An integrated modelling toolbox for water resources assessment and management in highland catchments: Model description

AGRICULTURAL SYSTEMS Agricultural Systems 89 (2006) 106–131 www.elsevier.com/locate/agsy

An integrated modelling toolbox for water resources assessment and management in highland catchments: Model description R.A. Letcher

a,c,*

, B.F.W. Croke a,c, A.J. Jakeman W.S. Merritt a,d

a,b

,

a

c

Integrated Catchment Assessment and Management Centre, The Australian National University, Canberra, ACT 0200, Australia b Centre for Resource and Environmental Studies, The Australian National University, Canberra, ACT 0200, Australia Department of Mathematics, The Australian National University, Canberra, ACT 0200, Australia d School of Resources, Environment and Society, The Australian National University, Canberra, ACT 0200, Australia Received 10 October 2003; received in revised form 21 June 2005; accepted 23 August 2005

Abstract Water and land resource competition and environmental degradation pose difficult questions for resource managers. In particular, the ensuing trade-offs between economic, environmental, and social factors and their spatiotemporal variability must be considered when implementing management policies. This paper describes an integrated modelling toolbox that has been developed for highland catchments – specifically the Mae Chaem catchment in Northern Thailand. This toolbox contains models of crop growth, erosion and rainfall-runoff, as well as household decision and socioeconomic impact models. The approach described advances and complements previous approaches by: considering more complex interactions between land-use decisions and the hydrological cycle; modelling household decisions based on uncertain expectations; and assessing impacts of changes not only on flows and household income, but also on subsistence production and erosion. An example of the types of trade-offs

*

Corresponding author. Tel.: +61 2 6125 8132; fax: +61 2 6125 8395. E-mail address: [email protected] (R.A. Letcher).

0308-521X/$ - see front matter  2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.agsy.2005.08.006

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and scenarios that can be assessed using the integrated modelling toolbox is also presented. This demonstrates that for the scenarios presented, the magnitude and direction of impacts simulated by the model is not dependent on climate. Further testing of the model is demonstrated in a companion paper. Overall, the plausibility of the model is shown.  2005 Elsevier Ltd. All rights reserved. Keywords: Integrated model; IWRAM; Socioeconomic modelling; Trade-offs; Water resource assessment and management

1. Introduction Effective water and land management involves consideration of complex, interacting processes, and often competing goals. In particular trade-offs between economic, social and environmental outcomes must be considered to improve the sustainability of catchment systems. These types of complex interactions lend themselves to analysis by modelling approaches. Integrated models are required to describe the links between economic, social and environmental system outcomes under various management and climatic regimes. A considerable, increasing and improving body of work already exists applying integrated approaches to water management problems (see for example, Greiner, 1999; OÕCallaghan, 1995; McKinney et al., 1999; Rosegrant et al., 2000; Letcher et al., 2004; Jakeman and Letcher, 2003). However, these integrated modelling approaches are at an early stage of development and are still being refined for various geographic areas and management issues. In particular, relatively few applications have been undertaken which integrate a broad range of disciplines or management considerations at similar levels of complexity. This paper outlines the development and application of one method of model integration that is applicable in many highland catchment situations. Our case study is a catchment in Northern Thailand. This application is of particular interest because: the level of complexity and treatment of the individual model components is similar, reflecting the Integrated Assessment paradigm underlying the system development (see, for example Jakeman and Letcher, 2003); the toolbox was developed using strong collaboration with both government agencies and universities in Thailand, and so represents the state-of-the-art in Thai River Management and modelling (Jakeman et al., 2005); and, the application demonstrates a conceptually strong and potentially transferable approach to integrated modelling for the broad type of catchment management question, not necessarily just for highland catchments. The following section reviews different approaches to integrated water resource assessment modelling to give context to the approach presented in this paper.

2. Integrated water resource assessment models Jakeman and Letcher (2003) provide an overview of the role of models in Integrated Assessment for catchment management, defining Integrated Assessment as

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a problem focused activity, that links research and policy, and that requires a level of disciplinary equilibration. This means that models developed as part of an integrated assessment should have similar levels of complexity and Ôdisciplinary robustnessÕ. In addition, the question of appropriate scales at which processes should be modelled and the way in which these should be integrated, given the differences in the scales of underlying processes, is a key issue in the development of integrated models. Integrated models for water resources assessment generally use one of two approaches to overcome difficulties created by the differences between the scale of component processes. The first of these is to disaggregate the entire system over a spatial grid. These grid cells may or may not be uniform in size, but generally do not represent any underlying system homogeneity. For example, neighbouring grid cells may have the same characteristics. Process descriptions and measurements are then assumed to be relevant at the grid scale, with the model considering all processes and interactions to occur over each grid cell. Characteristics, such as soil types or production values, are then assumed to be homogenous within the grid cell. This overcomes some problems with differences in process scales by considering the operation of all processes at a smaller spatial scale than that on which the smallest scale process occurs. Generally fluxes of various system components, such as rainfallrunoff, erosion, nutrients or even biodiversity or species populations, are modelled as interactions between neighbouring grid cells. This approach is very widely applied for model integration, particularly where ecological function of the terrestrial system is of primary importance. An example of this type of approach can be found in the NERC/ESRC land-use programme (NELUP). This is an integrated model for analysing the impacts of land-use and management options on catchment systems. The prototype modelling system has been developed on the River Tyne catchment in Northern England (OÕCallaghan, 1995). The NELUP model integrates modules describing the hydrology, ecology and economics of the catchment system. Descriptions of the model components can be found in Adams et al. (1995), Dunn et al. (1996), Rushton et al. (1995), Moxey et al. (1995) and Oglethorpe and OÕCallaghan (1995). Other examples of grid-based approaches to integration can be found in Vatn et al. (1997), Weber et al. (2001) and Mo¨ller and Kuhlmann (1999). MODULUS is an example of a grid-based integration approach where the size of grid cells is not the same for all components (Oxley et al., 2004; Engelen et al., 2000). This decision support system integrates grid-based models over a range of spatial and temporal resolutions. Component models include weather, hydrological and groundwater models, a plant growth model, an irrigation model, a decision-making model, a community model and constrained cellular automata for simulating land-use. The second type of approach commonly used to develop integrative models for water resources applications relies on a nodal-network or reach-based representation of the underlying system. This approach treats the river system as a series of connected nodes or reaches, between which flows and other fluxes are routed. Processes are aggregated and integrated on this nodal basis with process representation being on the basis of Ôrelatively homogenousÕ areas or groups. For example, farmers may be categorised into types based on the way in which their decisions interact with the

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hydrological cycle (e.g., extractive, non-extractive but affecting evapotranspiration). Farmers of each of these types are then represented by the model, and interactions with other system components are modelled at the aggregated nodal scale. Model outputs are usually available by node. This approach has been used widely for water quantity focused models, such as water allocation modelling, both from an operation and control perspective (usually engineering based) and a more integrated simulation perspective. An example of the approach being used to consider basin-wide water allocation is the system-wide initiative on water management (SWIM) model. SWIM is an integrated economic– hydrologic model that has been developed to consider water demand and supply and solute transport. It has been applied in the Maipo River Basin in Chile. While this application is specific to this catchment, the integrative framework has been developed to be generic enough for reapplication to other catchments and issues (McKinney et al., 1999; Rosegrant et al., 2000). The SWIM model is based on a nodal-network approach. Nodes are physical entities, representing demand and supply points. Demand nodes in the model include irrigation off-takes, industrial plants and households. Supply nodes includes rivers, reservoirs and groundwater aquifers (McKinney et al., 1999; Rosegrant et al., 2000). The model has been developed to account for the interactions between water allocation, farmer input choice, agricultural productivity, non-agricultural water demand, and resource degradation to estimate the social and economic gains from the allocation and efficiency of water use (McKinney et al., 1999; Rosegrant et al., 2000). It is intended to estimate the economic benefits of water use for different demand management instruments, including markets for tradeable water rights based on production and benefit functions with respect to the agricultural and urban and industrial sectors. The economic modelling component includes an optimisation model to estimate returns to water use. Both instream and offstream uses are modelled. Instream uses include flows for waste dilution and hydropower. Offstream uses include diversions for agriculture and municipal and industrial uses. Crop yields are calculated exogenously using a crop model that accounts for water, salinity and irrigation technology as variables. Hydrologic modelling is undertaken endogenously in the system. SWIM includes a component model for flow and salt balance and transport (McKinney et al., 1999; Rosegrant et al., 2000). A second example of this type of approach being used to consider water allocation is IQQM, a water resources planning model used for water management in Australia (DLWC, 1995; Simons et al., 1996). This model has also been applied elsewhere internationally. It uses a nodal-network description of the catchment system. Nodes in the model include tributary inflows, irrigation off-takes and urban supply. The model incorporates various water quantity and quality components including instream water quantity, instream water quality and rainfall-runoff modules. Flow routing takes places between flow nodes in the system. Simulated crop water demands are used to estimate irrigation demands throughout the regulated river system (i.e., river below a major water storage used for irrigation supply). For a regulated river system the model makes two passes of the river system in each day of the sim-

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ulation. Firstly, water ordered in the system, including channel losses, travel time, rainfall contributions and evaporation are calculated and summed to estimate total water demand in each day. This pass of the model starts at nodes in the lower reaches of the catchment and moves upstream to those in the upper catchment. The second pass of the model involves routing water from the upper reaches of the catchment to the lower reaches. This pass essentially simulates water supply given demands and availability. Other examples of this approach being applied include: an integrated hydrologic– economic model considering water allocation in the Namoi catchment, Australia (Letcher and Jakeman, 2003; Letcher et al., 2004); WaterWare, a decision support system for water allocation (ESS, 1999; Fedra and Jamieson, 1996; Jamieson and Fedra, 1996a; Jamieson and Fedra, 1996b); and, CATCHSCAPE, an agent-based model of water allocation and decision-making in Thailand (Becu et al., 2001). Nodal-network approaches developed to date have largely considered the direct links between irrigation extraction and river flows. That is, they have considered production and extraction decisions to be limited by flows and to impact flow directly through removal of flows from the stream. The influences of a change in landcover on the hydrological cycle are not included in these approaches. However, these types of effects are particularly important where substantial landcover changes are being experienced, such as in northern Thailand where deforestation for agricultural production is of concern. Previous approaches have also focused largely on issues associated with the quantity of flows, rather than water quality and erosion, and have largely considered economic impacts, without considering social implications such as changes in subsistence production. The model described in this paper has been developed to overcome many of these limitations, due to the features of the Thai case study. The approach used in the modelling to investigate the impacts of drivers – like policy changes, investments, climate – is scenario-based rather than overall optimisation (e.g., Schluter et al., 2005). Optimisation was not preferred because of the preference not to set some subjective Ôbest outcomeÕ. The aim was to explore and learn the nature of the system, the model and their response. A scenario-based approach was also felt to facilitate future changes to the integrated model use that allowed for uncertainty, such as to examine a ‘‘robust’’ decision policy so that the risk of wrong decisions is minimised (e.g., Pallottino et al., 2005). The following section provides a brief background to management issues in the case study area before a detailed description of the integrated modelling toolbox is provided.

3. Water resources management in Thailand In 1997, the Thai government adopted a ÔPolicy and Prospective Plan for Enhancement and Conservation of Natural Environmental Quality, 1997–2016Õ (UN, 2002). One major commitment relating to surface water resources in this Plan is to develop and conserve surface and groundwater sources at the basin level, taking

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into account socio-economic and environmental impacts (OEPP, 2002). Further details on water management in Thailand can be found in Sethaputra et al. (2002). The IWRAM project focused on subcatchments of the Mae Chaem catchment (40 km2), situated in the northwest of the Ping Basin (see Fig. 1). Resource competition is providing significant challenges to the development of policies aimed at allowing for the sustainable use, protection, conservation and management of land and water resources (Jakeman et al., 1997). Key environmental issues in the Mae Chaem are the distribution of dry-season flows between upland and lowland farmers, deforestation, increased rates of erosion from agricultural land, and surface water quality. In the Mae Chaem catchment, rapid agricultural intensification, rural development initiatives and government conservation policies have created tensions in land and water resource management. The IWRAM project involved active collaboration between Australian researchers and managers and researchers in Thailand. The focus catchments were chosen for research in collaboration with these partners.

Fig. 1. Location of the Mae Chaem catchment.

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3.1. Communities in the Mae Chaem The Mae Chaem catchment is settled by hilltribe people (Karen, Hmong, Akha and Lisu) and native Thai people. Hilltribes populations have migrated from Laos, Myanmar and China over the last century. The hilltribe people dominate upland areas, with Thai locals dominating populations in the lowlands (NRC, 1997). 3.2. Land cover Forest cover in the Mae Chaem catchment decreased by 10% between 1985 and 1990 to 2980 km2, then increased slightly from 1990 to 1995 (Merritt et al., 2004). This forested land was mainly converted to upland agriculture in the upper half of the catchment, with slight increases in the amount of paddy. Most of the land suitable for paddy cover has already been converted from its original forest cover. Agricultural activities are closely linked with population centres, with more remote upland catchments remaining under forest cover. 3.3. Policy Watersheds in Thailand have been classified on the basis of physical characteristics, such as landform, geology, soils, elevation and slope, as well as forest cover and environmental features of landscape units that interact with climate and human uses. The entire country has been classified into watershed classes (WSC) 1–5 (Krairapanond and Atkinson, 1998). Lower class numbers represent areas that are headwaters and are protected while higher classes correspond to those areas where more intensive land-uses are permitted. Much of the Mae Chaem catchment, particularly the northern and western regions, has been classified as WSC1A. This class is designed to protect the area from any exploitation of natural resources unless necessary for forest and ecological rehabilitation (Krairapanond and Atkinson, 1998). This classification allows for the residents of these areas to be relocated. However, evidence of the implementation of this policy is not generally seen in land cover maps of the area from the late 1990s. Existing areas of agriculture remain, regardless of the policy of relocation.

4. Description of the integrated toolbox 4.1. Nodal-network approach The IWRAM integrated modelling toolbox models resource management decisions as taking place at the household scale. This scale was chosen as it was considered that the household was the main driver of agricultural production decisions in Northern Thailand (Scoccimarro et al., 1999). The modelling uses a nodal-network where nodes represent aggregated points of extraction along the river system. Each node is associated with an area of land containing many households and land uses.

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This means that household extraction decisions in an area are aggregated and are modelled as occurring from a specific point along the river. Total water supply, simulated using the hydrological model (see Croke et al., 2004), is also an output at this point or node. Households in an area are divided into a number of representative Resource Management Units (RMU) and the decisions of individual households are aggregated by summing up decisions of each RMU type present at the node (see Section 5). The nodal structure is illustrated in Fig. 2 for the 43.5 km2 Mae Uam subcatchment. It shows two nodes – an upstream and downstream node at which spatial and temporal estimates of discharge are provided. Fig. 2 additionally illustrates the Land Development DepartmentÕs classification of land units in the sub-catchment. The LDD has developed a land unit approach that defines the given yield of a crop for a particular land unit (or land suitability class) based on the FAO land evaluation procedures (FAO, 1976). A single land unit reflects a combination of soil class and topography. Within the integrated modelling toolbox, land units are the modelling unit for the crop and erosion models. A full description of land units and their use within the IWRAM framework can be found in Merritt et al. (2004). Other applications of land unit methods in Thailand can be found in Liengsakul et al. (1993) and Kuneepong et al. (1990). Issues of crop water use exist with low slope land units suitable for paddy agriculture, whilst in steep lands susceptibility to erosion is an additional issue of concern. The nodal structure implemented in the integrated modelling toolbox allows these considerations to be described for different nodes. The toolbox provides the facility for a catchment to be disaggregated to separate these areas into distinct nodes, allowing output indicators to be

Fig. 2. The Mae Uam catchment showing the nodal structure implemented within the integrated modelling toolbox. Shading is used to illustrate the land unit distribution in the catchment.

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calculated separately for these areas. Hence, this structure allows the identification of potential differences in impacts between upstream and downstream areas. Households of the same RMU type are modelled as having the same access to land, water and labour at a node. This means that within any one nodal area the same land-use decisions are assumed to be made by each of these households in a specific RMU type. Household decisions for each RMU type present at the node are aggregated across individual RMUs and then across RMU types. This aggregate land-use decision is fed to the biophysical models in the system (referred to as the biophysical modelling toolbox) as an aggregated land-use and management decision for the node. Users of the model are able to change the total number of households of each RMU type at each node, as well as the access that each of these RMUs have to land, labour and water resources. In this way, they are able to explore changes that could occur in the catchment, for example as a result of forest clearing for agriculture, or migration into the catchment. 4.2. Model component integration The integrated modelling toolbox consists of a number of modelling components: household decision models (HDM); a decision disaggregation model (DDM); a biophysical modelling toolbox (BPT); and, a socioeconomic impact simulation model (SISM). The biophysical modelling toolbox consists of: a crop model, CATCHCROP (Perez et al., 2002); a hydrological modelling component (Croke et al., 2004) based on the IHACRES rainfall-runoff model (Croke and Jakeman, 2004; Jakeman and Hornberger, 1993); a water allocation model based on stakeholder recommendations for irrigation priority in the catchment; and an erosion model, based on a version of the universal soil loss equation (Wischmeier and Smith, 1978) modified for Thailand. A full description of the biophysical modelling toolbox can be found in Merritt et al. (2004). The way in which the socioeconomic models of the integrated modelling toolbox interact with this biophysical modelling toolbox is demonstrated in Fig. 3. Land-use decisions, based on expected returns and water availability, are simulated within the household decision models. These land-use decisions are disaggregated by land unit using the decision disaggregation model before being passed to the biophysical modelling toolbox, which simulates the impact of climate on crop yields, water use, water availability and erosion. Actual yields and water use are then passed from the biophysical modelling toolbox to the socioeconomic impact simulation model, where the impacts of actual yields on a series of socioeconomic indicators are calculated (see Section 4.7). The biophysical modelling toolbox operates at a number of key scales. Erosion is calculated on a land unit for a season/year given climatic inputs. Flow modelling is undertaken at a nodal scale, with flow models producing daily estimates of streamflow in response to daily rainfall and temperature inputs. The crop model runs on a 10-day time step, estimating water demands every 10 days, and yields over each season (wet and dry).

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Fig. 3. Framework and interactions between component models in the integrated modelling toolbox. Models interacting with the BPT models are household decision models (HDM), the decision disaggregation model (DDM) and the socioeconomic impact simulation model (SISM).

4.3. Household decision models (HDM) With decisions on land and water use being modelled in the integrated modelling toolbox as taking place at the household level, decisions are made in response to expectations of the level of land, water and labour available to a household. Households are classified into a number of different types, called Resource Management Units (RMU), on the basis of biophysical, economic and socio-cultural attributes. For a detailed discussion on Resource Management Units and their application in the IWRAM Project, see Scoccimarro et al. (1999). The classification system used in this paper was determined after consideration of detailed household level survey data. These data contained detailed biophysical, economic and social information on individual households as well as information on their production choices in several years. From these data it was determined that access to land and water was the main determinant of household resource use decisions. It should be noted that individual households are not modelled, but that separate household models are run for each household type, then the results aggregated by the number of households of each type. Thus, household models essentially estimate decisions on a Ôper household per RMUÕ basis. RMU types differ according to their access to land and water in the catchment. For example, one RMU type may contain households that own only irrigated paddy land, while households in another RMU may own some irrigated paddy and some rainfed upland fields. The types of RMU that may be seen in a catchment are summarised in Table 1. Classification of households into RMUs was undertaken using household survey data collected in the catchment. Only types 2, 3 and 8 are considered in this paper, as these were the only types seen in the survey data for

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Table 1 Resource Management Unit (RMU) types RMU

Description

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Rainfed paddy only Irrigated paddy only Rainfed upland only Irrigated upland only Rainfed and irrigated paddy Rainfed paddy and upland Rainfed paddy and irrigated upland Irrigated paddy and rainfed upland Irrigated paddy and upland Rainfed and irrigated upland Rainfed and irrigated paddy and rainfed upland All types Irrigated paddy and upland and rainfed upland Rainfed paddy and upland and irrigated upland

the Mae Uam subcatchment. Households of RMU2 have access to irrigated paddy land only. Households of RMU3 have access to rainfed upland fields only while those of RMU8 have access to both irrigated paddy fields and rainfed upland fields. Each household is assumed to be constrained in their activities by their access to land, water and labour. Households are modelled as aiming to generate as much household income as possible given a choice of crops, and expectations on the amount of land, water and labour that will be available to them. Social constraints, such as the desire to grow rice as a subsistence crop during the wet season, are included as constraints on household decision-making. For example, households are limited to growing mainly rice in the wet season in order to meet their subsistence needs. A level of 300 kg per year is assumed to be required per person to eliminate the subsistence deficit. Cash cropping is assumed to take place in the dry season. The model allows for different choices of fertiliser level on crops as well as for the choice of whether or not to irrigate a crop. The crops able to be chosen by each household type differ. For the Mae Uam: RMU2 is able to choose irrigated paddy rice in the wet and dry seasons, and irrigated sorghum in the dry season; RMU3 is able to choose rainfed upland rice in the wet season and rainfed sorghum in the dry season; and, RMU8 is able to choose irrigated paddy rice and rainfed sorghum, upland rice and groundnut in the wet season and irrigated and rainfed sorghum in the dry season. These crop choices were derived from the survey data. It is possible to run the integrated modelling toolbox over several years or for a single year. If the model is run over multiple years then the expected volume of irrigation water available to an RMU for each successive year (used in the household decision model) is updated on the basis of events in previous years. In the first year, the expected quantity of irrigation water is that which is initially assumed by the user. In all other years, the expected value is the actual amount of irrigation water used by the household in the previous year (i.e., naive expectations are assumed). Climate data for each year also affect flows, erosion, crop yields and irrigation demands, calculated in the biophysical modelling toolbox.

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Table 2 Initial values of key socioeconomic parameters Symbol

Name

Units

Model component

Instance

hm

Households

Number

DDM, SISM

RMU2, RMU3, RMU8, RMU2, RMU3, RMU8,

Initial value

pm

People

Number

SISM

All RMU, nodes

3

p

Rice cost

Baht per kg

SISM

–

7

pi

Price i

Baht per kg

HDM

Paddy rice Upland rice Sorghum Groundnut

6.6 6.9 8.2 2

w

Wage

Baht per day

HDM

–

H

Labour

Hours

HDM

All RMU, nodes and seasons

180

X

Water

ML

HDM

RMU2 RMU3 RMU8

304 0 260

node node node node node node

1 1 1 2 2 2

65 108 191 273 52 131

30

Table 3 Initial values of costs

Cost (baht per rai) Paddy rice Upland rice Sorghum Groundnut

No fertiliser

Half fertiliser

Full fertiliser

153 142.8 336 15.9

566.6 481 606.3 307.4

980.3 623.8 876.5 598.9

Linear programming is invoked to solve the constrained optimisation, using separate components for wet-season and dry-season decisions. At present only seasonal cropping decisions are able to be accounted for in the model. Decisions to grow perennial produce, such as fruit trees, are not currently incorporated in the model. The optimisation problem considered for both wet and dry-season production decisions is formulated as Eqs. (1)–(9) below. Base case values for key socioeconomic parameters are given in Tables 2 and 3. In most cases, base case values were determined from the household survey data. 4.3.1. HDM for RMU 2 and 3 Households of type RMU2 and RMU3 have access to either only paddy or only upland fields (see Table 1). Thus, the optimisation problem to be solved for the household is as follows:

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Maximise wðh1  h2 Þ þ

n X

ðpi Y i  ci Þai

ð1Þ

i¼1

subject to constraints: n X h 1  h2 þ li a i 6 H ;

ð2Þ

i¼1 n X i¼1 n X

W i ai 6 X t ;

ð3Þ

ai 6 A;

ð4Þ

i¼1

where w is the hourly wage in baht, h1 is the number of hours of household labour devoted to work off-farm, h2 is the number of hours of labour hired on the farm from outside the farm family, pi is the price per unit of yield of crop activity i, Yi is the expected yield per unit of land, ci is the variable cost per unit of land devoted to crop activity i, ai is the area of land devoted to crop activity i, li is the number of hours of labour required to work a unit of land devoted to crop activity i, H is the total number of hours of labour available to be worked by the farm household, Wi is the water use per unit land of crop activity i, Xt is the total water expected to be available to the household for the season (in year t) and A is the area of land available to the household (may be either paddy or upland depending on RMU type). 4.3.2. HDM for RMU 8 Households of RMU8 have access to both paddy and upland fields (see Table 1). Let activities 1, . . . , f be those activities undertaken on paddy land, and f + 1, . . . , n be those undertaken on upland fields. Then the optimisation problem solved by the household decision model is as follows: n X Maximise wðh1  h2 Þ þ ðpi Y i  ci Þai ð5Þ i¼1

subject to constraints: n X h 1  h2 þ li a i 6 H ;

ð6Þ

i¼1 n X

W i ai 6 X t ;

ð7Þ

ai 6 Ap ;

ð8Þ

i¼1 f X

i¼1 n X

a i 6 Au ;

ð9Þ

i¼f þ1

where Ap is the area of paddy land available to the household, Au is the area of upland, and all other variables are as previously defined.

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Decisions made at the household level are passed to the decision disaggregation model (DDM) before being fed through the biophysical modelling toolbox, where the impacts of actual water availability and aggregated decisions on yields, runoff and erosion are then simulated. 4.4. Decision disaggregation model (DDM) Land-use decisions must be disaggregated by land unit from the broad classification of paddy and upland used in the household decision model, in order to be passed as inputs to the biophysical modelling toolbox. The model used to disaggregate these land-use decisions assumes that land units are developed in proportion to their current development first, before development Ôspills overÕ onto other land units. The algorithm used to calculate land-use decisions by land unit is described below. 4.4.1. Disaggregating land types to land units Land is classified into only two types in the household decision model: paddy and upland. Production decisions simulated by the household decision model are output as a set of crop areas on both paddy and upland fields for the wet and dry season at the node. Users specify which land units correspond to each type of land (paddy or upland). It is assumed that all calculations described below relate to a single node. The procedure described below is applied separately to both paddy and upland land types. Let gi be the current agricultural area and ti be the total land area of land unit i. Clearly, gi 6 ti for all i. If bm is the area of paddy (or upland) belonging to a household of RMU type m and hm is the total number P of households of RMU type m, then the upper bound of total cultivated land is m bm hm . It follows that: X X bm hm 6 ti ; m

i

otherwise the land allotted to households exceeds the amount of such land available at the node. Let qi be the simulated area of land devoted to agriculture in land unit i. Let di be defined as follows: X g di ¼ bm hm P i . ð10Þ gj m j

If di 6 ti for all i then qi is set proportional to the percent of current agricultural land in land unit i, gi/Rjgj, namely qi ¼ d i .

ð11Þ

If di > ti for some i, then qi is determined by repeating the following steps iteratively until the condition qi = min(qi, ti) is met for all i. This process reallocates land that ‘‘spills over’’ current agricultural land in a land unit, ensuring that the area of simulated agricultural land is less than the total area of the land unit.

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For land units j such that dj  min(dj, tj) = 0 X tj  d j qj ¼ P  ðd i  minðd i ; ti ÞÞ þ minðd j ; tj Þ; n i ðtl  d l Þ

ð12Þ

l¼1

where n is the total number of such j, l is an index over these j, and i is the index over all land units. For land units j, where dj  min(dj, tj) 6¼ 0 qj ¼ minðd j ; tj Þ.

ð13Þ

The final allocation of paddy by land unit is then qi for land unit i. The same procedure is followed separately for both paddy and upland areas. 4.4.2. Disaggregating cropping activities to land units Let the households of RMU type m have areas am, k devoted to cropping activities (k) on paddy areas. Cropping activities are defined not only by the type of crop (e.g., rice, maize) but also the level of fertilisation of the crop (none, half and full) and whether or not the crop is irrigated. Note that it is possible for am, k = 0 for any m or k. Then P am;k hm m ck ¼ P P ; ð14Þ am;k hm m

k

where ck is the amount of paddy area given to crop activity k as a percentage of all agricultural area on ÔpaddyÕ land units. The disaggregation (by area) of cropping activity k by land unit i is sk;i ¼ qi  ck .

ð15Þ

This calculation is repeated for all cropping activities, on both paddy and upland areas. These sk, i are passed to the biophysical modelling toolbox as an aggregated land-use decision for the node. 4.5. Links to the biophysical modelling toolbox The total land-use decision for the residual subcatchment area contributing to each node, including remaining forest cover, is then passed as an input to the biophysical modelling toolbox. This toolbox calculates the pre-extraction flow on a daily time-step for the year, given rainfall and temperature. This flow is sensitive to changes in forest cover. The crop model then runs for each land unit and crop combination defined by the land-use decision for the node. The water demand is calculated by the crop model on a 10-day time step. A water allocation model, containing a crop prioritisation list defined by catchment stakeholders, is used to determine the order in which crops are able to access available water for irrigation. Crop demands are sequentially compared with water available for extraction. Yield

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penalties occur for crops that do not receive sufficient water. An erosion model is also run to calculate wet and dry-season erosion given the crop choice and climatic conditions. The biophysical modelling toolbox then outputs actual water available for each of the crops as well as the actual yield for each crop. The water available is used to update householdsÕ expectations of water availability in the next year in the household decision models. Actual yields are passed to the socioeconomic impact simulation model (SISM) to consider the impact of actual water availability on household performance. 4.6. Socioeconomic impact simulation model (SISM) The socioeconomic impact simulation model runs subsequent to the biophysical modelling toolbox to calculate the impact of actual yield and water availability on household income, and on total rice production per person, which is considered to be a social indicator of the impact of a scenario option. The algorithm used by this model is described below. 4.6.1. Calculate the area weighted average yield for each activity If qi is the final allocation of paddy/upland by land unit as given by the disaggregation procedure (see Section 6) and P am;j hm cj ¼ PmP ð16Þ am;j hm m

j

then sj,i = qi · cj. The area weighted average yield of activity j is then P sj;i y j;i i xj ¼ P ; sj;i

ð17Þ

i

where i corresponds to the land unit. 4.6.2. Calculate the total yield of different crops for each household in each RMU The total yield of an activity j for RMU m is tm;j ¼ am;j  xj .

ð18Þ

4.6.3. Calculate the rice production and deficit per person for each household in each RMU Let j be an cropping activity where the crop type grown is rice. Then total rice grown per person per year by each household of RMU m is P tm;j j rm ¼ ; ð19Þ pm

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where pm is the average number of people in households of RMU type m. The rice deficit is zero if rm is greater than 300 kg, otherwise it is 300  rm. The cost of the rice deficit is then  ð300  rm Þpm if rm < 300; Cm ¼ ð20Þ 0 otherwise; where pm is the price of purchasing rice per kg. 4.6.4. Calculate the labour deficit/surplus for the household The number of hours of household labour devoted to work off-farm (h1,m) and the number of hours of labour hired by the household from outside (h2,m) for each household (m) are given by the household decision models. Thus, the cost of hiring labour to RMU m is given by hcm ¼ h2;m  w

ð21Þ

and the total off-farm income obtained by the household is given by of m ¼ h1;m  w.

ð22Þ

4.6.5. Calculate the household cash for each year The total cash in each season for each household of type m is then given by ! X X cashm ¼ pj  tj  sj;i cj  hcm þ of m  C m ; ð23Þ j

i

where pk is the price of a unit of production of crop activity k and ck is the variable cost per unit area devoted to crop activity k. The total cash value for the year is given by the sum of wet and dry-season cash. 4.7. Biophysical and socioeconomic indicators Outputs of the integrated modelling toolbox are in the form of indicators. The biophysical indicators are the same as for the biophysical modelling toolbox (see Merritt et al., 2004) and have been aggregated at the node level here. These can be summarised as: 1. Crop yield (tonnes/ha). 2. Crop water demand (mm). Total crop water demand required for the crop to grow at full potential. 3. Irrigation (mm). Total irrigation applied throughout the season. If crop water demand does not exceed the amount of water available within the stream then irrigation is the same as crop water demand. 4. Residual streamflow (ML). This indicator shows wet-season, dry-season and annual streamflow following abstractions for crop irrigations. 5. Erosion (tonnes/ha). 6. Forest area (ha).

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The socioeconomic indicators are given by RMU, and by node. They allow changes in the social and economic ÔperformanceÕ of a household, due to different climatic and upstream land-use choice scenarios, to be investigated and potentially traded-off. Where a multi-year scenario is run, a time series chart of the output is provided. Tables of values are also given for all scenario runs. The indicators provided are: 1. Cash per household (baht). This indicator describes the Ôeconomic performanceÕ of households of each RMU type. 2. Total household income from agriculture (baht). This indicator describes the agricultural income from their land-use choices. 3. Off-farm (household) income (baht). This indicator shows the reliance of different households on off-farm income. 4. Hire cost (baht). This indicator shows the total wages paid per household to hired labour in each year. It shows the extent to which production relies on hired labour. 5. Rice production per person (kg). It is assumed that each person in a household requires 300 kg of rice to survive. This indicator shows how close households come to meeting their subsistence requirements. Most households have a strong preference to produce their own rice. 6. Cost of rice deficit (baht). This indicator shows the cost to the household of purchasing unmet rice requirements. In this paper, a reduced set of indicators is used to illustrate trade-offs. Biophysical indicators are annual streamflow remaining after extraction (flow), forest area (forest cover) and total erosion (erosion). The cost of the rice deficit by household (Rice RMUX, X = 2, 3, 8) and household cash (Cash RMUX, X = 2, 3, 8) are also provided as indicators of social and economic impacts of scenarios.

5. Examples of model output A detailed sensitivity analysis of the integrated modelling toolbox has been completed and is described in the companion paper (Letcher et al., this issue). This section provides a brief description of the results of two scenarios for which the model has been run to demonstrate the types of trade-offs able to be calculated by the model. The scenarios show the effects of agricultural expansion leading to increases in the land available to individual households as summarised in Table 4. The model was run over 5 years using climate data from 1989 to 1993. Results from both nodes (node 1 is upstream of node 2) are shown. Results from the two scenarios are shown in Figs. 4–7 for nodes 1 and 2, respectively. These figures demonstrate the trade-offs associated with increasing the amount of land available to households. Households of RMU2 receive a very small benefit (i.e.,

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Table 4 Scenario input assumptions by three RMU types in the Mae Uam subcatchment (areas in hectares) Base case

Scenario 1

Scenario 2

Paddy

Upland

Paddy

Upland

Paddy

Upland

Node 1 RMU 2 RMU 3 RMU 8

0.4 0 0.432

0 0.336 0.208

0.496 0 0.544

0 1.6 0.992

0.592 0 0.64

0 2.864 1.776

Node 2 RMU 2 RMU 3 RMU 8

0.496 0 0.368

0 0.4 0.192

0.56 0 0.416

0 0.912 0.432

0.624 0 0.464

0 1.424 0.688

Fig. 4. Change in indicator values from base case, Scenario 1, node 1.

increase in household cash) from this increase in land available. These households are constrained by their access to other resources (water and labour) more than land and so are not able to receive large benefits from this increase in area available. Households of RMU3 and RMU8 both have access to rainfed fields and benefit to a much greater extent than those of RMU2. In some years household income at these RMU types more than doubles under these scenarios. Also these households have a small rice deficit under the base case assumptions. Increases in land lead to the removal of this rice deficit. This means that increasing the land area available to these households helped them meet their subsistence requirements and increased their cash wealth. However, as can be seen in Figs. 4–7, these economic and social benefits are at the expense of environmental costs. Relatively small increases in flow are experienced at

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Fig. 5. Change in indicator values from base case, Scenario 2, node 1.

Fig. 6. Change in indicator values from base case, Scenario 1, node 2.

both nodes. This implies that the increase in flow resulting from a decrease in the area of forest cover is greater than the additional extraction occurring across the year. This relates to the way in which forest cover changes affect flow: a decrease in forest cover increases wet-season flows, increasing overall annual flows, but decreases dry-season flows which are used for irrigation. Thus, flow increases annually but less water is available for extraction in the periods when it is most required.

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Fig. 7. Change in indicator values from base case, Scenario 2, node 2.

Agricultural expansion also leads to large increases in erosion and substantial areas of forest cover are lost. As would be expected both costs and benefits are greater when larger areas of agricultural expansion occur. But the relative impacts are not proportional to the level of change in forest area in all cases. The change in household cash at RMU2 from the base case is less than proportionate to the change in forest cover. This is the case at both nodes, but the effect is more pronounced at node 1, possibly because flows are smaller at this upstream node. The increase in flows is also less than proportionate to the change in forest cover in most years for both nodes. The change in household cash is more than proportionate to the change in forest cover. The relative impacts of both scenarios are the same across all years, and show the same pattern for both nodes. Overall, these results are consistent with current understanding of the operation of this catchment system.

6. Discussion The integrated modelling toolbox in this paper demonstrates a method for integrating environmental, economic and social impact assessment for water resources management. The example in the previous section illustrates the types of scenarios and trade-offs that can be considered by the model. While the application presented is specific to the Mae Uam catchment in Northern Thailand, the modelling approach is more generally applicable. Testing of the model has been documented to some extent in this paper as well as in Letcher et al. (this issue), Croke et al. (2004), Merritt et al. (2004) and Merritt et al. (2005). Testing of the model against baseline conditions is possible but limited, given

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the reliance of model building and calibration on existing, sparse data sets. There is overall a lack of an independent data set on which to ÔvalidateÕ all aspects of the model. However, where data are available the model reproduces observed behaviour with a reasonable degree of accuracy or at least produces behaviour that is consistent with expectations and other observations in the literature. Overall the following observations support the plausibility of the model:  Some independent data were available for testing the accuracy of the hydrological model. This model was observed to be relatively accurate in reproducing the general pattern of streamflows. It was found to perform better on wet-season flows than dry-season flows. Given the paucity of data available to calibrate the procedure used to regionalise flows, the model was found to be relatively accurate (Croke et al., 2004).  The crop model was validated against observed data. Perez et al. (2002) summarise results from testing the model against 5 years of survey data. They found the model simulated yields that were close to observed values and that the model did a good job of discriminating between land units. They concluded that Ôthe simulated impact of the different crops on the hydrological balance is physically plausibleÕ.  The component models have been tested independently and as a group and have been found to reproduce behaviour that is expected and/or explainable. For example, changes in forest cover are expected to decrease dry-season flows while increasing wet-season flows. This behaviour is observed in the model. In terms of the biophysical models in the system Croke et al. (2004) and Merritt et al. (2004) found that the model behaviour was consistent with other observations in the literature.  The household models simulate changes in behaviour in response to changes in prices, costs and resource availability. The model accurately reflects survey data that were collected and used in its construction. This obviously does not validate the model but does indicate it has been constructed accurately. As demonstrated in this paper and in Letcher et al. (this issue), the model behaves in ways that are consistent with expectations. Overall simulated household behaviour and impacts on household performance are as expected. A key difference between the model in this paper and previously developed integrated models is the use of uncertain expectations as the basis for household decision-making. The implementation in this paper assumes naı¨ve expectations, that is, that farmers expect this yearÕs water availability to be the same as last yearÕs. However, the model framework means that it is relatively simple to assume different forms of expectation setting, so the effects of these assumptions could be tested in future work. Of more immediate concern is the effect of expectations-based decisions, particularly the impact of the initial value assumed for farmerÕs expectations of water availability and yield, on model results and recommendations. These effects are explored in more detail in the companion paper (Letcher et al., this issue) and do not appear to affect model recommendations.

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A related issue is the treatment of household cash as essentially temporally independent, that is, the assumption that cash does not carryover between years, or affect the decisions of households in future years. This assumption relates to the short-run nature of decision-making, and is of less importance given that Ôlonger-termÕ crop planting decisions are not currently considered by the model. Longer-term crop planting decisions, such as the decision to grow fruit trees or tea, which do not return income for several years, are not considered by the model but are an important part of family income in many cases. Modification of the approach to consider the constraints presented by available cash or credit, and longer-term planting or investment decisions would be an interesting and relevant future development path for the model. Another key issue when considering the robustness of the model is the sensitivity of the Ôqualitative resultÕ or recommendation to climate and uncertainty in parameter values. The scenarios demonstrated in this paper give a similar pattern of impacts, with the magnitude and direction of change of indicators being consistent across nodes and scenarios. Further testing of the modelÕs sensitivity to changes in parameter values is undertaken in Letcher et al. (this issue). Overall the model appears to provide consistent recommendations, regardless of climate or small levels of uncertainty in parameter values. Finally, the integrated model developed is balanced in terms of the complexity of each of the disciplinary components represented. Each component model runs on an appropriate ÔlumpedÕ spatial scale. Temporal scales vary between models, but essentially correspond to the largest appropriate temporal scale. For example, household decisions and erosion are simulated seasonally (twice yearly), while crop and flow models run over smaller temporal scales to allow meaningful comparison of water availability and demand. The style and detail of process representation for each of the components is also similar.

7. Conclusions This paper applies a nodal-network approach to water resources assessment, integrating production and extraction decisions, crop growth, erosion, rainfall-runoff and its interactions with forest cover, and household performance. The approach presented advances or complements the approaches developed in previous nodalnetwork studies to integration, as outlined in Section 2, in several ways: 1. More complicated interactions between production decisions and the hydrological cycle are considered (i.e., effects of changes in forest cover as well as direct extraction from the stream). 2. Decisions are simulated on the basis of uncertain information and updating expectations rather than perfect knowledge. This creates challenges to the integration between the component models. 3. An algorithm to disaggregate household level decisions made on the basis of paddy and upland fields available to different groups of households onto different soil and slope classes is developed.

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4. The model considers not only water allocation and the effects of decisions on water quantity, but also looks at erosion and subsistence production requirements. 5. The model uses a scenario based Ôwhat ifÕ approach to considering impacts and trade-offs, rather than using an optimisation based approach to allocating resources. This was required by decision makers in the Thai case study, who wished to understand possible trade-offs resulting from different development and management scenarios rather than be presented with the ÔbestÕ possible solution which would be constrained by a given objective. Thai government agencies and research groups have been actively involved in the IWRAM project. While the integrated modelling framework developed in this paper addresses specific management requirements of these groups, much of the focus of developing this framework has been in developing a shared understanding of the catchment system. In many ways the most rewarding outcomes of the project have been the process of development of the approach, the increased ÔintegratedÕ understanding this has engendered in all project partners, and the links that the approach has created between agencies and researchers in Thailand and Australia. The integrated model framework and lessons learnt in the development of this prototype system have since been adopted and extended to new catchments in Thailand by the Thai project partners.

Acknowledgements The authors express special thanks to the reviewers for their useful and detailed comments on the original manuscript, particularly for comments relating to the split of the manuscript into two separate papers. The IWRAM Project was funded by the Australian Centre for International Agricultural Research and the Royal Project Foundation of Thailand and involved many contributors to the key research components. The following people have made significant contributions to the IWRAM project: Nick Ardlie, Chris Buller, Thirayuth Chitchumnong, Barry Croke, Claude Dietrich, Tony Jakeman, Penporn Janekarnkij, Nootsuporn Krisdatarn, Padma Lal, Rebecca Letcher, Ken Menz, Wendy Merritt, Pascal Perez, Suwanna Praneetvatakul, Varaporn Punyawadee, Santhad Rojanasoonthon, Helen Ross, Somporn Sangawongse, Parisa Sanguantham, Kamron Saifuk, Sergei Schreider, Michelle Scoccimarro, Bandith Tansiri, Karn Trisophon, and Andrew Walker. The RMU concept and classification used in this paper were developed by Michelle Scoccimarro and Andrew Walker for the IWRAM Project. The economic survey that underpins much of this analysis was undertaken by a large team of staff and students from Mae Jo University (Chiang Mai) and Kasetsart University (Bangkok). References Adams, R., Dunn, S.M., Lunn, R., Mackay, R., OÕCallaghan, J.R., 1995. Assessing the performance of the NELUP hydrological models for river basin planning. Journal of Environmental Planning and Management 38 (1), 53–76.

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