On modelling the effects of afforestation on acidification in heterogeneous catchments at different spatial and temporal scales

doi:10.1016/S0022-1694(01)00433-4

Journal of Hydrology

Volume 250, Issues 1–4, 1 September 2001, Pages 149-169

https://doi.org/10.1016/S0022-1694(01)00433-4 Get rights and content

Abstract

A modelling approach is presented for simulating and predicting future changes in streamwater Gran alkalinity throughout a large, heterogeneous river system. The methodology is based on integrating End Member Mixing Analysis (EMMA), the Model of Acidification of Groundwater in Catchments (MAGIC) and spatial data describing the catchment characteristics stored on a Geographical Information System (GIS). These are integrated within a Functional Unit Network (FUN) to predict the changes in Gran alkalinity resulting from possible future changes in atmospheric deposition and land use (low intensity afforestation) in the River Dee catchment, NE Scotland. Model results indicate that declining sulphate and constant nitrogen deposition, combined with low intensity Scots pine (Pinus sylvestris) afforestation are unlikely to contribute significantly to streamwater acidification.

Introduction

Water quality models are widely used to assess the effects of diffuse pollution in catchment systems (e.g. Cosby et al., 1985a, Cosby et al., 1985b, Whitehead et al., 1998). Ideally for application to large catchments, such models should be able to incorporate and represent the processes that control the impact of drivers of change, such as atmospheric deposition and modifications to land management, on the water quality (Kendall, 1998). Given such drivers and the relative importance of catchment processes are likely to be highly heterogeneous in space and time, then such models should be able to simulate the spatial and temporal variations in water quality. Consideration of the temporal variations should include the long (decadal) and short (event) term changes, which are both necessary to establish the potential effects of chronic and episodic pollution on the streamwater biota (Ormerod and Jenkins, 1994). However, well-established difficulties arise when trying to simulate the transfer of water, and pollutant loads through catchment systems (Beven, 1993). In particular, problems occur which relate to spatial and temporal scaling (Wheater and Beck, 1995). Briefly, these arise because of two factors. Firstly, the structure and parameters of current models usually cannot adequately represent the spatial and temporal variability observed in water chemistry data (Blöschl and Sivapalan, 1995). Secondly, current water quality models are usually developed for small (<10 km²) scale research studies and these models are lumped representations of heterogeneous systems. Their transferability to larger scale catchments (>1000 km²), commensurate with the scale of water resource decision-making is generally unknown. Due to such difficulties, the predictions of future changes in water chemistry are uncertain. For example, many process-based, dynamic water quality models are over-parameterised resulting in non-unique parameter values and equifinality in model representation (Oreskes et al., 1994). Given this, and the inability of empirically based approaches to provide an assessment of long term water quality change, the utility of existing water quality models appears limited (Wheater and Beck, 1995). In this context, new approaches are being sought to overcome the problems associated with scale in water quality modelling. Increasingly, it is being recognised that a range of modelling approaches must be used in complex heterogeneous systems, and these should be brought together with data collected at appropriate spatial and temporal scales (Langan et al., 1997, Neal, 1997). Consistent with such a philosophy, the work presented in this paper investigates the potential of using a range of modelling approaches, together with a substantial spatial database, to form a modelling framework appropriate for the investigation and management of a relatively large catchment system. Specifically, conservative mixing concepts (End Member Mixing Analysis, EMMA, Christophersen et al., 1990) are combined with Model of Acidification of Groundwater in Catchments (MAGIC, Cosby et al., 1985a, Cosby et al., 1985b). Together with multiple regression analysis of catchment characteristics stored on a Geographical Information System (GIS), the models are integrated within a Functional Unit Network (FUN), which is used to predict the contemporary and future mean daily Gran alkalinity of streamwaters throughout the Dee catchment in response to likely afforestation scenarios. This integrated approach is explored as a first step in developing a modelling system that begins to account for catchment heterogeneity. The term ‘low intensity afforestation’ used in this paper refers to Scots pine regeneration and low density planting, which contrasts to the more intensive conifer plantations commonly associated with soil and streamwater acidification in the UK.

Section snippets

Study area

The River Dee catchment is an example of a large river system that is important as a regional resource in NE Scotland (Fig. 1). The characteristic acidic geology, soils and land use of the catchment make it sensitive to the impacts of acidification (Smart et al. 1998). The catchment is described in detail elsewhere (e.g. Langan et al., 1997). Simplistically, the catchment can be viewed as two main regions based upon elevation, land use and soil type. In the upland region (>300 m) to the west,

Methodology

A FUN is a methodology for dividing a river catchment into smaller, more homogenous units (Neal, 1997). In terms of water quality modelling, FUNs are based on the premise that the hydrology and/or water chemistry of the whole catchment can be characterised on the basis of component functional units (or types). These functional units are user-defined, and are assumed each to produce a characteristic water chemistry that typically equates to identifiable soil, land use or hydrological response

Defining the functional units

Using the results of the 1996/97 water quality survey, the 59 sites were divided qualitatively into three functional units (upland acidic, upland basic and cultivated) based upon streamwater Gran alkalinity concentrations and geological, soil and land use characteristics (Smart et al., 1998). It was then assumed that any catchment in the Dee basin could be described as a composite of these three fundamental functional units. This assumption was used because the three groups were determined

Simulating the temporal changes in the functional unit end members

The MAGIC model was used to simulate the long term (annual) changes in the end member Gran alkalinity of the units that may occur in response to: (i) reduced atmospheric deposition of sulphur (the Second Sulphur Protocol) and (ii) low and high intensity Scots pine afforestation.

Given the daily variations in streamwater Gran alkalinity in meso/large catchments (>50 km²) in the upland region can, to a first approximation, be explained as a mix of two chemically distinct waters or end members,

Spatial mixing using EMMA

The changes in the end members (the Δend members) are defined resulting from deposition and land use changes as follows: $Δ S=S_{t} −S_{1996}$ $Δ G=G_{t} −S_{1996}$ where ΔS and ΔG refer to the differences between the concentrations of the soilwater and groundwater end members of the functional units in the target year (S_t, G_t) and the respective concentrations in 1996 (S₁₉₉₆, G₁₉₉₆). The values of the Δend members are shown in Fig. 6a and b for the acid and basic units, respectively. At any site within the Dee

Derivation of site-specific end members

The end member concentrations at a specific site in the Dee catchment for a target year can be determined as follows: $S_{t,s} =S_{1996,s} + Δ Alk_{sw}$ $G_{t,s} =G_{1996,s} + Δ Alk_{gw}$ where S_t,s and G_t,s are the soilwater and groundwater end member concentrations of Gran alkalinity in the target year, t at site, s; S_1996,s and G_1996,s are the end member concentrations at site, s in reference year (1996); ΔAlk_sw and ΔAlk_gw are the changes in the end members at that site, calculated from , . The end member concentrations at

Temporal mixing using EMMA

The final stage is to use Eq. [7] to simulate the mean daily Gran alkalinity given the estimated end member Gran alkalinity (S_t,s and G_t,s) and a daily flow time series (Wade et al., 1999) $Alk_{soil} −Alk Alk_{soil} −Alk_{gw} =0.878 flow flow_{median}^{−0.35} −0.416$

where flow is the mean daily flow, (m³/s), flow_median the median of the sampled flows (m³/s), Alk the daily streamwater Gran Alkalinity, (μmol_c/l), Alk_soil the soilwater end member Gran alkalinity, (μmol_c/l) and Alk_gw is thegroundwater end member Gran

Temporal variations in streamwater acidity in 2010/2011

Fig. 7 shows an example of a simulated streamwater Gran alkalinity time series in 2010/11 (the target year) at Mar Lodge. The flow time series used in Eq. [7] to calculate the 2010/11 daily Gran alkalinity concentrations was that observed in 1996/97 at Mar Lodge. Clearly this is an over-simplification, but in a first instance it serves to highlight the possible differences to the water chemistry caused only by changes in the end member composition due to afforestation and reduced acid

Spatial variations in streamwater acidity in 2010 and 2056

As well as analysing the temporal variations in the daily mean Gran alkalinity it is also possible to draw maps of the spatial distribution of future water quality under various environmental change scenarios. Examples of such maps have been produced for the River Feugh subcatchment (Fig. 9). In each map, the Gran alkalinity concentration that is exceeded 95% of the time is shown. The 95 percentile concentrations in 2010 and 2056 generally exceed the pre-planting concentrations in 1996 due to

Discussion

The presented methodology represents progress towards a pragmatic approach for predicting water quality in heterogeneous catchments. By assessing changes in annual and mean daily variations in streamwater acidity throughout an upland region the potential impact of changes in SO₄ deposition and forest management has been examined. Thus the approach provides a means of estimating potential change at a range of spatial scales (10–1844 km²).

Although the regression equations relating the land cover

Acknowledgements

The authors thank Bob Ferrier and Rachel Helliwell for their useful comments and constructive criticism during the development of this work and Heather Browning for help with the illustrations. This research was funded by the Natural Environment Research Council/Scottish Executive (grant No. GT/02/1200) as part of the Special Topic, Large Scale Processes in Ecology and Hydrology.

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On modelling the effects of afforestation on acidification in heterogeneous catchments at different spatial and temporal scales

Abstract

Introduction

Section snippets

Study area

Methodology

Defining the functional units

Simulating the temporal changes in the functional unit end members

Spatial mixing using EMMA

Derivation of site-specific end members

Temporal mixing using EMMA

Temporal variations in streamwater acidity in 2010/2011

Spatial variations in streamwater acidity in 2010 and 2056

Discussion

Acknowledgements

Adv. Water Resour.

Environ. Pollut.

Water Res.

J. Hydrol.

Sci. Tot. Environ.

J. Hydrol.

J. Hydrol.

J. Hydrol.

J. Hydrol.

Ecol. Model.

J. Hydrol.

Sci. Tot. Environ.

Sci. Tot. Env.

J. Hydrol.

Environ. Pollut.

J. Hydrol.

Sci. Tot. Environ.

J. Hydrol.

Water Res.

Sci. Tot. Environ.

Land Capability Classification for Forestry in Britain

Scale issues in hydrological modelling: a review

Hydrol. Proc.

Time scales of acidification: a quantitative model for estimating freshwater acidification

Environ. Sci. Technol.

Modelling the effects of acid deposition: Assessment of a lumped parameter model of soil water and streamwater chemistry

Water Resour. Res.

Modelling the ecosystem effects of nitrogen deposition: model of ecosystem retention and loss of inorganic nitrogen (MERLIN)

Hydrol. Earth Syst. Sci.

Modelling water chemistry for a major Scottish river from catchment attributes

J. Appl. Ecol.

Simulation of the long term soil response to acid deposition in various buffer ranges

Water, Soil Air Pollut.

Blueprint for the UP modelling system for large scale hydrology

Hydrol. Earth Syst. Sci

Application of the MAGIC model to the Norway spruce stand at Solling Germany

Ecol. Model.