handle: 2117/345587
A certain acceptable level of risk in a major drainage system must be established since urban areas cannot be made entirely free from pluvial flooding. Among the diversity of flood risks in urban areas, direct damage to property has been extensively studied. A novel model approach (SFLOOD) to estimate flood damage to property in urban areas has been developed and presented herein. The model was conceptualised according to the knowledge of an insurance surveyor, acquired over many years on flood economic losses appraisals. It is a micro-scale-, depth-damage- and GIS-based model where water depth is the only hydrodynamic variable considered as a damage driver. The model testing has been conducted through the direct comparison of computed damage and damage appraisals provided by the Spanish public insurance company, Consorcio de Compensación de Seguros (CCS), for three actual flood events that occurred in Barcelona (Spain). Although a variety of uncertainties related to the flood damage estimates have been revealed here, the model is able to predict the order of magnitude of the actual damages according to the results obtained. The authors thank the RESCCUE project, which is funded by the EU H2020 (Grant Agreement No. 700174), whose support is gratefully acknowledged. The authors are also grateful to BCASA for allowing the use of their drainage model of Barcelona and for providing rainfall records. METEOCAT also contributed to this study by providing rainfall records from the rain gages they own, and the authors are grateful to them too. Finally, this study was conducted appropriately thanks to the contribution of the Consorcio de Compensación de Seguros (CCS), the institution that kindly provided insurance data to validate the tool presented herein. Peer Reviewed
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doi: 10.5194/tc-3-75-2009
Abstract. Energy balance based glacier melt models require accurate estimates of incoming longwave radiation but direct measurements are often not available. Multi-year near-surface meteorological data from Storglaciären, Northern Sweden, were used to evaluate commonly used longwave radiation parameterizations in a glacier environment under clear-sky and all-sky conditions. Parameterizations depending solely on air temperature performed worse than those which include water vapor pressure. All models tended to overestimate incoming longwave radiation during periods of low longwave radiation, while incoming longwave was underestimated when radiation was high. Under all-sky conditions root mean square error (RMSE) and mean bias error (MBE) were 17 to 20 W m−2 and −5 to 1 W m−2, respectively. Two attempts were made to circumvent the need of cloud cover data. First cloud fraction was parameterized as a function of the ratio, τ, of measured incoming shortwave radiation and calculated top of atmosphere radiation. Second, τ was related directly to the cloud factor (i.e. the increase in sky emissivity due to clouds). Despite large scatter between τ and both cloud fraction and the cloud factor, resulting calculations of hourly incoming longwave radiation for both approaches were only slightly more variable with RMSE roughly 3 W m−2 larger compared to using cloud observations as input. This is promising for longwave radiation modeling in areas where shortwave radiation data are available but cloud observations are not.
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Abstract Estimating the accurate longitudinal velocity fields in an open channel junction has a great impact on hydraulic structures such as irrigation and drainage channels, river systems and sewer networks. In this study, Genetic Programming (GP) and Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) were modeled and compared to find an analytical formulation that could present a continuous spatial description of velocity in open channel junction by using discrete information of laboratory measurements. Three direction coordinates of each point of the fluid flow and discharge ratio of main to tributary channel were used as inputs to the GP and ANN models. The training and testing of the models were performed according to the published experimental data from the related literature. To find the accurate prediction ability of GP and ANN models in cases with minor training dataset, the models were compared with various percents of allocated data to train dataset. New formulations were obtained from GP and ANN models that can be applied for practical longitudinal velocity field prediction in an open channel junction. The results showed that ANN model by Root Mean Squared Error (RMSE) of 0.068 performs better than GP model by RMSE of 0.162, and that ANN can model the longitudinal velocity field with small population of train dataset with high accuracy.
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Global precipitation patterns lead to differences in seasonal distributions of rainfall between locations in the form of alternating dry and wet seasons. Many locations experience a single wet and dry season per year, but some studies report the occurrence of two wet and dry seasons per year. This bimodal rainfall pattern is commonly associated with locations within the tropics but is reported outside the tropics as well. However, this information is fragmented, and studies of bimodality are mainly restricted to monthly rainfall totals. Here, we use a gridded global data set and simple harmonic analysis to provide a systematic overview of global bimodal rainfall and rain‐day frequency. We find good agreement between the various regional studies concerning bimodal precipitation and our global overview, showing that bimodal rainfall occurs on approximately 7% of the global land surface. In the tropics, regions of bimodal rainfall totals (P) and regions of bimodal rain‐day frequency (N) tend to overlap due to the presence of dry seasons that have zero precipitation. Outside the tropics, P and N are more independent, which leads to complex within‐year patterns of precipitation intensity. A secondary outcome of our results is an improved low‐dimensional global parameterization of monthly rainfall regimes. Our results provide the first gridded global overview of bimodal rainfall patterns and show the usefulness of simple mathematical approaches for detecting patterns in large data sets.
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citations | 32 | |
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doi: 10.13031/2013.18305
Lag time through a combine harvester was investigated using a stationary combine in the laboratory. Grain flow into the machine was modulated using electronically controlled gate valves. Base grain flows ranged from 0.91 to 6.36 kg s-1 in 0.91 kg s-1 steps. Flow perturbations of 0.91, 1.82, and 2.73 kg s-1 were introduced into the combine through a separate conduit. The results showed that lag time varied with the mass flow through the harvester. Grain flow was measured using both an experimental torque-based sensor and an impact plate-type sensor. Lag time was determined using an author-written software program, LagFinder. LagFinder was used to determine lag times for both the grain flow plate and torque-based sensors. Lag time increased with increasing flow rates. Applying varying lag times using a quadratic delay model to yield monitor output could be a simple way to improve the accuracy of yield maps over using constant lag times.
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doi: 10.1139/l97-048
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The effective monitoring of ballasted railway track beds is fundamental for maintaining safe operational conditions of railways and lowering maintenance costs. Railway ballast can be damaged over time by the breakdown of aggregates or by the upward migration of fine clay particles from the foundation, along with capillary water. This may cause critical track settlements. To that effect, early stage detection of fouling is of paramount importance. Within this context, ground penetrating radar (GPR) is a rapid nondestructive testing technique, which is being increasingly used for the assessment and health monitoring of railway track substructures. In this paper, we propose a novel and efficient signal processing approach based on entropy analysis, which was applied to GPR data for the assessment of the railway ballast conditions and the detection of fouling. In order to recreate a real-life scenario within the context of railway structures, four different ballast/pollutant mixes were introduced, ranging from clean to highly fouled ballast. GPR systems equipped with two different antennas, ground-coupled (600 and 1600 MHz) and air-coupled (1000 and 2000 MHz), were used for testing purposes. The proposed methodology aims at rapidly identifying distinctive areas of interest related to fouling, thereby lowering significantly the amount of data to be processed and the time required for specialist data processing. Prominent information on the use of suitable frequencies of investigation from the investigated set, as well as the relevant probability values of detection and false alarm, is provided.
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Changes in rainfall patterns due to climate change will change the hydrological elements in the stream and the riverbed changes that will occur consequently. In addition, dredging and multi-functional Weir installation was finished along the Nakdong River, South Korea in 2012 as a part of the Four Major River Restoration Project. Due to the project the river became wider and deeper with deeper water depth. If the characteristics of the watershed are maintained, it will return to the pre-maintenance stream, which is the dynamic equilibrium of the stream. Also, there are very few studies comparing and analyzing changes in river basins in connection with climate change. In this study, to develop the riverbed variation technique considering the climate change in the Nakdong River Basin. The fit of the model was analyzed by using GSTARS, which is a hydraulic model, by analyzing the changes of the riverbed according to the number of stream tubes and the sediment transport equation. As a result of the study, it was found that the riverbed change was appropriate when three stream tubes were used and the riverbed change was over estimated when the stream tubes were five. The Ackers and White and the Yang equations were found to be suitable for the riverbed changes. As the flow rate increases, the change of the riverbed becomes larger and the riverbed becomes narrower.
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Rainfall intensities measured at a few stations in Kerala during 2001–2005 using a disdrometer were found to be in reasonable agreement with the total rainfall measured using a manual rain gauge. The temporal distributions of rainfall intensity at different places and during different months show that rainfall is of low intensity (< 10 mm/hr), 65% to 90% of the time. This could be an indication of the relative prevalence of stratiform and cumuliform clouds. Rainfall was of intensity < 5 mm/hr for more than 95% of the time in Kochi in July 2002, which was a month seriously deficient in rainfall, indicating that the deficiency was probably due to the relative absence of cumuliform clouds. Cumulative distribution graphs are also plotted and fitted with the Weibull distribution. The fit parameters do not appear to have any consistent pattern. The higher intensities also contributed significantly to total rainfall most of the time, except in Munnar (a hill station). In this analysis also, the rainfall in Kochi in July 2002 was found to have less presence of high intensities. This supports the hypothesis that the rainfall deficiency was probably caused by the absence of conditions that favoured the formation of cumuliform clouds.
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citations | 19 | |
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doi: 10.1029/2003jd003575
The spatial distribution of frozen soil and snow cover at the start of the spring melt season plays an important role in the generation of spring runoff and in the exchange of energy between the land surface and the atmosphere. Field observations were made at the University of Minnesota's Rosemount Agricultural Experiment Station to identify statistical distributions that can be used to describe the spatial variability of frozen soil and snow in macroscale hydrology models. These probability distributions are used to develop algorithms that simulate the subgrid spatial variability of snow and soil ice content for application within the framework of the variable infiltration capacity macroscale hydrologic model. Point simulations show that the new snow algorithm increases the melt rate for thin snowpacks, and the new soil frost algorithm allows more drainage through the soil during the winter. Simulations of the Minnesota River show that the new snow algorithm makes little difference to regional streamflow but does play an important role in the regional energy balance, especially during the spring snowmelt season. The new soil frost algorithm has a larger impact on spring streamflow and plays a minor role in the surface energy balance during the spring soil thaw season.
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citations | 147 | |
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handle: 2117/345587
A certain acceptable level of risk in a major drainage system must be established since urban areas cannot be made entirely free from pluvial flooding. Among the diversity of flood risks in urban areas, direct damage to property has been extensively studied. A novel model approach (SFLOOD) to estimate flood damage to property in urban areas has been developed and presented herein. The model was conceptualised according to the knowledge of an insurance surveyor, acquired over many years on flood economic losses appraisals. It is a micro-scale-, depth-damage- and GIS-based model where water depth is the only hydrodynamic variable considered as a damage driver. The model testing has been conducted through the direct comparison of computed damage and damage appraisals provided by the Spanish public insurance company, Consorcio de Compensación de Seguros (CCS), for three actual flood events that occurred in Barcelona (Spain). Although a variety of uncertainties related to the flood damage estimates have been revealed here, the model is able to predict the order of magnitude of the actual damages according to the results obtained. The authors thank the RESCCUE project, which is funded by the EU H2020 (Grant Agreement No. 700174), whose support is gratefully acknowledged. The authors are also grateful to BCASA for allowing the use of their drainage model of Barcelona and for providing rainfall records. METEOCAT also contributed to this study by providing rainfall records from the rain gages they own, and the authors are grateful to them too. Finally, this study was conducted appropriately thanks to the contribution of the Consorcio de Compensación de Seguros (CCS), the institution that kindly provided insurance data to validate the tool presented herein. Peer Reviewed
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citations | 16 | |
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doi: 10.5194/tc-3-75-2009
Abstract. Energy balance based glacier melt models require accurate estimates of incoming longwave radiation but direct measurements are often not available. Multi-year near-surface meteorological data from Storglaciären, Northern Sweden, were used to evaluate commonly used longwave radiation parameterizations in a glacier environment under clear-sky and all-sky conditions. Parameterizations depending solely on air temperature performed worse than those which include water vapor pressure. All models tended to overestimate incoming longwave radiation during periods of low longwave radiation, while incoming longwave was underestimated when radiation was high. Under all-sky conditions root mean square error (RMSE) and mean bias error (MBE) were 17 to 20 W m−2 and −5 to 1 W m−2, respectively. Two attempts were made to circumvent the need of cloud cover data. First cloud fraction was parameterized as a function of the ratio, τ, of measured incoming shortwave radiation and calculated top of atmosphere radiation. Second, τ was related directly to the cloud factor (i.e. the increase in sky emissivity due to clouds). Despite large scatter between τ and both cloud fraction and the cloud factor, resulting calculations of hourly incoming longwave radiation for both approaches were only slightly more variable with RMSE roughly 3 W m−2 larger compared to using cloud observations as input. This is promising for longwave radiation modeling in areas where shortwave radiation data are available but cloud observations are not.
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citations | 52 | |
popularity | Top 10% | |
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Abstract Estimating the accurate longitudinal velocity fields in an open channel junction has a great impact on hydraulic structures such as irrigation and drainage channels, river systems and sewer networks. In this study, Genetic Programming (GP) and Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) were modeled and compared to find an analytical formulation that could present a continuous spatial description of velocity in open channel junction by using discrete information of laboratory measurements. Three direction coordinates of each point of the fluid flow and discharge ratio of main to tributary channel were used as inputs to the GP and ANN models. The training and testing of the models were performed according to the published experimental data from the related literature. To find the accurate prediction ability of GP and ANN models in cases with minor training dataset, the models were compared with various percents of allocated data to train dataset. New formulations were obtained from GP and ANN models that can be applied for practical longitudinal velocity field prediction in an open channel junction. The results showed that ANN model by Root Mean Squared Error (RMSE) of 0.068 performs better than GP model by RMSE of 0.162, and that ANN can model the longitudinal velocity field with small population of train dataset with high accuracy.
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citations | 30 | |
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influence | Top 10% | |
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Global precipitation patterns lead to differences in seasonal distributions of rainfall between locations in the form of alternating dry and wet seasons. Many locations experience a single wet and dry season per year, but some studies report the occurrence of two wet and dry seasons per year. This bimodal rainfall pattern is commonly associated with locations within the tropics but is reported outside the tropics as well. However, this information is fragmented, and studies of bimodality are mainly restricted to monthly rainfall totals. Here, we use a gridded global data set and simple harmonic analysis to provide a systematic overview of global bimodal rainfall and rain‐day frequency. We find good agreement between the various regional studies concerning bimodal precipitation and our global overview, showing that bimodal rainfall occurs on approximately 7% of the global land surface. In the tropics, regions of bimodal rainfall totals (P) and regions of bimodal rain‐day frequency (N) tend to overlap due to the presence of dry seasons that have zero precipitation. Outside the tropics, P and N are more independent, which leads to complex within‐year patterns of precipitation intensity. A secondary outcome of our results is an improved low‐dimensional global parameterization of monthly rainfall regimes. Our results provide the first gridded global overview of bimodal rainfall patterns and show the usefulness of simple mathematical approaches for detecting patterns in large data sets.