2019
DOI: 10.1029/2019wr024786
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Complementary Vantage Points: Integrating Hydrology and Economics for Sociohydrologic Knowledge Generation

Abstract: Because human and environmental systems in the Anthropocene are increasingly coupled, hydrologists and economists often find themselves studying the same systems from different vantage points. Here we argue that synthesis across economics and hydrology can help address two pressing sociohydrologic challenges: actionable prediction and the generation of transferable knowledge from place‐based studies. Specifically, we review (1) empirical methods and (2) theoretical approaches from economics and connect the two… Show more

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Cited by 41 publications
(38 citation statements)
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References 238 publications
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“…Rather, the primary objective of the model is a careful theoretical treatment of opportunity costs as a mechanism often thought to drive the relationship between climate change and conflict. In doing so, we elucidate the rich dynamics, and often counterintuitive outcomes, that emerge even under highly stylized theoretical representations of human behavior and climate (21).…”
Section: Significancementioning
confidence: 99%
“…Rather, the primary objective of the model is a careful theoretical treatment of opportunity costs as a mechanism often thought to drive the relationship between climate change and conflict. In doing so, we elucidate the rich dynamics, and often counterintuitive outcomes, that emerge even under highly stylized theoretical representations of human behavior and climate (21).…”
Section: Significancementioning
confidence: 99%
“…Borrowed from econometrics, the design focuses on isolating and quantifying the causal effect of a single driver-impervious cover-by controlling for other drivers of change (confounders). Recent reviews detail a variety of ways econometric approaches can improve our understanding of hydrologic change (Müller & Levy, 2019) and of coupled human-natural systems more broadly (Ferraro et al, 2019).…”
Section: Introductionmentioning
confidence: 99%
“…Empirically, we must overcome the challenges of combining social with hydrological and ecological data, of which the latter two measure features of a continuous spatio-temporal field, whereas the former is discrete, often static, and may not coincide with the spatial scale at which hydrological outcomes emerge. Advances in computer science and hydrological modeling can help us combine these different types of data into more integrated and causal analyses (Ferraro et al 2019, Müller andLevy 2019). Advances in digitization of historical records, natural language processing, machine learning, and remotely sensed imagery can help us address the paucity of data on institutions and social structure (e.g., Lansing et al 2017).…”
Section: Questions For Future Researchmentioning
confidence: 99%