The core idea behind the study is resilience, a concept that has moved to the center of disaster risk management worldwide. Rather than asking only how likely a flood is, resilience assessment asks how well a system, whether a city, a village, or an entire river basin, can absorb a disturbance, continue functioning, and recover afterward. While urban flood resilience has been studied extensively in recent years, with detailed indices developed for cities in China, Norway, Brazil, and elsewhere, the authors point out that resilience assessments at the watershed scale, where floods are actually generated and routed across the landscape, have remained largely unexplored. This gap matters because watershed-scale analysis captures interactions between upstream and downstream areas that city-level studies simply cannot see: how runoff generated on bare slopes travels toward settlements, how road networks and health infrastructure shape recovery capacity, and how social and economic conditions vary across an entire basin.

To structure their analysis, the researchers turned to the pressure-state-response framework, a conceptual model originally developed for environmental statistics by the OECD and Canadian statistician David Rapport in the late 1970s and early 1990s. The framework organizes information about an environmental system into three linked categories. Pressure describes the forces stressing the system, such as steep topography, intense rainfall, and hydrological conditions that promote runoff. State captures the current condition of the system, including social and economic characteristics of the population living with flood exposure. Response describes the capacity to act, encompassing flood-related infrastructure, health facilities, and other resources that determine how effectively a community can prepare for and recover from a disaster. By mapping dozens of variables from six domains, topography, meteorology, hydrology, social conditions, economic conditions, and flood-related infrastructure, onto these three categories, the team built a multidimensional portrait of resilience across the Gavkhoni Watershed.

One of the study’s key methodological innovations lies in how the variables were weighted. In many multi-criteria assessments, weights are assigned by expert judgment, which introduces subjectivity. Here, the researchers used the entropy method, a technique rooted in Claude Shannon’s 1948 mathematical theory of communication. Entropy weighting assigns importance to each variable based on how much informational variation it carries across the study area: variables that discriminate strongly between different locations receive higher weights, while those that are nearly uniform receive lower ones. This data-driven approach means the resilience map reflects the actual spatial structure of the watershed rather than the intuitions of a panel. The authors have made their Python code for entropy calculation freely available on GitHub, a transparency measure that allows other researchers to reproduce and adapt the method for their own regions.

The mathematical heart of the study is the catastrophe progression method, an approach derived from René Thom’s catastrophe theory, a branch of mathematics developed in the 1960s and 1970s to describe systems that change abruptly rather than smoothly. Catastrophe theory shows that certain continuous systems can undergo sudden jumps in behavior, and it classifies these jumps into a small number of canonical forms with names like the cusp, the swallowtail, and the butterfly, each defined by the number of control variables governing the system. In the catastrophe progression method, these forms become aggregation tools: instead of simply averaging indicators, the method combines them using the nonlinear formulas associated with each catastrophe model, allowing the composite score to reflect abrupt transitions and threshold effects that a linear average would smooth away. The technique has previously been applied to problems ranging from groundwater potential assessment to predicting coal and gas outbursts in mines.

Choubin and Dastranj applied these catastrophe models at three nested levels. First, within each subsystem, variables were aggregated using the cusp, swallowtail, or butterfly model depending on how many indicators belonged to that group. Second, the pressure, state, and response scores for each subsystem were combined. Finally, the comprehensive combination of pressure, state, and response across the entire framework produced a single resilience value for every location in the watershed, which was then translated into a flood resilience map. This hierarchical structure preserves the conceptual logic of the pressure-state-response model while exploiting the nonlinear mathematics of catastrophe theory, creating a hybrid conceptual-mathematical framework that the authors argue is well suited to the complexity of watershed systems.

To test whether their results were robust, the researchers performed a sensitivity analysis, systematically removing individual input variables and measuring how much the resulting resilience map changed. The findings are striking. The single most sensitive variable in the entire model turned out to be the number of outpatient health centers: removing it changed the resilience map by an average of about 16 percent. Road density ranked second in importance, with its removal producing a 5.5 percent change. These results carry a powerful practical message: in this watershed, the capacity of communities to respond to floods, embodied in healthcare access and transport connectivity, matters as much as or more than the physical drivers of flooding itself. A family’s ability to reach medical care after a disaster, and the ability of emergency services to reach affected areas, are quantifiably central to resilience.

The map itself delivers an urgent warning. About 12 percent of the Gavkhoni Watershed shows very low resilience to floods, and a further 28 percent shows low resilience. Taken together, roughly 40 percent of the basin, a very large area, requires serious measures to improve its resilience status. The authors emphasize that this finding should guide prioritization: with limited resources, watershed managers can target interventions, from infrastructure investment to social and economic support programs, at the areas where the map shows resilience is weakest. Because the framework separates pressure, state, and response, it also indicates what kind of intervention is most appropriate in each location, whether reducing the physical drivers of runoff, strengthening the socioeconomic fabric of exposed communities, or expanding response infrastructure such as roads and health facilities.

Beyond its immediate findings for central Iran, the study represents a significant methodological contribution to the growing field of resilience science. Most flood resilience research to date has concentrated on urban areas, where data are abundant and impacts are concentrated, but floods originate and propagate at the watershed scale, and resilience built in one part of a basin can be undermined by vulnerability in another. By demonstrating that the pressure-state-response framework and catastrophe progression models can be combined and operationalized across an entire basin, the researchers offer a template that can be adapted to other data-scarce regions around the world, many of which face intensifying flood hazards under climate change. The free availability of the underlying code lowers the barrier for other teams to replicate the approach.

The work, funded by the Iran National Science Foundation under project number 4026136, also underscores a broader shift in how societies think about disaster risk. For decades, the dominant paradigm was hazard prediction: build better models of where and when floods will strike. The resilience paradigm complements this by asking what makes a system able to bend without breaking. As the Gavkhoni study shows, answering that question requires looking far beyond rainfall and topography to the clinics, roads, livelihoods, and institutions that determine whether a flood becomes a catastrophe or a recoverable event. With roughly two-fifths of a major Iranian watershed flagged as having low or very low flood resilience, the study provides both a scientific framework and a practical roadmap for turning resilience from an abstract ideal into a mapped, measurable, and manageable target.

Subject of Research: Watershed-scale flood resilience assessment using the pressure-state-response framework and catastrophe progression models

Article Title: Watershed-scale flood resilience mapping using a pressure-state-response framework and catastrophe progression models

Article References: Choubin, B., & Dastranj, A. (2026). Watershed-scale flood resilience mapping using a pressure-state-response framework and catastrophe progression models. Natural Hazards, 122(21), Article 669. https://doi.org/10.1007/s11069-026-08445-7

Image Credits: AI Generated

DOI: 10.1007/s11069-026-08445-7

Keywords: flood resilience, watershed, pressure-state-response framework, catastrophe theory, catastrophe progression method, entropy weighting, Gavkhoni Watershed, Natural Hazards, sensitivity analysis, disaster risk management, Iran, flood mapping