48
4 Sefficiency (Sustainable Efficiency)
needs and the downstream requirements. This weight should consider all the benefits
that water brings to societies and natures: “Valuing water means recognizing and
considering all the benefits provided by water that encompass economic, social and
ecological dimensions. It takes many forms appropriate to local circumstances and
cultures. Safeguarding the poor, the vulnerable and the environment is required in
all instances” (UN-HLPW 2017). Hence, water benefits (interchangeable with water
values according to this citation and others) are fundamentally linked to the objectives
of a WUS, and include those that may not be quantifiable.
FIW4 is about the benefits, which stresses that multi-objective planning and
management should be the norm. Total benefits and costs vary due to various local
or national goals and even under different water allocation schemes. There are many
methods available to come up with those totals (Loucks and Van Beek 2005) and
then the weights. Presentation of these methods are beyond the scope of this book,
but they are routinely utilized in evaluating economic and social benefits of projects.
However, in many cases a competent estimate of the magnitude of the weight is
sufficient, which may be valuable as the first step in a learning process. At present,
the following is common practice in applications all over the world:
• Public water supply to people has a W bI of one, meaning that all the water that
enters into the water supply system (Inflow to a WUS) has the maximum benefit.
• For irrigation systems, the so-called effective precipitation (Brouwer and
Heibloem 1986) can be used to set the beneficial weight of precipitation (W bPP ).
This is doubly important for rainfed agriculture.
• The non-beneficial ET is routinely estimated or calculated at least for irrigation
systems.
• Evaporation from lakes and reservoirs are calculated with a small fraction
considered as beneficial.
Finally, gathering accurate data for the management of water systems is very
hard due to many factors, such as, bias (different from prejudice) and noise (chance
variability of judgments), shown in Fig. 4.2 (Kahneman et al. 2016). Noise is
one of the reasons that a learning process is needed because sometimes what a
stakeholder presents under one situation may be different from what he expresses
under another (consciously or not). Furthermore, decision makers and politicians
act with much noise for advancing their interests, which make the stakeholders
even noisier. However, action (Sect. 3.2) truly reveals the real intentions and true
mindsets (Sect. 3.2) of all involved in water management (or life in general). Due
to such inherent conditions and the idea of bounded rationality (Sect. 4.2), it is
suggested that equal weights (or its special case, unit weights) can sometimes be justifiable for complex systems such as, the NASDAQ-100 Equal Weighted Index Shares
(NASDAQ-100). This may be used for the beneficial weights (W bX ) at least in situations that reliable data is not available, an initial estimate is needed in the learning
process, or under urgent situations. Although the problem of noise is explained here
for W bX , but it is applicable for all types of data, including quality and quantity, and
should be persistently dealt with in all data handling.
4 Sefficiency (Sustainable Efficiency)
needs and the downstream requirements. This weight should consider all the benefits
that water brings to societies and natures: “Valuing water means recognizing and
considering all the benefits provided by water that encompass economic, social and
ecological dimensions. It takes many forms appropriate to local circumstances and
cultures. Safeguarding the poor, the vulnerable and the environment is required in
all instances” (UN-HLPW 2017). Hence, water benefits (interchangeable with water
values according to this citation and others) are fundamentally linked to the objectives
of a WUS, and include those that may not be quantifiable.
FIW4 is about the benefits, which stresses that multi-objective planning and
management should be the norm. Total benefits and costs vary due to various local
or national goals and even under different water allocation schemes. There are many
methods available to come up with those totals (Loucks and Van Beek 2005) and
then the weights. Presentation of these methods are beyond the scope of this book,
but they are routinely utilized in evaluating economic and social benefits of projects.
However, in many cases a competent estimate of the magnitude of the weight is
sufficient, which may be valuable as the first step in a learning process. At present,
the following is common practice in applications all over the world:
• Public water supply to people has a W bI of one, meaning that all the water that
enters into the water supply system (Inflow to a WUS) has the maximum benefit.
• For irrigation systems, the so-called effective precipitation (Brouwer and
Heibloem 1986) can be used to set the beneficial weight of precipitation (W bPP ).
This is doubly important for rainfed agriculture.
• The non-beneficial ET is routinely estimated or calculated at least for irrigation
systems.
• Evaporation from lakes and reservoirs are calculated with a small fraction
considered as beneficial.
Finally, gathering accurate data for the management of water systems is very
hard due to many factors, such as, bias (different from prejudice) and noise (chance
variability of judgments), shown in Fig. 4.2 (Kahneman et al. 2016). Noise is
one of the reasons that a learning process is needed because sometimes what a
stakeholder presents under one situation may be different from what he expresses
under another (consciously or not). Furthermore, decision makers and politicians
act with much noise for advancing their interests, which make the stakeholders
even noisier. However, action (Sect. 3.2) truly reveals the real intentions and true
mindsets (Sect. 3.2) of all involved in water management (or life in general). Due
to such inherent conditions and the idea of bounded rationality (Sect. 4.2), it is
suggested that equal weights (or its special case, unit weights) can sometimes be justifiable for complex systems such as, the NASDAQ-100 Equal Weighted Index Shares
(NASDAQ-100). This may be used for the beneficial weights (W bX ) at least in situations that reliable data is not available, an initial estimate is needed in the learning
process, or under urgent situations. Although the problem of noise is explained here
for W bX , but it is applicable for all types of data, including quality and quantity, and
should be persistently dealt with in all data handling.