Multi-criteria evaluation of CMIP5 GCMs for climate change impact analysis

Climate change is expected to have severe impacts on global hydrological cycle along with food-water-energy nexus. Currently, there are many climate models used in predicting important climatic variables. Though there have been advances in the field, there are still many problems to be resolved related to reliability, uncertainty, and computing needs, among many others. In the present work, we have analyzed performance of 20 different global climate models (GCMs) from Climate Model Intercomparison Project Phase 5 (CMIP5) dataset over the Columbia River Basin (CRB) in the Pacific Northwest USA. We demonstrate a statistical multicriteria approach, using univariate and multivariate techniques, for selecting suitable GCMs to be used for climate change impact analysis in the region. Univariate methods includes mean, standard deviation, coefficient of variation, relative change (variability), Mann-Kendall test, and Kolmogorov-Smirnov test (KS-test); whereas multivariate methods used were principal component analysis (PCA), singular value decomposition (SVD), canonical correlation analysis (CCA), and cluster analysis. The analysis is performed on raw GCM data, i.e., before bias correction, for precipitation and temperature climatic variables for all the 20 models to capture the reliability and nature of the particular model at regional scale. The analysis is based on spatially averaged datasets of GCMs and observation for the period of 1970 to 2000. Ranking is provided to each of the GCMs based on the performance evaluated against gridded observational data on various temporal scales (daily, monthly, and seasonal). Results have provided insight into each of the methods and various statistical properties addressed by them employed in ranking GCMs. Further; evaluation was also performed for raw GCM simulations against different sets of gridded observational dataset in the area.     

Forecasting monthly inflow with extreme seasonal variation using the hybrid SARIMA-ANN model

The optimal operation of dam reservoirs can be programmed and managed by predicting the inflow to these structures more accurately. To this end, there are various linear and nonlinear models. However, some hydrological problems like inflow with extreme seasonal variation are not purely linear or nonlinear. To improve the forecasting accuracy of this phenomenon, a linear Seasonal Auto Regressive Integrated Moving Average (SARIMA) model is combined with a nonlinear Artificial Neural Network (ANN) model. This new model is used to predict the monthly inflow to the Jamishan dam reservoir in West Iran. A comparison of the SARIMA and ANN models with the proposed hybrid model’s results is provided accordingly. More specifically, the models’ performance in forecasting base and flood flows is evaluated. The effect of changing the forecasting period length on the models’ accuracy is studied. The results of increasing the number of SARIMA model parameters up to five are investigated to achieve more accurate forecasting. The hybrid model predicts peak flood flows much better than the individual models, but SARIMA outperforms the other models in predicting base flow. The obtained results indicate that the hybrid model reduces the overall forecast error more than the ANN and SARIMA models. The coefficient of determination of the hybrid, ANN and SARIMA models were 0.72, 0.64 and 0.58, and the root mean squared error values were 1.02, 1.16 and 1.27 respectively, during the forecast period. Changing the forecasting length also indicated that these models can be used in the long term without increasing the forecast error.     

Consistency of precipitation products over the Arabian Peninsula and interactions with soil moisture and water storage

The regional-scale consistency between four precipitation products from the GPCC, TRMM, WM, and CMORPH datasets over the Arabian Peninsula was assessed. Their macroscale relationships were inter-compared with soil moisture and total water storage (TWS) estimates from AMSR-E and GRACE. The consistency analysis was studied with multivariate statistical hypothesis testing and Pearson correlation metrics for the period from January 2000 to December 2010. The products and GRACE estimates were assessed over a representative sub-domain (United Arab Emirates) with available in situ well observations. Next, geographically temporally weighted regression (GTWR) was employed to examine the interdependencies among the peninsula’s hydrological components. The results showed GPCC-TRMM recording the highest correlation (0.85) with insignificant mean differences over more than 90% of the peninsula. The highest GTWR predictive performance of TWS (R² = 0.84) was achieved with TRMM forcing, which indicates its potential to monitor changes in TWS over the arid peninsular region.     

Automatic urban building boundary extraction from high resolution aerial images using an innovative model of active contours

To present a new method for building boundary detection and extraction based on the active contour model, is the main objective of this research. Classical models of this type are associated with several shortcomings; they require extensive initialization, they are sensitive to noise, and adjustment issues often become problematic with complex images. In this research a new model of active contours has been proposed that is optimized for the automatic building extraction. This new active contour model, in comparison to the classical ones, can detect and extract the building boundaries more accurately, and is capable of avoiding detection of the boundaries of features in the neighborhood of buildings such as streets and trees. Finally, the detected building boundaries are generalized to obtain a regular shape for building boundaries. Tests with our proposed model demonstrate excellent accuracy in terms of building boundary extraction. However, due to the radiometric similarity between building roofs and the image background, our system fails to recognize a few buildings.     

Spatial and Temporal Distribution of Dust‐Bound Trace Metal Elements around Kuhdasht Watershed Area in Iran

Dust, as a source of trace metal elements, affects the health of society. The spatial and temporal concentrations of dust‐bound trace metals (Cd, Pb, Ni, Zn, Cu, and Mn) in Kuhdasht watershed (456 km²), Lorestan Province, Iran, is investigated. Dust is collected using glass traps placed in ten research stations in the region. The spatial and temporal distribution of dust trace metals are plotted using ARC‐GIS. The highest and the lowest concentrations of Zn (9751150 mg kg^?1), Pb (46.352.9 mg kg^?1), and Cd (2.443.30 mg kg^?1) are obtained in winter, of Ni (98110 mg kg^?1) and Cu in autumn (16.053.5 mg kg^?1), and of Mn in summer (385505 mg kg^?1). The spatial concentrations of dust‐bound trace metals indicate all, except Cu, show a decreasing trend from the mountains toward the plains, similar to that of soil and of dust, except for Zn, which shows higher concentrations in dust than in soil. The potential sources of dust‐bound trace metals and their rate of contamination are also investigated using the enrichment and contamination factors. The major sources of Cd and Zn in the dust of watershed are due to anthropogenic activities or from activities outside the borders.     

Static and dynamic analysis on slope stability using a DFN-DEM approach on the right abutment of the Karun 4 dam

Uncertainty in input fracture geometric parameters during analysis of the stability of jointed rock slopes is inevitable and therefore the stochastic discrete fracture network (DFN) — distinct element method (DEM) is an efficient modeling tool. In this research, potentially unstable conditions are detected in the right abutment of the Karun 4 dam and downstream of the dam body as a case study. Two extreme states with small and relatively large block sizes are selected and a series of numerical DEM models are generated using a number of validated DFN models. Stability of the rock slope is assessed in both static and dynamic loading states. Based on the design basis earthquake (DBE) and maximum credible earthquake (MCE) expected in the dam site, histories of seismic waves are applied to analyze the stability of the slope in dynamic earthquake conditions. The results indicate that a MCE is likely to trigger sliding of rock blocks on the rock slope major joint. Furthermore, the dynamic analysis also shows a local block failure by the DBE, which can consequently lead to slope instability over the long term. According to the seismic behavior of the two models, larger blocks are prone to greater instability and are less safe against earthquakes.

     

Runoff quality prediction from small urban catchments using SWMM

The RUNOFF block of EPA's storm water management model (SWMM) was used to simulate the quantity and quality of urban storm water runoff from four relatively small sites (i.e. 5·97–23·56 ha) in South Florida, each with a specific predominant land use (i.e. low density residential, high density residential, highway and commercial). The objectives of the study were to test the applicability of this model in small subtropical urban catchments and provide modellers with a way to select appropriate input parameters to be used in planning studies. A total of 58 storm events, measured by the US Geological Survey (USGS), provided hyetographs, hydrographs and pollutant loadings for biological oxygen demand (BOD₅), total suspended solids (TSS), total Kjeldahl nitrogen (TKN) and lead (Pb), and were used for calibration of the model. Several other catchment characteristics, also measured or estimated by USGS, were used in model input preparation. Application of the model was done using the Green–Ampt equation for infiltration loss computation, a pollutant accumulation equation using a power build-up equation dependent on the number of dry days, and a power wash-off equation dependent on the predicted runoff rate. Calibrated quantity input parameters are presented and compared with suggested values in the literature. The impervious depression storage was generally found to be the most sensitive calibration parameter, followed by the Manning's roughness coefficients of conduit and overland flow, the Green–Ampt infiltration parameters and, finally, the pervious depression storage. Calibrated quality input parameters are presented in the form of regression equations, as a function of rainfall depth and the number of antecedent dry days. A total of 16 independent rainfall events were used for verification of the model, which showed a good comparison with observed data for both hydrographs and pollutant loadings. Average model predictions for the four constituent concentrations from the verification runs also showed good agreement with NURP published values in Florida and US sites. © 1998 John Wiley & Sons, Ltd.     

The relationship among environmental variables,jellyfish and non‐gelatinous zooplankton: A case study in the north of the Gulf of Oman

Processes underlying the temporal and spatial variations observed in the distribution of jellyfish and non‐gelatinous zooplankton in the Gulf of Oman are not well understood. This information gap is clearly a major issue in controlling the harmful blooms of jellyfish and non‐gelatinous zooplankton. Samples of jellyfish and non‐gelatinous zooplankton were collected from six stations in Chabahar Bay and three stations in Pozm Bay within four seasons. At each station, environmental variables were also recorded from bottom and surface water. A total of 83 individuals of medusae representing four species of Scyphozoa (i.e., Cyanea nozakii, Chrysaora sp., Pelagia noctiluca, Catostylus tagi) and species of Hydrozoa (i.e., Diphyes sp., Rhacostoma sp., Aequorea spp.) were observed in the study area. A total of 70,727.25 individuals/m^?3 of non‐gelatinous zooplankton dominated by copepods and cladocerans were collected in nine stations within the four seasons. The results of a RELATE analysis yielded no significant association between species composition for jellyfish and non‐gelatinous zooplankton. Among environmental variables, water transparency, nitrite concentration, water depth and temperature were better associated with the total variation in jellyfish species composition than with that of non‐gelatinous zooplankton. Dissolved oxygen, pH, and phosphate concentration were significant environmental variables associated with the variation in the spatial and temporal distribution patterns of non‐gelatinous zooplankton assemblages. Although some jellyfish species (i.e., Rhacostoma sp., Pelagia noctiluca, Catostylus tagi) occur independently of non‐gelatinous zooplankton assemblages, other jellyfish (i.e., Chrysaora sp., Aequorea spp., Cyanea nozakii, Diphyes sp.) are strongly correlated with non‐gelatinous zooplankton assemblages.     

Investigation on the Effect of Geometrical and Geotechnical Parameters on Elongated Offshore Piles Using Fuzzy Inference Systems

Among numerous offshore structures used in oil extraction, jacket platforms are still the most favorable ones in shallow waters. In such structures, log piles are used to pin the substructure of the platform to the seabed. The pile’s geometrical and geotechnical properties are considered as the main parameters in designing these structures. In this study, ANSYS was used as the FE modeling software to study the geometrical and geotechnical properties of the offshore piles and their effects on supporting jacket platforms. For this purpose, the FE analysis has been done to provide the preliminary data for the fuzzy-logic post-process. The resulting data were implemented to create Fuzzy Inference System (FIS) classifications. The resultant data of the sensitivity analysis suggested that the orientation degree is the main factor in the pile’s geometrical behavior because piles which had the optimal operational degree of about 5° are more sustained. Finally, the results showed that the related fuzzified data supported the FE model and provided an insight for extended offshore pile designs.