Sociohydrological studies use interdisciplinary approaches to explore the complex interactions between physical and social water systems and increase our understanding of emergent and paradoxical system behaviors. The dynamics of community values and social cohesion, however, have received little attention in modeling studies due to quantification challenges. Social structures associated with community‐managed irrigation systems around the world, in particular, reflect these communities' experiences with a multitude of natural and social shocks. Using the Valdez acequia (a communally‐managed irrigation community in northern New Mexico) as a simulation case study, we evaluate the impact of that community's social structure in governing its responses to water availability stresses posed by climate change. Specifically, a system dynamics model (developed using insights from community stakeholders and multiple disciplines that captures biophysical, socioeconomic, and sociocultural dynamics of acequia systems) was used to generate counterfactual trajectories to explore how the community would behave with streamflow conditions expected under climate change. We found that earlier peak flows, combined with adaptive measures of shifting crop selection, allowed for greater production of higher value crops and fewer people leaving the acequia. The economic benefits were lost, however, if downstream water pressures increased. Even with significant reductions in agricultural profitability, feedbacks associated with community cohesion buffered the community's population and land parcel sizes from more detrimental impacts, indicating the community's resilience under natural and social stresses. Continued exploration of social structures is warranted to better understand these systems' responses to stress and identify possible leverage points for strengthening community resilience.

Inverters are a leading source of hardware failures and contribute to significant energy losses at photovoltaic (PV) sites. An understanding of failure modes within inverters requires evaluation of a dataset that captures insights from multiple characterization techniques (including field diagnostics, production data analysis, and current-voltage curves). One readily available dataset that can be leveraged to support such an evaluation are maintenance records, which are used to log all site-related technician activities, but vary in structuring of information. Using machine learning, this analysis evaluated a database of 55,000 maintenance records across 800+ sites to identify inverter-related records and consistently categorize them to gain insight into common failure modes within this critical asset. Communications, ground faults, heat management systems, and insulated gate bipolar transistors emerge as the most frequently discussed inverter subsystems. Further evaluation of these failure modes identified distinct variations in failure frequencies over time and across inverter types, with communication failure occurring more frequently in early years. These patterns can inform ongoing PV system reliability activities, including simulation analyses, spare parts inventory management, cost estimates for operations and maintenance, and development of standards for inverter testing. Advanced implementations of machine learning techniques coupled with standardization of asset labels and descriptions can extend these insights into actionable information that can support development of algorithms for condition-based maintenance, which could further reduce failures and associated energy losses at PV sites.

A spatiotemporal analysis of two well‐known agricultural drought indices, the Palmer Drought Severity Index (PDSI) and the Standardized Precipitation Index at a 9‐month scale (SPI‐9), is presented for Sri Lanka. The analysis was conducted based on monthly precipitation and temperature data from January 1881 to December 2010 using 13 stations distributed across the three climatic zones of the country. Principal component analysis shows that the first two principal components of PDSI and SPI‐9 are spatially comparable and could physically represent the two main monsoons. A wavelet analysis of these principal components' scores for both indices indicates a stronger association between the Northeastern monsoon and El‐Niño in recent decades. Correlation analysis with agricultural metrics suggests that different indices might be appropriate for each of the climatic zones in Sri Lanka. PDSI correlated best with the intermediate zone districts; SPI‐9 correlated best with the dry zone districts; but neither index correlated well with the wet zone districts.