Métodos estadísticos para manejar el riesgo de inundaciones y cambio climático

El cambio climático real y potencial y la variabilidad del clima se convertirán en un desafío cada vez mayor para los hidrólogos, ingenieros civiles, y planeadores interesadosen los riesgos de inundación. En general, no conocemos el riesgo de inundación existente en áreas particulares debido a que los registros tienden a ser limitados. Hay mayor incertidumbre en cuanto a nuestros estimadores cuantiles de inundación –de lo que la gente se imagina–. Si además, para nuestros análisis, tenemos en cuenta la variabilidad climática histórica y el cambio climático, entonces lo que sabemos es aún menos. En muchos casos, ni siquiera está claro si el calentamiento global va a incrementar o a disminuir el riesgo de inundación. Así que el desafío es usar toda la información que tenemos sobre inundaciones pasadas y el clima futuro, junto con una profunda comprensión acerca de los procesos hidrológicos, para predecir los riesgos de inundación en el futuro

Cramer-von Mises and Anderson-Darling goodness of fit tests for extreme value distributions with unknown parameters

The use of goodness of fit tests based on Cramer-von Mises and Anderson-Darling statistics is discussed, with reference to the composite hypothesis that a sample of observations comes from a distribution, FH, whose parameters are unspecified. When this is the case, the critical region of the test has to be redetermined for each hypothetical distribution FH. To avoid this difficulty, a transformation is proposed that produces a new test statistic which is independent of FH. This transformation involves three coefficients that are determined using the asymptotic theory of tests based on the empirical distribution function. A single table of coefficients is thus sufficient for carrying out the test with different hypothetical distributions; a set of probability models of common use in extreme value analysis is considered here, including the following: extreme value 1 and 2, normal and lognormal, generalized extreme value, three-parameter gamma, and log-Pearson type 3, in all cases with parameters estimated using maximum likelihood. Monte Carlo simulations are used to determine small sample corrections and to assess the power of the tests compared to alternative approaches

Modeling long-term persistence in hydroclimatic time series using a hidden state Markov model

A hidden state Markov (HSM) model is developed as a new approach for generating hydroclimatic time series with long-term persistence. The two-state HSM model is motivated by the fact that the interaction of global climatic mechanisms produces alternating wet and dry regimes in Australian hydroclimatic time series. The HSM model provides an explicit mechanism to stochastically simulate these quasi-cyclic wet and dry periods. This is conceptually sounder than the current stochastic models used for hydroclimatic time series simulation. Models such as the lag-one autoregressive (AR(1)) model have no explicit mechanism for simulating the wet and dry regimes. In this study the HSM model was calibrated to four long-term Australian hydroclimatic data sets. A Markov Chain Monte Carlo method known as the Gibbs sampler was used for model calibration. The results showed that the locations significantly influenced by tropical weather systems supported the assumptions of the HSM modeling framework and indicated a strong persistence structure. In contrast, the calibration of the AR(1) model to these data sets produced no statistically significant evidence of persistence.Mark Thyer and George Kucze

Water Resources Systems Planning and Management: An Introduction to Methods, Models and Applications

This 2005 version has been superseded by the 2017 edition, available in full here: http://hdl.handle.net/1813/48159Throughout history much of the world has witnessed ever-greater demands for reliable, high-quality and inexpensive water supplies for domestic consumption, agriculture and industry. In recent decades there have also been increasing demands for hydrological regimes that support healthy and diverse ecosystems, provide for water-based recreational activities, reduce if not prevent floods and droughts, and in some cases, provide for the production of hydropower and ensure water levels adequate for ship navigation. Water managers are challenged to meet these multiple and often conflicting demands. At the same time, public stakeholder interest groups have shown an increasing desire to take part in the water resources development and management decision making process. Added to all these management challenges are the uncertainties of natural water supplies and demands due to changes in our climate, changes in people's standards of living, changes in watershed land uses and changes in technology. How can managers develop, or redevelop and restore, and then manage water resources systems - systems ranging from small watersheds to those encompassing large river basins and coastal zones - in a way that meets society's changing objectives and goals? In other words, how can water resources systems become more integrated and sustainable

Reliability, Resiliency, Robustness, and Vulnerability Criteria for Water Resource Systems

Three criteria for evaluating the possible performance of water resource systems are discussed. These measures describe how likely a system is to fail (reliability), how quickly it recovers from failure (resiliency), and how severe the consequences of failure may be (vulnerability). These criteria can be used to assist in the evaluation and selection of alternative design and operating policies for a wide variety of water resource projects. The performance of a water supply reservoir with a variety of operating policies illustrates their use. When water resource investments are made there is little assurance that the predicted performance will coincide with the actual performance. Robustness is proposed as a measure of the likelihood that the actual cost of a proposed project will not exceed some fraction of the minimum possible cost of a system designed for the actual conditions that occur in the future. The robustness criterion is illustrated by its application to the planning of water supply systems in southwestern Sweden

Short-Term Hydropower Optimization Using A Time-Decomposition Algorithm

Hydropower operations optimization models select a sequence of releases from one or more reservoirs that maximizes the expected benefit while honoring many social and environmental constraints. A 3-tiered time-decomposition algorithm is adopted to compute optimal sub-daily releases for the Harris Station reservoir in Maine, USA. This involves solving nested optimization models, each with a different planning horizon and time-step, where the longer-term planning models inform the shorter-term models. This allows for rapid optimization of short-term operations, while efficiently considering seasonal objectives and constraints. In the case study presented, 6-hr release decisions in a weekly model are made by iteratively solving weekly, monthly, and annual models using sampling stochastic dynamic programming. A key consideration is how uncertainty is represented in each of the nested models. Uncertainty is inherent in hydropower operations optimization because the future availability of water and future energy prices are unknown at the time a decision is made. In order to ensure efficient operation of the hydropower system, it is often important that such uncertainties be well represented. Reservoir operations are simulated using release decisions from time-decomposition models with different representations of uncertainty. By comparing the operational efficiency of each model, the relative merits of different uncertainty representations are examined. In particular, we consider the value of inflow forecasts to inform the uncertainty model at various planning horizons and how this changes with seasonal hydrology. Summer inflows are generally low, and it is often desirable to operate at full head to maximize generated power per volume released. Still, brief and intense localized rainstorms can cause a spike in reservoir inflow, which can result in spilling. Not surprisingly we found that short-term forecasts are of most importance to summer reservoir performance, and longer-term forecasts contributed little to operational efficiency. In other periods of the year the relative importance of long- and short-term forecasts varies