[1] The stochastic nature of long-term dynamics of soil moisture, water table, and vegetation in wetland ecosystems driven by precipitation are investigated through this modeling study. A simple model is presented here to couple the hydrologic and vegetation dynamics via transpiration and ecosystem carrying capacity. The simulation results portray possible competition outcomes between plant species having different survival strategies in response to the fluctuating soil moisture and water tables under different rainfall conditions. Long-range correlations in the dynamics were detected in several of the key variables of wetland ecosystems, as the result of their dependence on the long-memory structure of the water table. The statistical structure of the modeled water table fluctuations is found to be similar to that obtained from a real case study, validating the ability of the model in capturing water table dynamics and suggesting its potential toward the quantification of the long-term dynamics of wetland vegetation.

Abstract. Robustness and resilience are concepts in systems thinking that have grown in importance and popularity. For many complex social-ecological systems, however, robustness and resilience are difficult to quantify and the connections and trade-offs between them difficult to study. Most studies have either focused on qualitative approaches to discuss their connections or considered only one of them under particular classes of disturbances. In this study, we present an analytical framework to address the linkage between robustness and resilience more systematically. Our analysis is based on a stylized dynamical model that operationalizes a widely used conceptual framework for social-ecological systems. The model enables us to rigorously delineate the boundaries of conditions under which the coupled system can be sustained in a long run, define robustness and resilience related to these boundaries, and consequently investigate their connections. The results reveal the trade-offs between robustness and resilience. They also show how the nature of such trade-offs varies with the choice of certain policies (e.g., taxation and investment in public infrastructure), internal stresses, and uncertainty in social-ecological settings.

With minimal moral hazard and adverse selection, weather index insurance promises financial resilience to farmers struck by harsh weather conditions through swift compensation at affordable premium. Despite these advantages, the very nature of indexing gives rise to production basis risk as the selected weather indexes do not sufficiently correspond to actual damages. To address this problem, we develop a stochastic yield model, built upon a stochastic soil moisture model driven by marked Poisson rainfall. Our analysis shows that even under similar temperature and rainfall amount yields can differ significantly; this was empirically supported by a 2‐year field experiment in which rain‐fed maize was grown under very similar total rainfall. Here, the year with more intense, less‐frequent rainfall produces a better yield—a rare counter evidence to most climate change projections. Through a stochastic yield model, we demonstrate the crucial roles of rainfall intensity and frequency in determining the yield. Importantly, the model allows us to compute rainfall pattern‐related basis risk inherent in cumulative rain index insurance. The model results and a case study herein clearly show that total rainfall is a poor indicator of yield, imposing unnecessary production basis risk on farmers and false‐positive payouts on insurers. Incorporating rainfall intensity and frequency in the design of rain index insurance can offer farmers better protection, while maintaining the attractive features of the weather index insurance and thus fulfilling its promise of financial resilience.

This paper quantifies wetland vegetation dynamics under drought, waterlogging, shading, and nutrient stresses within the coupled plant-soil-microbe system. A plant is characterized by three independent traits, namely leaf nitrogen (N) content, specific leaf area (SLA), and allometric carbon (C) allocation to rhizome storage, while plant growth is modelled through a dynamic plant allocation scheme. The modelling of N focuses on the internal cycle in which the aerobic and anaerobic processes are determined by the dynamics of oxygen controlled by plants, microbial aerobic processes, and hydrologic dynamics. The dynamics of water levels and soil moisture are described by a simple hydrologic model with stochastic rainfall and are decoupled from the plant-soil-microbe dynamics. Using the model to investigate the dynamics of sawgrass, the results, which are consistent with field observations in the southern Everglades, indicate that SLA decreases with increasing anaerobic condition. The lower SLA maintains high stomatal opening, while at the same time prevents cavitational collapse when sawgrass lowers its root : shoot ratio to reduce C cost in root anaerobic respiration. Given a naturally low but not too scarce level of phosphorus, net N mineralization is higher in the wetter hydrologic regimes because the increase in anaerobic decomposition and N mineralization compensate for the decline in those of aerobes; and the slower growing, more nitrogen efficient anaerobes compete less with plants for N. The optimal traits are the results of several counteracting trends of trade-offs in C and N economy differently influenced by trait combinations in different wetland environments.