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Messages - Pankaj Dey

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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.

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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

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.

The following users thanked this post: Sonali

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:
The following users thanked this post: Ila Chawla

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:
The following users thanked this post: Ila Chawla

Hydrological sciences / El Niño–Southern Oscillation complexity
« on: July 26, 2018, 08:45:25 PM »
El Niño events are characterized by surface warming of the tropical Pacific Ocean and weakening of equatorial trade winds that occur every few years. Such conditions are accompanied by changes in atmospheric and oceanic circulation, affecting global climate, marine and terrestrial ecosystems, fisheries and human activities. The alternation of warm El Niño and cold La Niña conditions, referred to as the El Niño–Southern Oscillation (ENSO), represents the strongest year-to-year fluctuation of the global climate system. Here we provide a synopsis of our current understanding of the spatio-temporal complexity of this important climate mode and its influence on the Earth system.

Link to paper:
The following users thanked this post: Sonali

Jakub Nowosad, a postdoc in the Space Informatics Lab at University of Cincinnati has develop these two courses on Spatial Analysis and GIS using R.

The relevant links are as follows:
Sample R Codes are also available with practical examples.

1. Introduction to Spatial Analysis using R.


2. GIS with R


3. Data Visualization and preprocessing

Link: 1.

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Interesting information / History of Hydrology Interviews
« on: July 07, 2018, 12:20:56 PM »
This video series provides source material that will be of particular interest to scientists and instructors in the field of hydrology. Through in-depth interviews captured on film, eminent hydrologists discuss achievements in hydrological science that have occurred during their careers. These interviews offer valuable insight into the progression of research in the field of hydrology during the second half of the 20th century.

1. Interview with Prof. Eric F Wood by Prof. M. Sivapalan.

2. Interview with Prof. M. Sivapalan by Prof. Ross Woods

3. Interview with Prof. Keith J Beven.

4. Interview with Georgia Destouni

5. Interview with Mike J Kirkby
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Data Visualisation can be defined as representing numbers with shapes – and no matter what these shapes look like (areas, lines, dots), they need to have a color. Sometimes colors just make the shapes visible, sometimes they encode data or categories themselves. We’ll focus mostly on the latter in this article. But we’ll also take a general look at colors and what to consider when choosing them:

Link to the article:
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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.
The following users thanked this post: Hemant Kumar

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.

The following users thanked this post: Hemant Kumar

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.

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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:

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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.
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