Transferable Decision-Support Framework Guides Water Allocation in Arid Reservoirs

In the drought-prone backlands of northeastern Brazil, where a single reservoir may be the only buffer between a community and catastrophe, researchers have unveiled a new graphical decision-support framework that could transform how water managers decide who gets water, when, and in what amounts. The study, led by Reginaldo Moura Brasil Neto and colleagues at the Federal University of Paraíba, in collaboration with the state’s water management agency, integrates hydrological simulation, reservoir shape analysis, and hierarchical clustering into a single operational tool designed to bring clarity to one of the most contentious decisions in water-scarce regions: allocation.

The research, published in the journal Water Resources Management, addresses a persistent weakness in conventional reservoir allocation. Traditional approaches, such as Brazil’s national methodology formalized by the National Water and Basic Sanitation Agency (ANA), rely on fixed operating rules, predefined hydrological states, and rigid planning horizons. These simplifications, the authors argue, fail to capture the enormous diversity of reservoir sizes and shapes found across semiarid landscapes—where storage capacities in their dataset ranged from less than 10 cubic hectometers to more than 700—and can obscure the real risks that decision-makers and water users face.

At the heart of the new framework are “allocation abaci”—graphical calculation charts that synthesize how a reservoir behaves under many combinations of initial storage, demand levels, and planning horizons. Rather than producing a single yes-or-no answer based on a fixed set of conditions, each abacus maps a continuous landscape of possibilities, allowing managers to see at a glance whether a proposed withdrawal is sustainable, how much water would remain at the end of a planning period, and how close the system would come to collapse. In total, the team generated 650 such abaci across five simulation horizons for each reservoir analyzed.

The study area is the state of Paraíba, a territory of roughly 56,000 square kilometers, about 70 percent of which lies within Brazil’s semiarid region—one of the most drought-vulnerable zones in the country. Rainfall there is extraordinarily variable, ranging from around 1,400 millimeters per year in the humid coastal Mata region to as little as 400 millimeters in the interior Borborema highlands. Meanwhile, potential evaporation remains relentlessly high, between 1,200 and 1,800 millimeters annually, and is relatively stable across the seasons. This climatic asymmetry—rain arriving in pulses, evaporation draining steadily year-round—is what makes storage-dominated water systems in the region so fragile, and why the researchers placed evaporative losses at the center of their methodology.

To characterize that evaporation, the team needed to know precisely how each reservoir’s geometry changes as water levels fluctuate. They compiled elevation–area–volume (EAV) curves for 130 monitored reservoirs, describing how each basin’s surface area expands or contracts as storage rises and falls. This matters because evaporation acts on the water surface: a shallow, sprawling reservoir that spreads rapidly as it fills loses proportionally far more water to the atmosphere than a deep, steep-walled one holding the same volume. The researchers then applied hierarchical cluster analysis—validated with standard statistical criteria including the Calinski–Harabasz index and silhouette coefficients—to group the reservoirs into five morphometrically distinct classes based on the shapes of their standardized elevation–area curves.

The clustering revealed striking differences in vulnerability. Reservoirs in Cluster 5, characterized by irregular geometries with limited surface area at low storage but rapid expansion near full capacity, proved the most susceptible to evaporative losses, particularly when starting from high storage levels. At the other extreme, Cluster 4 reservoirs, with more regular, roughly cylindrical shapes, showed the lowest sensitivity, losing only about 1.25 percent of their total volume per 10 percent decrease in initial storage, compared with roughly 2 percent for the most sensitive classes. Because reservoirs in Clusters 2, 3, and 1 dominate the regional network—comprising 66, 42, and 11 reservoirs respectively—the classification offers water agencies a practical way to stratify their infrastructure by inherent risk profile.

The simulation engine behind the abaci is deliberately conservative. Rather than attempting to forecast river inflows, which in intermittent semiarid streams are highly unreliable, the model assumes zero inflow and tracks only the “water bank” already in storage, depleted by evaporation and withdrawals. The researchers describe this as a precautionary screening baseline: it ensures that allocation decisions rest on tangible stored volumes rather than on uncertain recharge, providing a robust safety margin during the critical months when evaporation and consumption are the only predictable drivers of depletion. In more humid regions, they note, the framework could be adapted by adding stochastic inflow scenarios.

The team demonstrated the tool’s power by comparing it directly with the conventional ANA approach at the Acauã reservoir, one of Paraíba’s strategic storage sites. Under the traditional method, hydrological states such as the Green, Yellow, and Red categories are defined by guide curves tied to a fixed planning horizon—in this demonstration, corresponding to demands of 2,800, 2,000, and 1,200 liters per second. The new abaci reproduced those classifications but expanded the analysis across a continuum of horizons. For an 18-month planning period, the reservoir would need initial volumes of approximately 169, 128, and 86 cubic hectometers to satisfy the Green, Yellow, and Red state demands respectively. Stretch the horizon to 30 months, and even a completely full reservoir could no longer guarantee the Green state demand, while the Yellow and Red demands would require roughly 210 and 139 cubic hectometers. Read another way, at maximum initial storage, the Green demand could be sustained for only about 27 months, the Yellow for around 36, and the Red for more than 48.

These numbers carry real consequences for the region’s negotiation-based allocation system, in which government agencies, water users, and civil society jointly decide how to ration water during droughts. Because the abaci display not only whether a demand can be met but also the final storage percentage—the safety margin—for every scenario, stakeholders can explore both conservative and permissive options with a shared, transparent evidence base. The framework also distinguishes sharply between reservoirs: large systems such as Coremas and Acauã display high resilience, sustaining demands of 1,000 liters per second over long horizons with only moderate depletion, while Acauã collapses if demands exceed roughly 2,400 liters per second over 30 months. Medium reservoirs such as Araçagi and Capoeira are far more sensitive to both demand and horizon length, and small systems like Marés deplete rapidly due to limited storage and proportionally larger evaporative losses.

The study builds on a growing body of research documenting how evaporation from reservoirs represents a substantial and often overlooked drain on stored water, particularly in water-stressed regions. Recent work by Lorenzo-Lacruz and colleagues in 2025, along with studies by Zhao and Gao and by Nevermann and colleagues, has shown that intensive damming and regulation can intensify evaporative losses and erode usable storage. What the Brazilian team contributes is the translation of that concern into an operational allocation instrument—one that quantifies how evaporation interacts with reservoir geometry, demand, and time, and flags precisely when storage becomes vulnerable.

The authors are candid about the framework’s data requirements. Applying it elsewhere demands three core inputs: reliable and regularly updated EAV curves, which are especially important in reservoirs affected by sedimentation; representative and recent climatic data, particularly precipitation and evaporation, given that climate change is pushing evaporation rates upward; and realistic operational demand estimates reflecting local water use. The methodology is also tailored primarily to arid and semiarid settings, where the zero-inflow assumption is most defensible, though the authors emphasize that in humid regions it can be extended with additional water-balance components.

Beyond its technical contributions, the study carries a message about governance. Evidence from Brazil suggests that allocation processes grounded in transparent technical criteria, participatory mechanisms, and explicit strategic objectives achieve greater legitimacy among water users—improving the odds that hard decisions during droughts are actually followed. By replacing opaque rule charts and single-scenario assessments with intuitive graphics that any stakeholder can read, the framework aims to make negotiation itself more evidence-based. The researchers describe the tool as automated, replicable, and user-friendly, with immediate potential for adoption by Paraíba’s Executive Agency for Water Management and by other agencies confronting similar conditions.

Looking ahead, the authors call for curated data repositories and open, adaptable computational tools to broaden the framework’s applicability, and for validation across additional reservoir systems in different climatic, morphological, and governance contexts. As climate change intensifies hydrological uncertainty worldwide, the need for flexible, scenario-based alternatives to fixed operating rules is only expected to grow. In showing that reservoir shape—not just size or inflow—strongly controls both the magnitude and the pace of evaporative losses, this study offers water managers everywhere a deceptively simple but powerful idea: look the reservoir in its geometry, chart its fate graphically, and let the trade-offs speak for themselves.