No abstract

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.

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.

Abstract. The empirical attribution of rapid hydrologic change presents a unique data availability challenge in terms of establishing baseline prior conditions. On the one hand, one cannot go back in time to collect the necessary in situ data if it were not serendipitously collected when the change was taking place. On the other hand, modern satellite monitoring missions are often too recent to capture changes that are ancient enough to provide sufficient observations for adequate statistical inference. In that context, the four decades of continuous global high resolution monitoring enabled by the Landsat missions are an unrivaled source of information to study hydrologic change globally. However, extracting the relevant time series information in a systematic way across Landsat missions remains a monumental challenge. Cloud masking and inconsistent image quality often complicate the automatized interpretation of optical imagery. Focusing on the monitoring of lake water extents, we address this challenge by coupling supervised and unsupervised image classification techniques. Unsupervised classification is first used to detect water on unmasked (cloudless and high quality) pixels. Classification results are then compiled across images to estimate the inundation frequency of each pixel, hinging on the assumption that different pixels will be masked at different times. Inundation frequency is then leveraged to infer the inundation status of masked pixels on individual images through supervised classification. Applied to a representative set of global and rapidly changing lakes, the approach successfully captured water extent fluctuations obtained from in situ gauges (when applicable), or from other Landsat missions during overlapping time periods.