Environmental Engineering Reference
In-Depth Information
constraints for studying environmental and socio-economic related processes
—
particularly in consideration of global change processes. Such analyses require
long-term data collections of the processes and phenomena under consideration.
While the existing data collection infrastructure of weather stations allows for the
analysis of long-term trends, respective data of ecological or socio-economic pro-
cesses are not easily available over longer periods. Further development of state-of-
the-art knowledge about processes might make it important to gather new data with
the objective of densifying the measurement network, to account for process het-
erogeneity, or to incorporate innovative technologies (innovative instrumentation,
methods) into the data measurement. The densi
cation of a measurement network
acknowledges the existing data, which is in contrast to data-poor environments.
2.1.2 Data-Poor Environments
Data-poor environments generally lack infrastructure,
financial support, have dif-
ficult climate conditions (very cold, very dry) or are located in remote areas (low
population density). An example for such a data-poor environment is the Sahara
region, which is dry, low population and remote in relation to infrastructure. Basic
weather data assessment has a long history and is relatively simple (e.g. tempera-
ture, precipitation) compared to other data assessments (e.g. vegetation, soil).
Considering the low number of climate stations as indicator for data availability of
that region, we can assume that availability of other data that are more dif
cult to
measure will be much lower (Fig.
2
). Lacking
financial background, data-poor
environments offer low measurement infrastructure such as low instrumentation,
low measurement standardization and low number of quali
ed people for mea-
surements. All factors together lead to low data reliability and low-resolution of
final data products such as soil, vegetation or geological maps. This data scarcity
hinders effective management of resources, performing risk analyses and analyses
of actual and future environmental and socio-economic development. Data-poor
regions require focusing on effective sampling design (Sect.
2.2
) together with the
usage of affordable, but reliable data sources and technologies such as remote
sensing (Sect.
2.3.3
). The use of proxies (which need to be veri
ed under data-rich
conditions) becomes particularly important and is discussed in Sect.
2.3.1
.
2.2 Sampling Design
The design of any sampling strategy needs to be de
ned according to the sampling
purpose, research questions and the visualization objective. Water service-related
research questions, which need to be solved might be formulated as: Which is the
level of drought risk, erosion proneness and related soil fertility, or water quality in
a speci
c
water service? The assessment of environmental as well as social data requires the
c region? What are the consequences of land-use changes for a speci
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