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Topics - Pankaj Dey

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Information on global future gridded emissions and land-use scenarios is critical for many climate and global environmental modelling studies. Here, we generated such data using an integrated assessment model (IAM) and have made the data publicly available. Although the Coupled Model Inter-comparison Project Phase 6 (CMIP6) offers similar data, our dataset has two advantages. First, the data cover a full range and combinations of socioeconomic and climate mitigation levels, which are considered as a range of plausible futures in the climate research community. Second, we provide this dataset based on a single integrated assessment modelling framework that enables a focus on purely socioeconomic factors or climate mitigation levels, which is unavailable in CMIP6 data, since it incorporates the outcomes of each IAM scenario. We compared our data with existing gridded data to identify the characteristics of the dataset and found both agreements and disagreements. This dataset can contribute to global environmental modelling efforts, in particular for researchers who want to investigate socioeconomic and climate factors independently.

Link to paper:

Link to data:

Competition over limited water resources is one of the main concerns for the coming decades. Although water issues alone have not been the sole trigger for warfare in the past, tensions over freshwater management and use represent one of the main concerns in political relations between riparian states and may exacerbate existing tensions, increase regional instability and social unrest. Previous studies made great efforts to understand how international water management problems were addressed by actors in a more cooperative or confrontational way. In this study, we analyze what are the pre-conditions favoring the insurgence of water management issues in shared water bodies, rather than focusing on the way water issues are then managed among actors. We do so by proposing an innovative analysis of past episodes of conflict and cooperation over transboundary water resources (jointly defined as “hydro-political interactions”). On the one hand, we aim at highlighting the factors that are more relevant in determining water interactions across political boundaries. On the other hand, our objective is to map and monitor the evolution of the likelihood of experiencing hydro-political interactions over space and time, under changing socioeconomic and biophysical scenarios, through a spatially explicit data driven index. Historical cross-border water interactions were used as indicators of the magnitude of corresponding water joint-management issues. These were correlated with information about river basin freshwater availability, climate stress, human pressure on water resources, socioeconomic conditions (including institutional development and power imbalances), and topographic characteristics. This analysis allows for identification of the main factors that determine water interactions, such as water availability, population density, power imbalances, and climatic stressors. The proposed model was used to map at high spatial resolution the probability of experiencing hydro-political interactions worldwide. This baseline outline is then compared to four distinct climate and population density projections aimed to estimate trends for hydro-political interactions under future conditions (2050 and 2100), while considering two greenhouse gases emission scenarios (moderate and extreme climate change). The combination of climate and population growth dynamics is expected to impact negatively on the overall hydro-political risk by increasing the likelihood of water interactions in the transboundary river basins, with an average increase ranging between 74.9% (2050 – population and moderate climate change) to 95% (2100 - population and extreme climate change). Future demographic and climatic conditions are expected to exert particular pressure on already water stressed basins such as the Nile, the Ganges/Brahmaputra, the Indus, the Tigris/Euphrates, and the Colorado. The results of this work allow us to identify current and future areas where water issues are more likely to arise, and where cooperation over water should be actively pursued to avoid possible tensions especially under changing environmental conditions. From a policy perspective, the index presented in this study can be used to provide a sound quantitative basis to the assessment of the Sustainable Development Goal 6, Target 6.5 “Water resources management”, and in particular to indicator 6.5.2 “Transboundary cooperation”.



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.


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