Kate A. Brauman

Irrigation plays a critical role in global food production and climate adaptation and exercises profound influence over humanity's water use. Yet despite its critical importance, there is a persistent lack of understanding of fine-scale irrigation patterns across the planet, knowledge which is essential for informing global food security and sustainability targets. Utilizing either statistical downscaling or remote sensing approaches, existing global irrigation datasets are constrained by coarse spatial resolutions, a lack of timeliness, or varying robustness and reliability. To address this gap, here we integrate multi-source Earth observation and environmental datasets and use machine learning to develop a medium-resolution (30 m) global irrigated area dataset for the 2023/24 growing season. Within existing cropland extent, we leverage a newly compiled set of georeferenced irrigated (N=230,683) and non-irrigated (N=153,194) ground-truth points and integrate seasonal vegetation metrics derived from Landsat 8/9 imagery with agroecological-zone information and hydroclimatic and topographic variables. We subsequently develop and evaluate two machine-learning frameworks, a continental Agro-Ecological Zone (AEZ) tile-based framework and a continental-scale framework, and apply the best-performing approach for each continent. Evaluation using held-out test samples yielded a global accuracy of 80.5 ± 2.1%. The resulting maps were also validated against independent global and national irrigation datasets and statistics, demonstrating broad agreement in the spatial distribution of irrigated areas. This approach is robust and reliable because it is built on a harmonized global ground-truth database, incorporates multiple predictors, and is rigorously validated using independent datasets. All code, ground-truth, and data products are freely and publicly available and can serve as a robust, scale-neutral, and fully reproducible framework for fine-resolution irrigation mapping. These advances provide the critical and long-needed foundation for near-real-time monitoring and early warning systems, and fine-scale land and water resource management.

Global water footprint (WF) assessments guide critical water management decisions, yet their uncertainty remains poorly understood. While a few studies have assessed WF sensitivity to reference evapotranspiration and crop model inputs, uncertainty arising from harvested area (HA) variation has received little attention. This study quantified the uncertainty of WF for major crops (rice, maize, wheat, and soybeans) using five global HA datasets for the years 2000, 2010, and 2015. We applied statistical metrics, including bias, standard deviation, and coefficient of variation (CV), to quantify WF uncertainty and mapped spatial patterns to identify regional uncertainty hotspots. Results reveal substantial uncertainty in the total WF of the major crops, with average CVs of 28%, 32%, and 17% in 2000, 2010, and 2015, respectively. Green WF estimates exhibit even higher uncertainty than the total WF across all crops and years. In 2015, blue WF uncertainty exceeded total WF uncertainty for all crops except wheat, whereas in 2000 and 2010, this pattern was observed only for rice. Soybeans exhibited the highest variability in blue WF (24%, 28%, and 58% in 2000, 2010, and 2015) and green WF (53% in 2000, 69% in 2010), while wheat led in green WF variability in 2015 (35% CV). Hotspots of high WF variability of the major crops are found in China, India, Brazil, Southeast Asia, and parts of the US. Notably, WF uncertainty exceeds HA uncertainty, indicating amplification as uncertainty propagates from area inputs to WF estimates due to spatial variation in ET. These findings underscore the risk of misinformed water allocation and sustainability strategies, potentially exacerbating water and food insecurity. We highlight the need for systematic documentation of uncertainties and investment in crop area monitoring through enhanced field validation and farmer engagement.

While water has long been an object and mechanism of conflict, predicting water conflict remains a challenge. Little evidence supports strong, direct causal, or statistical links. Yet, connections between water and conflict remain relevant. As climate-driven water disturbances increase, it is imperative to understand how monitorable and predictable drivers like droughts and floods may affect political instability, of which conflict is a subset. Drawing from a variety of bodies of literature, we synthesize theory and case studies on water and conflict and integrate them into a generalized Pathways to Instability Framework. This framework presents a novel arrangement of conceptual categories that parse the biophysical and social elements that make up the multi-step, indirect links from water disturbance to instability. We demonstrate the framework’s usefulness by organizing literature on the onset of the Syrian Civil War and showing how disagreements among findings stem from studies on different links in the causal chain. The framework’s linear nature effaces specificity and depth in favor of simplicity, which helps evaluate the importance of different drivers. Acknowledging that policy contends with intertwined rather than standalone issues, the conceptual categories present discrete entry points in which policy-makers can devise and assess the value of interventions.

Temporal variation in the water footprint (WF) of crop production has been less studied compared to spatial variation. This study assessed global spatiotemporal trends in the WF of crop production from 1972 to 2018 at 5 arcmin resolutions using a process-based crop model, alongside statistical analysis to identify key socio-economic drivers. The global WF increased from 4,799 km³ in 1972 to 7,773 km³ in 2018, with blue and green WFs rising by 65% and 62%, respectively, driven by growing demands for food, feed, biofuels, and other uses. Crop area expansion (40%) was the primary contributor, while increased crop productivity through land intensification helped offset further increases, preventing a near doubling of the WF. Green water accounted for 81% of the WF increase, emphasizing the need for better green water management. Oil crops, cereals, and fruits were the largest contributors to the WF rise. Population growth, livestock production, agricultural exports, GDP, and biofuel demand were identified as key drivers, offering insights into targeted strategies for sustainable water use in agriculture.

“You were elected to lead the living, not the dead”: A computational analysis of social media discourse about Nigerian farmer-herder conflicts, Britt, Brian C, VanDyke, Matthew S, Hayes, Jameson L, Brauman, Kate A “You were elected to lead the living, not the dead”: A computational analysis of social media discourse about Nigerian farmer-herder conflicts, Britt, Brian C, VanDyke, Matthew S, Hayes, Jameson L, Brauman, Kate A

Crop production is among the most extensive human activities on the planet – with critical importance for global food security, land use, environmental burden, and climate. Yet despite the key role that croplands play in global land use and Earth systems, there remains little understanding of how spatial patterns of global crop cultivation have recently evolved and which crops have contributed most to these changes. Here we construct a new data library of subnational crop-specific irrigated and rainfed harvested area statistics and combine it with global gridded land cover products to develop a global gridded (5-arcminute) irrigated and rainfed cropped area (MIRCA-OS) dataset for the years 2000 to 2015 for 23 crop classes. These global data products support critical insights into the spatially detailed patterns of irrigated and rainfed cropland change since the start of the century and provide an improved foundation for a wide array of global assessments spanning agriculture, water resource management, land use change, climate impact, and sustainable development.

There are millions of river barriers worldwide, ranging from wooden locks to concrete dams, many of which form associated impoundments to store water in small ponds or large reservoirs. Besides their benefits, there is growing recognition of important environmental and social trade-offs related to these artificial structures. However, global datasets describing their characteristics and geographical distribution are often biased towards particular regions or specific applications, such as hydropower dams affecting fish migration, and are thus not globally consistent. Here, we present a new river barrier and reservoir database developed by the Global Dam Watch (GDW) consortium that integrates, harmonizes, and augments existing global datasets to support large-scale analyses. Data curation involved extensive quality control processes to create a single, globally consistent data repository of instream barriers and reservoirs that are co-registered to a digital river network. Version 1.0 of the GDW database contains 41,145 barrier locations and 35,295 associated reservoir polygons representing a cumulative storage capacity of 7,420 km³ and an artificial terrestrial surface water area of 304,600 km².

Projects designed to incentivize ecosystem management for societal benefits are becoming increasingly popular and are often touted as win–win solutions for social and environmental challenges. Yet, there are important concerns about the equity and justice implications of these programs, and there is strong evidence that a lack of attention to justice can exacerbate or create inequities. We focus on water funds, a subset of watershed ecosystem investment programs that are being scaled globally. We specifically discuss how three long‐standing Andean programs have shifted through time toward a greater focus on equity and justice. We argue that these shifts, while imperfect, have been critical to their durability and that, more generally, continued alignment of nature‐based investment programs with broader equity and justice concerns is essential for long‐term durability and success.