scholarly journals Effects of Spatial Scales on Runoff / Sediment Transport in Mountain Catchments (3) -Review for the Treatments of Numerical Models

2018 ◽  
Vol 31 (4) ◽  
pp. 245-261
Author(s):  
Yoshiyuki YOKOO ◽  
Satoshi NIWA ◽  
Taro UCHIDA ◽  
Marino HIRAOKA ◽  
Masanori KATSUYAMA ◽  
...  
2018 ◽  
Vol 31 (4) ◽  
pp. 232-244
Author(s):  
Yuko ASANO ◽  
Taro UCHIDA ◽  
Masanori KATSUYAMA ◽  
Marino HIRAOKA ◽  
Shigeru MIZUGAKI ◽  
...  

2021 ◽  
Vol 34 (3) ◽  
pp. 192-204
Author(s):  
Taro UCHIDA ◽  
Yuko ASANO ◽  
Marino HIRAOKA ◽  
Yoshiyuki YOKOO ◽  
Takashi GOMI ◽  
...  

2021 ◽  
Vol 9 (6) ◽  
pp. 600
Author(s):  
Hyun Dong Kim ◽  
Shin-ichi Aoki

When erosion occurs, sand beaches cannot maintain sufficient sand width, foreshore slopes become steeper due to frequent erosion effects, and beaches are trapped in a vicious cycle of vulnerability due to incident waves. Accordingly, beach nourishment can be used as a countermeasure to simultaneously minimize environmental impacts. However, beach nourishment is not a permanent solution and requires periodic renourishment after several years. To address this problem, minimizing the period of renourishment is an economical alternative. In the present study, using the Tuvaluan coast with its cross-sectional gravel nourishment site, four different test cases were selected for the hydraulic model experiment aimed at discovering an effective nourishment strategy to determine effective alternative methods. Numerical simulations were performed to reproduce gravel nourishment; however, none of these models simultaneously simulated the sediment transport of gravel and sand. Thus, an artificial neural network, a deep learning model, was developed using hydraulic model experiments as training datasets to analyze the possibility of simultaneously accomplishing the sediment transport of sand and gravel and supplement the shortcomings of the numerical models.


Ocean Science ◽  
2015 ◽  
Vol 11 (6) ◽  
pp. 879-896 ◽  
Author(s):  
M. Haller ◽  
F. Janssen ◽  
J. Siddorn ◽  
W. Petersen ◽  
S. Dick

Abstract. For understanding and forecasting of hydrodynamics in coastal regions, numerical models have served as an important tool for many years. In order to assess the model performance, we compared simulations to observational data of water temperature and salinity. Observations were available from FerryBox transects in the southern North Sea and, additionally, from a fixed platform of the MARNET network. More detailed analyses have been made at three different stations, located off the English eastern coast, at the Oyster Ground and in the German Bight. FerryBoxes installed on ships of opportunity (SoO) provide high-frequency surface measurements along selected tracks on a regular basis. The results of two operational hydrodynamic models have been evaluated for two different time periods: BSHcmod v4 (January 2009 to April 2012) and FOAM AMM7 NEMO (April 2011 to April 2012). While they adequately simulate temperature, both models underestimate salinity, especially near the coast in the southern North Sea. Statistical errors differ between the two models and between the measured parameters. The root mean square error (RMSE) of water temperatures amounts to 0.72 °C (BSHcmod v4) and 0.44 °C (AMM7), while for salinity the performance of BSHcmod is slightly better (0.68 compared to 1.1). The study results reveal weaknesses in both models, in terms of variability, absolute levels and limited spatial resolution. Simulation of the transition zone between the coasts and the open sea is still a demanding task for operational modelling. Thus, FerryBox data, combined with other observations with differing temporal and spatial scales, can serve as an invaluable tool not only for model evaluation, but also for model optimization by assimilation of such high-frequency observations.


2012 ◽  
Vol 5 (1) ◽  
pp. 223-230 ◽  
Author(s):  
S. Saux Picart ◽  
M. Butenschön ◽  
J. D. Shutler

Abstract. Complex numerical models of the Earth's environment, based around 3-D or 4-D time and space domains are routinely used for applications including climate predictions, weather forecasts, fishery management and environmental impact assessments. Quantitatively assessing the ability of these models to accurately reproduce geographical patterns at a range of spatial and temporal scales has always been a difficult problem to address. However, this is crucial if we are to rely on these models for decision making. Satellite data are potentially the only observational dataset able to cover the large spatial domains analysed by many types of geophysical models. Consequently optical wavelength satellite data is beginning to be used to evaluate model hindcast fields of terrestrial and marine environments. However, these satellite data invariably contain regions of occluded or missing data due to clouds, further complicating or impacting on any comparisons with the model. This work builds on a published methodology, that evaluates precipitation forecast using radar observations based on predefined absolute thresholds. It allows model skill to be evaluated at a range of spatial scales and rain intensities. Here we extend the original method to allow its generic application to a range of continuous and discontinuous geophysical data fields, and therefore allowing its use with optical satellite data. This is achieved through two major improvements to the original method: (i) all thresholds are determined based on the statistical distribution of the input data, so no a priori knowledge about the model fields being analysed is required and (ii) occluded data can be analysed without impacting on the metric results. The method can be used to assess a model's ability to simulate geographical patterns over a range of spatial scales. We illustrate how the method provides a compact and concise way of visualising the degree of agreement between spatial features in two datasets. The application of the new method, its handling of bias and occlusion and the advantages of the novel method are demonstrated through the analysis of model fields from a marine ecosystem model.


2021 ◽  
Author(s):  
Cristian Lussana ◽  
Thomas N. Nipen ◽  
Ivar A. Seierstad ◽  
Christoffer A. Elo

<p>Hourly precipitation is often simultaneously simulated by numerical models and observed by multiple data sources. Accurate precipitation fields based on all available information are valuable input for numerous applications and a critical aspect of climate monitoring. </p><p>Inverse problem theory offers an ideal framework for the combination of observations with a numerical model background. In particular, we have considered a modified ensemble optimal interpolation scheme. The deviations between background and observations are used to adjust for deficiencies in the ensemble. A data transformation based on Gaussian anamorphosis has been used to optimally exploit the potential of the spatial analysis, given that precipitation is approximated with a gamma distribution and the spatial analysis requires normally distributed variables. For each point, the spatial analysis returns the shape and rate parameters of its gamma distribution. </p><p>The ensemble-based statistical interpolation scheme with Gaussian anamorphosis for precipitation (EnSI-GAP) is implemented in a way that the covariance matrices are locally stationary, and the background error covariance matrix undergoes a localization process. Concepts and methods that are usually found in data assimilation are here applied to spatial analysis, where they have been adapted in an original way to represent precipitation at finer spatial scales than those resolved by the background, at least where the observational network is dense enough.</p><p>The EnSI-GAP setup requires the specification of a restricted number of parameters, and specifically, the explicit values of the error variances are not needed, since they are inferred from the available data. </p><p>The examples of applications presented over Norway provide a better understanding of EnSI-GAP. The data sources considered are those typically used at national meteorological services, such as local area models, weather radars, and in situ observations. For this last data source, measurements from both traditional and opportunistic sensors have been considered.</p>


2021 ◽  
Vol 13 (22) ◽  
pp. 12385
Author(s):  
Gabriele Lobaccaro ◽  
Koen De Ridder ◽  
Juan Angel Acero ◽  
Hans Hooyberghs ◽  
Dirk Lauwaet ◽  
...  

Urban analysis at different spatial scales (micro- and mesoscale) of local climate conditions is required to test typical artificial urban boundaries and related climate hazards such as high temperatures in built environments. The multitude of finishing materials and sheltering objects within built environments produce distinct patterns of different climate conditions, particularly during the daytime. The combination of high temperatures and intense solar radiation strongly perturb the environment by increasing the thermal heat stress at the pedestrian level. Therefore, it is becoming common practice to use numerical models and tools that enable multiple design and planning alternatives to be quantitatively and qualitatively tested to inform urban planners and decision-makers. These models and tools can be used to compare the relationships between the micro-climatic environment, the subjective thermal assessment, and the social behaviour, which can reveal the attractiveness and effectiveness of new urban spaces and lead to more sustainable and liveable public spaces. This review article presents the applications of selected environmental numerical models and tools to predict human thermal stress at the mesoscale (e.g., satellite thermal images and UrbClim) and the microscale (e.g., mobile measurements, ENVI-met, and UrbClim HR) focusing on case study cities in mid-latitude climate regions framed in two European research projects.


1988 ◽  
Vol 1 (21) ◽  
pp. 141
Author(s):  
Todd L. Walton ◽  
Philip L.F. Liu ◽  
Edward B. Hands

This paper examines the effects of random and deterministic cycling of wave direction on the updrift beach planform adjacent to a jetty. Results provided using a simplified numerical model cast in dimensionless form indicate the importance of the time series of wave direction in determining design jetty length for a given net sediment transport. Continuous cycling of • wave direction leads to the expected analytical solution. Simplications in the numerical model used restrict the applications to small wave angles, no diffraction, no reflection of waves off structure, no refraction, and no sand bypassing at jetty. The concept can be extended to more sophisticated numerical models.


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