Environmental Engineering Reference
In-Depth Information
control), cultural services (tourism, cultural heritage values, infrastructure), provi-
sioning services (vegetation growth, transportation, irrigation, hydropower), and
supporting services (groundwater recharge,
fishing, support to biodiversity, soil
conservation) (Renault et al.
2009
). Future water demand (not only) in Africa is
expected to increase due to a growing population and food demand. This problem is
intensi
ed by global change processes including climate change, ineffective water
management and economic globalization (Curmi et al.
2013
).
Successfully coping with water-related problems and handling future water
demands depend on effective water resources management. The management
requires data about the actual and future state of the environment (including water
resources) and socio-economics. The data assessment and management requires an
integrated approach where spatial data is a key for further systems analysis and
water management (Molina et al.
2014
). Data requirements and challenges for
integrated data analysis increase further when water management is addressed
considering its close interrelation with soil and land-use management and waste
management (Lal
2013
,
2014
). In spite of the associated challenges, adopting a
nexus approach to the management of water, soil and waste (WSW Nexus) is
increasingly recognized as a means to increase resource use ef
ciency and overall
sustainability (Kurian and Ardakanian
2014
), introductory chapter of this volume).
The WSW Nexus represents the resources perspective to the Water, Energy and
Food Security Nexus (Hoff
2011
), promoting synergies between sectors.
While data availability is an issue, at least equally important is the question of
how to make use of the data in a way that enables decision-makers to bridge from
good science to good practice. Since pure data are meaningless without context and
analysis with respect to a relevant question, an appropriate visualization technique
is needed to support water management and decision-makers. Nowadays, data
availability is continuously growing in many parts of the world due to fast tech-
nological developments (e.g. high-resolution remote sensing). For this reason,
'
big
data
, as a synonym for very large and complex data sets becomes an issue, as it is
so complex that it is dif
'
cult to process and analyse solely by looking at innu-
merable tables and
figures. Data visualization that integrates complex information
content is therefore mandatory for understanding and
filtering of data signi
cance
for a speci
c application (e.g. water management) (Molina et al.
2014
). The still
largely lacking data visualization with the aim of decision support might be due to
the high expertise and technical knowledge requirements for visualization workflow
(Kwakkel et al.
2014
). Recently, however, several (quite) easy to use software tools
have become available that can be used by researchers to disseminate their research
results in an adequate manner to support decision-makers.
This chapter aims to present the general workflow from data to visualization
supported by examples, paying special attention to water-related problems and
solutions in Africa. In order to support ease of access, also concerning costs, mainly
no cost or open source visualization tools are presented. As one speci
c example,
relevant in an African context, Water Point Mapping (WPM) is introduced.
Search WWH ::
Custom Search