Socio-hydrologic modeling to understand and mediate the competition for water between agriculture development and environmental health : Murrumbidgee River Basin, Australia

Competition for water between humans and ecosystems is set to become a flash point in the coming decades in many parts of the world. An entirely new and comprehensive quantitative framework is needed to establish a holistic understanding of that competition, thereby enabling the development of effective mediation strategies. This paper presents a modeling study centered on the Murrumbidgee River basin (MRB). The MRB has witnessed a unique system dynamics over the last 100 years as a result of interactions between patterns of water management and climate driven hydrological variability. Data analysis has revealed a pendulum swing between agricultural development and restoration of environmental health and ecosystem services over different stages of basin-scale water resource development. A parsimonious, stylized, quasi-distributed coupled socio-hydrologic system model that simulates the twoway coupling between human and hydrological systems of the MRB is used to mimic and explain dominant features of the pendulum swing. The model consists of coupled nonlinear ordinary differential equations that describe the interaction between five state variables that govern the co-evolution: reservoir storage, irrigated area, human population, ecosystem health, and environmental awareness. The model simulations track the propagation of the external climatic and socio-economic drivers through this coupled, complex system to the emergence of the pendulum swing. The model results point to a competition between human “productive” and environmental “restorative” forces that underpin the pendulum swing. Both the forces are endogenous, i.e., generated by the system dynamics in response to external drivers and mediated by humans through technology change and environmental awareness, respectively. Sensitivity analysis carried out with the model further reveals that socio-hydrologic modeling can be used as a tool to explain or gain insight into observed co-evolutionary dynamics of diverse human–water coupled systems. This paper therefore contributes to the ultimate development of a generic modeling framework that can be applied to human–water coupled systems in different climatic and socio-economic settings.

[1]  A. Hoekstra,et al.  Evolving water science in the Anthropocene , 2013 .

[2]  Lucien Wald,et al.  Using remotely sensed solar radiation data for reference evapotranspiration estimation at a daily time step , 2008 .

[3]  I. Rodríguez‐Iturbe,et al.  Socio‐hydrology: Use‐inspired water sustainability science for the Anthropocene , 2014 .

[4]  J. G. M. Cuypers,et al.  Brief paper: An analysis of Forrester's world dynamics model , 1974 .

[5]  Murugesu Sivapalan,et al.  Endogenous technological and population change under increasing water scarcity , 2013 .

[6]  G. Di Baldassarre,et al.  Towards understanding the dynamic behaviour of floodplains as human-water systems , 2013 .

[7]  Anthony J. Jakeman,et al.  Selecting among five common modelling approaches for integrated environmental assessment and management , 2013, Environ. Model. Softw..

[8]  Hubert H. G. Savenije,et al.  Equifinality, a blessing in disguise? , 2001 .

[9]  P. Romer Endogenous Technological Change , 1989, Journal of Political Economy.

[10]  Hubert H. G. Savenije,et al.  Water valuation at basin scale with application to western India , 2011 .

[11]  Murugesu Sivapalan,et al.  Downward approach to hydrological prediction , 2003 .

[12]  Vernon W. Ruttan,et al.  Factor Prices and Technical Change in Agricultural Development: The United States and Japan, 1880-1960 , 1970, Journal of Political Economy.

[13]  Saket Pande,et al.  Endogenous change: on cooperation and water availability in two ancient societies , 2014 .

[14]  Murugesu Sivapalan,et al.  Socio-hydrologic perspectives of the co-evolution of humans and water in the Tarim River basin, Western China: the Taiji-Tire model , 2013 .

[15]  Hung-Ju Chen,et al.  Environmental tax policy, habit formation and nonlinear dynamics , 2011 .

[16]  G. Likens,et al.  Technical Report: Human Alteration of the Global Nitrogen Cycle: Sources and Consequences , 1997 .

[17]  Helmuth Trischler The Anthropocene , 2016, NTM Zeitschrift für Geschichte der Wissenschaften, Technik und Medizin.

[18]  Manuel Pulido-Velazquez,et al.  Economic Optimization of Conjunctive Use of Surface Water and Groundwater at the Basin Scale , 2006 .

[19]  Saket Pande,et al.  Quantile hydrologic model selection and model structure deficiency assessment: 1. Theory , 2013 .

[20]  John N. Warfield,et al.  World dynamics , 1973 .

[21]  A. Greif,et al.  A Theory of Endogenous Institutional Change , 2004, American Political Science Review.

[22]  P. Romer Part 2 : The Problem of Development : A Conference of the Institute for the Study of Free Enterprise Systems , 2007 .

[23]  F. Chapin,et al.  Planetary boundaries: Exploring the safe operating space for humanity , 2009 .

[24]  G. Blöschl,et al.  Downward approach to hydrological prediction , 2003 .

[25]  Stefano Tarantola,et al.  Sensitivity analysis practices: Strategies for model-based inference , 2006, Reliab. Eng. Syst. Saf..

[26]  P. Reed,et al.  Hydrology and Earth System Sciences Discussions Comparing Sensitivity Analysis Methods to Advance Lumped Watershed Model Identification and Evaluation , 2022 .

[27]  M. Roderick Introduction to special section on Water Resources in the Murray‐Darling Basin: Past, present, and future , 2011 .

[28]  P. McIntyre,et al.  Global threats to human water security and river biodiversity , 2010, Nature.

[29]  Murugesu Sivapalan,et al.  Socio-hydrologic drivers of the pendulum swing between agricultural development and environmental health: a case study from Murrumbidgee River basin, Australia , 2014 .

[30]  Günter Blöschl,et al.  Socio-hydrology: conceptualising human-flood interactions , 2013 .

[31]  Giorgos Kallis,et al.  When is it coevolution , 2007 .

[32]  Giorgos Kallis,et al.  Coevolution in water resource development: The vicious cycle of water supply and demand in Athens, Greece , 2010 .

[33]  C. Vörösmarty,et al.  Global water resources: vulnerability from climate change and population growth. , 2000, Science.

[34]  Saket Pande,et al.  THE COSTATE VARIABLE IN A STOCHASTIC RENEWABLE RESOURCE MODEL , 2006 .

[35]  M. Falkenmark,et al.  Consumptive water use to feed humanity - curing a blind spot , 2004 .

[36]  P. Gleick,et al.  Peak water limits to freshwater withdrawal and use , 2010, Proceedings of the National Academy of Sciences.

[37]  Brett A. Bryan,et al.  Variance-based sensitivity analysis of a forest growth model , 2012 .

[38]  Zong-Liang Yang,et al.  Quantifying parameter sensitivity, interaction, and transferability in hydrologically enhanced versions of the Noah land surface model over transition zones during the warm season , 2010 .

[39]  M. Sivapalan,et al.  Threshold behaviour in hydrological systems as (human) geo-ecosystems: Manifestations, controls, implications , 2009 .

[40]  Hugh P. Possingham,et al.  Global insights into water resources, climate change and governance , 2013 .

[41]  M. Hipsey,et al.  A prototype framework for models of socio-hydrology: identification of key feedback loops with application to two Australian case-studies , 2014 .

[42]  Saket Pande,et al.  On hydrological model complexity, its geometrical interpretations and prediction uncertainty , 2013 .

[43]  Saltelli Andrea,et al.  Sensitivity Analysis for Nonlinear Mathematical Models. Numerical ExperienceSensitivity Analysis for Nonlinear Mathematical Models. Numerical Experience , 1995 .

[44]  T. Eicher,et al.  Interaction between Endogenous Human Capital and Technological Change , 1996 .

[45]  S. Simonovic,et al.  Global water resources modeling with an integrated model of the social-economic-environmental system , 2011 .

[46]  Peter A. Troch,et al.  The future of hydrology: An evolving science for a changing world , 2010 .

[47]  Saket Pande,et al.  Quantile hydrologic model selection and model structure deficiency assessment: 2. Applications , 2013 .

[48]  Praveen Kumar,et al.  Typology of hydrologic predictability , 2011 .

[49]  I. Sobol Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates , 2001 .

[50]  V. Srinivasan,et al.  Coevolution of water security in a developing city , 2013 .

[51]  Wesley W. Wallender,et al.  A spatially distributed hydroeconomic model to assess the effects of drought on land use, farm profits, and agricultural employment , 2009 .

[52]  E. O’Gorman Growing rice on the Murrumbidgee River: cultures, politics, and practices of food production and water use, 1900 to 2012 , 2013 .

[53]  P. Matgen,et al.  Understanding catchment behavior through stepwise model concept improvement , 2008 .

[54]  S. Pande,et al.  Endogenous change: On cooperation and water in ancient history , 2013 .

[55]  Anthony J. Jakeman,et al.  Integrated assessment and modelling: features, principles and examples for catchment management , 2003, Environ. Model. Softw..

[56]  Murugesu Sivapalan,et al.  Developing predictive insight into changing water systems: use-inspired hydrologic science for the Anthropocene , 2013 .

[57]  Daniel Campos,et al.  Stochastic model for population migration and the growth of human settlements during the Neolithic transition. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[58]  Murugesu Sivapalan,et al.  A prototype framework for models of socio-hydrology: identification of key feedback loops and parameterisation approach , 2014 .

[59]  Richard Howitt,et al.  Water transfers, agriculture, and groundwater management: a dynamic economic analysis. , 2003, Journal of environmental management.

[60]  E. Ostrom A General Framework for Analyzing Sustainability of Social-Ecological Systems , 2009, Science.

[61]  I. Sobola,et al.  Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates , 2001 .

[62]  G. Turner A Comparison of the Limits to Growth with Thirty Years of Reality , 2008 .

[63]  Sandjai Bhulai,et al.  Parameter-dependent convergence bounds and complexity measure for a class of conceptual hydrological models , 2012 .

[64]  P. Crutzen,et al.  The new world of the Anthropocene. , 2010, Environmental science & technology.

[65]  Y. Hayami,et al.  Factor Prices and Technical Change in Agricultural Development , 2011 .

[66]  G. Blöschl,et al.  Socio‐hydrology: A new science of people and water , 2012 .