Assessing uncertainty in VGI for emergency response

This research project examines the geographic data produced by volunteers via the Ushahidi web platform in response to the earthquake that struck Haiti in January 2010. Volunteers translated messages (text, e-mail, and voice) submitted by victims of the earthquake, categorized each message into a primary ‘emergency need’ category and subcategory, and georeferenced each message on a dynamic web-based map. Initial inspection of the categorized data indicated discrepancies between the emergency need submitted by victims and the subsequent categorization of the emergency need. Analysis of the main categorical data illustrated that 50% of the messages were mis-categorized by the volunteers, failing to convey the main idea of the victim’s message. At the subcategory level, approximately 73% of the messages failed to convey the main idea of the messages. These numbers are higher than the estimate of 36% error in categorization produced in an independent review of the Haiti Ushahidi database. While the volunteer response to the Haitian earthquake represents a paradigm shift in emergency response and victim empowerment that has been repeated in numerous natural and man-made disasters around the world, this study suggests the need for more research on the quality of the categorization (i.e., attribute data) of volunteered emergency data.     

Grid-enabled Monte Carlo analysis of the impacts of uncertain discharge rates on seawater intrusion in the Korba aquifer (Tunisia)

Abstract

The Korba aquifer, located in the north of Tunisia, suffers heavily from salinization due to seawater intrusion. In 2000, the aquifer was exploited from more than 9000 wells. The problem is that no precise information was recorded concerning the current extraction rates, their spatial distribution, or their evolution in time. In this study, a geostatistical model of the exploitation rates was constructed based on a multi-linear regression model combining incomplete direct data and exhaustive secondary information. The impacts of the uncertainty on the spatial distribution of the pumping rates on seawater intrusion were evaluated using a 3-D density-dependent groundwater model. To circumvent the large amount of computing time required to run transient models, the simulations were run in a parallel fashion on the Grid infrastructure provided by the Enabling Grid for E-Science in Europe project. Monte Carlo simulations results showed that 8.3% of the aquifer area is affected by input uncertainty.

Citation Kerrou, J., Renard, P., Lecca, G. & Tarhouni, J. (2010 Kerrou, J., Renard, P. and Tarhouni, J. 2010. Status of the Korba groundwater resources (Tunisia): observations and three-dimensional modelling of seawater intrusion. Hydrogeol. J., 18(5): 1173–1190. doi:10.1007/s10040-010-0573-5[Crossref], [Web of Science ®] , [Google Scholar]) Grid-enabled Monte Carlo analysis of the impacts of uncertain discharge rates on seawater intrusion in the Korba aquifer (Tunisia). Hydrol. Sci. J. 55(8), 1325–1336.     

A SEMANTIC REVIEW OF THE TERMINOLOGY OF GROUNDWATER MAPS

ABSTRACT

Relations of runoff to altitude may be denned for a hydrologically homogeneous region by using a limited number of streamflow records. Mean runoff from ungaged basins in this same region can then be computed on the basis of this derived relation. When these relations of runoff to altitude are applied to small areas, the runoff may be different from that indicated by the relation, because of local differences in geology, precipitation, vegetation, land slopes, and land use. Two methods are described for adjusting the relations for the effects of these variations; one based on a streamflow measurement at miscellaneous sites which is applicable to perennial streams, the other on measurement of two channel parameters which is applicable to either perennial or ephemeral streams.     

Visualizing perceived spatial data quality of 3D objects within virtual globes

Virtual globes (VGs) allow Internet users to view geographic data of heterogeneous quality created by other users. This article presents a new approach for collecting and visualizing information about the perceived quality of 3D data in VGs. It aims at improving users' awareness of the quality of 3D objects. Instead of relying on the existing metadata or on formal accuracy assessments that are often impossible in practice, we propose a crowd-sourced quality recommender system based on the five-star visualization method successful in other types of Web applications. Four alternative five-star visualizations were implemented in a Google Earth-based prototype and tested through a formal user evaluation. These tests helped identifying the most effective method for a 3D environment. Results indicate that while most websites use a visualization approach that shows a ‘number of stars’, this method was the least preferred by participants. Instead, participants ranked the ‘number within a star’ method highest as it allowed reducing the visual clutter in urban settings, suggesting that 3D environments such as VGs require different design approaches than 2D or non-geographic applications. Results also confirmed that expert and non-expert users in geographic data share similar preferences for the most and least preferred visualization methods.     

Modelling catchment inflows into Lake Victoria: uncertainties in rainfall–runoff modelling for the Nzoia River

Abstract

Climate and soil characteristics vary considerably around the Lake Victoria basin resulting in high spatial and temporal variability in catchment inflows. However, data for estimating the inflows are usually sparsely distributed and error-prone. Therefore, modelled estimates of the flows are highly uncertain, which could explain early difficulties in reproducing the lake water balance. The aim of this study was to improve the estimates of catchment flow to Lake Victoria. The WASMOD model was applied to the Nzoia River, one of the major tributaries to Lake Victoria. Uncertainty was assessed within the GLUE framework. During calibration, log-transformation was performed on both simulated and observed flows. The results showed that, despite its simple structure, WASMOD produces acceptable results for the basin. For a Nash-Sutcliffe efficiency (NS) threshold of 0.6, the percentage of observations bracketed by simulations (POBS) was 74%, the average relative interval length (ARIL) was 0.93, and the maximum NS value was 0.865. The residuals were shown to be homoscedastic, normally distributed and nearly independent. When the NS threshold was increased to 0.8, POBS decreased to 54% with an improvement of ARIL to 0.49, highlighting the effect of the subjective choice of likelihood threshold.

Citation Kizza, M., Rodhe, A., Xu, C.-Y. & Ntale, H. K. (2011) Modelling catchment inflows into Lake Victoria: uncertainties in rainfall–runoff modelling for the Nzoia River. Hydrol. Sci. J. 56(7), 1210–1226.     

高阶统计量及AIC方法在区域地震事件和直达P波初动识别中的应用

利用高阶统计量（偏斜度和峰度）与赤池信息量准则（简称AIC）相结合,进行区域地震事件实时检测和P波初至精细识别的新方法研究,通过处理山东地震台网记录的地震波资料,结果表明：应用高阶统计量（偏斜度和峰度,尤其是峰度）能够有效识别地震事件,降低地震事件的错误报警率和漏报率;与人工识别震相到时结果相比,根据Ske-AIC、Kur-AIC震相自动识别方法得到的震相到时的平均绝对值误差小.     

Propagating error in land-cover-change analyses: impact of temporal dependence under increased thematic complexity

We examined the impact of temporal dependence between patterns of error in classified time-series imagery through a simulation modeling approach. This research extended the land-cover-change simulation model we previously developed to investigate: (1) the assumption of temporal independence between patterns of error in classified time-series imagery; and (2) the interaction of patterns of change and patterns of error in a post-classification change analysis. In this research, the thematic complexity of the classified land-cover maps was increased by increasing the number of simulated land-cover classes. Simulating maps with increased categorical resolution permitted the incorporation of: (1) higher-order, more complex spatial and temporal interactions between land-cover classes; and (2) patterns of error that better reproduce the complex error interactions that often occur in time-series classified imagery. The overall modeling framework was divided into two primary components: (1) generation of a map representing true change; and (2) generation of a suite of change maps that had been perturbed by specific patterns of error. All component maps in the model were produced using simulated annealing, which enabled us to create a series of map realizations with user-defined spatial and temporal patterns. Comparing the true map of change to the error-perturbed maps of change using accuracy assessment statistics showed that increasing the temporal dependence between classification errors did not improve the accuracy of resulting maps of change when the categorical scale of the land-cover classified maps was increased. The increased structural complexity within the time series of maps effectively inhibited the impact of temporal dependence. However, results demonstrated that there are interactions between patterns of error and patterns of change in a post-classification change analysis. These interactions played a major role in determining the accuracy associated with the maps of change.     

A framework for uncertainty assessment in simulation models

In this article, we introduce a conceptual framework for systematic identification and assessment of sources of uncertainty in simulation models. This concept builds on a novel typology of uncertainty in model validation and extends the GIScience research focus on uncertainty in spatial data to uncertainty in simulation modelling. Such a concept helps a modeller to interpret and handle uncertainty in order to efficiently optimise a model and better understand simulation results.

To illustrate our approach, we apply the proposed framework for uncertainty assessment to the TREE LIne Model (TREELIM), an individual-based model that simulates forest succession at the alpine tree line. Using this example, uncertainty is identified in the modelling workflow during conceptualisation, formalisation, parameterisation, analysis and validation. With help of a set of indicators we quantify the emerging uncertainties and assess the overall model uncertainty as a function of all occurring sources of uncertainty.

An understanding of the sources of uncertainty in an ecological model proves beneficial for: (1) developing a structurally valid model in a systematic way; (2) deciding if further refinement of the conceptual model is beneficial for the modelling purpose; and (3) interpreting the overall model uncertainty by understanding its sources. Our approach results in a guideline for assessing uncertainty in the validation of simulation models in a feasible and defensible way, and thus functions as a toolbox for modellers. We consider this work as a contribution towards a general concept of uncertainty in spatially explicit simulation models.     

Reliability assessment of water structures subject to data scarcity using the SCS-CN model

ABSTRACT

When discharge measurements are not available, design of water structures relies on using frequency analysis of rainfall data and applying a rainfall–runoff model to estimate a hydrograph. The Soil Conservation Service (SCS) method estimates the design hydrograph first through a rainfall–runoff transformation and next by propagating runoff to the basin outlet via the SCS unit hydrograph (UH) method. The method uses two parameters, the Curve Number (CN) and the time of concentration (T_c). However, in data-scarce areas, the calibration of CN and T_c from nearby gauged watersheds is limited and subject to high uncertainties. Therefore, the inherent uncertainty/variability of the SCS parameters may have considerable ramifications on the safety of design. In this research, a reliability approach is used to evaluate the impact of incorporating the uncertainty of CN and T_c in flood design. The sensitivity of the probabilistic outcome against the uncertainty of input parameters is calculated using the First Order Reliability Method (FORM). The results of FORM are compared with the conventional SCS results, taking solely the uncertainty of the rainfall event. The relative importance of the uncertainty of the SCS parameters is also estimated. It is found that the conventional approach, used by many practitioners, might grossly underestimate the risk of failure of water structures, due to neglecting the probabilistic nature of the SCS parameters and especially the Curve Number. The most predominant factors against which the SCS-CN method is highly uncertain are when the average rainfall value is low (less than 20 mm) or its coefficient of variation is not significant (less than 0.5), i.e. when the resulting rainfall at the design return period is low. A case study is presented for Egypt using rainfall data and CN values driven from satellite information, to determine the regions of acceptance of the SCS-CN method.

EDITOR D. Koutsoyiannis; ASSOCIATE EDITOR A. Efstratiadis