Spatial sensitivity analysis for urban hotspots using cell phone traces

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.

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

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.

Optimal Cluster Analysis for Objective Regionalization of Seasonal Precipitation in Regions of High Spatial–Temporal Variability: Application to Western Ethiopia

Defining homogeneous precipitation regions is fundamental for hydrologic applications, yet nontrivial, particularly for regions with highly varied spatial–temporal patterns. Traditional approaches typically include aspects of subjective delineation around sparsely distributed precipitation stations. Here, hierarchical and nonhierarchical (k means) clustering techniques on a gridded dataset for objective and automatic delineation are evaluated. Using a spatial sensitivity analysis test, the k-means clustering method is found to produce much more stable cluster boundaries. To identify a reasonable optimal k, various performance indicators, including the within-cluster sum of square errors (WSS) metric, intra- and intercluster correlations, and postvisualization are evaluated. Two new objective selection metrics (difference in minimum WSS and difference in difference) are developed based on the elbow method and gap statistics, respectively, to determine k within a desired range. Consequently, eight homogenous regions are defined with relatively clear and smooth boundaries, as well as low intercluster correlations and high intracluster correlations. The underlying physical mechanisms for the regionalization outcomes not only help justify the optimal number of clusters selected, but also prove informative in understanding the local- and large-scale climate factors affecting Ethiopian summertime precipitation. A principal component linear regression model to produce cluster-level seasonal forecasts also proves skillful.

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

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.