Taking into account the climatic conditions of the semiarid region of Brazil, with its intermittent rivers and long periods of water scarcity, a dense network of surface reservoirs (on average one dam every $5{\mathrm{km}}^2$ ) of very different sizes has been built. The impact of such a network on water and sediment dynamics constitutes a remarkable challenge for hydrologists. The main objective of this work is to present a novel way of simulating water and sediment fluxes through such high-density reservoir networks, which enables the assessment of water and sediment retention in those structures. The new reservoir modeling approach has been coupled with the fully process-oriented and semidistributed hydrological WASA-SED model, which was tailored for semiarid hydroclimatological characteristics. This integrated modeling system was applied to the $933\text{-}{\mathrm{km}}^2$ Benguê catchment, located in semiarid northeastern Brazil, which has a network of 114 reservoirs with a wide range of surface areas (from 0.003 to 350 ha). The small reservoirs were grouped into size classes according to their storage capacity and a cascade routing scheme was applied to describe the upstream-downstream position of the classes; the large reservoirs were handled explicitly in the reservoir modeling approach. According to the model results, the proposed approach is capable of representing the water and sediment fluxes though the entire reservoir network with reasonable accuracy. In addition, the model shows that the dynamics of water and sediment within the Benguê catchment are strongly impacted by the presence of multiple reservoirs, which are able to retain approximately 21% of the generated runoff and almost 42% of the sediment yield of the catchment for the simulation period, from 2000 to 2012.

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 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 yield versus reliability curve, using a stochastic approach; (ii) generating the yield versus alert volume family of curves, using a water-balance approach; (iii) calibrating the key parameter T (depletion duration) 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.

The work evaluated the potential for discrimination of land use and occupation around reservoirs, using spectral information obtained by multispectral, hyperspectral satellites and images obtained with an ARP (remotely piloted aircraft). The research analyzed the performance of different images classification techniques applied to multispectral and hyperspectral sensors for the detection and differentiation of soil classes around the Paus Brancos and Marengo reservoirs, located in Settlement 25 of Maio. The classes identified based on surveys in campaigns carried out in 2014 and 2015 around the reservoirs were: water, macrophytes, exposed soil, native vegetation, agriculture, thin and ebbing vegetation, in addition to the cloud and cloud shadow targets. The performance of the classifiers applied to the image of the Hyperion sensor was, in general, superior to those obtained in Landsat 8 image, which can be explained by the high spectral resolution of the first, which facilitates the differentiation of targets with similar spectral response. For validation of the supervised classification method of Maximum Likelihood, Landsat 8 (08/24/2015) and Hyperion (08/28/2015) images were used. The results of the application indicated a good performance of the classifier associated with the RGB composition of the chosen Hyperion image (bands R - 51, G - 161, B - 19) in the detection of the classes around this reservoir, producing a Kappa coefficient of 0.83. The availability of data from the Hyperion sensor is very restricted, which hinders the development of continued research, thus the use of images surpassed by RPA is extremely viable.

This study aims to analyze the impacts of the reservoir network within Pereira de Miranda - CE catchment (also called Pentecoste) over sediment transport and storage capacity of the system. The survey of the "damming" was carried out using satellite images. We identified 502 erosion units, derived from overlaying maps of the Universal Soil Loss Equation parameters, which allowed the estimation of localized erosion in the basin and identification of areas potentially generating sediment. In order to estimate silting in Pentecoste reservoir, different system structure scenarios were considered. An average erosion rate of 59 t ha-1year-1 was estimated. According to the model, the silting of Pentecoste reservoir may vary from 1.1 to 2.6% per decade, depending on the scenario considered. It is also observed that the reservoirs upstream can retain up to 58% of the sediment that would reach the Pentecoste reservoir. Very small reservoirs with a capacity of up to 100,000 m³, although representing only 1.83% of the system water availability, are able to retain almost 8% of total sediment produced.

To increase drought preparedness in semi-arid regions many small and medium reservoirs have been built in recent decades. Together these reservoirs form a Dense Reservoir Network (DRN) and its pres...