further analysis the dry and wet years act as in the baseline scenario, further comments are not provided.

Results at intermediate points The concentrations have been analyzed at three intermediate points in the Regge catchment. These points are node 11, 20 and 35 and are shown in figure 3.8. The concentrations at these points are mostly substantially higher than at the outflow point for both N and P. The uncertainty bandwidth was expected to be higher at these intermediate points: since these points represent a smaller part of the subcatchment, fewer balancing out of extreme local

realizations of uncertainty factors was expected, resulting in larger bandwidths.

35 20 11

Figure 3.8 The location of the intermediate points 11, 20 and 35 for which the concentration N and P have been analyzed.

This is only found to be the case for node 35, which represent the smallest subcatchment part of the three nodes. Most of the land use in this area is extensive. At node 20, the bandwidth is even smaller than at the outflow point. This node represents roughly ¼ of the subcatchment including some intensive and some extensive agricultural parts. At node 11, representing roughly 1/3 of the catchment, being intensive agricultural parts, the differences are not that large as compared to the outflow points. The bandwidth is somewhat higher, as is the concentration.

As an illustration, annex 3 shows some maps of the mean and standard deviation of nitrogen concentrations in the upper groundwater throughout the catchment for a normal wet and dry year.

2. Reduction of the fertilization level by 50% alternative

Note that the measure is only implemented after 2006, all years before are equal to the baseline scenario.

Nitrogen This alternative shows a strong decrease of the nitrogen concentration for the surface water. Furthermore, a strong decrease of uncertainty is observed. It is expected that the main reason for the reduction of the uncertainty is the way uncertainty has been assessed for fertilization. As has already been described at the baseline scenario, the uncertainty is expressed relative to the amount of fertilization: the argumentation is followed that with the introduction of this measure, the content and application of fertilization can be better controlled (uncertainty due to implementation of the measure is not included). This implies that reduction of nitrogen concentration due the measure will reduce uncertainty as well.

The development over time and the differences between the wet, average and dry years show little difference to the baseline scenario. A selection of characteristics: • The concentration for the average year 2015 ranges between 2.52-2.86 mg/l, with median 2.68 mg/l. This means the target of 2.2 mg/l is not met. • The dry year 2013 ranges from 2.65-2.80 mg/l, with median 2.74 mg/l. The range and average for the dry year is not lower than for the average year, however the uncertainty bandwidth is smaller. • The wet year 2025 ranges from 2.89-3.54, with median 3.21 mg/l. • For the years 1996, 2012 and 2028 nothing changes.

Phosphorus The results show a decrease of the concentration in comparison to the baseline scenario and a decrease of the concentration in time. For the dry year, the effect is smaller than for the other years. Furthermore, a decrease in uncertainty is observed in comparison to the baseline scenario, for which the same explanation can be given as for nitrogen. The uncertainty also decreases in time. A selection of characteristics: • The concentration for the average year 2015 ranges between 0.41-0.47 mg/l, with median 0.44 mg/l. The target of 0.15 mg/l is not met. • The dry year 2013 ranges from 0.40-0.44 mg/l, with median 0.42 mg/l. • The wet year 2026 ranges from 0.39-0.46, with median 0.42 mg/l. • For the years 1996, 2012 and 2028 the average changes little over time, however, uncertainty even decreases more.

3. Increasing the volume of the small watercourses by a factor 2.

Note that the measure is only implemented after 2006, all years before are equal to the baseline scenario.

Nitrogen This alternative has little effect in comparison to the baseline scenario. The largest decrease in concentration can be seen for the wet years, where the median decreases by 0.5 mg/l. Also uncertainty decreases for these years (about 0.7 mg/l). For the dry year, hardly any changes can be seen and the average year is in between. A selection of characteristics: • The concentration for the average year 2015 ranges between 3.07-4.26 mg/l with median 3.46 mg/l. This means the target of 2.2 mg/l is not met. • The dry year 2013 ranges from 2.97-3.59 mg/l with median 3.19 mg/l. • The wet year 2025 ranges from 3.65-6.28 mg/l with median 4.44 mg/l. • For the years 1996, 2012 and 2028 nothing changes as well.

Phosphorus For phosphorus, nothing happens in comparison to the baseline scenario. Changes are in the order of magnitude of 0.01 mg/l.

4. Decrease of WWTP discharges by 50%

Note that the measure is only implemented after 2006, all years before are equal to the baseline scenario. Furthermore, it has to be noted that uncertainties related to the WWTP discharges are not included. Uncertainties in the data will not be very large, due to precise measurement possibilities. However, the relative size of this pollution source is large, which may cause this simplification to still have a substantial influence.

Nitrogen The concentration of nitrogen shows a large decrease with this alternative. The median reduces from 0.9 mg/l for the average and dry year to 0.7 mg/l for the wet year. The uncertainty bandwidth doesn’t change. A selection of characteristics: • The concentration for the average year 2015 ranges between 2.24-3.64 mg/l with median 2.66 mg/l. This means the target of 2.2 mg/l is not met, but is closely approached by the bottom of the uncertainty bandwidth. • The dry year 2013 ranges from 2.19-2.90 mg/l with median 2.44 mg/l. • The wet year 2025 ranges from 3.32-6.51 mg/l with median 4.25 mg/l. • The years 1996, 2012 and 2028 show a large decrease in concentration, not in uncertainty.

Phosphorus The concentration of phosphorus also reduces strongly with this alternative. The median reduces from 0.15 mg/l for the average and dry year to 0.10 mg/l for the wet year. The uncertainty bandwidth doesn’t change at first but increases in time. A selection of characteristics: • The concentration for the average year 2015 ranges between 0.30-0.45 mg/l with median 0.35 mg/l. This means the target of 0.15 mg/l is not met. • The dry year 2013 ranges from 0.26-0.32 mg/l with median 0.29 mg/l. • The wet year 2025 ranges from 0.37-0.67 mg/l with median 0.48 mg/l. • The years 1996, 2012 and 2028 show a large decrease in concentration, not in uncertainty.

5. Decrease of fertilisation and WWTP discharges by 50%

Nitrogen This alternative shows a combination of the characteristics of the individual measures. The concentration of nitrogen shows a large decrease as goes for the uncertainty. A selection of characteristics: • The concentration for the average year 2015 ranges between 1.69-2.02 mg/l with median 1.84 mg/l. • The dry year 2013 ranges from 1.76-1.90 mg/l with median 1.85 mg/l. • The wet year 2025 ranges from 2.24-2.87 mg/l with median 2.55 mg/l. • The years 1996, 2012 and 2028 show a large decrease in concentration, not in uncertainty.

This means that the target is met for dry and average years. For wet years the target is not met.

Phosphorus Again, a combination of the characteristics of the individual measures can be seen. The concentration and the uncertainty show a large decrease after introduction of the measures and the uncertainty shows a further decrease in time. A selection of characteristics: • The concentration for the average year 2015 ranges between 0.26-0.32 mg/l with median 0.29 mg/l. This means the target of 0.15 mg/l is not met. • The dry year 2013 ranges from 0.24-0.28 mg/l with median 0.26 mg/l. • The wet year 2025 ranges from 0.27-0.35 mg/l with median 0.30 mg/l. • The years 1996, 2012 and 2028 show a large decrease in concentration, not in uncertainty.

6. Combination of all measures

This alternative shows a combination of the characteristics of the individual measures. The concentration of nitrogen shows a large decrease as compared to the baseline scenario. The same goes for the uncertainty. In comparison to the measure with only a reduction of fertilisation and WWTP discharges, this alternative shows a decrease of concentration for the wet years of about 0.2 mg/l. For the average year a very small increase of concentration can be seen and for the dry year a very small decrease. A selection of characteristics: • The concentration for the average year 2015 ranges between 1.72-2.05 mg/l with median 1.87 mg/l. • The dry year 2013 ranges from 1.71-1.86 mg/l with median 1.8 mg/l. • The wet year 2025 ranges from 2.06-2.63 mg/l with median 2.34 mg/l. • The years 1996, 2012 and 2028 show a large decrease in concentration, not in uncertainty.

This means that the target is met for dry and average years. For wet years the target is within the uncertainty bandwidth. On average, the target is not met in wet years.

Phosphorus Again, a combination of the characteristics of the individual measures can be seen. The concentration and the uncertainty show a large decrease after introduction of the measures and in comparison to the baseline scenario and the uncertainty shows a further decrease in time. In comparison to the measure with only a reduction of fertilisation and WWTP discharges, only changes in the order of magnitude of 0.01 mg/l can be observed. A selection of characteristics: • The concentration for the average year 2015 ranges between 0.27-0.33 mg/l with median 0.29 mg/l. This means the target of 0.15 mg/l is not met. • The dry year 2013 ranges from 0.24-0.27 mg/l with median 0.25 mg/l. • The wet year 2025 ranges from 0.27-0.34 mg/l with median 0.30 mg/l. • The years 1996, 2012 and 2028 show a large decrease in concentration, not in uncertainty.

3.6 Summary of results

Influence uncertainties in inputs/ parameters individually Uncertainty in the phosphorus background concentration and storage capacity of the detailed surface water system had a much smaller effect on the results than expected. This may be partly explained by a balancing out of uncertainties in the input of these variables when considered on catchment scale. The specification of the scale of interest is very important for the selection and way of assessment of the uncertainties.

Outflow point versus individual points The concentrations on the considered intermediate points are higher since there has been less hydraulic retention and thinning down. Uncertainties are higher or lower, depending on the point. The specification of where the results are to be analysed has a large influence on the results. This needs to be selected strategically.

Meteorological variance The year-to-year variation due to meteorological variance is large. In general, this influence is larger than the influence of the selected uncertainties. In order to get a good impression of the status of the surface water, the concentrations should be normalised for the meteorological year before assessment of the concentrations.

Nitrogen In general, nitrogen reacts quickly to changes in input. Adaptation of the concentration is in the order of years. Uncertainty in the concentration converges in time and is smaller for dry years than for wet years.

Phosphorus The concentration of phosphorus reacts slowly to changes in input. Adaptation of the concentration is in the order of decades due to subsequent delivery. The median concentration doesn’t react very clearly on the meteorological year. Uncertainty bandwidths are smaller for dry years and larger for wet years. They diverge in time. The target is very likely not to be met for all alternatives.

Uncertainty Uncertainty is not very high and reduces further with reducing nutrient concentration. In general, dry years show a lower concentration and a lower uncertainty than wetter years. The summer concentration in these dry years is mostly caused by WWTP effluent; the contribution of diffuse sources decreases because of decreasing wash-off of nutrients. The low wash-off of and low contribution of diffuse sources can both explain part of the low uncertainty for dry years. Low wash- off will be less uncertain than high wash-off and though the contribution of diffuse sources for dry years is low, the uncertainty sources assessed are restricted to these diffuse sources. The lower uncertainty with reducing nutrient concentration can furthermore partly be explained by the inputs/ parameters chosen in the uncertainty analysis and their assessment. First and foremost, the uncertainty in fertilization input is set relative to the amount of fertilization. Less fertilization leads to less N and P added to the soil and to a smaller uncertainty bandwidth. Uncertainty in most of the other inputs/ parameters is dependent on the nutrient input.

Results of alternatives The increase of storage capacity is found to have little influence neither on the nitrogen and phosphorus concentration nor on their uncertainty. The reduction of WWTP loads and the reduction of fertilization are both found to have a substantial influence on the nitrogen and phosphorus concentration. The reduction of fertilization also has a decreasing impact on uncertainty bandwidth; this is not the case for reduction of WWTP input. For the combination of both alternatives, nitrogen and phosphorus concentration reduce further and uncertainty is small. For the combination of all measures, the results are almost equal to the results for WWTP and fertilization reduction together.

Deviating year The year 1996 has a very small uncertainty bandwidth and for phosphorus the concentration increases strongly for the baseline alternative, the decrease of fertilization alternative and the increase of storage capacity in the detailed surface water system alternative. No explanation has been found for the deviating characteristics.

Uncertainties in relation to alternatives Different measures can influence the uncertainty differently. Reduction of fertilisation shows a large decrease in the uncertainties assessed in this study. Based on these characteristics, it is an interesting alternative to consider. This knowledge is important as a basis of robust decisions.

N summer averaged concentration for baseline alternative at the outflow point 22

20 18 16 14 12 10 N conc(mg/l) 8 6 4 2 0

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 Year

P summer averaged concentration for baseline altermative at the outflow point

1.1 1 0.9 0.8

0.7 0.6 P concP (mg/l) 0.5 0.4 0.3

0.2

Figure 3.9 summer averaged concentration for baseline alternative for nitrogen (above) and phosphorus (below), form 1990- 2032 at the outflow point. Purple indicates the median.

39 N summer averaged concentration for baseline alternative at node 11 22 20 18 16 14 12 10 N conc(mg/l) 8 6 4

0 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 Year

P summer averaged concentration for baseline altermative at node 11 1.1

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Figure 3.10 summer averaged concentration for baseline alternative for nitrogen (above) and phosphorus (below), from 1990- 2032 at node 11. Purple indicates the median

N summer averaged concentration for baseline alternative at node 20 22 20 18 16 14 12

10 N conc(mg/l) 8 6 4 2 0 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 Year

P summer averaged concentration for baseline altermative at node 20

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) 0.8

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0.4

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0.11990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 Year 0 Figure 3.11 summer averaged concentration for baseline alternative for nitrogen (above) and phosphorus (below), form 1990- 2032 at node 20. Purple indicates the median.

N summer averaged concentration for baseline alternative at node 35 22 20 18 16 14 12

N conc(mg/l) 10 8 6 4 2

01990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 Year

P summer averaged concentration for baseline altermative at node 35

1.1

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0.3 0.2

Figure 3.12 summer averaged concentration for baseline alternative for nitrogen (above) and phosphorus (below), form 1990- 2032 at node 35. Purple indicates the median

42 N summer averaged concentration for baseline alternative a 18 16 Average year Dry year Wet year 14 12 10 8 6 4 N concentration (mg/l) 2 0 1999 2015 2031 1997 20132029 1993 2009 2025 1996 2012 2028

b N summer averaged concentration for reduction of fertilization 18 16 Average year Dry year Wet year 14 12 10 8 6 4 N concentration (mg/l) 2 0 1999 2015 2031 1997 20132029 1993 2009 2025 1996 2012 2028

N summer averaged concentration for increase watercourses c 18 16 Average year Dry year Wet year

14 12

10 8

6 4

N concentration (mg/l) 2

0 1999 2015 2031 1997 20132029 1993 2009 2025 1996 2012 2028

Figure 3.13 a-c: the effect and influence of uncertainty for the alternatives a) baseline alternative b) reduction of fertilization alternative c) increase of watercourses alternative.

N summer averaged concentration for decrease of WWTP discharges d 18 16 Average year Dry year Wet year 14 12 10 8 6 4 N concentration (mg/l) 2 0 1999 2015 2031 1997 20132029 1993 2009 2025 1996 2012 2028 N summer averaged concentration for decrease of fertilisation and WWTP discharges e 18 16 Average year Dry year Wet year 14 12 10 8 6 4 N concentration (mg/l) 2 0 1999 2015 2031 1997 20132029 1993 2009 2025 1996 2012 2028

N summer averaged concentration for combination of all measures f 18 16 Average year Dry year Wet year 14 12 10 8 6 4 N concentration (mg/l) 2 0 1999 2015 2031 1997 20132029 1993 2009 2025 1996 2012 2028

Figure 3.13 d-f: the effect and influence of uncertainty for the alternatives d) decrease of WWTP discharges e) decrease of fertilization and WWTP discharges and f) combination of all measures.

P summer averaged concentration for baseline alternative a Average year Dry year Wet year 0.8 0.75 0.7 0.65 0.6 0.55 0.5 0.45 0.4 0.35 0.3 0.25 0.2 0.15 P concentration (mg/l) 0.1 0.05 0 1999 2015 2031 1997 20132029 1994 2010 2026 1996 2012 2028

P summer averaged concentration for reduction of fertilisation

b 0.8 0.75 Average year Dry year Wet year 0.7 0.65 0.6 0.55 0.5 0.45 0.4 0.35 0.3 0.25 0.2 0.15 P concentration (mg/l) 0.1 0.05 0 1999 2015 2031 1997 20132029 1994 2010 2026 1996 2012 2028

P summer averaged concentration for increase watercourses

0.8 0.75 Average year Dry year Wet year c 0.7 0.65 0.6 0.55 0.5 0.45 0.4 0.35 0.3 0.25 0.2 0.15 P concentration (mg/l) 0.1 0.05 0 1999 2015 2031 1997 20132029 1994 2010 2026 1996 2012 2028

Figure 3.14 a-c: the effect and influence of uncertainty for the alternatives a) baseline alternative b) reduction of fertilization alternative c) increase of watercourses alternative.

P summer averaged concentration for decrease of WWTP d discharges 0.8 0.75 Average year Dry year Wet year 0.7 0.65 0.6 0.55 0.5 0.45 0.4 0.35 0.3 0.25 0.2 0.15 P concentration (mg/l) 0.1 0.05 0 1999 2015 2031 1997 20132029 1994 2010 2026 1996 2012 2028

P summer averaged concentration for decrease of fertilisation and e WWTP discharges 0.8 0.75 Average year Dry year Wet year 0.7 0.65 0.6 0.55 0.5 0.45 0.4 0.35 0.3 0.25 0.2 0.15 P concentration (mg/l) 0.1 0.05 0 1999 2015 2031 1997 20132029 1994 2010 2026 1996 2012 2028

P summer averaged concentration for combination of all measures f 0.8 0.75 Average year Dry year Wet year 0.7 0.65 0.6 0.55 0.5 0.45 0.4 0.35 0.3 0.25 0.2 0.15 P concentration (mg/l) 0.1 0.05 0 1999 2015 2031 1997 20132029 1994 2010 2026 1996 2012 2028

Figure 3.14 d-f: the effect and influence of uncertainty for the alternatives d) decrease of WWTP discharges e) decrease of fertilization and WWTP discharges and f) combination of all measures.

46 4 Analysis socio-economic impacts This chapter provides insight in the socio-economic impacts of the selected measures, and as such tests the guidelines prepared in HarmoniRiB’s task “Development of a concept for supporting decision making under uncertainty that integrates hydrological, ecological and economic assessments “. In chapter 4.1 the direct costs of the different measures are elaborated, including their uncertainty. In chapter 4.2 the other impacts of the measures are elaborated

4.1 Costs of measures

1. Baseline scenario The costs of the baseline scenario have been set to €0,-. The costs of all other alternatives are calculated relative to the baseline scenario.

2. Reduction of the fertilization level by 50% There are some technical solutions available through which a firm reduction of fertilization level can be achieved. Examples of measures are: precision application of manure and fertilizer, animal diets and restricted pasture. It is however unlikely that these measures can achieve a 50% reduction of fertilization level without yield reduction. In this study, the measure of fertilization reduction is introduced at once. The worst case will be that the proposed fertilization reduction leads to the closure of 50% of the farms. An indication of the costs can be found by looking at the compensation given to farmers who are bought out, which is some 40.000 euros/ha (Witteveen+Bos, 2006). In the best case, the farmers will experience costs of reduction of yield and disposal of manure. An estimate of these costs for the most serious fertilization policy currently considered, a reduction of fertilization level of 33%, is 200 euro/ha/year (source: oral presentation of a farmer connected to the Dutch agricultural organization LTO). In the best case, these costs can be linearly extrapolated to a reduction of 50% of fertilization. This will lead to costs of 300 euro/ha/year. It has to be noted that linear extrapolation is likely to be not applicable. There is a risk of reaching a breaking point in either the manure disposal market or the fertilization- yield relationship. The total amount of agricultural land that is influenced is 58700 ha. The execution time of the measure from 2006 until 2015 is 10 years.

The costs of this measure ranges from 175 million – 2.5 billion euros. The large range between the costs in the best case and in the worst case is due to the unpredictability of impact of the measure, including possible non-linear effects. No research is found giving a detailed calculation of impacts. This large range expresses the large uncertainty.

3. Increase of the storage capacity of the detailed surface water system by a factor 2 The total costs of this measure are brought about by the purchase and removal of soil.

The costs for the removal of soil can be calculated by volume of soil to be removed times the costs of removal. The volume of the soil to be removed is calculated to be 4,005,500 m3, calculated by: ((new profile)- (old profile))* (length of the ditches). The uncertainty of this calculation is estimated to be 10%. The costs of removal are estimated to be €14.30 (Tauw, 2005), for specifications see Annex 4. The uncertainty in this calculation is estimated to be 20% (of the costs without taxes). The total costs for removal of the soil sum up to 48 – 68 million euro.

The costs for purchase of the soil can be calculated by the land to be purchased times the costs for purchase of the land. The land to be purchased is calculated to be 3337900 m2, which is calculated by the following formula: ((width of the surface level of old profile) – (width of the surface level of new profile))* (length). Extra purchase to facilitate the work has not been included in the calculation. The uncertainty in the amount calculated is assumed to be 10%. The costs for purchase of 1 m2 of land are estimated to be €5.22 (HKV lijn in water, 2002), for specification see Annex 3. The uncertainty in this calculation is estimated to be 25% of the direct costs, indirect costs and incidental expenses. The total costs for purchase of the soil so sum up to 14 – 21 million euro.

The total costs of this measure are estimated to be 62 – 89 million euro.

4. Reduction of the WWTP discharges by 50% The costs of this measure are brought about by investment costs and management costs. The costs are specified in terms of the capacity of the WWTP in inhabitant equivalents (i.e.). The investment costs are 1-3 million euro per 10.000 i.e., the management costs are 0-30 euro per i.e. (Rebelgroup Rotterdam Royal Haskoning, 2005). The costs are dependent on the technique applied, for larger WWTP’s the costs are relatively lower. The economical life cycle of quaternary treatment implementation is 15 year. The measure is to be implemented for 700.000 i.e., which will bring the costs to 70 – 231 million euro.

5. Combination of reduction of fertilization level and WWTP discharges, both by 50% The costs will be the sum of the costs of both measures and range from 245 million – 2.5 billion euros.

6. Combination of all measures. The costs will be the sum of the costs of all measures and range from 307 million – 2.5 billion euro.

4.2 Other impacts This section describes the assessment of the measures’ impacts that are relevant for the evaluation beyond the physical impacts on the “good status” and the costs. The impacts that are considered are side- effects of the measures, public support and acceptance of Brussels. These three impacts will be elaborated for each of the alternatives.

1. Baseline scenario The side- effects and public support of the base-line scenario are taken to be neutral. This will be the reference for evaluation of the side- effects and public support of the other alternatives.

The acceptance by Brussels of the efforts in this alternative will very likely be low. The efforts of the actions taken in this scenario are not ‘everything that can reasonably be done’.

2. Reduction of the fertilization level by 50% Side- effects expected from introduction of this alternative are quite negative to negative. To start with, effects are expected on the national economy. Reduction of profit in the agricultural sector will influence the agricultural sector as well as other sectors, for which the effects will show in the national product. Next, effects are expected on the landscape and other functions in the area. The intervention in the everyday processes of the agricultural practice will be large and for some areas agricultural land use will cease to take place. This will give opportunity to other functions, e.g. ecological or recreational functions. On the other hand, the typical cultural-historical landscape will be threatened. As a last side- effect,

the reduction of the phosphorus application to the soil, can lead to the release of heavy metals that are now attached to the phosphorus (as for example happened in ‘de Kempen’ in the Netherlands).

Public support of this alternative is low. There will be heavy resistance of farmers when implementing this measure. The heavy influence of this alternative on the farmers and the national economy will not be accepted on political level unless there are no other alternatives and the necessity has been demonstrated.

The acceptance by Brussels is very likely to be quite low because the targets will likely still not be met. Though, acceptance will be larger than for the baseline scenario. The alternative requires a lot of effort, which will have quite some effect. However, there are other alternatives that can reasonably be taken and that are not included. The WFD requires all effort that can reasonably taken and it is hard to find argumentation that these requirements are met.

3. Increase of the storage capacity of the detailed surface water system by a factor 2 Side- effects if this alternative are the impact on the environment and the landscape. The evaluation of these side effects is dependent on the way the alternative is implemented. Disturbance of the ecology due to implementation of the alternative can be turned into an opportunity for implementation of ecological-friendly banks. Negative impacts on the attractiveness of the landscape can be avoided by a good implementation design. Another side- effect is the creation of more open water. In the Dutch Watermanagement for the twenty-first century directive (WB21), this is seen as an advantage because of the increase of the capacity of storage for water. Overall, the side- effects of this alternative are reasonably positive, however depending on the implementation they can range from positive to reasonably negative.

The advantages outlined for the side- effects will raise public support. However, a considerable amount of land needs to be excavated. Therefore, resistance of landowners is to be expected. Since the effect of the alternative is low, public support will be quite low. However, dependent on the implementation, public support can range from low to neutral.

The acceptance by Brussels of this alternative will very likely be low. The effects of the alternative are low; targets are not being met. Considerable effort is taken for this alternative. However, there are other alternatives that can reasonably be taken and that are not included. The WFD requires all effort that can reasonably taken and it is hard to find argumentation that these requirements are met.

4. Reduction of the WWTP discharges by 50% There are no specific side- effects related to the introduction of quaternary treatment for WWTP’s, so the side effects are evaluated as neutral. It has to be noted that a positive side- effect can be created when the quaternary treatment is designed for combined treatment of nutrients and other pollutants (e.g. priority substances). This is more cost- effective than individual implementation of measures.

People will need to pay more waterboard taxes. On the other hand, the usefulness of the implementation can be explained and understood. Public support therefore will range from quite low to quite high.

The acceptance by Brussels of this alternative will be reasonably low- neutral (neutral = acceptance). The targets are not being met, although the effect for nitrogen is substantial. It

may however be substantiated that this is all that can reasonably be done. In this case, it needs to be substantiated that measures for diffuse sources are not cost-effective. There is a large uncertainty whether this argumentation is accepted.

5. Combination of reduction of fertilization level and WWTP discharges, both by 50% Side- effects are the same as for the alternatives individually. The sum of the side- effects is quite negative to negative.

Public support is low to quite low as for the reduction of fertilization level alternative. However, public support might be slightly higher than for the alternative in which only a reduction of fertilization level is introduced. People might experience it as more fair, when the farmers are not the only ones who need to make a sacrifice. Still, the heavy influence of this alternative on the farmers and the national economy will not be accepted on political level unless there are no other alternatives and the necessity has been demonstrated.

The acceptance by Brussels of this alternative will be neutral (=acceptance) to quite low. The targets for nitrogen are likely being met. The targets for phosphorus will not be met. However, it can be substantiated that this is ‘everything that can reasonably be done’. Only when the measures don’t turn out to have the expected effect, or the people in Brussels don’t feel this is all that can be done, the acceptance can be quite low. This is however not expected.

6. Combination of all measures. Side- effects are the same as for the alternatives individually. The sum of the side- effects is negative to reasonably positive.

Public support is low. Public support can be compared to the alternative in which the WWTP discharges and the fertilisation level are reduced. However, in this alternative, more money is spend without having an demonstrably effect. Because of that, public support will be only low.

The acceptance of Brussels of this alternative will be neutral (=acceptance) to quite low. The same argumentation holds as is elaborated for the reduction of fertilizer and WWTP discharges alternative.

5 Evaluation and Comparison of Measures

5.1 Summary of Results from Impact Analysis The following matrix summarises the results of the impact analysis. The summary is shown for the year 2015, which is an average meteorological year. 2015 is the first year the nitrogen and phosphorus concentrations are confronted to the WFD targets. These targets do not necessarily need to be met. It can be seen in the modelling results, that concentrations mostly still show a slight decrease after 2015, although in most cases this is not much. Therefore, the concentrations in 2015 are also quite representative for the long- term development.

Criteria Good status Costs Further criteria Nitrog Phosphoru Costs Side Public Accep- en s conc. (million effects support tance Alternative conc. (mg/l) €) Brussels (mg/l) 1. Baseline 3.08- 0.44-0.59 0 0 0 -- scenario 4.51 2. Reduction of 2.52- 0.41-0.47 175- -/-- -- - fertilization 2.86 2,500 3. Increase of 3.07- 0.44-0.59 62-89 -/+ -/0 --/- storage cap. 4.26 4. Reduction of 2.24- 0.30-0.45 70-231 0 -/+ -/0 WWTP discharges 3.64 5. Reduction of 1.69- 0.26-0.32 245- --/- --/- 0/- fertilization and 2.02 2,500 WWTP discharges 6. Combination of 1.72- 0.27-0.33 307- --/+ --/- 0/- all measures 1.86 2,500

Explanation of the symbols: Side-effects Public support Acceptance by Brussels ++ = very positive ++ = very high 0 Acceptance + = positive + = high - Low 0 = neutral 0 = neutral -- Very low - = negative - = low -- = very negative -- = very low

As can be seen in chapter 3 ‘Integrated uncertainty analysis’, results deviate for different meteorological years. In wet years, the concentrations as presented in the table may be exceeded. In dry years, lower concentrations may be measured. In order to get a good impression of the status of the surface water, the concentrations should be normalised for the meteorological year.

A description of each of the alternatives will be given with special focus on the uncertainties.

Baseline alternative: Targets are not met. The uncertainty bandwidth is 1.4 mg/l for nitrogen and 0.15 mg/l for phosphorus, which is considerable. Costs, side effects and public acceptance for this alternative are used as a reference. It is quite certain that acceptance by Brussels is very low.

Fertilisation reduction by 50%: This alternative show a large decrease of nitrogen concentration, but not of phosphorus concentration. Both targets are not met. This alternative strongly decreases uncertainty with respect to the criteria for good status. The costs, on the other hand, show a very large uncertainty. The magnitude of the side effects is uncertain, but effects will be negative, especially for the economy. Public support will certainly be very low and acceptance of Brussels will be low, but higher as for the baseline alternative.

Increase of storage capacity of the detailed surface water system by a factor 2: This alternative reduces the uncertainty bandwidth for nitrogen. The effects on the concentration itself and the effects on phosphorus are minimal. Uncertainty in the costs is small as compared to the other alternatives. Acceptance and public support are uncertain, because they depend on the way the measure is introduced. They are however not very negative or low. Acceptance by Brussels is very low to low because of the small effect.

Reduction of WWTP discharges by 50%: This measure reduces both nitrogen and phosphorus concentrations to a large extend. The criterion for nitrogen is closely approached in the most advantageous case, but both criteria are not met. The uncertainty bandwidth has not decreased in comparison to the baseline scenario. Uncertainty is quite large for the criteria of good status. Uncertainty in the costs are considerable, but can be further reduced by a more detailed calculation. Side effects are not expected. Public support is uncertain but will not be very negative or positive. The alternative might be accepted by Brussels with a proper argumentation, however, this is uncertain since targets are not met and uncertainty is considerable.

Reduction of fertilization and WWTP discharges by 50%: The target for nitrogen concentration is met very likely; the total bandwidth is below the target. The reduction of phosphorus concentration is considerable, but the target is not met. Uncertainty for both criteria of good status is small. The costs, however, show a very large uncertainty. The magnitude of the side effects is uncertain, but effects will be negative because of the measure for fertilization. Public support will be very low to low. Support might increase because other measures are taken as well. The alternative will be likely accepted by Brussels with the proper argumentation on not meeting the target for phosphorus.

Combination of all measures: The target for nitrogen concentration is met; the total uncertainty bandwidth is below the target. The magnitude of the bandwidth has decreased in comparison the reduction of fertilization and WWTP discharge alternative. The reduction of phosphorus concentration has barely changed in comparison to this alternative. Costs are very uncertain, as are the side effects, which are a composition of all individual side effects. Public support is very low to low. The alternative will be likely accepted by Brussels with the proper argumentation on not meeting the target for phosphorus.

5.2 Comparison of measures

Since the target for phosphorus is not met for all alternatives, the comparison of measures will be focused on nitrogen, the costs and the other criteria. Interesting measures are the reduction of WWTP discharges by 50% and the reduction of WWTP discharges by 50% in combination with reduction of fertilization. The former doesn’t meet the target for nitrogen, but comes close. The alternative scores well on costs, side effects and public support, having only little uncertainty (public support will on average only be slightly negative or positive). A

disadvantage is that the uncertainty in the nitrogen concentration is considerable. The latter meets the target for nitrogen concentration. However, the alternative scores badly, including large uncertainties, on costs, side effects and public support. A large advantage is that uncertainty on the target for nitrogen concentration is low; the effects on good status for this alternative are quite certain.

A large uncertainty for the comparison of measures is the consequences for (non) acceptance by Brussels. When these consequences are bearable, the alternative in which only the WWTP discharges are reduced might get the preference. Though uncertainty in the effects is considerable; a nitrogen concentration of 3.64 mg/l would be highly undesirable.

Given the results of the analysis, the best solution would probably be to combine a strong reduction of the WWTP discharge (e.g. 50% as implemented in this study) with a less strong reduction of fertilization. Until a reduction of 30%, the costs and side effects are much better predictable, which would decrease uncertainty in the costs and presumably public support. In this case, the target would very likely be met and uncertainty would be not that large.

6 Conclusions

This chapter first discusses HarmoniRiB tools and methodologies used for this study. Next, a short summary of the results of the case study will be given followed by a discussion on the influence of the strategy and set-up of the case study on the results. This will be followed by general conclusions and learning points for the case study. The chapter ends with recommendations. In addition to this chapter, conclusions on the effects of the alternatives and the influence of uncertainties can be found in chapter 3.6. The complete assessment of alternatives including their uncertainties can be found in chapter 5.

Methodologies and tools The methodologies and tools for uncertainty assessment and error propagation have been successfully applied and appear promising. The guidance on uncertainty assessment developed in HarmoniRiB task 2.1 provided useful information for an overview on how to deal with specific uncertainty. However, the detail of this guidance is not sufficient to guide in a real uncertainty assessment of most data and a lot of uncertain model inputs and parameters are not covered. However, this guidance provided a good overview of how uncertainties can be dealt with. The guidance on uncertainty for socio-economic information has been used as a reference and global guide in this study. Also for this guidance the detail and scope of the information was mostly not sufficient for direct implementation. The uncertainty propagation software DUE has been used for creating realisation for uncertain inputs. The software is evaluated as convenient to use for the simpler data structures and distribution types. Finally, the uncertainty functionality of the database has only been applied for one uncertain input in this analysis, the phosphorus background concentration. The rest of the uncertain inputs and parameters were very model specific or the input was to complex for quick implementation in the database.

Conclusions on the results Given the results of the analysis, the best solution would probably be to combine a strong reduction of the WWTP discharge (e.g.50% as implemented in this study) with a less strong reduction of fertilization. This would likely lead to meeting the target for nitrogen with the smallest uncertainty on effects and socio-economic aspects. This is not one of the measures considered, but would be the average of two measures: the reduction of WWTP discharges by 50% and the reduction of WWTP discharges in combination with reduction of fertilization both by 50%. The target for phosphorus is very likely not met in either of the considered alternatives.

Important conclusions for the effects on the nutrient concentration are: - The specification of where the results are to be analysed has a large influence on the results. - The year-to-year variation due to meteorological variance is large - The scale of the analysis output is very important for the selection and assessment of uncertainties.

Discussion of the results The results of this study cannot be directly used for policy decisions. To a large extend, this study has the character of a theoretical exercise. To start with, the measures do not have a large overlay with reality. Implementation matters have not been considered in this study, neither were uncertainties related to implementation. Next, the criteria for nutrient concentrations are not yet formally established and thus have related uncertainty, which is not included in the study. Furthermore, not all uncertainties have been considered. The

uncertainties in data and parameters considered do not represent all important uncertainties. They are just a selection of important uncertainties. Due to choices that had to be made, other equally important or sometimes more important uncertainties are not included, such as uncertainty in water quantity/ discharge and uncertainty in WWTP load. Furthermore, there are other types of uncertainties not included as scenario uncertainty, model structure uncertainty and decision related uncertainty that have an effect on the results of nutrient concentrations.

During the assessment of the selected uncertainties and the further carrying out of the study, assumptions needed to be made for continuation of the study. These have been noted in this rapport. The most influencing are decision about how to deal with a lack of knowledge in assessing uncertainty, which is especially relevant for the assessment of uncertainty in fertilization and the assumptions about the variance of uncertainty in time; different decisions would have lead to different results. Next, choices made in the set-up of the analysis strategy are influential. The most influential are decisions about how to translate historical data into a scenario of the future, how to deal with meteorological variance and which output to consider. The socio-economic uncertainty has been assessed on a rough level, especially compared to the uncertainty in the effects of nutrient concentration.

Conclusions on learning points in the case study This case study has been a good exercise on how to perform an uncertainty analysis. It can be concluded that uncertainty analysis adds extra valuable information on the basis of which you can assess the alternatives; it gives information about how robust an alternative is. In the process of the analysis of the decision problem we encountered the following learning points: - The selection of uncertain input data and parameters to include is very important, but it was hard to see to consequences of our decisions on beforehand. The selection process took quite some time and discussion. With current knowledge we might have decided differently. - Due to lack of information about uncertainty bandwidth, uncertainty assessment is always subjective because of assumptions and choices made. There is a need for a good documentation. - For detailed, large models, it is time consuming to get a good insight in the uncertainties. Especially when there is the intention of not only showing a bandwidth, but also analyse the effects of different uncertainties. Other methods of uncertainty assessment might be used in addition to tackle this problem. - Monte Carlo simulation for large models takes quite some time. Calculation of one alternative took us four to five days on an entire network of computers.

Recommendations - There is a need for better guidance on how to make smart choices in the selection of uncertainties for uncertainty analysis. In this research, some of the selected uncertainties turned out to have not such a large influence. These guidelines need to elaborate on when a large effect of uncertainties can be expected and when this is not the case, anticipating on interaction of uncertainties and issues of scale. Smart choices of uncertainties can for example be made because some uncertainties enforce each other and some extinguish each other. Furthermore, the influence of uncertainties differs for the scale of the analysis. - Regarding uncertainty assessment, good documentation is needed about assumptions and choices made due to lack of information. - There is a need for guidance on smart uncertainty assessment for large models.

- Before confronting measured concentrations to WFD targets, it is recommended to normalise for the meteorological year in order to prevent false conclusions.

References

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Annex 1: NL-CAT

Scripts and databases The principal models embedded in dbSWAN (version 0.5.x) are the soil SWAP module and the ANIMO module. These models require extensive amounts of data to run, which are usually stored in a set of structured ASCII files. For regional studies the dbSWAN system offers a flexible mechanism for performing large numbers of SWAP and ANIMO runs. All data is stored inside a relational database. The so-called ‘runs’-table is related to all table groups in the database and offers the opportunity to adjust standard information stored in the tables. After starting dbSWAN, the following actions will be performed: 1. The database will be checked for errors. Execution will be halted if errors are found; 2. The ‘run’-table will be fetched. Each record in the ‘run’- table fully defines a specific SWAP and ANIMO simulation; 3. The information in record i of the ‘run’-table will be used to retrieve all necessary model input from the database; 4. Additional model input will be derived by means of pedotransfer functions and an extensive knowledgebase; 5. Model inputs will be sent to SWAP an ANIMO, and the models will be executed; 6. The simulation results will be checked for errors; 7. Post-processors will be applied to the simulation results. This step is optional. dbSWAN contains a large collection of user-adjustable post-processors. These will be triggered after each successful SWAP and ANIMO simulation; 8. The simulation results will be stored in an archive; 9. Steps 3-8 will be repeated until all records in the run-table have been processed. All output of SWAP and ANIMO are stored in a compressed data format. This format can be easily handled by existing post-processors for SWAP and ANIMO and can be readily converted to SWQN and SWQL inputs. Apart from offering a flexible interface to SWAP and ANIMO, dbSWAN also extends the functionality of these models.

Annex 2: Full uncertainty analyses

2.1 Uncertainty aspects of oxygen diffusion in soil

2.1.1 Summary An uncertainty analysis has been carried out for the parameters p1 and p2 in the oxygen diffusion relationship in soil:

D p2 = p1 ε Do

Where: D Oxygen diffusion coefficient in soil (L2 T-1) Do Oxygen diffusion coefficient in an air medium (L2 T-1) ε Soil air content (-) p1; p2 Parameters to be assessed experimentally (-)

The oxygen diffusion relationship is dependent on the soil texture. The justification of the assumption of spatial and temporal homogeneity depends on the variation of the soil texture in time and space. Since soil texture will be relatively stable in time, the effect of the assumption of no temporal variation is relatively small. Soil texture varies considerably in space on a local scale. Therefore, point measurements show a large variation. The autocorrelation length scale of the point measurements is much less than catchment scale so they can be assumed independent. When aggregating these point measurements to an average value for the parameters on catchment scale, the variations are smaller and spatial homogeneity can be assumed. When inspecting the results of the NL-Cat model on a more detailed level than the whole Regge catchment one should keep in mind that this aggregated uncertainty model will probably not be correct on this smaller scale. Regarding the representativeness of the data: the data are judged to be representative for the Regge soil characteristics. The amount of data however, is limited. Finally, this uncertainty analysis does not include the uncertainty in the data itself. Uncertainty due to random errors will balance itself out in the aggregation process. However, uncertainty due to any possible bias in the data is not included.

2.1.2 Introduction In ANIMO, the diffusion of oxygen in the soil gas phase has been modeled by the relative oxygen diffusion coefficient, which is a function of the soil air content:

D p2 = p1 ε Do

Where: D Oxygen diffusion coefficient in soil (L2 T-1) Do Oxygen diffusion coefficient in an air medium (L2 T-1) ε Soil air content (-) p1; p2 Parameters to be assessed experimentally (-)

Version 3.5 of the ANIMO model (Groenendijk et al., 1999) has been subjected to a sensitivity and limited uncertainty analysis (Groenenberg et al., 1999). The nitrogen leaching and the denitrification appeared to be very sensitive to the parameters p1 and p2 set for this function.

The parameters p1 and p2 are specific per soil type and per land use type. Table A2.1 presents the soil schematization and the assumed values for p1 and p2 per soil horizon as used in the EuroHarp project (Schoumans et al., 2006). The dominant soil type in the Regge catchment are Podzolic medium textured sandy soils.

Soil type Horizon Top Bottom Staring series unit p1 p2 according to Wosten Humic, medium Aanp 0 20 B02 0.3 1.5 textured, sandy D1 20 50 O16 0.3 1.7 topsoil over coarse textured, sandy D2 50 75 O16 0.3 1.7 subsoil C11 75 100 O02 1.5 3.0 Gx 100 1300 O02 1.5 3.0 Podzolic, medium Ap 0 25 B02 0.3 1.5 textured sandy soil B2 25 40 B02 0.3 1.5 B3 40 50 O02 1.5 3.0 C1g 50 100 O02 1.5 3.0 C1gx 100 1300 O02 1.5 3.0 Podzolic, medium Ap 0 20 B03 0.3 1.5 textured sandy soil B2 20 50 B03 0.3 1.5 over boulder clay C1g 50 100 O02 1.5 3.0 Dx 100 1300 O06 1.5 3.0

Table A2.1: the soil schematization and the assumed values for p1 and p2 per soil horizon as used in the EuroHarp project (Schoumans et al., 2006)

Due to lower oxygen concentrations, the transformation rates between root zone and groundwater level, called the subsoil zone, appears to be the most sensitive zone to oxygen limitation of transformation rates. Relative oxygen diffusion in subsoils of the Regge catchment has been described by only one combination of the (p1; p2) parameter set: D = 1.5 ε 3 . In the topsoil the aeration is less hampered by oxygen availability than in the Do subsoil and the denitrification and nitrogen leaching seems to be less sensitive to the assignment of the (p1; p2) parameters for this part of the soil. Our analysis of uncertainty aspects concerning the diffusion relation focuses only on the relation assumed for subsoils.

2.1.3 Uncertainty analysis Experimental data published by Bakker et al.(1987) have been used to assess the uncertainty aspects in the diffusion relation. This literature source presents the measurements of two sandy sub soils representative for the sandy sub soils in the Regge catchment (figure A2.1).

0.04

0.035

0.03

co Re 0.025 latieffi ve cie nt diff 0.02 suiD/ on Do (-) 0.015

0.01

0.005

0 0 0.05 0.1 0.15 0.2 0.25 0.3 Soil air content (-)

"humic loamy sand’ Sand arable land D/Do=0.3 (Eg)^1.5 D/Do=1.5 (Eg)^3.0

Figure A2.1: relative diffusion coefficient as a function of soil air content. Two series of experimental data published by Bakker et al (1987) and the relations used in the NL-CAT model for root zones (D/Do=0.3Eg1.5 ) and for subsoils (D/Do=1.5Eg3.0 )

The relation used in the NL-CAT model for the subsoils in the Regge-catchment complies with the experimental data published by Bakker et al.(1987).

The experimental data have been used to perform a multi-variate analysis and to assess the parameters p1 and p2 simultaneously. Bootstrapping (Efron & Tibshirani, 1994) has been performed to estimate the joint uncertainty distribution of parameters p1 and p2. A total of 1000 bootstrap samples have been drawn for this purpose. For each bootstrap sample, parameters p1 and p2 were estimated by means of nonlinear least squares regression. As an alternative, one may consider weighted nonlinear least squares regression, and give more weight to D/D0 ratio’s corresponding to lower soil air contents. In this way, more weight is assigned to observations that affect denitrification and nitrogen leaching most. However, no indication of the validity of this weighting is present, therefore the simple least squares regression has been applied.

The resulting realisations (or models representing the oxygen diffusion relationship) are given in Figure 2. The range of realisations (blue) reflects the uncertainty in the parameters p1 and p2. The measurements are point values, the scale of the analysis is for subsoil layers. It can be seen that the aggregated uncertainty for a subsoil layer is smaller then the original variation in the point values. Parameters p1 and p2 are highly correlated (figure A2.3). This result complies with findings of Bakker et al. (1987).

Results of the statistical analysis have been used to perform a number of realizations of the diffusion relation (figure A2.2) to be used in a Monte Carlo analysis of the NL-CAT model.

Figure A2.2: realizations of the diffusion relation for the sandy subsoils of the Regge-catchment in the NL-CAT model. In blue the different realisations of combinations of p1 and p2 are presented. In red the original point value measurements have been presented. 2 2 p p 2.0 3.0 4.0 5.0 012345

0 1020304050 012345

p1 p1

Figure A2.3 Scatter plots of parameter estimates obtained by fitting bootstrap samples. The only difference between the figures is the scale of the p1-axis.

To have a first impression of the impact of the variation of the diffusion coefficients, the realizations have been used in a Monte Carlo analysis of the ANIMO model using the data set of a readily available case: heavily fertilized maize field on a sandy soil at the Cranendonck experimental farm in the southern part of the Netherlands (Renaud et al., 2005) . Fig. 3 depicts the nitrogen concentration in the upper meter of the saturated zone.

400 Mean '5% 350 Median '95% 300

250

200

150

100 NO3 concentrationin groundwter (mg/l) 50

0 01/01/1974 01/01/1975 01/01/1976 31/12/1976 31/12/1977 31/12/1978 31/12/1979 30/12/1980 30/12/1981 30/12/1982 Date

Figure A2.4: nitrogen concentrations in groundwater of a maize plot at the Cranendonck experimental farm (Renaud et al., 2005) as a result of a Monte Carlo analysis using the p1 and p2 values obtained by fitting bootstrap samples.

In most of the winter/spring periods the range between the 95% and 5% amounts to 70 mg/l, but in the winter/spring period of 1978 the difference is 130 mg/l.

References

Bakker, J.W., F.R. Boone en P. Boekel, 1987. Diffusie van gassen in grond en zuurstofdiffusiecoefficienten in Nederlandse akkerbouwgronden. ICW, Wageningen. Rapport 20.

Efron, B. and R.J. Tibshirani, 1994. An Introduction to the Bootstrap. Monographs on Statistics and Applied Probability 57, CRC Press, 436 pp.

Groenenberg, J.E., C. van der Salm, E. Westein and P. Groenendijk, 1999. Gevoeligheidsanalyse en beperkte onzekerheidsanalyse van het model ANIMO. DLO-Staring Centrum, Wageningen. Rapport 446.

Groenendijk, P., L.V. Renaud and J. Roelsma, 2005. Prediction of Nitrogen and Phosphorus leaching to groundwater and surface waters; Process descriptions of the Animo 4.0 model. Wageningen, Alterra, Wageningen. Report 983.

Renaud, L.V., J. Roelsma and P. Groenendijk, 2005. User’s guide of the ANIMO 4.0 nutrient leaching model. Alterra, Wageningen. Report 224.

2.2 Uncertainty aspects of phosphorus background concentration

2.2.1 Summary An uncertainty analysis has been carried out for the variation of the mean phosphorus background concentration in the Regge catchment. In this analysis, the concentration as well as the uncertainty in the concentration are assumed constant throughout the catchment and in time. Point measurement concentration data have been aggregated to a mean concentration on catchment scale. Statistical analysis of the mean concentration resulted in a normal distribution, having a mean of 0.00016 kg/ m3 and a standard deviation of 0.000011 kg/ m3.

2.2.2 Introduction The phosphorus background concentration is the phosphorus concentration present in subsoil groundwater (from 1m below the surface until approximately 13m) and is not directly influenced by activities on the topsoil. In the model used for the case study (ANIMO, sub model of NL-CAT), the background concentration is assumed to be constant in space and time; one value for the total catchment. In reality this concentration will vary. A sensitivity analysis of an earlier version of the model, showed that the output of ANIMO is sensitive to this variable (Groenenberg et al., 1999). The simplification in the model is assumed to have a substantial effect on the output.

2.2.3 Uncertainty analysis In this uncertainty analysis data are sought that reflect the variation of phosphorus background concentration in the Regge catchment. First these data are analysed and next it is described how these will be used in the Monte Carlo uncertainty analysis of the HarmoniRiB case study.

Uncertainty analysis data Data on groundwater quality for the Regge area can be found in the national Dutch databases LMG (Landelijk Meetnet Grondwaterkwaliteit, RIVM) and Dino (TNO). LMG contains most data. Data on total phosphor concentration have been found starting from 1979 until 2003 for 13 measurement stations. When searching for data in Dino, no additional data for that period can be found. A selection of the period 1993-2003 has been made; since 1993 better documentation of measurements below the detection limit is present. The data have been provided by RIVM.

207

206 217 219

220

218

221

222 216

223 3

226 224

Figure A2.5: the measurement locations projected on a seepage map of the Regge catchment.

An overview of the thirteen measurement locations in/ around the Regge area is shown in figure A2.5. The measurement locations cover most of the area, some are located just outside the area at locations that have similar conditions. The depths of the measurements vary between 7 and 13 meter. The total analysis includes 139 measurements. All measurements are assumed to be independent.

The measured phosphorus concentration varies between < 0.062 and 0.465 g/m3. The detection limit for the phosphorus measurements is 0.062 g/m3. All measurements beneath the detection limit were originally reported as 0 g/m3. RIVM provided insight in the values that were measured in reality (the very low measurement values) in order to enable a better (statistical) analysis. However, the data beneath the detection limit have a larger uncertainty than the normal measurements. The data may even have a chaotic character. The use of data beneath this detection limit will be indicated in the further analysis.

The data have been analysed on their temporal and spatial variability.

Spatial variability Figure A2.6 shows the mean and variation of phosphorus concentration for the locations. There are large differences between locations. This implies that the area is spatially not very homogeneous regarding phosphorus concentration.

Mean and variation per location 0.6 mean 0.5 variat ion

0.4 0.3

0.2

phosphorus (mg/l) phosphorus 0.1 total concentration 0 200 202 204 206 208 210 212 214 216 218 220 222 224 226 228 location

Figure A2.6: the median and the spread of the measurements per location. Location 203 represents location 3.

RIVM has provided meta information for the data, which is used to find an explanation for the differences. The soil type, geological structure and origin of the groundwater are of importance for the phosphorus concentration measured. Some explanations of the differences in concentrations have been found, however not all differences can be explained.

Most measurement locations are located in sand soils, the main soil type in the Regge area. In the north of the area there are some peat soils, in which measurement locations 207 and 219 are located. The groundwater in peat soils has a larger phosphorus concentration by nature. Indeed, the concentrations of these measurement locations are above average. The ground at locations 206 and 207 is reported to be fermenting and sulphate reducing. These characteristics cause the high phosphor concentrations at these locations. Measurement locations 217, 220 and 221 are locations with a downward seepage flux, 206, 218 and 226 have an upward flux and the other locations have a difference in hydraulic head that is smaller than 0.03 m. The phosphorus concentrations in areas with a downward flux are influenced by groundwater that is younger in age and can be somewhat different in concentration. However, the data don’t show a clear effect.

Temporal variability The average value per year for all locations is plotted in figure A2.7. In general, the differences are not very large, with a small exception in 1994. For the total catchment, the variation in time is only small.

Average value per year

0.25

0.2

0.15

(mg/l) 0.1

0.05

0 concentration phosphorus Total 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Figure A2.7: The average values per year for all locations together.

In figure A2.8 some example locations are plotted.

Measurement location 3 Measurement location 206 0.5 0.5 0.45 0.45 0.4 0.4 0.35 0.35 0.3 0.3 0.25 0.25 0.2 0.2 0.15 0.15

Total phosphorus phosphorus Total 0.1 Total phosphorus phosphorus Total 0.1 concentration (mg/l) concentration (mg/l) concentration 0.05 0.05 0 0 1992 1994 1996 1998 2000 2002 2004 1992 1994 1996 1998 2000 2002 2004 year ye ar Measurement location 207 Measurement location 216 0.5 0.5 0.45 0.45 0.4 0.4 0.35 0.35 0.3 0.3 0.25 0.25 0.2 0.2 0.15 0.15

phosphorus Total 0.1

Total phosphorus phosphorus Total 0.1 concentration (mg/l) concentration 0.05 concentration (mg/l) 0.05 0 0 1992 1994 1996 1998 2000 2002 2004 1992 1994 1996 1998 2000 2002 2004 year year

Figure A2.8: Variation of measurements at the different measurement locations. Values < 0.062 g/m3 are beneath the detection limit.

Based on visual inspection trends in time are unlikely. Variation in concentration can be partly explained by the (natural) variation in groundwater levels over time. Since the

groundwater is a layered system, a fixed point in the soil will be situated in different groundwater layers in time, having different characteristics.

In figure A2.9 a frequency diagram of the measurements has been constructed

Frequency diagram 0.2 0.18 0.16 0.14 0.12 0.1 0.08

0.06

0.04 0.02 Fraction of measurements 0

0-0.03 0.27-0.3 0.3-0.33

0.03-0.06 0.06-0.09 0.09-0.12 0.12-0.15 0.15-0.18 0.18-0.21 0.21-0.24 0.24-0.27 0.33-0.36 0.36-0.39 0.39-0.42 0.42-0.45 0.45-0.48 Phophorus concentration intervals

Figure A2.9: Relative occurrence of phosphorus concentration values. The first two categories are values beneath the detection limit.

Monte Carlo analysis The value of phosphorus background concentration of interest is an averaged value for the total area and in time. As a result, the extreme values of the distribution of figure 3 measured at one measurement point, will always be balanced out by values at other measurement points at the total Regge scale. This means that the extreme realisations are not of interest in this case. Of interest is the standard deviation of the average value for the total area. This is defined as the standard deviation divided by the square root of the number of (independent) measurements. The more measurements taken in the area, the more secure one is about the average of the phosphorus background concentration in that area. The average (mean) of the phosphorus background concentration is 0.16 g/m3, the standard ⎛ 0.13 ⎞ deviation of the average is ⎜ ⎟ = 0.011. These values have been used for the Monte ⎝ 139 ⎠ Carlo analysis using random sampling.

Above data analysis showed temporal homogeneity but spatial heterogeneity. The input scale for the model is the total Regge catchment. Since the phosphorus concentration for the total Regge catchment is not always representative for a more local scale, one must be aware of the limitations for the phosphorus background concentration when analysing results on local scale. A change in model structure could be suggested. However, at the moment there is a lack of sufficient data and enough measurement points to make a subdivision in areas.

2.3 Uncertainty aspects of fertilisation

2.3.1 Summary An uncertainty analysis is carried out for the amount of fertiliser application in the Regge catchment. The uncertainty in the amount of fertiliser is determined in a two-step approach, first uncertainties relevant on catchment scale and next additional uncertainty on plot scale. On catchment scale, uncertainty caused by bias of the data is assessed. A uniform distribution, with upper and lower bounds ranging 75-125% from the original Euroharp input, has been set to reflect the variation in the total mean fertilisation application in the area (based on expert judgement). Next, variation in the amount of application within the area has been determined. The four original fertilisation areas from the Euroharp project have been maintained and the total mean application from the former step has been allocated to these regions by ratio. Next, statistical variation on plot scale from the so acquired mean has been determined. A statistical distribution has been obtained from field scale data that have been aggregated to plot scale (by dividing the standard deviation by the number of independent fields in a plot). The assumed autocorrelation of application on field scale is 3 (maize) or 6 (grass) fields. A distinction is made between the application to grass and maize land.

2.3.2 Introduction One of the main drivers that determine the surface water quality in rural areas is fertilisation. Fertilisation is a manageable factor and it often plays an important role in Programmes of Measures to reach a good ecological status of surface water bodies. Because it is one of the main nutrient inputs of the model, uncertainty in this input is expected to have a considerable impact on the results (expert judgement). The construction of the Euroharp model input for fertilisation is based on many assumptions. These include, amongst others, assumptions about the amount of slurry transport, about the relationship between animal numbers and fertilisation rate of the land, about N and P content of slurry, etc. An overview of the construction of the input is given at the end of this chapter. The impact of these assumptions is difficult to track. It can however be assumed that the fertilisation rate can deviate substantially from the Euroharp numbers. The fertilisation rates applied in Euroharp are shown in tables A2.2 and A2.3. The area is subdivided in four fertilisation sub districts, each having its own fertilisation rate. The total average application rate in the area is 410 kg N/ha for grass and 165 kg N/ha for maize. The fertilisation application is varied in time.

Table A2.2: the fertilisation rates for grass applied in Euroharp in kg N/ha Land use Fertilisation sub N effective Fraction total district application grass area Grass A 494 0.19 B 309 0.07 C 355 0.26 D 423 0.48 Total area 410 1

Table A2.3: the fertilisation rates for maize applied in Euroharp in kg N/ha Land use Fertilisation sub N effective Fraction total district application maize area Maize A 162 0.20 B 167 0.06 C 166 0.26 D 165 0.48 Total area 165 1

2.3.3 Uncertainty analysis The analysis will be performed for the fertilisation application on the main soil type in the area: sand. Since this soil type covers most of the area, the simplification is expected to have a small impact. The main land uses in the area are grass and maize. Arable crop is a minor land use in de area (roughly 7% of the total agricultural land) and has not been included in the analysis. The analysis is based on the effective application of N to the land, assuming that P application is proportional to N application.

Uncertainty analysis data Some data of fertilisation rates in the area have been found, which provide information on the distributions of the actual application rates of effective fertilisation dosages in the region. An overview for grassland and maize data is given below. These data will be used to find uncertainty distributions. However, there are some limitations to the use of these data. First, they are on field/ company scale and therefore do not cover for uncertainty due to the assumptions made on a higher scale. Next, the construction of these data has already been based on some assumptions and rules of thumb, like the standard excretion rates of animal slurry. Subsequently, these data are already (slightly) biased.

Grassland data Oenema et al., (2002) investigated nitrogen surpluses at field level for 17 farms in the “Cows & Opportunities” project. Application rates at field level of 198 fields completely utilized as grassland have been extracted from their report. They distinguish three different types of application: organic manure, fertilizer and manure produced by grazing cattle.

The total effective N dosage has been calculated from these data using the following formula:

N eff = N fert + 0.5N org.man + 0.1N gr.catt (1)

In figure A2.10 the marginal distribution of the effective N dosage is shown.

25 24 23

15 12 12

10 10 9

Percentage of fields (%) fields of Percentage 6

5 4 2

0 <100 100-150 150-200 200-250 250-300 300-350 350-400 400-450 >450 Application rate of total effective fertilizer (kg N/ha)

Figure A2.10: marginal distribution of total effective fertilizer application rate on grassland at field level in the “Cows & Opportunities” project. Normal distribution; Mean = 287.35 kg N/ha; Standard deviation = 96.38 kg N/ha.

The farmers cooperating in the “Cows & Opportunities” project are mostly modern progressive farmers, having a lower fertilisation application rate on average. This is one of the reasons that the distributions mean is some 25% lower than the applications applied in the Euroharp project. The distributions mean is considered not representative. Some tests show that the standard deviation of the “Cows and Opportunities” data for N fertiliser and the data of Reijneveld et al (2000) (see table 3, next paragraph) are of the same order of magnitude. The standard deviation of “Cows and Opportunities” is assumed to be representative for the standard deviation on field level for grass.

Maize data In the ‘Cows & Opportunities’ project a number of maize fields were included as well. However, the amount of data is relatively small. Reijneveld et al., 2000, provide an analysis of agricultural census data of dairy farms with respect to use of fertiliser. They classify the intensity of land use according to the quantity of milk produced per hectare. For the eastern sand district of the Netherlands information on average fertilizer application rates and standard deviations is presented in table A2.4.

Table A2.4: characteristics of Dairy farming in the eastern sand district of the Netherlands, based on 1997 census data (standard deviation between brackets) 10000-12000 12000-15000 >15000 kg/ha milk kg/ha milk kg/ha milk Life Stock Unit per ha 2.06 (0.44) 2.19 (0.31) 2.86 (0.55) N fertilizer on grassland (kg/ha) 279 (147) 217 (54.5) 293 (69.3) N fertilizer on silage maize (kg/ha) 34 (31) 25.7 (19.9) 24.1 (13.9) P2O5 fertilizer on grassland (kgha) 9.84 (12.2) 5.9 (6.9) 4.9 (7.5) P2O5 fertilizer on silage maize (kg/ha) 9.66 (6.52) 10.5 (7.8) 9.9 (5.1)

This table is based on 1502 data and shows that the N fertilizer use on silage maize for farms with 10000-12000 kg/ha milk is approximately 34 kg N/ha, having a standard deviation of 31 kg/N ha. The average value of 34 kg N/ha is quite low for the Regge area, based on expert judgement. Research from the Dutch LMM monitoring network (National monitoring network of effects of fertilisation policy) presented by Fraters et al., 1997 shows a distribution of manure application to non-grassland fields at LMM network-farms (figure A2.11), which is judged to be representative for the Regge.

Figure A2.11: marginal distribution of manure application rates to non-grassland fields at LMM network-farms in 1991-1995 (Fraters et al.,1997)

The bandwidth in the data of Fraters et al., (1997) represent the agricultural practise of the early years of the nineties. It can be expected that in more recent years the extreme high application rates will have lower frequency values or do not exist anymore. The distribution has a mean of 253 kg N/ha and a standard deviation of 112 kg N/ha.

Temporal dimension The data found are assumed to represent the fertilisation application in the year 2000. Except for the data of Fraters et al. (1997) the data are all being collected in the late nineties, which justifies this assumption. In the sampling strategy will be explained how these data will be used for other years.

Complementary use of expert judgement The fertilisation input to the NL-CAT model is very uncertain. Above data show uncertainty of data on field/ company level. This doesn’t cover all uncertainty. Two levels of uncertainty in the input data can be distinguished: uncertainty in the total fertilisation application level on catchment scale and regional deviation of the mean catchment fertilisation level on plot scale.

- The uncertainty in the total mean application level on catchment scale is caused by assumptions and rules of thumb in the construction of all fertilisation data (use of established methods) and assumptions made in aggregating data (e.g. about transport of slurry). The influence and direction of influence of the compiled assumptions is unknown. It is assumed that the Euroharp fertilisation is a likely estimation of the fertilisation level in the Regge. But, based on expert judgement, a fertilisation level that is 25% lower or higher is likely as well. - The regional deviation from this mean fertilisation level on catchment scale on field or farm scale is shown by the data in the former paragraph.

Monte Carlo analysis A two-step approach has been followed to create realisations for the area. First, realisations have been created for the total mean fertilisation application in the Regge area. These have been allocated to the four Euroharp fertilisation sub districts before further processing. Second, the regional deviations of these realisations have been determined.

Step 1: Total mean fertilisation application in the area: - An uniform distribution has been assumed, since we don’t have information on the type of distribution, - The original Euroharp application level (averaged over the total area) will be assumed to be the mean of the distribution, for grass this is 410 kg N/ha and for maize this is 165 kg N/ha. - Based on expert judgement, the lower and upper boundaries are defined as 25% of the original Euroharp application.

The mean fertilisation application for the total Regge has been allocated to the four original fertilisation sub districts of the Euroharp project, based on the original ratios. In Euroharp it has been determined which areas are more intensive and which are more extensive. The data used for determining the ratios are accurate enough to make the assumptions that these differences between areas are present. Taking the one overall mean of the above distributions would ignore this information. The exact magnitude of the differences between the regions is uncertain. We realise that this uncertainty is neglected. The current approach has been chosen to exclude the situation that an intensive area gets a lower fertilisation realisation than an extensive area.

Step 2: Regional variation in fertilisation application on plot scale: - Based on the information in the uncertainty analysis data paragraph, a normal distribution has been assumed. The distributions mean is determined in the former step. The standard deviations of the data in the uncertainty analysis data paragraph are assumed to be representative on field scale. This standard deviation has to be aggregated to plot level. The standard deviation per plot is the standard deviation per field divided by the square root of the number of independent fields. This is reflected in the formula:

STDb STDa = N ind

Where

N ind = number of independent observations for calculation of STDa

STDa = standard deviation of the original distribution, valid on a certain scale

STDb = standard deviation of the distribution of interest, on another scale

- Assumption for the number of independent fields per plot: o An average field is 2 ha (based on the Land use map deducted from satellite images and corrected by pictures taken from the air) o On the Land use map, land divided by a ditches or a fences is interpreted as multiple fields. However, the farmer will treat neighbouring fields in the same way. The assumption is that the management level of land will be 1 ½ fields (= 3 ha) for maize and 3 fields (= 6 ha) for grass. o To determine the number of independent plots, the plot area has been divided by the area of a management unit. o The standard deviation of “Cows and Opportunities” of 96 kg N/ha will be divided by the square root of the number of independent plots.

Table A2.5 gives an overview of the minimum and maximum standard deviation that has been applied.

Table A2.5: an overview of the minimum and maximum standard deviation that has been applied Standard deviation field (kg Standard deviation plot varies N/ha) between: Grass N effective 96.4 2.9-66.8 Maize N organic 112 3.8-54.9 manure Maize N fertilizer 31 1.1-15.2

Final processing to NL-CAT input NL-CAT needs more specific input than provided in this analysis, e.g. type of fertilization or type of slurry applied. To create this input, the original ratios of the Euroharp project have been used. We realize that these ratios are subject to uncertainty as well, although they will have a minor effect on the output as compared to the uncertainties included in the analysis. The P input to the model has been deducted from the N input. For every type of slurry, fixed ratios between N and P content have been set in the Euroharp project. Phosphorus fertilizer will be applied when sampling results in a low organic manure application combined with a relatively high N fertilizer dosage. The phosphorus fertilizer application has been calculated for grass as:

N eff − 250 Pfert = − Porg.mat − Pgr.catt ; Pfert ≥ 0 1+ 0.007()N eff − 250

And for maize: 0.25 * N fertilizer application rate

Temporal dimension Above process describes the creation of the realisations for the year 2000. The realisations for the years 1941-1999 have been deducted from the realisation in 2000. First, factors have been created, dividing the realisations for the plots in 2000 by the original Euroharp mean in 2000. Then the original Euroharp values for the years 1941- 1999 have been multiplied by these factors to get realisation for these years. This approach assumes that the direction and relative magnitude of the uncertainty remains constant in time. This assumption will be only

partly valid. It will for example. (to a large extend) be valid concerning uncertainty due to assumptions and rules of thumb in the construction of the fertilisation data. The land management of a farmer on a piece of land is also relatively stable in time. However, fertilisation regime and habits have changed over time. We feel however, that this is the best way, since we lack data for another approach.

2.3.4 Background: procedure followed to obtain fertilisation input data The explanation of the procedure is split up in two parts: the spatial and the temporal dimension. Each dimension will start with the data used to obtain the input data, followed by the explanation of the procedure. The main assumptions and uncertainties have been stressed.

Spatial dimension Data used: - Manure/ fertilizer use per LEI region. Source: LEI (Agricultural Economics Research Institute, the Netherlands, www.lei.wur.nl). With respect to agricultural data, the Netherlands is split up in LEI regions. The Regge catchment is situated in LEI region 8. The data are on national scale, so relatively uncertain on regional scale. - Number of livestock per farm for 1998. Source: CBS (Netherlands statistics, www.cbs.nl) Uncertainty is low (expert judgement) - Guidance information with respect to N and P production per animal (excretion rates). Source: LEI and CBS. Uncertainty is quite large (expert judgement).

Procedure and assumptions: NL-CAT is a regional model, so data with a regional aggregation level are needed. What is available are data on national level (LEI level) and on farm level. In order to get data on regional level, the data on farm level have been aggregated, satisfying the data on national level. This has been done by: - Dividing the Vecht catchment in extensive and intensive husbandry areas, based on farm data of livestock. The Regge catchment has been divided into four regions. Next, the regions are subdivided according to land use. For each of these regions (plots), a level of manure/ fertiliser has been set, based on data on farm level. - Allocate manure/ fertilisation to each area; making sure the total sum of the regions matches the LEI (national) loads.

The level of manure/ fertiliser for each region has been set following prevailing, generally accepted, agricultural relations. The first relation exists between the total animal population in combination with the land use, and the amount of manure/ fertiliser the land needs. The underlying assumption is that the land needs to provide for the feeding demand of the livestock, resulting in more need for fertilisation in intensive husbandry areas. These relations are different for different land use types. Next, the production of land has a relationship with the amount of fertilisation. This relationship has a certain optimum. These optima are published as guidelines for farmers.

In the Euroharp project an analysis of livestock numbers per municipality, resulting in nutrient demands, and the production capacity of agricultural fields is performed. This analysis has lead to a feasible fertilisation scenario in the Regge region (Schoumans et al. 2006). The analysis is done per fertilization region and for the different land use types (table 3). In case the manure production of the livestock in the area didn’t satisfy the demand, additional fertiliser is applied.

Assumption for these relations is that transport of manure/ fertilizer or livestock food is of minor importance. A transport component is present in the LEI data, but this component is quite uncertain.

For the manure applied to the land a certain composition has been assumed, the standard excretion rates. The excretion rates vary with the type of livestock. In literature, values are given for the composition of different types of manure. These data have been applied to estimate the values in table 3. In reality, the N and P concentrations in manure may have a considerable variation. A certain volume of manure can thus be related to different levels of fertilisation. . Temporal dimension: Extra data used: - Fertilisation trends in time from the model STONE (http://www.kennisonline.wur.nl/BO/BO-05/006/01/beschrijving.htm) over the period 1940- 1999. This national model is set up for all of the Netherlands and has a larger aggregation level than NL-CAT. The Regge is seen as one fertilisation area. Uncertainty unknown, but assumed of substantial influence, although not extremely high.

Above, the spatial distribution in fertilisation data has been determined. For the temporal development for the period 1940- 1999 the following steps and assumptions are made: - In 1940 the amount of manure is assumed to be evenly distributed over the area (no subregions). Maize wasn’t grown in the Netherlands yet and farmers used extensive farming methods. Using this assumption, the regions from NL-CAT and Stone can be equated for 1940, and the manure levels of Stone can be used. - For each of the 12 subregions (4 regions * 3 landuse) a factor is set for the increase of manure between 1940 and 1999. - The development of manure use in time of the model STONE is assumed to be representative. - The factor is used for the development of the manure level between 1940 and 1999. This development is not linear, but follows to temporal developments of manure use of the model Stone.

2.4 Uncertainty aspects of iron and aluminium content in the upper soil

2.4.1 Summary An uncertainty analysis is carried out for the iron aluminium concentration in the topsoil (1- 120 cm). For this purpose a geostatistical analysis has been applied: conditional sequential Gaussian (block) simulation. Uncertain model input has been created for each plot. The mean and standard deviation have been determined for each soil type and next the autocorrelation length scale for iron and aluminium concentration. The geostatistical analysis has been applied to a 250x250m grid scale, and was then aggregated to plot scale. Direct analysis on plot scale is not possible because of the nature of the plots, which are scattered over multiple locations in the area. Each realisation for the MC simulation is composed from (aggregated) samples for each plot.

2.4.2 Introduction The amount of iron and aluminium content in the top soil influences the amount of phosphor - introduced to the system by application on the topsoil - which reaches the groundwater and eventually the surface water. The iron and aluminium can bind the phosphor infiltrating to the ground, up until a certain maximum saturation level. A small amount of iron and aluminium can bind quite a lot of phosphorus. The amount of iron and aluminium is uncertain. In the NL-CAT model, four different iron and aluminium levels have been distinguished for the six different soil types in the area. For these four different levels, a vertical subdivision of iron and aluminium content is made over soil horizonts. This iron and aluminium content is set constant in space and time. In reality, especially variations in space will be present.

2.4.3 Uncertainty analysis The goal of the analysis is to find a variation of the iron aluminium concentration in the top soil (1-120 cm) in space on plot level. The assumption of this content being constant in time is kept, since soil content is assumed not to vary very much. Information has been found on the iron and aluminium content in the soil at certain measurement locations in the area. The

geostatistical analysis made it possible to get coverage for the total area. This is preferred over the method of allocating points to certain plots or areas. The amount of points is too little to cover all plots in such an analysis; only an average for larger areas could be obtained. In the geostatistical analyis, no vertical discretisation has been made for the soil horizonts, because of lack of measurement data for such an analysis. The average vertical concentrations have been the output of the analysis. Based on data in the original model, these average have been recalculated to bulk densities and these are assigned to the horizonts based on the original division of bulk densities over horizonts. The next paragraphs will first introduce the geostatistical model used and next the data used in the analysis. Then the results of the analysis are summarized.

Geostatistical model of soil aluminium and iron content The 0-120 cm average soil aluminium and iron content varies in space in a partially unpredictable way, as do most soil properties (Heuvelink and Webster 2001). As a result of this, maps of the spatial distribution of the aluminium and iron content will be uncertain to some degree. Geostatistical models explicitly address the uncertainty in soil properties by defining models that contain a stochastic residual term:

Z(x) = m(x) + ε(x) (1)

Here, Z is the soil property, x is a two-dimensional coordinate, m is a deterministic trend (explanatory part) and ε is a stochastic residual (unexplanatory part). We will assume that ε has zero mean, is normally distributed and possibly spatially correlated. We let m depend on soil type only:

K m(x) = ∑βk ⋅δk (x) (2) k =1 where K is the number of different soil types in the 1:50,000 soil map, βk is the mean value of the soil property for soil type k, δk(x)=1 if x lies in a mapping unit with soil type k, else δk(x)=0. Further, we will assume that the spatial covariance structure of ε satisfies:

Cov(ε(x),ε(x + h)) = σ(x) ⋅ σ(x + h) ⋅ρ( h ) (3) where the standard deviation σ depends on soil type, along similar lines as was done for m, and where ρ is the spatial correlation function of the standardised ε, which is assumed to depend on Euclidean distance between points only. The model proposed above accommodates differences in spatial variation between soil mapping units as well as spatial correlation as a function of distance. It was recently successfully applied to spatial modelling of groundwater level characteristics in the Netherlands (Finke et al. 2004) and bears much resemblance with common approaches in Digital Soil Mapping (Hengl et al. 2004, Dobos et al. 2005, Lagacherie et al. 2006).

The parameters of the model are the means βk and standard deviations σk per soil type and the correlation function ρ. These need to be derived from data, for both the soil aluminium and iron concentration. The data found and used are: the legend to the 1:50,000 soil map and point observations as contained in the ‘Landelijke Steekproef Kaarteenheden’ (LSK) (Finke et al. 2002) (a description of these data can be found in the next paragraph).

Once the parameters are estimated, we generate realisations of the geostatistical models using sequential conditional Gaussian simulation (Goovaerts 1997). The 1:50,000 soil map will appear in these realisations because the means and standard deviations vary between soil mapping units. In addition, the influence of point observations will also be visible because simulated values near observations points will be drawn towards the measured values. Simulations will be done using the Data Uncertainty Engine software (Brown and Heuvelink 2006, Heuvelink et al. 2006). Average values of the aluminium and iron content of 250 × 250 m grid cells will be simulated, since this is the size of a grid cell in the NL-CAT model. These grid cells will then be aggregated to plot size by block-kriging. In addition to simulated maps as required by the Monte Carlo uncertainty analysis we will also produce maps of the block-kriging predictions and standard deviations of aluminium and iron. These are identical to the mean or standard deviation of a very large number of simulated maps, respectively, and give useful information about the expected spatial distribution of aluminium and iron and the associated uncertainty.

Data sets used to parameterise the geostatistical model The legend to the 1:50,000 soil map of the Netherlands was generalised for the purpose of this work. Small soil types have been lumped together with other, similar soil types. The generalised soil map distinguishes between nine soil types in the Regge catchment, see Table A2.6. The generalised soil map is given in Figure A2.11.

Table A2.6. Soil types of the generalised Dutch 1:50,000 soil map that occur in the Regge catchment area. V Peat soils without mineral cover Iw Peat soils and shallow peat soils with sand cover Vw Shallow peat soils without mineral cover Hn Sandy soils, predominantly field podsols with signs of gley Hd Sandy soils, predominantly (dry) field podsols without signs of gley Pzg Sandy soils with gley, in stream valleys Ez Sandy soils with manure-rich earthy cover Zd Sandy soils, eolian deposits Kt Old clay soils with glacial till

Peat soils without mineral cover Peat soils and shallow peat soils with sand cover Shallow peat soils without mineral cover Sandy soils, predominantly field podsols with signs of gley Sandy soils, predominantly (dry) field podsols without signs of gley Sandy soils without gley, in stream valleys Sandy soils without manure-rich earthy cover Sandy soils, eolian deposits Old clay soils with glacial till

LSK- measurement location

Figure A2.12. Generalised soil map of the Regge catchment with LSK observation points indicated with a star.

Point observations of aluminium and iron content are taken from the LSK-dataset (Finke et al. 2002), which has 48 locations within the Regge catchment. In order to obtain more accurate estimates of the parameters of the geostatistical model, the dataset was enlarged to also contain observations outside the catchment, from soils similar to those of the Regge catchment (Figure 1). At first instance this yielded a total of 249 observations points, but after critical analysis of the data and judging the representativeness of the data for the soils of the Regge catchment the dataset was reduced to 168 observation points. The mean and standard deviation of the observations per soil type are given in Table A2.7. These values were assigned to the parameters βk and σk of the geostatistical model.

Table A2.7. Mean and standard deviation of average 0-120 cm aluminium and iron concentration per soil type, computed from 168 LSK-observations observations. soil type mean Aluminium standard deviation mean Iron standard deviation (mmol/kg) Aluminium (mmol/kg) (mmol/kg) Iron (mmol/kg)

v 40.6 6.9 42.8 36.9 iw 43.1 22.2 46.3 43.0 vw 23.5 12.1 16.9 21.3 hn 36.2 17.6 10.8 9.9 hd 48.0 9.8 10.9 6.4 pzg 14.8 7.9 40.4 34.6 ez 39.9 12.9 38.6 17.9 zd 38.5 22.2 11.2 7.4 kt 35.1 19.2 30.7 11.8

6.1.1 Results The 168 aluminium and iron observations were standardised by first subtracting the mean and next dividing by the standard deviation, using the values given in Table 2. Next variograms were computed and fitted on the standardised data. This yielded exponential variogram models for both soil properties:

h ρ ( h ) = 0.75⋅exp(− ) for h > 0 (4) Al 1500

h ρ ( h ) = 0.40⋅exp(− ) for h > 0 (5) Fel 3000

The fit of these functions is good. The fit is shown in figure A2.13. In the figures one sees some scatter due to the small lag used.

Figure A2.13: the fit of the variogram models for aluminium (left) and iron (right). The x-axis represent the distance and the y-axis the semi-variance.

Conditional sequential Gaussian (block) simulation on the standardised data yielded 400 simulated grid maps per soil property. Three example realisations for aluminium and iron are shown in Figure A2.14. The simulated maps were de-standardised by first multiplying per grid cell with the standard deviation of the soil type for that grid cell, and next adding the mean of the soil property for the grid cell. Averaging the 400 simulated maps and computing their standard deviation will yield maps that are close to the maps of the kriging estimate and standard deviation, which are shown in Figures A2.15 and A2.16. Finally, each of the 400 simulated maps of aluminium and iron were spatially aggregated to compute the average per NL-CAT plot. This yielded a table with 400 columns (the number of simulations) and 140 rows (the number of NL-CAT plots). The entries of the table are the average aluminium (iron) concentration of the cells occupied by the particular plot, for the particular simulation run. Negative values occasionally occur, which is a consequence from assuming a normal distribution for the uncertain aluminium and iron concentration. Since negative values are rare and not extremely large, a practical solution is to simply replace these values by zero. Some NL-CAT plots have no entries because these plots are either too small to have 250 × 250 m cells assigned to them or they are located in parts of the Regge catchment that are not covered by the Dutch 1:50,000 soil map (i.e., they lie in Germany).

Figure A2.14. Three example realisations of maps of the standardised aluminium (top) and iron (bottom) concentration, generated using conditional sequential Gaussian imulation.

Figure A2.15. Kriging estimate (left) and standard deviation (right) of the 0-120 cm aluminium concentration (mmol/kg).

Figure A2.16. Kriging estimate (left) and standard deviation (right) of the 0-120 cm iron concentration (mmol/kg).

References Brown, J.D. and G.B.M. Heuvelink (2006), Data Uncertainty Engine (DUE) User’s Manual. Universiteit van Amsterdam, 52 pp. Dobos, E., F. Carré, T. Hengl, H.I. Reuter and G. Tóth (Eds.) (2006), Digital Soil Mapping as a Support to Production of Functional Maps. EUR 22123 EN, 68 pp. Office for Official Publications of the European Communities, Luxembourg.

Finke, P.A., D.J. Brus, M.F.P. Bierkens, T. Hoogland, M. Knotters and F. De Vries (2004), Mapping groundwater dynamics using multiple sources of exhaustive high resolution data. Geoderma 123, 23–39. Finke, P.A., J.J. De Gruijter and R. Visschers (2002), Status 2001: national sampling of map units and applications. Stratified sampling and characterisation of Dutch soils. Wageningen: Alterra report 389 (in Dutch). Goovaerts, P. (1997), Geostatistics for Natural Resources Evaluation. Oxford University Press. Hengl, T., G.B.M. Heuvelink and A. Stein (2004), A generic framework for spatial prediction of soil properties based on regression-kriging. Geoderma 120, 75-93. Heuvelink, G.B.M., J.D. Brown and E.E. Van Loon (2006), Representing and simulating uncertain environmental variables in GIS. International Journal of GIS (forthcoming). Heuvelink, G.B.M. and R. Webster (2001), Modelling soil variation: past, present, and future. Geoderma 100, 269–301. Lagacherie, P., A.B. McBratney and M. Voltz (Eds.) (2006), Digital Soil Mapping: an Introductory Perspective. Developments in Soil Science 31, Elsevier, Amsterdam (in press).

2.5 Uncertainty aspects of storage capacity of detailed surface water system

2.5.1 Summary An uncertainty analysis has been carried out for the storage capacity of the detailed surface water system. To this respect, uncertainty introduced by the water levels has been analysed. An analysis has been performed per subcatchment for summer as well as winter water levels. Point measurement data on the water levels in the small watercourses have been analysed. For the summer period the exponential distribution has been found representative, for the winter period mostly the lognormal distribution. To aggregate the point measurements to subcatchment scale the distribution of the measurements scale has been used to sample a number of realisations n, with n based on the size of the subcatchment. The average of these n realisations forms one realisation for use in the model.

2.5.2 Introduction The HarmoniRiB case study is performed for part of the Vecht catchment: the Regge catchment. The applied watercourse schematisation is shown in figure A2.17.

# Node # Weir Main watercourses Small watercourses Subcatchments

1 # # # # # # # # # # 2 # ## #

# # # # # # # ## # # # # # # # # # # 3 # # # # # # # # # # # # # # # # #

# # # #

# #

# # Node # # Weir Main watercourses Small watercourses N 051015Kilometers Subcatchments

Figure A2.17: Regge catchment watercourse schematisation, upper right the total Vecht catchment is shown.

The main watercourses are explicitly modelled in the Vecht and Regge model. All small watercourses, channels and ditches are not included in this schematisation. This is not feasible considering the size of the modelled catchment and the density of the dewatering system. Still, the buffering effect on discharges, the storage of water and the retention of nutrients in these small watercourses can be significant and should be taken into account. In the Vecht and Regge model the small watercourses are therefore lumped together and added as ‘additional storage’. The total Regge catchment has been divided into subcatchments, for each subcatchment one additional storage node has been implemented. Altogether the additional storage represents a large ‘volume’ of watercourses. As these water volumes determine the hydraulic retention time in the surface water system they also influence the retention of nutrients and thus the concentrations at the outflow point. The exact dimensions, water levels and thus water volumes are however largely unknown or based on a few measurements at best and this might represent a large uncertainty in model outcomes. A small sensitivity analysis has been performed on this account of which the results are represented here.

Sensitivity analysis Weirs control the water levels in the additional storage sections. The level of these weirs has been varied in the analysis in order to see its influence on hydraulic retention time. Figure A2.18 shows the effect of weir variation, and thus water level variation, between 0 m and 1 m (‘ variant 0’ – ‘ variant 1m’). It is clear that the Hydraulic Retention Time (HRT) increases with increasing water levels. The range of the average water levels can roughly be situated between 0 and 43 cm.

Variant 0 25 Variant 0.1 m Variant 0.15 m Variant 0.2 m 20 Variant 0.25 m Variant 0.5 m 15 Variant 1 m

HRT (days) 10

0 91 95 96 97 99 100 101 102 131 135 136 137 139 140 141 142 Nodes representing small watercourses

Figure A2.18: Hydraulic Retention Time as a function of weir level variation per additional storage node.

The effect of the change in Hydraulic Retention Time (HRT) on the outflow concentrations in the Regge is shown in figure A2.19. The concentrations and the fluctuations clearly diminish with increasing water level and HRT in the additional storage.

Regge - N Concentration

Variant 0 Variant 0.1 Variant 0.15 Variant 0.2 Variant 0.25 Variant 0.5 Variant 1 Nitrogen [gN.m-3] 0 5 10 15 20

1993 1994 1995

Date Figure A2.19: effects of extra Hydraulic Retention Time on nitrogen concentration at the outflow point of the Regge catchment.

2.5.3 Uncertainty analysis Some background about the schematisation of the small watercourses can be found at the end of this appendix. Special attention is paid to the assumptions made and other factors that might have incorporated uncertainty.

The largest contribution to the uncertainty in the calculation of the total volume of the additional storage is introduced by the use of a standard cross section profile. This standard cross section profile consists of two parts: standard watercourse dimensions and standard water level (by standard weir level). On subcatchment scale, the variations in watercourse dimensions are expected to balance each other out to some extent. Furthermore, it has to be noted that for the construction of these watercourses, standards are applied. Data on the actual dimensions of the watercourses are not easy to obtain, so a quick check of the actual influence of this uncertain factor has not been possible. The water levels in the watercourses can have a substantial variation over subcatchments, depending on water management, dimensions, resistance to flow in the watercourses and drainage characteristics. Data on water levels in the Regge ditches and (small) watercourses have been found, enabling the elaboration of this uncertainty aspect.

Uncertainty analysis data Data on water levels in the Regge ditches and (small) watercourses are collected within the framework of the Groundwater Dynamics measurement program (Massop et al., in prep 2006). In the Regge catchment water depth in summer and winter situation have been measured for small watercourses on 226 locations. The objective of the measurement program was to measure the average highest (GHG) and lowest groundwater levels (GLG) and measurements were therefore preferably taken during a wet period in winter and dry period in summer. Using averages might therefore cause an overestimation of volumes for the winter period and an underestimation for the summer period.

Table A2.8 shows some statistics of the measurements. The measurements are not normally distributed but are skewed to the right, resulting in a large standard deviation.

Table A2.8 number of measurements, average depth, standard deviation and standard deviation of the average. Note that the large standard deviation is due to non normal distribution (large tail). Average Standard Deviation (cm) Period Count depth (cm) watercourse < 3m Winter 226 27 20 Summer 226 18 21

In figure A2.20 frequency diagrams of the water depth have been plotted.

water depth (winter) - watercourses < 3m water depth (summer) - watercourses < 3m

30 28.3 26.1 50 45.1 25 40 20 16 . 4 30 15 22.6

10 9.3 9.3 20

4.4 9.3 9.7 5 10

percentage of watercourses (%) watercourses of percentage 2.2 4.4 4.0

1. 3 1. 3 (%) watercourses of percentage 0.4 0.9 2.2 0.9 0.9 0.9 0 0 0-10 10-20 20-30 30-40 40-50 50-60 60-70 70-80 80-90 90- 100- 0-10 10-20 20-30 30-40 40-50 50-60 60-70 70-80 80-90 90- 100 110 100 waterdepth class (m) waterdepth class (m) Figure A2.20: frequency diagrams of the water depth for small watercourses

Above information is for the whole Regge catchment. For the model calculations, a division into subcatchments is made. The distribution of the measurements over these subcatchments and some statistics are shown in table A2.9.

Table A2.9: number of measurements, average water depths and standard deviation for each of the subcatchments in the Regge catchment. Subcatchment numbers are according to the numbers in NL-CAT. winter summer Average Average Number Depth St. dev. Depth St. dev. Subcatchment of meas. (cm) (cm) (cm) (cm) 6 4 28 5 0 0 24 14 17 7 9 8 25 12 22 11 11 12 26 45 22 17 9 13 27 30 33 35 16 20 28 13 36 20 23 15 29 48 25 13 24 25 30 9 43 21 42 30 32 17 29 14 19 20 34 34 25 16 21 20

Grand Total 226 27 20 18 21

These averages have to be interpreted with care as for many catchments less than 20 measurements were available. It can be seen that the average water level per subcatchment varies considerably. The GD study (Massop et al., in prep 2006) shows there can be large variation in water depths based on the hydrotype to which a watercourse belongs. The data have been divided into a summer and a winter period. The starting dates of these periods have been set to the first of March and the first of October respectively.

Uncertainty analysis An analysis has been made for both the winter and the summer period, since a clear difference in the data can be seen. Furthermore, the analysis has been done per subcatchment. One analysis for the total catchment doesn’t do justice to the inhomogeneity observed between the subcatchments. It has been realised that the analysis will be weak for some catchments, due to the limited number of measurements. However, the catchments having little measurements are small catchments, so the influence will be restricted. It has been assumed that all measurements are independent and representative.

Above statistical analysis is based on the water depth data. These water depths represent a water volume in a watercourse. For the calculations, it makes a difference whether and when we transform water depths to volumes. Averaging of water depths and then transform to volumes has not the same effect as averaging of the volumes that correspond to the individual water depths. It is considered more legitimate to follow the second approach (since it is the uncertainty in the total volume we are interested in eventually). This means all water depths first are transformed to water volumes, before starting the analysis. The watercourse cross section is represented by a trapezoid with base 1 and side slope 1:1 and standard length 1m. Table A2.10 shows the data used in the Monte Carlo analysis.

The approach followed Per subcatchment, the distribution has been determined for the summer as well as the winter period. A clear idea of the type of distribution could not always be retrieved given the small amount of measurements. Figure 4 shows that the overall distribution for the catchment is lognormal for the winter period and exponential for the summer period. These

distribution types have been applied for the subcatchments whenever reasonable. In the winter period, deviation from this principle has been necessary due to non log normality of the distribution for some subcatchments. An overview of the distributions applied can be found in table A2.10. The measurements are point values. The scale of interest is the subcatchment scale. The distribution of the measurements has been aggregated to the subcatchment scale. Standard procedure for a normal distribution or any other distribution constructed from a large number of measurements, is to divide the distributions standard deviation by the number of independent observations. However, the distributions are not normal and the number of measurements is not enough for application of this strategy (Olsson 2005, Kvanli et al. 1998). Another strategy has been followed to reduce the standard deviation proportionally to the number of independent observations (n). This strategy holds that in order to obtain one realisation, n samples have been drawn from the distribution for which the mean has been determined.

Table A2.10: summary of information used for realizations for the summer period (upper table) and the winter period (lower table). Averaged over Sub- Average Average no. of measure- catchment Depth (m) volume (m^3) Distribution Mean Lambda ments 6 0 0 Exponential 0.00 0.00 4 24 0.09 0.11 Exponential 0.10 9.57 14 25 0.11 0.14 Exponential 0.14 7.38 12 26 0.09 0.12 Exponential 0.12 8.45 45 27 0.16 0.22 Exponential 0.22 4.48 30 28 0.23 0.31 Exponential 0.31 3.25 13 29 0.24 0.36 Exponential 0.36 2.77 48 30 0.42 0.67 Exponential 0.67 1.49 9 32 0.19 0.26 Exponential 0.26 3.85 17 34 0.21 0.30 Exponential 0.30 3.36 34

Averaged over Sub- Average Average no. of measure- catchment Depth (m) volume (m^3) Distribution Mean STD ments 6 0.28 0.35 Lognormal 0.36 0.09 4 24 0.17 0.20 Lognormal 0.21 0.10 14 25 0.22 0.28 Lognormal 0.29 0.17 12 26 0.22 0.29 Lognormal 0.31 0.37 45 27 0.33 0.56 Exponential Mean = 0.56 Lambda = 1.8 30 28 0.36 0.52 Lognormal 0.54 0.45 13 29 0.25 0.34 Lognormal 0.37 0.35 48 30 0.43 0.65 Lognormal 0.67 0.49 9 32 0.29 0.40 Uniform Min = 0 Max = 0.7 17 34 0.25 0.33 Lognormal 0.35 0.35 34

2.5.4 Background: the schematisation of small watercourses in the model First, the schematisation procedure followed to calculate the additional storage for each subcatchment is explained, paying special attention to the assumptions made and other factors that might have incorporated uncertainty. Next a selection is made of which uncertainties to include in the uncertainty analysis. Finally, the method to come to an uncertainty distribution is explained.

Schematisation procedure for small watercourses For each subcatchment in the Regge, the small watercourses are lumped together and added as ‘additional storage’ (an additional volume) to the most upstream node. This means no spatial distribution of these watercourses is present. The additional storage of a node is derived from the Dutch top10vector map. This map holds data about the location and length of watercourses. The watercourses are classified by their average width. The following Top10Vector classes can be defined: Ditches Small watercourses < 3m Watercourses 3 – 6 m Primary watercourses

In the Regge schematisation ‘primary watercourses’ are explicitly modelled. ‘Small watercourses < 3m’ and ‘watercourses 3-6m’ form the additional storage which is calculated for each subcatchment. Ditches are not expected to hold much water and are therefore left out. A small part of the Regge catchment lies in Germany for which these data are not available. The average density of watercourses based on the Dutch Top10vector maps was used to calculate the additional storage in these areas.

The additional storage is presented by a representative cross section profile that is assigned a certain length and resistance coefficient. The following steps have been followed:

1. Determination of cross section profile

Waterdepth, bottom width (1m) and side slope (1:1) are derived from standards provided for the small watercourses class (<3m) (Massop et al., in prep 2006). The watercourses class (3-6m) is represented by the same cross section profile; this is compensated in the calculation of the length. A ‘fictive’ weir implements the average water depth; with weir level the same as the water level. In the real situation, the cross section profile will vary. It is highly unlikely that all profiles hold the same amount of water. This uncertainty aspect will be further elaborated in the next paragraph. The small watercourses are more frequently present than the watercourses. The representation of watercourses by the small watercourses profile is expected not to have a large effect on uncertainty.

2. Calculation of total length

The new length for the additional storage watercourse in each subcatchment is calculated by adding up the lengths of all watercourses in each Top10vector class. To compensate for the difference in dimensions between <3m and 3-6m watercourses the volume of the 3-6m watercourses is calculated based on their average depth and dimensions. Using this volume and the dimensions of the <3m watercourses a new length is then calculated and added to the total length of the <3m watercourses in the subcatchment. The total acquired length is attributed to the additional storage node. Uncertainty may be introduced by map uncertainty and misinterpretation. The scale of the map is 1:10.000.

3. Adjustment of resistance coefficient

The new very long watercourse now represents a proper storage but has an overestimated resistance when normal Chezy coefficients are being used. Therefore

the resistance coefficient must be multiplied with a correction factor (fsw) using the following formula:

Q L L f = tot = . sw Q l l sw max max

with:

Q 3 tot : Discharge of the total additional storage area (m /d). Q sw : Discharge of the total length of all additional storage as calculated in the model schematization (m3/d)

lmax : Maximum length to the main watercourse in reality (m) L : total length of all watercourses in one subcatchment (m)

The exact value of the Chezy coefficient however, is expected not to contribute to a variation in volume very much, because of the presence of the weir controlling the water level.

The actual supply of water to the additional storage section is calculated by the groundwater submodel of NL-CAT (SWAP).

References

Massop H.Th.L., J.W.J. van der Gaast en E.Hermans, in prep. Kenmerken van het ontwateringsstelsel in Nederland. Wageningen, Alterra

Gaast J.W.J. van der en P.J.T van Bakel, 1997. Differentiatie van waterlopen ten behoeve van het bestrijdingsmiddelenbeleid in Nederland. SC-DLO rapport 526.

Schoumans, O.F., P. Groenendijk, C. Siderius, 2005. NL-CAT application to six European catchments. Report 1205. Alterra, Wageningen.

Top10vector, Digital Topographic Map of the Netherlands, scale 1:10,000. Topografische Dienst Kadaster, Emmen, Netherlands

Annex 3: Nutrient concentrations in the upper groundwater

As an illustration, this annex shows the mean and the standard deviation of the nutrient concentrations in the upper groundwater throughout the catchment. The average year 1999 (figure A3.1), the wet year 1998 (figure A3.2) and the dry year 1997 are shown. Note that the wet year is not 1993, but 1998, which is the second wettest year.

Figure A3.1: Nitrate concentrations in upper groundwater in April 1999: normal year after wet winter period

Figure A3.2: Nitrate concentrations in upper groundwater in April 1998: wet year after two dry years

Figure A3.3: Nitrate concentrations in upper groundwater in April 1997: dry year after dry year (1996)

Annex 4: Cost calculations

The costs for the removal of soil are estimated to be €15.40 per m3 (HKV lijn in water, 2002). The method of calculation is explained in table A4.1.

Activity Unit Price in € a. Soil excavation 1 m3 3.50 b. Soil transport to depot, 1 m3 5.50 including further processing c. Incidental costs 2% (of a and b) 0.18 d. Realization costs 6% (of a and b) 0.54 e. General costs 8% (of a and b) 0.72 f. Profit and risk 4% (of a and b) 0.36 g. Unexpected costs 20% (of a-f) 2.16 h. Taxes (not over 19% (of a-g) 2.46 unexpected costs) Total costs 15.4 Table A4.1: method for calculation of the costs for removal of soil. Source: Tauw (2005)

The unexpected costs can be seen as the uncertainty in the calculation. These are defined to be 20%. The costs will most likely not be lower than the total costs minus the unexpected costs and not be higher than the costs including the unexpected costs. Therefore the bandwidth of this cost calculation will be €13.24- €15.40.

The costs for the purchase of the soil are estimated to be €5.70 per m2. The method of calculation has been adopted form HKV lijn in water (2002) and can be found in table A4.2.

Costs Price in € a. Direct costs land 3.00 b. Indirect costs (25% of a) 0.75 c. Incidental expenses (5% of a and b) 0.19 d. Unexpected costs (25% of a, b and c) 0.98 e. Taxes (19% of a, b, c and d 0.37 f. Costs of personal (10% of a and b) 0.38 g. Taxes personal costs (9.5% of f) 0.04 Total costs 5.71 Table A4.2: method of calculation of the costs for the purchase of soil. Source: HKV lijn in water (2002).

The direct costs of the land have been found to be 3 euro m2 (source: RPB, www.rpb.nl) over the year 2004. The unexpected costs can be seen as the uncertainty in the calculation. These are defined to be 25% of the direct costs, indirect costs and incidental expenses. The costs will most likely not be lower than the total costs minus the unexpected costs and not be higher than the costs including the unexpected costs. Therefore the bandwidth of this cost calculation will be €4.73- €5.71.