Environmental Engineering Reference
In-Depth Information
data to be measured in conjunction to their geographic location. One should be
aware that it is of importance, for the successful solving of the stated research
question, to decide early about the appropriate data dimension to be covered by the
sampling approach (Wang et al. 2012 ). The dimensions comprise 1D (point data),
2D (surfaces or depth information) or 3D (surfaces and depth information). For
decision-makers, the sampling design needs to be defensible under public and
c criticism. It needs to be taken into account that all approaches are related
to uncertainties (Molina et al. 2014 ). For this reason, the data quality and uncer-
tainties need to be documented and reported prior to further usage that canalize in
A comprehensive summary of important technologies for spatial measurements
that are useful in hydrology is given by Molina et al. ( 2014 ). For further infor-
mation about sampling and analysis of environmental data (multivariate geosta-
tistics), please refer to Chiles and Del
ner ( 2012 ), Mateu and Muller ( 2012 ) and
Wackernagel ( 2003 ).
2.3 Data Assessment Adequate for Data-Poor Environments
Visualization is always based on any kind of data. In water services context, data
comes not only from environmental assessments, but also from socio-economic
sources. In general, collected data can be stored in databases that are then available
to the stakeholders, such as researchers, students, decision-makers and the public.
Data sources for de
ning a sampling design that is adequate for data-sparse regions,
such as Africa, will briefly be summarized in the next sections.
2.3.1 Proxies
As Data Substitutes
The assessment and monitoring of system changes (e.g. water supply infrastructure,
climate change) related to water services might be based on speci
c indicators and
proxies. A proxy is a simpli
cation of reality that aims to substitute real data by an
estimation value that should, qualitatively or quantitatively, represent such real data.
They are applied to overcome data scarcity in data-poor environments or when
time-and-cost-consuming data surveys are not feasible. Since proxies are estima-
tions, they possess a varying degree of uncertainty, unless the proxy is veri
ed by
real data measurement analysis. Proxies can potentially guide and support decision-
makers by making them aware of the system conditions. This might
lead to
improved resource management, risk assessment and allocation of speci
c support.
Proxies can be applied for qualitative estimations that are based on simple obser-
vations (e.g. colour), and quantitative estimations that are based on mathematical
relationships (e.g. empirical, mathematical transfer functions). For instance, soil
colour describes qualitatively or quantitatively the carbon content (level of black
colour), iron content (level of reddishness) or a speci
c soil type (colour along soil
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