To detect landslides by object-based image analysis using criteria based on shape, color, texture, and, in particular, contextual information and process knowledge, candidate segments must be ...delineated properly. This has proved challenging in the past, since segments are mainly created using spectral and size criteria that are not consistent for landslides. This paper presents an approach to select objectively parameters for a region growing segmentation technique to outline landslides as individual segments and also addresses the scale dependence of landslides and false positives occurring in a natural landscape. Multiple scale parameters were determined using a plateau objective function derived from the spatial autocorrelation and intrasegment variance analysis, allowing for differently sized features to be identified. While a high-resolution Resourcesat-1 Linear Imaging and Self Scanning Sensor IV (5.8 m) multispectral image was used to create segments for landslide recognition, terrain curvature derived from a digital terrain model based on Cartosat-1 (2.5 m) data was used to create segments for subsequent landslide classification. Here, optimal segments were used in a knowledge-based classification approach with the thresholds of diagnostic parameters derived from If-means cluster analysis, to detect landslides of five different types, with an overall recognition accuracy of 76.9%. The approach, when tested in a geomorphologically dissimilar area, recognized landslides with an overall accuracy of 77.7%, without modification to the methodology. The multiscale classification-based segment optimization procedure was also able to reduce the error of commission significantly in comparison to a single-optimal-scale approach.
Urban flood hazard model needs rainfall with high spatial and temporal resolutions for flood hazard analysis to better simulate flood dynamics in complex urban environments. However, in many ...developing countries, such high-quality data are scarce. Data that exist are also spatially biased toward airports and urban areas in general, where these locations may not represent flood-prone areas. One way to gain insight into the rainfall data and its spatial patterns is through numerical weather prediction models. As their performance improves, these might serve as alternative rainfall data sources for producing optimal design storms required for flood hazard modeling in data-scarce areas. To gain such insight, we developed Weather Research and Forecasting (WRF) design storms based on the spatial distribution of high-intensity rainfall events simulated at high spatial and temporal resolutions. Firstly, three known storm events (i.e., 25 June 2012, 13 April 2016, and 16 April 2016) that caused the flood hazard in the study area are simulated using the WRF model. Secondly, the potential gridcell events that are able to trigger the localized flood hazard in the catchment are selected and translated to the WRF design storm form using a quantile expression. Finally, three different WRF design storms per event are constructed: Lower, median, and upper quantiles. The results are compared with the design storms of 2- and 10-year return periods constructed based on the alternating-block method to evaluate differences from a flood hazard assessment point of view. The method is tested in the case of Kampala city, Uganda. The comparison of the design storms indicates that the WRF model design storms properties are in good agreement with the alternating-block design storms. Mainly, the differences between the produced flood characteristics (e.g., hydrographs and the number of flood gird cells) when using WRF lower quantiles (WRFLs) versus 2-year and WRF upper quantiles (WRFUs) versus 10-year alternating-block storms are very minimal. The calculated aggregated performance statistics (F scores) for the simulated flood extent of WRF design storms benchmarked with the alternating-block storms also produced a higher score of 0.9 for both WRF lower quantiles versus 2-year and WRF upper quantile versus 10-year alternating-block storm. The result suggested that the WRF design storms can be considered an added value for flood hazard assessment as they are closer to real systems causing rainfall. However, more research is needed on which area can be considered as a representative area in the catchment. The result has practical application for flood risk assessment, which is the core of integrated flood management.
Numerical modeling is an important tool for prediction, analysis and understanding of the dynamics of land surface processes. To increase the usage and impact of such tools, it is crucial to decrease ...runtime by increasing computational efficiency. Dynamic processes such as water flow are typically described by higher-order differential equations. Solving these accurately requires numerical integration over time, where numerical errors depend on the time steps taken. Typically, flow simulation use the smallest required time steps in a model’s domain to simulate flow. In this paper, we analyze the usage of local time stepping, for catchment-scale simulation of land surface processes such as water flow, infiltration, slope stability and landslide runout. In such a scheme, temporal integration is cell specific, allowing for higher numerical efficiency. The implemented scheme works with fully free local time steps that are synchronized only for visualization. We implement this method in a monotonic upwind scheme for conservation laws (MUSCL). We investigate the influence on stability and the resulting changes in computation time and accuracy in a hydrology-coupled, catchment-scale flood simulation. Results show that local time stepping can be implemented in a total variation diminishing (TVD) numerical scheme that is second-order spatially accurate. Simulation results in both 1D dam-break scenarios and catchment-scale flash flood scenarios show insignificant changes in modeling result, while computation time reduces with over 50%. Finally, the method is successfully implemented in a multi-process lands surface model with hydrology, flooding, slope failure, and runout. The implementation of a local time stepping for computation of dynamic land surface processes could be implemented widely for increased computational efficiency without significant loss of accuracy.
An integrated, modeling method for shallow landslides, debris flows and catchment hydrology is developed and presented in this paper. Existing two-phase debris flow equations and an adaptation on the ...infinite slope method are coupled with a full hydrological catchment model. We test the approach on the 4 km2 Scaletta catchment, North-Eastern Sicily, where the 1-10-2009 convective storm caused debris flooding after 395 shallow landslides. Validation is done based on the landslide inventory and photographic evidence from the days after the event. Results show that the model can recreate the impact of both shallow landslides, debris flow runout, and debris floods with acceptable accuracy (91 percent inventory overlap with a 0.22 Cohens Kappa). General patterns in slope failure and runout are well-predicted, leading to a fully physically based prediction of rainfall induced debris flood behavior in the downstream areas, such as the creation of a debris fan at the coastal outlet.
•We develop a new open source multi-hazard model.•Our model simulates catchment hydrology, landslides, debris flows and flash flooding.•We apply a novel method for fast estimations of landslide failure volumes.•By including hydrology and debris flow runout, we simulate multi-hazard behavior.•We apply the model to a study case and found high accuracy in hazard behavior.
Soil (regolith) depth is a crucial input for modeling earth surface phenomena. However, most studies ignore its spatial variability. Techniques that map the spatial variability of soil depth are of ...three types: (1) physically-based; (2) empirico-statistical from environmental correlates; and (3) interpolation from point observations. In an anthropogenic landscape, soil depth does not depend primarily on natural processes, making it difficult to apply a physically-based approach. The present study compares empirico-statistical methods with geostatistical methods for predicting soil depth in such a landscape: Aruvikkal catchment (9.5 km
2) in the Western Ghats of Kerala, India. Regression kriging applied on blocks of 20 m by 20 m using the environmental covariates elevation, slope, aspect, curvature, wetness index, land use and distance from streams, proved to be the best predictor of soil depth. This model explains 52% of the variability of soil depth in the catchment; with a prediction variance of 0.05 to 0.19. A Gaussian simulation was attempted for a more realistic visualization of the depth, as opposed to the smooth kriging prediction. The most important explanatory variable of soil depth in this landscape is land use, as expected from the strong human intervention.
Global climate has changed over the past century. Precipitation amounts and intensities are increasing. In this study we investigated the response of seven soil erosion models to a few basic ...precipitation and vegetation related parameters using common data from one humid and one semi-arid watershed. Perturbations were made to inputs for rainfall intensities and amounts, and to ground surface cover and canopy cover. Principal results were that: soil erosion is likely to be more affected than runoff by changes in rainfall and cover, though both are likely to be significantly impacted; percent erosion and runoff will likely change more for each percent change in rainfall intensity and amount than to each percent change in either canopy or ground cover; changes in rainfall amount associated with changes in storm rainfall intensity will likely have a greater impact on runoff and erosion than simply changes in rainfall amount alone; changes in ground cover have a much greater impact on both runoff and erosion than changes in canopy cover alone. The results do not imply that future changes in rainfall will dominate over changes in land use, since land use changes can often be drastic. Given the types of precipitation changes that have occurred over the last century, and the expectations regarding changes over the next century, the results of this study suggest that there is a significant potential for climate change to increase global soil erosion rates unless offsetting conservation measures are taken.
PDF
