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Messages - Pankaj Dey

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The canopy layer urban heat island (UHI) effect, as manifested by elevated near-surface air temperatures in urban areas, exposes urban dwellers to additional heat stress in many cities, specially during heat waves. We simulate the urban climate of various generated cities under the same weather conditions. For mono-centric cities, we propose a linear combination of logarithmic city area and logarithmic gross building volume, which also captures the influence of building density. By studying various city shapes, we generalise and propose a reduced form to estimate UHI intensities based only on the structure of urban sites, as well as their relative distances. We conclude that in addition to the size, the UHI intensity of a city is directly related to the density and an amplifying effect that urban sites have on each other. Our approach can serve as a UHI rule of thumb for the comparison of urban development scenarios.

Information about the amount of fresh water in rivers is decreasing globally due to a loss of maintained gauges. Remote sensors, such as the NASA/CNES Surface Water and Ocean Topography (SWOT) mission, offer the possibility of extending the decaying gauge network by providing global open data access to rivers. However, the SWOT data is unlike the data that hydrologists know. Due to instrument noise, the river width, elevation, and slope, will need to be averaged over distances as long as 10\,km. Here, we show that these average parameters can be treated using the equations for point measurements, provided the equations replace the friction from the river bed by a larger effective friction. This increase accounts for the river scales that are unobserved due to averaging. We relate the increase in effective friction to river width, area, and slope variability that may be available through statistical studies or data from complimentary sensors. We find that the effective friction is dependent on the depth of the river, increasing as the river discharge decreases. The predictions from this paper can be incorporated to improve river discharge retrievals from the SWOT mission that will improve our understanding of global river networks.

Depletion and pollution of groundwater, Earth's largest and most accessible freshwater stock, is a global sustainability concern. A changing climate, marked by more frequent and intense hydrologic extremes, poses threats to groundwater recharge and amplifies groundwater use. However, widespread human development and contamination of groundwater reservoirs pose an immediate threat of resource extinction with impacts in many regions with dense population or intensive agriculture. A rapid increase in global groundwater studies has emerged, but this has also highlighted the extreme paucity of data for substantive trend analyses and assessment of the state of the global resource. Noting the difficulty in seeing and measuring this typically invisible resource, we discuss factors that determine the current state of global groundwater, including the uncertainties accompanying data and modeling, with an eye to identifying emerging issues and the prospects for informing local to global resource management in critical regions. We comment on some prospective management strategies.


There remains large intersimulation spread in the hydrologic responses to tropical volcanic eruptions, and identifying the sources of diverse responses has important implications for assessing the side effects of solar geoengineering and improving decadal predictions. Here, we show that the intersimulation spread in the global monsoon drying response strongly relates to diverse El Niño responses to tropical eruptions. Most of the coupled climate models simulate El Niño–like equatorial eastern Pacific warming after volcanic eruptions but with different amplitudes, which drive a large spread of summer monsoon weakening and corresponding precipitation reduction. Two factors are further identified for the diverse El Niño responses. Different volcanic forcings induce systematic differences in the Maritime Continent drying and subsequent westerly winds over equatorial western Pacific, varying El Niño intensity. The internally generated warm water volume over the equatorial western Pacific in the pre-eruption month also contributes to the diverse El Niño development.

Interesting information / Global threat of arsenic in groundwater
« on: May 23, 2020, 08:48:39 AM »
Arsenic is a metabolic poison that is present in minute quantities in most rock materials and, under certain natural conditions, can accumulate in aquifers and cause adverse health effects. Podgorski and Berg used measurements of arsenic in groundwater from ∼80 previous studies to train a machine-learning model with globally continuous predictor variables, including climate, soil, and topography (see the Perspective by Zheng). The output global map reveals the potential for hazard from arsenic contamination in groundwater, even in many places where there are sparse or no reported measurements. The highest-risk regions include areas of southern and central Asia and South America. Understanding arsenic hazard is especially essential in areas facing current or future water insecurity.


Naturally occurring arsenic in groundwater affects millions of people worldwide. We created a global prediction map of groundwater arsenic exceeding 10 micrograms per liter using a random forest machine-learning model based on 11 geospatial environmental parameters and more than 50,000 aggregated data points of measured groundwater arsenic concentration. Our global prediction map includes known arsenic-affected areas and previously undocumented areas of concern. By combining the global arsenic prediction model with household groundwater-usage statistics, we estimate that 94 million to 220 million people are potentially exposed to high arsenic concentrations in groundwater, the vast majority (94%) being in Asia. Because groundwater is increasingly used to support growing populations and buffer against water scarcity due to changing climate, this work is important to raise awareness, identify areas for safe wells, and help prioritize testing.

Link to paper:

Global climate models (GCMs) are developed to simulate past climate and produce projections of climate in future. Their roles in ascertaining regional issues and possible solutions in water resources planning/management are appreciated across the world. However, there is substantial uncertainty in the future projections of GCM(s) for practical and regional implementation which has attracted criticism by the water resources planners. The present paper aims at reviewing the selection of GCMs and focusing on performance indicators, ranking of GCMs and ensembling of GCMs and covering different geographical regions. In addition, this paper also proposes future research directions. 

Government policies during the COVID-19 pandemic have drastically altered patterns of energy demand around the world. Many international borders were closed and populations were confined to their homes, which reduced transport and changed consumption patterns. Here we compile government policies and activity data to estimate the decrease in CO2 emissions during forced confinements. Daily global CO2 emissions decreased by –17% (–11 to –25% for ±1σ) by early April 2020 compared with the mean 2019 levels, just under half from changes in surface transport. At their peak, emissions in individual countries decreased by –26% on average. The impact on 2020 annual emissions depends on the duration of the confinement, with a low estimate of –4% (–2 to –7%) if prepandemic conditions return by mid-June, and a high estimate of –7% (–3 to –13%) if some restrictions remain worldwide until the end of 2020. Government actions and economic incentives postcrisis will likely influence the global CO2 emissions path for decades.

The drought‐pluvial seesaw ‐‐ defined as the phenomenon of pluvials (wet spells) following droughts (dry spells) ‐‐ magnifies the impact of individual pluvial and drought events, yet has not been systematically evaluated, especially at the global scale. We apply an event coincidence analysis to explore the aggregated seesaw behavior based on land surface model simulations for the past seven decades (1950‐2016). We find that globally, about 5.9% and 7.6% of the land surface has experienced statistically significant (p<0.10) drought‐pluvial seesaw behavior during the boreal spring‐summer and fall‐winter, with an average 11.1% and 11.4% of all droughts being followed by pluvials in the following season, respectively. Although this global frequency pattern is modest and coherent changes cannot be detected at the sub‐continental scale, local hotspots of drought‐pluvial seesaw have become more frequent than either droughts or pluvials alone in the last three decades, albeit with a small percentage of area coverage.

Hydrological sciences / Community Water Model
« on: May 18, 2020, 06:41:45 PM »
Opensource model to examine how future water demand will evolve in response to socioeconomic change and how water availability will change in response to climate.
The Community Water Model allows the assessment of water supply and human and environmental water demands at both global and regional levels.
The hydrological model is open source and has been designed to link to other models, enabling the analysis of many different aspects of the water-energy-food-ecosystem nexus.

The model is the first step towards developing a next-generation global hydro-economic modeling framework, that can explore the economic trade-offs among different water management options, encompassing both water supply infrastructure and demand management.

The integrated modeling framework will consider water demand from agriculture, domestic, energy, industry, and the environment. It will also take into account the investment needed to alleviate future water scarcity, and provide a portfolio of economically optimal solutions. In addition, it will be able to track the energy requirements associated with the water supply system; for example, pumping, desalination, and inter-basin transfer. 

Rising population and growing economic development mean that water demand is expected to increase significantly, especially in developing regions. At the same time, climate change will have global, regional, and local impacts on water availability. Ensuring this changing supply can meet the increasing demand, without compromising the aquatic environment from which it is derived, is a huge challenge.  Accurate assessment of water supply, human water demand and the water demand of the environment required to maintain this supply, is essential to devise and assess potential strategies to overcome this challenge.
The Community Water Model is the first step towards developing an integrated modelling framework, which will be able to provide vital information to decision and policy makers.

Link to Tutorial and Source Code:

Remote sensing models that measure evapotranspiration directly from the Penman‐Monteith or Priestley‐Taylor equations; typically estimate the soil evaporation component over large areas using coarse spatial resolution relative humidity (RH) from geospatial climate datasets. As a result, the models tend to underperform in dry areas at local scales where moisture status is not well represented by surrounding areas. Earth observation sensors that monitor large‐scale global dynamics (e.g. MODIS) afford comparable spatial coverage and temporal frequency, but at a higher spatial resolution than geospatial climate datasets. In this study, we compared soil evaporation parameterized with optical and thermal indices derived from MODIS to RH‐based soil evaporation as implemented in the Priestley Taylor‐Jet Propulsion Laboratory (PT‐JPL) model. We evaluated the parameterizations by subtracting PT‐JPL transpiration from observation‐based flux tower evapotranspiration in agricultural fields across the contiguous United States. We compared the apparent thermal inertia (ATI) index, land surface water index (LSWI), normalized difference water index (NDWI), and a new index derived from red and shortwave infrared bands (soil moisture divergence index‐SMDI). Relationships were significant at the 95% confidence band. LSWI and SMDI explained 18‐33% of variance in 8‐day soil evaporation. This led to a 3‐11% increase in explained ET variance. LSWI and SMDI tended to perform better at the irrigated sites than RH. LSWI and SMDI led to markedly better performance over other indices at a seasonal time‐step. L‐band microwave backscatter can penetrate clouds and can distinguish soil from canopy moisture content. We are presently fusing red‐SWIR‐RADAR to improve soil evaporation estimation.

Plants and vegetation play a critical—but largely unpredictable—role in global environmental changes due to the multitude of contributing processes at widely different spatial and temporal scales. In this Perspective, we explore approaches to master this complexity and improve our ability to predict vegetation dynamics by explicitly taking account of principles that constrain plant and ecosystem behaviour: natural selection, self-organization and entropy maximization. These ideas are increasingly being used in vegetation models, but we argue that their full potential has yet to be realized. We demonstrate the power of natural selection-based optimality principles to predict photosynthetic and carbon allocation responses to multiple environmental drivers, as well as how individual plasticity leads to the predictable self-organization of forest canopies. We show how models of natural selection acting on a few key traits can generate realistic plant communities and how entropy maximization can identify the most probable outcomes of community dynamics in space- and time-varying environments. Finally, we present a roadmap indicating how these principles could be combined in a new generation of models with stronger theoretical foundations and an improved capacity to predict complex vegetation responses to environmental change.

Presently, the Indian Ocean (IO) resides in a climate state that prevents strong year-to-year climate variations. This may change under greenhouse warming, but the mechanisms remain uncertain, thus limiting our ability to predict future changes in climate extremes. Using climate model simulations, we uncover the emergence of a mode of climate variability capable of generating unprecedented sea surface temperature and rainfall fluctuations across the IO. This mode, which is inhibited under present-day conditions, becomes active in climate states with a shallow thermocline and vigorous upwelling, consistent with the predictions of continued greenhouse warming. These predictions are supported by modeling and proxy evidence of an active mode during glacial intervals that favored such a state. Because of its impact on hydrological variability, the emergence of such a mode would become a first-order source of climate-related risks for the densely populated IO rim.

High-resolution global maps of annual urban land coverage provide fundamental information of global environmental change and contribute to applications related to climate mitigation and urban planning for sustainable development. Here we map global annual urban dynamics from 1985 to 2015 at a 30 m resolution using numerous surface reflectance data from Landsat satellites. We find that global urban extent has expanded by 9,687 km2 per year. This rate is four times greater than previous reputable estimates from worldwide individual cities, suggesting an unprecedented rate of global urbanization. The rate of urban expansion is notably faster than that of population growth, indicating that the urban land area already exceeds what is needed to sustain population growth. Looking ahead, using these maps in conjunction with integrated assessment models can facilitate greater understanding of the complex environmental impacts of urbanization and help urban planners avoid natural hazards; for example, by limiting new development in flood risk zones.


Branger and McMillan propose and test a set of hydrological signatures derived from in situ soil moisture data that aim to be representative of soil moisture behavior at the watershed scale, which is suitable to use in diagnostic model evaluation.

sen2r is a scalable and flexible R package to enable downloading and preprocessing of Sentinel-2 satellite imagery via an accessible and easy to install interface. It allows the execution of several preprocessing steps which are commonly performed by Sentinel-2 users: searching the Sentinel-2 archive for datasets available over a spatial area of interest and in a defined time window, downloading them, applying the Sen2Cor atmospheric correction algorithm to compute surface reflectances, merging adjacent tiles, performing geometric transformations, applying a cloud mask, computing spectral indices and colour images. The package is designed to be accessible to a range of users, from beginners to skilled R users. It comes with a Graphical User Interface, which can be used to set the processing parameters and launch processing operations: this feature makes sen2r accessible also for novices with limited programming experience. High-level R functions, which enable customised image processing workflows and control over intermediate steps, can be useful to experienced remote sensing researchers. Thanks to those functions it is possible to easily schedule automatic processing chains, so to manage massive processing operations. This paper describes the main characteristics, functionalities and performance of the package and highlights its usefulness as the operational back-end of service-oriented architectures, as illustrated by the Saturno project.

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