Balancing Policy Coherence in Socio-hydrology

Renewed global mandates have encouraged widespread use of nature-based solutions (NBSs) for addressing overlapping benefits of hydro-environmental impact, social conditions (mental and physical health, sense of well-being, food security), biodiversity, and ecosystem restoration. However, the decision to implement nature-based solutions is posed with challenges regarding policy effectiveness and stakeholder buy-in. Such complexities may be assessed through the lens of socio-hydrology with enhanced approaches for incorporating human behavior into the modeling paradigm. Here, a novel framework is presented and demonstrated that applies stakeholder cognitive mapping and feedback loop analysis through a series of soft-systems approaches to elicit an understanding of the complex interactions between the physical processes served by NBSs and their human-directed management opportunities. The community-led application reveals a means for balancing complex governmental structures across multiple agencies toward policy coherence, where we identify synergies and conflicts between disparate management strategies regarding optimal allocation of NBS socio-hydrological benefits. A demonstrated case study in Houston, Texas reveals unexpected feedbacks resulting from interacting causal influences across the social and hydrological domains according to the norms in a given locale, such as the influence of climate, flood memory, advocacy, and benefit visualization. The flows of information throughout the cycle are transformed by belief systems embedded within the stakeholder cognition, which may be used to perform a semi-quantitative scenario-based assessment for identifying and better-understanding policy coherence amongst a plethora of NBS decision-making options. Typically, sole reliance upon scenario-based modeling obscures the feedback loop logic embedded within the system. Conversely, causal loop diagrams alone may quickly become convoluted, which are difficult to decipher from human visualization, particularly as we encounter double and triple loop influences. By combining both loop analysis with scenario-based modeling within one framework, we are better able to identify areas of policy coherence while also explaining the emergence of synergies and trade-offs according to causal loop logic. Such a framework is applicable to a variety of complex systems with overlapping socio-hydro-environmental processes that are influenced by political decision-making across institutional scales.