On the Value of River Network Information in Regional Frequency Analysis.

Extreme climatic conditions likely caused a massive fish mortality during the summer of 2001 in the St. Lawrence River. To corroborate this hypothesis, we used a physical habitat simulation approach incorporating hydraulic and water temperature models. Spawning Habitat Suitability Indices (HSI) for common carp (Cyprinus carpio) were developed using fuzzy logic and applied to the model outputs to estimate habitat weighted usable area during the event. The results revealed that areas suitable for common carp spawning (HSI > 0.3) were severely reduced by high water temperatures, which exceeded 28 °C during the mortality event. During the mortality event, the amount of suitable habitat was reduced to <200 ha/day, representing less than 15% of the maximum potential suitable habitat in the study reach. In addition, the availability of cooler habitats that could have been used as thermal refuges was also reduced. These results indicate that the high water temperature in spawning areas and reduced accessibility to thermal refuge habitats exposed the carp to substantial physiological and environmental stress. The high water temperatures were highly detrimental to the fish and eventually led to the observed mortalities. This study demonstrates the importance of including water temperature in habitat suitability models

Ground subsidence monitoring with SAR interferometry techniques in the rural area of Al Wagan, UAE.

In this work, we investigate the past and present land deformation in Al Wagan area in the United Arab Emirates. The area is primarily an agricultural region where dependence on groundwater is documented. Such a reliance on ground water resources in a region which is characterized by very low precipitation can lead to significant land subsidence as was observed in this study which identified fast and localized deformation trends. The quantification of ground deformations of large magnitude and small amplitude in this area with SAR Interferometry is a challenging task using moderate resolution data due to the incoherent surface background. Even though SAR acquisitions were sparse over this region, the available ENVISAT, ALOS and Sentinel-1A imagery was analysed with differential interferometry and the Small Baseline Subset technique in order to provide estimates about the evolution of the deformation pattern in a limited area. A clear evidence of subsidence phenomena has been identified in the study area. During the period 2003–2010 the subsidence was estimated to reach 18 cm/year as observed in the DInSAR processing results of data from ENVISAT and ALOS Satellites. However it appears to be slightly more stable during the recent past (Dec/2016–March/2018) as observed in the results with recent Sentinel-1 data where a maximum localized subsidence in the order of 10 cm was estimated. The depletion of the aquifer resources which is confirmed from groundwater level data is speculated to be the most probable cause

Diversity-driven ANN-based ensemble framework for seasonal low-flow analysis at ungauged sites.

Low-flow estimation at ungagged sites is a challenging task. Ensemble-based machine learning regression has recently been utilized in modeling hydrologic phenomena and showed improved performance compared to classical regional regression approaches. Ensemble modeling mainly revolves around developing a proper training framework of the individual learners and combiners. An ensemble framework is proposed in this study to drive the generalization ability of the sub-ensemble models and the ensemble combiners. Information mixtures between the subsamples are introduced and, unlike common ensemble frameworks, are explicitly devoted to the ensemble members as well as ensemble combiners. The homogeneity paradigm is developed via a two-stage resampling approach, which creates sub-samples with controlled information mixture levels for the training of the individual learners. Artificial neural networks are used as sub-ensemble members in combination with a number of ensemble integration techniques. The proposed model is applied to estimate summer and winter low-flow quantiles for catchments in the province of Québec, Canada. The results show significant improvement when compared to the other models presented in the literature. The obtained homogeneity levels from the optimum ensemble models demonstrate the importance of utilizing the diversity concept in ensemble learning applications

Evolution of the rainfall regime in the United Arab Emirates.

Arid and semiarid climates occupy more than 1/4 of the land surface of our planet, and are characterized by a strongly intermittent hydrologic regime, posing a major threat to the development of these regions. Despite this fact, a limited number of studies have focused on the climatic dynamics of precipitation in desert environments, assuming the rainfall input – and their temporal trends – as marginal compared with the evaporative component. Rainfall series at four meteorological stations in the United Arab Emirates (UAE) were analyzed for assessment of trends and detection of change points. The considered variables were total annual, seasonal and monthly rainfall; annual, seasonal and monthly maximum rainfall; and the number of rainy days per year, season and month. For the assessment of the significance of trends, the modified Mann–Kendall test and Theil-Sen’s test were applied. Results show that most annual series present decreasing trends, although not statistically significant at the 5% level. The analysis of monthly time series reveals strong decreasing trends mainly occurring in February and March. Many trends for these months are statistically significant at the 10% level and some trends are significant at the 5% level. These two months account for most of the total annual rainfall in the UAE. To investigate the presence of sudden changes in rainfall time-series, the cumulative sum method and a Bayesian multiple change point detection procedure were applied to annual rainfall series. Results indicate that a change point happened around 1999 at all stations. Analyses were performed to evaluate the evolution of characteristics before and after 1999. Student’s t-test and Levene’s test were applied to determine if a change in the mean and/or in the variance occurred at the change point. Results show that a decreasing shift in the mean has occurred in the total annual rainfall and the number of rainy days at all four stations, and that the variance has decreased for the total annual rainfall at two stations. Frequency analysis was also performed on data before and after the change point. Results show that rainfall quantile values are significantly lower after 1999. The change point around the year 1999 is linked to various global climate indices. It is observed that the change of phase of the Southern Oscillation Index (SOI) has strong impact over the UAE precipitation. A brief discussion is presented on dynamical basis, the teleconnections connecting the SOI and the change in precipitation regime in the UAE around the year 1999

Probability distributions of wind speed in the UAE.

For the evaluation of wind energy potential, probability density functions (pdfs) are usually used to describe wind speed distributions. The selection of the appropriate pdf reduces the wind power estimation error. The most widely used pdf for wind energy applications is the 2-parameter Weibull probability density function. In this study, a selection of pdfs are used to model hourly wind speed data recorded at 9 stations in the United Arab Emirates (UAE). Models used include parametric models, mixture models and one non-parametric model using the kernel density concept. A detailed comparison between these three approaches is carried out in the present work. The suitability of a distribution to fit the wind speed data is evaluated based on the log-likelihood, the coefficient of determination R 2 , the Chi-square statistic and the Kolmogorov–Smirnov statistic. Results indicate that, among the one-component parametric distributions, the Kappa and Generalized Gamma distributions provide generally the best fit to the wind speed data at all heights and for all stations. The Weibull was identified as the best 2-parameter distribution and performs better than some 3-parameter distributions such as the Generalized Extreme Value and 3-parameter Lognormal. For stations presenting a bimodal wind speed regime, mixture models or non-parametric models were found to be necessary to model adequately wind speeds. The two-component mixture distributions give a very good fit and are generally superior to non-parametric distributions

Coral Reefs of Abu Dhabi, United Arab Emirates: Analysis of Management Approaches in Light of International Best Practices and a Changing Climate.

The coasts and islands that flank Abu Dhabi, the United Arab Emirates (UAE)’s largest emirate, host the country’s most significant coastal and marine habitats including coral reefs. These reefs, although subject to a variety of pressures from urban and industrial encroachment and climate change, exhibit the highest thresholds for coral bleaching and mortality in the world. By reviewing and benchmarking global, regional and local coral reef conservation efforts, this study highlights the ecological importance and economic uniqueness of the UAE corals in light of the changing climate. The analysis provides a set of recommendations for coral reef management that includes an adapted institutional framework bringing together stakeholders, scientists, and managers. These recommendations are provided to guide coral reef conservation efforts regionally and in jurisdictions with comparable environmental challenges

Change detection using multi-scale convolutional feature maps of bi-temporal satellite high-resolution images

ABSTRACTChange detection in high-resolution satellite images is essential to understanding the land surface (e.g. agriculture and urban change) or maritime surface (e.g. oil spilling). Many deep-learning-based change detection methods have been proposed to enhance the performance of the classical techniques. However, the massive amount of satellite images and missing ground-truth images are still challenging concerns. In this paper, we propose a supervised deep network for change detection in bi-temporal remote sensing images. We feed multi-level features from convolutional networks of two images (feature-extraction) into one architecture (feature-difference) to have better shape and texture properties using a dual attention module We also utilize a multi-scale dice coefficient error function to decrease overlapping between changed and background pixel. The network is applied to public datasets (ACD, SYSU-CD and OSCD). We compare the proposed architecture with various attention modules and loss functions to verfiy the performance of the proposed method. We also compare the proposed method with the stateof-the-art methods in terms of three metrics: precision, recall and F1-score. The experimental outcomes confirm that the proposed method has good performance compared to benchmark methods

Improved classification of drainage networks using junction angles and secondary tributary lengths.

River networks in different regions have distinct characteristics generated by geological processes. These differences enable classification of drainage networks using several measures with many features of the networks. In this study, we propose a new approach that only uses the junction angles with secondary tributary lengths to directly classify different network types. This methodology is based on observations on 50 predefined channel networks. The cumulative distributions of secondary tributary lengths for different ranges of junction angles are used to obtain the descriptive values that are defined using a power-law representation. The averages of the values for the known networks are used to represent the classes, and any unclassified network can be classified based on the similarity of the representative values to those of the known classes. The methodology is applied to 10 networks in the United Arab Emirates and Oman and five networks in the USA, and the results are validated using the classification obtained with other methods

Long-term forecasting of wind speed in the UAE using nonlinear canonical correlation analysis (NLCCA).

Wind is one of the crucial renewable energy sources which is expected to bring solutions to the challenges of clean energy and the global issue of climate change. Several linear and nonlinear multivariate techniques have been used to predict the stochastic character of wind speed. Wind speed forecast with good accuracy has a positive impact on the reduction of electricity system cost and is essential for the effective power grid management. Over the past years, few studies have been done on the assessment of teleconnections and its possible effects on the long-term wind speed variability in the UAE region. In this study, nonlinear canonical correlation analysis (NLCCA) method is applied to study the relationship between global climate oscillation indices and meteorological variables, with a major emphasis on wind speed of UAE. The wind dataset was obtained from six ground stations spread within the country. The first mode of NLCCA captured the nonlinear mode of the teleconnection indices at different seasons, showing the symmetry between the warm states and the cool states. The strength of the nonlinear canonical correlation between the two sets of variables varies with the lead/lag time. The performance of the models is assessed by calculating error indices such as the relative root mean square error (rRMSE) and relative mean absolute error (MAER). The results indicated that NLCCA models provide more accurate information about the nonlinear intrinsic behavior of the dataset of variables than linear canonical correlation analysis (CCA) model in terms of the correlation and root mean square error

Modeling directional distributions of wind data in the United Arab Emirates at different elevations.

Modeling wind speed and direction are crucial in several applications such as the estimation of wind energy potential and the study of the long-term effects on engineering structures. While there have been several studies on modeling wind speed, studies on modeling wind direction are limited. In this work, we use a mixture of von Mises distributions to model wind direction. Finite mixtures of von Mises (FMVM) distributions are used to model wind directions at two sites in the United Arab Emirates. The parameters of the FMVM distribution are estimated using the least square method. The results of the research show that the FMVM is the best suited distribution model to fit wind direction at these two sites, compared to other distributions commonly used to model wind direction