The influence of irrigated agriculture, urbanisation and water scarcity on human-water system dynamics

Large dams have become a dominant water management strategy over the last century, but they are typically managed with limited understanding of how human responses to their construction and operation influence the achievement of water management objectives. In recent years, several behavioural response patterns to large dams in human-water systems have been identified, and quantitative models developed to capture these emergent phenomena. However, there is a gap between the understanding of these phenomena in a generalised sense and communicating their relevance to water managers in local contexts. In this study we applied a generalised human-water systems model of reservoir operations during droughts and floods to two case studies in Australia; one in the water-scarce, largely agricultural Lachlan River catchment, and the other in the coastal, highly-urbanised Hawkesbury–Nepean catchment. Modelling results coupled with a qualitative review of historical socioeconomic, hydroclimatic, and water management characteristics of each case study were compared to identify potential emergent phenomena and the characteristics contributing to their development. We found reservoir effects (where increases in water storage capacity increase vulnerability to water scarcity) and lock-in behaviours are inherent risks for large reservoirs. The levee effect, whereby infrastructure reducing the probability of flooding paradoxically increases vulnerability to floods, is a risk, particularly where urbanisation is high. Sequence effects, where measures to deal with one hydrological extreme exacerbate the effects of the other extreme, are likely when operational rules constrain the adaptation of operations to hydroclimatic conditions, or when water management interactions during drought and flood are poorly understood. Where there is economic incentive to increase water usage, supply–demand cycles and rebound effects are a risk. Sensitive downstream ecosystems and high competition for limited resources make shifts in values that redirect water management priorities (pendulum swings) more likely. Identifying these emergent phenomena and their driving characteristics can help water managers identify and focus on context-specific risks to enable a proactive management approach to current and future challenges.

1 Introduction

Dams and reservoirs have had a major role in water management strategy as a response to water scarcity in many parts of the world, providing significant social and economic benefits (Lehner et al., 2011). However, the rapid expansion of large reservoirs in the last century has led to widespread changes in river flow regimes and the degradation of downstream ecosystems (Vörösmarty et al., 2010). Moreover, there is growing evidence that large dams do not necessarily increase water security in the long term (Sivapalan et al., 2012), as most societies respond to increased water supply by increasing water demand and consumption (Di Baldassarre et al., 2018). Water managers making decisions about the construction and operation of large dams and the distribution of water resources, do so within a complex landscape, where the ‘best’ action is usually not obvious, and trade-offs between social, economic and environmental risks must be dealt with (Wu et al., 2023).

Sociohydrology focusses on understanding and explaining observed patterns of behaviours arising in human-water systems (sociohydrological phenomena) (Sivapalan et al., 2012). Local case studies examining the social, economic, and environmental conditions which co-produce observed behaviours have been used to develop explanatory models for these behaviours (e.g., Di Baldassarre et al., 2013; Kandasamy et al., 2014; van Emmerik et al., 2014). These case studies and explanatory models have contributed to the identification and generalised understanding of several sociohydrological phenomena in human-water systems that contain large dams (Table 1).

Table 1

Table 1. Sociohydrological phenomena in human-water systems that contain large dams: definitions and descriptions.

Water management practice could benefit from applying the generalised learnings about human-water systems to urgent global challenges such as increasing water scarcity, environmental degradation, and changes to drought and flood regimes (Di Baldassarre et al., 2019). However, direct application of a generalised model is complicated by the highly localised and context-based nature of human behaviours, which are informed by cultural norms and values and mediated through specific institutional arrangements (Mostert, 2018). This means the same water management intervention applied in different contexts could have different outcomes, depending on the specific evolution of the sociohydrological system over time (Yu et al., 2022). There is therefore a missing link between identifying sociohydrological phenomena in a general sense and the application of these learnings to water management practice and decision-making (Arheimer et al., 2024).

In the Australian water management context, water governance has historically been reactive to changing climatic and environmental conditions, with water crises typically triggering reforms (Anderies et al., 2006). There is a persistent norm preferencing infrastructural interventions over other approaches (Wyborn et al., 2023; Samnakay et al., 2024). In this context, analysis of human-water systems and their feedbacks can provide a counter-point to this dominant perspective in water resource management (Thompson et al., 2019), and could contribute to a more proactive and holistic approach.

In this study we apply a system dynamics model of the operation of dams in droughts and floods to two case studies in Australia. We consider the application of generalised models to local case studies has the potential to identify which sociohydrological phenomena are relevant for water managers to consider in a particular socio-economic and hydroclimatic context. Connecting generalised understanding to local contexts required a mixed methods approach, which combined a generalised quantitative model with a review of qualitative data on the historical characteristics of the human-water system.

Our research objectives in this study were to: (1) identify and compare emergent sociohydrological phenomena in two catchments in Australia; and (2) identify the specific features of each sociohydrological system driving the emergence of phenomena to inform water management decisions in local contexts.

2 Materials and methods

2.1 Case studies

The selected cases studies are the Lachlan River catchment (Case Study 1) and the Hawkesbury–Nepean River catchment (Case Study 2). The catchments share a boundary along the Great Dividing Range, with the Lachlan River draining inland to the west, and the Hawkesbury–Nepean River draining east to the Pacific Ocean (Figure 1).

Figure 1

Figure 1. Map showing locations of Case Study 1, Lachlan Catchment and Case Study 2, Hawkesbury–Nepean catchment, and other localities mentioned in the text. Inset: outline of Australia showing state boundaries (red) and the Murray–Darling Basin (blue) in relation to the position of the main map (blue rectangle).

The Lachlan River catchment (the Lachlan) is within the Murray–Darling Basin, which is sparsely populated (ca. 2 million people in 1.06 million km²), accounts for approximately 40% of agricultural production in Australia, and crosses the borders of five states and territories (MDBA, 2010). Major water reforms over the last 30 years culminated in the implementation of the 2012 Murray–Darling Basin Plan, intended to return water over-allocated for irrigation back to the environment (Colloff and Pittock, 2022). The major water storage in the Lachlan is Wyangala Dam, constructed in 1935 and expanded in 1971 to a capacity of 1,220 GL (WaterNSW, 2023). The headwaters of the Lachlan River are in the mountainous eastern parts of the catchment where average rainfall is the highest, with climate conditions becoming increasingly arid downstream, where evaporation generally exceeds rainfall throughout the year (Bureau of Meteorology, 2024a). Wyangala Dam supplies water primarily for irrigation, but other downstream uses are town water supply, stock and domestic, mining and major floodplain wetlands, which provide habitat for a number of threatened species (NSW Department of Planning and Environment, 2020). Water management in the Lachlan is the responsibility of the Murray–Darling Basin Authority and the Commonwealth Environmental Water Holder (for environmental water management) at the federal level, WaterNSW and the NSW Department of Department of Climate Change, Energy, the Environment and Water at the state level, and urban water supply is the responsibility of local government.

The Hawkesbury–Nepean catchment (the Hawkesbury) is on the east coast of New South Wales, and includes Australia’s largest city, Sydney, which has a population of approximately five million (Australian Bureau of Statistics, 2022). The primary water storage is Warragamba Dam, completed in 1960 with a storage capacity of 2,065 GL. The highest average annual rainfall in the catchment is on the coast (1,500 mm) and decreases towards the west where Warragamba Dam is located. The primary use of water from Warragamba Dam is for urban water supply, contributing up to 80% of Sydney’s water needs (Sydney Water, 2024). Operation of the dam is the responsibility of WaterNSW at the state level and Sydney Water, a state-owned corporation, is responsible for water distribution to consumers. Downstream of the dam, considerable floodplain development has occurred, and the floodplain of the Hawkesbury has the highest flood exposure in Australia (Infrastructure NSW, 2019).

In this study we hypothesise the dominance of irrigated agriculture or urbanisation, and the different climate characteristics and catchment water balance, will contribute to the emergence of different sociohydrological phenomena. The Lachlan was selected to examine the role of irrigated agriculture and water scarcity in shaping water management challenges. The Hawkesbury was chosen to investigate urban water use behaviours in response to drought, and how relatively abundant water availability and low competition and contestation affects management decisions. Additional considerations in case study selection was their proximity (Figure 1), similar administrative bodies, and the presence of a large dam constructed in the last century.

We combined quantitative modelling using a system dynamics model with qualitative analysis of the historical hydroclimate, water management decisions, and socioeconomic context to explore the presence of emergent phenomena. Qualitative data was derived from scientific articles, reports by government agencies, water authorities, local governments, media reports and online sources such as the Bureau of Meteorology website. This mixed methods case study approach (Harwell, 2011) is intended to generate generalizable understandings about specific human-water system features which drive specific emergent phenomena (Mostert, 2018).

2.2 Model description

The system dynamic model in this study contains five modelling sub-routines (Figure 2), namely:

1. The reservoir system models the dam inflows and outflows. Outflows include withdrawals for town water supply and irrigation, and downstream flows include environmental flows, spills and other operational requirements;

2. The flooding system models flood losses, and how flooding interacts with flood awareness and floodplain population density;

3. The drought system links water shortages (when withdrawals do not meet demand) to increasing drought awareness, and the subsequent reduction in per-capita water demand;

4. The population system models the increases in demand caused by population growth, and the reduction in population growth due to water shortages; and

5. The irrigation system simulates changes in irrigation water demands in response to water shortages and fluctuating water availability.

Figure 2

Figure 2. Causal loop diagram of the model showing the sub-routines, model variables, and the relationships between variables. Figure modified from Mazzoleni et al. (2021; figure 3 therein) to include irrigation sub-routine (Case Study 1) and connections between drought awareness of dam operators and per-capita water demand (Case Study 2). Sub-routines are the reservoir system (green), the flooding system (blue), the drought system (red), the population system (black) and the irrigation system (orange). Model parameters associated with each variable are included in parentheses. Arrows represent feedbacks between variables, and the symbols + and − represent positive and negative feedbacks, respectively.

Sources of data for model inputs and observations used to test model performance are detailed in Supplementary Table S2. The equations for the dam, drought, flood, and population systems are applied as by Mazzoleni et al. (2021) and are provided in the Supplementary Table S3 together with initial conditions and parameter values (Supplementary Tables S4, S5).

The main driving equation in the model is the dam storage water balance equation, which influences all other systems (Equation 1):

$\begin{array}{ll}{V}_{t+1}=V_t+Q_t^I\mathit{\Delta t}-Q_t^E\mathit{\Delta t}-Q_t^{\mathit{Bf}}\mathit{\Delta t}-Q_t^S\mathit{\Delta t}-W_t\mathit{\Delta t}& (1)\end{array}$

Where: $V_t$ is storage volume at time t, $Q_t^I\mathit{\Delta t}$ is the monthly total inflow to the dam, $Q_t^E\mathit{\Delta t}$ is the total monthly required environmental flow volume, $Q_t^{\mathit{Bf}}\mathit{\Delta t}$ is the minimum monthly outflow required to maintain river flow, $Q_t^S\mathit{\Delta t}$ are spills or controlled releases from the dam to maintain flood storage capacity, as required for dam safety, and $W_t\mathit{\Delta t}$ is the total of monthly withdrawals from the dam for consumptive purposes, including irrigation (Mazzoleni et al., 2021).

The irrigation component was newly developed for this study and affects the dam system by determining the volume of irrigation withdrawal, and the population system by limiting population growth when drought awareness is high. It is affected by dam storage volumes and actual dam withdrawals, simulating water shortages (when withdrawals cannot meet demand) and reductions in consumption when water availability is low, approximating the operational water sharing rules.

Irrigation water demands are calculated annually to reflect annual decision making on plantings and are based on the demand function of Gonzales and Ajami (2017), which captures rapid recovery of water demand after periods of drought. The maximum possible demand is limited by the dam operational full supply volume (OFSV) and the coefficient ( $c_{\mathit{\text{Dmax}}}$ ) representing the proportion of storage assumed to be accessible for irrigation on a monthly basis (Equation 2).

$\begin{array}{ll}{D}_{\max }=c_{\mathit{\text{Dmax}}}\mathit{\text{OFSV}}& (2)\end{array}$

Modelled irrigation demand ( $D_t^I$ ) (Equation 3) declines towards the minimum observed demand ( $D_{\min }^I$ ) if drought awareness is increased over the past year, and recovers if drought awareness has decreased. The rate at which demand recovers or declines is proportional to the drought awareness ( $A_t^{\mathit{DI}}$ ), and the sensitivity of demand to drought memory ( ${\alpha }_{\mathit{DI}}$ ), decaying over time at rate which reflects increases in water use efficiency ( ${\beta }_I$ ).

$D_{t+1\text{ (3)}}^I=\{\begin{array}{c}{D}_t^I-D_t^I\left[A_t^{\mathit{DI}}{\alpha }_{\mathit{DI}}\right(1-\frac{D_{\min }^I}{D_t^I})+{\beta }_I]\mathit{\Delta t}\text{if}{A}_t^{\mathit{DI}}>A_{t-12}^{\mathit{DI}}\\ D_t^I+D_t^I\left[A_t^{\mathit{DI}}{\alpha }_{\mathit{DI}}\right(1-\frac{D_t^I}{D_{\max }^I})-{\beta }_I]\mathit{\Delta t}\text{if}{A}_t^{\mathit{DI}}\le A_{t-12}^{\mathit{DI}}\end{array}$

Irrigators experience drought losses ( $L_t^{\mathit{DI}}$ ) when demands exceed withdrawals (Equation 4).

$\begin{array}{ll}{L}_t^{\mathit{DI}}={\left(\frac{D_t^I-W_t^I}{D_t^I}\right)}^2& (4)\end{array}$

Irrigators also experience profitability losses when demand is reduced in response to low water availability ( $L_t^P$ ). This approximates water sharing rules which reduce water available for irrigation as a priority when dam storage is low. Accordingly, irrigators make decisions about on-farm water use to manage reduced allocations. In the model, irrigation losses related to reduced profitability during periods of low water availability are calculated when dam storage volumes are below a threshold ( ${\tau }_I$ ) (Equation 5).

$L_t^P=\{\begin{array}{c}\frac{{\tau }_I-V_t/\mathit{\text{OFSV}}}{{\tau }_I}\text{if}\frac{V_t}{\mathit{\text{OFSV}}}<{\tau }_I\\ 0\text{otherwise}\end{array}\text{ (5)}$

Profitability losses are a reliable drought indicator, and can therefore be understood to raise drought awareness (Hughes et al., 2022). Drought awareness of irrigators ( $A_t^{\mathit{DI}}$ ) is computed by considering the maximum of water shortage and water demand losses (Equation 6).

$A_{t+1\text{ (6)}}^{\mathit{DI}}=\{\begin{array}{c}{A}_t^{\mathit{DI}}+L_t^{\mathit{DI}}(1-A_t^{\mathit{DI}})-{\mu }_{D_i}{A}_t^{\mathit{DI}}\mathit{\Delta t}\\ \mathit{\text{when}}{L}_t^{\mathit{DI}}>L_t^P\\ A_t^{\mathit{DI}}+L_t^P(1-A_t^{\mathit{DI}})-{\mu }_{D_i}{A}_t^{\mathit{DI}}\mathit{\Delta t}\\ \mathit{\text{when}}{L}_t^{\mathit{DI}}\le L_t^P\end{array}$

2.3 Model parameterisation

The model was parameterised to fit observation data where available or to reflect operational rules and physical characteristics of each case study. Parameter values were restricted to the range tested in the sensitivity analysis conducted by Mazzoleni et al. (2021). Model parameterisation values and methods are provided in more detail in the Supplementary Table S5.

A sensitivity analysis of key model variables to parameter values was performed to test the model robustness to parameter assumptions (details are included in Supplementary material). The sensitivity analysis showed that all variables of interest but particularly drought losses are sensitive to parameters which influence irrigation water demands. Where irrigation water demands are absent, drought losses are instead most sensitive to the fractional efficiency adoption rate (the rate at which water demands can be reduced).

2.4 Model scenarios

Model scenarios were designed to test the effects of major water management interventions in each case study. In Case Study 1, Lachlan Catchment, the goal is to test the role of the dam enlargement for enabling the growth of irrigated agriculture. Therefore, the following scenarios were modelled: Baseline, which approximates the real historical condition, including the construction of Wyangala Dam in 1935, the raise in the height of the dam in 1971, and where translucency flow rules are introduced in 1998; and No dam raise (NDR), whereby the dam is not raised in 1971 and remains at its initial capacity of 375 GL.

In Case Study 2, Hawkesbury–Nepean Catchment, the goal is to assess the effectiveness of public drought awareness programs and water restrictions in encouraging lower per-capita water use during droughts. Therefore, the following scenarios were modelled: Baseline, which approximates the current operating rules, which do not allow for dam operation for flood mitigation and include water restrictions during droughts; and Water exploitation (WE), where drought awareness is only raised when communities experience water shortages.

3 Results

3.1 Qualitative data review

In the Lachlan catchment, major sociohydrological changes are related to the prioritisation of water resources for irrigation agriculture purposes, which has driven the economic development of the region. Following colonial settlement in the region in the 1830s, river regulation and the establishment of European-style agriculture led to hydrological change and widespread environmental impacts (Leblanc et al., 2012). Agricultural expansion was possible only through the expropriation of land and water resources from First Nations peoples, the legacy of which continues to shape water justice issues in the Basin (Hartwig et al., 2020). Water reforms are typically triggered by water crises (Pittock, 2019), and have included the establishment of water markets, the capping of extractions, the re-allocation of water from irrigation to the environment through government ‘buy backs’ of water entitlements, and the introduction of environmental flow requirements.

In the Hawkesbury–Nepean Catchment, changes in water management have been driven by the need to provide secure and safe water to the population of Sydney (Davies and Wright, 2014). Floodplain development has occurred downstream of Warragamba Dam, exposing approximately 140,000 people to flood waters (NSW Government, 2021). Major water management decisions have been the construction of Warragamba Dam to support ongoing population growth, and the enforcement of water restrictions during drought periods to reduce per-capita water demands. More recently, the potential for the dam to be operated for flood mitigation has been investigated (e.g., Devanand et al., 2023).

A timeline of major changes in socio-economics, hydroclimate, and water management is presented for each case study (Figure 3). A more detailed review is provided in the Supplementary Table S1.

Figure 3

Figure 3. Timeline of major socio-economic, hydroclimatic and water management changes for Case Study 1, Lachlan Catchment (A) and Case Study 2, Hawkesbury–Nepean Catchment (B). See Supplementary Table S1 for details and references.

3.2 Quantitative model testing

For Case Study 1, Lachlan Catchment, the modelled storage volume of Wyangala Dam shows a good correlation with historical observations (correlation coefficient: 0.88), and captures significant drought-related drawdown periods in the early 1980s, the Millennium Drought (2000–2010) and the severe Tinderbox Drought (2017–2019) (Figure 4a). Modelled irrigation water demand approximates longer-term trends but does not reproduce the drop in demand observed in 1990 (Figure 4b). The comparison of modelled to observed growth in irrigation demand between 1987 and 2000 supports the model assumption that growth in irrigation demand is related to the catchment water storage capacity. Reductions in demand for irrigation water in wet years (1990, 2016 and 2021) are not captured by the model, which does not simulate soil moisture or on-farm water storage. The effect of the Millennium Drought on population growth is captured, although the model does not accurately simulate the steep growth in population after approximately 1980, and instead simulates a slower but more steady population increase (Figure 4c). The model generally captures flood events prior to the Millennium Drought, with some discrepancies in flood flow volume, but the model does not capture the drought-breaking flood events of 2010–2012 and 2016 (Figure 4d). Overall convergence of model results with observations indicates the model structure and assumptions realistically capture aspects of the dynamic relationships between dam operations, drought awareness, and irrigation water use.

Figure 4

Figure 4. Case study 1, Lachlan Catchment. Comparison of modelled results (blue) with observations (black) for (a) dam storage volume; (b) annual irrigation water demand (plotted in the first calendar year of the reported water year, from July–June); (c) population; and (d) downstream flood water height. Data sources: (a) daily storage volume measurements at river gauge 412,010, Wyangala Dam at Storage Gauge (Bureau of Meteorology, 2024b); (b) annual irrigation water use estimates for the Lachlan River, 1974–1988: Scott (1989); 1988–1999: Hope (2003); 2006–2020: Walsh et al. (2021); (c) population data: Australian Bureau of Statistics (2019); (d) maximum monthly water level at river gauge 412,002, Lachlan River at Cowra: WaterNSW (2024).

Model results for Case Study 2, Hawkesbury–Nepean Catchment, indicate the model effectively represents trends in dam storage volumes (correlation coefficient: 0.74) (Figure 5a). Trends in population growth are captured by the model, except for the effects of the COVID-19 pandemic in 2020 (Figure 5b). Modelled per-capita water demands reproduce the reductions associated with water restrictions in the 1990s and during the Millennium Drought (Figure 5c). Modelled total water demands reflect the balance between per-capita water demand changes and population growth (Figure 5d). There is limited data available for water consumption trends prior to 1990, so these are verified by convergence of dam storage volumes with observations. Where data is available, the model overestimates water consumption prior to 1990. Downstream flooding regime is adequately represented, with critical flood events captured, though with some discrepancies in flood water level (Figure 5e).

Figure 5

Figure 5. Case study 2, Hawkesbury–Nepean Catchment. Comparison of modelled results (blue) to observations (black) for (a) dam storage volume; (b) population; (c) per-capita water demand; (d) total annual water consumption; and (e) downstream flood water level. Data sources: (a) maximum monthly storage volume from daily observation data at Warragamba Dam station 212,243, Warragamba: WaterNSW (2024); (b) Sydney population data 1901–2001: Australian Bureau of Statistics (2019); 2001–2021: Australian Bureau of Statistics (2022); (c) per-capita water demand data (1991–2023): Sydney Water (2023); (d) annual Sydney water consumption data, 1972: NSW Water Conservation and Irrigation Commission (1973); 1985: Department of Primary Industries and Energy (1987); 1991–2023: Sydney Water (2023); (e) maximum monthly water level at stream gauge 212,201, Nepean River at Penrith: WaterNSW (2024).

The Nash-Sutcliffe Efficiency (NSE) was calculated to provide a quantitative comparison between modelled and observed values (Table 2) for variables with relevant observational data. NSE is a measure of deviation of modelled results from observations, considering the variability of observations from the mean. The calculated NSE shows that the model adequately approximates observations, particularly reservoir volumes, population and flood water level.

Table 2

Table 2. Calculated Nash-Sutcliffe Efficiency (NSW) for each case study, where observation data is available.

3.3 Identification of phenomena

3.3.1 Case study 1, Lachlan Catchment

The following sections refer to model results (Figure 6) and qualitative review findings to identify emergent sociohydrological phenomena.

Figure 6

Figure 6. Case study 1, Lachlan Catchment. Model results for the baseline (blue) and no dam raise (NDR) scenarios (green) scenarios for (a) dam storage; (b) irrigation water demand; (c) drought losses in society due to water shortages; (d) drought losses (including cumulative losses) of irrigators due to water shortages; (e) drought awareness of irrigators; and (f) drought losses (including cumulative losses) for irrigators due to reduced water availability.

3.3.1.1 Supply–demand cycles

Comparisons of dam storage (Figure 6a) and irrigation demand (Figure 6b) between baseline and NDR scenarios indicate supply demand cycles are evident. Following the raising of the wall of Wyangala Dam, under the baseline scenario, modelled irrigation water demand (Figure 6b) continues to increase to a maximum in the early 1990s, during a period of consistently high storage volumes (Figure 6a). This rapid growth is consistent with observed water demands (Figure 4b). In the two decades following the dam expansion, irrigation profits are buffered against variation in water availability (Figure 6f) compared to the NDR scenario, contributing to lower drought awareness of irrigators (Figure 6e) and more rapid growth in water demands (Figure 6b). By comparison, peak irrigation water demands in the NDR scenario are reached in the 1960s, and droughts between 1970 and 2000 serve to limit further growth in irrigation water demand. This dynamic is indicative of supply–demand cycles, where additional water supply stimulates growth in demands, potentially offsetting long-term gains in water security.

3.3.1.2 Reservoir effect (safe development paradox)

A comparison of the modelled drought losses in the baseline and NDR scenarios (Figures 6d,f), suggests that the reservoir effect is emergent in the Lachlan. Drought losses for irrigators are expressed as losses due to water shortages (when supply cannot meet demand) (Figure 6d) and profitability losses (when demand is reduced in response to low water availability, which avoids water shortages but reduces farm profits) (Figure 6f). The growth in irrigation water demand to a peak in the 1990s (Figure 6b) coincides with favourable climate conditions and water management policies. During this period, irrigators were protected from water shortages, and economic losses during droughts were buffered (Figures 6d,f), suggesting the expansion of Wyangala Dam contributed to improved water security during this time. Although more frequent losses are experienced under the NDR scenario, cumulative losses between scenarios are almost identical by the end of the modelled period for both profitability losses (LP) and losses due to water shortages (LD) (107 and 30, respectively, in baseline scenario, and 110 and 30, respectively, in the NDR scenario) (Figures 6d,f). This result is attributed to the high losses during the Millennium Drought under both scenarios, but especially the baseline. High losses during the Millennium Drought caused modelled drought awareness of irrigators to be at a maximum for almost a decade (Figure 6e).

The irrigation losses under baseline and NDR scenarios during the Millennium Drought are comparable only in relative terms because they are calculated as a ratio of supply and demand (shortage losses) and the fraction of dam storage below a threshold (profitability losses). In absolute terms the losses under the baseline scenario are much larger because of the higher growth of irrigation water use compared to the NDR scenario. These findings indicate the emergence of the reservoir effect, suggesting that significant growth in the irrigation industry following the expansion of Wyangala Dam has led to high vulnerability during extended droughts. The Wyangala Dam expansion does, however, appear to provide some protection against water shortages for society, as drought losses for society are larger in the NDR scenario than in the baseline (Figure 6c).

3.3.1.3 Levee effect (safe development paradox)

The safe-development paradox emerges when construction of infrastructure to mitigate flooding results in a collective perception that the risk of flooding has reduced. It leads to increased development on the floodplain, worsening the damage when floods occur. Agricultural development in flood-prone areas resulted in crop damage and insurance claims following floods in 2016 and 2022 (Insurance Council of Australia, 2024). There was an expectation, reflected in media reports, that Wyangala Dam could mitigate the effects of flooding (e.g., ABC News, 2021, 2022), indicating the possible emergence of the safe development paradox for agricultural development. However, the model does not capture interactions between flooding and irrigation water demand, so a levee effect cannot be identified definitively.

3.3.1.4 Sequence effect

The model results do not clearly indicate the emergence of sequence effects. However, there are interactions between droughts, floods and human interventions in the Murray–Darling Basin that can result in sequence effects. For example, an important risk to water resources is floodplain harvesting: the storage and capture of floodwater for irrigation purposes in large on-farm dams upstream (Pittock et al., 2023). Floodplain harvesting can be thought of as a strategy to achieve water security and drought management at the farm-scale, but by reducing the volume of floodwater returning to river channels, it restricts the benefit of flooding flows to downstream users and ecosystems (Pittock et al., 2023). Drought-flood interactions affect water quality risks, whereby management interventions during droughts can worsen the water quality once streamflow recovers (Beavis et al., 2023). For example, drought-breaking floods in 2010 leached bio-available carbon compounds from leaf litter that accumulated on floodplains during the Millennium Drought. Consumption of carbon compounds by aquatic heterotrophic bacteria within-channel resulted in oxygen depletion and major hypoxic blackwater events (Mccarthy et al., 2014). Water infrastructure, surface water diversions and irrigation practices during the drought, as well as climatic factors, all played a role in driving these water quality events (Whitworth et al., 2012). These examples show how drought-focussed management practices can re-shape flood effects in beneficial and detrimental ways, indicating the potential for sequence effects during both drought-to-flood (e.g., blackwater events) and flood-to-drought (e.g., floodplain harvesting) events. These mechanisms are related to the use of private, on-farm infrastructure and the effect of reservoir operations and land use changes on water quality, which are not captured in this model. As such, sequence effects are considered likely but are not corroborated by modelling results.

3.3.1.5 Rebound effect

Irrigation water demand was modelled based on an assumption that demand rebounds rapidly following drought, when drought awareness is high but decreasing (Gonzales and Ajami, 2017). Rapid recovery of irrigation water demand immediately following drought is apparent from the end of the Millennium Drought in 2010 (Figure 6b).

Rebound effects have occurred in the Murray–Darling Basin as a result of the government subsidised on-farm irrigation efficiency program, whereby irrigators receive funding for on-farm infrastructure upgrades and in exchange surrender a portion of their water savings for environmental use (Hughes et al., 2020). Irrigation efficiency projects have been shown to increase profitability, leading to an expansion of areas under irrigation, and hence higher water demands (Wheeler et al., 2020). This has also resulted in shifts towards higher-value perennial crops, that are more water intensive (such as almonds) and have inflexible water demands, and hence require more water during drought periods than annual crops such as cotton (Schirmer, 2017; Hughes et al., 2020). The on-farm irrigation efficiency program has included projects in the Lachlan (Hughes et al., 2020), and almonds constitute an increasing proportion of irrigated crop mix (Goesch et al., 2020). Therefore, it is likely that rebound effects are emergent in the Lachlan.

3.3.1.6 Pendulum swing effect

Reduced flows and altered flow regimes due to irrigation diversions and the operation of the dam, as well as a long history of land-use changes, have degraded downstream wetlands (Davies et al., 2010). Water governance has shifted in response to changing social values of water and increasing concerns about the effects of water management decisions on the environment (Wei et al., 2023). Kandasamy et al. (2014) demonstrated the emergence of the pendulum swing effect in the Murrumbidgee catchment, to the south of the Lachlan, identifying shifting values in response to environmental degradation and a declining contribution of irrigated agricultural production to the national economy as major drivers of policy changes. Water management rules in the Lachlan have changed in accord with broader water reforms in the Murray–Darling Basin, indicating the emergence of pendulum swing effects. The impacts of the Millennium Drought and high drought awareness at this time (Figure 6e) made water reforms more urgent, in a system where changes to water management policy are reactive and crisis-driven (Pittock, 2019).

3.3.1.7 Lock-in behaviour

The presence of supply–demand cycles and reservoir effects in the Lachlan indicate the construction and operation of Wyangala Dam has enabled the development of a large dependent irrigation sector. Due to the inland location and dry climate of the Lachlan, there are few options to increase water supply without incurring further environmental degradation. Options to increase private water supply of upstream irrigators via on-farm dams, floodplain harvesting or groundwater extraction will have negative impacts on downstream users and ecosystems (Pittock et al., 2023). Therefore, the Lachlan is characterised as having high contestation between users for scarce water resources. Changes in policy have resulted in water management measures intended to balance competing social, economic and environmental priorities, including re-allocation of water from irrigation to the environment. However, irrigator organisations represent a powerful lobby group with significant influence over water policy and management decisions (Grafton and Williams, 2020). The target volume of water to be recovered for the environment has been steadily stepped down, leading to a gap between what is required for the environment, and what is politically feasible to re-allocate (Colloff and Pittock, 2022).

Therefore, although there has been considerable water reform since the 1980s, there is little evidence that this is contributing to real social and environmental benefits (e.g., Bender et al., 2023; Colloff et al., 2024). This study provides further support to these findings, as modelling shows little evidence of real behavioural change in irrigation water demand since the introduction of managed environmental flows in 2009 (Figures 6a,b). Modelled water demand performs adequately prior to and following the change in dam operational rules, with no change to the behaviour response rules in the model. Instead, the main driver of demand continues to be water availability, and is therefore only indirectly affected by environmental flow rules. We therefore suggest lock-in behaviours may be evident in the Lachlan, where past decisions to increase water availability for irrigation purposes limits the effectiveness of water reforms with environmental objectives.

3.3.2 Case study 2, Hawkesbury–Nepean Catchment

Model results (Figure 7) and qualitative review findings are used to identify emergent sociohydrological phenomena, as detailed in the following sections.

Figure 7

Figure 7. Case study 2, Hawksbury–Nepean Catchment. Model results for the baseline (blue) and water exploitation (WE, green) scenarios for, (a) dam storage; (b) total annual water demand (unbroken line), per-capita water demand (dashed line); (c) drought awareness of reservoir operators (unbroken line) and society (dashed line); (d) drought losses of society; (e) flood awareness of reservoir operators (unbroken line) and society (dashed line); and (f) flood losses of society.

3.3.2.1 Supply–demand cycles

Supply–demand cycles emerge when increases in supply lead to increases in demand, beyond those which would be expected due to population growth (Di Baldassarre et al., 2018). Water consumption data prior to the construction of Warragamba Dam is scarce but indicates only a slight increase in per-capita water demands in the decade following dam construction (Figure 8). An increase in total water demand was observed over this time which appears to be more related to population growth. This rapid population growth is attributed to increased migration and the population boom following WWII, rather than the presence of the dam (Cook and Spearritt, 2021).

Figure 8

Figure 8. Effect of Warragamba Dam on Sydney water demands. Data sources: Sydney population data 1901–2001: (Australian Bureau of Statistics, 2019); 2001–2021: Australian Bureau of Statistics (2022); annual Sydney water consumption data, 1972: (NSW Water Conservation and Irrigation Commission, 1973); 1985: (Department of Primary Industries and Energy, 1987); 1991–2023: Sydney Water (2023); per-capita water demand prior to 1991 was calculated using population and total water demand data, with data for 1991–2023 from Sydney Water (2023).

The baseline scenario model shows per-capita water demand has declined since the implementation of water restrictions in the 1990s (Figure 7b), consistent with observations (Figure 5c). Per-capita reductions in consumption and demand are attributed to water conservation programs, including improvements in household water use efficiency, adoption of water conserving behaviours and fixing leaks in the water delivery network (Sydney Water, 2023). Following the Millennium Drought, population growth is the main driver of increases to total water demand (Figure 7b). Per-capita water demand has remained relatively stable until the 2017–2019 drought, during which per-capita demand increased slightly (Figure 8). We consider it likely that drought awareness-raising (Figure 7c) and water restrictions have been effective in reducing per-capita demand, preventing the emergence of supply–demand cycles.

3.3.2.2 Reservoir effect (safe development paradox)

Comparison of the baseline and water exploitation (WE) scenarios indicate that demand reduction strategies prevented dramatic water shortages during the Millennium Drought. In the baseline scenario, where increasing drought awareness of reservoir operators triggers proactive water management decisions (Figure 7c), per-capita water demands have declined (Figure 7b) when the dam volume is below a threshold. In the baseline scenario, urban demands are always met, so no drought losses occurred (Figure 7d), despite prolonged periods of drought. By comparison, in the WE scenario, where only experience of water shortages contributes to reduction in demand, significant drought losses are experienced during the Millennium Drought (Figure 7d). Water shortages prompted major behavioural change and reduced water consumption over a short period. This finding indicates reductions in per-capita water demand following droughts in the 80s and early 90s mitigated some impacts of the ten-year Millennium Drought, thus reducing the vulnerability of consumers to water scarcity.

However, despite a severe drought during 2017–2019, there has been little reduction in per-capita demands since the end of the Millennium Drought in 2010 and total consumption continues to rise due to population growth (Sydney Water, 2023). Despite some reduction, per-capita consumption is higher in Sydney than other Australian cities (Cook and Spearritt, 2021). Increased temperature due to climate change is likely to drive increases in per-capita consumption (Barker et al., 2020). For example, high temperatures likely contributed to the increase in per-capita demands observed during the 2017–2019 drought (Figure 8) (Sydney Water, 2023). Less rainfall under climate change will reduce inflows to Warragamba Dam, and water quality is expected to decline due to drought, bushfires and erosion upstream (Eco Logical Australia, 2023). These pressures and the reliance on Warragamba Dam for 80% of their water needs, leaves the Sydney population vulnerable to water scarcity, indicating the emergence of reservoir effects.

3.3.2.3 Levee effect (safe development paradox)

Modelling shows a significant discrepancy between flood awareness of reservoir operators and of society (Figure 7e). Warragamba Dam is used primarily for water supply and operational rules do not permit active flood management. However, the community perceives the dam as preventing major floods (Infrastructure NSW, 2019). About 30% of residents moved to new developments on the Hawkesbury floodplain in 2006–2011, during an extended period without significant flooding (1990–2021) (Infrastructure NSW, 2019). Perceptions of flood risk increase with the duration of residence; limited experience of flooding causes residents to underestimate risk (Masud et al., 2019). Flood preparedness in 2018 was low, with 67% of survey respondents indicating they had done nothing have done nothing to prepare for floods (Infrastructure NSW, 2019). These findings indicate emergence of the safe-development paradox, whereby the presence of the dam contributes to reduced flood risk awareness, preparedness and ultimately increases vulnerability to damage. Community awareness increased following major flooding in 2021 and 2022, though some misunderstanding remains about flood risk and likelihood (WMA Water, 2024).

3.3.2.4 Sequence effect

Creating airspace in Warragamba Dam has the potential to reduce downstream flooding (Infrastructure NSW, 2019). However, if a forecasted flood does not eventuate, the lowered volume of stored water would compromise water security. Keeping storage volume near capacity as a drought contingency increases flood risk, but lowering volume to capture floods can lead to water shortages (Wu et al., 2023). This interaction indicates sequence effects, where decisions to address one extreme exacerbate the other. This possibility does not clearly manifest in model results, as flood losses are relatively consistent between scenarios (Figure 7f). An exception is the flood in 1988, which is captured by the dam in the WE scenario but not in the baseline scenario (Figure 7f).

3.3.2.5 Rebound effect

Neither total nor per-capita water consumption appears subject to rebound effects (Figure 7b), as population is the major driver of demand. Recent additional supply from the desalination plant has not stimulated additional demand, which has been relatively stable since the end of the Millennium Drought. Reductions in per-capita water consumption during droughts may thus represent a permanent shift in water demand and rebound effects are considered absent.

3.3.2.6 Pendulum swing effect

The objective of water management is to ensure reliable urban water supply to the exclusion of other objectives such as flood mitigation. With the construction of the desalination plant in response to the Millennium Drought and increasing use of domestic rainwater tanks, water management is shifting from a centralised to a decentralised system, but the overall objective remains the same (Davies and Wright, 2014). A shift in policy priority to reduce flood exposure downstream of the dam has been assessed, but not yet implemented (e.g., Infrastructure NSW, 2019; Devanand et al., 2023). Therefore, pendulum swing effects have not been identified.

3.3.2.7 Lock-in behaviour

Water management options to address flood exposure downstream are constrained by the decision to predominantly rely on Warragamba Dam for urban water supply, the vulnerability of urban consumers to droughts, and the uncertainty in predicting future floods (Devanand et al., 2023; Wu et al., 2023). Water management is therefore constrained to one mode of dam operation as a result of past management decisions, indicating lock-in behaviour.

4 Discussion

4.1 Comparison of case study results

In this study we identified emergent sociohydrological phenomena in two catchments that can be attributed to the distinct characteristics of each sociohydrological system (summarised in Table 3).

Table 3

Table 3. Summary of emergent sociohydrological phenomena identified in each case study and the major drivers.

The levee effect is apparent in the Hawkesbury, where major floodplain development has occurred due to pressures on space for residential development and urbanisation. Options of water managers to reduce flood exposure are constrained, as the costs of relocating people are prohibitive (Infrastructure NSW, 2019). Water managers have more options to deal with the levee effect in the Lachlan, where space is less constrained, population sparser and costs of relocating infrastructure are lower. The extent of floodplain development in the Lachlan in relation to flood risk is not well documented. Further research could explore the relationship between flood risk and irrigated agriculture.

In the Hawkesbury, adaptive management of Warragamba Dam to manage both droughts and floods is constrained by the need to secure urban water supply. Therefore, sequence effects and lock-in behaviour are consequences of the reservoir effect. Strategies to diversify supply and reduce reliance on the dam for water supply create the opportunity for greater adaptive management. In the Lachlan, we identified the prospect of sequence effects via multiple pathways for which there is growing general understanding (Barendrecht et al., 2024). Droughts and floods tend to be managed separately in the Murray–Darling Basin and their interactions are typically not considered. The potential for sequence effects indicates water managers should consider interacting effects of their interventions and that floods and droughts be treated as two ends of a continuous hydrological spectrum rather than as separate, unrelated events (Ward et al., 2020).

Rebound effects are expected where there are strong economic incentives to consume more water. In the Lachlan, this is driven by water efficiency improvements in irrigated agriculture (Wheeler et al., 2020), and changes from annual to perennial crops with higher water requirements (Loch and Adamson, 2015). In the Hawkesbury, rebound effects have not been apparent historically, though may emerge following the introduction of new water supplies and efficiency improvements (Sharifi et al., 2024). Rebound effects are considered more likely in catchments with water-intensive industries, such as irrigated agriculture, and less likely in urban catchments.

Reservoir effects and lock-in behaviours were identified in both case studies, suggesting these are inherent risks in management of large dams. The driving characteristic of these phenomena is the presence of the dam itself. In the Hawkesbury, specific human-water system features present options to address reservoir effects through diversification of water sources to reduce drought vulnerability. For example, Sydney has access to seawater for desalination, providing additional supply (Sydney Desalination Plant, 2025). High local rainfall and domestic rainwater harvesting can reduce demand pressures on Warragamba Dam (Sountharajah et al., 2017). Decentralisation of urban water systems also has potential to relieve lock-in effects (Rabaey et al., 2020), and options to operate the dam more dynamically may arise as flood forecasting improves (Devanand et al., 2023).

By comparison, options to address reservoir effects and lock-in behaviours in the Lachlan are constrained by low water availability, leaving managers few options to increase supply without effects to downstream users and wetlands. For example, a recent proposal to expand storage of Wyangala Dam was rejected due to potential adverse environmental impacts (NSW Minister for Water, 2023). Unlike the Hawkesbury, diversifying supply via decentralisation and private infrastructure presents management challenges to water modelling and accounting, which form the basis for decisions on water allocations to irrigators and between consumptive and environmental uses (Pittock et al., 2023). In this context, decentralisation of water sources is unlikely to be a viable option to relieve demand for scarce water resources, and reduce reservoir effects. This is further complicated by the emergence of supply–demand cycles and rebound effects, with the high dependency of irrigators on the operation of Wyangala Dam interpreted as both a product of, and a constraint on, water supply options in the catchment. This indicates that water management strategies aiming to reduce scarcity by increasing supply or water-use efficiency are likely to instead stimulate demand, and would have limited effects on improving water security. Instead, attention should focus on strategies aiming to control or reduce demand (Di Baldassarre et al., 2019). The recent rejection of the NSW government proposal to expand the storage capacity of Wyangala Dam by 650 GL because of a lack of return on investment and adverse downstream environmental impacts suggests a slight shift in approach in this direction (NSW Minister for Water, 2023). However, the failure of the pendulum swing at the policy level to produce real change at the level of implementation suggests more transformative changes to water management are needed.

Although reservoir effects and lock-in behaviours are inherent risks for large dams, they will be lessened in catchments with options to diversify water sources and where there is alignment of water management objectives among users. In catchments with high competition for scarce water resources and economic incentives to increase irrigation demand, reservoir effects, supply–demand cycles, lock-in behaviours and rebound effects become interlinked and mutually reinforcing. The extent to which demand can be managed by farm-scale adaptation to less water remains to be seen.

In the Lachlan, water managers are trying to satisfy multiple, often conflicting objectives. Interventions to address some phenomena could exacerbate effects of others. Competition for resources drives trade-offs among management objectives, which shift together with societal values of water and changing economic and environmental conditions, producing pendulum swing effects (van Emmerik et al., 2014). By comparison, water management objectives in the Hawkesbury appear to have high coherence, and management options to diversify supply have potential to reduce risks associated with other phenomena. We consider the pendulum swing effect is more likely in catchments with vulnerable downstream wetlands and high competition for water, as in the Lachlan.

4.2 Study limitations

Confidence in the findings of this study would be improved by inclusion of more case studies from a range of catchments. Contextual analysis and the development of model scenarios could have benefitted from input from water managers. Despite these shortcomings, we consider the model framework as sufficiently generalisable to be adapted and tailored to a range of contexts and issues water managers might be required to investigate.

Model representation of the safe-development paradox and sequence effects in the Lachlan is limited by gaps in knowledge of drought-flood interactions and how irrigation water demand responds to extreme wet conditions. Drought awareness in each case study was modified to represent different mechanisms by which it is developed, highlighting the complexity of drought awareness-raising processes. Relating awareness only to experience of water shortages is too simplistic. In Australia, media reporting increasingly covers drought crises (Wei et al., 2015), and the effects of drought in irrigation areas have flow-on effects to the wider community, for example though increases in food prices. Therefore, it is likely that droughts in the Murray–Darling Basin affect drought-aware behaviours in Australian cities. This situation raises the question of whether the catchment is the most relevant scale to address droughts and their impacts (Fischer et al., 2021).

Further, the model was able to endogenously represent changes in urban and irrigation water demand as a result of variable water availability. More significant changes in water management, for example reservoir storage expansion, were instead modelled using scenarios. In both catchments, recent droughts have prompted calls to increase reservoir storage which were ultimately dismissed on social and environmental grounds. This suggests that the management response to drought in both catchments has changed over time, where social and environmental factors now serve to counteract pressure to increase supply. These feedback mechanisms are not included in this model, highlighting the value of qualitative data to complement quantitative modelling when understanding emergent behaviour in human-water systems.

An issue that warrants attention is the role of colonial processes in sociohydrological phenomena in Australia. Colonial processes enabled expansion of irrigation in the Murray–Darling Basin and served to exclude First Nations Peoples from participation in agricultural livelihoods (Hartwig et al., 2022). Historical and continuing injustices over water rights and access has led to major power imbalances: those who influence and benefit most from use of water resources tend to drive the emergence of sociohydrological phenomena to the detriment of those with least social power (Savelli et al., 2021). Power dynamics and how they play out in the management of water resources is an important sociohydrological issue which would benefit from further research (Konar et al., 2019).

4.3 Closing remarks

We compared two case studies to determine the characteristics of human-water systems that contribute to the development of emergent sociohydrological phenomena. The generalised model we present here can be used by water managers and decision-makers as a framework to consider and address behavioural responses to water policy and management interventions. This framework can be used to question current norms in water management practice, whether they are achieving objectives and explore options for change. A nuanced interpretation of the modelling requires a detailed contextual understanding of each catchment, which water managers are well-placed to contribute to and engage with. The identification of specific emergent phenomena and their attribution to the characteristics of the human-water system enables water managers to determine which phenomena are most important to consider, based on their knowledge and experience.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author. Information about publicly available datasets used in this study is contained within the article and Supplementary material.

Author contributions

IF: Writing – original draft, Data curation, Writing – review & editing, Investigation, Conceptualization, Methodology. MM: Writing – review & editing, Conceptualization, Supervision, Methodology. GB: Supervision, Writing – review & editing, Conceptualization. MC: Writing – review & editing.

Funding

The author(s) declare that financial support was received for the research and/or publication of this article. This article is based on research completed as part of a Master’s thesis at Uppsala University. The writing of this article has been partially funded by the Commonwealth Department of Education and Training through the Australia Government Research Training Program (to IF).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The authors declare that no Gen AI was used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

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Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/frwa.2025.1612580/full#supplementary-material

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