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Messages - Pankaj Dey

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Verification and validation of numerical models of natural systems is impossible. This is because natural systems are never closed and because model results are always nonunique. Models can be confirmed by the demonstration of agreement between observation and prediction, but confirmation is inherently partial. Complete confirmation is logically precluded by the fallacy of affirming the consequent and by incomplete access to natural phenomena. Models can only be evaluated in relative terms, and their predictive value is always open to question. The primary value of models is heuristic.

Interesting information / The Renaissance of Hydrology
« on: March 28, 2019, 09:50:00 PM »
Hydrology has evolved as a transdisciplinary, data-driven science in a remarkably short period of time.
Link to read more:

Interesting information / Spectral Characteristics Viewer
« on: March 27, 2019, 08:48:36 PM »
The Spectral Characteristics Viewer allows users to determine which satellite bands are best suited for their research application.
The user can select different satellites/bands and chose spectral characteristics for minerals, vegetation, or water etc.
 Bands: The RSR for the following satellites are currently available:

    Landsat 8 OLI
    Landsat 8 TIRS (not currently displayed in viewer)
    Landsat 7 ETM+
    Landsat 5 TM
    Landsat 4 TM
    Landsat 5 MSS
    Landsat 4 MSS
    Earth Observing-1 (EO-1) Advanced Land Imager (ALI) (Ref:
    Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) (Ref:
    Terra Moderate Resolution Imaging Spectroradiometer (MODIS) (Ref:

    SENTINEL-2A (also available in the Sentinel 2 Document Library)

Spectra: The USGS Spectroscopy Lab contains the spectra source information available in this viewer.

Convolve: While the full process of convolve is to simulate how a satellite sensor detects a surface feature in each spectral band, in this current viewer version, the Convolve function displays only the full width at half maximum values (FWHM) for the selected satellite bands; full convolve functionality will be included in a future update of this viewer.


Hydrological sciences / A global data set of soil hydraulic properties
« on: March 27, 2019, 11:46:53 AM »
Agroecosystem models, regional and global climate models, and numerical weather prediction models require adequate parameterization of soil hydraulic properties. These properties are fundamental for describing and predicting water and energy exchange processes at the transition zone between solid earth and atmosphere, and regulate evapotranspiration, infiltration and runoff generation. Hydraulic parameters describing the soil water retention (WRC) and hydraulic conductivity (HCC) curves are typically derived from soil texture via pedotransfer functions (PTFs). Resampling of those parameters for specific model grids is typically performed by different aggregation approaches such a spatial averaging and the use of dominant textural properties or soil classes. These aggregation approaches introduce uncertainty, bias and parameter inconsistencies throughout spatial scales due to nonlinear relationships between hydraulic parameters and soil texture. Therefore, we present a method to scale hydraulic parameters to individual model grids and provide a global data set that overcomes the mentioned problems. The approach is based on Miller–Miller scaling in the relaxed form by Warrick, that fits the parameters of the WRC through all sub-grid WRCs to provide an effective parameterization for the grid cell at model resolution; at the same time it preserves the information of sub-grid variability of the water retention curve by deriving local scaling parameters. Based on the Mualem–van Genuchten approach we also derive the unsaturated hydraulic conductivity from the water retention functions, thereby assuming that the local parameters are also valid for this function. In addition, via the Warrick scaling parameter λ, information on global sub-grid scaling variance is given that enables modellers to improve dynamical downscaling of (regional) climate models or to perturb hydraulic parameters for model ensemble output generation. The present analysis is based on the ROSETTA PTF of Schaap et al. (2001) applied to the SoilGrids1km data set of Hengl et al. (2014).

The example data set is provided at a global resolution of 0.25 degree at

Link to the paper:

This is an editorial published in Current Science by Professor V. V. Srinivas of Department of Civil Engineering, Indian Institute of Science, Bangalore.
The link to access the material is as follows:

Interesting information / Moving to a World Beyond “p < 0.05”
« on: March 26, 2019, 09:47:10 AM »
Some of you exploring this special issue of The American Statistician might be wondering if it’s a scolding from pedantic statisticians lecturing you about what not to do with p-values, without offering any real ideas of what to do about the very hard problem of separating signal from noise in data and making decisions under uncertainty. Fear not. In this issue, thanks to 43 innovative and thought-provoking papers from forward-looking statisticians, help is on the way.


The Beckman Center of the National Academies of Sciences and Engineering
University of California Irvine
June 19 - 21, 2019

Precipitation estimation and prediction at regional to global scales:
Advances in Hydroclimatology and Impact Studies

Conference Theme

Precipitation remains one of the most challenging processes to model and predict at the local, regional and global scales with significant implications for our ability to quantify water cycle dynamics, inform decision making, and predict hydro-geomorphic hazards in response to extremes. A key to these efforts is adequate observations across space and time scales to constrain and improve models, inform data assimilation efforts, and detect and attribute changes in large scale dynamics and regional extremes. Building on the previous International Precipitation Conferences (IPCs), IPC12 aims to bring together the international community to integrate research, discuss challenges and opportunities, and craft future directions. Innovative contributions are sought that focus on three main themes: (1) estimation of precipitation from multiple sensors; (2) water cycle dynamics and predictive modeling at local to global scales; and (3) hydrologic impacts of precipitation extremes and anticipated change.

The Soroosh Sorooshian Hydrometeorology Symposium

A special feature of IPC12 will be to honor the pioneering career of Distinguished Professor Soroosh Sorooshian in advancing hydrometeorology research and applications, providing community leadership, and mentoring a cadre of colleagues over the past four decades. As the founder of the Center for Hydrometeorology and Remote Sensing (CHRS), he has built global capacity for monitoring, forecast and mitigation of hydrologic disasters through the development of precipitation products, leveraging and extending the benefits of space and weather agencies' technological resources into applications that assist hydrologists and water resource managers worldwide with equitable access to relevant information. A trademark of CHRS is the PERSIANN product used worldwide for hydrologic prediction and water resources applications. The Soroosh Sorooshian Hydrometeorology Symposium will be integrated through IPC12 with special lectures, events and celebrations.

Kerala state (India) experienced a devastating flood event during the month of August 2018. While an extreme rainfall event (ERE) was the primary reason for this flood, there was criticism at various levels that the authorities failed to manage the flood effectively through reservoir operations. One of the worst affected basins, Periyar River Basin (PRB), received a 145 year return period rainfall. This study reports the results and analysis of a modelling exercise using HEC-HMS to simulate and analyse the role of dams, as well as reservoir operations, on the flood of August 2018. The results indicated that the role of releases from the major reservoirs in the PRB resulting in the flood havoc was less. The analysis suggested that reservoir operations could not have helped in avoiding the flood situation as only 16–21% peak attenuation was possible by emptying the reservoir in advance, as the bulk of runoff to the flooding was also contributed by the intermediate catchments without any reservoirs to control. Further, the attenuated flood peak due to advance emptying of the reservoir would still be almost double the safe carrying capacity of the river section at Neeleswaram. In addition, the reliability of the rainfall forecast at higher lead times is also a concern for the reservoir operation. It is noted that the probability of EREs of this kind in the month of August in PRB is very small (0.6%), and therefore any planned operation could not have helped in mitigating floods of such magnitude without a reliable EREs forecast coupled with reservoir inflow forecasting system and optimized set of reservoir operational policies.

Climate change has pushed the natural limits of our environment, creating extreme weather events that are more frequent and more intense in certain locations around the globe. There is evidence of increasing trends in temperature extremes in most countries of South Asia, while in a few regions, temperature extremes have been decreasing. Heatwaves have intensified, which has contributed to accelerating drought and extreme flood events in most South Asian countries. Overall changes in rainfall and temperature have led to alterations in water availability in this region. With few exceptions, the general phenomenon in most South Asian countries is that rainfall intensity has increased, but with a reduced number of wet days. Studies that associate rainfall and temperature in the region of South Asia are scarce and rainfall extremes have been studied more extensively than temperature extremes. In fact, temperature trends are spatially less coherent than rainfall trends in most south Asian countries. It is more likely correlated for the teleconnection and South Asian climate for influencing the temperature and rainfall pattern, rather than any other factors. When it comes to trend estimations, statistical slope detection metrics, such as simple linear regression, have been commonly used to detect and quantify mean trends for countries in the regions of South Asia. The application of robust nonparametric statistical tests lacks to quantify temperature and rainfall extremes, particularly in the small countries of South Asia. Statistical downscaling is recommended for better prediction accuracy as well as to find spatial coherence in trends.

Interesting information / Internship : Remote Sensing
« on: March 23, 2019, 05:20:50 PM »
Job Summary
A keen interest in the field of Remote Sensing is essential. We are looking for candidates who have some exposure in working with satellite data.

Required Experience, Skills and Qualifications

    Educational qualification: M.Tech preferable
    Should have knowledge about the Earth Observation Satellites & experience in using satellite data like Landsat, Sentinel, MODIS…. However, If you are not from Remote Sensing background, you should have done any project related to remote sensing.
    Should have experience in using any GIS software like QGIS, GRASS-GIS, ..
    Programming : Day in and day out we write programs for processing data. Basic knowledge in programming is desirable. We dont judge on what programming language you use to code, however we look for the originality. Preferrable : R or Python
    Experience in using Microwave satellite data is an added advantage.


Interesting information / Internship : Water resources
« on: March 23, 2019, 05:18:51 PM »
Job Summary

A keen interest in the field of Water resources is essential. We are looking for candidates who have hands on exposure in running any Water Resources model like (e.g., VIC, MODFLOW, HEC-HMS. etc)

Required Experience, Skills and Qualifications

    Educational qualification: M.Tech (Water Resource preferable). The candidates should have taken the courses on Surface and groundwater hydrology, fluid mechanics and open channel flow.
    We work on Free and Open Source software (FOSS), so having exposure in any of the below requirement is desirable.
    Programming : Day in and day out we write programs for processing data. Hope you have written some program in your graduation. We don’t judge on what programming language you use, however we look for its originality. Preferable : R or Python
    Operating system : Ability to work in the Linux (any flavour) OS will be of added advantage.

« on: March 23, 2019, 10:44:35 AM »
**Michigan State University and the Kansas Geological Survey/University of Kansas**
Position 1: Groundwater Sustainability Pathways for the High Plains Aquifer

Seeking a postdoctoral scholar with a passion for groundwater sustainability and a penchant for thinking big to help envision a sustainable future for the High Plains Aquifer. The successful candidate will lead integrated land surface-groundwater modelling efforts to evaluate agricultural practices for the past and future of the High Plains Aquifer at multiple spatial and temporal scales. The postdoc will be based at the Kansas Geological Survey (University of Kansas) and have the opportunity to collaborate widely within multi-institution NSF INFEWS and USDA NIFA projects to produce high-impact research.

This position is funded for 2 years with the opportunity for extension pending performance and funding availability, and includes an annual research/travel budget to support professional development. The preferred start date is September 2019 with flexibility for the right candidate. For more information, please contact Sam Zipper (
Full job posting:
Position 2: Food, Energy, and Water in the Amazon and Mekong River Basins
Seeking a postdoctoral scholar ready to take on large-scale modeling challenges in data-limited regions. The Amazon and Mekong River Basins are undergoing rapid hydrologic, climatic, and land use changes, affecting two of the world’s most important hydrologic systems and the people and ecosystems dependent upon them. The postdoc will lead integrated surface- and groundwater-modelling efforts at both watershed and regional basin scales to better understand these vital systems, and how they are affected directly by dams and indirectly via land use and climate changes. The successful candidate will interact with two large, interdisciplinary project teams including multiple US institutions as well as international collaborators.

This position is funded for 2 years with the opportunity for extension pending performance and funding availability. The start date for this position can be as early as May 2019, with flexibility for the right candidate. For more information, please contact David Hyndman ( For more information on the research group, please visit
Position 3: Water, Agriculture, and Nutrients in the Great Lakes Basin and California Central Valley
Seeking a postdoctoral scholar eager to quantify the role of agricultural practices in water and nutrient cycling in diverse agricultural landscapes spanning the US and Canadian Great Lakes Basin, as well as California’s Central Valley. The postdoc will lead efforts to develop integrated surface- and groundwater-models for these two regions, and to integrate new capabilities into those models. In particular we are looking to add explicit nutrient cycling and transport, informed by existing nutrient surface application and statistical transport models. We are working in those regions with a variety of collaborators in disciplines including remote sensing, ecology, agronomy, sociology, and economics to better understand the role that agriculture plays in water resources.

This position is funded for 2 years with the opportunity for extension pending performance and funding availability. The start date for this position can be as early as May 2019, with flexibility for the right candidate. For more information, please contact David Hyndman ( For more information on the research group, please visit
Application Details and Required Qualifications
Common qualifications for all three positions include:
expertise in groundwater and/or land surface modelling;
ability to work both independently and collaboratively;
strong communication skills as evidenced by peer-reviewed publications/conference presentations; and
a water-related Ph.D. by the start date.
Experience with integrated models, GIS, and high-performance computing are considered a plus.
Unique qualifications by position include:
Position 1: Coding experience (any of Python, R, FORTRAN, MATLAB, C, etc.) is strongly desired, experience working in irrigated agricultural landscapes is a plus
Position 2: Coding experience (any of Python, R, FORTRAN, MATLAB, C, etc.) is required, knowledge of dam operations and management is a plus.
Position 3: Coding experience (any of Python, R, FORTRAN, MATLAB, C, etc.) is required, knowledge of irrigated agricultural landscapes and snow hydrology is a plus.
To apply, send Sam Zipper ( an email with the subject line 'Water Postdoc' and the following materials as a single PDF file:
Short (1-2 page) cover letter including which position(s) you would like to be considered for, why you are excited about them, and how you meet the qualifications.
Full CV.
Contact information for 3 references.
If you are interested in position 1, please also submit materials via the KU HR portal to - you can use the same cover letter for all 3 positions.
For full consideration, submit your application by April 15, but review of applications will continue until suitable candidates are found.
Michigan State University is an Equal Employment Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability or protected veteran status or any other characteristic protected by law and University policy.
The University of Kansas prohibits discrimination on the basis of race, color, ethnicity, religion, sex, national origin, age, ancestry, disability, status as a veteran, sexual orientation, marital status, parental status, gender identity, gender expression and genetic information in the University's programs and activities. The following person has been designated to handle inquiries regarding the non-discrimination policies: Executive Director of the Office of Institutional Opportunity and Access,, 1246 W. Campus Road, Room 153A, Lawrence, KS, 66045, (785)864-6414, 711 TTY.

Rainfall runoff models are tools used by hydrologists in climate change assessments to estimate how future streamflow might change in response to a given (often hypothetical) climate scenario. For example, suppose we can assume that rainfall in a particular location is going to reduce by 20% in the future. Does this mean that streamflow will also reduce by 20%? Or will it be 10% less or 40% less? Although rainfall runoff models are among the best tools available, they are often not very good at answering this question. When tested on historical multiyear droughts, they often perform poorly, and we are unsure why. One problem is that when a model fails in this task, it is difficult to know what went wrong. Perhaps there was a problem with the data, since environmental monitoring is often subject to large errors. Perhaps the problem lay not with the model itself but with the way it was trained, or calibrated, to the data. Lastly, perhaps the model itself—its mathematical equations—need to be changed. To improve our estimates, we need a method to test which cause is behind the model failure; otherwise, we might make changes where none are warranted. This paper proposes such a method, in the form of a multistep framework that can isolate the causes of model failures. By ensuring that our attention is focused in the correct direction, this framework will help us to understand and make better estimates of how river flow will be altered by a changing climate.

A correct quantification of mass and energy exchange processes among Earth's land surface, groundwater, and atmosphere requires an accurate parameterization of soil hydraulic properties. Pedotransfer functions (PTFs) are useful in this regard because they estimate these otherwise difficult to obtain characteristics using texture and other ubiquitous soil data. Most PTFs estimate parameters of empirical hydraulic functions with modest accuracy. In a continued pursuit of improving global‐scale PTF estimates, we evaluated whether improvements can be obtained when estimating parameters of hydraulic functions that make physically based assumptions. To this end, we developed a PTF that estimates the parameters of the Kosugi retention and hydraulic conductivity functions (Kosugi, 1994,, 1996,, which explicitly assume a lognormal pore size distribution and apply the Young‐Laplace equation to derive a corresponding pressure head distribution. Using a previously developed combination of machine learning and bootstrapping, the developed five hierarchical PTFs allow for estimates under practical data‐poor to data‐rich conditions. Using an independent global data set containing nearly 50,000 samples (118,000 retention points), we demonstrated that the new Kosugi‐based PTFs outperformed two van Genuchten‐based PTFs calibrated on the same data. The new PTFs were applied to a 1 × 1 km2 global map of texture and bulk density, thus producing maps of the parameters, field capacity, wilting point, plant available water, and associated uncertainties. Soil hydraulic parameters exhibit a much larger variability in the Northern Hemisphere than in the Southern Hemisphere, which is likely due to the geographical distribution of climate zones that affect weathering and sedimentation processes.

This paper introduces the HydroGrid as an interconnected system of water. Through analogies with the electrical grid, we describe emerging technologies in monitoring, analysis, and control of the HydroGrid. We broadly discuss interdependent functions of the HydroGrid such as human and animal health services, food safety, and aquatic ecosystems health. The state of the HydroGrid is assessed using sustainability measures such as reliability, vulnerability, and resilience that originated in engineering reliability theory.

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