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Messages - Pankaj Dey

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Interesting information / Using R in Hydrology - EGU2019 Short Course
« on: April 10, 2019, 08:33:26 PM »
Please find the link of the resources of R in Hydrology currently going on at EGU.
The following users thanked this post: Hemant Kumar

Hydrological sciences / A global data set of soil hydraulic properties
« on: March 27, 2019, 11:46:53 AM »
Agroecosystem models, regional and global climate models, and numerical weather prediction models require adequate parameterization of soil hydraulic properties. These properties are fundamental for describing and predicting water and energy exchange processes at the transition zone between solid earth and atmosphere, and regulate evapotranspiration, infiltration and runoff generation. Hydraulic parameters describing the soil water retention (WRC) and hydraulic conductivity (HCC) curves are typically derived from soil texture via pedotransfer functions (PTFs). Resampling of those parameters for specific model grids is typically performed by different aggregation approaches such a spatial averaging and the use of dominant textural properties or soil classes. These aggregation approaches introduce uncertainty, bias and parameter inconsistencies throughout spatial scales due to nonlinear relationships between hydraulic parameters and soil texture. Therefore, we present a method to scale hydraulic parameters to individual model grids and provide a global data set that overcomes the mentioned problems. The approach is based on Miller–Miller scaling in the relaxed form by Warrick, that fits the parameters of the WRC through all sub-grid WRCs to provide an effective parameterization for the grid cell at model resolution; at the same time it preserves the information of sub-grid variability of the water retention curve by deriving local scaling parameters. Based on the Mualem–van Genuchten approach we also derive the unsaturated hydraulic conductivity from the water retention functions, thereby assuming that the local parameters are also valid for this function. In addition, via the Warrick scaling parameter λ, information on global sub-grid scaling variance is given that enables modellers to improve dynamical downscaling of (regional) climate models or to perturb hydraulic parameters for model ensemble output generation. The present analysis is based on the ROSETTA PTF of Schaap et al. (2001) applied to the SoilGrids1km data set of Hengl et al. (2014).

The example data set is provided at a global resolution of 0.25 degree at

Link to the paper:
The following users thanked this post: prayas

An accessible account of the ways in which the world's plant life affects the climate. It covers everything from tiny local microclimates created by plants to their effect on a global scale. If you’ve ever wondered how vegetation can create clouds, haze and rain, or how plants have an impact on the composition of greenhouse gases, then this book is required reading.

The following users thanked this post: Abhishek7

Misinterpretation and abuse of statistical tests, confidence intervals, and statistical power have been decried for decades, yet remain rampant. A key problem is that there are no interpretations of these concepts that are at once simple, intuitive, correct, and foolproof. Instead, correct use and interpretation of these statistics requires an attention to detail which seems to tax the patience of working scientists. This high cognitive demand has led to an epidemic of shortcut definitions and interpretations that are simply wrong, sometimes disastrously so—and yet these misinterpretations dominate much of the scientific literature. In light of this problem, we provide definitions and a discussion of basic statistics that are more general and critical than typically found in traditional introductory expositions. Our goal is to provide a resource for instructors, researchers, and consumers of statistics whose knowledge of statistical theory and technique may be limited but who wish to avoid and spot misinterpretations. We emphasize how violation of often unstated analysis protocols (such as selecting analyses for presentation based on the P values they produce) can lead to small P values even if the declared test hypothesis is correct, and can lead to large P values even if that hypothesis is incorrect. We then provide an explanatory list of 25 misinterpretations of P values, confidence intervals, and power. We conclude with guidelines for improving statistical interpretation and reporting.
In the words of R.A. Fisher:
"No scientific worker has a fixed level of significance at which from year to year, and in all circumstances, he rejects hypotheses; he rather gives his mind to each particular case in the light of his evidence and his ideas."
The following users thanked this post: prayas

The Center for Hydrometeorology and Remote Sensing (CHRS) has created the CHRS Data Portal to facilitate easy access to the three open data licensed satellite-based precipitation datasets generated by our Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) system: PERSIANN, PERSIANN-Cloud Classification System (CCS), and PERSIANN-Climate Data Record (CDR). These datasets have the potential for widespread use by various researchers, professionals including engineers, city planners, and so forth, as well as the community at large. Researchers at CHRS created the CHRS Data Portal with an emphasis on simplicity and the intention of fostering synergistic relationships with scientists and experts from around the world. The following paper presents an outline of the hosted datasets and features available on the CHRS Data Portal, an examination of the necessity of easily accessible public data, a comprehensive overview of the PERSIANN algorithms and datasets, and a walk-through of the procedure to access and obtain the data.


Portal Link:
The following users thanked this post: soumyashree Diixt

Hydrogeological conceptual models are collections of hypotheses describing the understanding of groundwater systems and they are considered one of the major sources of uncertainty in groundwater flow and transport modelling. A common method for characterizing the conceptual uncertainty is the multi-model approach, where alternative plausible conceptual models are developed and evaluated. This review aims to give an overview of how multiple alternative models have been developed, tested and used for predictions in the multi-model approach in international literature and to identify the remaining challenges.

The review shows that only a few guidelines for developing the multiple conceptual models exist, and these are rarely followed. The challenge of generating a mutually exclusive and collectively exhaustive range of plausible models is yet to be solved. Regarding conceptual model testing, the reviewed studies show that a challenge remains in finding data that is both suitable to discriminate between conceptual models and relevant to the model objective.

We argue that there is a need for a systematic approach to conceptual model building where all aspects of conceptualization relevant to the study objective are covered. For each conceptual issue identified, alternative models representing hypotheses that are mutually exclusive should be defined. Using a systematic, hypothesis based approach increases the transparency in the modelling workflow and therefore the confidence in the final model predictions, while also anticipating conceptual surprises. While the focus of this review is on hydrogeological applications, the concepts and challenges concerning model building and testing are applicable to spatio-temporal dynamical environmental systems models in general.

The authors shared the comment from a reviewer of this paper.

Reviewer: “This paper is a gem.  I cannot wait to have it available to require my groundwater modeling students to read and to reread it myself often."

The following users thanked this post: B N Priyanka, soumyashree Diixt

 Abstract. Earth system models (ESMs) are key tools for providing climate projections under different scenarios of human-induced forcing. ESMs include a large number of additional processes and feedbacks such as biogeochemical cycles that traditional physical climate models do not consider. Yet, some processes such as cloud dynamics and ecosystem functional response still have fairly high uncertainties. In this article, we present an overview of climate feedbacks for Earth system components currently included in state-of-the-art ESMs and discuss the challenges to evaluate and quantify them. Uncertainties in feedback quantification arise from the interdependencies of biogeochemical matter fluxes and physical properties, the spatial and temporal heterogeneity of processes, and the lack of long-term continuous observational data to constrain them. We present an outlook for promising approaches that can help quantifying and constraining the large number of feedbacks in ESMs in the future. The target group for this article includes generalists with a background in natural sciences and an interest in climate change as well as experts working in interdisciplinary climate research (researchers, lecturers, and students). This study updates and significantly expands upon the last comprehensive overview of climate feedbacks in ESMs, which was produced 15 years ago (NRC, 2003).
Citation: Heinze, C., Eyring, V., Friedlingstein, P., Jones, C., Balkanski, Y., Collins, W., Fichefet, T., Gao, S., Hall, A., Ivanova, D., Knorr, W., Knutti, R., Löw, A., Ponater, M., Schultz, M. G., Schulz, M., Siebesma, P., Teixeira, J., Tselioudis, G., and Vancoppenolle, M.: Climate feedbacks in the Earth system and prospects for their evaluation, Earth Syst. Dynam. Discuss.,

Link to the paper:
The following users thanked this post: soumyashree Diixt

1. More than 1.9 million tiles of MODIS daily reflectance data were used to create a 16- year record (2001 -2016) of daily water map for the entire globe.

2. The global daily water data set can be openly downloaded by potential users.

3. Global inland water has a dramatic seasonal variation ranging from approximately 3.8 million km2 in September to 1.5 million km2 in February within an annual cycle.
4. Short duration water bodies, sea level rise effects, various types of rice field use can be detected from the daily water data set.
Link to paper:
Link to data:
The following users thanked this post: nruthya

Interesting information / M_Map: A Mapping Package for MATLAB
« on: December 02, 2018, 10:29:27 PM »
M_Map is a set of mapping tools written for Matlab (it also works under Octave). M_Map includes:
  • Routines to project data in 19 different projections (and determine inverse mappings), using spherical and ellipsoidal earth-models.
  • A grid generation routine to make nice axes with limits either in lat/long terms or in planar X/Y terms.
  • A coastline database (with 1/4 degree resolution).
  • A global elevation database (1 degree resolution).
  • Hooks into freely available high-resolution coastline and bathymetry databases.
  • Other useful stuff.
The following users thanked this post: B N Priyanka

We’ve all heard it before: “Yeah, but the climate has ALWAYS changed.”
Oh, really? Well, this timeline of Earth’s average temperature shows just how much we’ve influenced the climate. This epic webcomic was created by Randall Munroe, the artist behind xkcd, one of our favorite places for simplifying complicated scientific concepts.
It’s pretty long, but bear with us.
The following users thanked this post: nruthya, Hemant Kumar

Hydrological sciences / hddtools: Hydrological Data Discovery Tools in R
« on: November 25, 2018, 11:13:58 AM »
hddtools stands for Hydrological Data Discovery Tools. This R package is an open source project designed to facilitate access to a variety of online open data sources relevant for hydrologists and, in general, environmental scientists and practitioners.
This typically implies the download of a metadata catalogue, selection of information needed, a formal request for dataset(s), de-compression, conversion, manual filtering and parsing. All those operations are made more efficient by re-usable functions.
Depending on the data license, functions can provide offline and/or online modes. When redistribution is allowed, for instance, a copy of the dataset is cached within the package and updated twice a year. This is the fastest option and also allows offline use of package's functions. When re-distribution is not allowed, only online mode is provided.
Link to manual:
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Deep learning (DL), a new generation of artificial neural network research, has transformed industries, daily lives, and various scientific disciplines in recent years. DL represents significant progress in the ability of neural networks to automatically engineer problem-relevant features and capture highly complex data distributions. I argue that DL can help address several major new and old challenges facing research in water sciences such as interdisciplinarity, data discoverability, hydrologic scaling, equifinality, and needs for parameter regionalization. This review paper is intended to provide water resources scientists and hydrologists in particular with a simple technical overview, transdisciplinary progress update, and a source of inspiration about the relevance of DL to water. The review reveals that various physical and geoscientific disciplines have utilized DL to address data challenges, improve efficiency, and gain scientific insights. DL is especially suited for information extraction from image-like data and sequential data. Techniques and experiences presented in other disciplines are of high relevance to water research. Meanwhile, less noticed is that DL may also serve as a scientific exploratory tool. A new area termed AI neuroscience, where scientists interpret the decision process of deep networks and derive insights, has been born. This budding subdiscipline has demonstrated methods including correlation-based analysis, inversion of network-extracted features, reduced-order approximations by interpretable models, and attribution of network decisions to inputs. Moreover, DL can also use data to condition neurons that mimic problem-specific fundamental organizing units, thus revealing emergent behaviors of these units. Vast opportunities exist for DL to propel advances in water sciences.

The following users thanked this post: prayas


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.

The following users thanked this post: Sat Kumar Tomer


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 (
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,, 2018.
The following users thanked this post: B N Priyanka

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

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.

The following users thanked this post: Sonali

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