Since 2013, hundreds of thousands of refugees have migrated southward to Jordan to escape the Syrian civil war that began in mid-2011. Evaluating impacts of conflict and migration on land use and transboundary water resources in an active war zone remains a challenge. However, spatial and statistical analyses of satellite imagery for the recent period of Syrian refugee mass migration provide evidence of rapid changes in land use, water use, and water management in the Yarmouk-Jordan river watershed shared by Syria, Jordan, and Israel. Conflict and consequent migration caused ∼50% decreases in both irrigated agriculture in Syria and retention of winter rainfall in Syrian dams, which gave rise to unexpected additional stream flow to downstream Jordan during the refugee migration period. Comparing premigration and postmigration periods, Syrian abandonment of irrigated agriculture accounts for half of the stream flow increase, with the other half attributable to recovery from a severe drought. Despite this increase, the Yarmouk River flow into Jordan is still substantially below the volume that was expected by Jordan under the 1953, 1987, and 2001 bilateral agreements with Syria.remote sensing | Landsat | reservoir | conflict | irrigation

The broader global community is navigating evolving climate risks, rapid energy transitions, and the growing recognition that sustainable future pathways will require fundamental transformations in our collective management of socio-environmental systems (de Vos et al.

Limited water availability, population growth, and climate change have resulted in freshwater crises in many countries. Jordan’s situation is emblematic, compounded by conflict-induced population shocks. Integrating knowledge across hydrology, climatology, agriculture, political science, geography, and economics, we present the Jordan Water Model, a nationwide coupled human–natural-engineered systems model that is used to evaluate Jordan’s freshwater security under climate and socioeconomic changes. The complex systems model simulates the trajectory of Jordan’s water system, representing dynamic interactions between a hierarchy of actors and the natural and engineered water environment. A multiagent modeling approach enables the quantification of impacts at the level of thousands of representative agents across sectors, allowing for the evaluation of both systemwide and distributional outcomes translated into a suite of water-security metrics (vulnerability, equity, shortage duration, and economic well-being). Model results indicate severe, potentially destabilizing, declines in freshwater security. Per capita water availability decreases by approximately 50% by the end of the century. Without intervening measures, >90% of the low-income household population experiences critical insecurity by the end of the century, receiving <40 L per capita per day. Widening disparity in freshwater use, lengthening shortage durations, and declining economic welfare are prevalent across narratives. To gain a foothold on its freshwater future, Jordan must enact a sweeping portfolio of ambitious interventions that include large-scale desalinization and comprehensive water sector reform, with model results revealing exponential improvements in water security through the coordination of supply- and demand-side measures.

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The role of individual and collective human action is increasingly recognized as a prominent and arguably paramount determinant in shaping the behavior, trajectory, and vulnerability of multisector systems. This human influence operates at multiple scales: from short‐term (hourly to daily) to long‐term (annually to centennial) timescales, and from the local to the global, pushing systems toward either desirable or undesirable outcomes. However, the effort to represent human systems in multisector models has been fragmented across philosophical, methodological, and disciplinary lines. To cohere insights across diverse modeling approaches, we present a new typology for classifying how human actors are represented in the broad suite of coupled human‐natural system models that are applied in MultiSector Dynamics (MSD) research. The typology conceptualizes a “sector” as a system‐of‐systems that includes a diverse group of human actors, defined across individual to collective social levels, involved in governing, provisioning, and utilizing products, goods, or services toward some human end. We trace the salient features of modeled representations of human systems by organizing the typology around two key questions: (a) Who are the actors in MSD systems and what are their actions? (b) How and for what purpose are these actors and actions operationalized in a computational model? We use this typology to critically examine existing models and chart the frontier of human systems modeling for MSD research.

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Abstract:The estimation of spatially-variable actual evapotranspiration (AET) is a critical challenge to regional water resources management. We propose a new remote sensing method, the Triangle Algorithm with Variable Edges (TAVE), to generate daily AET estimates based on satellite-derived land surface temperature and the vegetation index NDVI. The TAVE captures heterogeneity in AET across elevation zones and permits variability in determining local values of wet and dry end-member classes (known as edges). Compared to traditional triangle methods, TAVE introduces three unique features: (i) the discretization of the domain as overlapping elevation zones; (ii) a variable wet edge that is a function of elevation zone; and (iii) variable values of a combined-effect parameter (that accounts for aerodynamic and surface resistance, vapor pressure gradient, and soil moisture availability) along both wet and dry edges. With these features, TAVE effectively addresses the combined influence of terrain and water stress on semi-arid environment AET estimates. We demonstrate the effectiveness of this method in one of the driest countries in the world-Jordan, and compare it to a traditional triangle method (TA) and a global AET product (MOD16) over different land use types. In irrigated agricultural lands, TAVE matched the results of the single crop coefficient model (−3%), in contrast to substantial overestimation by TA (+234%) and underestimation by MOD16 (−50%). In forested (non-irrigated, water consuming) regions, TA and MOD16 produced AET average deviations 15.5 times and −3.5 times of those based on TAVE. As TAVE has a simple structure and low data requirements, it provides an efficient means to satisfy the increasing need for evapotranspiration estimation in data-scarce semi-arid regions. This study constitutes a much needed step towards the satellite-based quantification of agricultural water consumption in Jordan.

Jordan, with limited rainfall, has per capita water availability of 135 m 3 /yr making it one of the water-poorest countries in the world. We analyzed the most comprehensive modern rainfall data set to date, consisting of 44 years of daily measurements from 58 stations primarily in the western, populated and agricultural portion of Jordan over the period 1970-2013 to assess temporal trends, variability, and spatial patterns. From 1995 to 2013, 13 of 19 years showed rainfall less than the mean, which has a probability <8.35% of chance occurrence. We used nonparametric statistical analysis and found 38 of 58 stations experienced an annual rainfall decrease at an average rate of 1.2 mm/yr. Over all 58 stations, the average decrease was 0.41 mm/yr. The annual coefficient of variation of daily rainfall showed a long-term increase of 2-3% at 90% of stations. Analysis of annual variance of daily rainfall suggests decreasing variance in the low rainfall areas to the southwest and east and increasing variance in the high rainfall areas to the northwest, a pattern consistent with principal component analysis. Strict multiple hypothesis testing procedures using the k-familywise error rate approach reinforced and confirmed the statistically significant regional rainfall decline as well as the spatial patterns of increasing and decreasing rainfall variability.