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Record W2085923582 · doi:10.5194/hess-19-1905-2015

Why is the Arkavathy River drying? A multiple-hypothesis approach in a data-scarce region

2015· article· en· W2085923582 on OpenAlexfundno aff
Veena Srinivasan, Sally Thompson, Karthik Madhyastha, Gopal Penny, K. Jeremiah, Sharachchandra Lélé

Bibliographic record

VenueHydrology and earth system sciences · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsnot available
FundersOffice of International Science and EngineeringCentro Nacional de Investigaciones CardiovascularesInternational Development Research CentreUnited States Agency for International DevelopmentNational Science Foundation
KeywordsClimate changeDrainage basinEnvironmental scienceFragmentation (computing)Hydrology (agriculture)GroundwaterWater resource managementGeographyEcologyGeology

Abstract

fetched live from OpenAlex

Abstract. The developing world faces unique challenges in achieving water security as it is disproportionately exposed to stressors such as climate change while also undergoing demographic growth, agricultural intensification and industrialization. Investigative approaches are needed that can inform sound policy development and planning to address the water security challenge in the context of data scarcity. We investigated the "predictions under change" problem in the Thippagondanahalli (TG Halli) catchment of the Arkavathy sub-basin in South India. River inflows into the TG Halli reservoir have declined since the 1970s, and the reservoir is currently operating at only 20% of its built capacity. The mechanisms responsible for the drying of the river are not understood, resulting in uncoordinated and potentially counter-productive management responses. The objective of this study was to investigate potential explanations of the drying trend and thus obtain predictive insight. We used a multiple working hypothesis approach to investigate the decline in inflow into TG Halli reservoir. Five hypotheses were tested using data from field surveys and reliable secondary sources: (1) changes in rainfall amount, timing and storm intensity, (2) rising temperatures, (3) increased groundwater extraction, (4) expansion of eucalyptus plantations, and (5) increased fragmentation of the river channel. Our results indicate that proximate anthropogenic drivers of change such as groundwater pumping, expansion of eucalyptus plantations, and to a lesser extent channel fragmentation, are much more likely to have caused the decline in surface flows in the TG Halli catchment than changing climate. The case study shows that direct human interventions play a significant role in altering the hydrology of watersheds. The multiple working hypotheses approach presents a systematic way to quantify the relative contributions of anthropogenic drivers to hydrologic change. The approach not only yields a meaningful contribution to the policy debate, but also helps prioritize and design future primary research. The paper represents a first step toward "use-inspired" socio-hydrologic science.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.003
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.072
GPT teacher head0.234
Teacher spread0.161 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations73
Published2015
Admission routes1
Has abstractyes

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