Oscar Melo

This study analyses how climate and market shocks drive diversification strategies in Chilean small-scale fisheries (SSF) and their effects on household income. Using panel data (1998–2022) from CASEN surveys, fisheries landings, and environmental indices, we estimate instrumental variable models within a sustainable livelihoods and site assessment framework. The analysis operates at two complementary levels: a commune-year panel model, which captures how local fishing portfolio diversification responds to environmental variability (ENOS), declining resource abundance (CPUE), and first-sale prices; and a household-level cross-sectional model, which examines how diversification strategies are associated with income across heterogeneous fishing households. Results from the commune-level model show that fisheries diversification responds significantly to environmental variability (ENOS), declining resource abundance (CPUE), and market price signals, with notable regional variation—the central macrozone exhibits particularly strong climate-driven diversification responses. At the household level, diversification is positively associated with income, yet its effectiveness varies systematically across household types. Fishing-dependent households benefit more from income diversification beyond fisheries, while households with broader livelihood portfolios gain more from within-fisheries diversification. Regional heterogeneity is pronounced: central macrozones show lower incomes and greater climate-driven diversification, whereas northern regions exhibit mixed responses. Community-level economic diversification reduces diversification in fisheries, suggesting substitution effects. These findings highlight that diversification is an adaptive response to exogenous shocks, but its outcomes depend on household capabilities, spatial context, and local opportunity structures. Understanding these dynamics is essential for designing effective policies that enhance resilience and reduce vulnerability in SSFs facing growing environmental and economic pressures.

Understanding climate change risks requires moving beyond hazards alone and examining how climatic stress propagates through coupled natural and human systems. Semi-arid coastal basins are particularly exposed to these dynamics, where prolonged drought, intense human water use, and sensitive downstream ecosystems interact to shape complex and often unintended risk trajectories. Central Chile provides a compelling example, having experienced a nearly 15-year megadrought that has profoundly altered hydrological, ecological, and socio-economic conditions.In this study, we explore how climate-driven water scarcity is transmitted across semi-arid coastal basins, and how human adaptation responses reshape both short- and long-term risks. Using an integrated socio-ecological framework, we combine satellite remote sensing, hydroclimatic records, land-use and census data, and water rights information. Indicators include precipitation, streamflow and groundwater depth, standardized drought indices, vegetation dynamics derived from NDVI, urban expansion inferred from night-time lights (VIIRS), and surface water changes in small coastal lagoons quantified using NDWI.Our results reveal contrasting adaptation pathways across managed and unmanaged systems. Irrigated agriculture shows a high degree of apparent resilience, maintaining vegetation productivity during prolonged drought through intensified groundwater use and technological adaptation. However, this response is closely linked to accelerated groundwater depletion, streamflow collapse, and downstream ecological degradation, illustrating a clear case of maladaptation driven by short-term productivity gains. In contrast, natural shrublands and forests respond more directly to hydroclimatic variability, with forest systems exhibiting delayed and potentially threshold-like responses under sustained drought conditions.Coastal lagoons emerge as sentinel systems that integrate cumulative basin-scale stress. Satellite observations document a shift from persistent ocean connectivity to prolonged inlet closure during the megadrought, alongside shrinking water surfaces and signs of regime change at the land–sea interface. Overall, our findings highlight how uneven adaptation capacity and sector-specific responses can amplify cascading climate risks, underscoring the need for integrated, basin-scale adaptation strategies that explicitly consider cross-system feedbacks, ecological thresholds, and governance constraints.

Recent applications of Robust Decision Making (RDM) have demonstrated their value for exploring socio-hydrological vulnerabilities under deep uncertainty in water-scarce regions. A recent study in the semi-arid coastal Quilimarí basin (central Chile), used an RDM framework combining stakeholder engagement and an integrated WEAP-MODFLOW model to reveal critical trade-offs between agricultural production, drinking water security, groundwater depletion and saline intrusion under climate and development uncertainties. While this work provided valuable insights into system vulnerabilities and stressors, it remained focused on exploratory analysis rather than on the explicit design of adaptive strategies.This study builds directly on the Quilimarí RDM case study and advances the framework toward adaptive policy design, introducing two methodological innovations. First, we extend the RDM approach by integrating the Direct Policy Search (DPS) framework to identify robust and flexible water management strategies. Instead of evaluating a small set of predefined interventions, policies are formulated as adaptive decision rules that dynamically link observable system states such as groundwater levels, salinity thresholds or unmet drinking water demand, to management actions including abstraction restrictions, activation of alternative supplies or demand reallocation. This allows the systematic identification of pathways that evolve over time and remain robust across a wide ensemble of plausible hydroclimatic and socio-economic futures.Second, to enable the computational requirement of DPS in data and process intensive basins, we develop a surrogate model that emulates the behavior of the full integrated surface-groundwater system. The original WEAP-MODFLOW model for Quilimarí, which explicitly represents groundwater dynamics, agricultural water use, and seawater intrusion, is approximated using an LSTM (Long Short-Term Memory), a type of Recurrent Neural Networks (RNN), trained on large ensembles of simulation outputs. The surrogate model preserves key nonlinearities and memory effects inherent to groundwater systems while reducing computational costs by several orders of magnitude, making large-scale adaptive policy search feasible.The combined framework is applied to the Quilimarí basin to identify adaptive pathways that balance drinking water reliability, agricultural viability, and long-term groundwater sustainability under deep uncertainties as climate and land use change and growing population. Results show that the DPS using the surrogate model outperform static strategies identified in the original RDM analysis, particularly under severe drought and demand-growth scenarios, by avoiding maladaptation and reducing regret across objectives.By explicitly linking the vulnerability exploration with the design of adaptive strategies, this study shows how RDM can be operationalized into implementable and flexible water management policies. The approach is transferable to other semi-arid coastal basins that face strong groundwater dependence, institutional constraints, and profound climate and other uncertainties.

Water scarcity is a growing global challenge that threatens agricultural sustainability, food security, and the livelihoods of rural communities. Agriculture accounts for approximately 70–80% of global freshwater withdrawals, making it particularly vulnerable to declining water availability driven by climate change, population growth, and competing demand.Perennial crops face specific challenges under water scarcity due to their long investment horizons, high establishment costs, and limited flexibility in land reallocation. Unlike annual crops, perennial orchards are dynamic systems that are influenced by both short-term and cumulative conditions, which impact both production yields and overall tree performance. As a result, farmers’ behavioral responses to water scarcity differ markedly between perennial and annual systems, where most of the literature has focused.This study examines farmers’ dynamic decisions in perennial crop systems and the factors influencing changes in cultivated areas and crop abandonment in drought-prone contexts. Our case study is situated in a semi-arid region of Central Chile, where a three-decade decline in precipitation and a persistent megadrought since 2010 have resulted in reduced river flows, declining groundwater levels, intensified water competition, and significant land-use changes. In parallel, early in the decade, the region saw a shift toward permanent, high-value, and export-oriented crops—such as avocados, walnuts, and citrus—displacing annual production and livestock, reshaping the agricultural landscape, and the demand for water.To analyze farmers' decisions, we use data from a novel survey of 200 farmers from the Ligua-Petorca basins, which incorporates retrospective recall of past production outcomes to address dynamic decision-making processes. This approach allows the reconstruction of farmers’ past responses to water scarcity. Following various econometric approaches, which combine panel data models and limited dependent variable regressions, we find substantial heterogeneity in adaptation responses across farmers. Changes in cultivated areas are systematically associated with farm size, access to surface water, crop type, and the characteristics of farmers. Also, expected climatic conditions, proxied by recent trends, are found to influence crop area changes and abandonment.This research sheds light on the understanding of how farmers adapt to prolonged droughts in perennial production systems, an understudied topic in the literature but evermore relevant in the context of a changing climate. The proposed methodology enables addressing this issue in contexts where recurrent surveys are uncommon, such as in many semi-arid regions of lower-income countries. While recall-based data cannot replace true longitudinal panels, their use provides a feasible alternative for examining adaptation and abandonment processes in such environments, thereby expanding the empirical tools available for studying long-term agricultural responses to drought. Ignoring the differences between permanent crops and annuals implies overlooking the dynamic nature of perennial systems, misdiagnosing farmers’ constraints, and overestimating their flexibility. Observed reductions in cultivated area under prolonged water deficits are consistent with crop abandonment becoming a relevant outcome when adaptation pathways are limited in perennial systems. Recognizing these differences is essential for advancing research on agricultural adaptation and for informing the design of effective water and agricultural policies tailored to perennial systems under drought conditions.

South America is a global hotspot for land use and land cover (LULC) change, marked by dramatic agricultural land expansion and deforestation. While previous studies have documented land use and land cover changes in South America over recent decades, there is still a lack of spatially explicit and time-series maps of crop types that capture shifts in crop distribution. Therefore, developing high-resolution, long-term, and crop-specific datasets is crucial for advancing our understanding of human–environment interactions and for assessing the impacts of agricultural activities on carbon and biogeochemical cycles, biodiversity, and climate. In this study, we integrated multi-source data, including high-resolution remote sensing data, model-based data, and historical agricultural census data, to reconstruct the historical dynamics of four major commodity crops (i.e., soybean, maize, wheat, and rice) in South America at an annual timescale and 1 km × 1 km spatial resolution from 1950 to 2020. The results showed that soybean and maize cultivation expanded rapidly in South America by encroaching on other vegetation (i.e., forest, pasture/rangeland, and unmanaged grass/shrubland) over the past 70 years, whereas wheat and rice areas remained relatively stable. Specifically, soybean is one of the most dramatically expanded crops, increasing from essentially zero in 1950 to 48.8 Mha in 2020, resulting in a total loss of 23.92 Mha of other vegetation. In addition, the area of maize increased by a factor of 2.1 from 12.7 Mha in 1950 to 26.9 Mha in 2020. The newly developed crop type dataset provides important insights for assessing the impacts of cropland expansion on crop production, biodiversity, greenhouse gas emissions, and carbon and nitrogen cycles in South America. Moreover, these data are instrumental for developing national policies, sustainable trade, investment, and development strategies aimed at securing food supply and other human and environmental objectives in South America. The datasets are available at https://doi.org/10.5281/zenodo.14002960 (Xu et al., 2024).