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Messages - Sonali

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1
The work is carried out by Dr. Sonali Pattanayak along with Prof. Ravi S. Nanjundiah and Prof. D. Nagesh Kumar titled "Detection and attribution of climate change signal in South India maximum and minimum temperatures" got published in the Climate Research.

Abstract: South India has seen significant changes in climate. Previous studies have shown that southern part of India is more susceptible to impact of climate change than the rest of the country. A rigorous climate model-based detection and attribution analysis is performed to determine the root cause of the recent changes in climate over South India using fingerprint analysis. Modified Mann-Kendall test signalized non-stationariness in Tmax and Tmin in most of the season during the period 1950-2012. The diminishing cloud cover trend might be inducing significant changes in temperature during the considered time period. Significant downward trends in RH during most of the season could act as an evidence of the recent significant warming. The observed seasonal Tmax, Tmin change patterns are strongly associated with El Niño Southern Oscillation. Significant positive associations between South India temperatures and Niño3.4 are found in all the seasons. Deployment of fingerprint approach indicated that the natural internal variability obtained from 14 climate model control simulations could not explain these significant changes in Tmax (post-monsoon) and Tmin (pre- monsoon and monsoon) of South India. Moreover the experiment simulating natural external forcings (solar and volcanic) do not coincide with the observed signal strength. The dominant external factor leading to climate change is GHGs and its impact is eminent compared to other factors such as, land use change and anthropogenic aerosols. Anthropogenic signals are identifiable in observed changes in Tmax and Tmin of South India and these changes can be explained only when anthropogenic forcing are involved.

Sonali can be contacted at iisc.sonali@gmail.com

https://www.int-res.com/prepress/c01530.html
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2
Hydrological sciences / Critical Values for Sen’s Trend Analysis
« on: August 31, 2018, 03:26:54 PM »

Abstract
Trends in measured hydrologic data can greatly influence projected values; therefore, trends need to be identified and quantitatively modeled. First, it is necessary to verify whether or not a trend actually exists in the sample data. Tests of significance are most often used to verify whether or not a trend is statistically significant. Şen provided an easy-to-apply method of identifying the presence of trends in time series, but did not provide a quantitative method of verifying the statistical likelihood of an assumed trend in a measured time series [Şen, Z. 2012. “Innovative trend analysis methodology.” J. Hydrol. Eng. 17 (9): 1042–1046]. A method of quantifying Sen’s approach is developed herein, with critical values of the test statistic developed to provide a means of making objective decisions. Critical values are presented for rejection probabilities from 10% to 0.1%. The power of the test, which is similar to that of other trend tests, is also approximated; analyses indicate powers from 10% to 30% for small samples.

Link: https://ascelibrary.org/doi/abs/10.1061/%28ASCE%29HE.1943-5584.0001708
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3
Abstract
Streamflow time series are commonly derived from stage‐discharge rating curves but the uncertainty of the rating curve and resulting streamflow series are poorly understood. While different methods to quantify uncertainty in the stage–discharge relationship exist, there is limited understanding of how uncertainty estimates differ between methods due to different assumptions and methodological choices. We compared uncertainty estimates and stage–discharge rating curves from seven methods at three river locations of varying hydraulic complexity. Comparison of the estimated uncertainties revealed a wide range of estimates, particularly for high and low flows. At the simplest site on the Isère River (France), full width 95% uncertainties for the different methods ranged from 3%‐17% for median flows. In contrast, uncertainties were much higher and ranged from 41%‐200% for high flows in an extrapolated section of the rating curve at the Mahurangi River (New Zealand) and 28%‐101% for low flows at the Taf River (United Kingdom), where the hydraulic control is unstable at low flows. Differences between methods result from differences in the sources of uncertainty considered, differences in the handling of the time varying nature of rating curves, differences in the extent of hydraulic knowledge assumed, and differences in assumptions when extrapolating rating curves above or below the observed gaugings. Ultimately, the selection of an uncertainty method requires a match between user requirements and the assumptions made by the uncertainty method. Given the significant differences in uncertainty estimates between methods, we suggest that a clear statement of uncertainty assumptions be presented alongside streamflow uncertainty estimates.


Link: https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2018WR022708
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4
Hydrological sciences / El Niño–Southern Oscillation complexity
« on: July 26, 2018, 08:45:25 PM »
El Niño events are characterized by surface warming of the tropical Pacific Ocean and weakening of equatorial trade winds that occur every few years. Such conditions are accompanied by changes in atmospheric and oceanic circulation, affecting global climate, marine and terrestrial ecosystems, fisheries and human activities. The alternation of warm El Niño and cold La Niña conditions, referred to as the El Niño–Southern Oscillation (ENSO), represents the strongest year-to-year fluctuation of the global climate system. Here we provide a synopsis of our current understanding of the spatio-temporal complexity of this important climate mode and its influence on the Earth system.


Link to paper: https://www.nature.com/articles/s41586-018-0252-6
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5
River Linking may have the potential to accelerate global warming on a short term in addition to its possible adverse effect  on monsoon rain-fall in India on a long term.

Article :
Rajamani, V., Mohanty, U.C., Ramesh, R., Bhat, G.S., Vinayachandran, P.N., Sengupta, D., PrasannaKumar, S. and Kolli, R.K., 2006. Linking Indian rivers vs Bay of Bengal monsoon activity. Indian Academy of Sciences.
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6
Announcements / Opening for Ad-hoc Faculty in NIT Warangal
« on: December 04, 2017, 04:09:15 PM »
Vacancies are for Civil Department also.
Please find more details in the attachment.
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7
FREEWAT is an HORIZON 2020 project financed by the EU Commission under the call WATER INNOVATION: BOOSTING ITS VALUE FOR EUROPE.
FREEWAT main result is an open source and public domain GIS integrated modelling environment (the FREEWAT platform) for the simulation of water quantity and quality in surface water and groundwater with an integrated water management and planning module.
If you are interested in water management and in simulation tools (and you are especially dealing with groundwater management) please visit the Software and Training page of this web site.
FREEWAT is conceived as a composite plugin for the well-known QGIS (http://qgis.org)GIS open source desktop software.

As composite plugin, FREEWAT is designed as a modular ensemble of different tools: some of them can be used independently, while some modules require the preliminary execution of other tools. In this framework, the following tool classifications can be defined:

Tools for the analysis, interpretation and visualization of hydrogeological and hydrochemical data and quality issues, also focusing on advanced time series analysis, embedded in akvaGIS module.

Simulation of models related to the hydrological cycle and water resources management:  flow models, transport models, crop growth models, management and optimization models (also related to irrigation management and rural issues).

Tools to perform model calibration, sensitivity analysis and uncertainty quantifications.

Additional tools for general GIS operations to prepare input data, and post-processing functionalities (module OAT – Observation and Analysis Tool).

http://www.freewat.eu/
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8
Post your question/information / Never miss a scientific paper again!
« on: September 21, 2017, 03:25:37 PM »
It saved a lot of my time.

https://peer.us/
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9
Programming / EcoHydRology: A R Package
« on: September 08, 2017, 05:59:57 PM »
This package provides a flexible foundation for scientists, engineers, and policy makers to base teaching exercises as well as for more applied use to model complex eco-hydrological interactions.
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Link: https://cran.r-project.org/web/packages/EcoHydRology/EcoHydRology.pdf
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10
Study material / Online Free Courses on Remote Sensing
« on: September 06, 2017, 07:41:10 PM »
Online free courses on remote sensing:
1/ Earth observation from space (by ESA-course just started):
https://www.futurelearn.com/cour…/optical-earth-observation/
2/Monitoring the Oceans from Space (by EUMETSAT-starts on October, 16th): https://www.futurelearn.com/courses/oceans-from-space
3/Monitoring Climate from Space (by ESA-date TBA):
https://www.futurelearn.com/courses/climate-from-space
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11
Announcements / INDIA-UK Exchange
« on: September 06, 2017, 09:32:25 AM »
The India-UK Water Centre is inviting proposals from members of its Open Network of Water Scientists to apply for funding under one of two researcher exchange schemes. Funding is available to support at least two researcher exchanges to be undertaken during the period 01 January 2018 - 30 June 2018: at least one exchange by an Indian water scientist to the UK and at least one exchange by a UK water scientist to India.
1st September 2017: call opens
22nd September 2017: deadline for submission of application webform
10th October 2017: applicants notified of outcome
http://www.iukwc.org/open-call-invitation-apply-researcher-exchange-0
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13
Data / Relevant Datasets and their sources
« on: August 16, 2017, 05:42:38 PM »
Please find the attached document for datasets and their online links.


To read the "State of the Climate 2016" by American Meteorological Society, follow the link:


https://www.ametsoc.org/ams/index.cfm/publications/bulletin-of-the-american-meteorological-society-bams/state-of-the-climate/


Thank you,
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14
Announcements / 2017 AGU Fall Meeting
« on: July 02, 2017, 09:53:29 AM »
Early Abstract Submissions Deadline: July 26, 2017
Abstract Submissions Deadline: August 2, 2017
2017 Fall Meeting Begins: December 11, 2017
http://fallmeeting.agu.org/2017/
http://fallmeeting.agu.org/2017/abstract_overview/abstract-submissions/
Some Important Sessions:
General Surface Hydrology: https://agu.confex.com/agu/fm17/preliminaryview.cgi/Session24952
Emerging Technologies for Hydrologic Remote Sensing: https://agu.confex.com/agu/fm17/preliminaryview.cgi/Session26397
Applications of machine learning in hydrology: https://agu.confex.com/agu/fm17/preliminaryview.cgi/Session24633
Drones in Hydrology: How are Unmanned Aerial Vehicles Advancing our Understanding of Earth’s Critical Zone?: https://agu.confex.com/agu/fm17/preliminaryview.cgi/Session25667
Hydroclimatic Extremes: Drought: https://agu.confex.com/agu/fm17/preliminaryview.cgi/Session29430
Remote Sensing and Modeling of the Terrestrial Water Cycle: https://agu.confex.com/agu/fm17/preliminaryview.cgi/Session23042
Advances in hyperspectral infrared remote sensing in cloudy atmospheres: https://agu.confex.com/agu/fm17/preliminaryview.cgi/Session25458
Remote Sensing Applications for Water Resources Management, Including Droughts, Floods and Associated Water Cycle Extremes: https://agu.confex.com/agu/fm17/preliminaryview.cgi/Session23335
Evapotranspiration: Advances in In Situ Measurements and Remote Sensing Based Modeling Approaches: https://agu.confex.com/agu/fm17/preliminaryview.cgi/Session29394

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15
If you are dealing with huge datasets that are saved in .mat format, loading them to workspace eats lot of RAM. So, matlab has an option to read part of such files without loading them to memory. Have a look at this link:

https://in.mathworks.com/help/matlab/ref/matfile.html
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