Hydrological Guidelines for Reservoir Operation: Application to the Brazilian Semiarid Region

The Brazilian water legislation advocates that some uses have priority over others, but this aspect has never been clearly addressed, generating conflicts. Water authorities usually refer to hydrological models to justify their decisions on water allocation. However, a significant group of stakeholders does not feel qualified to discuss these models and is, therefore, excluded from the decision process. We hereby propose a hydrologically robust method to correlate water uses with their respective reservoir alert volumes, which should empower the less formally educated stakeholders. The method consists of: (i) generating the water discharge versus reliability curve, using a stochastic approach; (ii) generating the withdrawal discharge versus alert volume family of curves, using a water-balance approach; (iii) calibrating the key parameter T using field data; and (iv) associating each water use with its alert volume. We have applied the method to four of the largest reservoirs (2.10³ - 2.10² hm³) in the semi-arid Ceará State. The results indicate that low-priority water uses should be rationalized when the reservoir volume is below 20%; whereas uses with very high priority should start rationalization when it is below 11%. These hydrological guidelines should help enhance water governance among non-specialist stakeholders in water-scarce and reservoir-dependent regions.


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The Northeast of Brazil, where the semiarid Caatinga biome prevails, is home to 25 million 28 inhabitants with high water demand. Its rivers, however, are intermittent and groundwater is 29 limited and often salty [1]. To cope with the frequent and severe droughts, the water-supply policy number of reservoirs (one dam every 5 km² on average), and to the high residence time of the waters 75 within the reservoirs (which causes low levels of water quality [21]), the Caatinga biome has become 76 a challenging biome for water management [22]. Usually, River Basin Committees decide on water 77 release shortly after the rainy season, the key information being the stored reservoir volume. The  Coefficient of variation of inflow (-) 0.9 1.2 1.0 0.6 0.9 [3] Q90/average inflow (-) [ (1)

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In Equation (1), V(t) is the effectively stored reservoir volume at time t; QH is the discharge of the 117 direct precipitation over the lake; QR is the inflow discharge from the rivers; QG is the inflow 118 discharge from the groundwater; Qimp is the eventual import discharge from another basin by 119 transfer structures; QW is the withdrawal discharge; QE is the evaporation discharge; Qinf is the 120 infiltration discharge; QO is the overflow discharge through the outlet; and Qexp is the eventual

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In Equations (4) -(7), t is time; V0 is the reservoir volume in the beginning of the dry season; Vf is the 142 reservoir volume after the simulated depletion; T is the simulated depletion duration; Qi is the input 143 discharge; δQ is the difference between infiltration and groundwater discharges; EA is the 144 evaporation rate; A is the effectively flooded area of the reservoir; and φ is a parameter. According 151 152 The three parameters (Qi, Vf, and T) must be established. The model user elects two of them (Qi the reservoir dried out (Vf = 0) after the duration T. Since no inflow was assumed, no overflow 159 discharge through the outlet was expected either (QO = 0). The curves generated by Equation (7)

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the withdrawal discharges also decrease. From Figure 4 and     annual reliability); 17% for moderate priority uses (90% reliability); and 11% for very high priority 208 uses (99% reliability), such as human and animal supply.

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The fact that the derivative dQW/dG increases with reliability level means that, to obtain small 216 increments of high-reliability levels, the withdrawal discharge must be considerably reduced. This is 217 an important feature for decision making in systems designed to supply for high-reliability  (Table 2), whereas the smallest dam, Aracoiaba, yields less than 2 m³/s with the same reliability. Figure 3 and Table 1 indicate that Q90 is, on average, only 42% of the inflow, which means that 58% of

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The declining-demand trend when the stored volume is high means that demand decreases as 241 the stored volume increases above a threshold value (around 50%), and so do the withdrawal reason: in that case, despite water scarcity in the basin, the stakeholders fear the lack of water in the 245 near future. In fact, drought experiences strongly affect people emotionally [29], culturally [30] and the length of the dry season, i.e., the stakeholders try to use the available water as rationally as 248 possible before the next rainy season. Considering the differences in the catchment areas of the 249 reservoirs (size, precipitation, runoff), the constancy of the optimal T value suggests that it is 250 representative of the committees located in the Brazilian Semiarid region. What concerns the 251 highest-risk discharges (see the boxes in Figure 4), we noticed that, in the Araras and Pentecoste 252 reservoirs, this limit is low (below 10% of the storage capacity), showing that their stakeholders are 253 willing to take higher risks concerning the water supply of the following year. In the Orós and

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Aracoiaba reservoirs, observations differed (15% and 25%, respectively). The more conservative 255 policy in Orós is probably due to the dam's relevance for the regional water supply.

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Another important issue is the decision on how much water should be rationalized for each water 274 use in each situation. The hierarchical water-reliability policy, although necessary and helpful, is 275 also a source of conflicts. Take, for example, the case of Orós reservoir at 20% of its capacity. Very 276 low and low priority users will have to save water, but they will struggle to get as much as possible,

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whereas higher priority users will try to release as little as possible, so as to delay (or even avoid) 278 having to rationalize water themselves. An even worse scenario is that, in which all users have to

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We introduce a novel and hydrologically-sound method to provide a simple relation between 287 classes of water uses and their respective alert volumes. The method uses a new approach and 288 considers the input from committee stakeholders to classify water uses and to associate them with 289 the annual reliability level. Hydrological models associate withdrawal discharges with both the 290 reliability level and the alert volume. Our method was applied to four important reservoirs (2.10² -under water rationalization, but this has not happened until now. The field data shows that, when the stored reservoir volume is higher than 50%, demand decreases because of the relative abundance of water from other sources in the basin. When the stored volumes are low (typically below 25%), the 298 withdrawal discharges also decrease, most likely due to the fear of water scarcity in the near future.

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The field data also give evidence that the highest-risk discharges (i.e., those of the most external 300 points) are usually released when the reservoir volumes lie between 5% and 25% of the storage 301 capacity. Despite the water-priority policy's relevance, it is also a source of conflicts, with no 302 technical solution whatsoever. However, a democratic and representative committee seems to be the 303 best forum to decide such matters. The here-derived guidelines are simple and should help to 304 enhance water governance among the less educated stakeholders (in terms of hydrological 305 modeling) in water-scarce and reservoir-dependent regions.