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Messages - Pankaj Dey

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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:

Hydrological sciences / CRAN Task View: Hydrological Data and Modeling
« on: January 12, 2019, 10:34:17 AM »
This Task View contains information about packages broadly relevant to hydrology , defined as the movement, distribution and quality of water and water resources over a broad spatial scale of landscapes. Packages are broadly grouped according to their function; however, many have functionality that spans multiple categories. We also highlight other, existing resources that have related functions - for example, statistical analysis or spatial data processing.


Soil moisture observations are expected to play an important role in monitoring global climate trends. However, measuring soil moisture is challenging because of its high spatial and temporal variability. Point-scale in-situ measurements are scarce and, excluding model-based estimates, remote sensing remains the only practical way to observe soil moisture at a global scale. The ESA-led Soil Moisture and Ocean Salinity (SMOS) mission, launched in 2009, measures the Earth’s surface natural emissivity at L-band and provides highly accurate soil moisture information with a 3-day revisiting time. Using the first six full annual cycles of SMOS measurements (June 2010–June 2016), this study investigates the temporal variability of global surface soil moisture. The soil moisture time series are decomposed into a linear trend, interannual, seasonal, and high-frequency residual (i.e., subseasonal) components. The relative distribution of soil moisture variance among its temporal components is first illustrated at selected target sites representative of terrestrial biomes with distinct vegetation type and seasonality. A comparison with GLDAS-Noah and ERA5 modeled soil moisture at these sites shows general agreement in terms of temporal phase except in areas with limited temporal coverage in winter season due to snow. A comparison with ground-based estimates at one of the sites shows good agreement of both temporal phase and absolute magnitude. A global asseSMent of the dominant features and spatial distribution of soil moisture variability is then provided. Results show that, despite still being a relatively short data set, SMOS data provides coherent and reliable variability patterns at both seasonal and interannual scales. Subseasonal components are characterized as white noise. The observed linear trends, based upon one strong El Niño event in 2016, are consistent with the known El Niño Southern Oscillation (ENSO) teleconnections. This work provides new insight into recent changes in surface soil moisture and can help further our understanding of the terrestrial branch of the water cycle and of global patterns of climate anomalies. Also, it is an important support to multi-decadal soil moisture observational data records, hydrological studies and land data assimilation projects using remotely sensed observations.


Interesting information / Land Cover Classification with eo-learn
« on: January 09, 2019, 05:31:35 PM »
The availability of open Earth Observation (EO) data through the Copernicus and Landsat programs represents an unprecedented resource for many EO applications, ranging from land use and land cover (LULC) monitoring, crop monitoring and yield prediction, to disaster control, emergency services and humanitarian relief. Given the large amount of high spatial resolution data at high revisit frequency, frameworks able to automatically extract complex patterns in such spatio-temporal data are required. eo-learn aims at providing a set of tools to make prototyping of complex EO workflows as easy, fast, and accessible as possible.

Link to blog post chronologically:





Interesting information / R Interface to Python
« on: January 09, 2019, 04:05:18 PM »
The reticulate package provides a comprehensive set of tools for interoperability between Python and R. The package includes facilities for:

reticulated python

    Calling Python from R in a variety of ways including R Markdown, sourcing Python scripts, importing Python modules, and using Python interactively within an R session.

    Translation between R and Python objects (for example, between R and Pandas data frames, or between R matrices and NumPy arrays).

    Flexible binding to different versions of Python including virtual environments and Conda environments.

Reticulate embeds a Python session within your R session, enabling seamless, high-performance interoperability. If you are an R developer that uses Python for some of your work or a member of data science team that uses both languages, reticulate can dramatically streamline your workflow!


Complete hydrological time series are necessary for water resources management and modeling. This can be challenging in data scarce environments where data gaps are ubiquitous. In many applications, repetitive gaps can have unfortunate consequences including ineffective model calibration, unreliable timing of peak flows, and biased statistics. Here, Direct Sampling (DS) is used as a non-parametric stochastic method for infilling gaps in daily streamflow records. A thorough gap-filling framework including the selection of predictor stations and the optimization of the DS parameters is developed and applied to data collected in the Volta River basin, West Africa. Various synthetic missing data scenarios are developed to assess the performance of the method, followed by a real-case application to the existing gaps in the flow records. The contribution of this study includes the assessment of the method for different climatic zones and hydrological regimes and for different upstream-downstream relations among the gauging stations used for gap filling. Tested in various missing data conditions, the method allows a precise and reliable simulation of the missing data by using the data patterns available in other stations as predictor variables. The developed gap-filling framework is transferable to other hydrological applications, and it is promising for environmental modeling.
Link to paper:

Bias adjustment is often a necessity in estimating climate impacts because impact models usually rely on unbiased climate information, a requirement that climate model outputs rarely fulfil. Most currently used statistical bias-adjustment methods adjust each climate variable separately, even though impacts usually depend on multiple potentially dependent variables. Human heat stress, for instance, depends on temperature and relative humidity, two variables that are often strongly correlated. Whether univariate bias-adjustment methods effectively improve estimates of impacts that depend on multiple drivers is largely unknown, and the lack of long-term impact data prevents a direct comparison between model outputs and observations for many climate-related impacts. Here we use two hazard indicators, heat stress and a simple fire risk indicator, as proxies for more sophisticated impact models. We show that univariate bias-adjustment methods such as univariate quantile mapping often cannot effectively reduce biases in multivariate hazard estimates. In some cases, it even increases biases. These cases typically occur (i) when hazards depend equally strongly on more than one climatic driver, (ii) when models exhibit biases in the dependence structure of drivers and (iii) when univariate biases are relatively small. Using a perfect model approach, we further quantify the uncertainty in bias-adjusted hazard indicators due to internal variability and show how imperfect bias adjustment can amplify this uncertainty. Both issues can be addressed successfully with a statistical bias adjustment that corrects the multivariate dependence structure in addition to the marginal distributions of the climate drivers. Our results suggest that currently many modeled climate impacts are associated with uncertainties related to the choice of bias adjustment. We conclude that in cases where impacts depend on multiple dependent climate variables these uncertainties can be reduced using statistical bias-adjustment approaches that correct the variables' multivariate dependence structure.

Zscheischler, J., Fischer, E. M., and Lange, S.: The effect of univariate bias adjustment on multivariate hazard estimates, Earth Syst. Dynam., 10, 31-43,, 2019.

Semantic segmentation of land cover classes is fundamental for agricultural and economic development work, from sustainable forestry to urban planning, yet existing training datasets have significant limitations. To generate an open and comprehensive training library of high resolution Earth imagery and high quality land cover classifications, public Sentinel-2 data at 10 m spatial resolution was matched with accurate GlobeLand30 labels from 2010, which were filtered by agreement with an intermediary Sentinel-2 classification at 20 m produced during atmospheric correction. Scene-level classifications were predicted by Random Forests trained on valid reflectance data and the filtered labels, and achieved over 80% model accuracy for a variety of locations. Further work is required to aggregate individual scene classifications for annual labels and to test the approach in more locations, before crowdsourcing human validation. The goal is to create a sustained community-wide effort to generate image labels not only for land cover, but also very specific images for major agriculture crops across the world and other thematic categories of interest to the global development community.


Interesting information / Aqua Monitor
« on: January 05, 2019, 06:58:51 PM »
The Deltares Aqua Monitor is the first instrument that operates on a global scale, with a resolution of up to 30 metres, to show where water has been transformed into land and vice-versa. It uses freely available satellite data and the Google Earth Engine, a platform for the planetary-scale scientific analysis of geospatial datasets that is now open to the general public.


Link to software:

Link to paper:

Link to presentation given by the developer at AGU 2018:

Copulas and other multivariate models can give joint exceedance probabilities for multivariate events in the natural environment. However, the choice of the most appropriate multivariate model may not always be evident in the absence of knowledge of dependence structures. A simple nonparametric alternative is to approximate multivariate dependencies using “line mesh distributions”, introduced here as a data-based finite mixture of univariate distributions defined on a mesh of L = C(m, 2) lines extending through Euclidean n-space. That is, m data points in n-space define a total of L lines, where C() denotes the binomial coefficient. The utilitarian simplicity of the method has attraction for joint exceedance probabilities because just the data and a single bandwidth parameter within the 0, 1 interval are needed to define a line mesh distribution. All bivariate planes in these distributions have the same Pearson correlation coefficients as the corresponding data. Marginal means and variances are similarly preserved. Using an example from the literature, a 5-parameter bivariate Gumbel model is replaced with a 1-parameter line mesh distribution. A second illustration for three dimensions applies line mesh distributions to data simulated from a trivariate copula.


Interesting information / MODFLOW and More 2019
« on: January 03, 2019, 10:32:00 PM »
The MODFLOW and More conference series unites cutting-edge developments and practical applications of hydrologic models related to groundwater. The conference series focuses on MODFLOW, but encourages participation by developers and users of all types of models with diverse applications to help evolve the modeling capabilities of our profession. The conference brings together model users and developers to exchange ideas on the latest innovations in model applications, discuss the capabilities and limitations of currently available codes, and explore directions for future developments.

The theme for this year’s MODFLOW and More conference is “Groundwater Modeling and Beyond”. With recent challenges imposed by an ever changing climate and urban population growth, how can we adapt our modeling tools accordingly? Are our models of sustainable water yield accurate enough to effectively solve the problems we’re faced with as groundwater hydrologists? These questions (and more!) will be explored at this year’s conference.


Many environmental and geographical models, such as those used in land degradation, agro-ecological and climate studies, make use of spatially distributed inputs that are known imperfectly. The R package spup provides functions for examining the uncertainty propagation from input data and model parameters onto model outputs, via the environmental model. The functions include uncertainty model specification, stochastic simulation and propagation of uncertainty using Monte Carlo (MC) techniques. Uncertain variables are described by probability distributions. Both numerical and categorical data types are handled. Spatial auto-correlation within a variable and cross-correlation between variables is accommodated for. The MC realizations may be used as input to the environmental models written in or called from R. This article provides theoretical background and three worked examples that guide users through the application of spup.

The paper is attached with this post.

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 surface urban heat island (SUHI), which represents the difference of land surface temperature (LST) in urban relativity to neighboring non-urban surfaces, is usually measured using satellite LST data. Over the last few decades, advancements of remote sensing along with spatial science have considerably increased the number and quality of SUHI studies that form the major body of the urban heat island (UHI) literature. This paper provides a systematic review of satellite-based SUHI studies, from their origin in 1972 to the present. We find an exponentially increasing trend of SUHI research since 2005, with clear preferences for geographic areas, time of day, seasons, research foci, and platforms/sensors. The most frequently studied region and time period of research are China and summer daytime, respectively. Nearly two-thirds of the studies focus on the SUHI/LST variability at a local scale. The Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper (ETM+)/Thermal Infrared Sensor (TIRS) and Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) are the two most commonly-used satellite sensors and account for about 78% of the total publications. We systematically reviewed the main satellite/sensors, methods, key findings, and challenges of the SUHI research. Previous studies confirm that the large spatial (local to global scales) and temporal (diurnal, seasonal, and inter-annual) variations of SUHI are contributed by a variety of factors such as impervious surface area, vegetation cover, landscape structure, albedo, and climate. However, applications of SUHI research are largely impeded by a series of data and methodological limitations. Lastly, we propose key potential directions and opportunities for future efforts. Besides improving the quality and quantity of LST data, more attention should be focused on understudied regions/cities, methods to examine SUHI intensity, inter-annual variability and long-term trends of SUHI, scaling issues of SUHI, the relationship between surface and subsurface UHIs, and the integration of remote sensing with field observations and numeric modeling.


Studies of trends in streamflow data from across the globe are essential for understanding patterns and changes in water availability (e.g. regions of deficit and abundance) and evaluating the fidelity of global water availability models. This study evaluates historical trends in streamflow data, using a new data set of observations from over 30,000 sites around the world. The study is comprehensive, looking at changes in low flows (defined as the lowest day of flow in each year), average flows and high flows (the highest day of flow in each year). An interesting outcome is that where trends are present in a region, the direction of the trend is consistent across all indices for that region (consistently drier or wetter), as distinct from the possibility of stronger extremes (wetter maximums and drier minimums).


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