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Messages - Pankaj Dey

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1
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

2

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

3
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

4
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

5
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:



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6
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

7
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

8
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

9
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

10
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

11
IAHS President Günter Blöschl launches the new initiative of Unsolved Problems in Hydrology.


The introductory youtube video is as follows: https://www.youtube.com/watch?v=jyObwmNr7Ko


The sorted questions are as follows:


FLOODS AND DROUGHTS

Do we understand scour and erosion processes occurring during extreme floods?
Can we improve the estimation of extreme flood peak discharges?
How can we evaluate the performance of Flood Early Warning Systems, in terms of losses avoided as a result of a warning?
How do we improve drought (or flood) risk assessments?
How to use nature-based solutions to reduce flood risk and drought risks and increase the resilience of water resources?
How do droughts and floods shape hydrological risk awareness?
How are changes in vulnerability influencing trends in flood risk?
How to assess water scarcity by considering both water quantity and quality Do flood rich-poor periods exist? If so why?
Where and when do flood wave superpositions occur and what are the atmospheric, catchment and river network controls on this process?
Water scarcity assessment

SNOW AND ICE

How can we ensure that improved snowmelt models translate into improved capabilities to simulate streamflow from snowy watersheds?
How can small-scale variability of snow distribution be better represented in larger scale models, and what level of detail is needed for snowmelt runoffmodelling?
Under what conditions is snow melt a more efficient generator of streamflow and groundwater recharge than rainfall?
What is the effect of preferential deposition and lateral redistribution of snow on runoff generation in alpineheadwatersheds?
How to determine the snow water equivalent in mountain regions?

WATER QUALITY

Can we devise a combined eco-hydrology index of river health to balance human and ecological needs?
What is the role of water quality in the water-energy-food nexus?
How to describe human-water interactions in water quality models?
How do we identify the dominant process controlling water quality over different spatial scales?
What controls long-term spatio-temporal evolution of catchment water quality?

EVAPORATION AND PRECIPITATION

Soil evaporation and soil evaporation/transpiration partition How plants and grass works and interact with soil and atmosphere to produce evaporation?

SCALE AND SCALING

Will we ever find the best approach to extrapolate point scale data to the catchment scale?
Combining understanding gained at different spatial scales, e.g. generalizing lessons learned from case studies to larger scales.
How dominant hydrological processes emerge and disappear across the scales.
Can we trade space for time in hydrology?

MODELLING (GENERAL)

Can hydrological processes of highly urbanized watershed be realistically simulated/predicted?
What future for process based modelling beyond persistent dilettantism ?
How to solve the energy budget, the carbon budget and the sediment budget together to constrain hydrologic models results?
Which new mathematics to choose for the hydrology of this century?
Does machine learning have a real role in hydrological modelling
How can we really cope hydrological modeling with remote sensing measures ?
When will hydrological models (HMs) be robust enough to anticipate accurately future water conditions?
Is it possible to remove the independence condition in the multivariate frequency analysis (e.g., when using Copulas)?
What is the value of soil moisture observations for hydrologic predictions?
How can we identify the independent factors determining a nonlinearly evolving hydrologic response?
How can one identify the optimal sample dimension to use in multivariate analysis with copula functions?
Assessing the impact of non-stationary (epistemic) precipitation errors on hydrological model predictions

LANDSCAPE PROCESSES AND STREAMFLOW

Why we can not predict river runoff?
Why are the distribution of distances from a point in the catchment to the nearest river reach exponentially distributed?
Why / How does hydro-geomorphology follow thermodynamic laws - Coevolution, structure-function, emergence, anisotropy, scaling...
How can we explain the ubiquitous existence of patterns in hydrology providing constraints on heterogeneity and preferential flow of water through media Natural heterogeneity, thermodynamics and (yet again) closing the waterbalance
What controls the long term water balance, apart from aridity?

MEASUREMENTS AND DATA

Is it possible to accurately measure flow discharge using gauge-cams (or UAV-mounted cameras)?
A large number of inaccurate observations vs a few accurate measurements: what is our best choice?
Working with different data sources (and there varying spatial and temporal resolution), for example impact & vulnerability information, citizen science data, satellite data.
How can we accurately measure water fluxes in the subsurface (soil and groundwater) at a range of scales?
How can we detect and measure spatial hydrological patterns?
How to cost- efficiently observe multiple tracers at a high temporal frequency at various locations?

GROUNDWATER AND SOILS

Can we better account for the complex water flow dynamics in the vadosezone?
What controls the distribution and depth of actively circulating water in the subsurface?
It is time to change our mind to augmenting groundwater recharge by focus on water-bearing formation in uplands watershed not just in flood plains or alluvial fans!
What controls the source of water to wells?
Where and why is the largest global store of freshwater (groundwater) connected to other parts of the hydrologic cycle?
Why transport modeling in the subsurface is often inaccurate and fraught of uncertainty?
What are the main processes controlling transport and transformation of contaminants across scales?
Why assessing groundwater resources and their variation is space and time is a daunting, though dramatically needed, endeavour?
Why removal of contaminant from groundwater by pump-and-treat does not work?
Closing the mass-age balance by measurement

HYDROLOGICAL CHANGE

Why are some catchments more sensitive to land-use/cover change than others?
Is the hydrological cycle regionally accelerating under global warming?
Influence of climate variability on large rivers runoff
Dealing with non-stationarities, e.g differences in timescales between analysis tools & methods, modeling of non-stationary processes
Quantifying the human influence on hydrology and hydrological extremes at the catchment scale What is the real impact of man on the water volumes transferred to the sea by rivers?
Why are springs in mountains drying up?
How can we detect and attribute change in flood characteristics?
Sudden and abrupt changes of water management conditions?
Why do we see long term cycles in temperature, rainfall and river flows?

ASSORTED

Elementary physics of hydrological cycle.
Impact of solar activity on hydrological cycle of the Himalayan and Indian Peninsula Rivers.
Does hydrology needs non-equilibrium thermodynamics or even a new type of thermodynamics ?
How we can do hydrology science more open and replicable?
How can we link our hydrological science with stakeholders?


The list of problems are attached.
The following users thanked this post: Hemant Kumar

12
This article examines the effects of using leaves, something most students see every day and have some familiarity with, as an analogy for the concept of watersheds in an undergraduate water resources engineering course. The ultimate goal of the leaf/watershed analogy and associated instruction is to increase students’ understanding of hydrology principles, which in turn may facilitate better watershed management through increased public awareness, increased adoption of appropriate best management practices, and improved policy decisions. The assessment was performed with junior and senior undergraduate students enrolled in a Water Resource Engineering course. The assessment results showed that overall, students benefitted from the leaf analogy as a tool for learning watersheds. However, this effect varied depending on students’ learning style preferences.

Citation: Anandhi, A., Y. Yang, and M. Hubenthal. 2017. Using Leaves as a Model for Teaching Watershed Concepts in Natural Resources Science and Engineering Programs. Natural Sciences Education 46:170020. doi:10.4195/nse2017.09.0020
The following users thanked this post: Diwan

13
Announcements / WMO Fellowship Programme
« on: January 18, 2018, 02:53:44 PM »


The aim of the WMO Fellowship Programme is to support the education and training of qualified and suitable candidates, particularly from Least Developed and Developing Countries and Small Island Developing States. Please see the website for general information on WMO Fellowship Programme: https://public.wmo.int/en/resources/training/fellowships




WMO fellowship opportunities related to hydrology and water resources are available for the following institutions: Hohai University (Nanjing, China), Leibniz Universität (Hannover, Germany), UNESCO-IHE, Institute for Water Education (Delft, Netherlands), Russian State Hydrometeorological University (St. Petersburg, Russia), Indian Institute of Technology - Department of Hydrology (Roorkee, India), Universidad Nacional del Litoral (Santa Fe, Argentina).



The deadline for general nomination to WMO is 28 February 2018. Please note that any nomination should be endorsed by the Permanent Representative of the candidate's country of origin with WMO. For detailed information: https://www.wmo.int/edistrib_exped/grp_prs/_en/2017-12-13-DRA-ETR-ID41695_en.pdf
The following users thanked this post: denzilroy

14
We present TerraClimate, a dataset of high-spatial resolution (1/24°, ~4-km) monthly climate and climatic water balance for global terrestrial surfaces from 1958–2015. TerraClimate uses climatically aided interpolation, combining high-spatial resolution climatological normals from the WorldClim dataset, with coarser resolution time varying (i.e., monthly) data from other sources to produce a monthly dataset of precipitation, maximum and minimum temperature, wind speed, vapor pressure, and solar radiation. TerraClimate additionally produces monthly surface water balance datasets using a water balance model that incorporates reference evapotranspiration, precipitation, temperature, and interpolated plant extractable soil water capacity. These data provide important inputs for ecological and hydrological studies at global scales that require high spatial resolution and time varying climate and climatic water balance data. We validated spatiotemporal aspects of TerraClimate using annual temperature, precipitation, and calculated reference evapotranspiration from station data, as well as annual runoff from streamflow gauges. TerraClimate datasets showed noted improvement in overall mean absolute error and increased spatial realism relative to coarser resolution gridded datasets.


Link to paper: https://www.nature.com/articles/sdata2017191
Link to data: http://doi.org/10.7923/G43J3B0R
The following users thanked this post: Chandan Banerjee, Diwan

15
Interesting information / PICKING A COLOUR SCALE FOR SCIENTIFIC GRAPHICS
« on: January 10, 2018, 11:01:52 AM »
Here are some recommendations for making scientific graphics which help your audience understand your data as easily as possible. Your graphics should be striking, readily understandable, should avoid distorting the data (unless you really mean to), and be safe for those who are colourblind. Remember, there are no really “right” or “wrong” palettes (OK, maybe a few wrong ones), but studying a few simple rules and examples will help you communicate only what you intend.


Please find the link to the blog: https://betterfigures.org/2015/06/23/picking-a-colour-scale-for-scientific-graphics/
The following users thanked this post: Sat Kumar Tomer, Agilan

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