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Topics - Pankaj Dey

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Job Summary
The Cooperative Institute of Great Lakes Research (CIGLR) is seeking a postdoctoral scholar to lead research related to the development, testing, and deployment of hydrological models across the Great Lakes basin. Representative objectives of projects include calibration and verification of Weather Research and Forecasting (WRF)-Hydro and its meteorological forcings to support development of NOAA’s National Water Model, evaluation of potential empirical relationships between the risk of nutrient loading and land surface model parameters, and customization to improve local flood forecasting capabilities. The fellow will be expected to lead one or more of these projects, and will be given the intellectual freedom to pursue additional ideas of their own that contribute to the broader goals of hydrological modeling in the Great Lakes.
The postdoctoral scholar will work with a team of hydrological modelers at CIGLR, and in collaboration with modeling teams at the NOAA Great Lakes Environmental Research Laboratory (GLERL), University of Michigan, and the National Center for Atmospheric Research (NCAR) in Boulder, Colorado. The ideal candidate will have excellent communication skills, be prepared to guide technical support staff, report findings to internal and external audiences in reports and presentations, and lead publications in scientific journals.
The successful applicant’s appointment will be with CIGLR, which is part of the University of Michigan’s School for Environment and Sustainability located in Ann Arbor, Michigan. CIGLR is a collaboration between the University of Michigan and NOAA that brings together experts from academia and government research labs to work on pressing problems facing the Great Lakes region. The fellow will spend the majority of their time at the NOAA Great Lakes Environmental Research Laboratory in Ann Arbor and work in close collaboration with colleagues at the University of Michigan and the National Center for Atmospheric Research (NCAR).
The University of Michigan is consistently ranked among the top American public research universities, and Ann Arbor is routinely ranked as one of the best places to live in the U.S. due to its affordability, natural beauty, preservation of wooded areas, vibrant arts program, and lively downtown.
This position offers a highly competitive salary plus benefits. The initial appointment is for one year, with opportunity for extension based on performance, need, and availability of funds.
SEAS Diversity, Equity, and Inclusion Mission
At SEAS we are committed to creating and maintaining an inclusive and equitable environment that respects diverse experiences, promotes generous listening and communications, and discourages and restoratively responds to acts of discrimination, harassment, or injustice. Our commitment to diversity, equity and inclusion is deeply rooted in our values for a sustainable and just society
This position requires a Ph.D. in the natural sciences or engineering, with a background in hydrological science and modeling, and a solid record of scholarship.
To apply visit:
Applicants should prepare the following materials in a single PDF:•  Cover letter describing your qualifications related to the position and research accomplishments •  Curriculum vitae•  Contact information for three professional references•  Two representative publications
Applications are due by December 15, 2018.
U-M EEO/AA Statement
The University of Michigan is an equal opportunity/affirmative action employer.

Extreme weather and climate events, although rare at any particular location, can lead to different amount of loss to exposed human and natural systems, even to disasters. In this paper, the most authoritative definitions of “extreme events” given by the World Meteorological Organization and Intergovernmental Panel on Climate Change have been considered, as well as the underlying basic concepts, i.e., selected intensity levels, selected percentiles, multiples of the standard deviation, return period, distribution tails, imprint left, cause-effect relationships, natural disasters. Definitions and criteria have been tested with real world case studies using long instrumental records (300 years of daily temperature and 200 years of daily precipitation in Bologna, Italy) and proxy series (1000 years of Venice lagoon frozen over and 300 years of Po River outflow). The analysis reveals that each definition leads to particular consequences, e.g., in the peak over threshold theory, if the threshold is expressed in absolute terms, the number of extreme events may change with the climate period as tested with the Venice case study; as opposed, the relative definition based on percentiles or standard deviation will keep unchanged this frequency. Again, considering extreme events those external to the 10th or 90th percentile of the distribution may lead to a return period too short, e.g., 10 days for daily records, while a 10 year return period would require 1st or 99th percentiles, as tested with the daily temperature and precipitation. In addition, the distribution of a series may substantially change shape passing from daily to monthly and yearly averages as tested with the series taken as examples. The specific case of proxies is also considered analysing their uncertainties and categorization. The lagoon frozen over as a consequence of exceptionally severe winters constitutes an example of extremes based on an absolute threshold, as well as cause-effect relationship, and the return period was highly affected by the change of climate periods. The example of the overflow of the Po River suggests that the occurrence of extremes, and their intensity, may be altered by other factors that concur to the final result.


HydroSight is a highly flexible statistical toolbox for quantitative hydrogeological insights. It comprises of a powerful groundwater hydrograph time-series modelling and simulation framework plus a data quality analysis module. Multiple models can be built for one bore, allowing statistical identification of the dominant processes, or 100’s of bores can be modelled to quantify aquifer heterogeneity. This flexibility allows many novel applications such as:Separations of the impacts from pumping and drought over time.
  • Probabilistic estimation of aquifer hydraulic properties.
  • Estimation of the impacts of re-vegetation on groundwater level.
  • Exploration of groundwater management scenarios.
  • Interpolation and extrapolation irregularly observed hydrograph at a daily time-step.
The toolbox can be used from a highly flexible and stand-alone graphical user interface ( or programmatically from within Matlab 2014b (or later).

Interesting information / Targeting 1.5 °C
« on: October 10, 2018, 07:24:26 PM »
In December 2015, representatives from 195 nations met in Paris to negotiate an international agreement to combat climate change. The resulting ‘Paris Agreement’ codified an aspiration to limit the level of global temperature rise to 1.5 °C above pre-industrial levels —  lower than the previously generally agreed target of 2 °C. From a research standpoint, a more ambitious temperature target poses many questions that could draw scientific and intellectual attention and resources. Furthermore, the timescales in which researchers must decide how to engage with this new policy context is very short.
The Intergovernmental Panel on Climate Change has agreed to publish a special report on the costs and implications of the 1.5 °C target in 2018. In order to inform that process, researchers must decide which efforts to prioritise and begin work almost immediately. But deciding what can and should be delivered is far from trivial. This evolving collection draws together content from Nature Climate Change, Nature Geoscience, Nature Communications and Nature to provide comment on how research might best inform decisions about limiting climate warming as well as presenting pertinent new research that adressess this very question.


In regression, we assume noise is independent of all measured predictors. What happens if it isn’t?

A number of key assumptions underlie the linear regression model – among them linearity and normally distributed noise (error) terms with constant variance In this post, I consider an additional assumption: the unobserved noise is uncorrelated with any covariates or predictors in the model.


Global Warming of 1.5 °C an IPCC special report on the impacts of global warming of 1.5 °C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty


Interesting information / Digital tools for researchers
« on: October 07, 2018, 08:33:47 PM »
Here is a collection of digital tools that are designed to help researchers explore the millions of research articles available to this date. Search engines and curators help you to quickly find the articles you are interested in and stay up to date with the literature. Article visualization tools enhance your reading experience, for instance, by helping you navigate from a paper to another.


As a key variable in the climate system, soil moisture (SM) plays a central role in the earth's terrestrial water, energy, and biogeochemical cycles through its coupling with surface latent heat flux (LH). Despite the need to accurately represent SM/LH coupling in earth system models, we currently lack quantitative, observation‐based, and unbiased estimates of its strength. Here, we utilize the triple collocation (TC) approach introduced in Crow et al. (2015) to SM and LH products obtained from multiple satellite remote sensing platforms and land surface models (LSMs) to obtain unbiased global maps of SM/LH coupling strength. Results demonstrate that, relative to coupling strength estimates acquired directly from remote sensing‐based datasets, the application of TC generally enhances estimates of warm‐season SM/LH coupling, especially in the western United States, the Sahel, Central Asia, and Australia. However, relative to triple collocation estimates, LSMs (still) over‐predict SM/LH coupling strength along transitional climate regimes between wet and dry climates, such as the central Great Plains of North America, India, and coastal Australia. Specific climate zones with biased relations in LSMs are identified to geographically focus the re‐examination of LSM parameterizations. TC‐based coupling strength estimates are robust to our choice of LSM contributing SM and LH products to the TC analysis. Given their robustness, TC‐based coupling strength estimates can serve as an objective benchmark for investigating model predicted SM/LH coupling.



Department of Civil Engineering
Indian Institute of Science
Bangalore 560 012
Speaker:  Dr Gilles BOULET
                 Scientist, IRD, France
Title of the talk: High resolution remote sensing  (<100m) for 
       evapotranspiration  retrieval at global scale
Date and Time: 9th October, Tuesday
                             3.30 PM
Venue: Conference Hall, First floor, Civil Engineering Department
ALL ARE WELCOME                                                           
Coffee/ Tea: 3:15PM on 9th October 2018

The FloPy package consists of a set of Python scripts to run MODFLOW, MT3D, SEAWAT and other MODFLOW-related groundwater programs. FloPy enables you to run all these programs with Python scripts. The FloPy project started in 2009 and has grown to a fairly complete set of scripts with a growing user base. FloPy3 was released in December 2014 with a few great enhancements that make FloPy3 backwards incompatible. The first significant change is that FloPy3 uses zero-based indexing everywhere, which means that all layers, rows, columns, and stress periods start numbering at zero. This change was made for consistency as all array-indexing was already zero-based (as are all arrays in Python). This may take a little getting-used-to, but hopefully will avoid confusion in the future. A second significant enhancement concerns the ability to specify time-varying boundary conditions that are specified with a sequence of layer-row-column-values, like the WEL and GHB packages. A variety of flexible and readable ways have been implemented to specify these boundary conditions. FloPy is an open-source project and any assistance is welcomed. Please email the development team if you want to contribute.



Extreme precipitation events and flooding that cause losses to human lives and infrastructure have increased under the warming climate. In August 2018, the state of Kerala (India) witnessed large-scale flooding, which affected millions of people and caused 400 or more deaths. Here, we examine the return period of extreme rainfall and the potential role of reservoirs in the recent flooding in Kerala. We show that Kerala experienced 53% above normal rainfall during the monsoon season (till August 21st) of 2018. Moreover, 1, 2, and 3-day extreme rainfall in Kerala during August 2018 had return periods of 75, 200, and 100 years. Six out of seven major reservoirs were at more than 90% of their full capacity on August 8, 2018, before extreme rainfall in Kerala. Extreme rainfall at 1–15 days durations in August 2018 in the catchments upstream of the three major reservoirs (Idukki, Kakki, and Periyar) had the return period of more than 500 years. Extreme rainfall and almost full reservoirs resulted in a significant release of water in a short-span of time. Therefore, above normal seasonal rainfall (before August 8, 2018), high reservoir storage, and unprecedented extreme rainfall in the catchments where reservoirs are located worsened the flooding in Kerala. Reservoir operations need be improved using a skillful forecast of extreme rainfall at the longer lead time (4–7 days).


In this article, we suggest that giving greater prominence to the analysis of failures and errors would more fruitfully advance the hydrological sciences. As widely recognised by philosophers of science, we can all learn from our mistakes, and errors can lead to discovery if they are properly diagnosed. However, failure stories are very seldom communicated and published, even though they represent the bulk of the results obtained by researchers and modellers. This article is the result of passionate discussions held in a workshop called the Court of Miracles of Hydrology held in Paris in June 2008. The participants had been invited to present their unpublished experience with what could be called monsters, anomalies, outliers and failures in their everyday practice of hydrology. The review of these studies clearly shows that in-depth analysis of these observations and results that deviate from the expected norm blazes a trail that can only lead to progress.


Assessments of water balance changes, watershed response, and landscape evolution to climate change require representation of spatially and temporally varying rainfall fields over a drainage basin, as well as the flexibility to simply modify key driving climate variables (evaporative demand, overall wetness, storminess). An empirical–stochastic approach to the problem of rainstorm simulation enables statistical realism and the creation of multiple ensembles that allow for statistical characterization and/or time series of the driving rainfall over a fine grid for any climate scenario. Here, we provide details on the STOchastic Rainfall Model (STORM), which uses this approach to simulate drainage basin rainfall. STORM simulates individual storms based on Monte Carlo selection from probability density functions (PDFs) of storm area, storm duration, storm intensity at the core, and storm center location. The model accounts for seasonality, orography, and the probability of storm intensity for a given storm duration. STORM also generates time series of potential evapotranspiration (PET), which are required for most physically based applications. We explain how the model works and demonstrate its ability to simulate observed historical rainfall characteristics for a small watershed in southeast Arizona. We explain the data requirements for STORM and its flexibility for simulating rainfall for various classes of climate change. Finally, we discuss several potential applications of STORM.

Link to paper:

Link to Github Codes (Both MATLAB and Python):

Coastal aquifers embody the subsurface transition between terrestrial and marine systems, and form the almost invisible pathway for tremendous volumes of freshwater that flow to the ocean. Changing conditions of the earth’s landscapes and oceans can disrupt the fragile natural equilibrium between fresh and saltwater that exists in coastal zones. Among these, over-abstraction of groundwater is considered the leading man-made cause of seawater intrusion. Moreover, many of the world’s largest urban settings, where sources of contamination are profuse, have been built over the freshwater in coastal aquifers. Thus, coastal aquifers are important receptors of human impacts to water on Earth (Michael et al., 2017). This Special Issue on ‘Investigation and Management of Coastal Aquifers’ contains current scientific advances on the topic, dealing with the storage and quality of water, affected by stressors ranging in scale from point source contamination to global climate change.


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