Environmental Engineering Reference
In-Depth Information
Fig. 2 Global distribution of climate stations (
brown dots
) that deliver varying number of weather
global distribution of climate stations as shown in Fig.
2
, demonstrating a huge
variability at a global scale and data-scarce conditions in wide areas of Africa and
South America. What adds to the number of climate stations is the data complexity
(quality, number of measured variables) delivered by them. Stations in data-rich
environments deliver a large number of weather variables (e.g. dew point, global
radiation), while in data-poor environments; delivery is typically limited to basic
weather data (e.g. temperature and precipitation). Later on in this chapter, we
discuss a case study of Tanzania to highlight challenges in data-poor environments.
2.1.1 Data-Rich Environments
Data-rich regions generally have a long history of data collection, well-developed
data collection infrastructure (dense sampling network, standardized sampling
approaches, sophisticated instrumentation) along with a high degree of data reli-
ability. In these regions, data repeated measurements (e.g. weather and water
measurement stations) over longer periods are commonly carried out. Additionally,
large number of economic projects for water management (e.g. reservoirs, water
extraction wells) or agriculture (e.g. precision farming, erosion control) has led to
extensive data surveys. Scienti
c projects contribute to the data availability to some
degree through data collection, development of methods and instruments that
improve data assessment. For these reasons, a large number of detailed maps and
databases are available in data-rich regions that are frequently updated. In some
regions (e.g. USA), data assessed by public
financed institutions is openly acces-
sible to the public.
1
Nevertheless, even under data-rich conditions, there may be
1
For more information, see
https://www.data.gov/
or
http://project-open-data.github.io/
.
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