establishment of “Data Observatories” is attractive, in principle, to allow effective interdisciplinarity, forming an essential bridge over the
deep science-policy divide when considering
environmental governance (Kurian et al. 2016).
In fact, more scientific disciplines are involved
than the three core disciplines mentioned above
because of the UN Sustainable Development
Goals (SDGs) (https://sustainabledevelopment.
un.org) can be seen as the ultimate goals to be
reached and this would also involve agronomists,
climatologists, ecologists, economists and sociologists, to just mention a few (e.g., Bouma
2014; Blum 2016; Lal 2014; Keesstra et al.
2016). In 2015, 193 governments have signed a
commitment to reach these goals by 2030.
Data gathering and the associated information
technology involves a number of aspects:
(i) legacy data obtained from published reports
and other publications can, if necessary and relevant, be digitized and stored; (ii) data obtained
by modern sensing and monitoring techniques
are transmitted to central data storage facilities;
(iii) data are next systematically stored and made
accessible; (iv) algorithms can be developed that
allow combinations of data that are effective for
any design process, often requiring computer
modeling.
The objective of this paper is to present: (i) a
broad-brush analysis of the four data aspects,
mentioned above, for the three core scientific
disciplines: hydrology, soil science and engineering as it relates to waste management; (ii) a
discussion as to whether developments in these
disciplines serve the purpose to develop effective
inter- and transdisciplinary procedures characterizing the water-soil-waste nexus, and (iii) an
exploratory discussion on attractive future
developments to ensure that both the stakeholder
and policy arena are not only engaged but that
research also contributes to effective results in
the real world. To avoid a discourse that could
easily become too theoretical, conceptual and
abstract, a number of case studies will be considered, as mentioned by Kurian et al. (2016) and
Hettiarachchi and Ardakanian (2016b).
1.1 Developments in Hydrology
Soil and water regimes in the regions around the
world have been studied by many authors for at
least a hundred years (e.g., Hoekstra and
Mekkonen 2012; World Water Assessment Programme 2009). Measurements of soil water
contents and fluxes have been refined over the
years and now automated monitoring equipment
is widely available and applied. Automated tensiometers with transducers allow measurement of
soil water potentials in unsaturated soil; Time
Domain Reflectometry, Neutron probes and new
proximal and remote sensors based on the
reflection of specific wavelengths are widely
used to measure soil water contents (e.g., SSSA
2002; Viscarra Rossel et al. and Minashy 2010).
In addition, the advance of computers has
enabled the development of simulation models
for water regimes in soils and landscapes for
actual but also for future conditions, using projected future climatic data as input (e.g. Bonfante
and Bouma 2015; Bonfante et al. 2019, 2020).
This work is particularly relevant because the
future effects of climate change need to be
explored to propose appropriate action. The
implicit assumption in these models that soils are
isotropic and homogeneous needs modification
and soil survey data can be used to account for
the occurrence of heterogeneous flows due to the
occurrence of macropores or slowly permeable
soil horizons (e.g. Bouma 2016a). So far, such
soil survey data are hardly used in hydrological
modeling, illustrating the disciplinary gap
between soil science and hydrology. The following quote from Droogers and Bouma (2014),
is relevant in the context of considering modeling: “A huge number of hydrological models
exist, and applications are growing rapidly. The
number of pages on the Internet including “hydrological model” is over 5.8 million, using
Google in January 2014. Using the same search
engine with “water resources model” returns
150 million pages. The number of existing
hydrological simulation models is probably in
the tens of thousands. Even if we exclude the one16
J. Bouma
deep science-policy divide when considering
environmental governance (Kurian et al. 2016).
In fact, more scientific disciplines are involved
than the three core disciplines mentioned above
because of the UN Sustainable Development
Goals (SDGs) (https://sustainabledevelopment.
un.org) can be seen as the ultimate goals to be
reached and this would also involve agronomists,
climatologists, ecologists, economists and sociologists, to just mention a few (e.g., Bouma
2014; Blum 2016; Lal 2014; Keesstra et al.
2016). In 2015, 193 governments have signed a
commitment to reach these goals by 2030.
Data gathering and the associated information
technology involves a number of aspects:
(i) legacy data obtained from published reports
and other publications can, if necessary and relevant, be digitized and stored; (ii) data obtained
by modern sensing and monitoring techniques
are transmitted to central data storage facilities;
(iii) data are next systematically stored and made
accessible; (iv) algorithms can be developed that
allow combinations of data that are effective for
any design process, often requiring computer
modeling.
The objective of this paper is to present: (i) a
broad-brush analysis of the four data aspects,
mentioned above, for the three core scientific
disciplines: hydrology, soil science and engineering as it relates to waste management; (ii) a
discussion as to whether developments in these
disciplines serve the purpose to develop effective
inter- and transdisciplinary procedures characterizing the water-soil-waste nexus, and (iii) an
exploratory discussion on attractive future
developments to ensure that both the stakeholder
and policy arena are not only engaged but that
research also contributes to effective results in
the real world. To avoid a discourse that could
easily become too theoretical, conceptual and
abstract, a number of case studies will be considered, as mentioned by Kurian et al. (2016) and
Hettiarachchi and Ardakanian (2016b).
1.1 Developments in Hydrology
Soil and water regimes in the regions around the
world have been studied by many authors for at
least a hundred years (e.g., Hoekstra and
Mekkonen 2012; World Water Assessment Programme 2009). Measurements of soil water
contents and fluxes have been refined over the
years and now automated monitoring equipment
is widely available and applied. Automated tensiometers with transducers allow measurement of
soil water potentials in unsaturated soil; Time
Domain Reflectometry, Neutron probes and new
proximal and remote sensors based on the
reflection of specific wavelengths are widely
used to measure soil water contents (e.g., SSSA
2002; Viscarra Rossel et al. and Minashy 2010).
In addition, the advance of computers has
enabled the development of simulation models
for water regimes in soils and landscapes for
actual but also for future conditions, using projected future climatic data as input (e.g. Bonfante
and Bouma 2015; Bonfante et al. 2019, 2020).
This work is particularly relevant because the
future effects of climate change need to be
explored to propose appropriate action. The
implicit assumption in these models that soils are
isotropic and homogeneous needs modification
and soil survey data can be used to account for
the occurrence of heterogeneous flows due to the
occurrence of macropores or slowly permeable
soil horizons (e.g. Bouma 2016a). So far, such
soil survey data are hardly used in hydrological
modeling, illustrating the disciplinary gap
between soil science and hydrology. The following quote from Droogers and Bouma (2014),
is relevant in the context of considering modeling: “A huge number of hydrological models
exist, and applications are growing rapidly. The
number of pages on the Internet including “hydrological model” is over 5.8 million, using
Google in January 2014. Using the same search
engine with “water resources model” returns
150 million pages. The number of existing
hydrological simulation models is probably in
the tens of thousands. Even if we exclude the one16
J. Bouma