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Messages - Pankaj Dey

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Hydrological sciences / Scale Issues in Soil Hydrology
« on: July 16, 2019, 06:03:48 PM »
Soil hydrology is a key control for the functioning of the terrestrial environment. Many environmental issues that we need to tackle today are directly linked to soil water dynamics. This includes agricultural production and food security, nutrient cycling and carbon storage, prevention of soil degradation and erosion, and last but not least, clean water resources and flood protection. However, these problems need to be addressed at the scales of fields, regions, and landscapes, while soil water dynamics and soil hydraulic properties are well understood and typically measured at much smaller scales—the comfort zone of soil physics. An obvious problem is how to link these vastly different scales and how to profit from small-scale understanding to improve our capability to predict what is going on at the large scale. In this update, this problem is discussed based on insights gained during the last decades. As a synthesis, a two-step scaling approach is proposed for modeling soil water dynamics from local to landscape scales where the scale of the soil profile is the stepping stone.

Core Ideas
• The soil profile is the critical scale for representation of soil hydrology also at larger scales.
• Natural soils do not follow welldefined hydraulic properties.
• Concepts are needed to model hydraulic nonequilibrium and hysteresis.
• Spatial patterns of functional soil types need to be identified.
• These can account for vertical stratification of hydraulic properties and structural attributes.

Citation: Vogel, H.-J. 2019. Scale issues in soil hydrology. Vadose Zone J. 18:190001. doi:10.2136/vzj2019.01.0001

The properties of geographical phenomena vary with changes in the scale of measurement. The information observed at one scale often cannot be directly used as information at another scale. Scaling addresses these changes in properties in relation to the scale of measurement, and plays an important role in Earth sciences by providing information at the scale of interest, which may be required for a range of applications, and may be useful for inferring geographical patterns and processes. This paper presents a review of geospatial scaling methods for Earth science data. Based on spatial properties, we propose a methodological framework for scaling addressing upscaling, downscaling and side-scaling. This framework combines scale- independent and scale-dependent properties of geographical variables. It allows treatment of the varying spatial heterogeneity of geographical phenomena, combines spatial autocorrelation and heterogeneity, addresses scale- independent and scale-dependent factors, explores changes in information, incorporates geospatial Earth surface processes and uncertainties, and identifies the optimal scale(s) of models. This study shows that the classification of scaling methods according to various heterogeneities has great potential utility as an underpinning conceptual basis for advances in many Earth science research domains.

There is a strong inter-annual and inter-decadal variability in time series of flood-related variables, such as intense precipitation, high river discharge, flood magnitude, and flood loss at a range of spatial scales. Perhaps part of this variability is random or chaotic, but it is quite natural to seek driving factors, in a statistical sense. It is likely that climate variability (atmosphere–ocean oscillation) track plays an important role in the interpretation of the variability of flood-related characteristics, globally and, even more so, in several regions. The aim of this review paper is to create an inventory of information on spatially and temporally organized links of various climate-variability drivers with variability of characteristics of water abundance reported in scientific literature for a range of scales, from global to local. The climate variability indices examined in this paper are: El Niño-Southern Oscillations (ENSO), North Atlantic Oscillations (NAO), Atlantic Multi-decadal Oscillation (AMO), and Pacific Decadal Oscillations (PDO). A meta-analysis of results from many studies reported in scientific literature was carried out. The published results were collected and classified into categories after regions, climate variability modes, as well as flood-related variables: precipitation, river flow, and flood losses.

Soil is an important regulator of Earth system processes, but remains one of the least well-described data layers in Earth system models (ESMs). We reviewed global soil property maps from the perspective of ESMs, including soil physical and chemical and biological properties, which can also offer insights to soil data developers and users. These soil datasets provide model inputs, initial variables, and benchmark datasets. For modelling use, the dataset should be geographically continuous and scalable and have uncertainty estimates. The popular soil datasets used in ESMs are often based on limited soil profiles and coarse-resolution soil-type maps with various uncertainty sources. Updated and comprehensive soil information needs to be incorporated into ESMs. New generation soil datasets derived through digital soil mapping with abundant, harmonized, and quality-controlled soil observations and environmental covariates are preferred to those derived through the linkage method (i.e. taxotransfer rule-based method) for ESMs. SoilGrids has the highest accuracy and resolution among the global soil datasets, while other recently developed datasets offer useful compensation. Because there is no universal pedotransfer function, an ensemble of them may be more suitable for providing derived soil properties to ESMs. Aggregation and upscaling of soil data are needed for model use, but can be avoided by using a subgrid method in ESMs at the expense of increases in model complexity. Producing soil property maps in a time series still remains challenging. The uncertainties in soil data need to be estimated and incorporated into ESMs.

Plain Language Summary

Long-term groundwater resources monitoring is a costly affair at most parts of the globe. Satellite based estimations based on gravity measurements through Gravity Recovery and Climate Experiment sensors can provide groundwater resource information at relatively coarser spatial and temporal resolution. In this aspect, we used widely‐available, high‐resolution vegetation index data and investigate the possibility of using it as a proxy of groundwater storage. Artificial intelligence estimates show good performance of vegetation index on predicting future groundwater levels. The results are particularly encouraging in natural vegetation covered areas.


Interesting information / The Climate Data Toolbox for MATLAB
« on: June 29, 2019, 11:35:22 AM »
This article describes a collection of computer code that has recently been released to help scientists analyze many types of Earth science data. The code in this toolbox makes it easy to investigate things like global warming, El Niño, or other major climate‐related processes such as how winds affect ocean circulation. Although the toolbox was designed to be used by expert climate scientists, its instruction manual is well written, and beginners may be able to learn a great deal about coding and Earth science, simply by following along with the provided examples. The toolbox is intended to help scientists save time, help them ensure their analysis is accurate, and make it easy for other scientists to repeat the results of previous studies.

Link to article:

Link to Toolbox:

The representation of small‐scale soil and hydrological processes has been a challenge and a subject of debate since the pioneering work of Richardson and the dawn of numerical weather prediction. The recent leap to global scales with long‐term and large data offers new opportunities that place the long‐standing challenge of small‐scale representation in a more positive perspective. Global representation of soil processes require evaluation of the origins of the information used for global models and the parameterization via the so‐called pedotransfer functions. Parameters and processes benefit from application of physical constraints while replacing empirical approximations with small‐scale physics adapted for large‐scale processes. We provide an overview of the present state and opportunities for hydrology and soil communities in contemporary well‐connected, observable, global and big‐data realities.


Interesting information / Doing Hydrogeology in R
« on: June 26, 2019, 09:57:07 PM »
Use programming languages to interact with, analyze, and visualize data is an increasingly important skill for hydrogeologists to have. Coding-based science makes it easier to process and visualize large amounts of data and increase the reproducibility of your work, both for yourself and others.


Soil water content and matric potential are central hydrological state variables. A large variety of automated probesand sensor systems for state monitoring exists and is frequently applied. Most studies solely rely on the calibration by themanufacturers. Until now, there is no commonly agreed calibration procedure. Moreover, several opinions about the capabilitiesand reliabilities of specific sensing methods or sensor systems exist and compete.

A consortium of several institutions conducted a comparison study of currently available sensor systems for soil water5content and matric potential under field conditions. All probes have been installed in 0.2 m depth below surface following best practice procedure. We present the setup and the recorded data of 58 probes of 15 different systems measuring soil moisture and further 50 probes of 14 different systems for matric potential. The measuring campaign was conducted in the growing period of 2016. The monitoring data, results from pedophysical analyses of the soil and laboratory reference measurements for calibration are published in Jackisch et al. (2018,

Hydrological sciences / Comprehensive list of color palettes in r
« on: June 21, 2019, 11:52:27 AM »
The goal of this repository is to have a one stop destination for anyone looking for a color palette to use in r. If you would like to help/contribute please feel free post an issue, PR or send a email to
Further down the page is all the palettes available in the R ecosystem ordered alphabetically by package name. A list of palettes ordered by type can be found here Type sorted palettes to shorten the length of this page.

inferr builds upon the statistical tests provided in stats, provides additional and flexible input options and more detailed and structured test results. As of version 0.3, inferr includes a select set of parametric and non-parametric statistical tests which are listed below:

    One Sample t Test
    Paired Sample t Test
    Independent Sample t Test
    One Sample Proportion Test
    Two Sample Proportion Test
    One Sample Variance Test
    Two Sample Variance Test
    Binomial Test
    Chi Square Goodness of Fit Test
    Chi Square Independence Test
    Levene’s Test
    Cochran’s Q Test
    McNemar Test
    Runs Test for Randomness


 Meteolab is an open-source Matlab toolbox for statistical analysis and data mining in Meteorology, focusing on statistical downscaling methods.


Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change.

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