A Hybrid Model for Runoff Prediction Using Variational Mode Decomposition and Artificial Neural Network

Water Resources - Hydrological runoff prediction in a reliable and precise manner contributes significantly to the optimal management of hydropower resources. Considering the importance of runoff...     

Probabilistic seismic hazard analysis and spectral accelerations for United Arab Emirates

In recent years, the United Arab Emirates (UAE) has experienced an unprecedented growth which is coupled with the increase in seismic activity in the surroundings. Previous studies presents significant variations in their results whereas some recent studies although very detailed focus on only few cities. This study reviews the results of previous studies and presents new findings for the whole of UAE based on the improved source model and use of next generation attenuation (NGA) equations. The peak ground accelerations, spectral accelerations and deaggregation of hazard for major cities are presented. Moreover, the breakdown of the range of mapped spectral accelerations (S _0.2 and S ₁) is proposed to form the basis for the development of site amplification factors in subsequent studies. The results of this study indicate almost similar values of ground motion compared to some recently published studies and smaller values compared to some earlier studies.     

Forecasting flood risk in the Indus River system using hydrological parameters and its damage assessment

Hydrological parameters are among the widely used parameters in assessing flood risk. On the other hand, anticipated flood damages, in case of flooding, are estimated with the help of expected losses in areas nearer to the watercourse. The major source of almost every-year flooding in Pakistan is the Indus River system that comprises the major rivers of Pakistan. We first use observed data to construct simulated data models based on various probability distributions namely normal, lognormal, Weibull, largest extreme value, gamma-3, and log-Pearson type-3 distributions and thereby compute probable maximum flood. Secondly, we perform log-Pearson type-3 analysis with and without historic adjustment on the observed data series of 17 years to forecast floods with return periods T of 2, 5, 10, 25, 50, 100, and 200 years. We also categorize the river structures based on the risk of flooding. Lastly, we estimate risk of flood damages in terms of expected losses based on observed data. The present study reveals that the log-Pearson type-3 distribution is relatively better for estimating probable maximum flood. We use exceedence probability to assess the risk of flooding in the various structures of the said rivers. The analysis shows that flood damages in Pakistan may be reduced by increasing the design capacity of the structures and also by giving awareness to people about the flood-generating factors.     

A new bivariate Gamma distribution generated from functional scale parameter with application to drought data

Univariate and bivariate Gamma distributions are among the most widely used distributions in hydrological statistical modeling and applications. This article presents the construction of a new bivariate Gamma distribution which is generated from the functional scale parameter. The utilization of the proposed bivariate Gamma distribution for drought modeling is described by deriving the exact distribution of the inter-arrival time and the proportion of drought along with their moments, assuming that both the lengths of drought duration (X) and non-drought duration (Y) follow this bivariate Gamma distribution. The model parameters of this distribution are estimated by maximum likelihood method and an objective Bayesian analysis using Jeffreys prior and Markov Chain Monte Carlo method. These methods are applied to a real drought dataset from the State of Colorado, USA.     

Using geographically weighted regression kriging for crop yield mapping in West Africa

Geographical information systems support the application of statistical techniques to map spatially referenced crop data. To do this in the optimal way, errors and uncertainties have to be minimized that are often associated with operations on the data. This paper applies a spatial statistical approach to upscale crop yields from the field level toward the scale of Burkina Faso. Observed yields were related to the Normalized Difference Vegetation Index derived from SPOT-VEGETATION. The objective was to quantify the uncertainties at the subsequent steps. First, we applied a point pattern analysis to examine uncertainties due to the sampling network of field surveys in the country. Second, geographically weighted regression kriging (GWRK) was applied to upscale the yield observations and to quantify the corresponding uncertainty. The proposed method was demonstrated with the mapping of sorghum yields in Burkina Faso and results were compared with those from regression kriging (RK) and kriging with external drift using a local kriging neighborhood (KEDLN). The proposed method was validated with independent yield observations obtained from field surveys. We observed that the lower uncertainty range value increased by 39%, and the upper uncertainty range value decreased by 51%, when comparing GWRK with RK and KEDLN. Moreover, GWRK reduced the prediction error variance as compared to RK (20 vs. 31) and to KEDLN (20 vs. 39). We found that climate and topography had a major impact on the country’s sorghum yields. Further, the financial ability of farmers influenced the crop management and, thus, the sorghum crop yields. We concluded that GWRK effectively utilized information present in the covariate datasets and improved the accuracies of both the regional-scale mapping of sorghum yields and was able to quantify the associated uncertainty.     

Uncertainty analysis of runoff and sedimentation in a forested watershed using sequential uncertainty fitting method

The Soil and Water Assessment Tool(SWAT) was implemented in a small forested watershed of the Soan River Basin in northern Pakistan through application of the sequential uncertainty fitting(SUFI-2) method to investigate the associated uncertainty in runoff and sediment load estimation. The model was calibrated for a 10-year period(1991–2000) with an initial 4-year warm-up period(1987–1990), and was validated for the subsequent 10-year period(2001–2010). The model evaluation indices R~2(the coefficient of determination), NS(the Nash-Sutcliffe efficiency), and PBIAS(percent bias) for stream flows simulation indicated that there was a good agreement between the measured and simulated flows. To assess the uncertainty in the model outputs, p-factor(a 95% prediction uncertainty, 95PPU) and r-factors(average wideness width of the 95 PPU band divided by the standard deviation of the observed values) were taken into account. The 95 PPU band bracketed 72% of the observed data during the calibration and 67% during the validation. The r-factor was 0.81 during the calibration and 0.68 during the validation. For monthly sediment yield, the model evaluation coefficients(R~2 and NS) for the calibration were computed as 0.81 and 0.79, respectively; for validation, they were 0.78 and 0.74, respectively. Meanwhile, the 95 PPU covered more than 60% of the observed sediment data during calibration and validation. Moreover, improved model prediction and parameter estimation were observed with the increased number of iterations. However, the model performance became worse after the fourth iterations due to an unreasonable parameter estimation. Overall results indicated the applicability of the SWAT model with moderate levels of uncertainty during the calibration and high levels during the validation. Thus, this calibrated SWAT model can be used for assessment of water balance components, climate change studies, and land use management practices.     

Toxic hexavalent chromium reduction by Bacillus pumilis,Cellulosimicrobium cellulans and Exiguobacterium

Three bacterial strains B acillus pumilis,Cellulosimicrobium cellulans and Exiguobacterium were investigated when grown in Luria-Bertani(LB) medium at 500 μg/m L Cr(VI). The hexavalent chromium reduction was measured by growing the strains in De Leo and Ehrlich(1994) medium at 200 and 400 μg/m L K 2 Cr O 4. The optimal Cr(VI) reduction by strains B. pumilis,Exigubacterium and C. cellulans was 51%,39%,and 41%,respectively,at an initial K2 Cr O 4 concentration of 200 μg/m L at p H 3 and temperature 37°C. At an initial chromate concentration of 400 μg/m L,the Cr(VI) reduction by strains B. pumilis,Exigubacterium and C. cellulans was 24%,19%,and 18%,respectively at p H 3 at 37°C after 24 h. These strains have ability to reduce toxic hexavalent chromium to the less mobile trivalent chromium at a wide range of different environmental conditions and can be useful for the treatment of contaminated wastewater and soils.     

Heterogeneous data management and modeling for the gravimetric geoid model: a review study in Pakistan

Arabian Journal of Geosciences - The gravimetric geoid model can be used as vertical reference in surveying and other related technologies. It is being developed by NESCOM in collaboration with the...