Final Report of the DAUFIN project

DAUFIN = Data Assimulation within Unifying Framework for Improved river basiN modeling (EC 5th framework Project

On the uncertainty of stream networks derived from elevation data: the error propagation approach

DEM error propagation methodology is extended to the derivation of vector-based objects (stream networks) using geostatistical simulations. First, point sampled elevations are used to fit a variogram model. Next 100 DEM realizations are generated using conditional sequential Gaussian simulation; the stream network map is extracted for each of these realizations, and the collection of stream networks is analyzed to quantify the error propagation. At each grid cell, the probability of the occurrence of a stream and the propagated error are estimated. The method is illustrated using two small data sets: Baranja hill (30 m grid cell size; 16 512 pixels; 6367 sampled elevations), and Zlatibor (30 m grid cell size; 15 000 pixels; 2051 sampled elevations). All computations are run in the open source software for statistical computing R: package geoR is used to fit variogram; package gstat is used to run sequential Gaussian simulation; streams are extracted using the open source GIS SAGA via the RSAGA library. The resulting stream error map (Information entropy of a Bernoulli trial) clearly depicts areas where the extracted stream network is least precise – usually areas of low local relief and slightly convex (0–10 difference from the mean value). In both cases, significant parts of the study area (17.3% for Baranja Hill; 6.2% for Zlatibor) show high error (H>0.5) of locating streams. By correlating the propagated uncertainty of the derived stream network with various land surface parameters sampling of height measurements can be optimized so that delineated streams satisfy the required accuracy level. Such error propagation tool should become a standard functionality in any modern GIS. Remaining issue to be tackled is the computational burden of geostatistical simulations: this framework is at the moment limited to small data sets with several hundreds of points. Scripts and data sets used in this article are available on-line via the www.geomorphometry.org website and can be easily adopted/adjusted to any similar case study

A framework to classify error in animal-borne technologies

The deployment of novel, innovative, and increasingly miniaturized devices on fauna to collect data has increased. Yet, every animal-borne technology has its shortcomings, such as limitations in its precision or accuracy. These shortcomings, here labeled as “error,” are not yet studied systematically and a framework to identify and classify error does not exist. Here, we propose a classification scheme to synthesize error across technologies, discussing basic physical properties used by a technology to collect data, conversion of raw data into useful variables, and subjectivity in the parameters chosen. In addition, we outline a four-step framework to quantify error in animal-borne devices: to know, to identify, to evaluate, and to store. Both the classification scheme and framework are theoretical in nature. However, since mitigating error is essential to answer many biological questions, we believe they will be operationalized and facilitate future work to determine and quantify error in animal-borne technologies (ABT). Moreover, increasing the transparency of error will ensure the technique used to collect data moderates the biological questions and conclusions

Hydrologie symposium, georganiseerd door de KNAW, op 21 mei 2003 in het Trippenhuis te Amsterdam

Om een samenhangende visie mbt hydrologie te ontwikkelen, heeft de KNAW de werkgroep voorstudie verkenning hydrologie ingesteld. Deze werkgroep organiseerde een symposium, om eerste resultaten van hun bevindingen te presenteren. Wellicht volgt er een strategische verkenning, zoals de KNAW dat ook gedaan heeft voor de aardwetenschappe

Overland flow: interfacing models with measurements

Index words: overland flow, catchment scale, system identification, ensemble simulations.This study presents new techniques to identify scale-dependent overland flow models and use these for ensemble-based predictions. The techniques are developed on the basis of overland flow, rain, discharge, soil, vegetation and terrain observations that were collected over a three year period in two tropical catchments. The merits of the identification technique are its robustness with regard to unknown errors, the ability to adjust model resolution in response to data availability, and to interpret the entities of the identified model structures physically. Compared to a static regression model and a dynamic distributed model the predictive performance of the scale-dependent overland flow models is good, especially when using model ensembles. Further analysis of the scale-dependent models shows that rainfall largely determines overland flow when modelled at coarse resolutions, whereas soil moisture drives overland flow when defined at fine resolutions. Interestingly, the number of model parameters remains constant over the different resolutions. The use of the scale-dependent models for predictive purposes is demonstrated by applying Tikhonov regularization for recursive state as well as parameter estimation