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Messages - Pankaj Dey

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16
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: https://www.geosci-model-dev.net/11/3713/2018/


Link to Github Codes (Both MATLAB and Python): https://github.com/blissville71/STORM

17
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.


Link: https://www.sciencedirect.com/journal/journal-of-hydrology/vol/551

18
Google just released the (beta) version of a dataset search a couple days ago (Dataset Search Link), allowing users to effectively and efficiently search publicly available databases. With the use of Google-fu (a quick  graphic guide) or even regular Google skills, discovering data sets that might be buried in a web page are more readily discoverable.


Link to the blog: https://waterprogramming.wordpress.com/2018/09/07/google-dataset-search-beta-revolutionize-your-data-searches-and-database-exposure/?utm_source=dlvr.it&utm_medium=twitter


You can also subscribe to the blog called Water Programming: A Collaborative Research Blog
https://waterprogramming.wordpress.com/

Link to Google Dataset Search Engine: https://toolbox.google.com/datasetsearch

Google Dataset Guide's link: https://developers.google.com/search/docs/data-types/dataset

19
MODISTools is an R package for retrieving and using MODIS data subsets using ORNL DAAC web service (SOAP) for subsetting from Oak Ridge National Laboratory (ORNL). It provides a batch method for retrieving subsets of MODIS http://modis.gsfc.nasa.gov/ remotely sensed data and processing them to a format ready for user-friendly application in R, such as statistical modelling.


The most important function is MODISSubsets, for requesting subsets from a given MODIS product for multiple time-series. Each time-series is defined by a coordinate location (WGS-1984), a surrounding area of interest, and a start and end date. Automating this as a batch process reduces time, effort, and human error. Alternatively, MODISTransects expands upon MODISSubsets by extracting MODIS data along a transect, and its surrounding neighbourhood. Downloaded subsets are saved in ASCII files, but can be converted to ASCII grid files for use in a GIS environment. MODISSummaries computes summary statistics of downloaded subsets and organises summarised data back with the original input dataset, creating a csv file that can be easily used for modelling; this provides efficient storage of data and a transparent process from data collection, to processing, to a form that is ready for final use.


The functions were originally used for downloading vegetation indices data, but have been generalised to provide a package that performs the same functionality for any MODIS data that available through the web service. For a list of available MODIS products, see http://daac.ornl.gov/MODIS/MODIS-menu/products.html. Other minor functions -- including a lat-long coordinate conversion tool -- are included to aid this process.


Recent stable releases of this package have been checked and built on Windows, Mac, and Linux, and last checked on R 3.1.2 on 2014-12-22. MODISTools is written by Sean Tuck and Helen Phillips. This package can be used under the terms of the GNU GPLv3 license; feel free to use and modify as you wish, but please cite our work where appropriate. To cite MODISTools in publications, please use:


Tuck, S.L., Phillips, H.R.P., Hintzen, R.E., Scharlemann, J.P.W., Purvis, A. and Hudson, L.N. (2014) MODISTools -- downloading and processing MODIS remotely sensed data in R. Ecology and Evolution, 4 (24), 4658--4668. DOI: 10.1002/ece3.1273.


Some of the changes in recent updates:

New function, MODISGrid, that takes downloaded ASCII files and converts them into ASCII grid files, which can be used in a GIS environment.
  • Optional time-series plots for diagnostics as output from MODISSummaries.
  • MODISGrid now writes MODIS projection (PRJ) files for all ASCII raster grids, so their correct projection is defined. These files can now be loaded directly into a GIS environment.
  • MODISGrid now more flexibly deals with data files that contain multiple products.
  • Citation for publication in Ecology and Evolution added.
  • Documentation and 'Using MODISTools' vignette updated.

Github Link: https://github.com/mjwang1010/MODISTools

20

Abstract.
Knowledge of aquifer thickness is crucial for setting up numerical groundwater flow models to support groundwater resource management and control. Fresh groundwater reserves in coastal aquifers are particularly under threat of salinization and depletion as a result of climate change, sea-level rise, and excessive groundwater withdrawal under urbanization. To correctly assess the possible impacts of these pressures we need better information about subsurface conditions in coastal zones. Here, we propose a method that combines available global datasets to estimate, along the global coastline, the aquifer thickness in areas formed by unconsolidated sediments. To validate our final estimation results, we collected both borehole and literature data. Additionally, we performed a numerical modelling study to evaluate the effects of varying aquifer thickness and geological complexity on simulated saltwater intrusion. The results show that our aquifer thickness estimates can indeed be used for regional-scale groundwater flow modelling but that for local assessments additional geological information should be included. The final dataset has been made publicly available (https://doi.pangaea.de/10.1594/PANGAEA.880771).
Link: Zamrsky, D., Oude Essink, G. H. P., and Bierkens, M. F. P.: Estimating the thickness of unconsolidated coastal aquifers along the global coastline, Earth Syst. Sci. Data, 10, 1591-1603, https://doi.org/10.5194/essd-10-1591-2018, 2018.

21
Hydrological sciences / Critical Values for Sen’s Trend Analysis
« on: August 31, 2018, 03:26:54 PM »

Abstract
Trends in measured hydrologic data can greatly influence projected values; therefore, trends need to be identified and quantitatively modeled. First, it is necessary to verify whether or not a trend actually exists in the sample data. Tests of significance are most often used to verify whether or not a trend is statistically significant. Şen provided an easy-to-apply method of identifying the presence of trends in time series, but did not provide a quantitative method of verifying the statistical likelihood of an assumed trend in a measured time series [Şen, Z. 2012. “Innovative trend analysis methodology.” J. Hydrol. Eng. 17 (9): 1042–1046]. A method of quantifying Sen’s approach is developed herein, with critical values of the test statistic developed to provide a means of making objective decisions. Critical values are presented for rejection probabilities from 10% to 0.1%. The power of the test, which is similar to that of other trend tests, is also approximated; analyses indicate powers from 10% to 30% for small samples.

Link: https://ascelibrary.org/doi/abs/10.1061/%28ASCE%29HE.1943-5584.0001708

22
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.


Link: https://waterscarcityatlas.org/

23
Abstract
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.


Link: https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2018WR022708

24
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: https://link.springer.com/article/10.1007%2Fs10687-012-0155-0


Link to host website: http://www.assessment.ucar.edu/toolkit/

25
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.


Link: https://findajob.agu.org/job/8005367/water-quality-modeling-postdoc/?TrackID=26

26
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: lanl.jobs, search IRC67661: https://lanl.jobs/los-alamos-nm/vegetation-modeling-postdoc/241e492bdbf34f6bb232425e1eef5e9c/job/
www.lanl.gov/careersLos 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 applyhelp@lanl.gov or call 1-505-665-4444 option 1.




27
Abstract
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 : https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2017RG000591

28
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.


Link: https://jobs.exeter.ac.uk/hrpr_webrecruitment/wrd/run/ETREC107GF.open?VACANCY_ID=953161MVnP&WVID=3817591jNg&LANG=USA

29
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

30
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: https://github.com/ahmedaq/Making-elegant-Matlab-figures#generatepdf


2. Boxplots:  https://github.com/ahmedaq/Making-elegant-Matlab-figures#boxplot
                   https://github.com/kakearney/boxplot2-pkg
3. Heatmaps: https://github.com/ahmedaq/Making-elegant-Matlab-figures#heatmap


4. Confidence Interval or Bounded Lines: https://github.com/kakearney/boundedline-pkg


Thank you very much.

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