Eduardo Mario Mendiondo

IAHS decade on predictions in ungauged basins (PUB), 2003-2012: Shaping an exciting future for the hydrological sciences

Abstract Drainage basins in many parts of the world are ungauged or poorly gauged, and in some cases existing measurement networks are declining. The problem is compounded by the impacts of human-induced changes to the land surface and climate, occurring at the local, regional and global scales. Predictions of ungauged or poorly gauged basins under these conditions are highly uncertain. The IAHS Decade on Predictions in Ungauged Basins, or PUB, is a new initiative launched by the International Association of Hydrological Sciences (IAHS), aimed at formulating and implementing appropriate science programmes to engage and energize the scientific community, in a coordinated manner, towards achieving major advances in the capacity to make predictions in ungauged basins. The PUB scientific programme focuses on the estimation of predictive uncertainty, and its subsequent reduction, as its central theme. A general hydrological prediction system contains three components: (a) a model that describes the key processes of interest, (b) a set of parameters that represent those landscape properties that govern critical processes, and (c) appropriate meteorological inputs (where needed) that drive the basin response. Each of these three components of the prediction system, is either not known at all, or at best known imperfectly, due to the inherent multi-scale space—time heterogeneity of the hydrological system, especially in ungauged basins. PUB will therefore include a set of targeted scientific programmes that attempt to make inferences about climatic inputs, parameters and model structures from available but inadequate data and process knowledge, at the basin of interest and/or from other similar basins, with robust measures of the uncertainties involved, and their impacts on predictive uncertainty. Through generation of improved understanding, and methods for the efficient quantification of the underlying multi-scale heterogeneity of the basin and its response, PUB will inexorably lead to new, innovative methods for hydrological predictions in ungauged basins in different parts of the world, combined with significant reductions of predictive uncertainty. In this way, PUB will demonstrate the value of data, as well as provide the information needed to make predictions in ungauged basins, and assist in capacity building in the use of new technologies. This paper presents a summary of the science and implementation plan of PUB, with a call to the hydrological community to participate actively in the realization of these goals.

Development of a spatial decision support system for flood risk management in Brazil that combines volunteered geographic information with wireless sensor networks

Effective flood risk management requires updated information to ensure that the correct decisions can be made. This can be provided by Wireless Sensor Networks (WSN) which are a low-cost means of collecting updated information about rivers. Another valuable resource is Volunteered Geographic Information (VGI) which is a comparatively new means of improving the coverage of monitored areas because it is able to supply supplementary information to the WSN and thus support decision-making in flood risk management. However, there still remains the problem of how to combine WSN data with VGI. In this paper, an attempt is made to investigate AGORA-DS, which is a Spatial Decision Support System (SDSS) that is able to make flood risk management more effective by combining these data sources, i.e. WSN with VGI. This approach is built over a conceptual model that complies with the interoperable standards laid down by the Open Geospatial Consortium (OGC) - e.g. Sensor Observation Service (SOS) and Web Feature Service (WFS) - and seeks to combine and present unified information in a web-based decision support tool. This work was deployed in a real scenario of flood risk management in the town of Sao Carlos in Brazil. The evidence obtained from this deployment confirmed that interoperable standards can support the integration of data from distinct data sources. In addition, they also show that VGI is able to provide information about areas of the river basin which lack data since there is no appropriate station in the area. Hence it provides a valuable support for the WSN data. It can thus be concluded that AGORA-DS is able to combine information provided by WSN and VGI, and provide useful information for supporting flood risk management. HighlightsA conceptual framework integrates information from sensors and volunteers.Interoperable standards are employed for integrating the heterogeneous data sources.Lessons were learned from the deployment in a real scenario of flood risk management.Volunteered Geographic Information improves the coverage of monitored areas.

A middleware platform to support river monitoring using wireless sensor networks

Flooding is a critical global problem, which is growing more severe due to the effects of climate change. This problem is particularly acute in the state of São Paulo, Brazil, where flooding during the rainy season incurs significant financial and human costs. Another critical problem associated with flooding is the high level of pollution present in urban rivers. Efforts to address these problems focus upon three key research areas: river monitoring, modelling of river conditions and incident response. This paper introduces a rich next-generation middleware platform designed to support wireless sensor network based environmental monitoring along with a supporting hardware platform. This system has been deployed and evaluated in a real-world river monitoring scenario in the city of São Carlos, Brazil.

Water Scarcity Under Scenarios for Global Climate Change and Regional Development in Semiarid Northeastern Brazil

Abstract The state of Ceará, located in semiarid northeastern Brazil, suffers under irregularly recurring droughts that go along with water scarcity. Structural policies to control and reduce water scarcity, as water supply and demand management, should be seen as long-term planning, and thus must consider climate change and regional development. To this end, the present research proposes a model-based global change scenario. Water stress is assessed for 184 municipalities in Ceará between 2001 and 2025. For this purpose, four global change scenarios are developed, considering both global climate change and the effects of development policies. Climatic, hydrological, and water use models are applied and a proposed index computed for identification of long-term water stress. Application of the methodology in the focus area shows that, if no effective intervention measures are taken, up to almost 60 percent of the municipalities of the state may suffer under long-term water scarcity by 2025. On average, municipalities in the state of Ceará have a water shortage probability for the next 25 years ranging from 9 percent to 20 percent annually, depending on the scenario. The 10 percent most stressed municipalities have a probability of over 80 percent annually of facing water scarcity in the scenario period (25 years). Results also show that a decentralized development policy can compensate for the possible severe effects of climatic trends on future water availability over the scenario period.

A blue/green water-based accounting framework for assessment of water security

A comprehensive assessment of water security can incorporate several water-related concepts, while accounting for Blue and Green Water (BW and GW) types defined in accordance with the hydrological processes involved. Here we demonstrate how a quantitative analysis of provision probability and use of BW and GW can be conducted, so as to provide indicators of water scarcity and vulnerability at the basin level. To illustrate the approach, we use the Soil and Water Assessment Tool (SWAT) to model the hydrology of an agricultural basin (291 km2) within the Cantareira Water Supply System in Brazil. To provide a more comprehensive basis for decision making, we analyze the BW and GW-Footprint components against probabilistic levels (50th and 30th percentile) of freshwater availability for human activities, during a 23 year period. Several contrasting situations of BW provision are distinguished, using different hydrological-based methodologies for specifying monthly Environmental Flow Requirements (EFRs), and the risk of natural EFR violation is evaluated by use of a freshwater provision index. Our results reveal clear spatial and temporal patterns of water scarcity and vulnerability levels within the basin. Taking into account conservation targets for the basin, it appears that the more restrictive EFR methods are more appropriate than the method currently employed at the study basin. The blue/green water-based accounting framework developed here provides a useful integration of hydrologic, ecosystem and human needs information on a monthly basis, thereby improving our understanding of how and where water-related threats to human and aquatic ecosystem security can arise.

Bridging the gap between decision-making and emerging big data sources

With the emergence of big data and new data sources, a challenge posed to todays organizations consists of identifying how to align their decision-making and organizational processes to data that could help them make better-informed decisions. This paper presents a study in the context of disaster management in Brazil that applies oDMN+, a framework that connects decision-making with data sources through an extended modeling notation and a modeling process. The study results revealed that the framework is an effective approach for improving the understanding of how to leverage big data in the organizations decision-making. An extended model-based framework connects decision-making to big data sources.A better understanding of decision-making is achieved with the framework.A modeling process is outlined for systematically using the framework in practice.Decision-making can be improved through the use of standard models and notations.Lessons were learned from a case study on a Brazilian disaster early-warning center.

Improving the accuracy of a flood forecasting model by means of machine learning and chaos theory

AbstractMonitoring natural environments is a challenging task non account of their hostile features. The use of wireless sensor networks (WSNs) for data collection is a feasible method since these domains lack any infrastructure. However, further studies are required to handle the data collected for a better modeling of behavior and thus make it possible to forecast impending disasters. In light of this, in this paper an analysis is conducted on the use of data gathered from urban rivers to forecast flooding with a view to reducing the damage it causes. The data were collected by means of a WSN in São Carlos, São Paulo State, Brazil, which gathered and processed data about the river level and rainfall by means of machine learning techniques and employing chaos theory to model the time series; this meant that the inputs of the machine learning technique were the time series gathered by the WSN modeled on the basis of the immersion theorem. The WSNs were deployed by our group in the city of São Carlos where there have been serious problems caused by floods. After the data interdependence had been established by the immersion theorem, the artificial neural networks were investigated to determine their degree of accuracy in the forecasting models.

Sistema de informações geográficas para a gestão de programas municipais de cuidado a idosos

This paper describes the construction of an information system, integrating geographic and health care data for elderly people with dementia who were registered in Family Health Units in a municipality in the State of Sao Paulo, Brazil. The study is descriptive, based on a quantitative investigation conducted between June, 2006 and June, 2007. The methodological steps included the preparation of a database, registering the attributes of the elderly; of a second, geo-referenced registry; and the development of an operational platform to link these two information sets. In-home evaluations were conducted with 1048 elderly, 275 of whom presented cognitive impairments. The creation of this system enables a visualization of the geographic distribution of elderly with dementia, the investigation of geographic and health data associations; and the identification of risk and vulnerability factors. As such, geo-processing technologies enable important new possibilities for planning care initiatives in gerontology, improving public health program management.

Field investigations of the 2013–14 drought through quali-quantitative freshwater monitoring at the headwaters of the Cantareira System, Brazil

ABSTRACT Integrating seasonal patterns of water availability and land-use/land-cover change is crucial in watershed planning. Often, these are not considered under hydrological extremes affecting decision making. This article presents results from a multi-site, nested catchment experiment carried out during a dry period in the Cantareira Water Supply System, South-East Brazil, linking quali-quantitative freshwater monitoring to land-use/land-cover change. Results from 17 catchments show regional behaviour for nitrate loads and drainage areas (0.66–925 km2). An inverse correlation between forest cover and water yield was observed. Despite forest growth in spatial extent, nutrient loads showed potential hazards for water security.

Combining Wireless Sensor Networks and Machine Learning for Flash Flood Nowcasting

This paper addresses an investigation with machine learning (ML) classification techniques to assist in the problem of flash flood now casting. We have been attempting to build a Wireless Sensor Network (WSN) to collect measurements from a river located in an urban area. The machine learning classification methods were investigated with the aim of allowing flash flood now casting, which in turn allows the WSN to give alerts to the local population. We have evaluated several types of ML taking account of the different now casting stages (i.e. Number of future time steps to forecast). We have also evaluated different data representation to be used as input of the ML techniques. The results show that different data representation can lead to results significantly better for different stages of now casting.