Amin Elshorbagy

Estimation of missing streamflow data using principles of chaos theory

In this paper, missing consecutive streamflows are estimated, using the principles of chaos theory, in two steps. First, the existence of chaotic behavior in the daily flows of the river is investigated. The time delay embedding method of reconstructing the phase space of a time series is utilized to identify the characteristics of the nonlinear deterministic dynamics. Second, the analysis of chaos is used to configure two models employed to estimate the missing data, artificial neural networks (ANNs) and K-nearest neighbor (K-nn). The results indicate the utility of using the analysis of chaos for configuring the models. ANN model is configured using the identified correlation dimension (measure of chaos), and (K-nn) technique is applied within a subspace of the reconstructed attractor. ANNs show some superiority over K-nn in estimating the missing data of the English River, which is used as a case study.

Modelling the dynamics of the evapotranspiration process using genetic programming

Abstract Evapotranspiration constitutes one of the major components of the hydrological cycle and hence its accurate estimation is of vital importance to assess water availability and requirements. This study explores the utility of genetic programming (GP) to model the evapotranspiration process. An important characteristic of GP is that both the model structure and coefficients are simultaneously optimized. The method is applied in modelling eddy-covariance (EC)-measured latent heat (LE) as a function of net radiation (NR), ground temperature (GT), air temperature (AT), wind speed (WS) and relative humidity (RH). Two case studies having different climatic and topographic conditions are considered. The performance of the GP model is compared with artificial neural network (ANN) models and the traditional Penman-Monteith (PM) method. Results from the study indicate that both the datadriven models, GP and ANNs, performed better than the PM method. However, performance of the GP model is comparable with that of the ANN model. The GP-evolved models are dominated by NR and GT, indicating that these two inputs can represent most of the variance in LE. The results show that the GP-evolved equations are parsimonious and understandable, and are well suited to modelling the dynamics of the evapotranspiration process.

Noise reduction in chaotic hydrologic time series: facts and doubts

The issues of noise reduction and the reliability of its application to hydrologic time series are discussed. First, the concepts of noise, its effect, and noise reduction are briefly presented. Second, a few published articles in hydrology are critically reviewed with regard to the application of noise reduction to hydrologic data. Third, a case study of the English River, Ontario, Canada, is used to support the conclusions. It is found that the commonly used algorithm for noise reduction in hydrologic data might also remove a significant part of the original signal and introduce an artificial chaoticity to the data. It is recommended that current noise reduction algorithms should be applied with caution and used only for better estimation of chaotic invariants. The raw data should always be the basis for any further hydrologic analysis.

Object-oriented modeling approach to surface water quality management

The lack of sufficient water quality data in many places hinders the efforts of surface water quality modeling, and therefore affects the process of water quality management. In this paper, the potential of an object-oriented simulation environment for surface water quality management, based on the concepts of system dynamics (OO-SD), is discussed. The characteristics, along with a brief explanation, of the OO-SD approach are provided. A case study on the use of the OO-SD modeling approach for surface water quality management in southeastern Kentucky, USA, is described to highlight key features of the approach. In a later section, advantages and present shortcomings of the OO-SD approach to model hydrologic systems are discussed. The potential use of the proposed approach, especially in data-poor conditions, and the challenges that lie ahead of hydrologists to fully exploit such a modeling approach are identified.

Simulation of the hydrological processes on reconstructed watersheds using system dynamics

Abstract Reconstruction of disturbed watersheds is a common practice by the oil sands industry in northern Alberta, Canada. The reconstruction and restoration of the watershed hydrology are required as part of the reclamation mandated by Alberta Environment for mine closure. Assessment of the hydrological performance of the reconstructed watersheds is essential to ensure a sustainable reclamation strategy. A conceptual lumped system dynamics watershed (SDW) model is developed and calibrated in this study. The model, built within an object-based simulation environment, is capable of simulating the various hydrological processes in the reconstructed watersheds with good accuracy. STELLA Software is used as an object-based simulation environment that allows visual computations. The SDW model developed combines both physically-based and empirical formulations to replicate the hydrological system mathematically. The system dynamics approach along with the visual simulation environment help in developing a simulation-for-learning model, not only simulation for prediction. The model is successfully calibrated and validated; the results show that the SDW model is capable of simulating the various hydrological processes (soil moisture, evapotranspiration and runoff) with good accuracy. The SDW model can help in the assessment of the short- and long-term performances of the reconstructed watersheds, thus providing a useful decision-aid tool for the mining industry.

Toward understanding nonstationarity in climate and hydrology through tree ring proxy records

Natural proxy records of hydroclimatic behavior, such as tree ring chronologies, are a rich source of information of past climate-driven nonstationarities in hydrologic variables. In this study, we investigate tree ring chronologies that demonstrate significant correlations with streamflows, with the objective of identifying the spatiotemporal patterns and extents of nonstationarities in climate and hydrology, which are essentially representations of past “climate changes.” First and second-order nonstationarities are of particular interest in this study. As a prerequisite, we develop a methodology to assess the consistency and credibility of a regional network of tree ring chronologies as proxies for hydrologic regime. This methodology involves a cluster analysis of available tree ring data to understand and evaluate their dependence structure, and a regional temporal-consistency plot to assess the consistency of different chronologies over time. The major headwater tributaries of the Saskatchewan River basin (SaskRB), the main source of surface water in the Canadian Prairie Provinces, are used as the case study. Results indicate that stationarity might never have existed in the hydrology of the region, as the statistical properties of annual paleo-hydrologic proxy records across the basin, i.e., the mean and autocorrelation structure, have consistently undergone significant changes (nonstationarities) at different points in the history of the region. The spatial pattern of the changes in the mean statistic has been variable with time, indicating a time-varying cross-correlation structure across the tributaries of the SaskRB. Conversely, the changes in the autocorrelation structure across the basin have been in harmony over time. The results demonstrate that the 89 year period of observational record in this region is a poor representation of the long-term properties of the hydrologic regime, and shorter periods, e.g., 30 year periods, are by no means representative. This paper highlights the need to broaden the understanding of hydrologic characteristics in any basin beyond the limited observational records, as an improved understanding is essential for more reliable assessment and management of available water resources.

Managing water in complex systems: An integrated water resources model for Saskatchewan, Canada

Abstract Using a system dynamics approach, an integrated water resources system model is developed for scenario analysis of the Saskatchewan portion of the transboundary Saskatchewan River Basin in western Canada. The water resources component is constructed by emulating an existing Water Resources Management Model. Enhancements include an irrigation sub-model to estimate dynamic irrigation demand, including alternative potential evapotranspiration estimates, and an economic sub-model to estimate the value of water use for various sectors of the economy. Results reveal that the water resources system in Saskatchewan becomes increasingly sensitive to the selection of evapotranspiration algorithm as the irrigation area increases, due to competition between hydropower and agriculture. Preliminary results suggest that irrigation expansion would decrease hydropower production, but might increase the total direct economic benefits to Saskatchewan. However, indirect costs include reduction in lake levels and river flows.

Application of copula modelling to the performance assessment of reconstructed watersheds

The existence of interdependence among environmental variables has been qualitatively known for centuries. Recent studies have shown that copula modelling can provide a simple, yet powerful framework for modelling interdependence among hydrological data; however, still there are several studies which use outdated and superficial methods to perform this task. By considering the current state of knowledge, this study tries to introduce a pragmatic procedure to perform copula modelling in real-world problems. Our study uses copula modelling to find a notion for conditional quantities of the maximum annual water deficit with respect to the annual cumulative evapotranspiration. Therefore, by having an estimate for the annual cumulative evapotranspiration, the hydrological performance of the reconstructed watershed can be assessed even in nearby ungauged reconstructed watersheds with similar physical characteristics and reclamation strategy. Several competitive models are developed for joint description of these variables in a prototype reclaimed oil-sands mining site in northern Alberta, Canada. The developed joint models are compared and analyzed according to their convergence feasibility, overall accuracy, tail behaviour and a goodness-of-fit test. Our study concludes that copula modelling can be considered as a powerful option in practitioners’ toolkit. For the case under consideration, the Gumbel–Houguaard structure provides the most credible model of dependence. Moreover, our study provides some initial supports for the application of minimum distance methods for copula parameter estimation.

Data-driven modelling approaches for socio-hydrology: opportunities and challenges within the Panta Rhei Science Plan

ABSTRACT “Panta Rhei – Everything Flows” is the science plan for the International Association of Hydrological Sciences scientific decade 2013–2023. It is founded on the need for improved understanding of the mutual, two-way interactions occurring at the interface of hydrology and society, and their role in influencing future hydrologic system change. It calls for strategic research effort focused on the delivery of coupled, socio-hydrologic models. In this paper we explore and synthesize opportunities and challenges that socio-hydrology presents for data-driven modelling. We highlight the potential for a new era of collaboration between data-driven and more physically-based modellers that should improve our ability to model and manage socio-hydrologic systems. Crucially, we approach data-driven, conceptual and physical modelling paradigms as being complementary rather than competing, positioning them along a continuum of modelling approaches that reflects the relative extent to which hypotheses and/or data are available to inform the model development process. EDITOR D. Koutsoyiannis; ASSOCIATE EDITOR not assigned

Quantile-Based Downscaling of Precipitation Using Genetic Programming: Application to IDF Curves in Saskatoon

AbstractIntensity-duration-frequency (IDF) curves are commonly used in engineering planning and design. Considering the possible effects of climate change on extreme precipitation, it is crucial to analyze potential variations in IDF curves. This paper presents a quantile-based downscaling framework to update IDF curves using the projections of future precipitation obtained from general circulation models (GCMs). Genetic programming is applied to extract duration-variant and duration-invariant mathematical equations to map from daily extreme rainfall quantiles at the GCM scale to corresponding daily and subdaily extreme rainfall quantiles at the local scale. The proposed approach is applied to extract downscaling relationships and to investigate possible changes in the IDF curves for the City of Saskatoon, Canada. The results show that genetic programming is a promising tool for extracting mathematical mappings between extreme rainfall quantiles at the GCM and local scales. The duration-variant mappings w...