Appendix A – Modelling the impact of climate change on soils using UK climate projections

Work Package 1 & 2

Defra Project SP0571

Use of ‘UKCIP08 Scenarios’ to determine the

potential impact of climate change on the

pressures/threats to soils in England and Wales

Work Package 1 & 2

David Cooper1, Claire Foster2, Paul Hallett3, Peter Hobbs2

Brian Irvine4, Mike Kirkby4, Ragab Ragab5, Barry Rawlins2

Pete Smith6, Dave Spurgeon5, Andy Tye2

1Centre for Ecology and Hydrology, Bangor 2British Geological Survey 3Scottish Crop Research Institute (SCRI) 4Leeds University 5Centre for Ecology and Hydrology, Wallingford 6Aberdeen University

Contents

A1. Introduction ...... 5 A2. Carbon ...... 6 1. RothC ...... 6 2. ECOSSE ...... 6 3. Daycent ...... 7 4. DNDC ...... 7 Carbon references ...... 11 A3. Erosion ...... 13 A. Water ...... 13 1. RUSLE (Revised Universal Soil Loss Equation) ...... 13 2. Watershed Erosion Prediction Project (WEPP) ...... 14 3. EUROSEM (European Soil Erosion Model) ...... 14 4. PESERA (Pan–European Soil Erosion Risk Assessment) ...... 14 B. Wind ...... 15 1. Simplified RWEQ (Revised Wind Erosion eQuation) ...... 16 2. WEELS (Wind Erosion on European Lights Soils) ...... 16 3. WEPS (Wind Erosion Prediction System) ...... 17 4. WEQ (Wind Erosion Equation) ...... 17 Erosion references ...... 18 A4. Contaminants ...... 20 A. Diffuse Agricultural Contaminant Models ...... 21 A.1 Nitrate models ...... 21 A.2 N2O models ...... 26 A.3 Phosphorus models ...... 31 B. Pesticides and Organic Contaminants ...... 35 B.1. EUROPEARL ...... 36 B.2 POPPIE ...... 36 C. Acidification ...... 41 C.1 The Very Simple Dynamic Model (VSD) ...... 42 C.2 MAGIC ...... 42 D. Inorganic Contaminants ...... 46 E. Generic water / soil borne models ...... 48 E.1 Generic models for soil borne contaminants ...... 49 E.2 Generic models for water borne contaminants ...... 49 Contaminant references ...... 49 A5. Compaction...... 52 1. Workable Days ...... 53 2. Expert Model ...... 53 3. Mechanistic Model ...... 54 Compaction references ...... 58 A6. Landslides ...... 59 1. Antecedent Water Status Model ...... 60 2. Downscaling of General Circulation models (GCM’s) ...... 60 3. Threshold values ...... 61

3 4. Enhanced GeoSure model ...... 62 5. Slope Stability model ...... 63 Landslides references ...... 67 A7. Salinity ...... 68 A. Terrestrial (“Plot Scale”) ...... 68 A1. SALTMOD ...... 68 A2. HYDRUS ...... 68 A3. SALTMED ...... 69 B. Sea water intrusion ...... 71 B1. SWI ...... 71 B2. SEAWAT ...... 72 B3. SHARP ...... 72 B4. SUTRA ...... 72 Salinity references ...... 74 A8. Sealing ...... 75 Sealing references ...... 78 A9. Biodiversity ...... 81 1. GBMove ...... 81 2. Soil biodiversity model and other food web models ...... 81 3. PERPEST ...... 82 4. SOILPACS ...... 82 Biodiversity references ...... 86 A10. Conclusions and Options ...... 87 Carbon ...... 87 Erosion – water ...... 87 Erosion – wind ...... 87 Contaminants ...... 87 Landslides ...... 87 Salinity – terrestrial ...... 87 Salinity – sea water intrusion ...... 88 Sealing...... 88 Biodiversity ...... 88

4 A1. Introduction Climate change in England and Wales has the potential to affect the functioning of the soil. The nature of the effect is uncertain, but can be estimated through the use of models which approximate the cause and effect relationships between climatic drivers and the soil response. There are a number of potential changes in soil functions of particular concern, identified as “threats”, namely changes in:

• Soil carbon • Erosion by water and wind • Contaminant transport • Incidence and magnitude of landslides • Soil compaction • Salinity, either through non‐marine accumulation or through ingress of marine water • Surface sealing of agricultural soils through natural processes • Biodiversity

UKCIP09 climate change scenario data for England and Wales were released during 2009, updating previous data available. We have reviewed soil threat models which respond to climate change inputs and have chosen a number to be run with the scenarios to estimate the soil threat response based on specific criteria.

Not all models are suitable for application, and ten basic criteria have been selected against which they are judged. These are shown in Table 1.

Table 1. The model criteria

No. Criterion

1 Does it provide estimates of change in the soil threat as a function of climate change variables? 2 Are these at a space and time scale which can be used for national‐scale decision making? 3 Does it cover all aspects of the soil threat, particularly those of greatest perceived importance ‐ ecological, economic etc? 4 What is its track record in modelling historic data as a function of climate change variables? How well does it simulate? 5 Does it require additional parameters or driving variables, and are these available into the future? 6 Is it responsive to other changes, particularly land use? 7 Where are the likely bottle‐necks in applying the model? 8 If a model looks promising but requires a few changes, how readily can these be made? Can this be done within the scope of the project? 9 Cost implications 10 Compatibility with other models

UK specialists in each threat who form part of the project consortium have selected a small number of candidate models for each threat. Within the scope of the project it was not possible to consider

5 all models in use around the world. Candidate models were selected from those in common use in the UK and familiar to consortium members. These have been judged against the ten criteria. In this working paper the threats are addressed in the order above. For each threat there is a brief description of the candidate models, followed by tabulated judgements against each criterion, a summary table of suitability and a reference list. For some models not all criteria apply, or assessment is incomplete. In these cases the relevant row of the table of judgements is left blank. In the summary tables, green symbols indicate that the suitability criterion is met, orange that it is only partially met, and red that it is not met.

A2. Carbon The soil is a major repository of the world’s carbon. It may be a source or a sink, depending on the relative rates of processes transforming carbon between different compounds. Key amongst these are respiration and photosynthesis which directly influence the CO2 budget. These carbon transformation rates need to be understood at a global scale in order to assess the contribution of the soil to the global carbon budget.

Four soil carbon models are available for use spatially in the UK and Europe, all of which are used by the University of Aberdeen project team. These are described below and compared in Table 2. There are other SOC models (Smith, 2001 listed 33 known to be in existence at that time), many of which have been used by the University of Aberdeen. The models considered for this project are either widely used models or have been applied to extensively to UK soils. In a comparison of the performance of 9 SOM models carried out by Smith et al (1997), CENTURY (the precursor of DayCent), DNDC and RothC all performed favourably (ECOSSE was not developed at the time, but is built around RothC which performed well). The reader is referred to Smith et al. (1997) where all the models are summarised, but to expand briefly:

1. RothC

The RothC (Rothamsted Carbon) model (Coleman & Jenkinson, 1996) has been used to examine soil organic carbon (SOC) stock changes in the UK (Falloon et al., 2006) and Europe (Smith et al., 2005, 2006, 2007a). The RothC model simulates the decomposition of C in aerobic soils. Decomposition is dependent upon soil type, temperature, moisture and plant cover. Nitrogen and C dynamics are not interconnected.

2. ECOSSE

The ECOSSE (Estimating Carbon in Organic Soils ‐ Sequestration and Emissions) model was developed by combining RothC with the Rothamsted N model (Sundial; Bradbury et al., 1993; Smith et al., 1996) and further developing it for the simulation of anaerobic conditions for use on organic soils (Smith et al., 2007b). ECOSSE is capable of simulating anaerobic and aerobic soils and is the only model considered that can simulate anaerobic processes in highly organic soils. ECOSSE simulates

CO2, CH4 and N2O emissions in addition to SOC turnover. The ECOSSE soil carbon and nitrogen routines are now incorporated into the latest version of JULES. JULES is the Joint UK Land

6 Environment Simulator, the land surface model used in the Unified Model of the UK Met Office. It is the UK Community Land Surface model: www.jchmr.org/jules.

3. DayCent

DayCent (del Grosso et al., 2005) is the daily version of the CENTURY model (Parton et al., 1987), which along with RothC, is among the most widely tested and used SOC model in the world. The CENTURY model was developed to simulate long‐term (decades to centuries) SOM using a monthly time‐step. DayCent is a version of CENTURY that operates on a daily time‐step, providing higher temporal resolution. CENTURY is one of the most widely tested SOC models but since DayCent is derived from CENTURY it can also, by association, be considered to be widely tested. Like ECOSSE,

DayCent simulates CO2, CH4 and N2O emissions in addition to SOC turnover. It is being used by the University of Aberdeen team to examine N turnover in Europe as part of the EU‐funded NitroEurope‐ IP project (http://www.nitroeurope.eu/about).

4. DNDC

DNDC (Denitrification‐decomposition; Li et al., 1994) is a relatively complex process based model of soil C & N turnover which also simulates CO2, CH4 and N2O emissions in addition to SOC turnover. It is being used by the University of Aberdeen team to examine C and N turnover in Europe as part of the EU‐funded CarboEurope‐IP project (Wattenbach et al., 2009).

The characteristics of the SOC models, with respect to the model selection criteria, are shown in Table 2.

7

Table 2. Carbon model comments

No. Criterion RothC ECOSSE DayCent DNDC 1 Does it provide Yes Yes Yes Yes estimates of change in the soil threat as a function of climate change variables? 2 Are these at a space Yes already run for UK Yes already run for Scotland and Yes being run for Europe. Yes already run for UK at and time scale which and Europe Wales Cranfield may have run for county level by IGER for NGGI can be used for UK (National Greenhouse Gas national‐scale Inventory) decision making? 3 Does it cover all Not highly organic soils Yes ‐ includes highly organic soils Not highly organic soils Not highly organic soils aspects of the soil threat, particularly those of greatest perceived importance, ecological, economic etc? 4 What is its track Very widely tested Yes designed for historical land. Very widely tested (Del Widely tested (Li et al. (1994, record in modelling (Smith et al (2005)) use change (Smith et al (2010b)) Grosso et al. (2001, 2006)) 1996, 2000)) historic data as a ECOSSE is the most recently function of climate developed of the four models and change variables ? as such has been less widely used How well does it than the other models. However, it simulate? is primarily an extension of the RothC model (to allow simulation of organic soils) which has been widely tested.

8 No. Criterion RothC ECOSSE DayCent DNDC

5 Does it require All available for All available for UKCIP09 All available (or can be All available for UKCIP09 additional UKCIP09 derived) from UKCIP09 parameters or driving variables, and are these available into the future? 6 Is it responsive to Yes done previously Yes done previously Smith et al Yes done previously Del Yes done previously Li et al. other changes, Smith et al (2005) (2007b, 2010a, 2010b) Grosso et al (2001, 2006) (1994, 1996, 2000) particularly land use?

7 Where are the likely None as long as data is None as long as data is available None as long as data is None as long as data is bottle‐necks in available available available applying the model? 8 If a model looks Very few changes Very few changes needed only i/o Few changes needed i/o Few changes needed only i/o promising but needed only modification for any new UKCIP09 modification for any new modification for any new requires a few input/output (i/o) data format changes UKCIP09 data format UKCIP09 data format changes changes, how readily modification for any changes can these be made? new UKCIP09 data Can this be done format changes within the scope of the project? 9 Cost implications Could be run by Could be run by University of Could probably be run by Could probably be run by generally University of Aberdeen Aberdeen in the current project University of Aberdeen in University of Aberdeen in the in the current project budget. Being run for other the current project budget current project budget projects (e.g. ecosse, ecosse‐ii, (depending on progress on (depending on progress on budget. Being run for ecosse‐wales, quest‐gsi, quercc) to other projects). Being run other projects). Being run for other projects (e.g. look at other factors (e.g. land use for other projects (e.g. other projects (e.g. NGGI Falloon et al. change) synergy nitroeurope‐ip) to look at carboeurope, cctame) to look (2006) other factors (e.g. land use at other factors (e.g. land use change) synergy change) synergy carboeurope Smith et

9 No. Criterion RothC ECOSSE DayCent DNDC al. (2005), Smith et al. (2006), quest‐gsi Gottschalk et al. (in preparation)) to look at other factors (e.g. land use change) synergy

10 The assessment criteria for model selection are given in Table 3. This table shows that the RothC and ECOSSE models are the most appropriate for the task over the full range of assessment criteria. DayCent and DNDC were not shortlisted for the task primarily because they are relatively complex models that require more time/cost to modify and use (criteria 8 and 9 in Table 3). In addition, these models are not able to simulate highly organic soils (criterion 3 in Table 3). RothC and ECOSSE were shortlisted because they are relatively simple models that were more likely to meet the cost and time constraints of the project and still deliver results on par with the more complex models. Finally, ECOSSE was chosen over RothC because of its ability to simulate highly organic (anaerobic) soils, which constitute a significant part the UK soil carbon stocks.

Table 3. Carbon model assessment

Model Criterion RothC ECOSSE DayCent DNDC 1 2 3 4 5 6 7 8 9 10

When considering all the evaluation criteria, the RothC/ECOSSE models appear to be the most appropriate for application in England and Wales for assessing climate impacts using UKCP09 data. ECOSSE was selected over RothC for this project primarily because of its ability to simulate both highly organic and mineral soils whereas RothC is limited to mineral soils only, as described in Falloon et al. (2006) and Smith et al. (2007b). Comparing predictions from ECOSSE with those of the soil C & N routines coupled into JULES would also be a useful exercise.

Carbon references

Bradbury NJ, Whitmore AP, Hart PBS, Jenkinson, DS 1993. Modelling the fate of nitrogen in crop and soil in the years following application of 15N‐labelled fertilizer to winter wheat. Journal of Agricultural Science, Cambridge, 121, 363‐379.

Coleman K, Jenkinson DS (1996) RothC‐26.3‐ A Model for the turnover of carbon in soil. In: Evaluation of Soil Organic Matter Models Using Existing, Long‐Term Datasets, NATO ASI Series I, Vol.38 (eds. Powlson DS, Smith P, Smith JU). Springer‐Verlag, Heidelberg, Germany, pp. 237‐ 246.

Del Grosso SJ, Parton WJ, Mosier AR, Walsh MK, Ojima DS, Thornton PE 2006. DAYCENT national‐ scale simulations of nitrous oxide emissions from cropped soils in the United States. Journal of Environmental Quality 35, 1451‐1460.

11 Del Grosso, S.J., Parton, W.J., Mosier, A.R., Hartman, M.D., Keough, C.A., Peterson, G.A., Ojima, D.S. and Schimel, D.S. (2001) Simulated effects of land use, soil texture, and precipitation on N gas emissions using DAYCENT. In: Nitrogen in the Environment: Sources, Problems, and Management, (eds. R.F. Follett and J.L. Hatfield), Elsevier Science Publishers, The Netherlands, pp. 413–431.

Falloon, P., Smith, P., Bradley, R. I., Milne, R., Tomlinson, R., Viner, D. , Livermore, M & Brown, T. 2006. RothCUK – a dynamic modelling system for estimating changes in soil C from mineral soils at 1‐km resolution in the UK. Soil Use and Management 22, 274–288.

Li, C., Frolking, S. & Harriss, R. 1994. Modeling carbon biogeochemistry in agricultural soils. Global Biogeochemical Cycles. 8‐3. (237‐254)

Li, C., Narayanan, V. & Harriss, R.C. (1996). Model estimates of nitrous oxide emissions from agricultural lands in the United States. Global Biogeochemical Cycles. 10‐2, 297‐306.

Li, C.S. (2000). Modeling trace gas emissions from agricultural ecosystems. Nutrient Cycling in Agroecosystems. 58, 259‐276

Parton, W.J., Schimel, D.S., Cole, C.V. and Ojima, D.S., 1987. Analysis Of Factors Controlling Soil Organic‐Matter Levels In Great‐Plains Grasslands. Soil Science Society of America Journal, 51, 1173‐1179.

Smith JU, Bradbury NJ, Addiscott TM 1996. SUNDIAL: A PC‐based system for simulating nitrogen dynamics in arable land. Agronomy Journal 88, 38‐43.

Smith, J.U., Smith, P., Wattenbach, M., Zaehle, S., Hiederer, R., Jones, R.J.A., Montanarella, L., Rounsevell, M.D.A., Reginster, I., Ewert, F. (2005) Projected changes in mineral soil carbon of European croplands and grasslands, 1990‐2080. Global Change Biology 11, 2141–2152.

Smith, J.U., Smith, P., Wattenbach, M., Gottschalk, P., Romanenkov, V.A., Shevtsova, L.K., Sirotenko, O.D., Rukhovich, D.I., Koroleva, P.V., Romanenko, I.A. & Lisovoi, N.V. 2007a. Projected changes in cropland soil organic carbon stocks in European Russia and the Ukraine, 1990‐ 2070. Global Change Biology 13, 342‐356.

Smith J., Gottschalk P., Bellarby J., Chapman S., Lilly A., Towers W., Bell J., Coleman K., Nayak D., Richards M., Hillier J., Flynn H., Wattenbach M., Aitkenhead M., Yeluripurti J., Farmer J., Milne R., Thomson A., Evans C., Whitmore A., Falloon P., Smith P. (2010a) Estimating changes in national soil carbon stocks using ECOSSE I – a new model that includes upland organic soils. Part I. Model description and uncertainty in national scale simulations of Scotland. Clim Res (In press).

Smith J., Gottschalk P., Bellarby J., Chapman S., Lilly A., Towers W., Bell J., Coleman K., Nayak D., Richards M., Hillier J., Flynn H., Wattenbach M., Aitkenhead M., Yeluripurti J., Farmer J., Milne R., Thomson A., Evans C., Whitmore A., Falloon P., Smith P. (2010b) Estimating changes in Scottish soil carbon stocks using ECOSSE II – Application. Clim Res (In press).

Smith, P., Smith, J.U., Wattenbach, M., Meyer, J., Lindner, M., Zaehle, S., Hiederer, R., Jones, R.,

Montanarella, L., Rounsevell, M., Reginster, I., Kankaanpää, S. 2006. Projected changes in

12 mineral soil carbon of European forests, 1990‐2100. Canadian Journal of Soil Science 86, 159‐169.

Smith, P., Chapman, S.J., Scott, W.A., Black, H.I.J., Wattenbach, M., Milne, R., Campbell, C.D., Lilly, A., Ostle, N., Levy, P.E., Lumsdon, D.G., Miller, P., Towers, W., Zaehle, Z. and Smith, J.U. (2007) Climate change cannot be entirely responsible for soil carbon loss observed in England and Wales, 1978‐2003. Global Change Biology 13, 2605‐2609.

Smith, P., Smith, J.U., Flynn, H., Killham, K., Rangel‐Castro, I., Foereid, B., Aitkenhead, M., Chapman, S., Towers, W., Bell, J., Lumsdon, D., Milne, R., Thomson, A., Simmons, I., Skiba, U., Reynolds, B., Evans, C., Frogbrook, Z., Bradley, I., Whitmore, A., Falloon, P. 2007b. ECOSSE: Estimating Carbon in Organic Soils ‐ Sequestration and Emissions. Final Report. SEERAD Report. ISBN 978 0 7559 1498 2. 166pp.

Wattenbach, M., Leip, A., Hillier, J. G., Yeluripati, J.B., Gottschalk P., Ceschia, E., Beziat, P., Aubinet, Carrara, A., Cellier, P., Eugster, W., M., Jones, M., Kutsch, W.L., Lanigan, G., Moureaux, C., Moncrieff, J., Moors, E., Olioso, A., Osborne, B., Saunders, M. & Smith, P. The carbon balance of Europe’s croplands: pan‐European modelling estimates. Agriculture, Ecosystems & Environment (in review).

A3. Erosion

A. Water Of the ten generic criteria for model assessment, the third (see Table 1) was rejected for assessing water erosion models because none attempt to address the relative importance of other impacts of water‐based erosion, such as the off‐site costs. This criterion cannot therefore contribute to the assessment of the models. A further, specific criterion was added which assesses how well each model represents the complexity of the soil erosion process.

A review of the literature highlighted four well‐established models (RUSLE, PESERA, WEPP and EUROSEM) for further consideration in the assessment of soil erosion by water in relation to climate change across England and Wales. There are no available models designed specifically for organic‐ rich soils in the uplands or lowlands of England and Wales. Other available models were discounted because they could not be applied at the required spatial scale. The background to each of the four models is described below, with a focus on their strengths and weaknesses in relation to the criteria for model evaluation. A tabulated summary of each model in relation to the criteria is provided and the reasons for final model selection.

1. RUSLE (Revised Universal Soil Loss Equation) The Revised Universal Soil Loss Equation (RUSLE) computes average annual erosion from field slopes as a function of five factors and land management, specifically: a rainfall‐runoff erosivity factor, a soil erodibility factor, a slope length factor, slope steepness, a cover management factor and conservation practice (Renard et al., 1997). The RUSLE has been applied in the USA and in soil erosion studies worldwide (Merritt et al., 2003). RUSLE is an empirical overland flow or sheet‐rill erosion regression equation based primarily on observations. Its model outputs are grouped both spatially and temporally. As with most empirical models, the RUSLE is not event responsive,

13 providing only an annual estimate of soil loss. It does not account for rainfall‐runoff processes, and how these processes affect erosion, as well as the heterogeneities in inputs such as vegetation cover and soil types. It has been shown to perform well in predicting erosion for natural runoff plots and compares favourably with process‐driven models (Quinton, 2004). The main climate‐driven variable in the model is the energy‐intensity of rainfall. The main potential limitation of RUSLE is its empirical nature; it is not a process‐based model. Process‐based models (such as WEPP, PESERA and EUROSEM) should provide better estimates of long‐term soil erosion rates than empirical models such as RUSLE, although this has not been widely demonstrated in studies to date (Quinton, 2004).

2. Watershed Erosion Prediction Project (WEPP) The US Watershed Erosion Prediction Project (WEPP) developed a physically‐based model (Laflen et al., 1991) containing sub‐models which are parameterized by empirical relationships derived from plot studies. The model represents erosional, hydrological, plant growth, water use, hydraulic and soil processes and residue incorporation. The model output contains the runoff and erosion summary on a storm‐by‐storm, monthly, annual and average annual basis. The watershed simulation version of WEPP requires additional files to describe the watershed configuration, channel topography, the channel soils, channel management, and the channel hydraulic characteristics. A limitation of its use in the current study is the lack of a mechanism for applying it at a national, rather than catchment scale. This would require considerable modification which would entail significant project time and associated costs. Although validated against sites for which the model was developed, implementation on other sites in the UK and USA suggested that there was a large degree of uncertainty around model predictions (Brazier et al., 2000).

3. EUROSEM (European Soil Erosion Model) EUROSEM was developed as a distributed, event‐based hillslope model that predicts total runoff and soil loss, produces hydrographs and sediment graphs for each event. The modular structure represents rainfall, interception processes, infiltration, overland flow, sediment transport capacity and its deposition (Morgan et al., 1998). The model computes soil loss as a sediment discharge, defined as the product of the rate of runoff (m3 s‐1) and the sediment concentration in the flow (m3 m‐3). It requires hillslope‐scale data and has been applied to a range of slopes (Quinton, 1997), small catchments in Europe (Botterweg et al., 1998) and catchments in Kenya (Mati et al. 2006). Although used at the catchment‐scale using a raster grid approach, EUROSEM could not be implemented at the national‐scale without substantial effort by integrating the computer code with nationwide, topographic data. This is the main limitation to its application for UKCIP scenarios across England and Wales.

4. PESERA (Pan–European Soil Erosion Risk Assessment) The PESERA model (Pan–European Soil Erosion Risk Assessment) is a physically based, spatially distributed model developed to quantify soil erosion of environmentally sensitive areas relevant to a regional or European scale (Kirkby et al., 2008). The model combines the effect of topography, climate, vegetation cover and soil into a single integrated forecast of runoff and soil erosion. The PESERA model provides an estimate of current rates of soil erosion, averaged over a series of years with current climate and land use. The model represents a fundamental advance on previous models of comparable simplicity (e.g. RUSLE), by explicitly separating hydrology from sediment transport. It first estimates storm overland flow runoff, and then uses the runoff to estimate

14 sediment transport. The PESERA model was designed to provide an estimate of long‐term erosion, scaling up instantaneous sediment transport as a function of shear stress or flow power. First, it aggregates the relationship between event discharge and event sediment discharge, and secondly from single events to the aggregate of storm events across the relevant distribution of storms. This temporal scaling up provides the essential link between climate, defined by the distribution of rainfall events and long term sediment transport. The PESERA model has been applied at the European scale (1 km grid) and validation of the model has been undertaken at sites in Greece (Tsara et al., 2005), Italy and the Netherlands (Licciardello et al, 2009). The PESERA model is ideally suited to the national‐scale assessments required in the current project and needs no modification to utilise the climate change drive variables available under the UKCIP09 scenarios. It can respond to land use changes and has the advantage of representing the key physical processes which determine the long‐term average soil erosion rates across the landscape.

Table 4. Water erosion model assessment

Model Criterion RUSLE PESERA WEPP EUROSEM 1 2 3 4 5 6 7 8 9 10 Representation of process complexity

When considering all the evaluation criteria, the PESERA model ranks most highly and it has been selected in this study for the assessment of soil erosion by water. Its only potential weakness compared to the RUSLE model (which ranks the next highest) is the limited number of field‐based validation studies against which it has been tested. However, the RUSLE has been available for several more years than PESERA and so the significance of this observation is limited. The PESERA model represents the processes of soil erosion and can be implemented at a scale or pixel resolution of 250 m2 or 1000 m2. It is well‐suited to predicting average soil erosion rates in response to climate change data at the national‐scale.

B. Wind

In common with the water erosion models, the criteria for assessing aspects other than soil loss from wind erosion was rejected because none of the models provide such outputs. In addition, a criterion for the representation of the complexity of the wind erosion process was included. There are no wind erosion models designed specifically for organic soils in the uplands or lowlands of England and Wales; this is therefore not used as a criterion for model selection.

15 A review of the literature highlighted four models (simplified RWEQ, WEPP, WEPS and WEQ) worthy of further consideration in the assessment of soil erosion by wind in relation to climate change across England and Wales. Other available models such as TEAM (Texas Wind Erosion Analysis Model; Gregory et al., 2001) were discounted because they could not be applied at the required spatial scale. The background to each of the four models is described below, with a focus on their strengths and weaknesses in relation to the criteria for model evaluation. A tabulated summary of each model in relation to the criteria is provided and the reasons for final model selection (Table 5).

1. Simplified RWEQ (Revised Wind Erosion eQuation) The Revised Wind Erosion eQuation (RWEQ) represents the latest developments in wind erosion prediction from research undertaken in the USA since the 1930s. The precursor to RWEQ was the original Wind Erosion Equation (WEQ). The RWEQ estimates soil eroded and transported by wind between the soil surface and a height of two meters (Fryrear et al., 1998). The RWEQ differs from the Wind Erosion eQuation (WEQ) in that the central factor in the former is the wind, whilst in the latter soil erodibility is the dominant component. Full model implementation uses monthly weather data, soil property data and land management inputs. For site‐specific studies the latter includes detailed information on crops, tillage, dates of on‐site activity, irrigation and wind barrier information. For the purpose of national‐scale wind erosion estimation, many of these detailed datasets will not be available at the required spatial and temporal scales, so implementation of the full, process‐based version of the model is not possible. However, it is possible to use parameters within the RWEQ model for national scale erosion assessment by estimating the distribution of monthly wind erosion potential based on climatic factors and soil factors across the landscape. This approach was adopted in a previous report undertaken to assess national rates of wind erosion across England and Wales (Defra, 2006). The strengths of this approach include its reliance on empirical relationships which have been widely validated (Fryrear et al., 1998; Buschiazzo and Zobeck, 2008; Van Pelt et al., 2004; Zobeck et al., 2001) and the ease with which it relates to the dominant driver of erosion, wind strength. It can be implemented at the required temporal scales and requires no modification for application to the UK. A potential weakness is that it cannot be applied to soils with organic matter contents in excess of 5%. , so erosion estimates will not be available for upland landscapes dominated by peat, nor to areas of fenland or land converted from fens. In addition, the simplified application of RWEQ can only account for land use by modification of its soil factor to account for the occurrence of arable agriculture or other land use types.

2. WEELS (Wind Erosion on European Lights Soils) A wind erosion model was developed under a European Union (EU)‐funded research project ‘Wind Erosion on European Light Soils’ (WEELS). Its aim was the assessment of the spatial distribution of wind erosion risks in the North European Quaternary Plains and the prediction of long‐term wind‐ induced soil losses under different climate and land use scenarios. The project comprised field measurements at three representative test sites, located in Germany, England and Sweden as well as wind tunnel experiments with selected soils for process parameterisation. A central objective was to develop a spatially distributed wind erosion model that could operate on various time scales (hours to decades), accounting for different crop rotation periods and to simulate different management and climatic scenarios. To ensure wide applicability, work focused on the development and application of a process‐based wind erosion model that uses readily available or easily collected data for erosion risk assessments in model domains covering up to 5 km2. WEELS is based on a

16 modular structure combining available or easily sampled topographic and climatic data input. The first set of modules concern variations in climate erosivity (WIND, WIND EROSIVITY and SOIL MOISTURE) whilst the second relate to soil erodibility (SOIL ERODIBILITY, SOIL ROUGHNESS and LAND USE). It calculates the actual soil erosion in terms of actual erosion hours and total sediment transport. (Bohner et al., 2003). Although model validation was undertaken against two sets of data (an English and German site), its developers recommended that the model should be validated using independent data. The developers also commented that the model is restricted to only sandy soils because of the simple parameterisation of the soil water model. There is no indication in the literature or in documentation relating to the model to suggest that this major limitation (for model application across England and Wales) has been overcome. Despite the potential advantages of the representation of process complexity, the current version of the model is not well‐suited to a national‐scale estimation of climate change impacts on soil loss by wind due to the limitations relating to sandy soil and the scale at which the current version of the model could be implemented. The limited number of validation studies is also a potential weakness.

3. WEPS (Wind Erosion Prediction System) The Wind Erosion Prediction System (WEPS) is a process‐based, daily time‐step model that predicts soil erosion via simulation of the fundamental processes controlling wind erosion. WEPS calculates soil movement, estimate plant damage, and predicts PM‐10 emissions when wind speeds exceed the erosion threshold (Wagner et al., 2003). WEPS operates using a modular structure with databases (soils, climate, management and crops/decomposition) and submodels (including hydrology, management, soil, erosion and weather). WEPS was designed to replace the empirical Wind Erosion Equation (Woodruff and Siddoway, 1965). In contrast to WEQ which makes predictions for isolated field units, WEPS was designed to deal with non‐uniform areas and make predictions for specific areas of interest. WEQ was one‐dimensional (transects) whilst WEPS operates on areas (2 dimensions) and can make predictions for short – as opposed to only long – term intervals. WEPS simulates a wider range of processes on field surface conditions, whilst the WEQ relies upon user input to update these. WEPS includes feedbacks to change soil properties in the field according to weather and erosion. In recent validation studies in Argentina (Buschiazzo and Zobeck, 2008), WEPS performed well, but provided poorer long term predictions than WEQ. Its major limitation for the current project is that data are unavailable at the required spatial and temporal scales; the code would require very substantial alteration for its application.

4. WEQ (Wind Erosion Equation) The basis of the empirical WEQ model is the soil erodibility factor (I), which is potential soil erosion in tonnes per hectare per annum from an unsheltered field with a bare, smooth surface. These (I) values were determined from wind tunnel experiments and field measurements of soil erodibility based on climatic conditions near Kansas, USA. The other five inputs to WEQ are crustal stability, surface roughness, a climatic factor (wind speed, rainfall and temperature), field length, and vegetative cover. WEQ can therefore respond to climate variables and land use changes. It estimates average soil erosion for various field lengths. WEQ has been widely applied and validated (Fryrear et al., 2001; Buschiazzo and Zobeck, 2008). In the comparison of WEQ and RWEQ for 15 sites in the US over periods of 4 to 23 months, estimates from the latter were substantially better than the former (Fryrear et al., 2001). By contrast, WEQ provided somewhat better estimates of

17 long‐term soil loss in a validation study in the Argentinean Pampas using plots with different tillage conditions (Buschiazzo and Zobeck, 2008).

Table 5. Wind erosion model assessment

Model Criterion Simplified WEELS WEPS WEQ RWEQ 1 2 3 4 5 6 7 8 9 10 Representation of process complexity

When considering all the evaluation criteria, the simplified RWEQ model ranks most highly and it has been selected in this study for the assessment of soil erosion by wind. Its areas of potential weakness are its limited response to land use change and representation of process complexity, but on all other assessment factors it scores highly. Its proxy measure for soil erosion (Qmax or maximum transport capacity) can be computed at the required spatial and temporal scales to provide an assessment of erosion in response to climatic variables. The RWEQ model has been widely applied and validated. None of the models have been designed to predict soil erosion by wind for organic‐ rich soils.

Erosion references

Bohner, J., Schafer, W., Conrad, O., Gross, J. and Ringeler, A. 2003. The WEELS model: methods, results and limitations. Catena, 52, 289‐308.

Botterweg, P., Leek, R. Romstad, E. and Vatn, A. 1998. The EUROSEM‐GRIDSEM modeling system for erosion analyses under different natural and economic conditions. Ecological Modelling, 108, 115‐129. Brazier, R. 2004. Quantifying soil erosion by water in the UK: a review of monitoring and modelling approaches. Progress in Physical Geography, 28, 340‐365. Brazier, R. E., Beven, K. J., Freer, J. and Rowan, J. S. 2000. Equifinality and uncertainty in physically based soil erosion models: application of the GLUE methodology to WEPP‐the Water Erosion Prediction Project‐for sites in the UK and USA. Earth Surface Processes and Landforms, 25, 825‐845.

18 Buschiazzo, D. E. and Zobeck, T. M. 2008. Validation of WEQ, RWEQ and WEPS wind erosion for different arable land management systems in the Argentinean Pampas. Earth Surface Processes and Landforms, 33, 1839–1850. Defra, 2006. Scoping study of soil loss through wind erosion, tillage erosion and soil co‐extracted with root vegetables. Final Report SP08007. Defra, London. Fryrear, D. W., Saleh, A., Bilbro, J. D., Schomberg, H. M., Stout, J. E. and Zobeck, T. M. 1998. Revised Wind Erosion Equation. USDA Wind Erosion and Water Conservation Unit, Technical Bulletin No. 1. USDA, Texas. Fryrear, D. W., Sutherland, P.L., Davis, G. H. and Dollar, M. 2001. Wind erosion estimates with WEQ and RWEQ. In D. E. Stott and G. C. Steinhardt (Eds). Suataining the Global Farm. 10th International Soil Conservation Organization Meeting, 24‐29 May, 1999. Purdue University and USDA‐ARS National Soil Erosion Research Laboratory, pp 760‐765. Gregory, J. M. Vining, R., Peck, L and Wofford, K. 2001. TEAM: The Texas Tech Wind Erosion Analysis Model. In D. E. Stott and G. C. Steinhardt (Eds). Suataining the Global Farm. 10th International Soil Conservation Organization Meeting, 24‐29 May, 1999. Purdue University and USDA‐ARS National Soil Erosion Research Laboratory, pp 747‐750. Kirkby, M. J., Irvine, B. J., Jones, R.J.A, Govers, G. et al., 2008. The Pesera coarse scale erosion model for Europe: i – model rationale and implementation. European Journal of Soil Science, 59, 1293‐1306. Laflen, J.M., Lane, L.J., Foster, G.R., 1991. WEPP: A new generation of erosion prediction technology. Journal of Soil and Water Conservation 46, 34–38. Licciardello, F, Govers, G, Cerdan, O, Kirkby Mj, Vacca, A and Kwaad, FJPM, 2009. Evaluation of the PESERA model in two contrasting environments. Earth Surface Processes and Landforms 34, 5, 629‐640 Mati, B.M., Morgan, R.P.C. and Quinton, J.N. 2006. Application of EUROSEM to two catchments in Kenya. Earth Surface Processes and Landforms 37: 579‐588. Merritt, W. S., Letcher, R. A. & Jakeman, A. J. 2003. A review of erosion and sediment transport models. Environmental Modelling & Software, 18, 761. Morgan, R.P.C, Quinton, J.N., Smith, R.E., Govers, G, Poesen, J.W.A., Auerswald, K., Chisci, G., Torri, D., Styczen, M.E., Folly, A.J.V. 1998. The European soil erosion model (EUROSEM): documentation and user guide. Silsoe College, Cranfield University. Quinton, J.N. 1997. Reducing predictive uncertainty in model simulations: a comparison of two methods using the European Soil Erosion Model (EUROSEM). Catena, 30, 101 ‐ 117. Quinton, J. N. 2004. Erosion and Sediment Transport. In: Environmental Modelling. Finding Simplicity in Complexity. Eds. J. Wainwright and M. Mulligan. John Wiley and Sons Ltd, Chichester, p 187‐196. Renard, K.G., G.R. Foster, G.A. Weesies, D.K. McCool, and D.C. Yoder. 1997. Predicting Soil Erosion by Water: A Guide to Conservation Planning with the Revised Universal Soil Loss Equation (RUSLE). United States Department of Agriculture, Agriculture Handbook No. 703. USDA, Washington D.C. Tsara, M., Kosmas, C., Kirkby, M. J., Kosma, D. and Yassoglou, N. 2005. An evaluation of the PESERA soil erosion model and its application to a case study in Zakynthos, Greece. Soil Use and Management, 21, 377‐385.

19 Van Pelt, R. S., Zobeck, T. M., Potter, K. N., Stout, J. E. & Popham, T. W. 2004. Validation of the wind erosion stochastic simulator (WESS) and the revised wind erosion equation (RWEQ) for single events. Environmental Modelling & Software, 19, 191. Wagner, L. et al. 2003. The wind erosion prediction system (WEPS 1.0) – User Manual. USDA-ARS Wind Erosion Research Unit, Kansas, USA. Webb, N. P., McGowan, H. A., Phinn, S. R. & McTainsh, G. H. 2006. AUSLEM (AUStralian Land Erodibility Model): A tool for identifying wind erosion hazard in Australia. Geomorphology, 78, 179. Woodruff, N.P. and F.H. Siddoway. 1965. A wind erosion equation. Proceedings of the Soil Science Society of America, 29, 602‐608. Zobeck TM, van Pelt RS, Stout JE, Popham TW. 2001. Validation of the revised wind erosion equation (RWEQ) for single events and discrete periods. Proceedings of the International Symposium on Soil Erosion Research for the 21th Century, 3–5 January, Honolulu; 471–474. A4. Contaminants The review of contaminant models has been undertaken, dividing the models into 4 classes, these ‐ being (i) diffuse agricultural pollutants (e.g. NO3 , PO4, N2O), (ii) organics and pesticides, (iii) acidification and (iv) inorganic contaminants (e.g. PHE). The contaminants selected are based on those named in Defra Report SP0538 (Defra, 2005) and the EU Soil Thematic Strategy (Van Camp et al., 2004). A literature and web based review has been undertaken to identify candidate models. The project was aimed largely at identifying models that can be used for national mapping of contaminant threats. Therefore whilst a brief discussion of models that are available for each contaminant has been produced in the introduction to each section, only those that could be used or adapted for use for national scale mapping are discussed in greater detail for each category of contaminant.

With respect to the wide range of contaminants that pass through or are stored in soils, the UKCIP 09 scenarios likely to be of greatest importance are (i) changes in rainfall patterns that affect those contaminants liable to leaching, (ii) changes in intensity of rainfall which could affect the erosion of ‐ soil with sorbed contaminants such as PO4 and pesticides, (iii) changes in the length of time a soil may be saturated that could lead to a change in redox conditions and cause mobilisation of some contaminants (e.g. As, P and Cr in floodplain soils), and (iv) changes in organic C status and soil temperature that changes the plant uptake of nutrients, the speed of chemical reactions and the activity of microbial life based on respiration responses which may release contaminants that were once sequestered. In the case of (i) and (ii) increased leaching and erosion may particularly occur if changes in the pattern of rainfall occur during typical application or cultivation periods.

In addition to the potential impacts that the UKCIP 09 predictions may have on soil contaminants, the models ability to incorporate other climate change related parameters such as (i) change in land‐ use, (ii) change in cropping patterns including new crop types (e.g. sunflowers, grapes) being grown and (iii) factors such as greater compaction of land may need to be considered within the model parameterisation. An evaluation of the models for each contaminant will be made using the ten agreed criteria.

20 A. Diffuse Agricultural Contaminant Models The potential problem of diffuse agricultural contaminants has been recognised for many years. Thus, a considerable amount of work has been invested in producing models capable of operating on a range of scales including plot, field, catchment and national scales to aid policy decisions. Many of the best plot and field scale models have been incorporated into national scale model frameworks along with other sub‐models to describe processes such as leaching, plant uptake, and evapotranspiration. In addition, information from various national databases on soil properties such as the National Soil Inventory (NSI) soil databases, HOST (Boorman et al. 1995), agricultural census and climate are included. Therefore this effort has often resulted in one model evolving specifically for catchment or national scale that incorporates the best modelling approaches developed over a long period.

One such example is the MAGPIE framework managed by ADAS and the UK academic community (Defra, 2007a). It originally started as the framework for a nitrate leaching model (Lord and Anthony, 2000), but has since developed to include a number of nitrate and phosphorus pollution models that use the same databases. The MAGPIE frameworkl is based on several national databases and surveys along with sub‐models linked with a GIS system and interpolated on a 1km2 grid. The sources of input data come from (i) the annual agricultural census data (crops and livestock), (ii) land use derived from satellite data and CEH’s land cover map, (iii) climate information supplied by the MET office and (iv) soil type and property data (NSI).

A.1 Nitrate models There are a range of plot, field and catchment models that have been used to predict nitrate loss from soils. These include LEACHM (Sogbedji et al. (2001), GLEAMS (Webb et al. 2001). For example LEACHM predicts the transport of and transformation of nitrate in plots and requires three main types of initial input data, these being (i) soil parameters and hydrological properties (bulk density, particle size distribution, initial C & N content, initial water content, water retention parameters, hydraulic conductivity and nitrification and denitrification rates), (ii) surface and bottom conditions (fertiliser applications, rain/irrigation, weekly pan evaporation and temperature) and (iii) crop data (time of planting and emergence, date of root and crop maturity, date of harvest and crop cover) (Johnson et al. 1993; Ng et al. 2000). Models that have been developed for catchment / regional or national predictive modelling include SWAT (Arnold et al. 1998; Nietsch et al. 2005) and the models ‐ developed for NO3 leaching in England and Wales (NEAP‐N, and Evenflow) by ADAS and the academic community.

A1.1 NEAP‐N and EveNflow

These two nitrate models run under the MAGPIE framework. NEAP‐N describes nitrate leaching to soils and is the basis for defining Nitrate Vulnerable Zones throughout England and Wales (DEFRA, 2008). EveNFlow (Euroharp, 2004) is a development on NEAP‐N and can predict the nitrate leaching to rivers and water courses. These models include a series of sub‐models that have been developed to simulate different aspects of the N cycle in soils and interactions between vegetation and hydrology in agricultural ecosystems. For example NEAP‐N uses the sub‐models NCYCLE, NITCAT, MANNER, SUNDIAL, SLIM, The Burns Model, and MORECS to describe nitrate cycling under different agricultural systems. The water balance model MORECS is used to calculate the water balance with the difference between mean annual rainfall data and evapotranspiration used to calculate ‘annual excess rainfall (drainage volume)’. Outputs include estimates of potential and actual annual N loss

21 and water loss for each grid cell and land‐use. Table 6 shows the assessment of the nitrate models against the selection criteria.

A1.2 NIRAMS NIRAMS is the Scottish equivalent (Dunn et al. 2004a, 2004b) and is very similar to MAGPIE but includes a more comprehensive methodology for hydrological transport and routing to predict stream water N concentrations. These models use national land use data and agricultural practice census data that is updated annually.

A1.3 SWAT

The SWAT model (Arnold et al. 1998; Nietsch et al. 2005) has been used at catchment scale for predicting losses of nitrogen from soils. For modelling purposes river basins are split into sub‐basins to allow improved simulation of land uses and soil types. The model requires inputs relating to hydrology, ponds/wetlands, groundwater, weather, sedimentation, soil temperature, plant growth, nutrients and agricultural management. In addition to plant growth use of N, nitrate and organic N will be removed by soil through the mass flow of water. Fluxes of nitrate contained in runoff, lateral flow and percolation are estimated as products of the water flow and the average concentration of nitrate in the layer. Organic N attached to sediment can also be modelled.

22

Table 6. Nitrate model comments

No Criterion LEACHM SWAT NIRAMS NEAP‐N EveNFlow Scale Field scale Catchment scale National Scale, Uses National / catchment National / Catchment deterministic models NLEAP leaching Scale scale model 1 Does it provide Generally good Models solute and Predicts nitrate Describes nitrate Describes nitrate estimates of change estimation of water nutrient flow through leaching in soil or to leaching in soil. leaching to rivers and in the soil threat as a and solute flow at catchments. Would water courses. Would Would require new predicts function of climate plot or field scale require new water require new water water balance based concentrations change variables? balance based on balance based on on JULES. through ADAS JULES. JULES. MAGPIE system. Would require new water balance based on JULES. 2 Are these at a space No No Has been run for Yes With the Magpie and time scale which Scotland dataset it could be can be used for but is used at national‐scale catchment scale as decision making? moment 3 Does it cover all Not economic Not economic Not economic Not economic, not to Not economic aspects of the soil waters threat, particularly those of greatest perceived importance, ecological, economic etc? 4 What is its track Generally these Generally well but Generally well but Generally well but Generally well but record in modelling models simulate well scale reduces scale reduces scale reduces scale reduces historic data as a accuracy accuracy accuracy accuracy

23 No Criterion LEACHM SWAT NIRAMS NEAP‐N EveNFlow function of climate change variables? How well does it simulate? 5 Does it require Would require Would require E &W Would require E&W Has been run for Depends on data additional England and Wales national scale agri national scale agri E&W already for NVZ availability for water parameters or (E&W) national scale census & fertiliser census & fertiliser evaluation bodies driving variables, agri census & inputs and a new inputs and a new and are these fertiliser inputs and a model framework model framework available into the new model future? framework 6 Is it responsive to They can normally Forestry, natural Forestry, natural Forestry, natural other changes, model many crops. ecosystems and peat ecosystems and peat ecosystems and peat particularly land Possibly not forest or not ac‐counted for not ac‐counted for not accounted for use? peats 7 Where are the likely No agri census data No agri census data Within this project Within this project Within this project bottle‐necks in learning to use model learning to use model learning to use applying the model? model. Data hydro‐ graph availability for streams 8 If a model looks Within this project Within this project Within this project Within this project Within this project promising but No No No No No requires a few changes, how readily can these be made? Can this be done within the scope of the project? 9 Cost implications generally 10 Compatibility with These models are Has its own platform. Requires agri census Works within ADAS Works within ADAS other models – often used in Includes GLEAMS, data for E&W MAGPIE 2 system MAGPIE 2 system

24 No Criterion LEACHM SWAT NIRAMS NEAP‐N EveNFlow platform etc catchment / national LEACHM as models as sub‐ submodels models

25

Table 7. Nitrate model assessment

Criterion Model LEACHM SWAT NIRAMS NEAP‐N EveNFlow 1 2 3 4 5 6 7 8 9 10

Summary

All of the catchment scale / national scale models are based on the same major principles of hydrology, crop management, crop uptake and leaching. Whilst the SWAT model has been used for catchment scale modelling and thus has the capability of being upgraded to national scale, the realistic models for modelling nitrate losses within England and Wales are the NEAP‐N and EveNflow models that form part of the ADAS MAGPIE 2 framework. This is mainly due to the models operating within a framework where results from the agricultural census, satellite data from CEH land cover map along with Met office data are periodically updated. NIRAMS, which fundamentally operates in a similar manner to the ADAS model could be implemented but would require data bases to be transferred. However, the NEAP‐N and EvenFlow models would still require additional inputs from Jules (re: water balance) derived from ten new climate data to run. Thus, if nitrate modelling with future climate predictions was to be undertaken within this project in England and Wales the ADAS MAGPIE Framework models would be the most suitable. However, only present day cropping patterns could be modelled as there are no predictions for how climate may change variables such as cultivation practices, sowing dates, harvesting dates and crop types.

A.2 N2O models

A range of models exist for modelling N2O that operate from site specific such as the DeNit model (Reth et al. 2005) to those capable of being run at national scale. The national scale models include DNDC (Institute for the study of Earth, Oceans & Space, 2007), CENTURY/DAYCENT (Del Grosso, 2007) and ECOSSE (Smith et al, 2007). All national scale models would require input data that is UK‐ specific and collected from national agricultural and NSRI databases. This would include (i) soil characteristics (HOST classification for hydrological characteristics, organic C, pH, total porosity, clay proportion, water filled pore space at field capacity and permanent wilting point and saturated hydraulic conductivity, (ii) crop information (areas of crops in each county, type, height, optimum yield, C:N ratios of grain, root and shoot, water requirement, date of planting, harvest and tillage, straw disposal option), (iii) fertiliser use from the agricultural census, (iv) irrigation data, (v) weather data, and (vi) livestock data (numbers and type, N excretion, grazing season, application of FYM and slurry).

26

A.2.1 The DNDC model

The DNDC model is a general model of carbon‐nitrogen biogeochemistry in ecosystems. The model, ‐ although capable of modelling carbon storage and NO3 leaching, has mostly been used for estimating greenhouse gases from both agricultural and natural ecosystems (forests, wetlands). The DNDC model was originally written for US conditions and was used by Brown et al. (2002) to improve the estimation of N2O emissions from UK agricultural land for data taken from 1990. The first component consists of the (i) soil climate, (ii) crop growth and (iii) decomposition sub‐models and predicts soil temperature, moisture, pH, redox potential and substrate concentration profiles driven by ecological drivers (e.g. climate, soil, vegetation and anthropogenic activity). The second component consists of the nitrification, denitrification and fermentation sub‐models and predicts

NO, N2O, N2, CH4 and NH3 fluxes based on the input variables. The modelling is based on classical laws of biology, physics and chemistry along with empirical equations.

Brown et al. (2002) ran the DNDC model on a county basis for the UK The three dominant soils in each county were selected on the basis of areal coverage on agricultural land and included minimum and maximum organic C. Where data was not available, pedo‐transfer functions were used for saturated hydraulic conductivity, water holding capacity and bulk density. Data for organic soils of the UK was included but some of the data requirements were limited. When run by Brown et al. (2002), model validation was carried out using 16 datasets from contrasting soil, crop and fertiliser types. Agreements between estimated and measured data were found to be very good. Sensitivity analysis was carried out by changing the weather variables (temperature and precipitation) and changes in farming practice (timing, rate and form of N fertiliser).

The DNDC model has been run for the UK and has achieved good results and from the literature appears to be the model most often used solely for N2O. It has been used in many countries such as Canada and the US Recently, new forest and wetland modules have been added which are indications that it will be able to model changes in land use. Potential problems cited by Brown et al.

(2002) suggest that it cannot model N2O from high organic C soils, there was a lack of N2O data from housing of animals or storage of manure that meant relying on the IPCC methodology of estimates from ‘indirect sources’. Nitrogen leached was included (IPCC, 1997) as no estimates of denitrification and nitrification in fresh water is available. New crop types may need to be included if agriculture changes (e.g. Sunflower, vineyards).

A.2.2 The Daycent Model

The Daycent model would require the same UK specific input variables as described in the introduction. Smith et al. (2008) compared predictions from the DAYCENT model to those from the DNDC model in Eastern Canada. Whereas both models were found to under predict soil water content, the DNDC model accurately predicted average seasonal N2O emissions whereas DAYCENT under predicted by 32 to 58%. However, the DNDC model lacks the ability to model organic soils.

A.2.3 The Ecosse Model

27 ECOSSE, however, can estimate N2O from peat soils. Predictions of N2O emissions from DAYCENT and DNDC are fairly well tested, whilst those from ECOSSE are being tested and improved in the NITROEUROPE‐IP project (pers comm. Pete Smith).

28

Table 8. N2O model comments

No Criterion DenNit DNDC CENTURY / DAYCENT ECOSSE Scale Site specific Can be run at national Can be run at national Can be run at national scale scale scale 1 Does it provide estimates Requires soil moisture, Has soil moisture, temp, Has soil moisture, temp, Has soil moisture, temp, of change in the soil temperature, pH, crop growth, crop growth, crop growth, threat as a function of ammonium and nitrate decomposition & N cycle decomposition & N cycle decomposition & N cycle climate change concentration as drivers. submodels. Does CH4 as submodels. submodels. ECOSSE has variables? well the ability to do Peat 2 Are these at a space and No Has already been used at Has already been used at N2O predictions within time scale which can be UK scale UK scale Ecosse are still being used for national‐scale tested. However, model decision making? has been run at national scale (Scotland & Wales) for C. Will be run for England as part of this project. 3 Does it cover all aspects Yes, but not economic. Yes, but not economic. Yes, but not economic. Yes, but not economic. of the soil threat, No peat module No peat module particularly those of greatest perceived importance, ecological, economic etc? 4 What is its track record Generally very well. Has been well tested and Has been well tested and Is still being validated for in modelling historic data validated for N2O validated for N2O N2O as a function of climate emissions emissions change variables? How well does it simulate? 5 Does it require additional Not for site specific Mechanist‐ically based Mechanist‐ically based parameters or driving modelling. model so major processes model so major pro‐

29 No Criterion DenNit DNDC CENTURY / DAYCENT ECOSSE variables, and are these are modelled. Will cesses are modelled. Will available into the future? require other data‐bases require other data‐bases such as fertiliser and agri‐ such as fertiliser and agri practice to be continually practice to be continually updated for new updated for new scenarios scenarios 6 Is it responsive to other As long as data is Agriculturally based. New Yes changes, particularly available to parameterise forest and wetlands land use? it modules have been added. 7 Where are the likely Not national scale bottle‐necks in applying the model? 8 If a model looks Few changes needed Few changes needed Few changes needed promising but requires a few changes, how readily can these be made? Can this be done within the scope of the project? 9 Cost implications Aberdeen running as part Aberdeen running as part Aberdeen running as part generally of project of project of project 10 Compatibility with other models – platform etc

30 Table 9. N2O model assessment

Criterion Model DenNit DNDC CENTURY / ECOSSE DAYCENT 1 2 3 4 5 6 7 8 9 10

Summary

Three of the models reviewed (DAYCENT, DNDC and ECOSSE) are capable of being used to model regional or national N2O emissions. Whilst the DNDC model has been found to be accurate, a major flaw is that it does not predict losses from organic rich soils. The ECOSSE model has the ability to model the linked soil carbon/nitrogen cycles in organic rich soils, including the ability to model predictions of N2O losses. This is an important feature to achieve an overall N2O emission map of England and Wales due to the large areas of upland peat and organic rich soils in both countries. However, the Ecosse model is currently under testing for N2O and has beenrun within the “Carbon” section of the present project.

A.3 Phosphorus models As with the nitrate, models that have examined the losses of phosphorus (P) from soil have been developed over many years. Within the UK these have been subsumed into the PSYCHIC model based within the ADAS MAGPIE framework. Other models have been developed for field and plot scales such as LEACHM and GLEAMS.

A.3.1. PSYCHIC Model

Within the ADAS MAGPIE framework, two models exist that can be used to assess P loss from soils at a national scale. These are PIT and PYSCHIC (Davison et al. 2008; Heathwaite et al. 2003; Heathwaite et al. 2005; Liu et al. 2005). The evolution of these models is such that PIT has now been largely subsumed into PYSCHIC which is now capable of being run nationally (pers. Comm. Eunice Lord). The PSYCHIC model predicts P and suspended sediment mobilisation in land runoff. Transfer pathways include the release of desorbable soil P, detachment of suspended sediment and associated particulate P, incidental losses from manure and fertiliser applications, losses from hard standings, the transport of all these sources to watercourses in under‐drainage (where present) and via surface pathways, and losses of dissolved P from point sources (Davison et al. 2008). Catchment scale studies have already been undertaken in the Hampshire Avon and Herefordshire Wye (Stromqvist et al. 2008). Updates to how PSYCHIC works have been undertaken within the PEDAL project (DEFRA, 2007b).

31 Input data includes (i) the area of major crops; livestock numbers by type (combination of agricultural census, CEH land cover and other databases) from the MAGPIE framework, (ii) P applications by month as manures and excretal returns to land, by livestock type from data taken from the Manure Management Database (derived from Defra census and survey data), (iii) dominant soil series (1km2) over England and Wales (NATMAP 2), (iv) soil series characteristics (PSD, HOST class, bulk density, and OC under arable and grass) from SOILSERIES (NSRI), (v) monthly climatic data: rain, rain days, wind speed, sun hours, max and min temperature from the Mean Climate Drainage Model (MCDM) (Barrow et al. 1993), (vi) the index of proximity to surface water from the Connectivity database, (vii) mean slope per 1km2 derived from 50 x 50m DEM and (viii) number of people per 1 km2 derived from population statistics of the 1991 census. In addition, soil compaction is included in the model. This is simulated by assigning a fraction of the agricultural land to bare soil with a bulk density of 135 %. Outputs of the model produced by Stromqvist et al. (2008b) include catchment maps of (i) mobilisation in surface runoff, (ii) diffuse P loss, (iii) mobilisation of P in drain‐flow and (iv) total P loss.

New variables may need to be considered for the future e.g. new crop types. The climate inputs for the MCMD database will need to be updated or replaced by those from UKCIP 09. Compacted land can be adjusted to take into account changes in machinery type and traffic periods if typical agricultural practice is altered. Stromqvist et al. (2008b) suggest a number of factors that may constrain the statistical performance of the model. One concern is by using climate data; short term behaviour of the system may not be modelled particularly well. However the authors suggest that comparisons between PSYCHIC and longer term monitoring are less prone to the confounding effects of inter‐annual variation in catchment rainfall and diffuse pollutant fluxes. The authors suggest that PSYCHIC is more appropriate to characterising longer‐term catchment response. Other issues that Stromqvist et al. (2008b) accept they need to consider include (i) channel bank erosion as sources of suspended sediment and particulate P, and (ii) channel bed storage and remobilisation.

A.3.2. SWAT

The SWAT model (Arnold et al. 1998) has been used at catchment scale (Kirsch et al. 2002….. amongst many others) for predicting losses of phosphorus to soils. The model requires inputs relating to hydrology, weather, sedimentation, soil temperature, plant growth, nutrients and agricultural management. The model is linked to ARCVIEW so that digital elevation models can be incorporated, which is especially important for phosphate transport by soil erosion which is determined by the Modified Universal Soil Loss Equation (MUSCLE) where the rainfall energy factor is replaced with a runoff factor. The model can be used to simulate organic N in surface runoff, soluble phosphorus movement and organic and mineral P attached to sediment in surface runoff (Neitsch, 2005).

32

Table 10. Phosphorus model comments

No Criterion LEACHM, GLEAMS SWAT PSYCHIC PIT Scale Field / plot scales Catchment scale Catchment scale. Works Catchment / National through MAGPIE system scale. 1x1km resolution 1 Does it provide estimates Generally model water Models solute and Provides estimates of Provides estimates of of change in the soil and solute flow at plot or nutrient flow through diffuse P loss from diffuse P loss from agri threat as a function of field scale accurately catchments. Would agricultural land to land to surface waters as climate change require new water surface waters as monthly annual P loss variables? balance based on JULES. P loss 2 Are these at a space and No No 1 km2 resolution, 1 km2 resolution, time scale which can be sensitive to land use and sensitive to land use and used for national‐scale management & management & decision making? environmental factors environmental factors such as climate, soil type such as climate, soil type & topography & topography 3 Does it cover all aspects Not economic Not economic Gives data regarding Not economic factors but of the soil threat, losses of P to waters but addresses leaching of P to particularly those of no economic analyses waters. greatest perceived importance, ecological, economic etc? 4 What is its track record in Generally these models Generally well but scale Results from Davison et Results from two modelling historic data as simulate well reduces accuracy al. (2008) mixed for both catchments (Windermere a function of climate long and short term & Windrush) produced change variables? How studies good estimates (Liu et al. well does it simulate? 2005) 5 Does it require additional Would require E &W Would require E &W No No parameters or driving national scale agri census national scale agri census variables, and are these & fertiliser inputs and a & fertiliser inputs and a available into the future? new model framework new model framework

33 No Criterion LEACHM, GLEAMS SWAT PSYCHIC PIT 6 Is it responsive to other They can normally model If relevant Only parameterised for Only parameterised for changes, particularly land many crops or other parameterisation data for arable and grassland arable and grassland use? landuses if data is avail‐ new land use e.g. forestry agriculture agriculture able was available 7 Where are the likely No agri census data No agri census data Within this project yes – Within this project yes – bottle‐necks in applying Learning to use MAGPIE & Learning to use MAGPIE & the model? PSYCHIC, data transfer PIT, data transfer 8 If a model looks Within this project No Within this project No No No promising but requires a few changes, how readily can these be made? Can this be done within the scope of the project? 9 Cost implications generally 10 Compatibility with other These models are often Has its own platform. Works through ADAS Works through ADAS models – platform etc used in catchment / Includes GLEAMS, MAGPIE system MAGPIE system national models as sub‐ LEACHM as submodels models

34 Table 11. Phosphorus model assessment

Criterion Model LEACHM, SWAT PSYCHIC PIT GLEAMS 1 2 3 4 5 6 7 8 9 10

Summary

As for nitrate, the catchment / regional / national models developed for losses of phosphorus have been developed by selecting the best attributes of early plot / field models and upscaling with additional national data sources relating to soil, hydrology and agricultural practice. Thus the SWAT and Psychic models are two models which require very similar inputs. However, the PSYCHIC model has advantages in that it has been developed in the UK and uses the databases already present within the MAGPIE framework to utilise. For this reason, the PSYCHIC model is considered the best choice for use.

B. Pesticides and Organic Contaminants The receptors for pesticides and organic contaminants are mainly considered to be ground and surface waters. Very effective plot scale models have been developed such as PEARL, PELMO and PRZM which are the models recommended by the EU FOCUS (Forum for the Co‐ordination of Pesticide Fate Models and their use) project (FOCUS, 1995) and GLEAMS (Webb et al. 2001) for assessing pesticide leaching. The LEACHM model also has a pesticide module (Johnson et al, 1993). Catchment scale models include SWATCATCH (Neitsch et al. 2005) and POPPIE (Brown et al. 2002). There are no UK national scale models actively being developed to predict leaching of pesticides. However, some previous attempts have been made to examine aspects of leaching on a larger scale. These include (i) the European wide EUROPEARL model (Tiktak et al. 2004) and (ii) an attempt to add a national scale groundwater component to the EA’s POPPIE model which was developed by Holman et al. (2004). This latter model was used to predict potential atrazine and isoproturon leaching. Similar methodologies in terms of data inputs and model behaviour have been used in models. Data is taken from national or European databases regarding climate and soil properties. Applicable well tested sub‐models for pesticide leaching are used to determine pesticide behaviour (e.g. kd, breakdown and metabolites) to produce predictive maps of pesticide loss for catchment / national / international scale based on agricultural land use.

35 B.1.EUROPEARL The EUROPEARL model (Tiktak et al. 2004) was a Pan‐European spatially distributed leaching model for Plant Protection Products (PPP). It is based on the European Soil Map, the European Soil Database and the Pan‐European Climate database. Thus, predictions for potential pesticide leaching are at fairly low resolution.

The PEARL model includes sub‐models and inputs including (i) the SWAP (SOIL WATER ATMOSPHERIC PLANT SYSTEM) model which uses a finite difference method to solve Richard’s equation with the upper boundary of the model situated at the top of the crop canopy, (ii) daily rainfall fluxes are input into the model and (iii) reference evapotranspiration calculated according to the FAO modified Penman‐Monteith approach (Allen et al. 1998). The soil system where PPP’s and their metabolites reside are considered as equilibrium and non equilibrium domains. The equilibrium domain is partitioned into adsorbed, solution and gaseous phases. Sorption is described through by a Freundlich equation or pH dependent Freundlich equation and transformations of PPP’s are described by first order rate equations and a number of reduction factors which account for soil temperature, soil moisture and depth of soil. The uptake of PPP’s is taken proportional to the root water uptake and an empirical transpiration stream concentration factor. Soil data at horizon level from databases including bulk density, organic matter, pH(H2O) and texture and climate data is split into eight major European climate zones. PEARL has several application options including spaying to the soil surface, injection and incorporation by tillage.

Output reported by Tiktak et al. (2004) included (i) potential leaching based on 10 x 10 km2 grid which is the highest resolution of the Euro soil map 1:1000000, (ii) a map of Europe covering the eight major climatic zones based on maps of long term averages of annual precipitation and temperature, (iii) spatial balances of water flux according to arable crop and (iv) leaching risk according to 20th and 80th percentile.

B.2 POPPIE In the UK, the Environment Agency (EA) currently operates the Prediction of Pesticide Pollution in the Environment (POPPIE) system, initially developed to predict pesticide concentrations in surface waters and to support the design of pesticide monitoring programs. Currently, it exists for the prediction of pesticides in surface waters outlets at a field or catchment scale. The surface water component of the POPPIE model is based on the USDA developed SWAT (Soil and Water Assessment Tool). SWAT compiles information about weather, soil properties, topography, natural vegetation and cropping practices within a customised ARCview interface. Sub‐models within SWAT include (i) algorithms for the movement of soluble and sorbed forms of pesticides from land to stream which were taken from EPIC (Williams, 1995), and (ii) a simple mass balance to model the transformation and transport of pesticides in streams represented as a well mixed layer of water overlying a homogenous substrate. To assess over land components of the hydrological cycle including sheet flow, channel flow, unsaturated sub‐surface flow and saturated groundwater flow the MIKE‐SHE model is used, whilst additional modules allow the simulation of pesticide transport, biodegradation and transport via macropore flow. The MIKE‐SHE model requires an extensive list of input data and this restricts its use to highly characterised catchments. To reduce computing times the water calculations are carried out in advance and cannot be changed by the user. Properties and pesticide scenarios are entered via an interface.

36 A groundwater component for POPPIE was developed by Holman et al. (2004) and was used at UK national scale to predict the movement of pesticides to groundwaters. This was carried out for atreazine and isoproturon. The model, used for England and Wales is regarded by the authors as a first attempt at integrating preferential flow into the evaluation of potential pesticide contaminations of groundwater. Limitations include (i) not using a preferential flow model to simulate water and solute flow in the unsaturated zone, (ii) whilst using MACRO, adjustments to crop height were not made, (iii) application dates were not adjusted to rainfall and possibly most importantly and (iv) no integration of detailed information on the unsaturated zone of aquifers was made. Since the publication of Holman et al. (2004), no further work has been carried out on this model.

B.3 SWAT‐Catch

The SWAT model (Arnold et al. 1998; Neitsch et al. 2005) has been used at catchment scale (….. amongst many others) for predicting losses of pesticides from soils. Like its operation for nitrates and phosphates it is based on the same principles where, for modelling purposes, river basins can be split into sub‐basins to allow improved simulation of land uses and soil types. The model requires inputs relating to hydrology, ponds/wetlands, groundwater, weather, sedimentation, soil temperature, plant growth, nutrients and agricultural management. The pesticide transport algorithms in SWAT were taken from EPIC (Williams, 1995) to give adsorption coefficients and these are combined with transport algorithms. Transport of pesticides attached to sorbed sediment is included.

The models outlined represent the attempts that have been made to date to predict the potential pesticide leaching to ground and surface waters and at the moment none provide a truly satisfactory approach because of (i) data limitation and (ii) technical limitations for nationwide coverage; and as such modelling was not undertaken as part of this project. Table 12 presents comments on the two models described here (Euro‐Pearl and POPPIE) and also others that have not been detailed as they do not fit the assessment criteria. Testing of new pesticides is still carried out using those transport models recommended by FOCUS. Some new advances are being made including the SPIDER model which simulates pesticide transport to ditches (Renaud et al. 2008).

37

Table 12. Pesticide model comments

No Criterion PEARL, PELMO, SPIDER SWAT‐CATCH POPPIE EURO‐PEARL MACRO, CRACK‐NP, Approach GLEAMS, OPUS, PRZM3 Scale Field, Plot Scale Field scale to drains / Catchment scale Mainly Catchment Continent wide Some used as basis small catchments (up scale. Combines Examines potential of FOCUS EU to 10 km2) models of pesticide for pesticide leaching pesticide regulations transport through unsaturated zone with GIS to produce risk maps. 1 Does it provide Generally model Models water flow Models solute and Models water flow Hydrology, weather estimates of change water and pesticide and pesticides nutrient flow so that can be and crop type in the soil threat as flow at plot or field leaching to ditches through catchments. adjusted from JULES considered on a function of climate scale accurately. Would require new 50x50km grid change variables? water balance based on JULES. 2 Are these at a space No No No At present only The approach is but and time scale catchments grid would need to which can be used modelled to surface be reduced for UK for national‐scale waters. Has a decision making? ground‐water component that has been used on national scale.

38 No Criterion PEARL, PELMO, SPIDER SWAT‐CATCH POPPIE EURO‐PEARL MACRO, CRACK‐NP, Approach GLEAMS, OPUS, PRZM3 (Holman et al. 2004). 3 Does it cover all Not economic Designed to cover Not economic Not economic Not economic aspects of the soil ecological risk. Not threat, particularly economic those of greatest perceived importance, ecological, economic etc? 4 What is its track Generally these Reasonable Generally well but Uses SWAT‐ At 50 x 50 km scale it record in modelling models simulate well predictions scale reduces CATCH(Generally can’t have good historic data as a accuracy well but scale resolution function of climate reduces accuracy) change variables? How well does it simulate? 5 Does it require Would require Model works for Would require E &W No national scale Would require E &W additional England and Wales small catchments national scale agri databases for national scale agri parameters or (E&W) national scale census & fertiliser unsaturated zone. census & fertiliser driving variables, agri census & inputs and a new Ground‐water model inputs and a new and are these fertiliser inputs and a model framework done for UK model framework available into the new model future? framework 6 Is it responsive to They can normally Designed for If relevant If relevant Designed for other changes, model many crops. agricultural land parameterisation parameterisation agriculture particularly land Possibly not forest or data for new land data for new land use? peats use e.g. forestry was use e.g. forestry was avail‐able avail‐able 7 Where are the likely No agri census data Not national scale No agri census data No national scale UK scale databases

39 No Criterion PEARL, PELMO, SPIDER SWAT‐CATCH POPPIE EURO‐PEARL MACRO, CRACK‐NP, Approach GLEAMS, OPUS, PRZM3 bottle‐necks in Would require databases would need to be applying the model? familiarisation with linked model 8 If a model looks Within this project Within this project Within this project Within this project Within this project promising but No No No No No requires a few changes, how readily can these be made? Can this be done within the scope of the project? 9 Cost implications generally 10 Compatibility with These models are Has its own POPPIE is the GIS other models – often used in platform. Includes and database platform etc catchment / national GLEAMS, LEACHM as framework models as sub‐ submodels models

40

Table 13. Pesticide model assessment

Criterion Model PEARL, SPIDER SWAT‐CATCH POPPIE EUROPEARL PELMO, Approach MACRO, CRACK‐NP, GLEAMS, OPUS, PRZM3 1 2 3 4 5 6 7 8 9 10

Summary

At present there are no national scale pesticide models for the UK. Catchment models exist of which POPPIE and SWAT‐Catch appear to be the current models of choice. The Europearl approach was of low resolution and the methodology has been superseded by the catchment ones. Therefore either POPPIE or SWAT‐Catch could be upgraded to national scale, depending whether sufficient information was available for individual pesticides and for catchment characteristics.

C. Acidification A review of possible effects of climate change on soils (Defra, 2005) suggested that the input of acidifying aerosols and increased leaching of basic cations could lead to acidification of soils. Again a range of models is available that work on different scales. The SAFE model works on profile scale (Alveteg et al. 1995; Sverdrup et al. 1995; Reynolds, 1997). It is a dynamic, multi layer soil chemistry model that calculates the weathering rate and soil water chemistry. It requires inputs of physical and chemical properties of the soil profile as well as time series of nutrient and contaminant deposition, nutrient uptake and precipitation. A similar model is the SMART2 model (Kros et al. 1995) which is a simple one‐compartment soil acidification and nutrient model that includes the major hydrological and biogeochemical processes in the vegetation, litter and mineral soil. The model predicts changes in pH, aluminium, base cations, nitrate and sulphate concentrations in the soil solution and solid phase characteristics such as carbonate content, base saturation and readily available aluminium content. The SMART 2 model is a single layer soil model and its time step is one year. From these original early soil profile models the process understanding has been developed for the current state of the art catchment / national models to be developed such as MAGIC and VSD. For example the MAGIC model incorporates many of the SMART 2 models capabilities (Riends et al. 2008). In the

41 recent report on Critical Loads and Dynamic Modelling (CEH, 2007), two larger scale models were examined; the MAGIC (Cosby et al. 1985a & b; Hardekopf et al. 2008) and VSD (Posch et al. 2009; Reinds et al. 2008) models. The MAGIC model was used primarily for regional modelling, over 53 sites whilst VSD was used for UK wide modelling because of its greater simplicity. Whilst both models are similar and produce similar results if VSD is configured appropriately, the VSD model has advantages that it can be run from an Access database, a simpler calibration routine that enables large numbers of model runs and that it has already been run for the UK.

C.1 The Very Simple Dynamic Model (VSD) The VSD model was run for the UK on a 1 x 1 km grid. Data sources are existing datasets including the NSI soils and property database, and EMEP gridded deposition or from appropriate defaults. Some additional data were collected for 133 representative sensitive soil types (CEC, base saturation, C:N ratios) as well as information from 80 locations to derive vegetation specific default relationships between soil C:N ratios and % N immobilisation. Inputs for the VSD model are given in Appendix 2.

Hydrological characteristics require the soil water content and precipitation surplus. In this instance Reinds et al. (2008) used the hydrological model WATBAL. National outputs from the model are reported in CEH (2007), and for example include (i) modelled target loads for acidity for sensitive habitat types, (ii) Soil C:N and pH predictions for a range of ecosystems including acid grassland, heathland, broadleaf woodland, conifer forest, bog as well as combined habitats and (iii) predictions of organic C:N and soil solution pH.

The predictions of organic C:N and soil solution pH were compared to independent samples from the Countryside Survey (CS) and some weak relationships were found. This may have been as a result of differences in measurement techniques and like for like measurements (e.g. soil pH measured in water for CS whilst model is predicting in‐situ pH). In addition, VSD simulations were parameterised on a constrained set of input data as compared to real world data and this could lead to a restricted distribution compared to the observed distribution. However the authors found no indication of bias in their C:N and soil pH predictions as compared to actual data from the CS survey.

The following improvements were suggested by the authors to improve the model performance. These included (i) to improve the estimates of the biologically active C pool based on NSRI estimates of total soil C. In minerals soils much of the organic C may be recalcitrant and this could lead to over‐ estimation of the capacity of the soil to immobilise N; (ii) to refine calibration parameters such as CEC and base saturation; (iii) to undertake further sampling so there is less reliance on national default datasets; (iv) for less lumping together of heterogenous soil types into single classes; (v) for more allowance of regional‐scale variations as a consequence of factors such as climate, base saturation and deposition history and (vi) for input parameters such as weathering rates that are hard to measure to be calibrated using MAGIC for different soil types. This would require new measurements of soil pore waters for sufficient samples from each soil type. Such measurements would require critical loads to be re‐calculated.

C.2 MAGIC The MAGIC model has been calibrated on a series of 53 sites within the UK and provides a resource for model testing in support of national modelling efforts (CEH, 2007). It is in effect very similar to

42 the VSD model in its function. However, as of yet no national scale output has been published. Output from the Magic model shows time trends (150 years) of how soil properties such as organic C, soil C:N, N immobilisation and inorganic N leaching change at individual sites with respect to acidification. Improvements for the MAGIC model were outlined in (CEH, 2007) to enable it to perform in a manner similar to the VSD model and to handle national scale modelling. These include (i) non constant soil C pools, to enable N‐induced C accumulations to be simulated better with respect to N saturation, (ii) more flexible input files to allow improved climate data to be included and (iii) options to use defaults instead of calibrating variable such as weathering rates.

43

Table 14. Acidification model comments

No Criterion VSD MAGIC SAFE SMART2 Scale National Scale1x1 km Catchment Scale Soil Profile scale Soil Profile scale grid Incorporates PROFILE model 1 Does it provide Precipitation and related Precipitation and related Precipitation values can Precipitation and related estimates of change in values can be varied values can be varied be added; temp values the soil threat as a accounted for in function of climate equations change variables? 2 Are these at a space and Yes. uses national Currently only operates Individual profiles. Can Individual soil profiles time scale which can be datasets of values at catchment scale be used to predict over used for national‐scale time decision making? 3 Does it cover all aspects Covers soil & water Covers soil & water Covers soil acidification Doesn’t cover some of the soil threat, acidification hence acidification hence and hence ecological parts of N cycle; particularly those of ecological threat but not ecological threat but not threat complexation of Al3+ & greatest perceived economic Can predict economic Can predict interaction between soil importance, ecological, with time back and with time back and solution & vegetation economic etc? forwards forwards 4 What is its track record Depends on quality of Depends on quality of Depends on quality of Depends on quality of in modelling historic input data & their input data & their input data & their input data & their data as a function of variation in soil variation in soil variation in soil variation in soil climate change variables? How well does it simulate? 5 Does it require No No NO Doesn’t cover some additional parameters parts of N cycle; or driving variables, and complexation of Al3+ & are these available into interaction between soil

44 No Criterion VSD MAGIC SAFE SMART2 the future? solution & vegetation 6 Is it responsive to other Model considers Model considers Model considers plant Model considers changes, particularly vegetation and thus vegetation and thus interaction vegetation and thus land use? changes in C & N, changes in C & N, changes in C & N, throughfall etc throughfall throughfall, etc 7 Where are the likely Has been used at UK Developing ability to run Will need to put into a Will need to put into a bottle‐necks in applying national scale at national scale national computing national computing the model? framework framework 8 If a model looks Not within the scope of Not within the scope of Not within the scope of Not within the scope of promising but requires this project this project this project this project a few changes, how readily can these be made? Can this be done within the scope of the project? 9 Cost implications Computing development Hasn’t been configured Hasn’t been configured generally required from to run at national scale to run at national scale catchment to national scale 10 Compatibility with Utilises various sub‐ Developed as stand Good. has been other models – platform models within its alone program incorporated into etc framework. However catchment and national this is really stand alone models

45

Table 15. Acidification model assessment

Criterion Model VSD MAGIC SAFE SMART2 1 2 3 4 5 6 7 8 9 10

Summary

As with the other contaminants, there has been a progressive development of modelling capability from single soil profile acidification models (SAFE, SMART2) to catchment / national models that often incorporate the best attributes of these models and therefore build on them. The state of the art models now would appear to be the MAGIC and VSD models. For the purposes of national modelling in the UK, the VSD would be the first choice as it is simpler to utilise and has already been used for national scale modelling.

D. Inorganic Contaminants Inorganic contaminants include those elements described as ‘PHE’s (Potentially Harmful Elements). Whilst, there are national soil inventories of heavy metal concentrations in soils such as the Soil Geochemical Atlas of England & Wales (McGrath & Loveland, 1992) and the G‐BASE geochemical survey (British Geological Survey), there are currently no available models for determining the effects that climate change may have on the mobility and speciation of PHE’s on a national scale. Typical models for predicting solubility and mineral phases of heavy metals typically require information that is site specific and of greater detail such as DOC, an estimate of labile metal, pore water concentrations, pH and organic carbon. Much of this detail is not held at national scale. Models such as WHAM (Tipping, 1994) are regularly used to undertake soil pore water speciation. Other models may include the Nica‐Donnan model (Kinniburgh et al. 1996) now incorporated within Visual Minteq, and PhreeqC (Parkhurst & Appelo, 2000). The examples given in this review are WHAM and Phreeqc.

46

Table 16. Inorganic model comments

No Criterion WHAM PHREEQC Scale Site specific Site specific 1 Does it provide estimates of change in the soil Model will incorporate site Model will incorporate site specific threat as a function of climate change variables? specific factors. No redox factors. Redox included but weak DOC component but very good DOC model model 2 Are these at a space and time scale which can be No No used for national‐scale decision making? 3 Does it cover all aspects of the soil threat, Only covers site specific soil Only covers site specific soil threats particularly those of greatest perceived threats importance, ecological, economic etc? 4 What is its track record in modelling historic data N/A N/A as a function of climate change variables? How well does it simulate? 5 Does it require additional parameters or driving No No variables, and are these available into the future? 6 Is it responsive to other changes, particularly land Site specific Site specific use? 7 Where are the likely bottle‐necks in applying the Not national Not national model? 8 If a model looks promising but requires a few NA NA changes, how readily can these be made? Can this be done within the scope of the project? 9 Cost implications generally Could run scenarios Could run scenarios 10 Compatibility with other models – platform etc

47

Table 18. Inorganic model assessment

Model Criterion WHAM PHREEQC 1 2 3 4 5 6 7 8 9 10

Summary

Modelling of inorganic contaminants such as metal solubility largely depends on specific measurements for sites that are not normally contained within our national soil inventories (e.g reactive metals, DOC). However, model capability is very good. WHAM with its DOC model is capable of speciation calculations and used in the SCAMP sub‐model mode, solution metal concentrations can be made. Phreeqc is capable of arrange of geochemical calculations including sorption calculations.

E. Generic water / soil borne models The list of contaminants discussed previously can also be divided into soil and water borne contaminants. This definition could potentially take into account many other contaminants for which no specific models have been developed, but can still be vulnerable to potential redistribution to ground or surface waters due to climate change impacts. Table 19 categorises contaminants according to their possible mode of re‐distribution.

Table 19. List of potential soil and water borne soil contaminants

Soil Borne Water Borne

Phosphates, PHE, PAH’s, PCB’s, Nitrates, PHE, Pesticides, Pathogens, Pesticides, Endocrine disruptors (e.g. nano‐materials, Pharmaceuticals, Plasticisers, Estradiol), nano‐materials Chlorinated phenols, Benzene, Low molecular weight PAH’s, Endocrine disruptors (e.g. Plasticisers, Estradiol)

48 E.1 Generic models for soil borne contaminants The selection of models for the potential erosion of soil through wind or water (and carrying soil‐ borne contaminants) will reflect the choices made in the erosion part of this report. These are the PESERA model for water erosion and WEQ model for wind erosion and transport of soil borne contaminants.

E.2 Generic models for water borne contaminants The generic model used for potential changes in contaminants being transported through leaching could be run by examining ‘precipitation surplus’. This could be calculated through the JULES model on a national scale.

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Arnold, J.G., Srinivasan, R., Muttiah, R.S. & Williams, J.R. 1998. Large area hydrologic modelling and assessment Part 1: Model development. Journal of the American Water Resources Association, 34(1), 73‐89.

Boorman, D.B., Hollis, J.M. & Lilly, A. 1995. Hydrology of soil types: a hydrologically‐based classification of the soils of the United Kingdom. IH Report No. 126, Institute of Hydrology.

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Cosby, B.J., Hornberger, G.M., Galloway, J.N., Wright, R.F., 1985a. Modelling the effects of acid deposition: assessment of a lumped‐parameter model of soil water and streamwater chemistry. Water Resour.Res., 21,51.

Cosby, B.J., Wright, R.F., Hornberger, G.M., Galloway, J.N., 1985b. Modelling the effects of acid deposition: estimation of long‐term water quality responses in a small forested catchment. Water Resour. Res., 21, 1591.

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Dunn, S.M., Vinten, A.J.A, Lilly, A., DeGroote, J., Sutton, M.A. & McGechan, M. Nitrogen Risk Assessment Model for Scotland 1. Hydrological transport and model testing. Hydrology and Earth system Sciences, 8(2), 205‐219.

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Institute for the Study of Earth, Oceans and Space. 2007. User’s Guide for the DNDC model (Version 9.1), University of New Hampshire.

Hardekopf, E.W., Horecky, J., Kopacek, J., Stuchlik, E. 2008. Predicting lomg term recovery of a strongly acidified stream using MAGIC and climate models (Litavka, Czech Republic). Hydrology and Earth System Science, 12(2), 479‐490.

Heathwaite, A.L., Frase, A.I., Johnes, P.J., Hutchins, M., Lord, E., Butterfield, D. 2003. The Phosphorus Indicators Tool: a simple model of diffuse P loss from agricultural land to water. Soil Use and Management, 19, 1‐11.

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Jarvis, N.J., Jansson, P‐E. Dik, & Messing, I. 1991. Modelling water and solute transport in macroporous soil. 1. Model description and sensitivity analysis. Eur. J. Soil Sci., 42(1), 59‐70.

50 Johnson, A.D., Cabrera, M.L., Hargrove, W.L., McCracken, D.V. & Harbers, G.W. 1993. Estimating nitrate leaching and soil water dynamics with LEACHM, Proceedings of the 1993 Georgia Water Resources Conference, University of Georgia, 371‐374.

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A5. Compaction Soil compaction is recognised as a current threat (Schafer et al. 1992; van den Akker et al. 2003). It occurs when an external mechanical stress from equipment or livestock exceeds the mechanical stability of soil (Bailey et al. 1995). Various properties control the susceptibility of soil to compaction, including previous stress history (Keller and Arvidsson 2007), texture (O'Sullivan et al. 1999), organic matter (Zhang et al. 2005) and soil structure (Horn and Fleige 2009). Most of these properties also regulate the capacity of soil to recover from compaction either through subsequent cultivation (Watts and Dexter 2000) or the inherent resilience under natural weathering (Gregory et al. 2007).

Water content is the greatest temporal variable influencing soil compaction, so precipitation changes that may be predicted for the future could have major implications (Cooper et al. 1997). Most dry soils compact little, whereas soils wetter than field capacity can be highly susceptible. Predicting the susceptibility to compaction, however, is fraught with uncertainty because of the complexity of stress transmission through soil and confounding factors such as the influence of soil structure (Defossez and Richard 2002).

Subsoil compaction is viewed as a far greater threat (van den Akker et al. 2003) than topsoil compaction as the latter can be ameliorated to some extent by soil cultivation and natural processes (Barre et al. 2009; Zhang et al. 2005). The impacts of subsoil compaction include reduced crop yields, poor drainage and increased overland flow (Horn et al. 2005; Horn et al. 2007; Pietola et al.

52 2005). It also alters microbial habitat in soil (Gregory et al. 2007), so it could have implications to biodiversity (Young et al. 1998).

A review of the literature found three major methods used to predict the susceptibility of soil to compaction:

1. Workable Days – based only on the water content of the soil. 2. Expert Model – based on both water content of the soil and expert judgement on the mechanical stability of the soil when wet. 3. Mechanistic Model – based on the mechanical stability of soil at different water contents/potentials, water content and the stress transmitted externally from traffic or trampling. Most of these models have been applied and developed for arable soils, although some have been adapted to use for livestock trampling or forest operations. Data on compaction is far more abundant for arable soils than any other land use.

1. Workable Days After a major search of the literature, we found only one attempt at predicting the impact of climate change on soil compaction at large‐scale. Cooper et al. (1997) used predictions from the Scottish Climate Change Group to model the influence on Workable Days where tractors can access fields without causing severe compaction. They defined Workable Days as soils with water contents drier than 110% of field capacity. This cut‐off was logical from an agronomical stand‐point because many soils are resistant to compaction at field capacity. However, in Scotland a cut‐off of field capacity resulted in hardly any work days for some soils and sites, so a water content of 110% of field capacity was used. Other researchers have used similar cut‐offs, although it varies from 100% to 110% depending on the study.

In the paper by Cooper et al. (1997) and its subsequent application to soils in Sweden by de Toro and Hansson (2004), the SOIL‐model was used to predict soil water content in the top 10 cm. This is a multi‐layer model that can simulate the content and movement of water from the surface down to specified depths. Water transport equations are based on Brooks and Corey. Macropore flow, freeze‐thaw and snow melt are also included in the SOIL‐model.

Workable days can be defined as ‘tillage’, ‘harvest’ and ‘slurry spreading’ workdays by extracting subsets of dates that are relevant to each of these activities. These would require modification if climate change will alter crop growing seasons.

The soil water model is the only complex aspect of the ‘Workable Days’ approach. It could be easily adapted to use with JULES, simply by identifying days drier than 110% field capacity.

2. Expert Model Various expert models of soil compaction exist, but the approach presented by Jones et al. (2003) was selected as the most relevant for predicting climate change impacts in the UK because it was developed using our national soil survey data. Broad classes of soil susceptibility are defined based on soil texture and packing density (bulk density corrected for clay content). These are then combined with climatic data to define the vulnerability to compaction. The climatic data is based on

53 days wetter than field capacity, so the approach is in essence an extension to the Workable Days method. However, there are only 5 broad classes of climate defined and no consideration is made for the timing of traffic on soils.

In comparison to the Workable Days method, the Expert Model can identify soils that are mechanically stable when wet. This could alleviate the need to use an arbitrary value of 110% field capacity as a cut‐off. More climatic classes could also be defined so that the output would be more sensitive to changes in climate. Adapting JULES to provide the output would not be difficult as all of the input parameters are used already for modelling water content.

3. Mechanistic Model Mechanistic models vary in complexity from deterministic models that require considerable data (Thu et al. 2007; Defossez et al. 2003) to qualitative models that can be applied across different soil types (Schafer et al. 1992; Horn and Fleige 2003; Horn and Fleige 2009). For predictions at large‐ scale, complex soil compaction models can not be applied as they require considerable data. More readily available data can be used to assess the influence of agricultural machinery on stress propagation to soil (van den Akker 2004), and its resistance to deformation (O'Sullivan et al. 1999). In these models the weight of the vehicle (or animal) and the contact area with soil are accounted for and the stress distribution with depth is calculated. The models can therefore simulate specific impacts, such heavy tractors with low‐ground pressure tyres or lighter animals with small hoof‐ prints.

These models require data on the mechanical behaviour of soil. The angle of internal friction and cohesion of soil can be determined from direct shear or triaxial tests (van den Akker 2004). Another parameter that is often required is the precompression stress (Horn and Fleige 2003). It indicates the stress that causes plastic deformation in uniaxial compression tests, although its determination from stress‐strain data is subjective (Gregory et al. 2006) and the physical relevance of the precompression stress has been questioned (Keller and Arvidsson 2007).

Where mechanical data is not available, pedotransfer functions may be used to provide estimates based on more readily available soil data (Horn and Fleige 2003). Although the pedotransfer functions are sometimes based on poor correlations with soil properties, the approach offers hope for directing policy using data that is currently available. The approach was developed originally to map subsoil compaction risk in Germany (Horn and Fleige 2003) and it has been extended to mapping subsoil compaction risk in Pennsylvania (Fritton 2008). It has not been applied to the UK.

Mechanistic models would allow for specific threats, such as cattle trampling or heavy harvesting equipment, to be considered separately. The pedotransfer functions described include soil carbon as a variable, so climate change impacts on this soil property could also be considered. Implementing the models would be the most difficult of the three different major methods that have been described. JULES could be extended by including the pedotransfer functions, but there are numerous multiple regressions that need to be selected based on soil properties, so it is not straight‐forward. Given the financial resources available, the mechanistic models are probably beyond the scope of the project.

54

Table 20. Compaction models comments

No. Criterion Workable Days Expert Model Mechanistic Model 1 Does it provide estimates of Only approach used to date to Would require modification as it Possible. Predicts compaction change in the soil threat as a model effects of climate change. has only been applied to the stresses at depth based on a range function of climate change Used historical data. Description current threat of compaction. Soil of soil properties including water variables? limited to days available for traffic moisture deficit defines impact of and carbon. on land. changing precipitation.

2 Are these at a space and time scale Yes. Yes. It has been applied to model Yes. If the pedotransfer functions which can be used for national‐ threat of compaction for all of can be incorporated into JULES scale decision making? Europe. Good for decision making. then it will provide output at the same scale.

3 Does it cover all aspects of the soil No. It is a simple approach that will Provides an expert opinion of Of all models, most likely to give an threat, particularly those of rely heavily on JULES output. Based subsoil compaction threat. No assessment of extent of change. greatest perceived importance, entirely on working days and not direct estimate of extent of With much more work, it would be ecological, economic etc? risk of compaction. damage. Also, the approach is base possible to examine feed‐forward on a general vulnerability impacts of increased compaction classification for an entire year. one year, influencing compaction in With climate change it could be subsequent years. Influence of that for very short times of the year hydraulic conductivity could be when tillage etc. is important, assessed. Air permeability changes rainfall makes soils very susceptible and microbial habitat could also be to compaction and this may not be determined. picked up with the expert system defined so far. 4 What is its track record in One paper has been published. It None. However, with JULES output None. Applied to current threat in modelling historic data as a was valuable. Quality of simulation the soil moisture deficit changes Europe and also in Pennsylania. function of climate change verified against a few soil types in could be used to model the risk Only used for subsoil. Model variables? Scotland. now versus with climate change. appears acceptable but based on

55 No. Criterion Workable Days Expert Model Mechanistic Model How well does it simulate? pedotransfer functions so considerable error is possible. 5 Does it require additional It could be used with a small Yes. JULES output will provide the Most of the parameters can be parameters or driving variables, modification to JULES output. Very moisture deficit data. It is limited predicted using other models to be and are these available into the simple 'yes/no' decision based on to a subsoil risk. Model could be used in this project including water future? 110% Field Capacity being too wet modified to also account for a data from JULES and carbon data for traffic. Such data can be topsoil risk, particularly from for Ecosse. Integrating these will obtained from JULES and it would livestock, although this will be present a considerable challenge. be a major advance to existing work based on expert judgement so in this area. prone to error and bias.

6 Is it responsive to other changes, Depends on inputs for JULES. If Does not account for soil carbon Only model where different particularly land use? carbon content could be included directly. Soil structure only compaction scenarios (i.e. tractor then yes. Also, 'expert' judgement considered by bulk density. Prone weight, livestock etc.) could be could be used to discriminate to expert bias if applied nationally. considered. arable vs. livestock damage. 7 Where are the likely bottle‐necks Simple output only on working Expert judgement required to Would require additional code to in applying the model? days. Interpretation may be define vulnerability classes. This is JULES to incorporate pedotransfer difficult. prone to error. functions. The soil variables would need to be refined as the equations are based on German soil taxonomy. 8 If a model looks promising but Changes are possible by integrating JULES will advance this model by JULES would need additional code. requires a few changes, how JULES and this model. providing more descriptive This is possible. readily can these be made? Can predictions of soil water contents this be done within the scope of and potential. the project? 9 Cost implications generally Cheapest of all options. Medium. Possible within the Highest cost of all proposed project but AD model is more models. deterministic and less prone to expert bias. 10 Compatibility with other models – JULES should provide the output JULES will provide the output for Could use information produced by

56 No. Criterion Workable Days Expert Model Mechanistic Model platform etc with minimal changes to the model. soil moisture. Soil survey data both JULES and Ecosse if additional needed to define vulnerability code was added to these existing classes. models.

57

Table 21. Compaction model assessment

Model Criterion Workable Expert Mechanical Days 1 2 3 4 5 6 7 8 9 10

On the basis of this assessment we recommend that the Workable Days model be used as the soil compaction threat model for use with UKCIP09 scenarios.

Compaction references Bailey A. C., Raper R. L., Johnson C. E. & Burt E. C. 1995. An Integrated Approach to Soil Compaction Prediction. Journal of Agricultural Engineering Research 61: 73‐80. Barre P., McKenzie B. M. & Hallett P. D. 2009. Earthworms bring compacted and loose soil to a similar mechanical state. Soil Biology & Biochemistry 41: 656‐658. Cooper G., McGechan M. B. & Vinten A. J. A. 1997. The influence of a changed climate on soil workability and available workdays in Scotland. Journal of Agricultural Engineering Research 68: 253‐269. de Toro A. & Hansson P. A. 2004. Analysis of field machinery performance based on daily soil workability status using discrete event simulation or on average workday probability. Agricultural Systems 79: 109‐129. Defossez P. & Richard G. 2002. Models of soil compaction due to traffic and their evaluation. Soil & Tillage Research 67: 41‐64. Defossez P., Richard G., Boizard H. & O'Sullivan M. F. 2003. Modeling change in soil compaction due to agricultural traffic as function of soil water content. Geoderma 116: 89‐105. Fritton D. D. 2008. Evaluation of pedotransfer and measurement approaches to avoid soil compaction. Soil & Tillage Research 99: 268‐278. Gregory A. S., Watts C. W., Whalley W. R., Kuan H. L., Griffiths B. S., Hallett P. D. & Whitmore A. P. 2007. Physical resilience of soil to field compaction and the interactions with plant growth and microbial community structure. European Journal of Soil Science 58: 1221‐1232. Gregory A. S., Whalley W. R., Watts C. W., Bird N. R. A., Hallett P. D. & Whitmore A. P. 2006. Calculation of the compression index and precompression stress from soil compression test data. Soil & Tillage Research 89: 45‐57.

58 Horn R. & Fleige H. 2003. A method for assessing the impact of load on mechanical stability and on physical properties of soils. Soil & Tillage Research 73: 89‐99. Horn R. & Fleige H. 2009. Risk assessment of subsoil compaction for arable soils in Northwest Germany at farm scale. Soil & Tillage Research 102: 201‐208. Horn R., Fleige H., Richter F. H., Czyz E. A., Dexter A., az‐Pereira E., Dumitru E., Enarche R., Mayol F., Rajkai K., de la Rosa D. & Simota C. 2005. SIDASS project ‐ Part 5: Prediction of mechanical strength of arable soils and its effects on physical properties at various map scales. Soil & Tillage Research 82: 47‐56. Horn R., Vossbrink J., Peth S. & Becker S. 2007. Impact of modem forest vehicles on soil physical properties. Forest Ecology and Management 248: 56‐63. Jones R. J. A., Spoor G. & Thomasson A. J. 2003. Vulnerability of subsoils in Europe to compaction: a preliminary analysis. Soil & Tillage Research 73: 131‐143. Keller T. & Arvidsson J. 2007. Compressive properties of some Swedish and Danish structured agricultural soils measured in uniaxial compression tests. European Journal of Soil Science 58: 1373‐1381. O'Sullivan M. F., Henshall J. K. & Dickson J. W. 1999. A simplified method for estimating soil compaction. Soil & Tillage Research 49: 325‐335. Pietola L., Horn R. & Yli‐Halla M. 2005. Effects of trampling by cattle on the hydraulic and mechanical properties of soil. Soil & Tillage Research 82: 99‐108. Schafer R. L., Johnson C. E., Koolen A. J., Gupta S. C. & Horn R. 1992. Future‐Research Needs in Soil Compaction. Transactions of the ASAE 35: 1761‐1770. Thu T. M., Rahardjo H. & Leong E. C. 2007. Critical state behavior of a compacted silt specimen. Soils and Foundations 47: 749‐755. van den Akker J. J. H. 2004. SOCOMO: a soil compaction model to calculate soil stresses and the subsoil carrying capacity. Soil & Tillage Research 79: 113‐127. van den Akker J. J. H., Arvidsson J. & Horn R. 2003. Introduction to the special issue on experiences with the impact and prevention of subsoil compaction in the European Union. Soil & Tillage Research 73: 1‐8. Watts C. W. and Dexter A. R. Intensity of tillage of wet soil and the effects on soil structural condition. 2000. ISTRO 2000. Young I. M., Blanchart E., Chenu C., Dangerfield M., Fragoso C., Grimaldi M. & Ingram J. J. M. L. 1998. The interaction of soil biota and soil structure under global change. Global Change Biology 4. Zhang B., Horn R. & Hallett P. D. 2005. Mechanical resilience of degraded soil amended with organic matter. Soil Science Society of America Journal 69: 864‐871.

A6. Landslides Numerous models have been produced globally to try to predict the effects of rainfall on landsliding. Five classes of model and individual models are assessed: Antecedent Water Status Models, Downscaling of General Circulation Models (GCM’s), Threshold based models, the Enhanced

59 GeoSure model and the Slope stability model. A review of the data requirements is given and a judgement made on whether this approach would be suitable for the current study. National scale modelling of climate change and landsliding has so far not been tackled. The varied nature of the topography and geology of GB would make any up‐scaling of local models inherently difficult and scientifically invalid.

1. Antecedent Water Status Model Developed by Crozier (1999) and others, the model is applied to shallow, rainfall triggered landslides within the urban environment of Wellington, New Zealand. The model calculates an index of soil water over the preceding ten days before a storm event. The model has been used to empirically identify a threshold for rainfall triggered landslides whilst producing a probability of a landslide occurring within the city during the following 24hrs. The model’s ability to predict the probability of a landslide occurring relies on the assumption that a critical water content (CWC) is required to initiate failures, which is composed of both antecedent rainfall and event water. However, it is important to determine which climatic parameters are important in the initiation of landsliding. In the case of Wellington it is believed that antecedent rainfall is critical whilst in other areas storm characteristics are the dominant triggering factor (Glade, 1997 and Kim et al., 1992). Problems arise through the extrapolation of point data to represent regional conditions and this problem will increase with the size of catchment and non‐homogeneity of physical parameters.

Model requirements:

• Knowledge of which climatic factors trigger landslides.

• Detailed knowledge of the frequency and magnitude of rainfall within an area.

• Access to rainfall data in the order of years.

• Only predicts landslides of a certain nature and with a certain triggering factor. One of the problems with trying to apply this approach in GB is that it is very rare for a single rainfall event to trigger a large number of landslides. In the August 2004 heavy rain in the Scottish Highlands triggered a number of debris flows, but these were in the magnitude of tens of failures. In Wellington (NZ) landsliding peaked at 20 failures a day but this was continuous for several days. The production of a graph to indicate the maximum landslide triggering threshold would not be meaningful with such a small number of landslides per event. The fact that this approach only predicts landslides of a certain type with a certain triggering factor also makes this model unsuitable for application across a large area, especially in a geologically varied country with many different landslide types such as GB. The model is not considered suitable for the present project

2. Downscaling of General Circulation models (GCM’s) Climatic factors are proven to be among those that trigger instability, and GCM’s are useful in providing future climate projections. GCM’s provide a global coupled ocean‐atmosphere model; however the patterns produced are large scale and have in the past been downscaled to provide information for regional/local studies (Dehn et al., 2000). GCM’s have previously produced models at a resolution of 100‐500km; regional downscaled climatic models at 50km resolution are still too low for local studies. With the improved resolution of the UKCIP09 data this downscaling may not be necessary. Buma and Dehn (1998) present a case study involving GCM downscaling which showed it

60 was possible to model the impact of future climate on a particular landslide in SE France. The method undertaken by the authors involved understanding the hydrological and slope stability parameters of a particular landslide and being able to carry out a back analysis, onto which climatic parameters could be overlain. Buma and Dehn (1998) use the correlation of mean monthly sea level pressures and monthly precipitation. However, for some landslides such as the Alvera Mudslide (Italy) described by Dehn et al., (2000) it would be necessary to have precipitation at a daily resolution.

Application - Alvera Mudslide, Italy

Hydrological and slope stability modelling were used along with climatic variables which were recorded at weather stations near the slide. Daily precipitation was recorded as well as minimum and maximum air temperature. Recorded peaks in the ground water levels following precipitation were associated with accelerated movement of the mudslide. Once it was known what the impact of current meteorological conditions was on the mudslide the use of data from downscaled GCM’s could be incorporated into the model. A key factor emerging from the Alvera model was that of winter temperature. This was found to influence snow precipitation and snow melt, and in turn water infiltration and instability, particularly in the spring. As such this factor would be less significant in GB.

Model requirements: • Monitored active landslide

• Daily precipitation values

• Daily min and max. temperatures

• Detailed knowledge of the hydrological regime

• Ability/data to back‐analyse the landslide A major limitation of using GCM’s to predict future landsliding is the assumption that the relationships studied under present conditions will continue to be relevant with a changing climate (Dehn and Buma, 1999). The amount of data needed to produce these types of models for a single landslide is outside the current feasibility of this study. It would also not be possible to carry out this type of modelling nationwide. The model is not considered suitable for the present project.

3. Threshold values Dixon and Brook (2007) point out that threshold types of studies are highly site, region and material specific and it is therefore not possible to use values taken from the literature to apply to landslides with different behaviour in different climates and geological material. It is therefore not applicable to further explore this technique within the context of trying to predict future landslide behaviour nationally in a changing climate. Threshold values used can be derived from empirical, semi‐ empirical or physically based data (Crosta, 2003). They have been used to predict likely future behaviour in relation to climate change and also as part of early warning systems.

Application – Dolomites

61 Debris flow thresholds have been modelled in countries such as Italy where intense rainfall events can lead to numerous debris flow type failures. The threshold for debris flow activity in the Cancia area of NE Italy models the potential for failures in terms of mean intensity, duration and mean annual precipitation (Bacchini and Zannoni, 2003). An important factor in the amount of rainfall which triggers a debris flow event is the permeability of the ground. If the material is highly permeable the period of rainfall which triggers a debris flow event may be short compared to the situation for less permeable material. In the example given by Bacchini and Zannoni (2003) in the Dolomites one landslide has been periodically active since 1868 and rainfall gauges have been used to provide rates of precipitation leading up to the reactivations.

Application - Mam Tor

Mam Tor is a large deep seated failure within Derbyshire. The initial failure of this landslide took place thousands of years ago but it is still periodically active. A study by Dixon and Brook (2007) looked to link between rainfall and movement and to use this to forecast the future activity rate brought about by climate change. The results of the study were that a Rainfall Threshold Analysis Model (RTA) was developed. The results of the study were obtained from a detailed survey of movement over 8 years combined with historical movement records. The thresholds proposed relate to the cumulative affects of rainfall. It was shown that 210mm of rainfall in the preceding month triggers movement if it follows 750mm of rainfall in the preceding six months. The RTA model used UKCIP02 climate predictions to calculate return periods for the exceedance of these limits under a differing future climate. The findings of this study are only applicable to landslides with a similar geometry and sub‐surface conditions. The climatic conditions of sites where these thresholds would be applied would also need to be similar.

Model Requirements • Detailed, long term rainfall data. • Long term monitoring of an active landslide. • Climatic events that trigger numerous failures.

Numerous site specific studies of landslides with detailed climatic records are not currently available. Dixon and Brook (2007) also highlight the major issue that unless a site was similar n geometry and sub surface conditions it would not be possible to use the model outside of the test site. The model is not considered suitable for the present project.

4. Enhanced GeoSure model At a national scale, landslides in GB can be considered to fall into three broad types: deep seated slides (1), shallow slides and flows (2), and coastal landslides (3). Each of these landslide types will respond to different environmental inputs and must be considered differently. As has been seen in the case studies given above the most commonly modelled landslides are those of a shallow nature as they usually respond to short term intense rainfall events. Another reason is that in some areas across the world these short intense storms can trigger many hundreds of events. A detailed understanding of the relationship between landslides and rainfall in GB is outside of the scope of this study as landslides’ response to climatic triggers is not fully understood for the present climate, so the changes brought about by future climate change are not known.

62 At present, the information and understanding with which to make a scientifically valid numerical model of all landslide activity is not available. This approach takes into account landslide type, the existence of recorded landslides, the geotechnical properties of the ground and hydrogeology. The method proposed here utilises three datasets unique to the British Geological Survey (BGS) – the GeoSure landslide susceptibility model, the BGS debris flow potential model and the BGS Quaternary Domain model. Reference will also be made to the BGS National Landslide Database and National geotechnical Database.

• Derive GB Landslide Domain Model using BGS GeoSure 1:50k Landslide model and BGS Quaternary Domain Model along with Debris Flow study. • Inclusion of permeability data into model. • Merge attributed Landslide Domain model with UKCIP09 scenarios using UKCIP09 scenarios (rainfall intensity). • Heuristic analysis of GB landslide domains with reference to UKCIP09 scenarios.

5. Slope Stability model Deterministic slope stability models, as used in ground engineering, are well‐established (>70 years) and there are a variety of analytical methods available. Typically, these are static 2‐D ‘limit equilibrium’ or ‘finite element’ models and input to them consists of surface topography, geotechnical properties (density and strength) and piezometric levels. Recently, probabilistic elements have been incorporated in some cases. The output of the models is a factor of safety (against sliding) and in some cases deformation vectors. Whilst used extensively for engineering structures such as cuttings and embankments these same models may be used for natural slopes and naturally‐occurring landslides with the notable exception of flow types. The usual types of ‘natural’ analysis are either of current stability (known present‐day conditions) or back‐analysis of antecedent stability (estimated pre‐landslide conditions). Modelling of present‐day slope stability is possible where sub‐surface geotechnical and hydrogeological data are available (there are approximately 50 landslides in GB which have this). Knowledge of the locations of the principal slip surface is essential for ‘limit equilibrium’ methods, but not for ‘finite element’ methods. As the great majority of GB landslides are re‐activations of pre‐existing landslides, mostly dating from post‐glacial times (e.g. last 10k years), back‐analysis is difficult owing to a lack of knowledge of local post‐glacial ground conditions.

The above models can be used re‐iteratively in ‘what‐if’ scenarios by adjusting key parameters. For example, the overall piezometric level can be raised to determine the point at which the factor of safety equals unity and hence the conditions under which failure will occur, all other factors being equal.

Whilst the above models are site specific and are capable of dealing with quite complex landslides, the simplest form is the ‘infinite slope analysis’ (e.g. Schmidt & Dehn, 2000). This crude model has been around for many years in a variety of forms and may be applicable to universal predictive models of the ‘Geosure’ type (Geosure in fact already has most of the elements of the model within it). However, the infinite slope model does not cater for rotational, complex or deep‐seated landslides.

63 Importantly, slope stability models of the type described above do not take account of rainfall or the relationship between rainfall and piezometric levels within a slope. Such a relationship would have to take into account the following:

• Permeability & specific yield (or other transmission factors) • Porosity (or related capacity factor) • Infiltration (land‐use, vegetation, run‐off) • Phreatic/piezometric depths • Artesian conditions (regional geology)

It may be that existing hydrological models could be adapted to provide the link between rainfall and piezometric levels and hence bridge the gap between environmental factors and mechanical behaviour of a slope.

Back‐analysis of individual landslides using slope stability models, of the type described above, allows for the possibility of establishing the likely ground water conditions under which the event actually occurred. Such hind‐casting in‐turn may permit fore‐casting of a repeat movement, or partial re‐activation, to be made using the same sub‐surface model, but of course with the present‐ day topography instead of the (past) pre‐slip topography.

Table 22 shows the assessment for those models not rejected on the basis of earlier considerations.

64

Table 22. Landscape model comments

Criterion Enhanced Slope No. Geosure stability model 1 Does it provide estimates of GeoSure does not provide numerical estimates of the The slope stability model could provide numerical change in the soil threat as a change in soil threat. The model would act as a susceptibility estimates of the change in soil threat but only for function of climate change map which was tailored to future climate change. The specific sites where there is a prior knowledge of the variables? Enhanced GeoSure model would flag areas where sub‐surface and where enough environmental variables landsliding was likely to increase with future changes in were known. rainfall. 2 Are these at a space and time The model would be at a national scale and at present does The slope stability model does not provide estimates at scale which can be used for not take into consideration any temporal variables a space and time scale which can be used for national‐ national‐scale decision making? scale decision making 3 Does it cover all aspects of the soil At present the model does not take into account any The model does not take into account any ecological or threat, particularly those of ecological or economical variables. economical variables. greatest perceived importance, ecological, economic etc? 4 What is its track record in GeoSure has not been used to model historic data as a The ‘model’ represents a raft of models which have modelling historic data as a function of climate change. It is a model that predicts been applied in an ad‐hoc fashion to historic events for function of climate change present susceptibility to landsliding under current variable a variety of purposes, in some cases successfully variables? How well does it simulate? 5 Does it require additional The main driving variables are already contained within the The model would require additional variables, which parameters or driving variables, model. It is proposed that some enhancements are made. may be available in the future, possibly via other (e.g. and are these available into the These variables are currently available as static data. hydrological) models future? 6 Is it responsive to other changes, Land use is currently not taken into account within the Currently it is not responsive to land‐use changes, with particularly land use? current version of GeoSure. It could be included within the the exception of topographic changes, the addition of

65 Criterion Enhanced Slope No. Geosure stability model enhanced version within the scope of the project structures or engineered remediation such as drainage 7 Where are the likely bottle‐necks It is not foreseen that there will be any bottle necks as this The bottle‐necks are mainly to do with choice of model in applying the model? model is already up and running on a national scale and its validation in different landslide domains and gathering of the historic and environmental data 8 If a model looks promising but Enhancements planned for GeoSure are based upon pre‐ The models cannot be changed in their present requires a few changes, how existing data so can be included within the scope of this commercially available form. Other equivalent non‐ readily can these be made? Can project commercial/academic models may be available which this be done within the scope of can be changed. the project? 9 Cost implications generally It is not foreseen that there are any particular cost Cost will include obtaining rainfall data and possibly implications for this model other data should it be required to link with a hydrological model 10 Compatibility with other models – platform etc

66

Table 23. Landslides model assessment

Model Enhanced Slope Criterion Geosure stability model 1 2 3 4 5 6 7 8 9 10

Our review indicates that at present there are no landslide models which could be used to quantitatively predict change in the incidence or severity of landslides in England and Wales in response to climate change.

Landslides references British Geological Survey holds most of the references listed below, and copies may be obtained via the library service subject to copyright legislation (contact [email protected] for details). The library catalogue is available at: http://geolib.bgs.ac.uk. Bromhead, E. 2006 Climate change and failure mechanisms of natural slopes. Climate Impact Forecasting for Slopes (CLIFFS), Loughborough University/EPSRC. http://cliffs.lboro.ac.uk/ Buma, J. and Dehn, M. 1998. A method for predicting the impact of climate change on slope stability. Environmental Geology 35 (2‐3). pp190‐196 Crosta G.B., 2004 Introduction to the special issue on rainfall‐triggered landslides and debris flows. Engineering Geology Volume: 73 (3‐4). pp191‐192. Crozier, M. J. 1999. Prediction of rainfall triggered landslides: A test of the antecedent water status model. Earth Surface Processes and Landforms. 24. pp825‐833. Dehn, M., Buma, J., 1998. Modelling future landslide activity based on general circulation models. Geomorphology. 30. pp175‐187 Dehn, M., Burger, G. Buma, J. and Gasparetto, P. 2000. Impact of climate change on slope stability using expanded downscaling. Engineering Geology. 55. pp193‐204. Forster, A. and Culshaw, M. G., 2004 Implications of climate change for hazardous ground conditions in the UK, Geology Today 20 (2) pp61‐67 Glade, T. 1997. The temporal and spatial occurrence of rainstorm triggered landslide events in New Zealand. Unpublished PhD. Victoria University of Wellington.

67 Gostelow, T. P. 1991. Properties of soils relevant to natural slope stability. In: Almeida‐Teixeira, M.E., Fantechi, R., Oliveria, R. and Gomes Coelho, A (Eds.). Prevention and control of landslides and other mass movements. Commission of the European Communities. Kim, S. K., Hong, W. P. and Kim, Y. M. 1992. Prediction of rainfall‐triggered landslides in Korea, In: Bell, D. H.; (Ed.), Landslides, Proceedings of the Sixth International Symposium on Landslides, Christchurch, February 1992, Vol. 2, Balkema, Rotterdam. pp989‐994. Lee, M. 2006 How does climate change affect the assessment of landslide risk? Climate Impact Forecasting for Slopes (CLIFFS), Loughborough University/EPSRC. http://cliffs.lboro.ac.uk/ Schmidt, M and Dehn. M. 2000. Examining the links between climate change and landslide activity using GCM’s: Case studies from Italy and New Zealand. In McLaren, S.J. and Kniveton, D.R. (eds.) Linking climate change to land surface change. Kluwer Academic Publishers. A7. Salinity

A. Terrestrial (“Plot Scale”)

One possible source of soil salinity in England and Wales is inundation by seawater. This causes flooding at the soil surface, with infiltration leading to salination of the soil. By its nature, this is local to low‐lying coastal areas. Salination models of this effect consider the infiltration process once inundation has occurred, but not the probability of inundation. Such models are 1‐dimensional and are referred to as plot‐scale. Three models have been selected for assessment.

A1. SALTMOD The model was developed by Oosterbaan (2002). It simulates and predicts the salinity of soil moisture, ground and drainage water (groundwater), the depth of the water table, drain discharge and leaching of salts in irrigated agricultural lands under different (geo) hydrologic conditions, varying water management options, including the (re) use of ground water for irrigation by pumping from wells (conjunctive use), and several crop rotation schedules. The computation in this model is based on seasonal water balances of agricultural lands. Day to day water balances are not considered. Saltmod contains four different reservoirs, three of which are in the soil profile: 1. a surface reservoir, 2. an upper (shallow) soil reservoir or root zone, 3. an intermediate soil reservoir or transition zone; and 4. a deep reservoir or aquifer. The water balances are calculated for each reservoir separately. The salt balances are calculated for each reservoir separately. They are based on their water balances, using the salt concentrations of the incoming and outgoing water.

A2. HYDRUS The model was developed by Simunek (2005), and has now reached version 4 (http://www.pc‐ progress.cz/Pg_Hydrus_1D.htm). The program is a finite element model for simulating the one‐ dimensional movement of water, heat, and multiple solutes in variably saturated media. The program numerically solves the Richards' equation for saturated‐unsaturated water flow and Fickian‐based advection dispersion equations for heat and solute transport. The flow equation incorporates a sink term to account for water uptake by plant roots. The heat transport equation considers conduction as well as convection with flowing water. The solute transport equations consider advective‐dispersive transport in the liquid phase, and diffusion in the gaseous phase. One of its applications is to the salinisation and reclamation processes, through salt leaching. There is

68 also a 2‐d version of HYDRUS. This can be purchased from the International Groundwater Modelling Centre (IGWMC).

A3. SALTMED The model was developed by Ragab (2002) and Ragab et al. (2005), and also at http://www.ceh‐wallingford.ac.uk/research/cairoworkshop. The model is designed to simulate water and solute movement under different agricultural water management. The model runs with different water qualities ranging from fresh water to saline water (brackish groundwater or brine water) and simulates the soil salinity evolution with time. The SALTMED model includes evapotranspiration, plant water uptake, water and solute transport under different irrigation systems, drainage, crop growth and dry matter production and nitrogen processes (mineralization, nitrification, denitrification, N‐uptake, N‐ leaching). The model has been developed under two different EU funded projects, SALTMED and SAFIR. The model has been tested against field experimental data. The model simulates the processes on a daily basis.

69

Table 24. Plot scale salinity model comments

No. Criterion All models 1 Does it provide estimates of change in the soil Yes threat as a function of climate change variables? 2 Are these at a space and time scale which can be No, they are for plot scale. used for national‐scale decision making? 3 Does it cover all aspects of the soil threat, No, they do not account for ecological and economical aspect, only soil salinity particularly those of greatest perceived and crop response to salinity. However crop yield has economical value. importance, ecological, economic etc? 4 What is its track record in modelling historic data Not used for prediction under climate change but are used with previously as a function of climate change variables? collected data. Quite well with existing data as seen in literature. How well does it simulate? 5 Does it require additional parameters or driving Yes, it will need rainfall intensity, duration, sea level rise and any changes in soil variables, and are these available into the future? physical and hydraulic properties over time and as function of soil salinity level. 6 Is it responsive to other changes, particularly Yes, plants will respond in different ways to salinity according to their tolerance land use? level. 7 Where are the likely bottle‐necks in applying the Models are for plot scale (for specific land cover and soil type) not for country model? scale, data on rainfall intensity and duration, sea level, the change in soil hydraulic and physical parameters with salinity not well established. 8 If a model looks promising but requires a few The SALTMED model code is available and might only need minor changes for changes, how readily can these be made? Can salinity‐hydraulic properties relation. SALTMED could be changed to account for this be done within the scope of the project? salinity –soil hydraulic parameter by the current developer. It should not take more than 10 days of work from a developer. HYDRUS 2 Code is not available for changes. SALTMOD code is available but the model is a seasonal conceptual model and might not be the best one to use. 9 Cost implications generally 10 Compatibility with other models – platform etc

70

Table 25. Plot scale salinity model assessment

Model Criterion SALTMED HYDRUS 1 & SALTMOD 2 1 2 3 4 5 6 7 8 9 10

The SALTMED model code is available and the model is the most suitable for the plot scale soil salinity threat. The suggested plan is to run the SALTMED model with different combinations of vegetation on different soil types (based on their geographic distribution in England and Wales) where the sea water is input at the surface. The change in rainfall, temperature and other climatic factors could be input in “what if” scenarios. SALTMED is expected to show the impact on soil salinity and plant growth under each scenario. Then, using GIS , the results can be extrapolated elsewhere in England and Wales (where sea water is likely to flood the soil surface) with vegetation and soil types taken from national scale databases (LCM2007 and LandIS).

B. Sea water intrusion

Sea water intrusion occurs when groundwater becomes contaminated with seawater, and is a subsurface process in contrast to inundation. Out of many models reported in the literature, four sea water intrusion models were selected for consideration. The four models are SWI, SHARP, SEAWAT and SUTRA.

B1. SWI

The Sea Water Intrusion (SWI) model (Bakker & Schaars, 2003) is a variation of the MODFLOW model with two water densities but runs at a smaller spatial scale than MODFLOW (hundreds of metres or less). SWI simulates the evolution of the three‐dimensional density distribution through time. The effects of the density distribution on the flow are taken into account explicitly. The SWI package can simulate interface flow, stratified flow, and continuously‐varying density flow. The main advantage of the SWI package is that each aquifer can be modelled with a single layer of cells. An existing MODFLOW model of a coastal aquifer can be modified to simulate seawater intrusion through the addition of one input file.

71 B2. SEAWAT

SEAWAT (v.4; Langevin et al. 2008) simulates three‐dimensional variable‐density groundwater flow. It is based on MODFLOW (v 2000)/MT3DMS to simulate groundwater flow coupled with multi‐ species solute and heat transport. The model accounts for viscosity variations which impact on GW flow and thermal diffusivity as function of temperature.

B3. SHARP

SHARP (Essaid, 1990) is a quasi three dimensional finite difference model to simulate a fresh water and saltwater flow separated by a sharp interface in layered coastal aquifers when the transition zone is small relative to thickness of the aquifer.

B4. SUTRA

SUTRA (v. 2.1; Voss and Provost, 2008) is a two or three dimensional saturated‐unsaturated, variable density groundwater flow with solute or energy transport. The model allows irregular meshes and accepts input data from separate files.

72

Table 26. Sea water intrusion model comments

No. Criterion All models 1 Does it provide estimates of change in the soil threat Yes but mostly applied to the subsurface layers of the aquifer with sub as a function of climate change variables? surface horizontal saline water intrusion. They do not account for surface inundation by the sea. They are aquifer based for coastal areas. 2 Are these at a space and time scale which can be used for national‐scale No, only applicable to single costal aquifers. decision making? 3 Does it cover all aspects of the soil threat, particularly those of greatest No, covers only the subsurface threat due to seawater intrusion into the perceived importance, ecological, economic etc? coastal aquifer. They do not account for economical & ecological aspects.

4 What is its track record in modelling historic data as Yes, can use historical data. Good simulation examples in literature. a function of climate change variables? How well does it simulate? 5 Does it require additional parameters or driving Yes, sea water density, temperature and level but not sure of future variables, and are these available into the future? availability of density and temperature values. 6 Is it responsive to other changes, particularly land use? Yes, but indirectly via the effect of land use on recharge rate and subsequently the groundwater levels. Water use via abstraction has direct effect. 7 Where are the likely bottle‐necks in applying the model? Only applicable to aquifer scale in costal areas, requires a lot of data on the geology, aquiferproperties, aquifer nature and boundaries, abstraction, groundwater level and sea water level, density, etc. They are site / aquifer specific. 8 If a model looks promising but requires a few changes, They are available but doubtful if they can be modified to upscale from how readily can these be made? Can this be done within the scope of single aquifer to England and Wales country scale. They are aquifer based the project? for coastal areas with sub surface horizontal saline water intrusion. They do not account for surface inundation by the sea. (no surface vertical saline intrusion). 9 Cost implications generally 10 Compatibility with other models – platform etc

73

Table 27. Seawater intrusion model assessment

Model Criterion SWI SEAWAT SHARP SUTRA 1 2 3 4 5 6 7 8 9 10

The sea water intrusion models are aquifer specific and run for each aquifer separately. They are suitable to investigate the aquifer threat rather than soil threat. Since this project is focused on soil threats, sea water intrusion models are not considered relevant.

Salinity references

Bakker, M and Schaars F. 2003. The Sea Water Intrusion (SWI) package manual Version 0.2. Artesia Water Research Unlimited Schoonhoven, the Netherlands and Department of Biological and Agricultural Engineering, University of Georgia, Athens, USA. Essaid, H.I., 1990. The computer model SHARP, a quasi‐three‐ dimensional finite‐difference model to simulate freshwater and saltwater flow in layered coastal aquifer systems: U.S. Geological Survey Water‐Resources Investigations Report 90‐4130, 181 p. Essaid, H.I., 1990. A multilayered sharp interface model of coupled freshwater and saltwater flow in coastal systems—model development and application: Water Resources Research, v. 26, no. 7, p. 1431‐1454. Essaid, H.I., 1992. Simulation of freshwater and saltwater flow in the coastal aquifer system of the Purisima Formation in the Soquel‐Aptos Basin, Santa Cruz County, California: U.S. Geological Survey Water‐Resources Investigations Report 91‐ 4148,35 p. Guo W and Langevin CD. 2002. User’s guide to SEAWAT: a computer program for simulation of three‐ dimensional variable‐density ground‐water flow. US Geological Survey. Techniques of water‐ resources investigations Book 6‐Chapter A7. Tallahassee, Florida, USA. Langevin, C.D., Thorne, D.T., Jr., Dausman, A.M., Sukop, M.C., and Guo, Weixing, 2008. SEAWAT Version 4: A Computer Program for Simulation of Multi‐Species Solute and Heat Transport: U.S. Geological Survey Techniques and Methods Book 6, Chapter A22, 39 p. Misut PE, Yulinsky W, Cohen D, St. Germain D, Voss CI and Monti Jr. J. A SUTRA model of seawater intrusion in Western Long Island, New York, USA. http://www.geo.sunysb.edu/lig/Conferences/abstracts‐03/Misut.htm

74 Ragab, R. 2002. A Holistic Generic Integrated Approach for Irrigation, Crop and Field Management: The SALTMED Model. J. of Environmental Modelling & Software. 17(4):345‐361. Ragab, R. (Editor), 2005. Advances in integrated management of fresh and saline water for sustainable crop production: Modelling and practical solutions. International Journal of Agricultural Water Management (Special Issue), volume 78‐ Issues 1‐2, pages 1‐164. Elsevier, Amsterdam. The Netherlands. Ragab, R., Malash, N., Abdel Gawad, G., Arslan, A. and Ghaibeh, A. 2005. A holistic generic integrated approach for irrigation, crop and field management: 1. The SALTMED model and its application using field data from Egypt and Syria. International Journal of Agricultural Water Management, 78 (1‐2) 67‐88. Ragab, R., Malash, N., Abdel Gawad, G., Arslan, A. and Ghaibeh, A. 2005. A holistic generic integrated approach for irrigation, crop and field management: 2. The SALTMED model validation using field data of five growing seasons from Egypt and Syria. International Journal of Agricultural Water Management, 78 (1‐2) 89‐107. Secunda S, Collin ML and Melloul AJ. 1998. Groundwater vulnerability assessment using a composite model combining DRASTIC with extensive agricultural land use in Israel’s Sharon region. Journal of Environmental Management 54: 39–57. Šimünek J, Šejna M and van Genuchten MTh. 1999. The HYDRUS‐2D software package for simulating two‐dimensional movement of water, heat, and multiple solutes in variably saturated media. Version 2.0, IGWMC‐TPS‐53, International Ground Water Modeling Center, Colorado School of Mines, Golden, Colorado, p. 251. Šimünek J, Šejna M and van Genuchten MTh. 1998. The HYDRUS‐1D software package for simulating the one‐dimensional movement of water, heat, and multiple solutes in variably saturated media. Version 2.0, IGWMC‐TPS‐70, International Ground Water Modeling Center, Colorado School of Mines, Golden, Colorado, p. 202. Voss, C. I., and Provost, A.M., 2008. SUTRA, A model for saturated‐unsaturated variable‐density ground‐water flow with solute or energy transport, U.S. Geological Survey Water‐Resources Investigations Report 02‐4231, 270 p.

A8. Sealing Soil sealing is considered here to be the crusting of agricultural soil with reduced infiltration capacity, rather than the construction of paved areas with low permeability. There are numerous models of infiltration through soils, whether sealed or partially sealed (assuming no infiltration through a sealed soil). These include: 1. Q = ‐ K d(h+z)/dz (Darcy Law), 2. Empirical: Kostiakovv (1932), Horton (1940), SCS curve number method (USDA, 1972), 3. Physically based models (based on Richard’s Equations (Marshal and Holmes, 1979), 4. Models based on approximation, Green & Ampt (1911), Philips (1957). These models are separate from models which simulate the process of a change in the physical structure of the soil surface, altering infiltration rates. Assouline and Mualem (1997) developed a dynamic model that relates the formation of a seal at the surface of a bare soil exposed to water

75 drops impacts to the initial soil mechanical and hydraulic properties as well as the physical characteristics of the regional rainfall or the applied irrigation. At the soil surface, the disturbance resulting from raindrop impact and imbibitions is expressed in terms of the bulk density increase, taken as a function of rainfall intensity, I, and time of exposure to rainfall, t. Farres (1978) found that the observed crust thickness, dc, increases with cumulative rainfall, R.

76

Table 28. Sealing model comments

No. Criterion All models 1 Does it provide estimates of change in the soil Yes threat as a function of climate change variables? 2 Are these at a space and time scale which can be No, they are for plot scale. used for national‐scale decision making? 3 Does it cover all aspects of the soil threat, No, they do not account for ecological and economical aspect, only soil hydrology particularly those of greatest perceived importance, ecological, economic etc? 4 What is its track record in modelling historic data Not used for prediction under climate change but are used with previously collected as a function of climate change variables? How data. Simulates quite will with existing data as seen in literature. well does it simulate? 5 Does it require additional parameters or driving Yes, it will need rainfall intensity, duration and possible changes in soil physical and variables, and are these available into the hydraulic properties. future? 6 Is it responsive to other changes, particularly Not directly, mostly directly to raindrop impact on soil surface. Land cover has land use? indirect influence. 7 Where are the likely bottle‐necks in applying the Models are for plot scale (for specific land cover and soil type) not for country scale, model? data on rainfall intensity and duration, the change in soil hydraulic and physical parameters with climate change not well established, and calibration might be needed. 8 If a model looks promising but requires a few These models need to be coded. A software developer will be needed to write the changes, how readily can these be made? Can codes. It should not take more than 10 days of work from a developer. this be done within the scope of the project? 9 Cost implications generally 10 Compatibility with other models – platform etc

77

Table 29. Sealing model assessment

Model Darcy type Horton type Conceptual Approximation based on based on models based models, reduced flux infiltration based on Green & Ampt, Criterion at the and saturated and Philips surface conductivity seal layer of reduction fixed thickness 1 2 3 4 5 6 7 8 9 10

While there are several models which can model changes in infiltration following change in surface soil characteristics leading to sealing, only the model of Assouline and Mualem simulates changes in soil sealing under changed climatic conditions. This requires rainfall intensity variables which are not available from either UKCIP09 or JULES.

Sealing references

Ahuja LR. 1983. Modeling infiltration into crusted soils by the Green‐Ampt approach. Soil Sci. Soc. Am. J. 47:412‐418.

Assouline S. 2006. Modeling the relationship between soil bulk density and the water retention curve. Vadose Zone Journal 5:554–563.

Assouline S. 2004. Rainfall‐induced soil surface sealing: A critical review of observations, conceptual models, and solutions. Vadose Zone Journal 3:570–591.

Assouline S and Mualem Y. 1997. Moeling the dynamics of seal formation and its effects on infiltration as related to soil and rainfall characteristics. Water Resour. Res. 33:1527‐1536.

Baumhardt RL, Romkens MJM, Whisler FD and Parlange JY. 1990. Modelling infiltration into a sealing soil. Water Resour. Res. 26:2497‐2505.

Brackensiek DL and Rawls WJ. 1983. Agricultural management effects on water processes. II. Green and Ampt parameters for crusting soils. Trans. ASAE 26:1753‐1757.

78 Cerdan O, Souchère V, Lecomte V, Couturier A, Le Bissonnais Y. 2001. Incorporating soil surface crusting processes in an expert‐based runoff model: Sealing and transfer by runoff and erosion related to agricultural management. Catena 46:189–205.

Chu ST, Onstad CA and Rawls WJ. 1986. Field evaluation of layered Green‐Ampt model for transient crust conditions. Trans. ASAE 29:1268‐1272, 1277.

De Roo APJ, Wesseling CG and Ritsema CJ. 1996. LISEM: A single‐event physically based hydrological and soil erosion model for drainage basins. I: Theory, input and output. Hydrological processes 10:1107‐1 117.

Edwards WM and Larson WE. 1969. Infiltration of water into soils as influenced by surface seal development. Trans. ASAE 12:463‐465, 470.

Farres P. 1978. The role of time and aggregate size in the crusting process. Earth Surf. Processes. 3:243‐254.

Fohrer N, Berkenhagen J, Hecker JM and Rudolph A. 1999. Changing soil and surface conditions during rainfall ‐ Single rainstorm/subsequent rainstorms. Catena 37:355‐375.

Geeves, GW. 1997. Aggregate breakdown and soil surface sealing under rainfall. Centre for Resource and Environmental Studies. A thesis submitted for the degree of Doctor of Philosophy of the Australian National University.

Green WH and Ampt GA. 1911. Studies on soil physics. 1. The flow of air and water through soils. J. Agric. Sci. 4:1‐24.

Hessel R, Jetten V, Liu B, Zhang Y and Stolte J. 2003. Calibration of the LISEM model for a small loess plateau catchment. Catena, 2003 – Elsevier

Hillel D and Gardner WR. 1970. Transient infiltration into crust topped profiles. Soil Sci. 109:69‐76.

Hillel D and Gardner WR. 1969. Steady infiltration into crust topped profiles. Soil Sci. 108:137‐142.

Horton, R. E. 1940. An approach towards physical interpretation of infiltration –capacity. Proceedings of Soil Science Society of America.5, 399‐417.

Kostiakov, A. N. 1932. On the dynamics of the coefficient of water –percolation in soils and on the necessity of studying it from a dynamic point of view for processes of amelioration. Trans. 6th Comm. Int. Soc. Soil Sci. Russian Pt A. pp 15‐21.

Lado M, Ben‐Hur M and Shainberg I. 2007. Clay mineralogy, ionic composition, and pH effects on hydraulic properties of depositional seals. Soil Sci. Soc. Am. J. 71:314–321.

Lado M, Ben‐Hur M and Assouline S. 2005. Effects of effluent irrigation on seal formation, infiltration, and soil loss during rainfall. Soil Sci. Soc. Am. J. 69:1432–1439.

Marshal, T. J, and Holmes, J. W. 1979. Soil Physics. Cambridge University press. Cambridge, pp345.

79 Moore ID. 1981a. Infiltration equations modified for surface effect. J. Irrig. Drainage Div. ASAE 107:71‐86.

Moore ID. 1981b. Effect of surface sealing on infiltration. Trans. ASAE 24:1546‐ 1553.

Mualem Y and Assouline S. 1989. Modeling soil seal as a non‐uniform layer. Water Resour. Res. 25:2101‐2108.

Morin J and Benyamini Y. 1977. Rainfall infiltration into bare soils. Water Resour. Res. 13: 813‐817.

Mucher HJ and De Ploey J. 1977. Rainfall induced soil seal. B. Application of a new model to saturated soils. Catena 17:205‐218.

Mualem Y, Assouline S and Rohdenburg H. 1990a. Rainfall induced soil seal. A. A critical review of observations and models. Catena 17:185‐203.

Mualem Y, Assouline S and Rohdenburg H. 1990b. Rainfall induced soil seal. B. Application of a new model to saturated soils. Catena 17:205‐218.

Mualem Y, Assouline S and Rohdenburg H. 1990c. Rainfall induced soil seal. C. A dynamic model with kinetic energy instead of cumulative rainfall as independent variable. Catena 17:289‐303.

Nachtergaele J, Poesen J, Vandekerckhove L, Oostwoud Wijdenes D and Roxo M. 2001. Testing the ephemeral gully erosion model (EGEM) for two Mediterranean environments. Earth Surface Processes and Landforms 26:17–30.

Parlange JY, Hogarth WL and Parlange MB. 1984. Optimal analysis of surface crusts. Soil Sci. Soc. Am. J. 48:494‐497.

Philip, J. R. 1957. The theory of infiltration. 1. The infiltration equation and its solution. Soil Science. 83, 345‐357

Ragab RA. 1983. The effect of sprinkler and energy of falling drops on soil surface sealing. Soil Science 136(2):117 123.

Romkens MJM, Baumhardt RL, Parlange MB, Whisler FD, Parlange JY and Prasad SN. 1986. Rain induced surface seals: Their effect on ponding and infiltration. Ann. Geophys. 4:417‐424.

Roth CH. 1997. Bulk density of surface crusts: Depth functions and relationships to texture. Catena 29:223‐237.

Stange CF and Horn R. 2005. Modeling the soil water retention curve for conditions of variable porosity. Vadose Zone Journal 4:602–613.

United States Department of Agriculture, Soil Conservation Service. 1972. National Engineering Handbook, Hydrology Section 4. Washington DC, USA

Valentin C and Bresson LM. 1998. Methods for Assessment of Soil Degradation. Catena 1 7: 205‐218. Vandevaere J‐PM, Vauclin M, Haverkamp R, Peugeot C, Thony J‐L and Gilfedder M. 1998. A simple model of infiltration into crusted soils. Soil Sci. 163:9‐21.

80 A9. Biodiversity The limited knowledge about the effect of climate drivers on the structure of soil communities means that there are currently no modelling tools that can be taken of the shelf and use to predict changes in soil biodiversity that may be expected under different climate scenarios. Because no specific models are available, the only approaches feasible that can currently be taken are either to utilise broad empirical relationship to assess how communities may changes under different climate scenarios or to highlight potential approaches that are currently used in other areas so that these can be applied towards address climate effects on soil biodiversity.

As an example of the first type of (empirical) approach, analysis of the data from CEH Countryside Survey program indicated an influence of rainfall on the relative abundance of springtail and mites present in site soil. Results indicated that mite frequency increases in soils from locations with high rainfall, whereas springtails were more common in areas with lower rainfall. Since other soil properties, such as pH, trace metal concentration, and organic carbon may all be correlated with total rainfall amount, it is not currently clear whether the mechanism behind this community shift are linked directly to changes in soil water balance or one of the other co‐correlated parameters. Nonetheless relationships such as this may be use to predict potential change in rainfall community structure under different rainfall scenarios. Among those model approaches that may be suitable (after completion of further work) for prediction of potential climate effects on soil biodiversity, a number of approaches can be identified. These are briefly listed below and summarised in the following table.

1. GBMove This model has been developed to allow prediction of the impacts of changing soil and climatic conditions on plant species composition and biomass in UK Priority Habitats. The model has been developed so that it can test scenarios of the impact of multiple drivers on Priority Habitat patches. The approach links a dynamic soil model to empirical species niche models for 971 higher plants, 233 bryophytes and 74 lichens derived from large species occurrence databases that empirical, data‐ intensive modelling of species’ realised niches along climatic and abiotic gradients. The linkage of the species models to outputs from the dynamic soil model was based on simulations of annual change in soil chemistry derived from predictions of historical and future atmospheric S and N deposition. On the basis of these input soil conditions were modelled and used to define change in conditions across site and their consequences for each of included plant species The current GBMove approach can (and has) been used to predict potential changes in the distribution of plant species under different climate scenarios. Implementation of a similar approach for soil biodiversity is feasible. However, to achieve this will require a substantial increase in the availability of occurrence data for soil dwelling species from which realised niches can be defined. Current survey of soil microbial and invertebrate diversity and utilisation of high throughput molecular analysis might be useful in the long run to obtain such data, but this is not achievable within the project timescale.

2. Soil biodiversity model and other food web models Models are available that simulates carbon flow through the soil community (http://www.nerc.ac.uk/publications/other/documents/soilbio_soilmodel.pdf). These utilises data on food web composition and energy flow to calculate the carbon flows between groups. With this

81 information we can then simulate applying a carbon isotope to the soil and covering plants and tracking changes in 13C concentration as it is transferred from producers to consumers and recyclers. While this model utilises information of the composition of the soil community to track the flow of added carbon through soil, the model is not applicable to addressing how environmental drivers may affect community composition. As such the approach is not applicable within the context of this project

3. PERPEST PERPEST is a model that Predicts the Ecological Risks of PESTicides in freshwater ecosystems. In particular, taxa specific effects of pesticide residues are considered. The system predicts the effects of a particular concentration of a pesticide on various (community) endpoints, based on empirical data extracted from the literature. The method that it uses is called Case‐Based Reasoning (CBR), a technique that solves new problems (e.g., what is the effect of pesticide A?) by using past experience (e.g., published microcosm experiments for related pesticide B, C, D etc.). As the problem of predicting community effects of a pesticide are somewhat analogous to that of predicting the community effects of climatic change long term, then it may be that the approach could be adapted for prediction of climate effects using both survey and climate manipulation experiment results as input data. This work would require resource well beyond those available in this project.

4. SOILPACS A traditional approach to community characterisation is using morphological keys and classic biodiversity‐based comparisons. A working example of this for UK rivers is the UK River Invertebrate Prediction and Classification System (RIVPACS) (Clarke et al. 2003). While the classic identification and enumeration may represent the gold standard for assessment of stressor impacts, attempts to develop a system for soil communities analogous to RIVPACS (aka SOILPACS) have foundered because the morphology‐based taxonomy is currently not fit‐for‐purpose for the most abundant soil taxa (Weeks et al. 1997). Specifically, identification of diverse soil invertebrate taxa such as the nematodes, orabatid mites and springtails requires considerable expertise: a rapidly diminishing commodity as the number of taxonomists in research declines. New developments in molecular genetics may again help here. However, this would require and investment of time and resources well outside the scope of this project.

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Table 30. Biodiversity model comments

Soil biodiversity model No. Criterion GBMove Perpest SOILPACs types models (dynamic models of C flow)

1 Does it provide estimates No. Model is used to No. Model is to track No. Model developed to No. Model does not exist of change in the soil threat predict effects of carbon flow not model predict effects of pesticides yet. But RIVPACS is being as a function of climate eutrophication and other community change by on stream invertebrate used for this in waters change variables? variables on plant driving climate variables. communities communities. 2 Are these at a space and Model has been used for No. Model current site No. It is constructed for site This is done within time scale which can be National scale assessment specific, but spatial element specific risk assessment. RIVPACS. used for national‐scale of N effects. is promised. decision making? 3 Does it cover all aspects of No. Model would need to No. Model tracks No. It is an aquatic model. Potentially, but would the soil threat, particularly be adapted for this consequences of soil depend on availability of those of greatest perceived purpose. biodiversity change of soil C suitable input data. importance, ecological, flow and not risk of change economic etc? due to climate (or other factors) 4 What is its track record in None, but has been used None. None, but has proved a RIVPACS is a well respected modelling historic data as a for N prediction to some reliable model approach for and widely replicated function of climate change effect. predicting pesticide risks. approach. variables? How well does it

83 Soil biodiversity model No. Criterion GBMove Perpest SOILPACs types models (dynamic models of C flow)

simulate?

5 Does it require additional Requires information on Requires inputs of soil Yes. Model obviously need Yes and they are probably parameters or driving the niche requirement of biodiversity and can be complete parameterisation. available now. variables, and are these individual species. For optimised for particular The reason mentioned here available into the future? plants this can include soil food chain linkages. though is that the case moisture. based reasoning approach that is uses could transfer well to the prediction of climate sensitivity as field studies of these effects are included in the literature. The output would be highly compatible with the aims of this project. 6 Is it responsive to other Yes. Has been used for N Yes. Model developed to Yes. It works well for It is likely that land use changes, particularly land and land use can be simulate effects of N pesticides being initially would be an important use? included. addition on C flow though parameterised using data driver of change. soil food webs. from mesocosm trials 7 Where are the likely Obtaining climate envelope Data needed on climate Data needed on climate Getting the baseline data bottle‐necks in applying information for soil species effects on the structure of effects on the structure of for parameterising the the model? will be the biggest soil communities. soil communities. But model challenge studies of this type are available.

84 Soil biodiversity model No. Criterion GBMove Perpest SOILPACs types models (dynamic models of C flow)

8 If a model looks promising No, the work required is of Probably the way to start is Way to start development Definitely not. Developing a but requires a few a scale that could not be to conduct a meta‐analysis is to collate climate change SOILPACS system would be changes, how readily can completed within this of climate change study to studies of the effects of a major undertaking. these be made? Can this be project. develop scenarios for soil climate parameters on the done within the scope of community change. This structure of soil the project? model could be use to test communities. This model feedback consequences. could be use to predict Meta‐analysis probably not scenario effects. In this feasible in this project. project a review of the number of possible data‐ sets is possible. 9 Cost implications generally Model framework is Model is freely available. Model would need to be Expensive to develop. available. Focus to allow Parameterising it will take parameterised from scratch Would need many £1000s extension needs to be on some time. but basic framework freely (indeed millions) of collecting data to test available. investment. Long‐term feasibility for climate commitment. change effects on soils. 10 Compatibility with other Could unify climate It exists which is a start. Good at what it does, but Would unify soils with UK models – platform etc approach for soil this is not climate or soils approach to assessing biodiversity with related water quality. eutrophication work.

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Table 31. Biodiversity model assessment

Model Criterion GBMove Soil PerPest SOILPACS biodiversity 1 2 3 4 5 6 7 8 9 10

Our review indicates that at present there are no models which could be used to quantitatively predict change in soil biodiversity in England and Wales in response to climate change.

Biodiversity references

Anderson JM 1975. The Enigma of soil animal diversity. In: Vanek, J. (Ed.), Progress in Soil Zoology. Junk B. V., The Hague, pp. 51‐58.

Brussard L, Kuyper TW, Didden WAM, de Goede RGM, Bloem J 2004. Biological soil quality from biomass to biodiversity – importance and resilience to management stress and disturbance. In Managing Soil Quality: Challenges in Modern Agriculture (Eds Schjonning P, Elmholt S, Christensen BT). CAB International.

Clarke RT, Wright JF, Furse MT 2003. RIVPACS models for predicting the expected macroinvertebrate fauna and assessing the ecological quality of rivers. Ecol. Mod.160, 219‐233.

Parisi V, Menta C, Gardi C, Jacomini C, Mozzanica E 2005. Microarthropod communities as a tool to assess soil quality and biodiversity: a new approach in Italy. Agr. Ecosy. Env., 105, 323‐333.

Pinceel J, Jordaens K, van Houtte N, de Winter AJ, Backeljau T 2004. Molecular and morphological data reveal cryptic taxonomic diversity in the terrestrial slug complex Arion subfuscus/fuscus (Mollusca, Pulmonata, Arionidae) in continental north‐west Europe. Biol. J. Lin. Soc. 83, 23‐ 38.

Ruf A, Beck L, Dreher P, Hund‐Rinke K, Rombke J, Spelda J 2003. A biological classification concept for the assessment of soil quality: "biological soil classification scheme" (BBSK). Agr. Ecosyst. Env., 98, 263‐271.

86 Van Straalen NM 1997. Community structure of soil arthropods, bioindicators of soil quality. In: Pankhurst, C.E., Doube, B.M., Gupta, V.V.S.R. (Eds.), Bioindicators of Soil Health. CAB International, Wallingford, pp. 235‐264.

Weeks JM, Svendsen C, Roy D, Eversham B, Black H, Hopkin SP, Wright JF 1997. A demonstration of the feasibility of SOILPACS ‐ Soil Invertebrate Prediction and Classification Scheme, Report to the UK Environment Agency.

A10. Conclusions and Options

Carbon Soil carbon modelling in response to climate change is well established. The most suitable option, ECOSSE was developed by a project partner and is in routine use with other climate scenario data. ECOSSE suitable for use, modelling to be undertaken

Erosion – water The situation here is much as for carbon. The recommended model is PESERA. PESERA suitable for use, modelling to be undertaken

Erosion – wind The recommended model is RWEQ. This has not been run before by project partners, but in view of the model’s simplicity this is considered a low risk. The model requires an estimate of wind speed, which is not directly available from JULES or UKCIP09. Wind speed is one variable which will be estimated externally to UKCIP, since it is required by a number of models. RWEQ suitable for use ‐ modelling to be undertaken.

Contaminants This covers a wide range of potential variables to be modelled, and there are a number of well‐ established models in existence. It would be unrealistic to model all variables of interest in the current project. All models assessed are external to the project and require the assistance or cooperation of the originators or owners. This entails some risk, and may require use of contingency funds. Decision on models to run, bearing in mind available resources. Outcome – run VSD for acidification, PSYCHIC for phosphorus. Do not run models for other contaminants because of lack of resources within the project.

Landslides There are no quantitative models which could simulate the effect of climate change on the incidence or severity of landslides. For this threat, some progress might be made in developing a model, which is likely to be statistical. Decision on whether to work on a new model. Outcome – no modelling.

Salinity – terrestrial There are models which simulate the infiltration of saline water through the soil, this being assumed derived from temporary seawater inundation across the land surface. They do not model the extent of inundation, and this aspect of modelling would not be predictable. SALTMED is the recommended model, although full coverage of England and Wales may be problematic since probabilities of

87 inundation are unknown. Decision on whether SALTMED can usefully be run at England and Wales scale. Outcome – limited modelling with SALTMED.

Salinity – sea water intrusion While there are models capable of simulating seawater intrusion, this form of salinisation does not affect soil, so is not considered relevant to the project. No modelling.

Sealing One model has been identified which can simulate changes in sealing, but this has not been coded. It also uses rainfall intensity variables which are not available from UKCIP09 or JULES. Confirm no modelling. Outcome – no modelling.

Biodiversity There are no quantitative models which could simulate the effect of climate change on the soil biodiversity. Any biological response to climate change is inherently difficult to predict quantitatively. Decision on whether to work on a new model. Outcome – no modelling.

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