Adverse weather delays forty-five percent of construction projects worldwide, costing project owners and contractors billions of dollars in additional expenses and lost revenue each year. Additionally, changes in climate are expected to increase the frequency and intensity of weather conditions that cause these construction delays. Researchers have investigated the effect of weather on several aspects of construction. Still, no previous study comprehensively (1) identifies and quantifies the risks weather imposes on construction projects, (2) categorizes modeling and simulation approaches developed, and (3) summarizes mitigation strategies and adaptation techniques to provide best management practices for the construction industry. This paper accomplishes these goals through a systematic state-of-the-art review of 3207 articles published between 1972 and October 2020. This review identified extreme temperatures, precipitation, and high winds as the most impactful weather conditions on construction. Despite the prevalence of climate-focused delay studies, existing research fails to account for future climate in the modeling and identification of delay mitigation strategies. Accordingly, planners and project managers can use this research to identify weather-vulnerable activities, account for changing climate in projects, and build administrative or organizational capacity to assist in mitigating weather delays in construction. The cumulative contribution of this review will enable sustainable construction scheduling that is robust to a changing climate.

Water rights law and corresponding markets exist to promote economic water resource use efficiency by permitting water rights holders to trade allocations. In some regions, hydrologic uncertainty drives annual assignment of per‐water right allocation values. Water rights holders, specifically those involved in agricultural production, may use collaborative water resource decision making to mitigate allocation uncertainty and promote economic and social efficiency. Such is the case in semi‐arid North Chile, where interactions between representative farmer groups, treated as resource competitive growers’ cooperatives, and modeled at water market‐scale, can provide both price and water right allocation distribution signals for unregulated, temporary water right markets. For the range of feasible per‐water right allocation values, a coupled agricultural‐economic model is developed to describe the equilibrium distribution of water, the corresponding market price of water rights, and the net surplus generated by collaboration between competing agricultural uses. A static, demand‐based allocation redistribution ruleset is generated by which the cooperatives are constrained to abide. Water right supply and demand inequality impacts at the market‐scale are used to characterize market performance under existing water rights law, and to evaluate the efficacy of intercooperative collaboration over the period 2000–2015. Exclusive intercooperative water trading, following a demand‐based ruleset, produces joint mean annual expected profits 24%–122% larger than a case of no‐interaction, depending on initial rights distribution. The broader insights of this research suggest that water rights holders engaged in agriculture can achieve enhanced benefits by forming crop‐type cooperatives and implementing demand‐based allocation redistribution rulesets.

In many semi-arid regions, multisectoral demands often stress available water supplies. Such is the case in the Elqui River valley of northern Chile, which draws on a limited-capacity reservoir to allocate 25 000 water rights. Delayed infrastructure investment forces water managers to address demand-based allocation strategies, particularly in dry years, which are realized through reductions in the volume associated with each water right. Skillful season-ahead streamflow forecasts have the potential to inform managers with an indication of future conditions to guide reservoir allocations. This work evaluates season-ahead statistical prediction models of October–January (growing season) streamflow at multiple lead times associated with manager and user decision points, and links predictions with a reservoir allocation tool. Skillful results (streamflow forecasts outperform climatology) are produced for short lead times (1 September: ranked probability skill score (RPSS) of 0.31, categorical hit skill score of 61 %). At longer lead times, climatological skill exceeds forecast skill due to fewer observations of precipitation. However, coupling the 1 September statistical forecast model with a sea surface temperature phase and strength statistical model allows for equally skillful categorical streamflow forecasts to be produced for a 1 May lead, triggered for 60 % of years (1950–2015), suggesting forecasts need not be strictly deterministic to be useful for water rights holders. An early (1 May) categorical indication of expected conditions is reinforced with a deterministic forecast (1 September) as more observations of local variables become available. The reservoir allocation model is skillful at the 1 September lead (categorical hit skill score of 53 %); skill improves to 79 % when categorical allocation prediction certainty exceeds 80 %. This result implies that allocation efficiency may improve when forecasts are integrated into reservoir decision frameworks. The methods applied here advance the understanding of the mechanisms and timing responsible for moisture transport to the Elqui Valley and provide a unique application of streamflow forecasting in the prediction of water right allocations.

Airfield pavement systems support the global economy, passenger travel, and national defense. Accurate pavement degradation predictions are critical inputs for maintenance and repair decisions, and when skillful, they may reduce the need for time-intensive, costly physical inspections that disrupt airfield operations. Existing airport pavement management systems (APMS) compute expected degradation as a function of pavement type and age, but they do not account for local climate and traffic conditions and they are not built to adapt to future changes in either mode of variability. This paper implements a bias-reduced statistical model that reveals the effects of local conditions using observed historical climatic and aircraft traffic data. Model performance is evaluated using a diverse data set from nine Air Force installations, encompassing three major Köppen-Geiger climate zones in the contiguous United States and representing a wide range of aircraft. Environmental factors are more impactful on pavement degradation than aircraft traffic; a climate-only model produces ${\mathrm{R}}^2$ values as high as 0.84, while traffic improves explained variance across installations (${\mathrm{R}}^2$ = 0.86–0.97) for the most heavily trafficked pavement family. This work illustrates the impactful role of climate in pavement degradation and demands implementation into the current APMS. Airfield asset managers can use this adaptable framework to more accurately determine sources of local degradation and inform sustainable pavement design and management practices.