Abedalrazq F. Khalil

Stochastic simulation model for nonstationary time series using an autoregressive wavelet decomposition: Applications to rainfall and temperature

[1]xa0A time series simulation scheme based on wavelet decomposition coupled to an autoregressive model is presented for hydroclimatic series that exhibit band-limited low-frequency variability. Many nonlinear dynamical systems generate time series that appear to have amplitude- and frequency-modulated oscillations that may correspond to the recurrence of different solution regimes. The use of wavelet decomposition followed by an autoregressive model of each leading component is explored as a model for such time series. The first example considered is the Lorenz-84 low-order model of extratropical circulation, which has been used to illustrate how chaos and intransitivity (multiple stable solutions) can lead to low-frequency variability. The central England temperature (CET) time series, the NINO3.4 series that is a surrogate for El Nino–Southern Oscillation, and seasonal rainfall from Everglades National Park, Florida, are then modeled with this approach. The proposed simulation model yields better results than a traditional linear autoregressive (AR) time series model in terms of reproducing the time-frequency properties of the observed rainfall, while preserving the statistics usually reproduced by the AR models.

El Nino-Southern Oscillation-based index insurance for floods: Statistical risk analyses and application to Peru

Received 23 June 2006; revised 18 June 2007; accepted 3 July 2007; published 17 October 2007. [1] Index insurance has recently been advocated as a useful risk transfer tool for disaster management situations where rapid fiscal relief is desirable and where estimating insured losses may be difficult, time consuming, or subject to manipulation and falsification. For climate-related hazards, a rainfall or temperature index may be proposed. However, rainfall may be highly spatially variable relative to the gauge network, and in many locations, data are inadequate to develop an index because of short time series and the spatial dispersion of stations. In such cases, it may be helpful to consider a climate proxy index as a regional rainfall index. This is particularly useful if a long record is available for the climate index through an independent source and it is well correlated with the

Episodic interannual climate oscillations and their influence on seasonal rainfall in the Everglades National Park

[1]xa0The restoration of the Everglades in Florida is an exemplary ecosystem project. A basic challenge of the restoration project is to operate the hydrologic control structures in a manner that allows the right quantity and quality of water to be delivered at the right times to the right locations. An understanding of long-term variations in seasonal rainfall as well as prospects for the upcoming season are of interest for operational planning. This paper aims to characterize the interannual variability in seasonal rainfall in the Everglades and to identify regions of Pacific and Atlantic oceans whose sea surface temperatures (SSTs) may be the carriers of the low-frequency information associated with Everglades rainfall. It is now known that interannual and interdecadal quasi-oscillatory phenomena modulate continental rainfall in many places. The amplitudes of these “oscillations” vary with time, and they conform to activity in specific frequency bands. The dominant low-frequency modes also vary by season. Identifying the climate modes that influence specific low-frequency aspects of rainfall is a challenge that is addressed here using wavelet analysis to diagnose the time-varying low-frequency structure and independent component analysis to identify the spatial modes of variation of the low-frequency signals. The combined approach is termed wavelet-independent component analysis (WICA). In addition to identifying dominant timescales of quasi-oscillatory phenomena that modulate interannual rainfall in the Everglades National Park, we investigate how the amplitude (power) associated with these interannual modes varies at decadal or longer timescales. The analyses presented motivate the need for the development of methods for the analysis and simulation of nonstationary hydroclimatic phenomena. The connection between the resulting low-frequency rainfall modes and sea surface temperatures (SSTs) is then established using correlation analysis using concurrent and preceding season SSTs. The results provide the motivation for the development of a new generation of simulation and forecasting models for rainfall that could directly use such low-frequency information.

Predictive downscaling based on non-homogeneous hidden Markov models

Abstract Weather-state models have been shown to be effective in downscaling the synoptic atmospheric information to local daily precipitation patterns. We explore the ability of non-homogeneous hidden Markov models (NHMM) to downscale regional seasonal climate data to daily rainfall at a collection of gauging sites. The predictors used are: ensemble means of seasonal rainfall as forecast by the DEMETER and ECHAM models, and the preceding seasonal outgoing long-wave radiation (OLR). As the downscaling of seasonal GCM-based predictions lacks the ability to capture the intra-seasonal variability, we augment the seasonal GCM-driven inputs with statistically-driven predictions of the monthly rainfall amounts. The pooling effect of combining seasonal and monthly estimates of the regional rainfall enhances the capacity of the NHMM to simulate the stochastic characteristics of rainfall fields. The monthly rainfall prediction is derived from a wide range of climate precursors such as the El Niño-Southern Oscillation, local sea-level pressure, and sea-surface temperature. Application of the methodology to data from the Everglades National Park region in South Florida, USA is presented for the seasons May–July and August–September using a 22-year sequence of seasonal data from eight rainfall stations. The model skill in capturing the seasonal and intra-seasonal rainfall attributes at each station is demonstrated graphically and using simple statistical measures of efficiency. The hidden states derived from NHMM are qualitatively analysed and shown to correspond to the dominant synoptic-scale features of rainfall generating mechanisms, which reinforces the argument that physical processes are appropriately captured. Citation Khalil, A. F., Kwon, H.-H., Lall, U. & Kaheil, Y. H. (2010) Predictive downscaling based on non-homogeneous hidden Markov models. Hydrol. Sci. J. 55(3), 333–350.

Bayesian deduction for redundancy detection in groundwater quality monitoring networks

[1]xa0A new methodology for designing a network for monitoring ambient, long-term groundwater quality is presented in this paper. The methodology is based on a sparse Bayesian learning approach known as a relevance vector machine (RVM) which produces probabilistic predictions that quantify the uncertainty in both the data and the model parameters. A reliable and parsimonious network configuration that is pertinent to the physics of the case study, revealed through understanding of the information content of the available data, is sought through application of the RVM. The methodology has been employed to reduce redundancy in the network for monitoring nitrate (NO3−) in the West Bank Palestinian National Authority aquifers to illustrate the potential for use of RVMs in optimal groundwater monitoring and to explore possible trade-offs between different monitoring objectives, e.g., monitoring cost versus uncertainty in groundwater. A sparse monitoring network configuration produced by the RVM-based model indicates that only 32% of the existing monitoring sites in the aquifer are sufficient to characterize the nitrate state. Proof of correctness and accuracy using rigorous statistical tests is presented.