Indian Forum for Water Adroit

Show Posts

This section allows you to view all posts made by this member. Note that you can only see posts made in areas you currently have access to.


Messages - Pankaj Dey

Pages: [1] 2 3 4
1
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

2
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

3
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

4
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

5
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

6
Data Visualisation can be defined as representing numbers with shapes – and no matter what these shapes look like (areas, lines, dots), they need to have a color. Sometimes colors just make the shapes visible, sometimes they encode data or categories themselves. We’ll focus mostly on the latter in this article. But we’ll also take a general look at colors and what to consider when choosing them:


Link to the article: https://blog.datawrapper.de/colors/
The following users thanked this post: denzilroy

7

Verifying that a statistically significant result is scientifically meaningful is not only good scientific practice, it is a natural way to control the Type I error rate. Here we introduce a novel extension of the p-value—a second-generation p-value (pδ)–that formally accounts for scientific relevance and leverages this natural Type I Error control. The approach relies on a pre-specified interval null hypothesis that represents the collection of effect sizes that are scientifically uninteresting or are practically null. The second-generation p-value is the proportion of data-supported hypotheses that are also null hypotheses. As such, second-generation p-values indicate when the data are compatible with null hypotheses (pδ = 1), or with alternative hypotheses (pδ = 0), or when the data are inconclusive (0 < pδ < 1). Moreover, second-generation p-values provide a proper scientific adjustment for multiple comparisons and reduce false discovery rates. This is an advance for environments rich in data, where traditional p-value adjustments are needlessly punitive. Second-generation p-values promote transparency, rigor and reproducibility of scientific results by a priori specifying which candidate hypotheses are practically meaningful and by providing a more reliable statistical summary of when the data are compatible with alternative or null hypotheses.


Link to paper: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0188299
Advantage over old concept of p-values is shown in a figure attached to the post.
The following users thanked this post: Hemant Kumar

8
Floods, wildfires, heatwaves and droughts often result from a combination of interacting physical processes across multiple spatial and temporal scales. The combination of processes (climate drivers and hazards) leading to a significant impact is referred to as a ‘compound event’. Traditional risk assessment methods typically only consider one driver and/or hazard at a time, potentially leading to underestimation of risk, as the processes that cause extreme events often interact and are spatially and/or temporally dependent. Here we show how a better understanding of compound events may improve projections of potential high-impact events, and can provide a bridge between climate scientists, engineers, social scientists, impact modellers and decision-makers, who need to work closely together to understand these complex events.


Link: https://www.nature.com/articles/s41558-018-0156-3
The following users thanked this post: Hemant Kumar

9
Demonstrating the “unit hydrograph” and flow routing processes involving active student participation – a university lecture experiment

 The unit hydrograph (UH) has been one of the most widely employed hydrological modelling techniques to predict rainfall–runoff behaviour of hydrological catchments, and is still used to this day. Its concept is based on the idea that a unit of effective precipitation per time unit (e.g. mm h−1) will always lead to a specific catchment response in runoff. Given its relevance, the UH is an important topic that is addressed in most (engineering) hydrology courses at all academic levels. While the principles of the UH seem to be simple and easy to understand, teaching experiences in the past suggest strong difficulties in students' perception of the UH theory and application. In order to facilitate a deeper understanding of the theory and application of the UH for students, we developed a simple and cheap lecture theatre experiment which involved active student participation. The seating of the students in the lecture theatre represented the hydrological catchment in its size and form. A set of plastic balls, prepared with a piece of magnetic strip to be tacked to any white/black board, each represented a unit amount of effective precipitation. The balls are evenly distributed over the lecture theatre and routed by some given rules down the catchment to the catchment outlet, where the resulting hydrograph is monitored and illustrated at the black/white board. The experiment allowed an illustration of the underlying principles of the UH, including stationarity, linearity, and superposition of the generated runoff and subsequent routing. In addition, some variations of the experimental setup extended the UH concept to demonstrate the impact of elevation, different runoff regimes, and non-uniform precipitation events on the resulting hydrograph. In summary, our own experience in the classroom, a first set of student exams, as well as student feedback and formal evaluation suggest that the integration of such an experiment deepened the learning experience by active participation. The experiment also initialized a more experienced based discussion of the theory and assumptions behind the UH. Finally, the experiment was a welcome break within a 3 h lecture setting, and great fun to prepare and run.


Link: https://www.hydrol-earth-syst-sci.net/22/2607/2018/hess-22-2607-2018.pdf
The following users thanked this post: subash, Diwan

10
At the EGU General Assembly 2018 in Vienna, “Hydroinformatics for hydrology” short course (SC) was run for the fourth time. The previous themes of the SC were data-driven and hybrid techniques, data assimilation, and geostatistical modelling. And this year the focus was extreme value modelling. Participants of the SC were given a state-of-the-science overview of different aspects in extreme value analysis along with relevant case studies. Available R functions for extreme value analysis were also introduced. Thanks to Hugo’s excellent lecture, we now know common issues and pitfalls in using extreme value models (i.e. modelling choices and assumptions). We would like to thank Dr. Hugo Winterfrom EDF Energy for delivering the lecture. You can find his lecture slides (and exercises) in the attachments:



The following users thanked this post: saurabh.01jsr

11
A postdoc position is immediately available in the area of hydrological modeling. We are particularly interested in those who have interests and experience in modeling large scale water and nutrient cycles because of the climate changes.

Appointment is initially for one year, with subsequent years possible pending on availability of funds and performance.
Salary is competitive and includes fringe benefits.

Applicants should send an inquiry with a cv to Professor Chen Zhu (che...@indiana.edu).

You can also visit our web site for our research activities at www.indiana.edu/~hydrogeo.

Indiana University is an Equal Opportunity/Affirmative Action employer.
Women and minorities are especially encouraged to apply.
The following users thanked this post: atul.4200@gmail.com

12
Hydrological sciences / Using R in Hydrology - EGU2018 Short Course
« on: April 22, 2018, 02:37:21 PM »
This was a short course conducted during EGU this year. The course was divided into six workflows as follows:


Introduction to the short course - Louise Slater
  • Accessing hydrological data using web APIs (a demo of the rnrfa package) - Claudia Vitolo
  • Processing, modelling and visualising hydrological data in R (tidyverse; piping, mapping and nesting) - Alexander Hurley
  • Extracting netCDF climate data for hydrological analyses (reading and visualising gridded data) - Louise Slater
  • Hydrological modelling and teaching modelling (airGR and airGRteaching) - Guillaume Thirel
  • Typical hydrological tasks in R (List columns, Leaflet and coordinate transformation, Open Street Maps) - Tobias Gauster

Please follow the github link to access the necessary materials: https://github.com/hydrosoc/rhydro_EGU18/



The following users thanked this post: Sat Kumar Tomer, Alok Pandey

13
Streamflow data is highly relevant for a variety of socio-economic as well as ecological analyses or applications, but a high-resolution global streamflow dataset is yet lacking. We created FLO1K, a consistent streamflow dataset at a resolution of 30 arc seconds (~1 km) and global coverage. FLO1K comprises mean, maximum and minimum annual flow for each year in the period 1960–2015, provided as spatially continuous gridded layers. We mapped streamflow by means of artificial neural networks (ANNs) regression. An ensemble of ANNs were fitted on monthly streamflow observations from 6600 monitoring stations worldwide, i.e., minimum and maximum annual flows represent the lowest and highest mean monthly flows for a given year. As covariates we used the upstream-catchment physiography (area, surface slope, elevation) and year-specific climatic variables (precipitation, temperature, potential evapotranspiration, aridity index and seasonality indices). Confronting the maps with independent data indicated good agreement (R2 values up to 91%). FLO1K delivers essential data for freshwater ecology and water resources analyses at a global scale and yet high spatial resolution.


Link to the paper: https://www.nature.com/articles/sdata201852.pdf


Link to data shared in figshare: https://figshare.com/collections/FLO1K_global_maps_of_mean_maximum_and_minimum_annual_streamflow_at_1_km_resolution_from_1960_through_2015/3890224
The following users thanked this post: prayas, Hemant Kumar

14
Regression techniques are one of the most popular statistical techniques used for predictive modeling and data mining tasks. On average, analytics professionals know only 2-3 types of regression which are commonly used in real world. They are linear and logistic regression. But the fact is there are more than 10 types of regression algorithms designed for various types of analysis. Each type has its own significance. Every analyst must know which form of regression to use depending on type of data and distribution.


Please find the link: https://www.listendata.com/2018/03/regression-analysis.html
The following users thanked this post: Chandan Banerjee, prayas

15
Announcements / 2018 Water Travel Award
« on: March 26, 2018, 06:32:20 PM »
We are pleased to announce that the “2018 Water Travel Award” is now open to receive applications from postdoctoral and Ph.D. researchers who plan to participate in an international conference during July–December 2018. The award will consist of three prizes each of 800 CHF(Swiss Francs).
The awardee will be determined after assessment by an evaluation committee, chaired by the Editor-in-Chief Prof. Arjen Y. Hoekstra, and also includes Prof. John W. Day, Prof. Kwok-wing Chau, Prof. Roy C. Sidle, Prof. Laodong Guo and Prof. Thilo Hofmann.
Candidates should fulfil the following criteria:Postdoctoral fellows (within three years of receiving their Ph.D.) or Ph.D. students undertaking water resources research.
  • They must present their own, original work as a poster or oral presentation at the conference for which the travel award application is being made.
Applicants are required to submit the following documents (Please provide the entire package in a PDF file):Outline of current and future work (1 page).
  • CV, including a complete list of publications.
  • Details of the conference to be attended, together with a copy of the abstract and acceptance letter or anticipated date of decision.
  • Current grant funding and travel budget, if any, and why the support of this award would be beneficial.
  • A letter of recommendation from their supervisor, research director or department head (1 page).
Please apply by clicking the button above before 30 April 2018. The decision will be announced in June 2018.
Link :http://www.mdpi.com/journal/water/awards
The following users thanked this post: B N Priyanka, Diwan

Pages: [1] 2 3 4