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Messages - Pankaj Dey

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High-resolution information on climatic conditions is essential to many applications in environmental and ecological sciences. Here we present the CHELSA (Climatologies at high resolution for the earth’s land surface areas) data of downscaled model output temperature and precipitation estimates of the ERA-Interim climatic reanalysis to a high resolution of 30 arc sec. The temperature algorithm is based on statistical downscaling of atmospheric temperatures. The precipitation algorithm incorporates orographic predictors including wind fields, valley exposition, and boundary layer height, with a subsequent bias correction. The resulting data consist of a monthly temperature and precipitation climatology for the years 1979–2013. We compare the data derived from the CHELSA algorithm with other standard gridded products and station data from the Global Historical Climate Network. We compare the performance of the new climatologies in species distribution modelling and show that we can increase the accuracy of species range predictions. We further show that CHELSA climatological data has a similar accuracy as other products for temperature, but that its predictions of precipitation patterns are better.

Link to paper:

Link to data:

Observing surface water is essential for ecological and hydrological studies. This paper reviews the current status of detecting, extracting and monitoring surface water using optical remote sensing, especially progress in the last decade. It also discusses the current status and challenges in this field. For example, it was found that pixel unmixing and reconstruction, and spatio‐temporal fusion are two common and low‐cost approaches to enhance surface water monitoring. Remote sensing data have been integrated with in situ river flow to model spatio‐temporal dynamics of surface water. Recent studies have also proved that the river discharge can be estimated using only optical remote sensing imagery. This will be a breakthrough for hydrological studies in ungauged areas. Optical sensors are also easily obscured by clouds and vegetation. This limitation can be reduced by integrating optical data with Synthetic Aperture Radar (SAR) data and Digital Elevation Model (DEM) data. There is increasing demand of monitoring global water dynamics at high resolutions. It is now easy to achieve with the development of big data and cloud computation techniques. Enhanced global or regional water monitoring in the future requires integrated use of multiple sources of remote sensing data.


Hydrologic soil groups (HSGs) are a fundamental component of the USDA curve-number (CN) method for estimation of rainfall runoff; yet these data are not readily available in a format or spatial-resolution suitable for regional- and global-scale modeling applications. We developed a globally consistent, gridded dataset defining HSGs from soil texture, bedrock depth, and groundwater. The resulting data product—HYSOGs250m—represents runoff potential at 250 m spatial resolution. Our analysis indicates that the global distribution of soil is dominated by moderately high runoff potential, followed by moderately low, high, and low runoff potential. Low runoff potential, sandy soils are found primarily in parts of the Sahara and Arabian Deserts. High runoff potential soils occur predominantly within tropical and subtropical regions. No clear pattern could be discerned for moderately low runoff potential soils, as they occur in arid and humid environments and at both high and low elevations. Potential applications of this data include CN-based runoff modeling, flood risk assessment, and as a covariate for biogeographical analysis of vegetation distributions.

Link to paper:

Link to dataset:

Description of dataset: This dataset - HYSOGs250m - represents a globally consistent, gridded dataset of hydrologic soil groups (HSGs) with a geographical resolution of 1/480 decimal degrees, corresponding to a projected resolution of approximately 250-m. These data were developed to support USDA-based curve-number runoff modeling at regional and continental scales. Classification of HSGs was derived from soil texture classes and depth to bedrock provided by the Food and Agriculture Organization soilGrids250m system.


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.

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.


Speaker: Dr. Dennis Helder
Time: 18 May 2018 (Friday) @ 3:00 pm
Venue: DESE Auditorium , IISc
Radiometric calibration of optical remote sensing satellite sensors is a necessary step so that the data acquired by these systems can be placed on an absolute scale of radiance or reflectance. This fundamental step converts the raw digital numbers recorded by the satellite into physical units and thus turns a ‘pretty picture’ into a scientific data set. There are two basic approaches to performing radiometric calibration. The first is to design calibration systems into the instrument itself. Often this is done by incorporating diffuser panels and/or lamps into the instrument. However, this approach adds significant additional cost and weight. Thus, many satellites do not incorporate such onboard systems. The second approach is most often termed ‘vicarious calibration’ and it involves using information acquired from a distance such as information from the earth imagery itself. The South Dakota State University Image Processing Laboratory has specialized in vicarious calibration of remote sensing imagery for over 25 years. In this presentation the fundamental concepts of vicarious calibration will be presented, examples of the surface reflectance method and the pseudo invariant calibration site (PICS) method will be provided, and application of these methods to the Landsat image archive, from 1972 to the present, will be given.
About Speaker:
Dr. Dennis Helder has been involved with the characterization and calibration of space borne and airborne  remote sensing imaging systems for over 25 years. Initial work focused on characterization and removal of radiometric artifacts of the Landsat TM and MSS sensors. More recent work has emphasized development of vicarious radiometric calibration approaches for a variety of optical remote sensing systems as well as on-orbit point spread function estimation. Dr. Helder has served on several NASA and USGS EROS science teams including Landsat 7, Landsat 8, and EO-1. He is currently Associate Dean for Engineering Research and Distinguished Professor of Electrical Engineering at South Dakota State University and is also on detail to USGS EROS as director of the EROS CalVal Center of Excellence.

JASP is statistically inclusive as it offers both frequentist and Bayesian analysis methods. Indeed, the primary motivation for JASP is to make it easier for statistical practitioners to conduct Bayesian analyses. We firmly believe that Bayesian statistics deserves to be applied more often and more widely than it is today, and that there is more to statistical inference than the frequentist p-value. A pragmatist may argue that –irrespective of one’s statistical convictions– it is prudent to report the results from both paradigms; when the results point in the same direction, this bolsters one’s confidence in the conclusions, but when the results are in blatant contradiction, this will weaken one’s confidence.


Youtube video link:

We are recruiting a new postdoc for an exciting position to assess the role of dams in delivering improved food security in developing countries as part of the FutureDams research centre led by the University of Manchester. The selected candidate will use remote sensing, field datasets, and crop models to evaluate impacts of historic and future dam developments on agricultural productivity and livelihoods, and investigate trade-offs between agriculture and other water users (e.g. hydropower, environment). Research activities will focus on three primary case study river basins: the Nile and Volta basins in Africa, and the Salween River basin in Myanmar. The position is available for a period of 3 years starting from summer 2018 or as soon as possible thereafter. Further details and the project application form can be accessed by clicking the buttons below.


Demonstrating the “unit hydrograph” and flow routing processes involving active student participation – a university lecture experiment

 The unit hydrograph (UH) has been one of the most widely employed hydrological modelling techniques to predict rainfall–runoff behaviour of hydrological catchments, and is still used to this day. Its concept is based on the idea that a unit of effective precipitation per time unit (e.g. mm h−1) will always lead to a specific catchment response in runoff. Given its relevance, the UH is an important topic that is addressed in most (engineering) hydrology courses at all academic levels. While the principles of the UH seem to be simple and easy to understand, teaching experiences in the past suggest strong difficulties in students' perception of the UH theory and application. In order to facilitate a deeper understanding of the theory and application of the UH for students, we developed a simple and cheap lecture theatre experiment which involved active student participation. The seating of the students in the lecture theatre represented the hydrological catchment in its size and form. A set of plastic balls, prepared with a piece of magnetic strip to be tacked to any white/black board, each represented a unit amount of effective precipitation. The balls are evenly distributed over the lecture theatre and routed by some given rules down the catchment to the catchment outlet, where the resulting hydrograph is monitored and illustrated at the black/white board. The experiment allowed an illustration of the underlying principles of the UH, including stationarity, linearity, and superposition of the generated runoff and subsequent routing. In addition, some variations of the experimental setup extended the UH concept to demonstrate the impact of elevation, different runoff regimes, and non-uniform precipitation events on the resulting hydrograph. In summary, our own experience in the classroom, a first set of student exams, as well as student feedback and formal evaluation suggest that the integration of such an experiment deepened the learning experience by active participation. The experiment also initialized a more experienced based discussion of the theory and assumptions behind the UH. Finally, the experiment was a welcome break within a 3 h lecture setting, and great fun to prepare and run.


The recent levelling of global mean temperatures after the late 1990s, the so-called global warming hiatus or slowdown, ignited a surge of scientific interest into natural global mean surface temperature variability, observed temperature biases, and climate communication, but many questions remain about how these findings relate to variations in more societally relevant temperature extremes. Here we show that both summertime warm and wintertime cold extreme occurrences increased over land during the so-called hiatus period, and that these increases occurred for distinct reasons. The increase in cold extremes is associated with an atmospheric circulation pattern resembling the warm Arctic-cold continents pattern, whereas the increase in warm extremes is tied to a pattern of sea surface temperatures resembling the Atlantic Multidecadal Oscillation. These findings indicate that large-scale factors responsible for the most societally relevant temperature variations over continents are distinct from those of global mean surface temperature.


At the EGU General Assembly 2018 in Vienna, “Hydroinformatics for hydrology” short course (SC) was run for the fourth time. The previous themes of the SC were data-driven and hybrid techniques, data assimilation, and geostatistical modelling. And this year the focus was extreme value modelling. Participants of the SC were given a state-of-the-science overview of different aspects in extreme value analysis along with relevant case studies. Available R functions for extreme value analysis were also introduced. Thanks to Hugo’s excellent lecture, we now know common issues and pitfalls in using extreme value models (i.e. modelling choices and assumptions). We would like to thank Dr. Hugo Winterfrom EDF Energy for delivering the lecture. You can find his lecture slides (and exercises) in the attachments:

A postdoc position is immediately available in the area of hydrological modeling. We are particularly interested in those who have interests and experience in modeling large scale water and nutrient cycles because of the climate changes.

Appointment is initially for one year, with subsequent years possible pending on availability of funds and performance.
Salary is competitive and includes fringe benefits.

Applicants should send an inquiry with a cv to Professor Chen Zhu (

You can also visit our web site for our research activities at

Indiana University is an Equal Opportunity/Affirmative Action employer.
Women and minorities are especially encouraged to apply.

Based on the earlier post, a meeting was organised at EGU 2018 to finalise and categorize the problems into three status: gold, silver and out.

The following are the excerpts of the meeting held at EGU 2018.

We had excellent meetings on Friday 13 April 2018 (Splinter meeting at EGU in Vienna) and on Saturday 14 April (Vienna Catchment Symposium at TU Wien) with about 60 and 110 people attending, respectively.

These are the questions resulting from the LinkedIn discussion, the Friday Splinter meeting and additional email contributions received before Friday.

On Saturday we had three rounds of discussions in four break out groups and one final plenary discussion. In each round we discussed the questions, merged them, split them and reworded them as needed followed by a voting on prioritising the questions. The voting was for gold/silver/bronze/remove in each of the three break out group rounds. Only the gold and silver ones were retained for the plenary with an additional round of voting (by the entire plenary) for gold, silver or removing them from the list. The idea of the process was to whittle down the 260 questions initially proposed to a more coherent and smaller set of those questions deemed most important by the participants. The process resulted in 16 gold and 29 silver questions which are posted here.

A paper drafting team (Günter Blöschl, Elena Toth, Jeff McDonnell, Gia Destouni, Antonio Chambel, Elena Volpi, Jim Kirchner, Marc Bierkens, Christine Stumpp, Christophe Cudennec, Hubert Savenije, Siva Sivapalan, Aldo Fiori) has been formed to
- check whether there are any obvious ‘holes’ in the list and propose a small number of additional questions if needed
- wordsmith the questions
- start with an initial draft of the summary paper.

The updated list of questions will be circulated among the co-authors (those who have substantially contributed to the process which will be around 160 scientists) with a final voting on the list, and the co-authors will also be asked to provide suggestions for any changes to the paper draft.

The plan is to submit the paper to HSJ.

Many thanks again for all your contributions


Please find the attached pdf file for the document.

Abstract. This article extends a previous study Seneviratne et al. (2016) to provide regional analyses of changes in climate extremes as a function of projected changes in global mean temperature. We introduce the DROUGHT-HEAT Regional Climate Atlas, an interactive tool to analyse and display a range of well-established climate extremes and water-cycle indices and their changes as a function of global warming. These projections are based on simulations from the fifth phase of the Coupled Model Intercomparison Project (CMIP5). A selection of example results are presented here, but users can visualize specific indices of interest using the online tool. This implementation enables a direct assessment of regional climate changes associated with global mean temperature targets, such as the 2 and 1.5° limits agreed within the 2015 Paris Agreement.

Link to paper:

Link to interactive ploting wen interface:

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