Originally published on August 14, 2012 at The Spout
Making a decision on the best way to operate a reservoir during a flooding or high inflow event is a multi-objective and challenging task; and depending on the size of the reservoir, the level of success is usually highly affected by water level in the reservoir before the event occurs. Therefore, decision making before and during these events is too complicated to be handled only by reservoir operation planners’ judgement. A Risk-Informed Decision Making (RIDM) framework to acquire all the necessary information from multiple sources and providing planners with the collected information and a pre-designed and solid guideline to follow in order to make recommendations to decision makers seems to be the best approach to deal with this task.
The framework, as a guideline, requires gathering information on possible inflow scenarios for the probable high inflow or flooding period. These scenarios are inputted into simulation and/or optimization models developed for the task of reservoir operation. The outputs of these models are probability density functions for the value of the objectives function(s) and variables of the model(s). The outputs are generated separately for each different operational alternative. In order to analyze the performance of operational alternatives on each objective, a number of streamflow impact curves are coupled with the outputs of simulation and/or optimization models to translate the variables values into meaningful data to evaluate the alternatives performance and generate a performance matrix for each alternative.
Finally, the performance matrices, decision makers’ desirable risk-taking level on each objective, and relative importance of the objectives are inputted into a Multi-Criteria Decision Making (MCDM) software package. The outcome is a ranking of the operational alternatives for the task of reservoir operation during the flooding or high inflow period. If the recommended alternative is acceptable to decision makers, the corresponding operational plan might be implemented. Otherwise, the decision makers might order developing new operational alternatives and reiterating the process.
Originally published on May 20, 2012 at The Spout
Decision Making could be defined as a process in which a decision maker makes a specific choice among several existing choices. Multi-Criteria Decision Making (MCDM) as its name suggests pertains to a decision making situation where the decision maker considers multiple criteria in order to make a choice. Benjamin Franklin is allegedly the earliest known person to create a simple method to solve this type of problems. As a result of the rapid growth of operations research during and after World War II, numerous methods have been invented to help decision makers face the challenge of MCDM problems. As an invaluable book on MCDM, Smart Choices by Hammond, Keeney, and Raiffa is strongly recommended to interested readers. Moreover, for a thorough description of MCDM history and other information and resources related to MCDM, you may have a look at MCDM Society website. I also spent some time working on developing a new method for solving MCDM problems with the use of fuzzy numbers, which enable more flexibility in taking account of uncertainties, and the results are published in a paper that might be of interest to you. With a simple search into literature, the variety of approaches and techniques to solve MCDM problems will be revealed.
MCDM techniques can be very helpful in solving water related problems due to the fact that most of the large-scale water related decisions impact multiple active components in a watershed system. (This concept was the base for the definition of Integrated Water Resources Management by Global WaterPartnership in 2000). If you have read the previous post, one of the applications of MCDM in water resources planning and management is in planning for reservoir operation. In fact, MCDM is somehow an impartible component of a Risk-informed Decision Making framework for reservoir operation during floods. I hope to be able to explain each of the components of such a framework through a number of posts in the future and explain how they connect and create a coherent framework in the end.
Originally published on February 21, 2012 at The Spout
Once more busy with an interesting project at BC Hydro that might be of interest to you too. Decision making for planning and management of water resources can be an utterly challenging task where several stakeholders with a diverse range of interests and consequently several objectives are part of the process. As an example, operating a hydro-power dam is a task normally done with consideration of several competing objectives such as maximizing power generation and minimizing adverse environmental impacts. They are competing where a long term operational plan demands storing water in the reservoir for later power generation while there is a minimum required flow to be released for a healthy river environment known as environmental flow. Case by case, there might be several other objectives and concerns such as recreational opportunities, water supply for residential and/or irrigational use, navigation etc. This process becomes much more challenging where the reservoir is also being used for controlling floods. The difficult task of reservoir operation planning for minimizing flood damage during flooding periods accompanied by other operational objectives and exacerbated by the lack of time for decision making won't probably be successful unless there is a comprehensive Risk-Informed Decision Making (RIDM) framework. The framework should utilize advanced inflow forecast and scenario generation methods and be able to inform decision makers of the risks involved with each possible decision.
For a better understanding of an RIDM framework, you might be interested in looking at NASA's RIDM handbook which is available for download at NASA's website.
Originally published on October 26, 2011 at The Spout
If you have read the previous posts, you know that as opposed to my fellow contributors, my approach to water problems is from an engineering perspective. So one more time I intend to share with you an engineer's perspective through the application of statistics to solve water related problems.
The main topic that I intend to introduce to you today is called moment matching, but first I will explain why we use moment matching for planning and management of water resources. In order to plan the long-term operation of a hydropower reservoir - through utilizing approaches such as stochastic dynamic programming - operation engineers need to have access to the forecasts of future inflows into the reservoir. Clearly, a great deal of uncertainty is inevitable in forecasting future inflows and operations engineers have to consider the pertaining risks. One of the useful methods to managing uncertainties is generating multiple inflow scenarios. Usually inflow scenario trees, which consider the dependency of the inflow at a time step to the inflow at the previous time step, are the desirable structure. Generating multiple potential scenarios of future events through analyzing the available data from similar events in the past is a common approach in many areas of research with numerous practical applications. Similarly, there are several statistical methods to generate scenarios of the inflows into a reservoir through the use of recorded inflows in the past. One of these methods I have been working with recently is called moment matching. It is not always possible to detect the statistical distribution of an event, which is the basis of several inflow forecasting methods, with the available historical data. In these instances, moment matching could be a useful surrogate. As its name implies, moment matching is a method that generates scenarios that match the statistical moments of the historically recorded data. Which moments to include is up to the researchers and depends on the specific problem they are trying to solve. For the case of inflow scenarios, for instance, there some studies have used moment matching for generating scenarios that match the first four statistical moments (expected value, standard deviation, skewness, kurtosis) and also the correlation coefficients (could be serial, seasonal, or spatial correlation) between historical inflows. In 2003, Michael Kaut and his colleagues wrote a paper on developing an efficient algorithm for scenario generation with the use of moment matching. If you are interested in learning more, or if you think the algorithm could be of use to you, do not hesitate to visit Michael Kaut’s website for free access to a full source code of the algorithm.
Originally published on June 9, 2011 at The Spout
As a Hydrotechnical Engineering (one of the areas of specialization in Civil Engineering) student, my area of focus is reservoir operation during high flow events. Although I just use the inflow data other specialists provide me with, hydrology and inflow forecast is the starting point of reservoir operation planning process. Forest harvesting as an influential element which can change the inflow amount until forest regrowth is complete, has been a controversial area of study in forest hydrology.
In the eyes of the public and usually policy-makers, logging trees and removing forests increases the magnitude of floods and consequently exacerbates its destructive impacts. On the other hand, historically, forest hydrologists have tended to rely on the chronological pairing analysis of peak flow events to study the impact of forest harvesting on peak flows which has led them to have opposite views ("Forest impact on floods due to extreme rainfall and snowmelt in four Latin American environments 1: Field data analysis" as one of the most recent examples). Throughout the years, many scientists have demanded that the public and policymakers improve their understanding of natural phenomena such as floods. But is it the public who need to improve their understanding of the behavior of nature?
In order to study the impacts of forest harvesting on peak flows, there are usually two small neighboring watersheds (which are quite similar) that are chosen, one as control watershed which remains unchanged and the other one as treatment watershed which is clear-cut, or treated in another way depending on the objectives of the study. Using statistical methods, the relationship between paired peak flows, which stem from the same meteorological events in the neighboring watersheds, is captured and used to figure out how this relationship changes as the result of clear-cutting in the treatment watershed. This type of pairing of the events is known as chronological pairing. To avoid getting too technical or going through statistical complexities involved in making inferences from the graph results of this type of pairing, let us suffice to say that the related studies tend to conclude that the impact of forest harvesting on the magnitude of peak flows diminishes as the size of the meteorological event increases so that the highest peak flows are almost unchanged. Moreover, in some cases there are suggestions that peak flows with the return period of higher than a particular number of years, like 10, are not affected by logging.
Recently, Younes Alila, a professor at the Department of Forestry in University of British Columbia, and his colleagues have published a paper on the topic utilizing frequency-based pairing of events. In the paper, the authors reveal how chronological pairing of events leads to an irrelevant hypothesis and how the blind use of some statistical methods to support the hypothesis without giving sufficient thought to the process has misled forest hydrologists for several decades. The authors, through appropriate and insightful use of statistical methods accompanied with physical reasoning of natural phenomena, finally prove that the public view on the impact of forest harvesting on floods turns out to be closer to reality than traditional view in the forest hydrology studies.
The publication of the paper as a new paradigm to dismiss years of controversy over the impact of forest harvesting on flood magnitude, at least for small watersheds, has been objected by some scientists who have spent several years of their career on similar studies with opposite conclusions. For further reading, below you can find the links to the original paper, a critique to the paper, and the reply of the authors to the critique.
Forests and floods: A new paradigm sheds light on age-old controversies.
Comment on “Forest and floods: A new paradigm sheds light on age-old controversies” by Younes Alila et al.
Reply to comment by Jack Lewis et al. on “Forests and floods: A new paradigm sheds light on age-old controversies”
M. H. (Ali) Alipour is a Ph.D. candidate and recipient of Trustees Doctoral Fellowship at the University of Central Florida (Orlando). His research includes water resources planning and management, hydrology, and ecohydraulics.