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Sink Areas (sink + area)
Selected AbstractsErosion models: quality of spatial predictionsHYDROLOGICAL PROCESSES, Issue 5 2003Victor Jetten Abstract An Erratum has been published for this article in Hydrological Processes 18(3) 2004, 595. An overview is given on the predictive quality of spatially distributed runoff and erosion models. A summary is given of the results of model comparison workshops organized by the Global Change and Terrestrial Ecosystems Focus 3 programme, as well as other results obtained by individual researchers. The results concur with the generally held viewpoint in the literature that the predictive quality of distributed models is moderately good for total discharge at the outlet, and not very good for net soil loss. This is only true if extensive calibration is done: uncalibrated results are generally bad. The more simple lumped models seem to perform equally well as the more complex distributed models, although the latter produce more detailed spatially distributed results that can aid the researcher. All these results are outlet based: models are tested on lumped discharge and soil loss or on hydrographs and sedigraphs. Surprisingly few tests have been done on the comparison of simulated and modelled erosion patterns, although this may arguably be just as important in the sense of designing anti-erosion measures and determining source and sink areas. Two studies are shown in which the spatial performance of the erosion model LISEM (Limburg soil erosion model) is analysed. It seems that: (i) the model is very sensitive to the resolution (grid cell size); (ii) the spatial pattern prediction is not very good; (iii) the performance becomes better when the results are resampled to a lower resolution and (iv) the results are improved when certain processes in the model (in this case gully incision) are restricted to so called ,critical areas', selected from the digital elevation model with simple rules. The difficulties associated with calibrating and validating spatially distributed soil erosion models are, to a large extent, due to the large spatial and temporal variability of soil erosion phenomena and the uncertainty associated with the input parameter values used in models to predict these processes. They will, therefore, not be solved by constructing even more complete, and therefore more complex, models. However, the situation may be improved by using more spatial information for model calibration and validation rather than output data only and by using ,optimal' models, describing only the dominant processes operating in a given landscape. Copyright © 2003 John Wiley & Sons, Ltd. [source] ,The National Stream Quality Accounting Network: a flux-based approach to monitoring the water quality of large riversHYDROLOGICAL PROCESSES, Issue 7 2001Richard P. Hooper Abstract Estimating the annual mass flux at a network of fixed stations is one approach to characterizing water quality of large rivers. The interpretive context provided by annual flux includes identifying source and sink areas for constituents and estimating the loadings to receiving waters, such as reservoirs or the ocean. Since 1995, the US Geological Survey's National Stream Quality Accounting Network (NASQAN) has employed this approach at a network of 39 stations in four of the largest river basins of the USA: the Mississippi, the Columbia, the Colorado and the Rio Grande. In this paper, the design of NASQAN is described and its effectiveness at characterizing the water quality of these rivers is evaluated using data from the first 3 years of operation. A broad range of constituents was measured by NASQAN, including trace organic and inorganic chemicals, major ions, sediment and nutrients. Where possible, a regression model relating concentration to discharge and season was used to interpolate between chemical observations for flux estimation. For water-quality network design, the most important finding from NASQAN was the importance of having a specific objective (that is, estimating annual mass flux) and, from that, an explicitly stated data analysis strategy, namely the use of regression models to interpolate between observations. The use of such models aided in the design of sampling strategy and provided a context for data review. The regression models essentially form null hypotheses for concentration variation that can be evaluated by the observed data. The feedback between network operation and data collection established by the hypothesis tests places the water-quality network on a firm scientific footing. Published in 2001 by John Wiley & Sons, Ltd. [source] Local environmental effects and spatial effects in macroecological studies using mapped abundance classes: the case of the rook Corvus frugilegus in ScotlandJOURNAL OF ANIMAL ECOLOGY, Issue 5 2006A. GIMONA Summary 1The study of the spatial pattern of species abundance is complicated by statistical problems, such as spatial autocorrelation of the abundance data, which lead to the confusion of environmental effects and dispersal. 2Atlas-derived data for the rook in Scotland are used as a case study to propose an approach for assessing the likely contribution of dispersal and local environmental effects, based on a Bayesian Conditional Autoregressive (CAR) approach. 3The availability of moist grasslands is a key factor explaining the spatial pattern of abundance. This is influenced by a combination of climatic and soil-related factors. A direct link to soil properties is for the first time reported for the wide-scale distribution of a bird species. In addition, for this species, dispersal seems to contribute significantly to the spatial pattern and produces a smoother than expected decline in abundance at the north-western edge of its distribution range. Areas where dispersal is most likely to be important are highlighted. 4The approach described can help ecologists make more efficient use of atlas data for the investigation of the structure of species abundance, and can highlight potential sink areas at the landscape and regional scale. 5Bayesian spatial models can deal with data autocorrelation in atlas-type data, while clearly communicating uncertainty through the estimation of the full posterior probability distribution of all parameters. [source] An empirical test of source,sink dynamics induced by huntingJOURNAL OF APPLIED ECOLOGY, Issue 5 2005ANDRÉS J. NOVARO Summary 1Under the source,sink model, persistence of populations in habitat sinks, where deaths outnumber births, depends on dispersal from high-quality habitat sources, where births outnumber deaths. The persistence of the regional population depends on the proportion of sink relative to source habitat. 2Hunting that occurs in some parts of the landscape and not in others can create patches where deaths outnumber births. We tested whether hunting of culpeo foxes Pseudalopex culpaeus, which is patchily distributed in relatively homogeneous habitat in Argentine Patagonia, induces source,sink dynamics. 3On Patagonian sheep ranches, culpeos are hunted for fur and to protect sheep, and on cattle ranches hunting is usually banned. We monitored culpeo densities using scent stations and estimated survival, fecundity and dispersal by radio-tracking 44 culpeos and analysing carcasses collected from hunters on two cattle and four sheep ranches between 1989 and 1997. 4Survival of juvenile culpeos was lower on hunted than unhunted ranches, mainly as a result of hunting mortality. Reproduction could not compensate for high mortality on hunted ranches. Interruption of hunting led to an increase in juvenile survival, indicating that hunting and natural mortality were not compensatory. We concluded that sheep ranches were sinks because of the high mortality and that sink populations may be maintained by dispersal from cattle ranches. 5We used a simulation model to assess implications of changes in the proportion of source and sink areas on population dynamics. The percentage of land on cattle ranches in the study area was 37%. Current hunting pressure on culpeos would not be sustainable if that percentage fell below 30%. 6Synthesis and applications. Source,sink dynamics may occur in landscapes where hunting is intense and spatially heterogeneous. Wildlife management traditionally monitors demographic rates to evaluate the sustainability of hunting, but our results suggest that the size and spatial arrangement of areas with and without hunting should be considered as well. In regions where enforcement and monitoring are limited, securing large and regularly distributed source areas for hunted species may be more effective than trying to regulate harvest size. [source] Identification of sediment source and sink areas in a Himalayan watershed using GIS and remote sensingLAND DEGRADATION AND DEVELOPMENT, Issue 6 2009M. K. Jain Abstract Erosion is a natural geomorphic process occurring continually over the Earth's surface and it largely depends on topography, vegetation, soil and climatic variables, and therefore, exhibits pronounced spatial variability due to catchment heterogeneity and climatic variation. This problem can be circumvented by discretizing the catchment into approximately homogeneous sub-areas using GIS. In this study, the remote sensing and GIS techniques (through Imagine®8.6 and ArcGIS®9.1 software) were used for derivation of spatial information, catchment discretization, data processing etc. for the Himalayan Chaukhutia watershed (India). Various thematic layers for different factors of USLE were generated and overlaid to compute spatially distributed gross soil erosion maps for the watershed using 18-year rainfall data. The concept of transport limited accumulation was formulated and used in ArcGIS® for generating the transport capacity maps. Using these maps, the gross soil erosion was routed to the catchment outlet using hydrological drainage paths, for derivation of transport capacity limited sediment outflow maps. These maps depict the amount of sediment rate from a particular grid in spatial domain and the pixel value of the outlet grid indicates the sediment yield at the outlet of the watershed. Up on testing, the proposed method simulated the annual sediment yield with less than ±40% error. Copyright © 2009 John Wiley & Sons, Ltd. [source] | ||||||||||