Representing uncertainty in continental-scale gridded precipitation fields for agrometeorological modeling

This work proposes a relatively simple methodology for creating ensembles of precipitation inputs that are consistent with the spatial and temporal scale necessary for regional crop modeling. A high-quality reference precipitation dataset [the European Land Data Assimilation System (ELDAS)] was used as a basis to define the uncertainty in an operational precipitation database [the Crop Growth Monitoring System (CGMS)]. The distributions of precipitation residuals (CGMS ¿ ELDAS) were determined for classes of CGMS precipitation and transformed to a Gaussian distribution using normal score transformations. In cases of zero CGMS precipitation, the occurrence of rainfall was controlled by an indicator variable. The resulting normal-score-transformed precipitation residuals appeared to be approximately multivariate Gaussian and exhibited strong spatial correlation; however, temporal correlation was very weak. An ensemble of 100 precipitation realizations was created based on back-transformed spatially correlated Gaussian residuals and indicator realizations. Quantile¿quantile plots of 100 realizations against the ELDAS reference data for selected sites revealed similar distributions (except for the 100th percentile, owing to some large residuals in the realizations). The semivariograms of realizations for sampled days showed considerable variability in the overall variance; the range of the spatial correlation was similar to that of the ELDAS reference dataset. The intermittency characteristics of wet and dry periods were reproduced well for most of the selected sites, but the method failed to reproduce the dry period statistics in semiarid areas (e.g., southern Spain). Finally, a case study demonstrates how rainfall ensembles can be used in operational crop modeling and crop yield forecasting

Research activities in regional crop modelling and yield forecasting

CGMS is being applied successfully within the MARS Crop Yield Forecasting System for qualitative monitoring of the growing season and for making quantitative crop yield forecasts. Nevertheless, there are large uncertainties related to applying crop growth models over large areas

CORINE land cover database of the Netherlands: monitoring land cover changes between 1986 and 2000

This paper describes the methodology for updating the CORINE 1986 database for the Netherlands to CLC2000 and the accuracy of the resulting database. The methodology consisted of computer-assisted visual interpretation of satellite images. Furthermore, topographic maps (analogue and digital), aerial photographs and the national land cover database of the Netherlands (LGN4) were used as additional information in the interpretation and verification processes. During the first step, the geometry and thematic contents of the CORINE 1986 land cover interpretation were revised. Next, the CORINE 1986 was updated on the basis of Landsat 7 ETM images of 1999 and 2000. Land cover changes larger than 5 ha were digitized into the database and labeled to ensure that the land cover changes could be discriminated from other changes in the database. Furthermore, each polygon has an attribute label for the land cover class in 1986 as well as 2000. This procedure ensured consistency between the three databases, because the CORINE 1986 classification, the CORINE 2000 classification as well as the land cover changes could be generated from the same database. The validation was based on a stratified random sample whose true land cover types were taken from aerial photographs. The validation revealed an overall accuracy of nearly 95% for the CLC2000 database (level 3) when taking into account that patches smaller than 25 hectares are not allowed in the CLC database. Omitting this condition reduces the accuracy by 30%. The changes in the CLCchange database have a user and producer's accuracy of 76.1 and 91.1%. The producer's accuracy indicates an overestimation of changes of almost 10%. Comparing the number of changes with the Dutch National database also suggests a slight overestimation of changes

Evaluation of MSG-derived global radiation estimates for application in a regional crop model

Crop monitoring systems that rely on agrometeorologic models require estimates of global radiation. These estimates are difficult to obtain due to the limited number of weather stations that measure this variable. In the present study, we validated the global radiation estimates derived from MeteoSat Second Generation (MSG) and evaluated their use in the European Crop Growth Monitoring System (CGMS). A validation with measurements from four CarboEurope flux towers showed that the MSG estimates are accurate and unbiased (standard deviation between 30 and 51 W/m2). Moreover, a comparison with global radiation estimates from about 300 operational weather stations throughout Europe confirmed that the quality of the MSG product is high and spatially uniform. We also made an intercomparison between the MSG product and the ECMWF (ERA-INTERIM) and CGMS products at 25 km resolution, thus demonstrating that the CGMS and ECMWF products generally underestimate radiation. Nevertheless, the CGMS product showed irregular spatial patterns of local over- and underestimation, while the ECMWF product consistently underestimated. A trend analysis using a seasonal Mann-Kendall test between 2005 and 2009 did not reveal any significant monotonic trends in the MSG radiation estimates, except for 1 location out of 15. Finally, when we applied the WOFOST crop model for maize throughout Europe, the simulated potential total biomass increased due to higher estimates of global radiation made by MSG. In contrast, the water-limited simulated total-biomass generally decreased due to a higher reference evapotranspiration, causing faster depletion of soil moisture and increased water stress

Evaluatie prototype TOP10-21ste eeuw

De Topografische Dienst Nederland (TDN) wenst haar geo-informatie product "TOP10 vector" te vernieuwen. Als laatste fase in dit vernieuwingsproces is het prototype getest en geëvalueerd aan de op gebruikerseisen van huidige en potentiële gebruikers gebaseerde gebruikersspecificaties

A generic data schema for crop experiment data in food security research

In agricultural research targeted at food security, crop experiments in fields are a crucial source of information for statistical or model based analyses or purely a system description. In these crop experiments or field trials, crop responses are investigated to a change a management or in different climatic or soil conditions, and thus provide an understanding of production potential in different circumstances. Though crucial, these crop experiments are currently poorly available to the crop research community, which proves an obstacle to developments in the domain. The aim of this paper is to propose a generic data schema, Spatial Temporal Attribute Catalogue, that can be used to store data on agricultural systems compiled with many different purposes and scopes. The generic data schema covers aspects of soil, climate, location, crop management and crop variety characteristics. The data schema is developed in a context of different ongoing and past efforts in structuring this crop experiment data, e.g. the AgMIP crop experiment database, the Global Yield Gap Atlas, and the MOCASSIN project on winterkill. Future developments on the data schema include assessing the possibilities to broaden it to different domains (i.e. socio-economic, ecology, and animal sciences) and the use of semantic technologies for storage and availability