Drought is a natural hazard and, as such, has to be understood as a natural feature of climate. Whether or not a drought becomes a disaster depends on its social, economic, and environmental impacts [1
]. Therefore, the key to understanding drought is to acknowledge its different dimensions. Drought affects both surface and groundwater resources and can lead to reduced water supply for in-home consumption and agricultural and industrial activities. Furthermore, it can deteriorate water quality by rising nitrate, ammonium, and phosphate concentrations, and disturb riparian habitats [2
]. Agriculture is the most affected sector by droughts, but many other sectors may suffer relevant losses, including energy production, tourism and recreation, transportation, urban water supply, and the environment. Sustained drought can cause social, economic, and energy crises, even leading to migration from affected zones (often rural and agricultural-focused) to other regions or nearby countries [4
]. Drought is not the only issue that water resource systems have to face regarding water availability. Water scarcity refers to continued unsustainable use of water resources and it can be influenced by water management [5
]. Increasing water demand due to population growth and the development of the agricultural, energy, and industrial sectors has increased the frequency of water scarcity events that occur when there is a lack of freshwater to meet the demands [6
]. Climate change is expected to further aggravate water scarcity because of the increase in drought frequency, severity, and duration [7
There is an increasing concern worldwide about the ineffectiveness of most common drought management practices, largely based on crisis management and on treating symptoms (impacts) rather than the underlying causes associated with them [7
]. The European Union has promoted the move from crisis management to drought risk management since 2007 [9
]. However, there are gaps in the current water scarcity and droughts policy of the EU, including [10
]: conceptual gaps on the understanding of causal relationships between drivers, pressures, status, and impacts; limited data on current and future water demand and availability; policy, governance and implementation gaps regarding measures to increase water supply and to target pressures and impacts caused by droughts.
Drought management plans are tools that aim to reduce the impact of droughts in water resource systems providing a framework for proactive, risk-based management [9
]. A coordinated drought plan includes monitoring, early warning and information systems, impact assessment procedures, risk management measures, preparedness plans, and emergency response programs. Without these plans, nations will continue responding drought in a reactive, crisis management mode [7
]. A key feature of drought management plans is the use of indices to establish a link between the state of the river basin and the measures to be taken [11
]. Drought indices have been developed for assessing drought parameters including intensity, duration, severity, and spatial extent, and are effective tools in the monitoring and management of droughts [2
]. However, traditional drought indexes often fail at detecting critical events in highly regulated systems, where natural water availability is conditioned by the operation of water infrastructures such as dams, diversions, and pumping wells. Here, ad hoc index formulations are usually adopted based on empirical combinations of several significant hydro-meteorological variables through customized formulations [13
]. A system of drought indicators based on levels or thresholds depending upon the degree of water scarcity, and several management actions aiming to mitigate critical situations have been developed in the Jucar River system [11
]. The creation and institutionalization of multi-sector partnerships have reinforced the development of efficient drought management [15
]. To support drought management, scientific approaches including drought characterization, development of risk indicators, and the analysis of economic instruments for risk mitigation are involved in conjunction with the identification, selection, and prioritization of measures to lessen the effects of drought [16
]. Decision support systems (DSS) have been developed to study effective drought management strategies, as they are considered one of the most effective tools for integrated water resource management [6
]. The use of DSS tools for drought risk management has been increasing during the last decades [17
]. Studying resource allocation requires the development of DSS able to apply drought management strategies and to dynamically evaluate the status of water resource systems [12
]. Multi-criteria decision analysis tools (MCDA) are also oriented to assist the decision-making process in the operation of water resource systems. Nevertheless, a major problem in developing MCDA processes is to understand the risk associated with persistent drought conditions, as risk management involves subjective considerations [6
]. The water sector’s importance for other sectors requires policies and management strategies that are aware of the potential widespread impacts [21
]. Very often, undesired effects can be derived from the execution of drought management strategies. For example, increased groundwater extraction to compensate for the reduction of surface water availability can lower base flows of rivers and streams, and reduce the piezometric level of aquifers [22
]. These unexpected consequences can affect river biota, agriculture income, and urban supply in ways that are more damaging or long-lasting in time than the aforementioned drought. Consequently, there is a need for management models able to simulate the complex interactions between different sectors and activities to study the response of water resource systems to drought management strategies.
System dynamics is a theory of system structure and a set of tools for representing complex systems and analyzing their dynamic behavior [23
]. This methodology is particularly useful for studying complex water resource systems with interacting elements and policies, whose behavior cannot be easily predicted [24
]. The development of system dynamics models to analyze and improve water resource management has a tradition that dates back to the late 1960s. Since then, and thanks to the development of computer technology and user-friendly system dynamics software, all types of qualitative models have been developed for improving system understanding in water resource systems. However, system dynamics have not been yet applied to highly regulated and complex water resource systems for testing drought management strategies with a quantitative approach and integrating a drought early warning system.
The objective of this paper is to develop a decision support system (DSS) based on system dynamics for the efficient drought management of the Jucar River system. The DSS simulates the management of the Jucar multi-reservoir system integrating monthly-defined reservoir operating rules, stream-aquifer interaction and conjunctive use of surface and groundwater, drought management measures (linked to a system state index), and all this taking into account current water demands and allocation criteria. The tool allows studying the effect of policy and management measures in the system, and it serves as a steppingstone towards the understanding of water resource systems as a holistic system. The DSS provides quantitative results comparable to the historical records for the calibration and validation period. The calibrated model facilitates the design, testing, and selection of new drought management strategies. Section 2
introduces the system dynamics modeling method, details some applications of the methodology for the management of water resource systems and describes the Jucar River system case study. Section 2
also introduces and describes the main features of the system dynamics model developed for the case of study. Section 3
shows and discusses the results, first validating the behavior of the model and later discussing the hydrological and economic results for the simulated scenarios. Finally, Section 4
exposes the conclusions.
This paper presents a system dynamics DSS for drought management of the Jucar River system, taking into account the combination of a state index and several drought management strategies. The resulting DSS showed the potential of system dynamics for simulating the management of multi-reservoir systems, integrating monthly-defined operating rules for the reservoirs, stream-aquifer interaction, conflicting water demands, and drought management strategies. The model adequately reproduces the operation of the system and is able to produce accurate quantitative results, as shown by the comparison with the historical records.
The DSS takes advantage of the holistic concept that drives the methodology and incorporates components from different disciplines (hydrology, economics, social sciences, laws, etc.) into its modular structure. The state index subsystem is an example of how it is possible to integrate policies and management strategies into a water resource model using a system dynamics approach. Likewise, water policy or legislation has been incorporated into the model—e.g., the Alarcon agreement.
The DSS opens up the possibility of analyzing different drought management strategies and assessing the interactions, feedbacks, and impacts within and between multiple sectors and variables.
Results showed that drought management strategies have a net positive effect in the Jucar River system from both the economic (agriculture) and the water management perspective. The defined measures lowered agricultural losses for the 2005–2008 drought period and increased the amount of stored water during drought allowing the faster recovery of the system. Although the model provides quantitative results similar to the historical data available, the main goal of a system dynamics model is neither to forecast nor to optimize, but studying patterns, trends, and interactions between different variables of the model [24
]. Modeling and dynamically simulating the change in water resources over time provides a scientifically defensible basis for proactive management strategies, enhancing our prospects to maximize the adaptive capacity of the system as a whole [29
Moreover, the same methodology used to study drought management strategies can be applied to study the impact of different realities and inputs into the system. The DSS model developed for the Jucar River system uses a quantitative approach for its simulation. Consequently, it requires numeric data and well-tuned equations to capture the behavior of the system in detail. Qualitative variables and inputs can also be implemented in this kind of model. Qualitative modeling often introduces “soft” variables to study the general patterns of behavior of the model, rather than precise numbers [56
]. In this case, qualitative modeling can be restricted to new subsystems for the testing of different non-easily quantifiable hypothesis.
The model herein presented was successfully developed for the Jucar case study and it could be replicated in any basin or system where enough information and data are available. The development of quantitative system dynamics models requires the use of a large volume of data coming from different fields (from hydrological to economic and reservoir data) as well as a deep understanding of the system structure and behavior. Very often, the most complex issue of this type of model is the development of the monthly operating rules for the reservoirs. In this case, the final rules were inferred using fuzzy logic, but additional tests showed that it is possible to simulate the operation of the system using other approaches and calibrating the rules with the historical records for the releases and water storage of the reservoirs. Although the model is able to reproduce the stream aquifer interaction between the Jucar River and the Mancha Oriental aquifer, it simulates neither groundwater heads nor aquifer storage. Groundwater head specifically is a determinant factor for the Mancha Oriental aquifer, as it has suffered continuous drops in groundwater levels due to intense pumping since the early 1970s. To assess the effect of drought policies on groundwater levels, it would be necessary to apply a detailed groundwater model, such as finite-difference model, coupling it with the system dynamics model either through scripting, wrapping, or spreadsheet coupling [57
The model developed using system dynamics for the Jucar River system has the potential to grow and increase its scope by integrating new dynamics that can modify the behavior of the whole system. Future lines of work include linking the agricultural demand subsystem and a land-use subsystem, which would allow for introducing changes in agricultural land use based on economic benefit from previous years and on changes in land-use policies. System dynamics provides an excellent framework to study trade-offs that land use changes can introduce in specific sectors and communities [58
]. Furthermore, it is already possible to activate population growths or losses over time to study how changes in urban demand can affect the system. These functionalities are required to test the effect of different climate change narratives within the next decades, which is also a future line of research to explore.