Labour-market in a border-area; searching for jobs and the influence of borders

At the moment borders, border-related problems, and the process of tearing down borders are very much in the centre of interest. Especially in Europe a lot of scientific work is done with regard to borders of countries, to determine their role in the ongoing integration process. In this respect border-regions are considered to be able to play a catalytic role. The borderland economies on both sides of a national border in this view have to be changed into one transborder economy. Initiatives to encourage cross-border integration however are not always successful. To our opinion, one of the main reasons lies in the fact that the border has many faces. The effect of a border differs, depending on the type of interaction (e.g. economic, social-cultural or institutional) and the nature of the region it defines. This paper tries to formulate a conceptual framework, within which the different properties of borders and border-regions are taken into account. Next this model is applied to the regional labour-market in the Nijmegen-Arnhem border-area in the eastern part of the Netherlands. The most important questions to be answered are: - Are there effects stemming from the fact the regional labour-market in the Arnhem-Nijmegen is part of a (peripheral) borderland economy? - What are the effects of the border with regard to the interaction of the region Arnhem-Nijmegen with the neighbouring region in Germany? - Is a part of the "natural" labour-market cut off by the national border, or put in other words, what would happen if the Dutch-German border would disappear completely? Keywords: Borders, Regional labour-markets, Transition

Anatomy of extraordinary rainfall and flash flood in a Dutch lowland catchment

On 26 August 2010 the eastern part of The Netherlands and the bordering part of Germany were struck by a series of rainfall events lasting for more than a day. Over an area of 740 km2 more than 120 mm of rainfall were observed in 24 h. This extreme event resulted in local flooding of city centres, highways and agricultural fields, and considerable financial loss. In this paper we report on the unprecedented flash flood triggered by this exceptionally heavy rainfall event in the 6.5 km2 Hupsel Brook catchment, which has been the experimental watershed employed by Wageningen University since the 1960s. This study aims to improve our understanding of the dynamics of such lowland flash floods. We present a detailed hydrometeorological analysis of this extreme event, focusing on its synoptic meteorological characteristics, its space-time rainfall dynamics as observed with rain gauges, weather radar and a microwave link, as well as the measured soil moisture, groundwater and discharge response of the catchment. At the Hupsel Brook catchment 160 mm of rainfall was observed in 24 h, corresponding to an estimated return period of well over 1000 years. As a result, discharge at the catchment outlet increased from 4.4 × 10-3 to nearly 5 m3 s-1. Within 7 h discharge rose from 5 × 10-2 to 4.5 m3 s-1. The catchment response can be divided into four phases: (1) soil moisture reservoir filling, (2) groundwater response, (3) surface depression filling and surface runoff and (4) backwater feedback. The first 35 mm of rainfall were stored in the soil without a significant increase in discharge. Relatively dry initial conditions (in comparison to those for past discharge extremes) prevented an even faster and more extreme hydrological response

Uncertainty of effective roughness parameters calibrated on bare agricultural land using Sentinel-1 SAR

Uncertainty of roughness parameters has effect on soil moisture retrievals with backscatter models from Synthetic Aperture Radar observations. The uncertainty of soil moisture retrievals is important information for the usability of these estimates. In this paper we introduce a methodology to estimate the uncertainty of effective roughness parameters in the Integral Equation Method surface backscatter model, using a Bayesian Markov Chain Monte Carlo approach. Using Sentinel-1 imagery we demonstrate the methodology for a selected field, showing the posterior uncertainty distributions of the roughness parameters, and the effect on the backscatter model simulations and soil moisture inversions. The estimated total uncertainty of the soil moisture retrievals with the optimum parameter set is 0.043 m3/m3, which is slightly higher than the root mean square error of 0.040 m3/m3 of the retrievals compared to in situ soil moisture measurements

Calibration and Validation of the Sentinels Geophysical Observation Models

We present a method to calibrate and validate observational models that interrelate remotely sensed energy fluxes to geophysical variables of land and water surfaces. Coincident sets of remote sensing observation of visible and microwave radiations and geophysical data are assembled and subdivided into calibration (Cal) and validation (Val) data sets. Each Cal/Val pair is used to derive the coefficients (from the Cal set) and the accuracy (from the Val set) of the observation model. Combining the results from all Cal/Val pairs provides probability distributions of the model coefficients and model errors. The method is generic and demonstrated using comprehensive matchup sets from two very different disciplines: soil moisture and water quality. The results demonstrate that the method provides robust model coefficients and quantitative measure of the model uncertainty. This approach can be adopted for the calibration/validation of satellite products of land and water surfaces, and the resulting uncertainty can be used as input to data assimilation schemes

Temporal Stability of Surface Roughness Effects on Radar Based Soil Moisture Retrieval During the Corn Growth Cycle

A representative soil surface roughness parameterization needed for the retrieval of soil moisture from active microwave satellite observation is difficult to obtain through either in-situ measurements or remote sensing-based inversion techniques. Typically, for the retrieval of soil moisture, temporal variations in surface roughness are assumed to be negligible. Although previous investigations have suggested that this assumption might be reasonable for natural vegetation covers (Moran et al. 2002, Thoma et al. 2006), insitu measurements over plowed agricultural fields (Callens et al. 2006) have shown that the soil surface roughness can change considerably over time. This paper reports on the temporal stability of surface roughness effects on radar observations and soil moisture retrieved from these radar observations collected once a week during a corn growth cycle (May 10th - October 2002). The data set employed was collected during the Optimizing Production Inputs for Economic and Environmental Enhancement (OPE3) field campaign covering this 2002 corn growth cycle and consists of dual-polarized (HH and VV) L-band (1.6 GHz) acquired at view angles of 15, 35, and 55 degrees. Cross-polarized L baud radar data were also collected as part of this experiment, but are not used in the analysis reported on here. After accounting for vegetation effects on radar observations, time-invariant optimum roughness parameters were determined using the Integral Equation Method (IEM) and radar observations acquired over bare soil and cropped conditions (the complete radar data set includes entire corn growth cycle). The optimum roughness parameters, soil moisture retrieval uncertainty, temporal distribution of retrieval errors and its relationship with the weather conditions (e.g. rainfall and wind speed) have been analyzed. It is shown that over the corn growth cycle, temporal roughness variations due to weathering by rain are responsible for almost 50% of soil moisture retrieval uncertainty depending on the sensing configuration. The effects of surface roughness variations are found to be smallest for observations acquired at a view angle of 55 degrees and HH polarization. A possible explanation for this result is that at 55 degrees and HH polarization the effect of vertical surface height changes on the observed radar response are limited because the microwaves travel parallel to the incident plane and as a result will not interact directly with vertically oriented soil structures

Understanding bi-project management: Engineering complex industrial construction projects

Abstract Engineering large industrial construction projects is usually a complex task involving several co-operating actors. This paper investigates a specific type of such projects, labelled bi-project management. Bi-project management is characterised by two main actors, each of whom manage a part of the project: the owner of the installation (the client organisation) responsible for the engineering of the production process part, and an engineering office responsible for the construction related part. This paper describes and analyses what an engineering office can do to improve control of its part of the project, knowing that its part must be completed in advance; in addition, it is dependent on the client's part and must adapt to any changes the client may make. A framework for analysis and control has been developed, which distinguishes four areas. Two areas (key documents and basic interaction structure) are based on normal project control practices. The two remaining areas have been added to deal with the technological uncertainty and planning structure of the client organisation. The framework helps elucidate the specific nature of bi-project management. Effective bi-project management should not only apply normal project management practices, but also anticipate risks and postpone work to the last possible moment. A main topic for future research is to identify the underlying causes for uncertainty in these types of projects.