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Messages - Pankaj Dey

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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:

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:

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 :

A collection of statistical tools for objective (non-supervised) applications of the Regional Frequency Analysis methods in hydrology. The package refers to the index-value method and, more precisely, helps the hydrologist to: (1) regionalize the index-value; (2) form homogeneous regions with similar growth curves; (3) fit distribution functions to the empirical regional growth curves.

Link to the reference manual:

Add-on package to the 'airGR' package that simplifies its use and is aimed at being used for teaching hydrology. The package provides 1) three functions that allow to complete very simply a hydrological modelling exercise 2) plotting functions to help students to explore observed data and to interpret the results of calibration and simulation of the GR ('Génie rural') models 3) a 'Shiny' graphical interface that allows for displaying the impact of model parameters on hydrographs and models internal variables.

For details please visit the following links:



In this paper, we present a comprehensive review of the data sources and estimation methods of 30 currently available global precipitation data sets, including gauge-based, satellite-related, and reanalysis data sets. We analyzed the discrepancies between the data sets from daily to annual timescales and found large differences in both the magnitude and the variability of precipitation estimates. The magnitude of annual precipitation estimates over global land deviated by as much as 300 mm/yr among the products. Reanalysis data sets had a larger degree of variability than the other types of data sets. The degree of variability in precipitation estimates also varied by region. Large differences in annual and seasonal estimates were found in tropical oceans, complex mountain areas, northern Africa, and some high-latitude regions. Overall, the variability associated with extreme precipitation estimates was slightly greater at lower latitudes than at higher latitudes. The reliability of precipitation data sets is mainly limited by the number and spatial coverage of surface stations, the satellite algorithms, and the data assimilation models. The inconsistencies described limit the capability of the products for climate monitoring, attribution, and model validation.



Experimental work in hydrology is in decline. Based on a community survey, Blume et al. showed that the hydrological community associates experimental work with greater risks. One of the main issues with experimental work is the higher chance on negative results (defined here as when the expected or wanted result was not observed despite careful experimental design, planning and execution), resulting in a longer and more difficult publishing process. Reporting on negative results would avoid putting time and resources in repeating experiments that lead to negative results, and give experimental hydrologists the scientific recognition they deserve. With this commentary, we propose four potential solutions to encourage reporting on negative results, which might contribute to a stimulation of experimental hydrology.


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 relationship between rainfall variability and economic growth is complex, and tends to be significant in economies like India where agriculture plays a major role in economic output and food security. This paper seeks to provide insight into this relationship using Indian state-level economic and rainfall data from 1961 to 2012. We examine all 15 Indian states with populations exceeding 20m as of 2000, totalling 920m people, about 12% of the global population. Physical and human geography vary greatly between, and even within, these states, reflecting the global range of water security challenges and providing an analogue for a range of global economic development and environmental conditions. We identify three patterns of interdependence between rainfall variability and economic growth: i) Continuous Correlation of rainfall and economic growth rates, ii) Decayed Correlation from a significant to an insignificant relationship, and iii) Never Correlated i.e. no significant observable correlation between rainfall and growth. Sensitivity to rainfall variability is somewhat less in wetter states. Investment in irrigation infrastructure has helped states to reduce their economic sensitivity to rainfall variability, with three of the four states that have Decayed Correlation of growth with rainfall having the highest percentage expansion in irrigated areas of the 15 states. Greater use of groundwater supplies (rather than surface water) does not, however, appear to influence the sensitivity of economic growth to rainfall variability. The relationship between rainfall-growth correlation and long term income is complex; states which are correlated generally appear to be growing faster than states which are not correlated, but that growth is occurring from a lower per capita income level. Finally, confirming national trends for India, the paper does not find that economic diversification away from agriculture has reduced economic sensitivity to rainfall variability. The observation that growth in economically-diversified states can still be dependent on rainfall invites further research into the ways in which rainfall either directly, or through other hydro-climatic variables, influences the general economy.


Interesting information / ArXives of Earth science
« on: March 01, 2018, 10:47:39 AM »
Preprint servers afford a platform for sharing research before peer review. We are pleased that two dedicated preprint servers have opened for the Earth sciences and welcome submissions that have been posted there first.

Link for more details:

Assessing the accuracy of gridded climate datasets is highly relevant to climate-change impact studies, since evaluation, bias-correction and statistical downscaling of climate models commonly use these products as reference. Among all impact studies those addressing hydrological fluxes are the most affected by errors and biases plaguing these data. This paper introduces a framework, coined Hydrological Coherence Test (HyCoT), for assessing the hydrological coherence of gridded datasets with hydrological observations. HyCoT provides a framework for excluding meteorological forcing datasets not complying with observations, as function of the particular goal at hand. The proposed methodology allows falsifying the hypothesis that a given dataset is coherent with hydrological observations on the basis of the performance of hydrological modeling measured by a metric selected by the modeler. HyCoT is demonstrated in the Adige catchment (southeastern Alps, Italy) for streamflow analysis, using a distributed hydrological model. The comparison covers the period 1989-2008 and includes five gridded daily meteorological datasets: E-OBS, MSWEP, MESAN, APGD and ADIGE. The analysis highlights that APGD and ADIGE, the datasets with highest effective resolution, display similar spatio-temporal precipitation patterns and produce the largest hydrological efficiency indexes. Lower performances are observed for E-OBS, MESAN and MSWEP, especially in small catchments. HyCoT reveals deficiencies in the representation of spatio-temporal patterns of gridded climate datasets, which cannot be corrected by simply rescaling the meteorological forcing fields, as often done in bias-correction of climate model outputs. We recommend this framework to assess the hydrological coherence of gridded datasets to be used in large-scale hydro-climatic studies.


Theoretically, if the distribution of daily rainfall is known or justifiably assumed, then one could argue, based on extreme value theory, that the distribution of the annual maxima of daily rainfall would resemble one of the three limiting types: (a) type I, known as Gumbel; (b) type II, known as Fréchet; and (c) type III, known as reversed Weibull. Yet, the parent distribution usually is not known and often only records of annual maxima are available. Thus, the question that naturally arises is which one of the three types better describes the annual maxima of daily rainfall. The question is of great importance as the naive adoption of a particular type may lead to serious underestimation or overestimation of the return period assigned to specific rainfall amounts. To answer this question, we analyze the annual maximum daily rainfall of 15,137 records from all over the world, with lengths varying from 40 to 163 years. We fit the generalized extreme value (GEV) distribution, which comprises the three limiting types as special cases for specific values of its shape parameter, and analyze the fitting results focusing on the behavior of the shape parameter. The analysis reveals that (a) the record length strongly affects the estimate of the GEV shape parameter and long records are needed for reliable estimates; (b) when the effect of the record length is corrected, the shape parameter varies in a narrow range; (c) the geographical location of the globe may affect the value of the shape parameter; and (d) the winner of this battle is the Fréchet law.


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 spatial distribution of subsurface parameters such as permeability are increasingly relevant for regional to global climate, land surface and hydrologic models that are integrating groundwater dynamics and interactions. Despite the large fraction of unconsolidated sediments on Earth's surface with a wide range of permeability values, current global, high-resolution permeability maps distinguish solely fine-grained and coarse-grained unconsolidated sediments. Representative permeability values are derived for a wide variety of unconsolidated sediments and applied to a new global map of unconsolidated sediments to produce the first geologically-constrained, two-layer global map of shallower and deeper permeability. The new mean logarithmic permeability of the Earth’ surface is-12.7 + 1.7 m2 being one order of magnitude higher than derived from previous maps which is consistent with the dominance of the coarser sediments. The new dataset will benefit a variety of scientific applications including the next generation of climate, land surface and hydrology models at regional to global scales.


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