Conceptual modeling for improved understanding of the Rio Grande/Río Bravo socio-environmental system

Abstract

Social processes are essential components of human-environment systems and their dynamics. However, modeling a tightly coupled socio-environmental system over a large area and across wide social and environmental diversity presents several challenges, given the complexity of the interactions and their spatial heterogeneity. The transboundary Rio Grande/Río Bravo (RGB) Basin is an excellent case study to address these challenges. Water scarcity and over-allocation of water are present in a highly engineered system with extensive damming and a complex structure of agreements and compacts that govern the distribution of hydrological resources among users. Since no basin-wide approaches to modeling the RGB as a socio-environmental system exist, we attempt to close this gap. Building on data collected through extensive ethnographic fieldwork, we used a structured, collaborative, and integrative approach for documenting existing knowledge on and modeling of the RGB socio-environmental system. We assess different models for conceptualizing human behavior applied in the RGB, identify a need to redefine the (spatial) boundaries of the system and produce inductively generated knowledge about the interlinkages of social processes with environmental system components in the form of a semi-quantitative conceptual model. Our research demonstrates an alternative to ad-hoc approaches to defining “the social” in socio-environmental models and is a first step towards the development of a basin-wide computer simulation model of the RGB socio-environmental system.

Learning across disciplines in socio-environmental problem framing

“…However, few studies address social learning during the earliest phase of an SES research team, the problem framing phase. The modelling community has described problem framing as including: defining the purpose of the research, deciding the disciplines to be involved, bridging epistemologies across disciplines, synchronizing mental models, deciding on scales of interest, conceptualizing the system, and identifying sources of uncertainty (Argent et al, 2016;Badham et al, 2019;Elsawah et al, 2020;Hamilton et al, 2015;Jakeman et al, 2006;Koch et al, 2019;Villamor et al, 2020;Voinov et al, 2016;Voinov & Bousquet, 2010). This requires time to iterate over many possible problem framings and converge on a shared, integrated conceptualization (Jakeman et al, 2006).…”

A socio-environmental geodatabase for integrative research in the transboundary Rio Grande/Río Bravo basin

“…Geospatial operations included the projection of the final datasets to the most appropriate projected coordinate system that minimized areal distortion for the whole RGB (North America Albers Equal Area Conic), and clipping datasets to the RGB boundary. Instead of using the hydrological basin boundary, we used the spatial boundaries of the Rio Grande/ Río Bravo socio-environmental system as delineated by Koch et al 27 . For all political jurisdictional boundary information (national, state, or county), given the spatial mismatch with the catchment boundary, we prioritized the use of administrative boundaries over catchment boundaries in locations where they overlap.…”

“…In the RGB, a basin level database synthesizing hydrologic, administrative, land-use/cover, and water management datasets, was also developed at a finer resolution 25,26 . However, we are not aware of a similar approach to capture the social heterogeneity and complexity across the basin 27 . Hence, we compiled a comprehensive socio-environmental geodatabase for the RGB, encompassing geospatial data sets related to Water & Land Governance, Hydrology, Water Use & Hydraulic Infrastructures, Socio-Economics, and Biophysical Environment.…”

“…2. The geodatabase can support spatially explicit and integrated modelling for the RGB 27 . We are currently developing a simulation model for the RGB that draws on water and land governance datasets (political jurisdiction boundaries, land management and protected areas, irrigation district outlines), as well as hydrologic and biophysical environment datasets (river network, elevation, water bodies, gauges, land-use), socio-economic (population), and water use and hydraulic infrastructures (dams).…”

Unlikely alliances and their implications for resource management in the American West

“…Given the challenges and complexities outlined above in understanding unlikely alliances, key advances in our understanding will potentially be generated by scholars who use creative methods to overcome these challenges, particularly in light of the declining efficacy of surveybased research. Innovations in methodology may involve combining qualitative and quantitative methods in interesting ways, for example pairing ethnography with computational modeling (Koch et al 2019) or field experiments (Levy Paluck 2010).…”