5. METHODS: MODELLING IMPACTS OF CLIMATE CHANGE ON THE
5.1 Selection of the ACRU Modelling System
The ACRU agrohydrological modelling system was developed within the School of Bioresources Engineering and Environmental Hydrology at the University of KwaZulu-Natal (formerly the Department of Agricultural Engineering at the University of Natal) in Pietermaritzburg. The theoretical background, concepts and capabilities of the ACRU model are detailed in Hydrology and Agrohydrology: A Text to Accompany the ACRU 3.00 Agrohydrological Modelling System by Schulze (1995). A summary of the concepts of the ACRU model and its water budget is presented below.
5.1.1 Concepts on which the model is based
The ACRU modelling system (Schulze, 1995) has been designed according to the modelling philosophies represented in Figures 5.1 and 5.2. It is a daily time step, physical-conceptual model, where variables (rather than optimised parameter values) are estimated from physically based characteristics of the catchment. ACRU is a multi-purpose model which integrates the various water budgeting and runoff generation components of the terrestrial hydrological system (Figure 5.1). Revolving around daily multi-layer soil water budgeting, the model has been developed essentially into a versatile total evaporation model (Figure 5.2), structured to be sensitive to dynamic climate and land cover factors – both of which are necessities for climate change impacts assessments (Schulze, 1995).
Importantly, ACRU can operate at multiple scales from being a point model or as a lumped small-catchments model, to large catchments or at national scale. When applying the model over large catchments or at national scale, where heterogeneous climates, land uses and soils render the lumped modelling approach less appropriate, ACRU operates as a distributed cell-type model. In distributed mode, individual subcatchments are identified, discretised and flows can take place from
“exterior” through “interior” cells (subcatchments) according to a predetermined
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Figure 5.1 The ACRU agrohydrological modelling system: Concepts and linkages (after Schulze, 1995)
TOTAL EVAPORATION
BASEFLOW
QUICKFLOW
RUNOFF
INTERCEPTION PRECIPITATION
GROUNDWATER STORE INTERMEDIATE STORE
SUBSOIL
TOPSOIL
SURFACE LAYER STORE STORMFLOW
Figure 5.2 The ACRU agrohydrological modelling system: Schematic of its general structure (after Schulze, 1995)
RESERVOIR LAND USE IRRIGATION SUPPLY IRRIGATION DEMAND CLIMATIC
CATCHMENT
SOILS
AGRONOMIC LAND CHANGE
HYDROLOGICAL LOCATIONAL
SOIL WATER BUDGETING/
TOTAL EVAPORATION MODELLING
POINT or LUMPED or DISTRIBUTED MODES
or G.I.S. LINKED
DYNAMIC TIME or
ANNUAL CYCLIC CHANGE
INPUTS
MODEL
OPERATIONAL MODES
SIMULATION OPTIONS /
OUT- PUT
Daily Monthly Annual Risk Analyses
SPECIFIC
COMPONENTS OBJECTIVES /
RUNOFF COMPONENTS
RESERVOIR
STATUS YIELD
IRRIGATION DEMAND
IRRIGATION SUPPLY
LAND USE IMPACTS
CROP YIELD
Stormflow Baseflow Peak Discharge Hydrograph : - generation - routing EV Analyses
Outflows:
- overflow - normal flow - seepage - abstractions Interbasin
transfers
Sediment - generation Reservoir - siltation
Crop Demand Application:
- on demand - fixed cycle - deficit
From : - reservoir - river - river and
Return flows reservoir
Gradual change Abrupt change Total Tillage Wetlands
Practices
Maize Winter Wheat Sugarcane
- dryland - irrigated - profit / loss
SEDIMENT CLIMATE
CHANGE
Off-Channel storage
- fixed amount
CO2 T E P
- off channel storage
Evaporation Primary
Productivity
ACRU MODEL
COMBINATIONS
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configuration, with each subcatchment able to generate individually requested outputs, which may be different to those of other subcatchments or with different levels of input/information (Schulze, 1995).
Furthermore, the model includes a dynamic input option to facilitate modelling hydrological responses to climate or land use or management changes in a time series. A dynamic input file is then accessed each year of the simulation, with the new variable inputs to be used from that year onwards (Schulze, 1995).
The ACRU model has been linked to the Southern African National Quaternary and Quinary Catchments Databases (QCD and QnCD, respectively) for applications at a range of scales in South Africa, Lesotho and Swaziland for studies involving, inter alia, water resources assessments, design flood estimation, the calculation of low flows and/or the impacts of climate change.
5.1.2 Synopsis of the general structure of the ACRU model for water budgeting
The streamflow generation water balance of the effective rainfall, i.e. rainfall that is not abstracted as plant interception, comprises (Schulze, 1995; van Zyl and Lorentz, 2003):
• Rapid, event-based runoff (stormflow).
• Evapotranspiration losses from the soil.
• Slower baseflow from a groundwater store.
Stormflow is controlled by the magnitude of the effective rainfall and the antecedent soil water content to a specified depth in the soil profile. The stormflow depth generated by an event is estimated using an equation developed by the Soil Conservation Service (SCS) of the United States Department of Agriculture (USDA) that has been adapted South African conditions. Not all the stormflow generated from a rainfall event reaches the catchment outlet on the same day. Rather, stormflow is divided into quickflow (i.e. same day response) and delayed stormflow (cf. Figure 5.2), and is controlled by a release rate parameter (Schulze, 1995; van Zyl and Lorentz, 2003; Schulze, 2009b).
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Rainfall that is not abstracted as plant interception or removed as stormflow, enters through the surface layers of the soil, where the water is moved up and down between the top- and subsoil horizons. These processes are based on the soil water retention at critical thresholds (e.g. field capacity), on soil texture and/or impeding layers, and the volumetric water content between horizons. Slow, unsaturated up- and downward soil water redistribution is also accounted for. The process of evapotranspiration occurs simultaneously from various soil horizons, and is driven by a reference potential evaporation, representing the atmospheric demand, which may be estimated by a number of methods. Furthermore, evapotranspiration is controlled by various vegetation parameters, soil water content and atmospheric demand (Schulze, 1995; van Zyl and Lorentz, 2003; Schulze, 2009b).
Baseflow is generated from excess water percolating through the bottom of the active root zone, into the intermediate (vadose) zone, and then into the groundwater stores.
Baseflow is released from this store to the catchment outlet on a daily basis at an exponential rate of decay, dependant on the volume in storage and a decay rate constant (Schulze, 1995; van Zyl and Lorentz, 2003; Schulze, 2009b).
For more details on the above processes, including all equations, the reader is referred to Schulze (1995). More detailed explanations of the methodologies utilised for the calculation of the various hydro-climatic hazards assessed in this study are presented in the relevant sections of Chapter 7.
5.1.3 Suitability of the ACRU model as a tool for climate change impacts studies on hydrological processes and water resources in the Orange River Catchment
Not only does the ACRU modelling system meet many of the criteria/requirements outlined in the introduction to this chapter, but the generation of streamflow with the ACRU model has been verified against observed outputs from 44 catchments worldwide in 31 independent studies. Of the 44 catchments, 10 were international catchments in the USA, Germany, Swaziland, Zimbabwe and Eritrea, and with those verifications undertaken in nine of the 31 separate studies. The remainder of the verifications were performed on South African catchments. Specific design hydrology
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verifications have been undertaken in four separate studies at seven hydro- climatically diverse sets of catchments in the USA, and in five South African studies at three diverse hydro-climatic locations (Schulze, 2008b).
In addition to these verification studies, the ACRU model has been used extensively in decision-making in southern Africa and internationally, in water resources related research and applications in all four countries in which the Orange River Catchment exists, viz. Botswana, Lesotho, Namibia and South Africa; as well as in Mozambique, Swaziland, Canada, Chile, Germany and the USA (Schulze and Smithers, 2004).
For the reasons presented above, and despite several shortcomings (Schulze, 2005d), ACRU is believed to be a modelling system highly suitable for evaluating impacts of climate change on the hydrology and water resources of southern Africa and, hence, the Orange River Catchment.