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Messages - Pankaj Dey

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 Abstract. Earth system models (ESMs) are key tools for providing climate projections under different scenarios of human-induced forcing. ESMs include a large number of additional processes and feedbacks such as biogeochemical cycles that traditional physical climate models do not consider. Yet, some processes such as cloud dynamics and ecosystem functional response still have fairly high uncertainties. In this article, we present an overview of climate feedbacks for Earth system components currently included in state-of-the-art ESMs and discuss the challenges to evaluate and quantify them. Uncertainties in feedback quantification arise from the interdependencies of biogeochemical matter fluxes and physical properties, the spatial and temporal heterogeneity of processes, and the lack of long-term continuous observational data to constrain them. We present an outlook for promising approaches that can help quantifying and constraining the large number of feedbacks in ESMs in the future. The target group for this article includes generalists with a background in natural sciences and an interest in climate change as well as experts working in interdisciplinary climate research (researchers, lecturers, and students). This study updates and significantly expands upon the last comprehensive overview of climate feedbacks in ESMs, which was produced 15 years ago (NRC, 2003).
Citation: Heinze, C., Eyring, V., Friedlingstein, P., Jones, C., Balkanski, Y., Collins, W., Fichefet, T., Gao, S., Hall, A., Ivanova, D., Knorr, W., Knutti, R., Löw, A., Ponater, M., Schultz, M. G., Schulz, M., Siebesma, P., Teixeira, J., Tselioudis, G., and Vancoppenolle, M.: Climate feedbacks in the Earth system and prospects for their evaluation, Earth Syst. Dynam. Discuss.,

Link to the paper: https://www.earth-syst-dynam-discuss.net/esd-2018-84/
The following users thanked this post: soumyashree Diixt

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1. More than 1.9 million tiles of MODIS daily reflectance data were used to create a 16- year record (2001 -2016) of daily water map for the entire globe.

2. The global daily water data set can be openly downloaded by potential users.

3. Global inland water has a dramatic seasonal variation ranging from approximately 3.8 million km2 in September to 1.5 million km2 in February within an annual cycle.
4. Short duration water bodies, sea level rise effects, various types of rice field use can be detected from the daily water data set.
Link to paper: https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2018WR023060?af=R
Link to data: http://data.ess.tsinghua.edu.cn/modis_500_2001_2016_waterbody.html
The following users thanked this post: nruthya

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Interesting information / M_Map: A Mapping Package for MATLAB
« on: December 02, 2018, 10:29:27 PM »
M_Map is a set of mapping tools written for Matlab (it also works under Octave). M_Map includes:
 
  • Routines to project data in 19 different projections (and determine inverse mappings), using spherical and ellipsoidal earth-models.
  • A grid generation routine to make nice axes with limits either in lat/long terms or in planar X/Y terms.
  • A coastline database (with 1/4 degree resolution).
  • A global elevation database (1 degree resolution).
  • Hooks into freely available high-resolution coastline and bathymetry databases.
  • Other useful stuff.
Link: https://www.eoas.ubc.ca/~rich/map.html
The following users thanked this post: B N Priyanka

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We’ve all heard it before: “Yeah, but the climate has ALWAYS changed.”
Oh, really? Well, this timeline of Earth’s average temperature shows just how much we’ve influenced the climate. This epic webcomic was created by Randall Munroe, the artist behind xkcd, one of our favorite places for simplifying complicated scientific concepts.
It’s pretty long, but bear with us.
Link: https://grist.org/science/when-someone-tells-you-the-climate-is-always-changing-show-them-this-cartoon/amp/?__twitter_impression=true
The following users thanked this post: nruthya, Hemant Kumar

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Hydrological sciences / hddtools: Hydrological Data Discovery Tools in R
« on: November 25, 2018, 11:13:58 AM »
hddtools stands for Hydrological Data Discovery Tools. This R package is an open source project designed to facilitate access to a variety of online open data sources relevant for hydrologists and, in general, environmental scientists and practitioners.
This typically implies the download of a metadata catalogue, selection of information needed, a formal request for dataset(s), de-compression, conversion, manual filtering and parsing. All those operations are made more efficient by re-usable functions.
Depending on the data license, functions can provide offline and/or online modes. When redistribution is allowed, for instance, a copy of the dataset is cached within the package and updated twice a year. This is the fastest option and also allows offline use of package's functions. When re-distribution is not allowed, only online mode is provided.
Link to manual: https://cran.r-project.org/web/packages/hddtools/hddtools.pdf
The following users thanked this post: nirmalkbcaos15@gmail.com

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Deep learning (DL), a new generation of artificial neural network research, has transformed industries, daily lives, and various scientific disciplines in recent years. DL represents significant progress in the ability of neural networks to automatically engineer problem-relevant features and capture highly complex data distributions. I argue that DL can help address several major new and old challenges facing research in water sciences such as interdisciplinarity, data discoverability, hydrologic scaling, equifinality, and needs for parameter regionalization. This review paper is intended to provide water resources scientists and hydrologists in particular with a simple technical overview, transdisciplinary progress update, and a source of inspiration about the relevance of DL to water. The review reveals that various physical and geoscientific disciplines have utilized DL to address data challenges, improve efficiency, and gain scientific insights. DL is especially suited for information extraction from image-like data and sequential data. Techniques and experiences presented in other disciplines are of high relevance to water research. Meanwhile, less noticed is that DL may also serve as a scientific exploratory tool. A new area termed AI neuroscience, where scientists interpret the decision process of deep networks and derive insights, has been born. This budding subdiscipline has demonstrated methods including correlation-based analysis, inversion of network-extracted features, reduced-order approximations by interpretable models, and attribution of network decisions to inputs. Moreover, DL can also use data to condition neurons that mimic problem-specific fundamental organizing units, thus revealing emergent behaviors of these units. Vast opportunities exist for DL to propel advances in water sciences.


Link: https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2018WR022643
The following users thanked this post: prayas

7

In regression, we assume noise is independent of all measured predictors. What happens if it isn’t?

A number of key assumptions underlie the linear regression model – among them linearity and normally distributed noise (error) terms with constant variance In this post, I consider an additional assumption: the unobserved noise is uncorrelated with any covariates or predictors in the model.


Link: https://www.rdatagen.net/post/linear-regression-models-assume-noise-is-independent/
The following users thanked this post: Sat Kumar Tomer

8

Abstract.
Knowledge of aquifer thickness is crucial for setting up numerical groundwater flow models to support groundwater resource management and control. Fresh groundwater reserves in coastal aquifers are particularly under threat of salinization and depletion as a result of climate change, sea-level rise, and excessive groundwater withdrawal under urbanization. To correctly assess the possible impacts of these pressures we need better information about subsurface conditions in coastal zones. Here, we propose a method that combines available global datasets to estimate, along the global coastline, the aquifer thickness in areas formed by unconsolidated sediments. To validate our final estimation results, we collected both borehole and literature data. Additionally, we performed a numerical modelling study to evaluate the effects of varying aquifer thickness and geological complexity on simulated saltwater intrusion. The results show that our aquifer thickness estimates can indeed be used for regional-scale groundwater flow modelling but that for local assessments additional geological information should be included. The final dataset has been made publicly available (https://doi.pangaea.de/10.1594/PANGAEA.880771).
Link: Zamrsky, D., Oude Essink, G. H. P., and Bierkens, M. F. P.: Estimating the thickness of unconsolidated coastal aquifers along the global coastline, Earth Syst. Sci. Data, 10, 1591-1603, https://doi.org/10.5194/essd-10-1591-2018, 2018.
The following users thanked this post: B N Priyanka

9
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
The following users thanked this post: Sonali

10
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
The following users thanked this post: Sonali

11
Interesting information / The Effects of Tropical Vegetation On Rainfall
« on: August 14, 2018, 10:58:27 PM »
Vegetation modifies land-surface properties, mediating the exchange of energy, moisture, trace gases, and aerosols between the land and the atmosphere. These exchanges influence the atmosphere on local, regional, and global scales. Through altering surface properties, vegetation change can impact on weather and climate. We review current understanding of the processes through which tropical land-cover change (LCC) affects rainfall. Tropical deforestation leads to reduced evapotranspiration, increasing surface temperatures by 1–3 K and causing boundary layer circulations, which in turn increase rainfall over some regions and reduce it elsewhere. On larger scales, deforestation leads to reductions in moisture recycling, reducing regional rainfall by up to 40%. Impacts of future tropical LCC on rainfall are uncertain but could be of similar magnitude to those caused by climate change. Climate and sustainable development policies need to account for the impacts of tropical LCC on local and regional rainfall.


Link to paper: https://www.annualreviews.org/doi/abs/10.1146/annurev-environ-102017-030136
The following users thanked this post: Ila Chawla

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In recent decades India has undergone substantial land use/land cover change as a result of population growth and economic development. Historical land use/land cover maps are necessary to quantify the impact of change at global and regional scales, improve predictions about the quantity and location of future change and support planning decisions. Here, a regional land use change model driven by district-level inventory data is used to generate an annual time series of high-resolution gridded land use/land cover maps for the Indian subcontinent between 1960–2010. The allocation procedure is based on statistical analysis of the relationship between contemporary land use/land cover and various spatially explicit covariates. A comparison of the simulated map for 1985 against remotely-sensed land use/land cover maps for 1985 and 2005 reveals considerable discrepancy between the simulated and remote sensing maps, much of which arises due to differences in the amount of land use/land cover change between the inventory data and the remote sensing maps.


Link to paper: https://www.nature.com/articles/sdata2018159


Link to data: https://figshare.com/collections/A_spatio-temporal_land_use_land_cover_reconstruction_for_India_1960_2010_/3967329
The following users thanked this post: Ila Chawla

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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
The following users thanked this post: Sonali

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Jakub Nowosad, a postdoc in the Space Informatics Lab at University of Cincinnati has develop these two courses on Spatial Analysis and GIS using R.


The relevant links are as follows:
Sample R Codes are also available with practical examples.


1. Introduction to Spatial Analysis using R.


Link: https://nowosad.github.io/presentations/2017/intro_to_spatial_analysis/
Slides: https://cdn.rawgit.com/Nowosad/Intro_to_spatial_analysis/05676e29/Intro_to_spatial_analysis.html#1


2. GIS with R


Link: https://nowosad.github.io/presentations/2017/gis_with_r_start/
Slides: https://cdn.rawgit.com/Nowosad/gis_with_r_how_to_start/aea08f46/gis_with_r_start.html#1


3. Data Visualization and preprocessing


Link: 1. https://nowosad.github.io/presentations/2017/intro_to_data_visulalization/
        2. https://cdn.rawgit.com/Nowosad/Intro_to_data_processing/5d0da6a7/Intro_to_data_processing.html#1



The following users thanked this post: subash

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Interesting information / History of Hydrology Interviews
« on: July 07, 2018, 12:20:56 PM »
This video series provides source material that will be of particular interest to scientists and instructors in the field of hydrology. Through in-depth interviews captured on film, eminent hydrologists discuss achievements in hydrological science that have occurred during their careers. These interviews offer valuable insight into the progression of research in the field of hydrology during the second half of the 20th century.


1. Interview with Prof. Eric F Wood by Prof. M. Sivapalan.
https://www.youtube.com/watch?v=-XZytAeYr7I&t=2869s

2. Interview with Prof. M. Sivapalan by Prof. Ross Woods
https://www.youtube.com/watch?v=nrKhicA9IfU&t=1067s

3. Interview with Prof. Keith J Beven.
https://www.youtube.com/watch?v=y8_EtGeLc4c&t=605s

4. Interview with Georgia Destouni
https://www.youtube.com/watch?v=Yf5P-SzsN44&t=15s

5. Interview with Mike J Kirkby
https://www.youtube.com/watch?v=6-asqTNlVPo
The following users thanked this post: subash, Diwan

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