Simulating the effects of spatial configurations of agricultural ditch drainage networks on surface runoff from agricultural catchments

The study of runoff is a crucial issue because it is closely related to flooding, water quality and erosion. In cultivated catchments, agricultural ditch drainage networks are known to influence runoff. As anthropogenic elements, agricultural ditch drainage networks can therefore be altered to better manage surface runoff in cultivated catchments. However, the relationship between the spatial configuration, i.e. the density and the topology, of agricultural ditch drainage networks and surface runoff in cultivated catchments is not understood. We studied this relationship by using a random network simulator that was coupled to a distributed hydrological model. The simulations explored a large variety of spatial configurations corresponding to a thousand stochastic agricultural ditch drainage networks on a 6.4 km² Mediterranean cultivated catchment. Next, several distributed hydrological functions were used to compute water flow paths and runoff for each simulation. The results showed that (i) denser networks increased the drained volume and the peak discharge and decreased hillslopes runoff, (ii) greater network density did not affect the surface runoff any further above a given network density, (iii) the correlation between network density and runoff was weaker for small subcatchments (< 2 km²) where the variability in the drained area that resulted from changes in agricultural ditch drainage networks increased the variability of runoff and (iv) the actual agricultural ditch drainage network appeared to be well optimized for managing runoff as compared with the simulated networks. Finally, our results highlighted the role of agricultural ditch drainage networks in intercepting and decreasing overland flow on hillslopes and increasing runoff in drainage networks. Copyright © 2011 John Wiley & Sons, Ltd.     

Spatial imaginary and geography: A plea for the geography of representations

After twenty years of work on the geography of representations, how is it still possible to define geography as “the science of space”, ie as direct knowledge of material reality? This conception of the discipline — based on Cartesian precepts of evidence (eg the observer's independent certainty), reductionism (ie a disaggregation into sets of simple elements), causality (ie the presupposition of a linear linkage between cause and effect) and exhaustiveness (ie the certainty that nothing essential has been omitted) — has been thrown into question by the geography of representations' holistic approach. How can our scientific practices be separated from our interior existence with its affective and emotional aspects? Is not scientific action an extension of being? Mustn't the geographer, above and beyond the observation of concrete phenomena, also understand the subtle and complex — at times random and hidden — links which unite human beings and their life-space, be it from the viewpoint of the poet, or of all those who take alternative approaches to geography? What I would like to demonstrate is (1) how in an historically and socially given environment, the individual constructs his own reality in linking together the structural, functional and symbolic; (2) how the representation of the landscape is related to our existential experience; and (3) how the imaginary and the real are connected in each place.     

Producer Services Research in Europe*

Four directions in producer services research in Europe are identified: the economic role of producer services for urban and regional development; the location of advanced producer services; the role of regional structures and innovative “milieu”; and local development and labor markets. Issues for future research include the role of producer services in the urban structuring and/or restructuring of world cities; the relation between producer services and interregional disparities; the integration of producer services into networks; and the impacts of the liberalization of producer services.     

Quantitative assessment of dissolved radiotracers in the English Channel: Sources, average impact of la Hague reprocessing plant and conservative behaviour (1983, 1986, 1988, 1994)

Knowledge of long-term movements of water-masses in the English Channel has been substantially improved using hydrodynamic modelling coupled with radio-tracers studies; nevertheless, the precision of results so obtained is still largely dependent on measurement precision. New tools are now available to make more accurate determinations of radio-tracer distribution: (1) Repositioning of station locations at the same tide reference-time, giving a homogeneous spatial data set, coupled with the possibility of interpolating and quantifying the amounts of dissolved radioactivity flowing through the English Channel; (2) the first measurements of tritium (³H) in seawater on a large scale in the English Channel demonstrate that this fully conservative radionuclide is a clearly identifiable marker of industrial releases; (3) recent campaigns carried out during the FLUXMANCHE II CCE (1994) programme show the general distribution of dissolved radionuclides ¹³⁷Cs, ¹³⁴Cs, ⁶⁰Co, ¹²⁵Sb, ¹⁰⁶Ru and ³H in the English Channel and the Irish Sea; and (4) the re-utilisation of data from previous campaigns (1983, 1986, 1988) provides indications, at any given location in the English Channel, about the average dilution and distribution of releases derived from the La Hague reprocessing plant. Excesses and losses of radionuclides are now quantified with respect to known source terms; estimates of losses are provided for non-conservative radionuclides, while an excess of ¹³⁷Cs was observed in the English Channel during the period 1983–1994. This excess, which has the same order of magnitude as the quantities released from La Hague plant in the English Channel, could be explained by about 1% of the Sellafield reprocessing plant releases entering the Channel. These results confirm and give a more detailed picture of the previously known distribution of water masses in the English Channel. They lead to clear information about transit times and dilution at this scale, and provide directly comparable data for the validation of hydrodynamic models.     

Earthquakes,catastrophic sediment additions and the response of urban stream communities

Disturbance events can regularly impact stream ecosystems; however, large-scale catastrophic disturbances are rare. From September 2010 to September 2011 Christchurch City experienced over 8500 earthquakes including a magnitude 7.1. One consequence was catastrophic additions of silt and sand into waterways throughout the city. Of 161 km of permanent waterways, 102 km (63%) were affected by earthquake siltation. Benthic invertebrates and fish communities were compared across 16 streams with differing siltation. Invertebrate taxonomic richness decreased significantly (mean 17 taxa reduced to 10 taxa) and EPT taxa (Ephemeroptera, Plecoptera and Trichoptera) were removed entirely from streams receiving heavy siltation. Fish richness and density decreased significantly, with fish absent from some heavily silted streams. Many of these urban streams are sourced from springs and their stable flows and low gradient limit their ability to flush sediment. We predict that without human intervention there will be a long-term sediment legacy and it may take many years for these streams to recover from this catastrophic disturbance.     

Comparison of LiDAR waveform processing methods for very shallow water bathymetry using Raman,near‐infrared and green signals

Airborne light detection and ranging (LiDAR) bathymetry appears to be a useful technology for bed topography mapping of non‐navigable areas, offering high data density and a high acquisition rate. However, few studies have focused on continental waters, in particular, on very shallow waters (<2 m) where it is difficult to extract the surface and bottom positions that are typically mixed in the green LiDAR signal. This paper proposes two new processing methods for depth extraction based on the use of different LiDAR signals [green, near‐infrared (NIR), Raman] of the SHOALS‐1000T sensor. They have been tested on a very shallow coastal area (Golfe du Morbihan, France) as an analogy to very shallow rivers. The first method is based on a combination of mathematical and heuristic methods using the green and the NIR LiDAR signals to cross validate the information delivered by each signal. The second method extracts water depths from the Raman signal using statistical methods such as principal components analysis (PCA) and classification and regression tree (CART) analysis. The obtained results are then compared to the reference depths, and the performances of the different methods, as well as their advantages/disadvantages are evaluated. The green/NIR method supplies 42% more points compared to the operator process, with an equivalent mean error (?4·2 cm verusu ?4·5 cm) and a smaller standard deviation (25·3 cm verusu 33·5 cm). The Raman processing method provides very scattered results (standard deviation of 40·3 cm) with the lowest mean error (?3·1 cm) and 40% more points. The minimum detectable depth is also improved by the two presented methods, being around 1 m for the green/NIR approach and 0·5 m for the statistical approach, compared to 1·5 m for the data processed by the operator. Despite its ability to measure other parameters like water temperature, the Raman method needed a large amount of reference data to provide reliable depth measurements, as opposed to the green/NIR method. Copyright © 2010 John Wiley & Sons, Ltd.     

Improving the assessment of ICESat water altimetry accuracy accounting for autocorrelation

Given that water resources are scarce and are strained by competing demands, it has become crucial to develop and improve techniques to observe the temporal and spatial variations in the inland water volume. Due to the lack of data and the heterogeneity of water level stations, remote sensing, and especially altimetry from space, appear as complementary techniques for water level monitoring. In addition to spatial resolution and sampling rates in space or time, one of the most relevant criteria for satellite altimetry on inland water is the accuracy of the elevation data. Here, the accuracy of ICESat LIDAR altimetry product is assessed over the Great Lakes in North America. The accuracy assessment method used in this paper emphasizes on autocorrelation in high temporal frequency ICESat measurements. It also considers uncertainties resulting from both in situ lake level reference data. A probabilistic upscaling process was developed. This process is based on several successive ICESat shots averaged in a spatial transect accounting for autocorrelation between successive shots. The method also applies pre-processing of the ICESat data with saturation correction of ICESat waveforms, spatial filtering to avoid measurement disturbance from the land–water transition effects on waveform saturation and data selection to avoid trends in water elevations across space. Initially this paper analyzes 237 collected ICESat transects, consistent with the available hydrometric ground stations for four of the Great Lakes. By adapting a geostatistical framework, a high frequency autocorrelation between successive shot elevation values was observed and then modeled for 45% of the 237 transects. The modeled autocorrelation was therefore used to estimate water elevations at the transect scale and the resulting uncertainty for the 117 transects without trend. This uncertainty was 8 times greater than the usual computed uncertainty, when no temporal correlation is taken into account. This temporal correlation, corresponding to approximately 11 consecutive ICESat shots, could be linked to low transmitted ICESat GLAS energy and to poor weather conditions. Assuming Gaussian uncertainties for both reference data and ICESat data upscaled at the transect scale, we derived GLAS deviations statistics by averaging the results at station and lake scales. An overall bias of −4.6 cm (underestimation) and an overall standard deviation of 11.6 cm were computed for all lakes. Results demonstrated the relevance of taking autocorrelation into account in satellite data uncertainty assesment.