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Millions of people displaced by conflicts have found refuge in water-scarce countries, where their perceived effect on water availability has shaped local water security discourses. Using an annual global data set, we explain the effects of refugee migrations on the host countries’ water stress through the food demand displaced by refugees and the water necessary to produce that food. The water footprint of refugee displacement increased by nearly 75% globally between 2005 and 2016. Although minimal in most countries, implications can be severe in countries already facing severe water stress. For example, refugees may have contributed up to 75 percentage points to water stress in Jordan. While water considerations should not, alone, determine trade and migration policy, we find that small changes to current international food supply flows and refugee resettlement procedures can potentially ease the effect of refugee displacement on water stress in water-vulnerable countries.

 

Abstract The ongoing agrarian transition from smallholder farming to large-scale commercial agriculture promoted by transnational large-scale land acquisitions (LSLAs) often aims to increase crop yields through the expansion of irrigation. LSLAs are playing an increasingly prominent role in this transition. Yet it remains unknown whether foreign LSLAs by agribusinesses target areas based on specific hydrological conditions and whether these investments compete with the water needs of existing local users. Here we combine process-based crop and hydrological modelling, agricultural statistics, and georeferenced information on individual transnational LSLAs to evaluate emergence of water scarcity associated with LSLAs. While conditions of blue water scarcity already existed prior to land acquisitions, these deals substantially exacerbate blue water scarcity through both the adoption of water-intensive crops and the expansion of irrigated cultivation. These effects lead to new rival water uses in 105 of the 160 studied LSLAs (67% of the acquired land). Combined with our findings that investors target land with preferential access to surface and groundwater resources to support irrigation, this suggests that LSLAs often appropriate water resources to the detriment of local users. 

Abstract. Streamflow regimes are rapidly changing in many regions of the world. Attribution of these changes to specific hydrological processes and their underlying climatic and anthropogenic drivers is essential to formulate an effective water policy. Traditional approaches to hydrologic attribution rely on the ability to infer hydrological processes through the development of catchment-scale hydrological models. However, such approaches are challenging to implement in practice due to limitations in using models to accurately associate changes in observed outcomes with corresponding drivers. Here we present an alternative approach that leverages the method of multiple hypotheses to attribute changes in streamflow in the Upper Jhelum watershed, an important tributary headwater region of the Indus basin, where a dramatic decline in streamflow since 2000 has yet to be adequately attributed to its corresponding drivers. We generate and empirically evaluate a series of alternative and complementary hypotheses concerning distinct components of the water balance. This process allows a holistic understanding of watershed-scale processes to be developed, even though the catchment-scale water balance remains open. Using remote sensing and secondary data, we explore changes in climate, surface water, and groundwater. The evidence reveals that climate, rather than land use, had a considerably stronger influence on reductions in streamflow, both through reduced precipitation and increased evapotranspiration. Baseflow analyses suggest different mechanisms affecting streamflow decline in upstream and downstream regions, respectively. These findings offer promising avenues for future research in the Upper Jhelum watershed, and an alternative approach to hydrological attribution in data-scarce regions. 

Abstract. The empirical attribution of hydrologic change presents a unique data availability challenge in terms of establishing baseline prior conditions, as one cannot go back in time to retrospectively collect the necessary data. Although global remote sensing data can alleviate this challenge, most satellite missions are too recent to capture changes that happened long ago enough to provide sufficient observations for adequate statistical inference. In that context, the 4 decades of continuous global high-resolution monitoring enabled by the Landsat missions are an unrivaled source of information. However, constructing a time series of land cover observation across Landsat missions remains a significant challenge because cloud masking and inconsistent image quality complicate the automatized interpretation of optical imagery. Focusing on the monitoring of lake water extent, we present an automatized gap-filling approach to infer the class (wet or dry) of pixels masked by clouds or sensing errors. The classification outcome of unmasked pixels is compiled across images taken on different dates to estimate the inundation frequency of each pixel, based on the assumption that different pixels are masked at different times. The inundation frequency is then used to infer the inundation status of masked pixels on individual images through supervised classification. Applied to a variety of global lakes with substantial long term or seasonal fluctuations, the approach successfully captured water extent variations obtained from in situ gauges (where applicable), or from other Landsat missions during overlapping time periods. Although sensitive to classification errors in the input imagery, the gap-filling algorithm is straightforward to implement on Google's Earth Engine platform and stands as a scalable approach to reliably monitor, and ultimately attribute, historical changes in water bodies. 

The common‐pool nature of groundwater resources creates incentives to over pump that contribute to their rapid global depletion. In transboundary aquifers, users are separated by a territorial border and might face substantially different economic and hydrogeologic conditions that can alternatively dampen or amplify incentives to over pump. We develop a theoretical model that couples principles of game theory and groundwater flow to capture the combined effect of well locations and user asymmetries on pumping incentives. We find that heterogeneities across users (here referred to as asymmetries) in terms of either energy cost, groundwater profitability or aquifer response tend to dampen incentives to over pump. However, combinations of two or more types of asymmetry can substantially amplify common‐pool overdraft, particularly when the same user simultaneously faces comparatively higher costs (or aquifer response) and profitability. We use this theoretical insight to interpret the emergence of the Disi agreement between Saudi Arabia and Jordan in association with the Disi‐Amman water pipeline. By using bounded non‐dimensional parameters to encode user asymmetries and groundwater connectivity, the theory provides a tractable generalized framework to understand the premature depletion of shared aquifers, whether transboundary or not.

 

Foreign investors have acquired approximately 90 million hectares of land for agriculture over the past two decades. The effects of these investments on local food security remain unknown. While additional cropland and intensified agriculture could potentially increase crop production, preferential targeting of prime agricultural land and transitions toward export-bound crops might affect local access to nutritious foods. We test these hypotheses in a global systematic analysis of the food security implications of existing land concessions. We combine agricultural, remote sensing, and household survey data (available in 11 sub-Saharan African countries) with georeferenced information on 160 land acquisitions in 39 countries. We find that the intended changes in cultivated crop types generally imply transitions toward energy-rich, but nutrient-poor, crops that are predominantly destined for export markets. Specific impacts on food production and access vary substantially across regions. Deals likely have little effect on food security in eastern Europe and Latin America, where they predominantly occur within agricultural areas with current export-oriented crops, and where agriculture would have both expanded and intensified regardless of the land deals. This contrasts with Asia and sub-Saharan Africa, where deals are associated with both an expansion and intensification (in Asia) of crop production. Deals in these regions also shift production away from local staples and coincide with a gradually decreasing dietary diversity among the surveyed households in sub-Saharan Africa. Together, these findings point to a paradox, where land deals can simultaneously increase crop production and threaten local food security.

 

Groundwater supports essential societal and ecological functions by acting as a reservoir that buffers against natural variability. Increasing water scarcity and climate variability have resulted in more intensive management of groundwater resources, but groundwater often remains difficult to understand and manage. With this in mind, we develop a simple platform that provides a straightforward, web‐based user interface applicable to a wide variety of end‐user scenarios. Groundwater behavior is modeled using the method of images in a new R package, anem, which serves as the engine for the web platform, anem‐app, produced using R Shiny. Both tools allow users to define aquifer properties and pumping wells, view maps of hydraulic head, and simulate particle tracking under steady‐state conditions. These tools have the advantage of being platform independent and open source, so that they are freely available to anyone with a web browser and internet connection (anem‐app) or computing platform with R installed (anem). We designed both tools to lower the learning curve and up‐front costs to building simple groundwater models. The simplicity of the web application allows exploration of groundwater behavior under various conditions, and should be especially valuable in low‐budget applications where advanced analysis may not be practical or necessary. Integration with the R language allows for advanced analysis and deeper exploration of groundwater dynamics. In this manuscript, we describe how anem and anem‐app are built in the R environment and demonstrate how they might be used by planners or stakeholders.