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Interesting information / GSDR: A global sub-daily rainfall dataset
« Last post by Pankaj Dey on Today at 06:16:35 PM »
Extreme short duration rainfall can cause devastating flooding which puts lives, infrastructure, and natural ecosystems at risk. It is therefore essential to understand how this type of extreme rainfall will change in a warmer world. A significant barrier to answering this question is the lack of sub-daily rainfall data available at the global scale. To this end, a global sub-daily rainfall dataset based on gauged observations has been collated. The dataset is highly variable in its spatial coverage, record length, completeness and, in its raw form, quality. This presents significant difficulties for many types of analyses. The dataset currently comprises 23,687 gauges with an average record length of 13 years. Apart from a few exceptions, the earliest records begin in the 1950s. The global sub-daily rainfall dataset (GSDR) has wide applications, including improving our understanding of the nature and drivers of sub-daily rainfall extremes, the improvement and validation of high resolution climate models, and developing a high resolution gridded sub-daily rainfall dataset of indices.
The Department of Civil and Environmental Engineering at Portland State University has one Postdoctoral scholar position open. The successful candidate will support riverine hydrodynamic, transport and water quality modeling projects. The tasks involved will include but are not limited to the following:

1) Develop riverine hydrodynamic, transport and water quality modeling of nutrients and toxic chemicals,
2) Modify/improve existing hydrodynamic and water quality models and validate new capabilities,
3) Apply 1D and 2D numerical models, conduct model runs and process models results, and
4) Contribute to the dissemination of the team’s scientific advances through publications.

The initial appointment is for one year, renewable for additional years contingent upon satisfactory performance, mutual agreement, and viability of funds.

Successful candidates will have a Ph.D. in hydrology, hydraulics, environmental engineering, chemical oceanography, biogeochemistry, or a related field.

The candidate must have experience in numerical model and programming such as Fortran, Python, etc.

The starting annual salary rate for this position will be between $50,004 and $61,308, depending on level of experience. The starting salary may be negotiable above this range, however it will be dependent upon the knowledge, experience, skills and abilities of the chosen candidate, the budget of the hiring department, and approval from HR. PSU’s excellent benefits package includes 95% premium paid healthcare; a generous retirement and vacation package; and reduced tuition rates for employee, spouse or dependent at any of the Oregon Public Universities.

Submit an application from at the link below:
Leaf area index (LAI) is a critical vegetation structural variable and is essential in the feedback of vegetation to the climate system. The advancement of the global Earth Observation (EO) has enabled the development of global LAI products and boosted global Earth system modeling studies. This overview provides a comprehensive analysis of LAI field measurements and remote sensing estimation methods, the product validation methods and product uncertainties, and the application of LAI in global studies. First, the paper clarifies some definitions related to LAI and introduces methods to determine LAI from field measurements and remote sensing observations. After introducing some major global LAI products, progress made in temporal compositing and prospects for future LAI estimation are discussed. Subsequently, the overview presents various LAI product validation schemes, uncertainties in global moderate resolution LAI products and high resolution reference data. Finally, applications of LAI in global vegetation change, land surface modeling, and agricultural studies are presented. It is recommended that (1) continued efforts are taken to advance LAI estimation algorithms and provide high temporal and spatial resolution products from current and forthcoming missions; (2) further validation studies be conducted to address the inadequacy of current validation studies, especially for under‐represented regions and seasons; and (3) new research frontiers, such as machine learning algorithms, LiDAR technology, and unmanned aerial vehicles (UAV) be pursued to broaden the production and application of LAI.
Models / Re: Anybody working with VIC model
« Last post by hsalehi on May 19, 2019, 04:02:52 PM »
Hi to all
I want run VIC Hydrology model.
About this, i prepared soil parameter,vegetation parameter and Forcing filse.
I con't prepared Vegetation Library.
for this, i have downloaded LAI and Albedo data for 12 month from MODIS but i don't know how i can use them.
who can help me?
Hydrological sciences / Course: Spatial Modeling with Geostatistics
« Last post by Pankaj Dey on May 18, 2019, 11:34:11 AM »
"Everything Geoscientists and Data Scientists Need to Know About Geostatistics"

Prof. Michael J. Pyrcz, Ph.D., P.Eng. Hildebrand Department of Petroleum & Geosystems Engineering University of Texas at Austin

This repository includes the lectures for this short course. The suppliemental materials with workflows, demonstrations, hands-on and data are here:

Michael Pyrcz is an associate professor at the University of Texas at Austin. He teaches and consults on the practice of geostatistical reservoir modeling and conducts research on new geostatistical methods to improve reservoir modeling and uncertainty for conventional and unconventional reservoirs. He has published over 40 peer reviewed technical articles, a textbook with Oxford University Press, and is an associated editor with Computers & Geosciences. For more details see or follow him on Twitter @GeostatsGuy.
Course Objectives

Class will be accessible to geoscientists and data scientists with no previous experience with geostatistics. We will build up from data integration to spatial estimation and simulation along with uncertainty modeling to support decision making. After completion the students will understand: (1) the benefits and uses of geostatistics, (2) the common spatial and uncertainty modeling workflows, (3) how to better integrate their domain knowledge into the geostatistical model.

Course Outline
For the following 2-day class outline it is assumed that: (1) the class will be taught in English without translation, and (2) 1/3 of time will be guided hands-on practice

    Exploratory Data Analysis (a) Sampling theory, stationarity, data debiasing (b) Random variables and functions (c) Univariate statistics, multivariate statistics (d) Geostatistics and big data analytics

    Spatial Data Analysis (a) Trend modeling (b) Variogram calculation, interpretation and modeling (c) Scaling relations (d) Training images

    Estimation (a) Interpolation (b) Kriging estimation (c) Predrill prediction

    Stochastic Simulation (a) Simulation paradigm (b) Gaussian simulation (c) Minimum acceptance and uncertainty checks

    Uncertainty Management (a) (Spatial) bootstrap (b) Model post-processing (c) Uncertainty workflows

    Machine Learning for Subsurface (a) Estimation variance (b) Multivariate analysis (c) Decision Tree

I am open to revisions to the outline to accommodate student learning needs.
Link to youtube lectures:
Hydrological sciences / Course: Subsurface Heterogeneity Modeling
« Last post by Pankaj Dey on May 18, 2019, 11:29:34 AM »
Subsurface heterogeneity modeling course.
Course Objectives:
You will gain:

    knowledge concerning basic data analytics and geostatistics for subsurface modeling.

Course Agenda

    Introduction: objectives, plan
    General Overview - essential concepts from geostatistics
    Data analytics - definitions, bootstrap, declustering
    Spatial continuity - variogram calculation and modeling, trend modeling and spatial estimations
    Limitations with Geostatistics

The Instructor:
Michael Pyrcz, Associate Professor, University of Texas at Austin

Novel Data Analytics, Geostatistics and Machine Learning Subsurface Solutions

With over 17 years of experience in subsurface consulting, research and development, Michael has returned to academia driven by his passion for teaching and enthusiasm for enhancing engineers' and geoscientists' impact in subsurface resource development.
Link to Youtube Lectures:
Link to Github Lectures:
Interesting information / Hydrology Postdoc at CTU Prague [CZE]
« Last post by Pankaj Dey on May 17, 2019, 10:32:35 AM »
Junior Postdoctoral Researcher position is available in the framework of the Operational Programme Research, Development and Education call “International Mobility of Researchers” at Czech Technical University in Prague, Faculty of Civil Engineering, Dept. of Hydraulics and Hydrology.

Duration of this full-time position is 12 months, expected starting date is fall 2019. Salary is sufficient for covering the cost of living in the Czech Republic (gross monthly salary of about 2,500 EUR + family allowance).

The research will be focused on processes affecting the hydrological regime of forest catchments in headwater areas exposed to changing climate. The researcher will take part in: The evaluation and interpretation of existing datasets acquired by hydroclimatological monitoring and soil sampling at the experimental sites located in the Jizera Mountains and Bohemian Forest, Czech Republic; Evaluation and modeling of laboratory experiments on soil columns and/or field experiments to determine hydraulic properties of individual compartments of the soil-plant-atmosphere system; Publishing of the research results. The candidate is required to have PhD in hydrology or related branch of environmental science/engineering and have experience in the field of subsurface hydrology and hydrological modeling.

The official announcement of this position and more detailed information can be found at

The closing date for applications is 8 June 2019.
Junior Postdoctoral Researcher position is available in the framework of the Operational Programme Research, Development and Education call “International Mobility of Researchers” at Czech Technical University in Prague, Faculty of Civil Engineering, Dept. of Hydraulics and Hydrology.

Your research will focus on the development of a real-time reconstruction procedure for rainfall mapping at city scale using path-integrated rainfall observations retrieved from microwave links, radio connections used within cellular networks.

You will be part of the interdisciplinary research project Tel4Rain (“High resolution rainfall product using telecommunication microwave links”), where we collaborate with hydrologists, urban water engineers, radio engineers and IT specialists as well as with relevant stakeholders (e.g. City of Prague and T-Mobile CZ,). The position requires good interpersonal skills with the desire and the ability to work collaboratively and effectively in a team environment. The candidate will be encouraged to collaborate actively with our international partners and expected to present the results on a relevant international scientific conference and/or in a peer-reviewed journal.


The ideal candidate must have a recent Ph.D. degree in applied mathematics, geostatistics, data analysis, meteorology, hydrology, remote sensing or similar discipline. Programming skills are required, academic skills proven by relevant publication record are desired.


This full-time position is funded from EU Mobility program for duration of 6 months (start from September 1st 2019). The position is open for a researcher from a foreign country or a researcher from Czech Republic who has spent at least 2 years during the period of past 3 years doing research abroad. The salary based on the conditions of the call covers the cost of living in the Czech Republic (2.400 EUR brutto app. 1.750 EUR netto per month + family allowance).

The official announcement of this position and more detailed information can be found at

The closing date for applications is 8 June 2019.

For further information, please contact Dr. Vojtěch Bareš,
Interesting information / Key determinants of global land-use projections
« Last post by Pankaj Dey on May 15, 2019, 05:29:54 PM »
Land use is at the core of various sustainable development goals. Long-term climate foresight studies have structured their recent analyses around five socio-economic pathways (SSPs), with consistent storylines of future macroeconomic and societal developments; however, model quantification of these scenarios shows substantial heterogeneity in land-use projections. Here we build on a recently developed sensitivity approach to identify how future land use depends on six distinct socio-economic drivers (population, wealth, consumption preferences, agricultural productivity, land-use regulation, and trade) and their interactions. Spread across models arises mostly from diverging sensitivities to long-term drivers and from various representations of land-use regulation and trade, calling for reconciliation efforts and more empirical research. Most influential determinants for future cropland and pasture extent are population and agricultural efficiency. Furthermore, land-use regulation and consumption changes can play a key role in reducing both land use and food-security risks, and need to be central elements in sustainable development strategies.
Link to paper:
Please find the attachment.
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