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Messages - Hemant Kumar

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Interesting information / Using R in Hydrology - EGU2019 Short Course
« on: April 10, 2019, 08:33:26 PM »
Please find the link of the resources of R in Hydrology currently going on at EGU.
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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.
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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:
Advantage over old concept of p-values is shown in a figure attached to the post.
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Recently, a comment was published in Hydrology and Earth System Science - Discussion (HESS-D) ( on an article published in the same journal (HESS) in 2018 ( The nature of the comment does not contribute to constructive scientific debate. It is more like a rant, a vicious attack on the authors and their expertise, rather than on their work. This has evoked serious reactions from the hydrologic community (8 short comments in the last 3 days). I feel it is important for the Indian hydrologic community to be aware of such trolling and scientific harassments (thanks to Nandita Basu for pointing it out, and would request people to comment if they feel the need for it. Of course, all this is my personal opinion.
Link to original paper:
Link to commentary:

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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.

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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:

Link to data shared in figshare:
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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.
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Job Location: Noida
Modeler – Hydrology or Water Resources, RMS  - NOIDA


RMS is the world's leading provider of analytics and decision science solutions for the quantification and management of catastrophe risks.  The models and consulting services of RMS are used by hundreds of insurance and reinsurance companies, hedge funds, corporations, and governments to assess a wide-range of natural and man-made perils such as - earthquake, flood, windstorm, terrorism, and disease pandemic.  RMS continues to grow and diversify to service and meet the requirement of its clients through research and technology innovation and superior client service.  The company's strength lies in its ability to use and develop the skills of its people across a wide remit of business activities.


The Model Development Department at RMS is responsible for the development of models to assess the risk from natural and man-made catastrophes. A group in Noida works in close collaboration with colleagues in Europe and US, to help quantifying catastrophe risk across all of RMS's products and services.

Objective of the role

Looking for a skilled scientist/technical specialist as catastrophe risk modeler, or a similar role to be a part of model development team that develops probabilistic flood models. He/ she should have an in-depth understanding of hydrodynamics, hydrology, hydraulics, and a good understanding of the integration of data and models. He/ she should have good problem-solving and communication abilities and adapt to work in a quick-paced and project oriented environment.

Key Accountabilities

·         Responsible for understanding, analyzing & developing hydrological models for development of flood hazard model, loss model, calibration and validation
·         Communication and interaction with colleagues in global offices
·         Responsible for research driven process enhancement initiatives in flood modeling
Experience Required

·         Sound knowledge of hydrological and hydraulic processes and modelling
·         Command in one or more of the programming languages: Python, R, FORTRAN, MATLAB
·         Knowledge of C#, C++, JavaScript would be valued
·         Linux/Shell scripting for data management
·         Experience working with large datasets
·         Basic knowledge and interest in catastrophe risk insurance industry
·         D. or Post Graduate in Water Resource Engineering/Hydraulics Engineering/Hydrology with 3 to 5 years or research or industry project experience from a reputed institute
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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.”

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The work carried out by Ila Chawla along with Prof. P.P. Mujumdar titled "Partitioning Uncertainty in Streamflow Projections under Nonstationary Model Conditions" was recently published in the Advances in Water Resources Journal.

In this work, the authors, in a novel attempt, have addressed the possibility of nonstationary hydrological model (here they have used the VIC land surface model coupled with a routing model). They found that the hydrological model parameters, which are influenced by climate variables, vary over time, and thus may not be assumed to be stationary. Further, the work also involves attribution the total uncertainty in the streamflow projections to multiple effects such as, model parameters, GCM simulations, emission scenarios, land use scenarios, the assumption of hydrological model stationarity, along with internal variability of the model. The Upper Ganga Basin (UGB) is considered as a case study for this analysis.

Further details on the work can be found here:

Authors are glad to share their article with interested readers.

Correspondence to:
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Announcements / Prof. Mike Wallace's visit to CAOS (November 8-12, 2016)
« on: November 03, 2016, 10:16:37 AM »

Prof. john m. wallace of the university of Washington, seattle will be
visiting CAOS during the period November 8-12, 2016.

he is scheduled to give two talks, one on November 8 (Tuesday) and one on
November 10, both at 4pm.

1) The tropical atmospheric signature of ENSO (8/11/2016)

2) The dominant patterns of variability of global SST (10/11/2016).


Speaker: John M Wallace
         University of Washington, Seattle, USA

Date:  November 8, 2016 (Tuesday)

Time: 4PM

Venue: CAOS Seminar Hall


The atmospheric signature of ENSO, obtained by regressing fields of
geopotential height Z, wind, vertical velocity, and rainfall upon the
Nino 3.4 index, is partitioned into zonally symmetric and eddy
components. The zonally symmetric component is mechanically forced by
the weakening of the upper tropospheric equatorial stationary waves
and thermally forced by the equatorward shifting of the tropical
rainfall during El Nino (and vice versa). The eddy component is made
up of a planetary wave signature, the sum of baroclinic and barotropic
contributions, plus a residual. The baroclinic contribution is defined
as the leading EOF of the eddy Z regression coefficient matrix: the
EOFs are horizontal patterns and PCs are vertical profiles of their
amplitude and polarity. It exhibits a nearly equatorially symmetric
planetary wave structure comprising three dumbbell-shaped features
suggestive of equatorial Rossby waves. The weaker barotropic
contribution reflects the more top-heavy vertical profiles of
planetary wave amplitude in the cooler regions of the tropics versus
more bottom-heavy profiles in the warmer regions. The planetary wave
component accounts for nearly all the eddy ENSO signature in the free
atmosphere. The residual is dominated by a shallow, convergent,
boundary layer signature forced by the weakening of the equatorial
cold tongue in SST. The anomalous boundary layer convergence drives a
deep convection signature whose upper tropospheric outflow is an
integral part of the planetary wave signature of ENSO.


Title: The dominant patterns of variability of global SST

Speaker: John M Wallace
         University of Washington, Seattle, USA

Date:  November 10, 2016 (Thursday)

Time: 4PM

Venue: CAOS Seminar Hall


Empirical  orthogonal function  (EOF) analysis  of global  sea surface
temperature yields  modes in which interannual  variability associated
with  ENSO and  the lower  frequency variability  associated with  the
Pacific  decadal  oscillation  (PDO)  and  the  Atlantic  multidecadal
oscillation (AMO) are scrambled together with one another and with the
signature of global warming. Some  of the scrambling results from mode
mixing --  a term  I will  use in reference  to commingled  modes with
different  time   scales.  Using  sequences  of   pairwise  orthogonal
rotations of  the EOFs, it is  possible, without recourse to  a priori
filtering, to  recover a  relatively smooth, monotonic  global warming
signature along with dynamical modes  that resemble ENSO, the PDO, and
the AMO.  Novel elements  in the  rotation protocol  are (1)  a simple
algorithm to eliminate mode mixing between the dynamical modes and the
global warming  mode by transferring the  linear trends in the  PCs of
the former into the PCs of the latter and (2) a rationale for choosing
a rotation angle  that reduces or eliminates  correlations between the
paired PCs in  different specified frequency ranges.  The algorithm in
(1) is used  to compare the contributions of the  Atlantic and Pacific
dynamical modes  to the variance of  global mean SST (GSST)  about its
own trend line. The Pacific modes account for a larger fraction of the
variance but the  Atlantic mode plays an important  role in modulating
the rate of rise of GSST on the multidecadal time scale.
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Post your question/information / Re: Cauvery (Kaveri) River Water Dispute
« on: October 07, 2016, 01:54:07 PM »
Hi Alok,

             In the past it was a hydrological problem, but now it has been turned into social, cultural even legal problem.  Some times, I was thinking, instead of dividing the state with respect to language or other criteria, we should have divided the states with respect to hydrological basis. 

Thanks for sharing.
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