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Record W2944658919 · doi:10.9734/ajraf/2019/v3i230036

Quantitative and Qualitative Perspectives of Forest-Water Interactions at Catchment Scales

2019· article· en· W2944658919 on OpenAlexaff
Murari Lal Gaur

Bibliographic record

VenueAsian Journal of Research in Agriculture and Forestry · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsWater cyclePopulationEnvironmental scienceClimate changeLand coverGeographyHydrology (agriculture)Land usePhysical geographyEcologyGeology

Abstract

fetched live from OpenAlex

Sustaining a resilient and reliable water cycle is a global challenge, which inevitably needs proper understanding and action at many levels. One quarter of the world’s population depends on water from forested catchments, where behavior of atmospheric water nonetheless governs the forest-water interactions and thus the ultimate water availability. As per a coarse estimation the water vapors comprise one quarter of 1% of atmospheric mass being equivalent to just 2.5 centimeters of liquid water over the entire Earth. Such water availability raises more tangible concerns for most people than do temperature and carbon. Ever escalating populations and living standards are badly impacting the earth’s surface in variety of ways, as 1.5 million Km2 of dense tree cover were reported to be lost between 2000-2012, leading to highly impeded access to fresh water. Majority of studies of how forest land use and its change influences climate and hydrology rely on models (mostly imperfect owing to pitiable/incomplete process understandings and poor parameterization). It is projected that land cover changes have caused a 5 to 6 % reduction in global atmospheric wetness. A plethora of alike estimations/inferences are included herein to offer relevant R&D insights on core theme of this paper, by encircling reviews of few global observations and findings towards forest influences on quality and quantity of water. With increasing demand for agricultural and urban land (owing to population/affluent life-styles) majority of forests are put under pressure. At this juncture tropical regions like India remains more crucial, as their water and land use policies are often influenced to big extent by many perceived effects from hydrological functioning of forested catchments. This paper offers certain food for thought by summarizing relevant scientific consensus of key aspects of forest-water relationships and couple of wider aspects towards ‘forest-water interactions’ and ‘water quality and pollution facets. Apprehensions and knowledge gaps about hydrological impacts of forest management and also the emerging futuristic R&D issues are elaborated with specified line of sights on effects of forests and forest management on various stream flow parameters, soil erosion, stream sedimentation, water quality, landslides and water uses. Owing to their inherent capabilities and capacities, the forests govern available moisture for tree growth, evapotranspiration (ET), soil infiltration, ground water recharge, and runoff; hence could be projected as futuristic ‘water towers’. Hydraulic redistribution of water in soil remains other important activities by the forest, where tree root structures plays a vital role to facilitate both upward and downward water dynamics. Even under low to intermediate tree cover each tree remains capable to improve soil hydraulic properties even up to 25 m from its canopy edge, with higher hydrologic gains in comparison to associated additional losses (transpiration and interception). Among most profound and alarming insights offered by this write up are; critical knowledge gaps on understanding importance of forests to water, trends of findings from a few catchments based hydrological experiments on water yield, roles forest may play in regulating water fluxes and rainfall patterns. Other key messages offered for water and forest policy makers includes issues like water use by forests, flood flows, water quality, erosion, climate change, energy forest, and forest water productivities.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.180
Threshold uncertainty score0.190

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.028
GPT teacher head0.355
Teacher spread0.327 · 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 teacher head, 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

Citations3
Published2019
Admission routes1
Has abstractyes

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