Significance: North American and European countries built many large dams until 1975, after which both started to abandon a significant part of their installed hydropower because of the negative social and environmental impacts. However, there has been a recent trend of new large hydropower dams being built in developing countries, particularly in megabiodiversity river basins, such as the Amazon, the Congo, and the Mekong. The socioeconomic and environmental damages in these river systems are even greater than the early costs in North America and Europe. This paper discusses how the hydropower sector needs to not only focus on energy production but also, include the negative social and environmental externalities caused by dams and recognize the unsustainability of current common practices.

Irrigation’s effects on precipitation during an exceptionally dry summer (June–August 2012) in the United States were quantified by incorporating a novel dynamic irrigation scheme into the Weather Research and Forecasting (WRF) Model. The scheme is designed to represent a typical application strategy for farmlands across the conterminous United States (CONUS) and a satellite-derived irrigation map was incorporated into the WRF-Noah-Mosaic module to realistically trigger the irrigation. Results show that this new irrigation approach can dynamically generate irrigation water amounts that are in close agreement with the actual irrigation water amounts across the high plains (HP), where the prescribed scheme best matches real-world irrigation practices. Surface energy and water budgets have been substantially altered by irrigation, leading to modified large-scale atmospheric circulations. In the studied dry summer, irrigation was found to strengthen the dominant interior high pressure system over the southern and central United States and deepen the trough over the upper Midwest. For the HP and central United States, the rainfall amount is slightly reduced over irrigated areas, likely as a result of a reduction in both local convection and large-scale moisture convergence resulting from interactions and feedbacks between the land surface and atmosphere. In areas downwind of heavily irrigated regions, precipitation is enhanced, resulting in a 20%–100% reduction in the dry biases (relative to the observations) simulated over a large portion of the downwind areas without irrigation in the model. The introduction of irrigation reduces the overall mean biases and root-mean-square errors in the simulated daily precipitation over the CONUS.

Climate change impacts food production systems, particularly in locations with large, vulnerable populations. Elevated greenhouse gases (GHG), as well as land cover/land use change (LCLUC), can influence regional climate dynamics. Biophysical factors such as topography, soil type, and seasonal rainfall can strongly affect crop yields. We used a regional climate model derived from the Regional Atmospheric Modeling System (RAMS) to compare the effects of projected future GHG and future LCLUC on spatial variability of crop yields in East Africa. Crop yields were estimated with a process-based simulation model. The results suggest that: (1) GHG-influenced and LCLUC-influenced yield changes are highly heterogeneous across this region; (2) LCLUC effects are significant drivers of yield change; and (3) high spatial variability in yield is indicated for several key agricultural sub-regions of East Africa. Food production risk when considered at the household scale is largely dependent on the occurrence of extremes, so mean yield in some cases may be an incomplete predictor of risk. The broad range of projected crop yields reflects enormous variability in key parameters that underlie regional food security; hence, donor institutions’ strategies and investments might benefit from considering the spatial distribution around mean impacts for a given region. Ultimately, global assessments of food security risk would benefit from including regional and local assessments of climate impacts on food production. This may be less of a consideration in other regions. This study supports the concept that LCLUC is a first-order factor in assessing food production risk.