Systems Archetypes and Generic Structures for
Reservoir Management
Babak Bahaddin Saeed Langarudi David Andersen
University at Albany lang@nmsu.edu david.andersen@ albany.edu
bbahaddin@ albany.edu Water Resources Research Institute, University at Albany
New Mexico State University
Keywords: Archetypes, Generic Structures, Reservoir Effect, Supply-Demand Cycle
In the recent movements among water scientist, feedback mechanisms are progressively becoming
prominent. Following this trend and in their recent article, Di Baldassarre et al. [2018] introduce two long-
term dynamics in reservoir management, the supply-demand cycle and reservoir effect which are caused
by feedback mechanisms between human and natural systems. However, after acknowledging the
difficulties in quantifying such causal relationship, the authors call on water managers, social scientists,
policymakers, economists, ecologists and hydrologists to collaborate and develop analytical tools capturing
the long-term dynamics produced by the interactions of physical, social and technical processes. Inspired
by this call, this paper constitutes a first step to bridging qualitative water resources analysis and quantified
simulation models. To this end, we have used system dynamics modeling and its embedded concepts such
as systems archetypes and generic structures to study human-nature interaction in the context of water
resources management.
The supply-demand cycle takes place when increasing water supply enables agricultural, industrial, or urban
expansion, which in turn leads to increasing competition for water resources [Kallis, 2010, Scarrow, 2014].
The reservoir effect states that the construction of the reservoirs reduces the incentive for adaptive actions
at individual or community levels. The lack of incentive for adaptive actions reduces the resiliency of the
system against the negative impacts of water shortages during severe droughts. Di Baldassarre et al. [2018]
portray these two mechanisms with a simple causal loop diagram (Figure 1). These mechanisms could be
represented using systems archetypes analogies. The paper explains how supply-demand cycle and
reservoir effect can conveniently match the structure of the Fixes that Backfire archetype. Besides the fixes
that backfire archetype, other archetypal structures are explained in the paper which could be used for
explaining the post-dynamics of expansion in water supply infrastructure
Water
Demand
( +
(R1)
"7 Supply
®
(B1) Economic
Reservoir Damage
Storage +
+\__ Public
epenianes Pressure ~ +
tC Vulnerability
Figure 1) supply-demand cycle (upper loop) and reservoir effects (lower loop) -- adopted from De Baldassarre et al (2018).
While archetypes can be helpful for getting structural insights, they cannot provide behavioral analysis. To
overcome the limitations of system archetypes, simulation models can be generated. These mathematical
models include all key variables and their causal relationships that are necessary to generate the behavior
of desired variables. In this paper, we created one exemplary generic structure that includes both the supply-
demand cycle and also the reservoir effect. With the help of this generic structure we were able to simulate
the model and generate the overtime behavior of each variable. This model helped us to test our initial
hypotheses with different what-if scenarios. The behavior of the main variables are shown below.
Z Vulnerability Public Pressure L
Vulnerability v
Change N
praia Required Additional
Damage E
R2
Reservoir
Effect Water Shortage H
Provoked Bl tion
Vulnerability Q RateT Potential Addition to
Capacity Capacity K
hs Adjustment Storage
‘Availability A Chae
Carrying
Capacity W
Socioeconomic
R1
Supply-Demand Water Suppys| 599° | “Recharging
Cycle Rate F
Demand Net
Change M | average
a Demand D
Supply-Demand Cycle Vulnerability
600 14
450 1 1
y z
5 300 Es
150 2
0 “2
0 10 20 30 40 50 60 10 20 30 40 50 60
‘Time (Year) Time (Y ear)
Figure 2) stock- diagram of the suppl cycle and reservoir
Despite the value archetypes as a first approach to understanding systems (Senge et al, 1994), itis crucially
important to note that archetypes merely constitute a hypothesis concerning the structure link. For the
rigorous testing of such hypotheses, the use of computer simulation is strongly encouraged (Lane and Smart,
1996 - quotation from John Sterman). Dowling et al. (1995) reported in detail the difficulties that they
experienced in attempting to represent two archetypes as stock and flow diagrams in order to simulate their
behavior. Their experience adds weight to the idea that many archetypes are too poorly posed to be recreated
as formal models (Lane and Smart, 1996). Archetypes need to be seen as products of formal dynamic
models; not the origin. The practice of archetypes without a more sophisticated supporting model can cause
ambiguity and inconclusiveness.
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