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

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

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

Short-Term Optimization Model With ESP Forecasts For Columbia Hydropower System With Optimized Multi-Turbine Powerhouses

Hydroelectric generation is the major source of electric energy in the Pacific Northwestern region of the United States, and efficient operation of that system while meeting environmental constraints and reserve capacity demands is an important economic, environmental, and social issue. This paper describes efforts to develop a new stochastic short-term scheduling model (with perhaps a 3-week planning horizon) for the ten major reservoirs operated by the federal Bonneville Power Administration (BPA) on the Columbia and Snake River systems. The analysis incorporates time-delays (up to 24 hours in a model with time steps increasing from 6 hours initially perhaps to 24 hours); non-economic turbine dispatch with operational constraints; and inflow and load uncertainty (reflecting wind generation) through use of Ensemble Streamflow Predictions (ESP) augmented to include load uncertainties (ESLP). Synthetic ESLPs will be generated for the model testing effort. The intent is to evaluate the benefits of alternative representations of uncertainty subject to all of the operational constraints, both physical and those that result from environmental concerns. Large BPA storage projects can include many turbines of different types; for example, Grand Coulee has 27 turbines of 4 different types. To make system optimization faster and more reliable, concave “powerhouse” functions are pre-computed which are as economically efficient as possible given estimated turbine performance characteristics, and operational dispatch and release constraints. Powerhouse generation functions are forced to be concave if such constraints are consistent with the data; in other cases mandated fish-passage constraints result in non-economic turbine dispatch sequences and often limit allowable discharge ranges, both of which complicate the computation of the loading of individual turbines and the optimization of the hydropower system. Pre-computation of powerhouse functions is an effective decomposition technique for this large stochastic nonlinear optimization problem

Robustness of water resources systems

When water resource systems 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

Value of regional information using bulletin 17B and LP3 distribution

To improve the accuracy of quantile estimators, Bulletin 17B recommends a number of procedures to improve at-site estimators using regional information. Several procedures, including two recommended by the Bulletin, are considered here. Because the data available at a site is generally limited, the skewness estimator can be particularly unstable. When fitting the LP3 distribution, Bulletin 17B recommends combining the station skew with a regional skew using the inverse of their mean square errors as weights. Previous studies have demonstrated the impact of a more precise regional skewness estimator on quantile estimator precision. To improve quantile estimates computed using short records, the Bulletin also suggests combining the at-site quantile estimate with a regional quantile estimate using their effective record lengths as the weights. Potential problems with this weighted estimator are discussed here. Two examples compare the precision of the Bulletin 17B weighted quantile estimator to several alternative estimators which employ different combinations of at-site and regional information, including an index flood procedure which did poorly. The simple Bulletin 17B weighting of at-site and regional regression quantile estimates performs nearly as well as more complex alternatives, and for short records provides a substantial improvement in quantile accuracy. However, when the regional standard deviation and skew are very informative and the regional mean estimator is relatively imprecise, more accurate estimates can be obtained by weighting each of the three sample moments separately with regional estimators of those same statistics. © 2007 ASCE

Incorporating climate change and variability into Bulletin 17B LP3 model

The current techniques for flood frequency analysis presented in Bulletin 17B assume annual maximum floods are stationary, meaning that the distribution of flood flows is not significantly affected by climatic trends or long-term cycles (i.e. decadal variations). Observed trends in stream flows raise concern as to whether or not this assumption is valid. This paper considers how the Bulletin 17B framework might be modified to account for nonstationarity in flood records due to climate variability. In order to improve estimates/forecasts obtained using the LP3 model, the effects of climate variability may be incorporated into updated estimates of the mean, standard deviation, and perhaps the skew by regressing the LP3 parameters on climatic indices describing the Pacific Decadal Oscillation and Northern Atlantic Oscillation. The effects of climatic cycles occurring over a shorter time frame, such as El Niño, are averaged into estimates made using the procedures of Bulletin 17B. However, the effects of El Niño are likely to affect the magnitude of annual maximum stream flows, and thus would impact flood risk in a given year. El Niño effects are incorporated into forecasts by regressing the LP3 parameters on sea surface temperatures. © 2007 ASCE

Getting from here to where? Flood frequency analysis and climate

Modeling variations in flood risk due to climate change and climate variability are a challenge to our profession. Flood-risk computations by United States (U.S.) federal agencies follow guidelines in Bulletin 17 for which the latest update 17B was published in 1982. Efforts are underway to update that remarkable document. Additional guidance in the Bulletin as to how to address variation in flood risk over time would be welcome. Extensions of the log-Pearson type 3 model to include changes in flood risk over time would be relatively easy mathematically. Here an example of the use of a sea surface temperature anomaly to anticipate changes in flood risk from year to year in the U.S. illustrates this opportunity. Efforts to project the trend in the Mississippi River flood series beg the question as to whether an observed trend will continue unabated, has reached its maximum, or is really nothing other than climate variability. We are challenged with the question raised by Milly and others: Is stationarity dead? Overall, we do not know the present flood risk at a site because of limited flood records. If we allow for historical climate variability and climate change, we know even less. But the issue is not whether stationarity is dead - the issue is how to use all the information available to reliably forecast flood risk in the future: Where do we go from here? © 2011 American Water Resources Association