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Messages - Pankaj Dey

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

The introductory youtube video is as follows:

The sorted questions are as follows:


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


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?


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?


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


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?


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


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?


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?


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


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?


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

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

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:

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:
The following users thanked this post: denzilroy

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:
Link to data:
The following users thanked this post: Chandan Banerjee, Diwan

« 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:
The following users thanked this post: Sat Kumar Tomer, Agilan

Models / Google Earth Engine
« on: November 13, 2017, 05:55:12 PM »
Earth Engine is a platform for petabyte-scale scientific analysis and visualization of geospatial datasets, both for public benefit and for business and government users.
Earth Engine stores satellite imagery, organizes it, and makes it available for the first time for global-scale data mining. The public data archive includes historical earth imagery going back more than forty years, and new imagery is collected every day. Earth Engine also provides APIs in JavaScript and Python, as well as other tools, to enable the analysis of large datasets.

The following users thanked this post: Agilan, gowri

Announcements / The Future of Water Cycle Earth Observing Systems
« on: November 10, 2017, 06:15:00 PM »
Union Symposium at the 2011 General Assembly of the European Geosciences Union. (Credit: EGU/

Link to the video:
The following users thanked this post: prashantbhagawati

AbstractInspired by the work of Newton, Darwin, and Wegener, this paper tracks the drivers and dynamics that have shaped the growth of hydrological understanding over the last century. On the basis of an interpretation of this history, the paper then speculates about what kind of future is in store for hydrology and how we can better prepare for it. The historical narrative underpinning this analysis indicates that progress in hydrological understanding is brought about by changing societal needs and technological opportunities: new ideas are generated by hydrologists through addressing societal needs with the technologies of their time. We suggest that progress in hydrological understanding over the last century has expressed itself through repeated cycles of euphoria and disenchantment, which have served as stimuli for the progress. The progress, for it to happen, also needed inspirational leaders as well as a supportive scientific community that provided the backdrop to major advances in the field. The paper concludes that, in a similar way to how Newton, Darwin, and Wegener conducted their research, hydrology too can benefit from synthesis activities aimed at “connecting the dots.”

The following users thanked this post: Hemant Kumar

Data / CCI Toolbox
« on: November 07, 2017, 12:10:46 PM »
In 2009, ESA, the European Space Agency, has launched the Climate Change Initiative (CCI), a programme to respond the need for climate-quality satellite data as expressed by GCOS, the Global Climate Observing System that supports the UNFCCC, the United Nations Framework Convention on Climate Change.
In the ESA CCI programme 14 Essential Climate Variables (ECV) are produced by individual expert teams, and cross-cutting activities provide coordination, harmonisation and support. The CCI Toolbox and the CCI Open Data Portal are the two main technical support projects within the programme. The CCI Open Data Portal will provide a single point of harmonised access to a subset of mature and validated ECV-related data products. The CCI Toolbox will provide tools that support visualisation, analysis and processing across CCI and other climate data products.
Please follow the links for more information:
The following users thanked this post: Agilan

The Variable Infiltration Capacity (VIC) hydrologic and river routing model simulates the water and energy fluxes that occur near the land surface and provides useful information regarding the quantity and timing of available water within a watershed system. However, despite its popularity, wider adoption is hampered by the considerable effort required to prepare model inputs and calibrate the model parameters. This study presents a user-friendly software package, named VIC-Automated Setup Toolkit (VIC-ASSIST), accessible through an intuitive MATLAB graphical user interface. VIC-ASSIST enables users to navigate the model building process through prompts and automation, with the intention to promote the use of the model for practical, educational, and research purposes. The automated processes include watershed delineation, climate and geographical input set-up, model parameter calibration, sensitivity analysis, and graphical output generation. We demonstrate the package's utilities in various case studies.


The following users thanked this post: Diwan

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.
The following users thanked this post: Sonali, Himanshu Mishra

Daily temperature values are generally computed as the average of the daily minimum and maximum observations, which can lead to biases in the estimation of daily averaged values. This study examines the impacts of these biases on the calculation of climatology and trends in temperature extremes at 409 sites in North America with at least 25 years of complete hourly records. Our results show that the calculation of daily temperature based on the average of minimum and maximum daily readings leads to an overestimation of the daily values of ~10+% when focusing on extremes and values above (below) high (low) thresholds. Moreover, the effects of the data processing method on trend estimation are generally small, even though the use of the daily minimum and maximum readings reduces the power of trend detection (~5-10% fewer trends detected in comparison with the reference data).

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

Due to inherent bias the climate model simulated precipitation and temperature cannot be used to drive a hydrological model without pre-processing e statistical downscaling. This often consists of reducing the bias in the climate model simulations (bias correction) and/or transformation of the observed data in order to match the projected changes (delta change). The validation of the statistical downscaling methods is typically limited to the scale for which the transformation was calibrated and the driving variables (precipitation and temperature) of the hydrological model. The paper introduces an R package ”musica” which provides ready to use tools for routine validation of statistical downscaling methods at multiple time scales as well as several advanced methods for statistical downscaling. The musica package is used to validate simulated runoff. It is shown that using conventional methods for downscaling of precipitation and temperature often leads to substantial biases in simulated runoff at all time scales.

The following users thanked this post: Alok Pandey, Diwan

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:

Thank you,
The following users thanked this post: Sonali, P KABBILAWSH

Study material / Lattice: Multivariate Data Visualization with R (Book)
« on: August 12, 2017, 06:04:34 PM »
Lattice brings the proven design of Trellis graphics (originally developed for S by William S. Cleveland and colleagues at Bell Labs) to R, considerably expanding its capabilities in the process. Lattice is a powerful and elegant high level data visualization system that is sufficient for most everyday graphics needs, yet flexible enough to be easily extended to handle demands of cutting edge research. Written by the author of the lattice system, this book describes it in considerable depth, beginning with the essentials and systematically delving into specific low levels details as necessary. No prior experience with lattice is required to read the book, although basic familiarity with R is assumed.

The book contains close to150 figures produced with lattice. Many of the examples emphasize principles of good graphical design; almost all use real data sets that are publicly available in various R packages. All code and figures in the book are also available online, along with supplementary material covering more advanced topics.

Deepayan Sarkar won the 2004 John M. Chambers Statistical Software Award for writing lattice while he was a graduate student in Statistics at the University of Wisconsin-Madison. He is currently doing postdoctoral research in the Computational Biology program at the Fred Hutchinson Cancer Research Center, a member of the R Core Team, and an active participant on the R mailing lists.

The following users thanked this post: Sat Kumar Tomer

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