Please use this identifier to cite or link to this item: http://hdl.handle.net/10889/11109
Title: Environmental data management and decision support for river basins : application in Alfeios river
Other Titles: Διαχείριση περιβαλλοντικής πληροφορίας και λήψη αποφάσεων σε υδρολογικές λεκάνες : εφαρμογή στον Αλφειό ποταμό
Authors: Μπεκρή, Ελένη
Keywords: Correction technique
Fuzzy programming
Data reconciliation
Water and agricultural future scenarios
Alfeios river
River discharge measurements
River pollutant loads
Tracers
Optimal water allocation
Linear programming
Inexact two-stage stochastic programming
Keywords (translated): Τεχνική διόρθωσης
Αλφειός ποταμός
Διαχείριση υδάτινων πόρων
Ασαφής λογική
Στοχαστικός λογισμός
Abstract: The EU Water Framework Directive 2000/60/EC (WFD) has set as necessity the formulation and implementation of Integrated River Basin Management (IRBM) plans for all EU member states. The backbone of river basin management is the monitoring of qualitative and quantitative river characteristics. A common difficulty in many rivers is the absence of permanent measurement equipment combined with low financial means and time restrictions for implementing monitoring programs. Alternatively, quick measurement methods of low cost and reliability (e.g. floats, air bubbles release) could be employed to estimate river discharges. Moreover, river basins are exposed to a plethora of environmental stresses, resulting in degradation of their quantitative and qualitative status. This led to the reduction of the availability of clean water as well as to increasing competition among water users. It has given rise to the need for optimal water allocation for each river unit. In most countries water resources management is scourged by high uncertainty and by imprecise and limited data, which may be easier approximated through estimates of intervals. Due to these difficulties and complexities the need to adapt and apply optimal water allocation methodologies under uncertainty has arisen. The present PhD research is focused on two main challenges of river basin management. The first part aims to propose and to develop the conceptual, mathematical and computational framework of an original correction technique of quick river discharge measurements in ungauged rivers. A methodological framework is developed based on the principles of volume and pollutant mass conservation, considering intermittent nonmeasurable latent quantities. Parallel measurements of discharge and natural tracers for representative cross-sections of a river and its tributaries are required. The water volume conservation is combined with pollutant/tracers mass balance expressed synchronously not only for each single node of a river, but also for all possible multiple-node combinations covering the entire river. This “divide and conquer” process relies on linear optimization. According to the WFD the river monitoring programs should determine apart from the level of predefined pollutants also their mass load. Discharge data are essential for the estimation of loads of sediments or chemical pollutants of a river or stream. Therefore, the proposed methodology enables the estimation of river discharges with higher accuracy and reliability compared to the initial discharge estimates. Subsequently, it enables the estimation of more reliable pollution loads. It intends to decrease duration, work force and xlii expense of river monitoring programs along with their management plans. The suggested methodology was successfully implemented to the Alfeios river in Greece including tributaries, where only limited short-term quantitative and qualitative measurement data are available. It enabled the estimation of: (a) corrected discharges, pollutant concentrations and pollution loads for eight combinations of initial values as estimated from the qualitative analysis of the river basin, (b) a best/worst case (Min/Max) interval and the corresponding error of the computed/optimized river discharges, pollutant concentrations and pollution loads for the cross-sections of the main river and its tributaries, where tracer concentrations were measured, and (c) the unknown not directly measured parameters, including latent flow rate, the correspondin pollutant concentrations and pollution loads of each river node. Based on these results the methodology succeeded in restricting the errors of the corrected mean discharge values of all measured cross-sections. The resulting error of the corrected latent discharges is much wider compared to the corresponding error for the measured cross-sections. However, it is of note that the determination of a hypothetical unknown latent discharge and subsequently the correction of its estimation, even if it is relatively inaccurate, is very important and useful, since the direct measurement of the latent discharge, and generally of the assumed latent terms, is impossible. Besides, it is worth underscoring that the combination of the single-node balances together with all possible multiple-node balances based on the previous findings resulted in a considerable reduction of the river discharge interval of the ensemble of the cross-sections for the Alfeios river. The direct confirmation of the corrected river discharges with simultaneous accurate measurements is hampered by the lack of such precise measurements. Thus, the consistency of the proposed methodology based on the linear form of the optimization problem was compared with the results from the nonlinear model and the following conclusion can be extracted. Firstly, the value ranges of the nonlinear model lie into similar but not exactly the same value region as the ranges of the linear model. Both ranges have a wide common value region, whereas the ranges from the nonlinear model are wider. Secondly, by comparing each type of model (linear and nonlinear) with the measurements, for both of them the measured discharge values and the corrected ones are linearly connected. More precisely, through the t-test statistics it is proven that the results from the linear as well as from the nonlinear model are not overestimated or underestimated based on the measurements. This result confirms the consistency of the resulting solutions from the optimization process with the measurements. The second part of the present PhD thesis aims at proposing a decision support (DS) framework for optimal water allocation under uncertain system conditions in a real and complex multi-tributary and multi-period water resources system, and more precisely in the Alfeios River Basin. Firstly, an inexact two-stage stochastic programming technique (ITSP) with deterministic-boundary intervals (Huang and Loucks, 2000) and secondly, a similar in terms of concept but more sophisticated and advanced methodology (FBISP) (Li et al., 2009) embody the core of the proposed DS frame. Both hybrid methods are based on the concept that in real-world problems, some uncertainties may indeed exist as ambiguous intervals, since planners and engineers may not have enough information and data to specify probability distributions, and therefore, find it much easier and realistic to define fluctuation ranges for these uncertainties. The ITSP method combines ordinary two-stage stochastic programming with uncertainties expressed as deterministic boundary intervals and is simpler and easier to follow up compared to FBISP. Stable intervals for optimized water allocation targets and probabilistic water allocation volumes and shortages are estimated under a baseline scenario and four water and agricultural policy future scenarios. On the other hand, the FSBIP methodology combines an ordinary multi-stage stochastic programming with uncertainties expressed as fuzzy-boundary intervals. Upper- and lower-bound solution intervals for optimized water allocation targets and probabilistic water allocation volumes and shortages are also estimated under the same baseline scenario and future scenarios for an optimistic and a pessimistic attitude of the decision makers. In both methods the uncertainty of the random water inflows is incorporated through the simultaneous generation of stochastic equal-probability hydrologic scenarios at various inflow positions, instead of using the scenario-tree approach which is commonly used in most applications of these methodologies. The comparison of the corresponding results of the FBISP method with that of ITSP revealed that the results are consistent and compatible. In addition, the incorporation of the fuzzy nature of the uncertainties in the FBISP results in a more analytic and fine approximation of the effect of the uncertainties on the minimum and maximum values of the boundaries of the results, providing also a more complicated structure of the results.
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