José-Luis Molina

Object-Oriented Bayesian Networks for Participatory Water Management: Two Case Studies in Spain

Object-oriented Bayesian networks (OOBNs) have recently been introduced to model water systems that can be represented as repetitive patterns. This paper shows the way in which OOBNs can be used as a groundwater management decision support system in two Spanish case studies. The two areas, in the southern and eastern parts of inland Spain, are characterized by a semiarid climate, water scarcity, and frequent droughts; consequently, the agrarian economy in both cases depends on the provision of irrigation from groundwater sources. Both case studies are illustrative examples of conflict among various water actors, complexity, and uncertainty about the consequences of water management actions. Each study is approached from a different viewpoint: one from an agroeconomic and the other from a hydrogeological perspective. The sites display different degrees of aquifer overexploitation and agrarian profitability. This indicates that, in each case, the effects generated by water management interventions and the t...

Comparative Analysis of System Dynamics and Object-Oriented Bayesian Networks Modelling for Water Systems Management

This paper presents a comparative analysis of System Dynamics Modelling (SDM) and Object-Oriented Bayesian Networks (OOBN). Both techniques are extensively used for water resources modelling due to their flexibility, effectiveness in assessing different management options, ease of operation and suitability for encouraging stakeholder involvement. Conversely, both approaches have several important differences that make them complementary. For example, while SDM is more suitable for simulating the feedback dynamics of processes, OOBN modelling is a powerful tool for modelling systems with uncertain inputs (or outputs) characterised by probability distributions. This comparative analysis is applied to the Kairouan aquifer system, Tunisia, where the aquifer plays an essential role for socio-economic development in the region. Both models produced comparable results using baseline data, and show their complementarity through a suite of scenario tests. It is shown that reducing pumping of groundwater to coastal cities may prove the key to reducing the current aquifer deficit, though local demand reduction must be considered to preserve the agricultural economy. It is suggested that water management assessment should be tackled using both approaches to complement each other, adding depth and insight, and giving a more coherent picture of the problem being addressed, allowing for robust policy decisions to be made.

Reviewing Bayesian Networks potentials for climate change impacts assessment and management: A multi-risk perspective

The evaluation and management of climate change impacts on natural and human systems required the adoption of a multi-risk perspective in which the effect of multiple stressors, processes and interconnections are simultaneously modelled. Despite Bayesian Networks (BNs) are popular integrated modelling tools to deal with uncertain and complex domains, their application in the context of climate change still represent a limited explored field. The paper, drawing on the review of existing applications in the field of environmental management, discusses the potential and limitation of applying BNs to improve current climate change risk assessment procedures. Main potentials include the advantage to consider multiple stressors and endpoints in the same framework, their flexibility in dealing and communicate with the uncertainty of climate projections and the opportunity to perform scenario analysis. Some limitations (i.e. representation of temporal and spatial dynamics, quantitative validation), however, should be overcome to boost BNs use in climate change impacts assessment and management.

The Social Sustainable Aquifer Yield: An Indicator for the Analysis and Assessment of the Integrated Aquifers Management

Integrated Aquifers Management (IAM) demands innovative tools and methods that are able to consider as much perspectives as possible. This research is aimed to design, apply and provide an indicator named Social Sustainable Aquifer Yield (SSAY), expressed in units of time that includes pure hydrological variables as well as social ones. The indicator is defined as the relation between the Residence Time, which is the relation between aquifer Storage (S) and Recharge (R) (S/R), and the relation between the aquifer Pumping (P) and the new variable named Aquifer Social Yield (ASY). The whole indicator is defined by this formula: (S/R)/(P/ASY). The assessment of the residence time is essential in aquifers with at least one of the following features: i) high hydraulic diffusivity, and ii) small volume of reserves. Finally, the variable ASY is defined as the average perception from the stakeholders about the maximum acceptable aquifer exploitation. This indicator has been successfully applied in the aquifers located in southern Jaen province (South Spain) belonging to the Water System SE4-Jaén Water Supply. The results probe the high utility of the indicator, especially in regards to the public participation processes.

Causal Reasoning for the Analysis of Rivers Runoff Temporal Behavior

The accurate knowledge about the influence of time in the behavior of rivers systems is crucial for a proper river basin water management. Traditional techniques such as correlograms or ARMA models have been widespread used over the last decades providing the analyzer with an average behaviour of temporal influence of hydrological series. In the last decade, the development of techniques, under the discipline of artificial intelligent, have increased the range of available analytical tools. On the other hand, hydrological processes have a very strong random nature and they are driven by its high uncertainty and variability. Consequently, it is necessary to build tools, able to incorporate these peculiarities in their analytical functioning. Causal Reasoning through Bayesian Networks (BNs) allows processing and analysing hydrological series, incorporating and assessing all their variability. Causality driven by Bayes´ theorem is used here to dynamically identify, characterize and quantify the influence of time (dependence) for each time step in annual run-off series in five Spanish River basins. Therefore, BNs arise as a powerful tool for getting a deeper understanding on the knowledge of temporal behaviour of hydrological series because this analysis is dynamic and implemented specifically for temporal iterations (decision variables). Implications and applications of this research are largely aimed to improve and optimize the design and dimensioning of hydraulic infrastructures, as well as reducing the risk of negative impacts produced by extreme events such as several droughts or floods, among others.

HidroMap: A New Tool for Irrigation Monitoring and Management Using Free Satellite Imagery

Proper control and planning of water resource use, especially in those catchments with large surface, climatic variability and intensive irrigation activity, is essential for a sustainable water management. Decision support systems based on useful tools involving main stakeholders and hydrological planning offices of the river basins play a key role. The free availability of Earth observation products with high temporal resolution, such as the European Sentinel-2B, has allowed us to combine remote sensing with cadastral and agronomic data. This paper introduces HidroMap to the scientific community, an open source tool as a geographic information system (GIS) organized in two different modules, desktop-GIS and web-GIS, with complementary functions and based on PostgreSQL/PostGIS database. Through an effective methodology HidroMap allows monitoring irrigation activity, managing unregulated irrigation, and optimizing available fluvial surveillance resources using satellite imagery. This is possible thanks to the automatic download, processing and storage of satellite products within field data provided by the River Surveillance Agency (RSA) and the Hydrological Planning Office (HPO). The tool was successfully validated in Duero Hydrographic Basin along the 2017 summer irrigation period. In conclusion, HidroMap comprised an important support tool for water management tasks and decision making tackled by Duero Hydrographic Confederation which can be adapted to any additional need and transferred to other river basin organizations.

Flood Hazard Assessment Supported by Reduced Cost Aerial Precision Photogrammetry

Increasing flood hazards worldwide due to the intensification of hydrological events and the development of adaptation-mitigation strategies are key challenges that society must address. To minimize flood damages, one of the crucial factors is the identification of flood prone areas through fluvial hydraulic modelling in which a detailed knowledge of the terrain plays an important role for reliable results. Recent studies have demonstrated the suitability of the Reduced Cost Aerial Precision Photogrammetry (RC-APP) technique for fluvial applications by accurate-detailed-reliable Digital Terrain Models (DTMs, up to: ≈100 point/m2; vertical-uncertainty: ±0.06 m). This work aims to provide an optimal relationship between point densities and vertical-uncertainties to generate more reliable fluvial hazard maps by fluvial-DTMs. This is performed through hydraulic models supported by geometric models that are obtained from a joint strategy based on Structure from Motion and Cloth Simulation Filtering algorithms. Furthermore, to evaluate vertical-DTM, uncertainty is proposed as an alternative approach based on the method of robust estimators. This offers an error dispersion value analogous to the concept of standard deviation of a Gaussian distribution without requiring normality tests. This paper reinforces the suitability of new geomatic solutions as a reliable-competitive source of accurate DTMs at the service of a flood hazard assessment.

A novel planning approach for the water, sanitation and hygiene (WaSH) sector: The use of object-oriented bayesian networks

Abstract Conventional approaches to design and plan water, sanitation, and hygiene (WaSH) interventions are not suitable for capturing the increasing complexity of the context in which these services are delivered. Multidimensional tools are needed to unravel the links between access to basic services and the socio-economic drivers of poverty. This paper applies an object-oriented Bayesian network to reflect the main issues that determine access to WaSH services. A national Program in Kenya has been analyzed as initial case study. The main findings suggest that the proposed approach is able to accommodate local conditions and to represent an accurate reflection of the complexities of WaSH issues, incorporating the uncertainty intrinsic to service delivery processes. Results indicate those areas in which policy makers should prioritize efforts and resources. Similarly, the study shows the effects of sector interventions, as well as the foreseen impact of various scenarios related to the national Program.

Object-Oriented Modelling as a Decision-Making Tool in Agriculturally Overexploited Karstic Aquifers

Object-Oriented Bayesian Networks (OOBNs) utilise the power of Object-Oriented Programming (OOP) and offer a novel approach to the problems of integrated water management. This paper describes the building of an OOBN Decision Support System (DSS) that allows complex domains to be described in terms of inter-related objects. Thus, the DSS structure is able to represent an accurate reflection of a complex real-world water system made for an aquifer that has been used as an example of a successful application. In this research, conventional Bayesian Networks (BNs) are used to describe the probabilistic relationships between variables (objects) within each network. A network is a group of objects that can be described as a class. Different classes can possess similar sets of objects and be linked through other networks having common variables. Classes inherit commonly used states and behavior from other classes in a hierarchical way. This model of networks represents a participatory DSS for helping water managers.