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Topics - Pankaj Dey

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A perception has emerged, based on several studies, that satellite-based reflectances are limited in terms of their ability to predict gross primary production (GPP) globally at weekly temporal scales. The basis for this inference is in part that reflectances, particularly expressed in the form of vegetation indices (VIs), convey information about potential rather than actual photosynthesis, and they are sensitive to non-green substances (e.g., soil, woody branches, and snow) as well as to chlorophyll. Previous works have suggested that processing and quality control of satellite-based reflectance data play an important role in their interpretation. In this study, we use high quality reflectance data from the MODerate-resolution Imaging Spectroradiometer (MODIS) data to train neural networks that are used to upscale GPP estimated from eddy covariance flux tower measurements globally. We quantify the ability of the machine learning approaches to capture GPP variability at daily to interannual time scales. Our results show that MODIS reflectances, when paired only with potential short-wave radiation, are able to capture a large fraction of GPP variability (approximately 77%) at daily to weekly time scales. Additional meteorological information (temperature, water vapor deficit, soil water content, ET, and incident radiation) captures only a few more percent of the GPP variability. The meteorological information is used most effectively when information about plant functional type and climate classification is included. We show that machine learning can be a useful tool for estimating GPP uncertainties as well as GPP itself from upscaling methods. Our estimated global annual mean GPP for 2007 is 142.5 ± 7.7 Pg C y which is higher than some other satellite-based estimates but within the range of other reported observation-, model-, and hybrid-based values.

An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of opportunities to quantify a diversity of biochemical and structural vegetation properties. The processing requirements for such large data streams require reliable retrieval techniques enabling the spatiotemporally explicit quantification of biophysical variables. With the aim of preparing for this new era of Earth observation, this review summarizes the state-of-the-art retrieval methods that have been applied in experimental imaging spectroscopy studies inferring all kinds of vegetation biophysical variables. Identified retrieval methods are categorized into: (1) parametric regression, including vegetation indices, shape indices and spectral transformations; (2) nonparametric regression, including linear and nonlinear machine learning regression algorithms; (3) physically based, including inversion of radiative transfer models (RTMs) using numerical optimization and look-up table approaches; and (4) hybrid regression methods, which combine RTM simulations with machine learning regression methods. For each of these categories, an overview of widely applied methods with application to mapping vegetation properties is given. In view of processing imaging spectroscopy data, a critical aspect involves the challenge of dealing with spectral multicollinearity. The ability to provide robust estimates, retrieval uncertainties and acceptable retrieval processing speed are other important aspects in view of operational processing. Recommendations towards new-generation spectroscopy-based processing chains for operational production of biophysical variables are given.

Janus is an open source Python package for agent-based modeling (ABM) of land use and land cover change (LULCC). Many ABMs of LULCC have been created across platforms, some of which are not ideal for large scale, high resolution scenarios. This model provides a simple object-oriented framework for creating ABMs specific to LULCC. The organizational philosophy of the modeling framework is to create software objects (agents) that are associated with specific and contextual attributes which are isolated from where those agents exist in the spatial setting of the model, and still provide clear linkages between the agent, their environment, and other agents in the simulation. In this way, the framework allows for assembly of LULCC ABMs with low (programmatic) overhead, making the models extensible and providing clear mechanisms for integrating them with process-oriented biophysical models. Provided with Janus is a suite of geospatial data preprocessing tools that can use arbitrary land cover products as an input. Crop choice decisions are based on potential crop prices, these can be created synthetically, or drawn from integrated human-Earth systems models such as the Global Change Assessment Model. Janus is publicly accessible through GitHub and provides an example dataset for testing.
Code repository:
Link to paper:

Hydrological sciences / GRDC Station Catalogue
« on: June 27, 2020, 10:55:40 PM »
Download global #discharge (#river flow) data with few mouse clicks. Great effort by Global Runoff Data Centre (GRDC), Koblenz (Germany).

 The “Ecohydrologic separation” hypothesis challenged assumptions of translatory flow through the rooting zone. However, studies claiming to test ecohydrologic separation have largely diverged from testing how water infiltrates and recharges the rooting zone, towards identifying isotopic differences between stream water and plant water. We suggest that differences should exist among the isotopic compositions of water in plants, streams, and other subsurface pools in most scenarios and that ecohydrologic separation is not solely about observing fractionated isotope ratios in plant water. The discussion of ecohydrologic separation should refocus on how heterogeneous infiltration and root uptake processes lead to such differences. More generally, we propose that research objectives should involve interpreting isotope data in the context of processes, rather than settling on describing data patterns that have confounded interpretations (i.e., that plant and stream water isotopically differ). Consequently, we outline areas where plant and soil water stable isotope data can progress us towards improved understanding and representation of soil‐water transport and plant‐water recharge.
  Key points 
  • Isotope ratios of plant water should differ from water flowing in soils to streams and so we need to move beyond confirming this difference
  • To move beyond identifying ecohydrologic separation towards understanding it, we provide a framework for assessing soil water flow processes
  • By focusing on dynamics of how water infiltrates into the subsurface and becomes available to plants we can better interpret past findings

While agricultural expansion and management practices are critical for increasing global food production, there is limited understanding of how they impact fluxes of carbon, water, and energy from the land surface to the atmosphere. Global land models are useful for understanding these possible climate impacts, yet few global land models explicitly represent crops or crop management given the complexity of interactions between human decisions, crop phenology, and land processes at global scales. Our analysis illustrates that representing specific crop types, as well as irrigation and fertilization, in the Community Land Model (CLM) increases the amount of carbon that plants draw out the atmosphere and also changes patterns of evapotranspiration. Additionally, crop yield estimates from CLM compare well to observed crop yields until ~1990, when modeled crop yields level off. This occurs because CLM does not represent management practices associated with modern agricultural intensification. Overall, our results illustrate the impact that crop expansion and management may have on climate and highlight that global models should represent specific crop types and crop management to accurately capture carbon, water, and energy fluxes from the land surface.

Land cover is the physical material at the surface of the Earth. As the cause and result of global environmental change, land cover change (LCC) influences the global energy balance and biogeochemical cycles. Continuous and dynamic monitoring of global LC is urgently needed. Effective monitoring and comprehensive analysis of LCC at the global scale are rare. With the latest version of GLASS (Global Land Surface Satellite) CDRs (climate data records) from 1982 to 2015, we built the first record of 34-year-long annual dynamics of global land cover  (GLASS-GLC) at 5 km resolution using the Google Earth Engine (GEE) platform. Compared to earlier global land cover (LC) products, GLASS-GLC is characterized by high consistency, more detail, and longer temporal coverage. The average overall accuracy for the 34 years each with seven classes, including cropland, forest, grassland, shrubland, tundra, barren land, and snow/ice, is 82.81 % based on 2431 test sample units. We implemented a systematic uncertainty analysis and carried out a comprehensive spatiotemporal pattern analysis. Significant changes at various scales were found, including barren land loss and cropland gain in the tropics, forest gain in the Northern Hemisphere, and grassland loss in Asia. A global quantitative analysis of human factors showed that the average human impact level in areas with significant LCC was about 25.49 %. The anthropogenic influence has a strong correlation with the noticeable vegetation gain, especially for forest. Based on GLASS-GLC, we can conduct long-term LCC analysis, improve our understanding of global environmental change, and mitigate its negative impact. GLASS-GLC will be further applied in Earth system modeling to facilitate research on global carbon and water cycling, vegetation dynamics, and climate change. The GLASS-GLC data set presented in this article is available at  (Liu et al., 2020).
Link to the paper:

 Abstract. Data on global agricultural production are usually available as statistics at administrative units, which does not give any diversity and spatial patterns thus is less informative for subsequent spatially explicit agricultural and environmental analyses. In the second part of the two-paper series, we introduce SPAM2010 – the latest global spatially explicit datasets on agricultural production circa year 2010 – and elaborate on the improvement of the SPAM (Spatial Production Allocation Model) dataset family since year 2000. SPAM2010 adds further methodological and data enhancements to the available crop downscaling modeling: it not only applies the latest global synergy cropland layer (see Lu et al., submitted to the current journal) and other relevant data, but also expands the estimates of crop area, yield and production from 20 to 42 major crops under four farming systems across a global 5 arc-minute grid. All the SPAM maps are freely available at the MapSPAM website (, which not only acts as a tool for validating and improving the performance of the SPAM maps by collecting feedbacks from users, but also dedicates as platform providing archived global agricultural production maps for better targeting the Sustainable Development Goals by making proper agricultural and rural development policies and investments. In particular, SPAM2010 can be downloaded via an open-data repository (DOI:, IFPRI, 2019).
          Paper Citation:     Yu, Q., You, L., Wood-Sichra, U., Ru, Y., Joglekar, A. K. B., Fritz, S., Xiong, W., Lu, M., Wu, W., and Yang, P.: A cultivated planet in 2010: 2. the global gridded agricultural production maps, Earth Syst. Sci. Data Discuss.,, in review, 2020.

In the tropics, the majority of high‐intensity precipitation comes from the organization of multiple thunderstorms into a convective system. These systems are not yet well‐represented in global models, and studies of how their precipitation changes with long‐term modes of climate variability like the El Niño Southern Oscillation have been limited. Using long‐term satellite data, we find here that the most intense precipitation becomes two times more probable for the deepest systems and 20% less probable for the least deep systems during El Niño. With a budget for the ascent rate within these systems, we illustrate how favorable moisture structure acts as a buoyancy source in the first case and unfavorable circulation acts as a buoyancy sink in the latter case.

Interesting information / Satellite Imagery Datasets: A Repository
« on: June 20, 2020, 07:08:29 PM »
List of aerial and satellite imagery datasets with annotations for computer vision and deep learning. Newest datasets at the top of each category (Instance segmentation, object detection, semantic segmentation, scene classification, other).


Climate change will impact every aspect of biophysical systems and society. However, unlike other components of the climate system, the impact of climate change on the groundwater system has only recently received attention. This focus is due to the realization that groundwater is a vital freshwater resource crucial to global food and water security, and is essential in sustaining ecosystems and human adaptation to climate variability and change. This paper synthesizes findings on the direct and indirect impacts of climate change on the entire groundwater system and each component. Also, we appraise the use of coupled groundwater-climate and land surface models in groundwater hydrology as a means of improving existing knowledge of climate change-groundwater interaction, finding that most models anticipate decreases in groundwater recharge, storage and levels, particularly in the arid/semi-arid tropics. Reducing uncertainties in future climate projections and improving our understanding of the physical processes underlying models to improve their simulation of real-world conditions remain a priority for climate and earth scientists. Despite the enormous progress made, there are still few and inadequate local and regional aquifer studies, especially in less developed regions. The paper proposes two key considerations. First, physical basis: the need for a deeper grasp of complex physical processes and feedback mechanism with the use of more sophisticated models. Second, the need to understand the socioeconomic dimensions of climate-groundwater interaction through multidisciplinary synergy, leading to the development of better adaptation strategies and groundwater-climate change adaptation modelling.


• Presents a comprehensive review on Climate change influences on groundwater system.
• Highlights future considerations in research areas including, but not limited to, methodology, processes, mechanisms and climate variability
• Proposes two key strategies, Physical basis and Socioeconomic dimension, for post groundwater-climate change research.

Link to paper:

An increasing population in conjunction with a changing climate necessitates a detailed understanding of water abundance at multiple spatial and temporal scales. Remote sensing has provided massive data volumes to track fluctuations in water quantity, yet contextualizing water abundance with other local, regional, and global trends remains challenging by often requiring large computational resources to combine multiple data sources into analytically-friendly formats. To bridge this gap and facilitate future freshwater research opportunities, we harmonized existing global datasets to create the Global Lake area, Climate, and Population (GLCP) dataset. The GLCP is a compilation of lake surface area for 1.42 + million lakes and reservoirs of at least 10 ha in size from 1995 to 2015 with co-located basin-level temperature, precipitation, and population data. The GLCP was created with FAIR (findable, accessible, interoperable, reusable) data principles in mind and retains unique identifiers from parent datasets to expedite interoperability. The GLCP offers critical data for basic and applied investigations of lake surface area and water quantity at local, regional, and global scales.

Stochastic simulation has a prominent position in a variety of scientific domains including those of environmental and water resources sciences. This is due to the numerous applications that can benefit from it, such as risk-related studies. In such domains, stochastic models are typically used to generate synthetic weather data with the desired properties, often resembling those of hydrometeorological observations, which are then used to drive deterministic models of the understudy system. However, generating synthetic weather data with the desired properties is not an easy task. This is due to the peculiarities of such processes, i.e., non-Gaussianity, intermittency, dependence, and periodicity, and the limited availability of open-source software for such purposes. This work aims to simplify the synthetic data generation procedure by providing an R-package called anySim, specifically designed for the simulation of non-Gaussian correlated random variables, stochastic processes at single and multiple temporal scales, and random fields. The functionality of the package is demonstrated through seven simulation studies, accompanied by code snippets, which resemble real-world cases of stochastic simulation (i.e., generation of synthetic weather data) of hydrometeorological processes and fields (e.g., rainfall, streamflow, temperature, etc.), across several spatial and temporal scales (ranging from annual down to 10-min simulations).

The Penman‐Monteith equation is used widely to estimate evapotranspiration (E ) and to understand its governing physics. I present an alternative to the Penman‐Monteith equation that has both practical and theoretical advantages, at no appreciable cost. In particular, the new equation requires no additional assumptions, empiricism, or computational cost compared with the Penman‐Monteith equation. Practically, the new equation is consistently more accurate over a wide range of conditions when compared with eddy covariance observations: The new equation has lower errors compared with Penman‐Monteith estimates of ET at all of the 79 eddy covariance sites available for the analysis. Using the new equation reduces errors, on average, by 67% , from 8.55 to 2.81 [W m−2]. At night, the improvement is even greater (92% reduction in error; from 1.26 to 0.097 [W m−2]). This improvement is achieved without calibration. Theoretically, the new equation corrects a conceptual error in the Penman‐Monteith equation, in which the Penman‐Monteith equation incorrectly implies that E from a saturated surface into a saturated, turbulent atmosphere (“equilibrium” E ) is exactly equivalent to E from an unsaturated surface into an unsaturated, laminar atmosphere. The conceptual error is traced back to the failure of the Penman‐Monteith equation in important limiting cases; these errors are eliminated by the new equation. I use the new equation to revise an existing theory of land‐atmosphere coupling affected by the conceptual error in the Penman‐Monteith equation and to reassess several common but incorrect definitions of equilibrium E .
This technical report is open access:

Historical temperature measurements are the basis of global climate datasets like HadCRUT4. This dataset contains many missing values, particularly for periods before the mid-twentieth century, although recent years are also incomplete. Here we demonstrate that artificial intelligence can skilfully fill these observational gaps when combined with numerical climate model data. We show that recently developed image inpainting techniques perform accurate monthly reconstructions via transfer learning using either 20CR (Twentieth-Century Reanalysis) or the CMIP5 (Coupled Model Intercomparison Project Phase 5) experiments. The resulting global annual mean temperature time series exhibit high Pearson correlation coefficients (≥0.9941) and low root mean squared errors (≤0.0547 °C) as compared with the original data. These techniques also provide advantages relative to state-of-the-art kriging interpolation and principal component analysis-based infilling. When applied to HadCRUT4, our method restores a missing spatial pattern of the documented El Niño from July 1877. With respect to the global mean temperature time series, a HadCRUT4 reconstruction by our method points to a cooler nineteenth century, a less apparent hiatus in the twenty-first century, an even warmer 2016 being the warmest year on record and a stronger global trend between 1850 and 2018 relative to previous estimates. We propose image inpainting as an approach to reconstruct missing climate information and thereby reduce uncertainties and biases in climate records.

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