scholarly journals Spatial Sensitivity Analysis and Structure Design of the Ring Electrostatic Electrode

2021 ◽  
Vol 804 (4) ◽  
pp. 042051
Author(s):  
Jiange Chen ◽  
Min Jiao ◽  
Dewen Li ◽  
Siyuan Lu ◽  
Gelai Yan
Keyword(s):  
Sensitivity Analysis ◽  
Structure Design ◽  
Spatial Sensitivity ◽  
Spatial Sensitivity Analysis

scholarly journals Spatial sensitivity analysis of snow cover data in a distributed rainfall–runoff model

2014 ◽  
Vol 11 (10) ◽  
pp. 11987-12025 ◽  
Author(s):  
T. Berezowski ◽  
J. Nossent ◽  
J. Chormański ◽  
O. Batelaan
Keyword(s):  
Sensitivity Analysis ◽  
Distributed Data ◽  
Rainfall Runoff ◽  
Spatial Sensitivity ◽  
Wetspa Model ◽  
Spatial Parameters ◽  
Spatial Sensitivity Analysis ◽  
Rainfall Runoff Model ◽  
Different Response ◽  
Runoff Model

Abstract. As the availability of spatially distributed data sets for distributed rainfall–runoff modelling is strongly growing, more attention should be paid to the influence of the quality of the data on the calibration. While a lot of progress has been made on using distributed data in simulations of hydrological models, sensitivity of spatial data with respect to model results is not well understood. In this paper we develop a spatial sensitivity analysis (SA) method for snow cover fraction input data (SCF) for a distributed rainfall–runoff model to investigate if the model is differently subjected to SCF uncertainty in different zones of the model. The analysis was focused on the relation between the SCF sensitivity and the physical, spatial parameters and processes of a distributed rainfall–runoff model. The methodology is tested for the Biebrza River catchment, Poland for which a distributed WetSpa model is setup to simulate two years of daily runoff. The SA uses the Latin-Hypercube One-factor-At-a-Time (LH-OAT) algorithm, which uses different response functions for each 4 km × 4 km snow zone. The results show that the spatial patterns of sensitivity can be easily interpreted by co-occurrence of different environmental factors such as: geomorphology, soil texture, land-use, precipitation and temperature. Moreover, the spatial pattern of sensitivity under different response functions is related to different spatial parameters and physical processes. The results clearly show that the LH-OAT algorithm is suitable for the spatial sensitivity analysis approach and that the SCF is spatially sensitive in the WetSpa model.

scholarly journals Spatial sensitivity analysis of snow cover data in a distributed rainfall-runoff model

2015 ◽  
Vol 19 (4) ◽  
pp. 1887-1904 ◽  
Author(s):  
T. Berezowski ◽  
J. Nossent ◽  
J. Chormański ◽  
O. Batelaan
Keyword(s):  
Sensitivity Analysis ◽  
Spatial Data ◽  
Distributed Data ◽  
Rainfall Runoff ◽  
Spatial Sensitivity ◽  
Wetspa Model ◽  
Spatial Sensitivity Analysis ◽  
Rainfall Runoff Model ◽  
Different Response ◽  
Runoff Model

Abstract. As the availability of spatially distributed data sets for distributed rainfall-runoff modelling is strongly increasing, more attention should be paid to the influence of the quality of the data on the calibration. While a lot of progress has been made on using distributed data in simulations of hydrological models, sensitivity of spatial data with respect to model results is not well understood. In this paper we develop a spatial sensitivity analysis method for spatial input data (snow cover fraction – SCF) for a distributed rainfall-runoff model to investigate when the model is differently subjected to SCF uncertainty in different zones of the model. The analysis was focussed on the relation between the SCF sensitivity and the physical and spatial parameters and processes of a distributed rainfall-runoff model. The methodology is tested for the Biebrza River catchment, Poland, for which a distributed WetSpa model is set up to simulate 2 years of daily runoff. The sensitivity analysis uses the Latin-Hypercube One-factor-At-a-Time (LH-OAT) algorithm, which employs different response functions for each spatial parameter representing a 4 × 4 km snow zone. The results show that the spatial patterns of sensitivity can be easily interpreted by co-occurrence of different environmental factors such as geomorphology, soil texture, land use, precipitation and temperature. Moreover, the spatial pattern of sensitivity under different response functions is related to different spatial parameters and physical processes. The results clearly show that the LH-OAT algorithm is suitable for our spatial sensitivity analysis approach and that the SCF is spatially sensitive in the WetSpa model. The developed method can be easily applied to other models and other spatial data.

scholarly journals Spatial Sensitivity Analysis of Simulated Land Surface Patterns in a Catchment Model Using a Set of Innovative Spatial Performance Metrics

2017 ◽  
Vol 18 (4) ◽  
pp. 1121-1142 ◽  
Author(s):  
Julian Koch ◽  
Gorka Mendiguren ◽  
Gregoire Mariethoz ◽  
Simon Stisen
Keyword(s):  
Sensitivity Analysis ◽  
Spatial Patterns ◽  
Land Surface ◽  
Performance Metrics ◽  
Surface Model ◽  
Model Parameters ◽  
Spatial Sensitivity ◽  
Spatial Performance ◽  
Surface Patterns ◽  
Spatial Sensitivity Analysis

Abstract Distributed hydrological models simulate states and fluxes of water and energy in the terrestrial hydrosphere at each cell. The predicted spatial patterns result from complex nonlinear relationships and feedbacks. Spatial patterns are often neglected during the modeling process, and therefore a spatial sensitivity analysis framework that highlights their importance is proposed. This study features a comprehensive analysis of spatial patterns of actual evapotranspiration (ET) and land surface temperature (LST), with the aim of quantifying the extent to which forcing data and model parameters drive these patterns. This framework is applied on a distributed model [MIKE Système Hydrologique Européen (MIKE SHE)] coupled to a land surface model [Shuttleworth and Wallace–Evapotranspiration (SW-ET)] of a catchment in Denmark. Twenty-two scenarios are defined, each having a simplified representation of a potential driver of spatial variability. A baseline model that incorporates full spatial detail is used to assess sensitivity. High sensitivity can be attested in scenarios where the simulated spatial patterns differ significantly from the baseline. The core novelty of this study is that the analysis is based on a set of innovative spatial performance metrics that enable a reliable spatial pattern comparison. Overall, LST is very sensitive to air temperature and wind speed whereas ET is rather driven by vegetation. Both are sensitive to groundwater coupling and precipitation. The conclusions may be limited to the selected catchment and to the applied modeling system, but the suggested framework is generically relevant for the modeling community. While the applied metrics focus on specific spatial information, they partly exhibit redundant information. Thus, a combination of metrics is the ideal approach to evaluate spatial patterns in models outputs.

scholarly journals Spatial sensitivity analysis for urban hotspots using cell phone traces

2021 ◽  
pp. 239980832098584
Author(s):  
Jiahui Wu ◽  
Enrique Frias-Martinez ◽  
Vanessa Frias-Martinez
Keyword(s):  
Sensitivity Analysis ◽  
Cell Phone ◽  
Large Scale ◽  
Urban Environments ◽  
Urban Life ◽  
Spatial Sensitivity ◽  
Methodological Choices ◽  
Spatial Sensitivity Analysis ◽  
Spatio Temporal ◽  
The Impact

Urban hotspots can be used to model the structure of urban environments and to study or predict various aspects of urban life. An increasing interest in the analysis of urban hotspots has been triggered by the emergence of pervasive technologies that produce massive amounts of spatio-temporal data including cell phone traces (or Call Detail Records). Although hotspot analyses using cell phone traces are extensive, there is no consensus among researchers about the process followed to compute them in terms of four important methodological choices: city boundaries, spatial units, interpolation methods, and hotspot variables. Using a large-scale CDR dataset from Mexico, we provide an interpretable systematic spatial sensitivity analysis of the impact that these methodological choices might have on the stability of the hotspot variables in both static and dynamic settings.

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