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Messages - Pankaj Dey

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What's driving the greening of planet Earth? New research points to China and India contributing with land-use management:

Satellite data show increasing leaf area of vegetation due to direct factors (human land-use management) and indirect factors (such as climate change, CO2 fertilization, nitrogen deposition and recovery from natural disturbances). Among these, climate change and CO2 fertilization effects seem to be the dominant drivers. However, recent satellite data (2000–2017) reveal a greening pattern that is strikingly prominent in China and India and overlaps with croplands world-wide. China alone accounts for 25% of the global net increase in leaf area with only 6.6% of global vegetated area. The greening in China is from forests (42%) and croplands (32%), but in India is mostly from croplands (82%) with minor contribution from forests (4.4%). China is engineering ambitious programmes to conserve and expand forests with the goal of mitigating land degradation, air pollution and climate change. Food production in China and India has increased by over 35% since 2000 mostly owing to an increase in harvested area through multiple cropping facilitated by fertilizer use and surface- and/or groundwater irrigation. Our results indicate that the direct factor is a key driver of the ‘Greening Earth’, accounting for over a third, and probably more, of the observed net increase in green leaf area. They highlight the need for a realistic representation of human land-use practices in Earth system models.

The open-source programming language R has gained a central place in the hydrological sciences over the last decade, driven by the availability of diverse hydro-meteorological data archives and the development of open-source computational tools. The growth of R's usage in hydrology is reflected in the number of newly published hydrological packages, the strengthening of online user communities, and the popularity of training courses and events. In this paper, we explore the benefits and advantages of R's usage in hydrology, such as: the democratization of data science and numerical literacy, the enhancement of reproducible research and open science, the access to statistical tools, the ease of connecting R to and from other languages, and the support provided by a growing community. This paper provides an overview of important packages at every step of the hydrological workflow, from the retrieval of hydro-meteorological data, to spatial analysis and cartography, hydrological modelling, statistics, and the design of static and dynamic visualizations, presentations and documents. We discuss some of the challenges that arise when using R in hydrology and useful tools to overcome them, including the use of hydrological libraries, documentation and vignettes (long-form guides that illustrate how to use packages); the role of Integrated Development Environments (IDEs); and the challenges of Big Data and parallel computing in hydrology. Last, this paper provides a roadmap for R's future within hydrology, with R packages as a driver of progress in the hydrological sciences, Application Programming Interfaces (APIs) providing new avenues for data acquisition and provision, enhanced teaching of hydrology in R, and the continued growth of the community via short courses and events.


Interesting information / Do Dams Increase Water Use?
« on: February 20, 2019, 11:23:27 AM »
Reservoirs may promote waste by creating a false sense of water security.

A novel approach to stochastic rainfall generation that can reproduce various statistical characteristics of observed rainfall at hourly to yearly timescales is presented. The model uses a seasonal autoregressive integrated moving average (SARIMA) model to generate monthly rainfall. Then, it downscales the generated monthly rainfall to the hourly aggregation level using the Modified Bartlett–Lewis Rectangular Pulse (MBLRP) model, a type of Poisson cluster rainfall model. Here, the MBLRP model is carefully calibrated such that it can reproduce the sub-daily statistical properties of observed rainfall. This was achieved by first generating a set of fine-scale rainfall statistics reflecting the complex correlation structure between rainfall mean, variance, auto-covariance, and proportion of dry periods, and then coupling it to the generated monthly rainfall, which were used as the basis of the MBLRP parameterization. The approach was tested on 34 gauges located in the Midwest to the east coast of the continental United States with a variety of rainfall characteristics. The results of the test suggest that our hybrid model accurately reproduces the first- to the third-order statistics as well as the intermittency properties from the hourly to the annual timescales, and the statistical behaviour of monthly maxima and extreme values of the observed rainfall were reproduced well.
Link to paper:, 2019
Link to the application:

Not only does rainfall influence soil moisture, but soil moisture can also actively influence rainfall. Current climate models do not represent such two‐way relationships correctly, mainly due to uncertainty in the latter. Our understanding of models' weaknesses in simulating these processes is relatively low, and this is the focus of this study. Here we investigate how afternoon rainfall occurrence is affected by morning soil moisture conditions from three perspectives: relative soil moisture of the region where it rains compared to (1) surrounding regions (spatial feedback), (2) its long‐term mean (temporal feedback), and (3) the spatial heterogeneity of soil moisture (heterogeneity feedback). In models, the afternoon rainfall preferably occurs over regions that are wetter than their surroundings, as opposed to observations. Models show better agreement with observations in the temporal and heterogeneity feedback, but large differences across the models remain. We suggest that the combined effect of these three relationships in models may contribute to their biases in the persistence of precipitation.

Hydrological sciences / lulcc: Land Use Change Modelling in R
« on: February 17, 2019, 04:13:50 PM »
We present the lulcc software package, an objectoriented framework for land use change modelling written in the R programming language. The contribution of the work is to resolve the following limitations associated with the current land use change modelling paradigm: (1) the source code for model implementations is frequently unavailable, severely compromising the reproducibility of scientific results and making it impossible for members of the community to improve or adapt models for their own purposes; (2) ensemble experiments to capture model structural uncertainty are difficult because of fundamental differences between implementations of alternative models; and (3) additional software is required because existing applications frequently perform only the spatial allocation of change. The package includes a stochastic ordered allocation procedure as well as an implementation of the CLUE-S algorithm. We demonstrate its functionality by simulating land use change at the Plum Island Ecosystems site, using a data set included with the package. It is envisaged that lulcc will enable future model development and comparison within an open environment.

Link to GMD paper:
Link to R Manual:

Hydrological sciences / CatchX: Catchment Water Explorer App
« on: February 13, 2019, 06:23:41 PM »
The CatchX platform is designed to provide easy access to the latest scientific hydrology data. It processes the data for each river catchment globally and allows you to explore and visualise that data for a chosen time period and compare the behaviour of different catchments. The specific datasets used are precipitation (e.g. rainfall and snow), temperature, evapotranspiration, water runoff from land, and landcover types. These variables represent the basics of a water balance for each catchment.

SatPy is a python library for reading and manipulating meteorological remote sensing data and writing it to various image and data file formats. SatPy comes with the ability to make various RGB composites directly from satellite instrument channel data or higher level processing output. The pyresample package is used to resample data to different uniform areas or grids. Various atmospheric corrections and visual enhancements are also provided, either directly in SatPy or from those in the PySpectral and TrollImage packages.

Go to the project page for source code and downloads.

It is designed to be easily extendable to support any meteorological satellite by the creation of plugins (readers, compositors, writers, etc). The table at the bottom of this page shows the input formats supported by the base SatPy installation.

SAMO conferences are devoted to advances in research on sensitivity analysis methods and their interdisciplinary applications, they are held every third year. The aim of the SAMO conference in Barcelona is to bring together researchers involved in the development and improvement of methods and strategies and users of sensitivity analysis in all disciplines of science, including physics, operations research, chemistry, biology, nanotechnology, engineering, environmental science, nuclear and industrial safety, economics and finance, and many others.

SAMO conferences, held every third year, are devoted to advances in research on sensitivity analysis methods and their interdisciplinary applications. The aim of the SAMO conference in Barcelona is to bring together researchers involved in the development and improvement of methods and strategies and users of sensitivity analysis in all disciplines of science, including physics, operational research, chemistry, biology, nanotechnology, engineering, environmental science, nuclear and industrial safety, economics and finance, and many others.Please visit the website of SAMO 19; you will be able to submit your abstract until 6th of April, and the deadline for registering is the 1st of July.
The proceedings of this conference is considered to be published as a special issue in Environmental Modelling and Software or Reliability Engineering and System Safety

    decision-making under uncertainty,model, model calibration, model development, model output, model reduction, model validation, modelling, Monte Carlo, quality-assurance, quantification, reliability and robustness analysis, sensitivity analysis,uncertainty analysis

Surface urban heat island (SUHI) refers to higher land surface temperature (LST) in urban than in rural areas. The increased SUHI intensity (SUHII, urban LST minus rural) was mainly attributed to increased anthropogenic heat emission and built‐up areas, and reductions in vegetation in urban areas in the literature. However, this study showed that the increased vegetation (i.e. greening) in rural areas was a significant and widespread driver for the increased daytime SUHII around the world during 2001–2017. The implication of this study is that urban LST may increase much faster than rural LST in future global warming.

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."

Interesting information / Drought and famine in India, 1870‐2016
« on: February 04, 2019, 10:10:24 AM »
India witnessed some of the most famous famines during the late 19th and early 20th century. These famines caused millions of deaths primarily due to widespread crop failure. However, the role of agricultural drought in these famines remains unrecognized. Using station based observations and simulations from a hydrological model, we reconstructed agricultural droughts and established a linkage between famines and droughts over India. We find that a majority of famines were caused by large‐scale and severe soil moisture droughts that hampered the food production. However, one famine was completely resulted due to the failure of policy during the British Era. Expansion of irrigation, better public distribution system, rural employment, and transportation reduced the impact of drought on the lives of people after the independence.

MARRMoT is a novel rainfall-runoff model comparison framework that allows objective comparison between different conceptual hydrological model structures. The framework provides Matlab code for 46 unique model structures, standardized parameter ranges across all model structures and robust numerical implementation of each model. The framework is provided with extensive documentation, a User Manual and several workflow scripts that give examples of how to use the framework. MARRMoT is based around individual flux functions and aggregated model functions, allowing a wide range of possible applications.
Link to paper:
Link to github page:

Noting a strong imperative to understand precipitation extremes, and that considerable uncertainty affects observational data sets, this paper compares the representation of extremes in a number of widely used daily gridded products, derived from rain gauge data, satellite retrieval and reanalysis for the conterminous United States. Analysis is based upon the concept of “tail dependence” arising in multivariate extreme value theory, and we infer the level of temporal dependence in the joint tail of the precipitation probability distribution for pairwise comparisons of products. In this way, we consider the range of products more like an ensemble and examine the relationships between members, and do not attempt to define, or compare products to, some ground truth. Linear correlation between products is also computed. Considerable discrepancy between groups of products, both annually and seasonally, is linked to source data and complex terrain. In particular, products based on rain gauge data showed remarkable similarity, but differed considerably, showing almost total loss of extremal dependence during DJF in mountainous regions, when compared with satellite products. Additionally, simulated re-forecasts revealed reasonable temporal agreement with large scale generated extremes. The diversity and extent of discrepancies identified across all products raises important questions about their use, and we urge caution, particularly for products derived from satellite data.

Human societies evolved alongside rivers, but how has the relationship between human settlement locations and water resources evolved over time? We conducted a dynamic analysis in the conterminous US to assess the coevolution of humans and water resources from 1790 to 2010. Here we show that humans moved closer to major rivers in pre-industrial periods but have moved farther from major rivers after 1870, demonstrating the dynamics of human reliance on rivers for trade and transport. We show that humans were preferentially attracted to areas overlying major aquifers since industrialization due to the emergent accessibility of groundwater in the 20th century. Regional heterogeneity resulted in diverse trajectories of settlement proximity to major rivers, with the attractiveness of rivers increasing in arid regions and decreasing in humid areas. Our results reveal a historical coevolution of human-water systems, which could inform water management and contribute to societal adaptation to future climate change.

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