Calibrating Environment Canada's MESH Modelling System over the Great Lakes Basin

This paper reports on recent progress towards improved predictions of a land surface-hydrological modelling system, Modélisation Environmentale–Surface et Hydrologie (MESH), via its calibration over the Laurentian Great Lakes Basin. Accordingly, a “global” calibration strategy is utilized in which parameters for all land class types are calibrated simultaneously to a number of sub-basins and then validated in time and space. Model performance was evaluated based on four performance metrics, including the Nash-Sutcliffe (NS) coefficient and simulated compared with observed hydrographs. Results from two calibration approaches indicate that in the model validation mode, the global strategy generates better results than an alternative calibration strategy, referred to as the “individual” strategy, in which parameters are calibrated individually to a single sub-basin with a dominant land type and then validated in a different sub-basin with the same dominant land type. The global calibration strategy was relatively successful despite the large number of calibration parameters (51) and relatively small number of model evaluations (1000) used in the automatic calibration procedure. The NS values for spatial validation range from 0.10 to 0.72 with a median of 0.41 for the 15 sub-basins considered. Results also confirm that a careful model calibration and validation is needed before any application of the model.     

Predicting the expansion of an urban boundary using spatial logistic regression and hybrid raster–vector routines with remote sensing and GIS

This paper presents an urban growth boundary model (UGBM) which utilizes spatial logistic regression (SLR), remote sensing, and GIS to simulate the differentially expanding geometry of a dynamic urban boundary over decadal time periods. SLR is used as the core algorithm in a UGBM quantifying how biophysical factors influence the rate at which all edges of an urban boundary expand over time. Spatial drivers selected from a raster-based environment are used as input predictor variables to the SLR UGBM, the output response variable being the distance between time-separated urban boundary intersections along arcs extending radially from a point centered at the urban core relative to the maximum distance. Percent area match (PAM) quantity and location goodness-of-fit metrics, fit of the predicted distance versus observed distance, and the sensitivity of the SLR UGBM to the contribution of each predictor variable are used to assess the agreement between predicted and observed urban boundaries. The model is built, tested, and validated using satellite images of the city of Las Vegas, United States of America, collected in 1990, 2000, and 2010. We compare urban boundary simulation of full and reduced SLR UGBMs to a null UGBM lacking in specificity of predictor variables. Results indicate that the SLR UGBM has a better goodness of fit compared to a null UGBM using PAM quantity and location goodness-of-fit metrics. Then, we use the SLR UGBM to predict urban boundary expansion between the years 2000 and 2010 and describe how this model can be used to plan ahead for future boundary expansions given what is known about current edge conditions.     

Hybrid modelling approach to prairie hydrology: fusing data-driven and process-based hydrological models

Abstract

Much of the prairie region in North America is characterized by relatively flat terrain with many depressions on the landscape. The hydrological response (runoff) is a combination of the conventional runoff from the contributing areas and the occasional overflow from the non-contributing areas (depressions). In this study, we promote the use of a hybrid modelling structure to predict runoff generation from prairie landscapes. More specifically, the Soil and Water Assessment Tool (SWAT) is fused with artificial neural networks (ANNs), so that SWAT and the ANN module deal with the contributing and non-contributing areas, respectively. A detailed experimental study is performed to select the best set of inputs, training algorithms and hidden neurons. The results obtained in this study suggest that the fusion of process-based and data-driven models can provide improved modelling capabilities for representing the highly nonlinear nature of the hydrological processes in prairie landscapes.

Editor D. Koutsoyiannis; Associate editor L. See     

Identification of homogenous regions in Gorganrood basin (Iran) for the purpose of regionalization

Estimation of flood in basins with poor condition of hydrometric stations as in quantity and quality is a dominant problem around the world, mainly in developing country where lack of funds and human resources cause more limitation in number of gauging stations. One of the areas that experience frequent floods and also suffer from small number of stations in Iran is Gorganrood basin. So there is a great need for the estimation and prediction of runoff in this area to prevent any future floods. Due to insufficient station in this area, direct prediction of flood is not applicable. Regional flood frequency analysis is a practical and widely used solution for these situations, which involves the identification of homogenous regions. Gorganrood region was hydrologically homogenized according to the extracted parameters that influence the floods. One of these parameters was Normalized Difference Vegetation Index (NDVI) driven from MODIS images. Curvature is another parameter that relates to topographic attributes. From factor analysis, the most appropriate variables were selected. According to these parameters (NDVI, curvature, area, slope…), the regions were classified into homogenous regions. For the purpose of homogenization, hierarchical (wards) clustering, fuzzy clustering and Kohonen method were applied. L-moment technique was used for the investigation of the results. The heterogeneity measure for one of the groups (Group 1) was more than two; therefore some modifications were applied. The region was grouped into two homogenous subregions. All of the clustering methods showed same results. The models showed that class 4 of NDVI is influential on flood in some return periods. The resulted models can be applied in future studies in different aspects of practical hydrology.     

Stability Analysis of Rock Slopes Against Block-Flexure Toppling Failure

Block-flexure is the most common type of toppling failure in rock slopes. In this case, some rock blocks fail due to tensile bending stresses and some overturn under their own weights. In this paper, first, a literature review of toppling failures is summarized. Then, a theoretical model is proposed for rock slopes with a potential for block-flexure toppling instability. Next, a new analytical approach is presented for the stability analysis of such slopes. Finally, a special computer code is developed for a quick stability assessment of the failures based on the proposed method. This code receives the rock slope parameters from the user as the input data and predicts its stability, along with the corresponding factor of safety against the failure, as the output. In addition, two case studies are used for practical verification of the proposed approach and the corresponding computer code as well.     

A correlation between P-wave velocity and Schmidt hardness with mechanical properties of travertine building stones

As known, P-wave velocity and Schmidt hardness are non-destructive tests, which have been used for many years in geological, geotechnical, and civil engineering as an index tests for a quick assessment of rocks mechanical properties due to its rapidity and easiness, and non-destructiveness. The purpose of this study is to investigate the correlation between P-wave velocity and Schmidt hardness with some of mechanical properties of travertine building stones by empirical equations. Moreover, we have compared the accuracy of P-wave velocity and Schmidt hardness to estimate the mechanical properties of rocks. For this purpose, 15 types of travertine have been collected from various quarries of Iran and tested. The tests include the determination of P-wave velocity and Schmidt hardness, and mechanical properties include the unconfined compressive strength, Brazilian tensile strength, and point load strength. Using data analysis, empirical equations have been developed for estimating the mechanical properties from P-wave velocity and Schmidt hardness. To check the validity of the empirical equations, a t test was performed, which confirmed the validity of the proposed empirical equations. Moreover, the results show that P-wave velocity appears to be more reliable than the Schmidt hardness for estimating the mechanical properties. Consequently, we propose empirical equations avoiding from cumbersome and time consuming tests for determining the mechanical properties of rocks.     

Exploration of gold mineralization in a tropical region using Earth Observing-1 (EO1) and JERS-1 SAR data: a case study from Bau gold field,Sarawak, Malaysia

Bau gold mining district, located near Kuching, Sarawak, Malaysia, is a Carlin style gold deposits. Geological analyses coupled with remote sensing data were used to detect hydrothermal alteration rocks and structure elements associated with this type of gold mineralization. Image processing techniques, including principal components analysis, linear spectral unmixing, and Laplacian algorithms, were employed to carry out spectrolithological–structural mapping of mineralized zones, using Advanced Land Imager, Hyperion, and JERS-1 synthetic aperture radar scenes covering the study area and surrounding terrain. Hydrothermally alteration mineral zones were detected along the SSW to NNE structural trend of the Tai Parit fault that corresponds to the areas of occurrence of the gold mineralization in the Bau limestone. The results show that potentially interesting areas are observable by the methods used, despite limited bedrock exposure in this region and the constraints imposed by the tropical environment.