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Record W2124884607 · doi:10.1201/b11863-22

Snow Cover Monitoring from Remote-Sensing Satellites : Possibilities for Drought Assessment

2012· book-chapter· en· W2124884607 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicHydrology and Drought Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsRemote sensingSnow coverCover (algebra)SnowEnvironmental scienceGeographyMeteorologyEngineering

Abstract

fetched live from OpenAlex

Snow cover is an important earth surface characteristic because it inžuences partitioning of the surface radiation, energy, and hydrologic budgets. Snow is also an important source of moisture for agricultural crops and water supply in many higher latitude or mountainous areas. For instance, snowmelt provides approximately 50%–80% of the annual runoff in the western United States (Pagano and Garen, 2006) and Canadian Prairies (Gray et al., 1989; Fang and Pomeroy, 2007), which 15.1 Introduction .................................................................................................. 359 15.2 Snow Mapping with Optical Satellite Observations ..................................... 361 15.2.1 Physical Principles and Sensor Characteristics ................................ 361 15.2.2 Primary Optical-Based Products ...................................................... 363 15.2.3 Validation of Optical Products ......................................................... 365 15.3 Snow Mapping with Microwave Satellite Observations ...............................366 15.3.1 Physical Principles and Instrument Characteristics .........................366 15.3.2 Primary Microwave-Based Products ................................................ 369 15.3.3 Validation of Microwave Products ................................................... 371 15.3.4 Data Assimilation Approaches for Estimating Snow Water Equivalent............................................................................... 372 15.4 Snow Cover Monitoring Using Synergy of Optical and Microwave Remote-Sensing Techniques ......................................................................... 374 15.5 Application of Satellite-Based Snow Products for Drought Identi–cation and Monitoring ....................................................................... 375 15.6 Conclusions ................................................................................................... 382 References .............................................................................................................. 383 substantially impacts warm season hydrology. Limited soil moisture reserves from the winter period can result in agricultural drought (i.e., severe early growing season vegetation stress if rainfall de–cits occur during that period), which can be prolonged or intensi–ed well into the growing season if relatively dry conditions persist. Snow cover de–cits can also result in hydrological drought (i.e., severe de–cits in surface and subsurface water reserves including soil moisture, streamžow, reservoir and lake levels, and groundwater) since snowmelt runoff is the primary source of moisture to recharge these reserves for a wide range of agricultural, commercial, ecological, and municipal purposes. Semiarid regions that rely on snowmelt are especially vulnerable to winter moisture shortfalls since these areas are more likely to experience frequent droughts. In the Canadian Prairies, more than half the years of three decades (1910-1920, 1930-1939, and 1980-1989) were in drought. Wheaton et al. (2005) reported exceptionally low precipitation and low snow cover in the winter of 2000-2001, with the greatest anomalies of precipitation in Alberta and western Saskatchewan along with near-normal temperature in most of southern Canada. The reduced snowfall led to lower snow accumulation. A loss in agricultural production over Canada by an estimated $3.6 billion in 2001-2002 was attributed to this drought. Fang and Pomeroy (2008) analyzed the impacts of the most recent and severe drought of 1999/2004-2005 for part of the Canadian Prairies on the water supply of a wetland basin by using a physically based cold region hydrologic modeling system. Simulation results showed that much lower winter precipitation, less snow accumulation, and shorter snow cover duration were associated with much lower discharge from snowmelt runoff to the wetland area during much of the drought period of 1999/2004-2005 than during the nondrought period of 2005/2006.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.023
GPT teacher head0.263
Teacher spread0.240 · 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

Citations7
Published2012
Admission routes1
Has abstractyes

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