Reimagining the past – use of counterfactual trajectories in socio-hydrological modelling: the case of Chennai, India
Bibliographic record
Abstract
Abstract. The developing world is rapidly urbanizing. One of the challenges associated with this growth will be to supply water to growing cities of the developing world. Traditional planning tools fare poorly over 30–50 year time horizons because these systems are changing so rapidly. Models that hold land use, economic patterns, governance systems or technology static over a long planning horizon could result in inaccurate predictions leading to sub-optimal or paradoxical outcomes. Most models fail to account for adaptive responses by humans that in turn influence water resource availability, resulting in coevolution of the human–water system. Is a particular trajectory inevitable given a city's natural resource endowment, is the trajectory purely driven by policy or are there tipping points in the evolution of a city's growth that shift it from one trajectory onto another? Socio-hydrology has been defined as a new science of water and people that will explicitly account for such bi-directional feedbacks. However, a particular challenge in incorporating such feedbacks is imagining technological, social and political futures that could fundamentally alter future water demand, allocation and use. This paper offers an alternative approach – the use of counterfactual trajectories – that allows policy insights to be gleaned without having to predict social futures. The approach allows us to "reimagine the past"; to observe how outcomes would differ if different decisions had been made. The paper presents a "socio-hydrological" model that simulates the feedbacks between the human, engineered and hydrological systems in Chennai, India over a 40-year period. The model offers several interesting insights. First, the study demonstrates that urban household water security goes beyond piped water supply. When piped supply fails, users turn to their own wells. If the wells dry up, consumers purchase expensive tanker water or curtail water use and thus become water insecure. Second, unsurprisingly, different initial conditions result in different trajectories. But initial advantages in piped infrastructure are eroded if the utility is unable to expand the piped system to keep up with growth. Both infrastructure and sound management decisions are necessary to ensure household water security although the impacts of mismanagement may not manifest until much later when the population has grown and a multi-year drought strikes. Third, natural resource endowments can limit the benefits of good policy and infrastructure. Cities can boost recharge through artificial recharge schemes. However, cities underlain by productive aquifers can better rely on groundwater as a buffer against drought, compared to cities with unproductive aquifers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".