4,913 research outputs found
Socio-hydrological modelling: a review asking “why, what and how?”
Interactions between humans and the environment are occurring on a scale that
has never previously been seen; the scale of human interaction with the water
cycle, along with the coupling present between social and hydrological
systems, means that decisions that impact water also impact people. Models
are often used to assist in decision-making regarding hydrological systems,
and so in order for effective decisions to be made regarding water resource
management, these interactions and feedbacks should be accounted for in
models used to analyse systems in which water and humans interact. This paper
reviews literature surrounding aspects of socio-hydrological modelling. It
begins with background information regarding the current state of
socio-hydrology as a discipline, before covering reasons for modelling and
potential applications. Some important concepts that underlie
socio-hydrological modelling efforts are then discussed, including ways of
viewing socio-hydrological systems, space and time in modelling, complexity,
data and model conceptualisation. Several modelling approaches are described,
the stages in their development detailed and their applicability to
socio-hydrological cases discussed. Gaps in research are then highlighted to
guide directions for future research. The review of literature suggests that
the nature of socio-hydrological study, being interdisciplinary, focusing on
complex interactions between human and natural systems, and dealing with long
horizons, is such that modelling will always present a challenge; it is,
however, the task of the modeller to use the wide range of tools afforded to
them to overcome these challenges as much as possible. The focus in
socio-hydrology is on understanding the human–water system in a holistic
sense, which differs from the problem solving focus of other water management
fields, and as such models in socio-hydrology should be developed with a view
to gaining new insight into these dynamics. There is an essential choice that
socio-hydrological modellers face in deciding between representing individual
system processes or viewing the system from a more abstracted level and
modelling it as such; using these different approaches has implications for
model development, applicability and the insight that they are capable of
giving, and so the decision regarding how to model the system requires
thorough consideration of, among other things, the nature of understanding
that is sought
An open and extensible framework for spatially explicit land use change modelling in R: the lulccR package (0.1.0)
Land use change has important consequences for biodiversity and the
sustainability of ecosystem services, as well as for global
environmental change. Spatially explicit land use change models
improve our understanding of the processes driving change and make
predictions about the quantity and location of future and past
change. Here we present the lulccR package, an object-oriented
framework for land use change modelling written in the R programming
language. The contribution of the work is to resolve the following
limitations associated with the current land use change modelling
paradigm: (1) the source code for model implementations is
frequently unavailable, severely compromising the reproducibility of
scientific results and making it impossible for members of the
community to improve or adapt models for their own purposes; (2)
ensemble experiments to capture model structural uncertainty are
difficult because of fundamental differences between implementations
of different models; (3) different aspects of the modelling
procedure must be performed in different environments because
existing applications usually only perform the spatial allocation of
change. The package includes a stochastic ordered allocation
procedure as well as an implementation of the widely used CLUE-S
algorithm. We demonstrate its functionality by simulating land use
change at the Plum Island Ecosystems site, using a dataset included
with the package. It is envisaged that lulccR will enable future
model development and comparison within an open environment
Hydrological Models as Web Services: An Implementation using OGC Standards
<p>Presentation for the HIC 2012 - 10th International Conference on Hydroinformatics. "Understanding Changing Climate and Environment and Finding Solutions" Hamburg, Germany July 14-18, 2012</p>
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Uncertainties in climate change projections and regional downscaling: implications for water resources management
Climate change is expected to have a large impact on water resources worldwide. A major problem in assessing the potential impact of a changing climate on these resources is the difference in spatial scale between available climate change projections and water resources management. Regional climate models (RCMs) are often used for the spatial disaggregation of the outputs of global circulation models. However, RCMs are time-intensive to run and typically only a small number of model runs is available for a certain region of interest. This paper investigates the value of the improved representation of local climate processes by a regional climate model for water resources management in the tropical Andes of Ecuador. This region has a complex hydrology and its water resources are under pressure. Compared to the IPCC AR4 model ensemble, the regional climate model PRECIS does indeed capture local gradients better than global models, but locally the model is prone to large discrepancies between observed and modelled precipitation. It is concluded that a further increase in resolution is necessary to represent local gradients properly. Furthermore, to assess the uncertainty in downscaling, an ensemble of regional climate models should be implemented. Finally, translating the climate variables to streamflow using a hydrological model constitutes a smaller but not negligible source of uncertainty
Local DRLs and automated risk estimation in paediatric interventional cardiology
Introduction : Cardiac catheterization procedures result in high radiation doses and often multiple procedures are necessary for congenital heart disease patients. However, diagnostic reference levels (DRL) remain scarce. Our first goal was finding the optimal DRL parameter and determining appropriate DRLs. The second goal was to calculate organ doses (OD), effective doses (ED) and lifetime attributable risks (LAR) per procedure and to provide conversion factors based on dose area product (DAP).
Materials and methods : DRLs are calculated for each procedure type, as the 75th percentile of the cumulative value per procedure from the corresponding parameter. All irradiation events in the DICOM Structured Reports were automatically processed and simulated using PCXMC, resulting in OD, ED and LAR. Using a Kruskal Wallis H test and subsequent pairwise comparisons, differences in median values of the DRL parameter between procedure types were assessed.
Results : Linear regression showed a strong correlation and narrow confidence interval between DAP and product of body weight and fluoroscopy time (BWxFT), even when all procedures (diagnostic and interventional) are combined. Only 15% of the pairwise comparisons were statistically significant for DAP normalized to BWxFT (DAP(BWxFT)). The latter pairs contained less frequent procedure types with significant outliers. For DAP normalized to BW (DAP(BW)), 38% of the pairwise comparisons showed statistically significant differences. Conversion factors from DAP(BW) to OD and ED were reported for various weight groups, due to the higher correlation between DAP(BW) and both OD and ED than between DAP and both OD and ED.
Conclusions : The P75 of DAP(BWxFT) for all procedures combined serves as an appropriate DRL value. This facilitates local DRL determination in smaller paediatric centres, which often have insufficient data to produce appropriate DRLs for different procedure types. Conversion factors are more reliable starting from DAP(BW) instead of DAP and should be used according to the appropriate BW group
Data Mining of Hydrological Model Performance
Multi-objective criteria have long been used to infer hydrological simulations and fit the natural world. On the other hand, modelling frameworks are also becoming more and more popular as identification of the processes occurring in a catchment is still a very uncertain matter. In theory, multi-objective criteria and multi-model frameworks should be used in combination so that the ’representation’ of the catchment is fitted to the observations, not only the simulated results. In practise those approaches are highly computationally demanding. The modeller is often obliged to find a compromise reducing either the number of objective functions or model structures taken into consideration. This compromise is becoming obsolete using parallel computing. In the present study we investigate the extend to which model selection algorithms and regionalisation techniques can be improved by such facilities and highlight the challenges that still need to be addressed. The model simulations are obtained using an ensemble of conceptual lumped models (FUSE by Clark et al. 2008), but techniques and suggestions are of general use and applicable to any modelling frameworks. In particular we developed a novel model selection algorithm tuned to drastically reduce the subjectivity in the analysis. The procedure was automated and coupled with redundancy reduction techniques such as PCA and Cluster Analysis. Results show that the actual model ’representation’ has the shape of a set of complementing model structures. It is also possible to capture intra-annum dynamics of the response as the algorithm recognises subtle variations in the selected model structures in different seasons. Similar variations can be found analysing different catchments. This suggests the same methodology would be suitable for analysing spatial patterns in the distribution of suitable model structures and maybe long term dynamics in relation with expedited climate modifications. Although the mentioned methodology has proven to be successful with regards to the case study, some limitations are worth noting. If this is going to be applied to the more general case of ’models of everywhere’, for instance, there could be dominant processes not described in the FUSE framework. Further studies could therefore extend the current framework to include routines able to simulate missing processes
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