Abstract
Abstract. The complex interactions and feedbacks between humans and water are critically important issues but remain poorly understood in the newly proposed discipline of socio-hydrology (Sivapalan et al., 2012). An exploratory model with the appropriate level of simplification can be valuable for improving our understanding of the co-evolution and self-organization of socio-hydrological systems driven by interactions and feedbacks operating at different scales. In this study, a simplified conceptual socio-hydrological model based on logistic growth curves is developed for the Tarim River basin in western China and is used to illustrate the explanatory power of such a co-evolutionary model. The study area is the main stream of the Tarim River, which is divided into two modeling units. The socio-hydrological system is composed of four sub-systems, i.e., the hydrological, ecological, economic, and social sub-systems. In each modeling unit, the hydrological equation focusing on water balance is coupled to the other three evolutionary equations to represent the dynamics of the social sub-system (denoted by population), the economic sub-system (denoted by irrigated crop area ratio), and the ecological sub-system (denoted by natural vegetation cover), each of which is expressed in terms of a logistic growth curve. Four feedback loops are identified to represent the complex interactions among different sub-systems and different spatial units, of which two are inner loops occurring within each separate unit and the other two are outer loops linking the two modeling units. The feedback mechanisms are incorporated into the constitutive relations for model parameters, i.e., the colonization and mortality rates in the logistic growth curves that are jointly determined by the state variables of all sub-systems. The co-evolution of the Tarim socio-hydrological system is then analyzed with this conceptual model to gain insights into the overall system dynamics and its sensitivity to the external drivers and internal system variables. The results show a costly pendulum swing between a balanced distribution of socio-economic and natural ecologic resources among the upper and lower reaches and a highly skewed distribution towards the upper reach. This evolution is principally driven by the attitudinal changes occurring within water resources management policies that reflect the evolving community awareness of society to concerns regarding the ecology and environment.
Highlights
In the emergent Anthropocene, the competition for water between humans and ecosystems leads to complex interactions between hydrologic and social systems
A new trans-disciplinary science of sociohydrology has been proposed which aims at understanding and predicting the dynamics and co-evolution of coupled human–water systems (Sivapalan et al, 2012)
Four ordinary differential equations are used to describe the dynamics of the hydrological sub-system represented by water storage, ecological subsystem represented by natural vegetation cover, economic sub-system represented by irrigated crop area, and social sub-system represented by human population
Summary
In the emergent Anthropocene, the competition for water between humans and ecosystems leads to complex interactions between hydrologic and social systems. Liu et al.: A conceptual socio-hydrological model of the co-evolution of humans and water significant feedbacks that need to be understood, assessed, modeled and predicted (Montanari et al, 2013) These feedback mechanisms between the hydrological and social systems are often ignored in traditional hydrology. Di Baldassarre et al (2013a, b) developed a simple dynamic model to represent the interactions and feedbacks between hydrological and social processes in the case of flooding, and found that a simple conceptual model is able to reproduce reciprocal effects between floods and people and the generation of emergent patterns from the coupled system dynamics. Coevolution of the hydrological and associated systems (including society, economy and ecology) needs to be recognized and incorporated within a suitable modeling approach, in order to predict their reaction to future human or environmental changes (Montanari et al, 2013), which is the aim of this study. The paper concludes with the main results and recommendations for future research
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