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Messages - Pankaj Dey

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Interesting information / Water Scarcity Atlas
« on: August 27, 2018, 06:46:04 PM »
This Global Water Scarcity Atlas provides an introduction to water scarcity and showcases analyses that cover the whole world, based on cutting-edge research.Water scarcity means there is not enough water to go around. There is a need to reduce demand or increase supply - or someone loses out. Whatever happens, when water scarcity hits, the world cannot stay the same.


Streamflow time series are commonly derived from stage‐discharge rating curves but the uncertainty of the rating curve and resulting streamflow series are poorly understood. While different methods to quantify uncertainty in the stage–discharge relationship exist, there is limited understanding of how uncertainty estimates differ between methods due to different assumptions and methodological choices. We compared uncertainty estimates and stage–discharge rating curves from seven methods at three river locations of varying hydraulic complexity. Comparison of the estimated uncertainties revealed a wide range of estimates, particularly for high and low flows. At the simplest site on the Isère River (France), full width 95% uncertainties for the different methods ranged from 3%‐17% for median flows. In contrast, uncertainties were much higher and ranged from 41%‐200% for high flows in an extrapolated section of the rating curve at the Mahurangi River (New Zealand) and 28%‐101% for low flows at the Taf River (United Kingdom), where the hydraulic control is unstable at low flows. Differences between methods result from differences in the sources of uncertainty considered, differences in the handling of the time varying nature of rating curves, differences in the extent of hydraulic knowledge assumed, and differences in assumptions when extrapolating rating curves above or below the observed gaugings. Ultimately, the selection of an uncertainty method requires a match between user requirements and the assumptions made by the uncertainty method. Given the significant differences in uncertainty estimates between methods, we suggest that a clear statement of uncertainty assumptions be presented alongside streamflow uncertainty estimates.


Hydrological sciences / A software review for extreme value analysis
« on: August 25, 2018, 05:39:28 PM »
Extreme value methodology is being increasingly used by practitioners from a wide range of fields. The importance of accurately modeling extreme events has intensified, particularly in environmental science where such events can be seen as a barometer for climate change. These analyses require tools that must be simple to use, but must also implement complex statistical models and produce resulting inferences. This document presents a review of the software that is currently available to scientists for the statistical modeling of extreme events. We discuss all software known to the authors, both proprietary and open source, targeting different data types and application areas. It is our intention that this article will simplify the process of understanding the available software, and will help promote the methodology to an expansive set of scientific disciplines.

Link to paper:

Link to host website:

Interesting information / Water Quality Modeling PostDoc
« on: August 24, 2018, 08:57:13 PM »
Position function: The Department of Agricultural and Biological Engineering at Mississippi State University in cooperation with NOAA is searching for a postdoctoral research associate to conduct research on coastal water quality dynamics and modeling in Biscayne Bay, Florida. The position will be housed and advised at Mississippi State University and also co-advised by the NOAA Atlantic Oceanographic and Meteorological Laboratory in Miami, Florida.
Essential duties and responsibilities: Conduct research on spatially and temporally variable water quality dynamics and land cover in Biscayne Bay, Florida, develop situationally appropriate models, write reports and peer reviewed publications, and other duties assigned by supervisor.
Minimum qualifications: PhD in environmental science, engineering, or related field
ABD/degree pending: Yes
Preferred qualifications: Experience with coastal watershed processes, nutrient dynamics, and phytoplankton dynamics
Knowledge, skills and abilities: GIS modeling, water quality modeling, computer coding, and statistical software
Working conditions and physical effort: The applicant must have the ability to perform routine office work and field work in inclement weather in the summer climate, including long hours and weekends. Extended periods of time may be spent on-site at Biscayne Bay, Florida.
Instructions for applying: Apply on-line by submitting a cover letter, resume, copy of your PhD transcript, and list of three references.


Interesting information / Vegetation Modeling Postdoc
« on: August 24, 2018, 08:51:51 PM »

Vegetation Modeling Postdoc
Los Alamos National Laboratory
Los Alamos, New Mexico
The Earth and Environmental Sciences (EES) Division at Los Alamos National Laboratory (LANL) is seeking applications from diverse postdoctoral candidates with expertise in modeling vegetation dynamics under various environmental conditions (e.g., coastal, tropics and arctic) for spring 2019. The successful candidate will improve the current state-of-the-art dynamic vegetation model, the DOE-sponsored Functionally Assembled Terrestrial Simulator (FATES), to better represent vegetation responses to water/nutrient limitations and salinity stress; parameterize and evaluate the model with observations from field or remote sensing; and test hypotheses related to vegetation responses to changes in future environmental conditions. The successful candidate will work within a multi-disciplinary team of plant physiologists, ecologists, hydrologists, geomorphologists and applied mathematicians from LANL and other national laboratories. Applications will be reviewed as received.
Requirements:Ph.D. in Ecology, Environmental Science, Earth System Modeling or a closely related field within the past five years (or soon to be completed)
  • Demonstrated experience in using models to predict and understand vegetation dynamics under different environmental conditions
Proven quantitative analysis skills with experience in one or more of the following programing and analysis languages: Python, C, Matlab and R
  • Proven ability to work in a highly collaborative team setting
Desired:Diverse research experience with a preference for Earth System modeling
  • Demonstrated experience in the analysis and incorporation of data and observations for model development and testing
  • Demonstrated experience in model development and high-performance computing
  • Scientific excellence as evidenced by publication in refereed journals
We Are Delivering Scientific Excellence Los Alamos National Laboratory is more than a place to work. It is a catalyst for discovery, innovation and achievement. It’s one of the reasons we attract world-class talent who contribute greatly to our outstanding culture. Professional development, work/life balance and a diverse and inclusive team foster lasting career satisfaction. Our on-site cafeterias and medical, fitness and breastfeeding facilities, education assistance and generous compensation and benefits reflect our commitment to providing our people with all they need for personal and professional growth.Apply now:, search IRC67661: Alamos National Laboratory is an equal opportunity employer and supports a diverse and inclusive workforce. All employment practices are based on qualification and merit, without regards to race, color, national origin, ancestry, religion, age, sex, gender identity, sexual orientation or preference, marital status or spousal affiliation, physical or mental disability, medical conditions, pregnancy, status as a protected veteran, genetic information, or citizenship within the limits imposed by federal laws and regulations. The Laboratory is also committed to making our workplace accessible to individuals with disabilities and will provide reasonable accommodations, upon request, for individuals to participate in the application and hiring process. To request such an accommodation, please send an email to or call 1-505-665-4444 option 1.

Water availability is a major factor constraining humanity's ability to meet the future food and energy needs of a growing and increasingly affluent human population. Water plays an important role in the production of energy, including renewable energy sources and the extraction of unconventional fossil fuels that are expected to become important players in future energy security. The emergent competition for water between the food and energy systems is increasingly recognized in the concept of the “food‐energy‐water nexus.” The nexus between food and water is made even more complex by the globalization of agriculture and rapid growth in food trade, which results in a massive virtual transfer of water among regions and plays an important role in the food and water security of some regions. This review explores multiple components of the food‐energy‐water nexus and highlights possible approaches that could be used to meet food and energy security with the limited renewable water resources of the planet. Despite clear tensions inherent in meeting the growing and changing demand for food and energy in the 21st century, the inherent linkages among food, water, and energy systems can offer an opportunity for synergistic strategies aimed at resilient food, water, and energy security, such as the circular economy.

Link :

4 year Postdoc available at University of Exeter to work on the exciting links between tipping point precursors and Emergent Constraints on future climate change.


bigleaf is an R package for the calculation of physical (e.g. aerodynamic conductance, surface temperature) and physiological (e.g. canopy conductance, water-use efficiency) ecosystem properties from eddy covariance data and accompanying meteorological measurements. All calculations are based on a 'big-leaf' representation of the vegetation and return representative bulk ecosystem/canopy variables.

Link to R Manual:
CitationKnauer J, El-Madany TS, Zaehle S, Migliavacca M (2018) Bigleaf—An R package for the calculation of physical and physiological ecosystem properties from eddy covariance data. PLoS ONE

Hydrological sciences / Creating aesthetically pleasing plots in MATLAB
« on: August 16, 2018, 11:08:56 PM »
Dear All,

Most of us use MATLAB as one of the programming tools as well as for data visualization. Different kind of plots such as Probability Density Function (PDF), Boxplot, Heatmap, creating beautiful confidence interval around statistic, are often used in Hydrology and Water Resources Engineering.

Here, I am sharing some of the links from Github to do all kinds of plots mentioned above. If you have a Github account, fork them for future reference. All the functions are explained step by step.

1. Probability Density Function:

2. Boxplots:
3. Heatmaps:

4. Confidence Interval or Bounded Lines:

Thank you very much.

Interesting information / The Effects of Tropical Vegetation On Rainfall
« on: August 14, 2018, 10:58:27 PM »
Vegetation modifies land-surface properties, mediating the exchange of energy, moisture, trace gases, and aerosols between the land and the atmosphere. These exchanges influence the atmosphere on local, regional, and global scales. Through altering surface properties, vegetation change can impact on weather and climate. We review current understanding of the processes through which tropical land-cover change (LCC) affects rainfall. Tropical deforestation leads to reduced evapotranspiration, increasing surface temperatures by 1–3 K and causing boundary layer circulations, which in turn increase rainfall over some regions and reduce it elsewhere. On larger scales, deforestation leads to reductions in moisture recycling, reducing regional rainfall by up to 40%. Impacts of future tropical LCC on rainfall are uncertain but could be of similar magnitude to those caused by climate change. Climate and sustainable development policies need to account for the impacts of tropical LCC on local and regional rainfall.

Link to paper:

In recent decades India has undergone substantial land use/land cover change as a result of population growth and economic development. Historical land use/land cover maps are necessary to quantify the impact of change at global and regional scales, improve predictions about the quantity and location of future change and support planning decisions. Here, a regional land use change model driven by district-level inventory data is used to generate an annual time series of high-resolution gridded land use/land cover maps for the Indian subcontinent between 1960–2010. The allocation procedure is based on statistical analysis of the relationship between contemporary land use/land cover and various spatially explicit covariates. A comparison of the simulated map for 1985 against remotely-sensed land use/land cover maps for 1985 and 2005 reveals considerable discrepancy between the simulated and remote sensing maps, much of which arises due to differences in the amount of land use/land cover change between the inventory data and the remote sensing maps.

Link to paper:

Link to data:

While analyzing geospatial data, easy visualization is often needed that allows for quick plotting, and simple, but easy interactivity. Additionally, visualizing geospatial data in projected coordinates is also desirable. The 'quickmapr' package provides a simple method to visualize 'sp', 'sf' (via coercion to 'sp'), and 'raster' objects, allows for basic zooming, panning, identifying,labeling, selecting, and measuring spatial objects. Importantly, it does not require that the data be in geographic coordinates.


Reliable meteorological data are a basic requirement for hydrological and ecological studies at the landscape scale. Given the large spatial variation of meteorology over complex terrains, meteorological records from a single weather station are often not representative of entire landscapes. Studies made on multiple sites over a landscape require different meteorological series for each site; and other studies may require meteorological data series for all grid cells of a landscape, in a continuous way. In these cases, spatial correlation between the meteorology series of different sites or cells must be taken into account. For example, the sequence of days with rain of contiguous cells will normally be the same or very similar, even if precipitation amounts may differ. Finally, studies addressing the impacts of climate change on forests and landscapes require downscaling coarse-scale predictions of global or regional climate models to the landscape scale. When downscaling predictions for several locations in a landscape, spatial correlation of predictions is also important.

With the aim to assist research of climatic impacts on forests, the R package meteoland provides utilities to estimate daily weather variables at any position over complex terrains:

 1. Spatial interpolation of daily weather records from meteorological stations.

 2. Statistical correction of meteorological data series (e.g. from climate models).

 Spatial interpolation is required when meteorology for the area and period of interest cannot be obtained from local sensors. The nearest weather station may not have data for the period of interest or it may be located too far away to be representative of the target area. Correcting the biases of a meteorological data series containing biases using a more accurate meteorological series is necessary when the more accurate series does not cover the period of interest and the less accurate series does. The less accurate series may be at coarser scale, as with climate model predictions or climate reanalysis data. In this case one can speak of statistical correction adn downscaling. However, one may also correct the predictions of climate models using reanalysis data estimated at the same spatial resolution.

Link to paper:

Link to R Manual:

Link to User Guide:

Gramm is a powerful plotting toolbox which allows to quickly create complex, publication-quality figures in Matlab, and is inspired by R's ggplot2 library. As a reference to this inspiration, gramm stands for GRAMmar of graphics for Matlab.



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