The challenges and possibilities of earthquake predictions using non-seismic precursors

Abstract The catastrophic magnitude of life and monetary losses associated with earthquakes deserve serious attention and mitigation measures. However, in addition to the pre-earthquake and post-earthquake alleviation actions, the scientific community indeed needs to reconsider the possibilities of earthquake predictions using non-seismic precursors. A significant number of studies in the recent decades have reported several possible earthquake precursors such as anomalies in electric field, magnetic field, gas/aerosol emissions, ionospheric signals, ground water level, land surface temperature, surface deformations, animal behaviour, thermal infrared signals, atmospheric gravity waves, and lightning. Such substantial number of scientific articles and reported anomalous signals cannot be overlooked without a thoughtful appraisal. Here, we provide an opinion on the way forward for earthquake prediction in terms of challenges and possibilities while using non-seismic precursors. A general point of concern is the widely varying arrival times and the amplitudes of the anomalies, putting a question mark on their universal applicability as earthquake markers. However, a unifying concept which does not only define the physical basis of either all or most of these anomalies but which also streamlines their characterisation procedure must be the focus of future earthquake precursory research. Advancements in developing the adaptable instrumentation for in-situ observations of the claimed non-seismic precursors must be the next step and the satellite observations should not be taken as a replacement for field-based research. We support the need to standardise the precursor detection techniques and to employ a global-scale monitoring system for making any possible earthquake predictions reliable.

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A near-global, high resolution land surface parameter dataset for the variable infiltration capacity model

Scientific Data ◽

10.1038/s41597-021-00999-4 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Jacob R. Schaperow ◽

Dongyue Li ◽

Steven A. Margulis ◽

Dennis P. Lettenmaier

Keyword(s):

Land Surface ◽

Global Scale ◽

Radiation Budget ◽

Soil Parameters ◽

Variable Infiltration Capacity ◽

Infiltration Capacity ◽

Vic Model ◽

Spatial Coverage ◽

Land Surface Parameter ◽

Vegetation Parameters

AbstractHydrologic models predict the spatial and temporal distribution of water and energy at the land surface. Currently, parameter availability limits global-scale hydrologic modelling to very coarse resolution, hindering researchers from resolving fine-scale variability. With the aim of addressing this problem, we present a set of globally consistent soil and vegetation parameters for the Variable Infiltration Capacity (VIC) model at 1/16° resolution (approximately 6 km at the equator), with spatial coverage from 60°S to 85°N. Soil parameters derived from interpolated soil profiles and vegetation parameters estimated from space-based MODIS measurements have been compiled into input files for both the Classic and Image drivers of the VIC model, version 5. Geographical subsetting codes are provided, as well. Our dataset provides all necessary land surface parameters to run the VIC model at regional to global scale. We evaluate VICGlobal’s ability to simulate the water balance in the Upper Colorado River basin and 12 smaller basins in the CONUS, and their ability to simulate the radiation budget at six SURFRAD stations in the CONUS.

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Tiling soil textures for terrestrial ecosystem modelling via clustering analysis: a case study with CLASS-CTEM (version 2.1)

10.5194/gmd-2017-3 ◽

2017 ◽

Author(s):

Joe R. Melton ◽

Reinel Sospedra-Alfonso ◽

Kelly E. McCusker

Keyword(s):

Soil Texture ◽

Land Surface ◽

Clustering Analysis ◽

Clustering Algorithms ◽

Terrestrial Ecosystem ◽

Grid Cell ◽

Moisture Stress ◽

Ecosystem Model ◽

Global Scale ◽

Terrestrial Ecosystem Model

Abstract. We investigate the application of clustering algorithms to represent sub-grid scale variability in soil texture for use in a global-scale terrestrial ecosystem model. Our model, the coupled Canadian Land Surface Scheme – Canadian Terrestrial Ecosystem Model (CLASS-CTEM), is typically implemented at a coarse spatial resolution (ca. 2.8° × 2.8°) due to its use as the land surface component of the Canadian Earth System Model (CanESM). CLASS-CTEM can, however, be run with tiling of the land surface as a means to represent sub-grid heterogeneity. We first determined that the model was sensitive to tiling of the soil textures via an idealized test case before attempting to cluster soil textures globally. To cluster a high-resolution soil texture dataset onto our coarse model grid, we use two linked algorithms (OPTICS (Ankerst et al., 1999; Daszykowski et al., 2002) and Sander et al. (2003)) to provide tiles of representative soil textures for use as CLASS-CTEM inputs. The clustering process results in, on average, about three tiles per CLASS-CTEM grid cell with most cells having four or less tiles. Results from CLASS-CTEM simulations conducted with the tiled inputs (Cluster) versus those using a simple grid-mean soil texture (Gridmean) show CLASS-CTEM, at least on a global scale, is relatively insensitive to the tiled soil textures, however differences can be large in arid or peatland regions. The Cluster simulation has generally lower soil moisture and lower overall vegetation productivity than the Gridmean simulation except in arid regions where plant productivity increases. In these dry regions, the influence of the tiling is stronger due to the general state of vegetation moisture stress which allows a single tile, whose soil texture retains more plant available water, to yield much higher productivity. Although the use of clustering analysis appears promising as a means to represent sub-grid heterogeneity, soil textures appear to be reasonably represented for global scale simulations using a simple grid-mean value.

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Twitter reveals human mobility dynamics during the COVID-19 pandemic

PLoS ONE ◽

10.1371/journal.pone.0241957 ◽

2020 ◽

Vol 15 (11) ◽

pp. e0241957 ◽

Cited By ~ 2

Author(s):

Xiao Huang ◽

Zhenlong Li ◽

Yuqin Jiang ◽

Xiaoming Li ◽

Dwayne Porter

Keyword(s):

Time Window ◽

Human Mobility ◽

Global Scale ◽

Mitigation Measures ◽

Mobility Patterns ◽

Protection Measures ◽

Social Challenges ◽

Rapid Spread ◽

Collaborative Efforts ◽

State Scale

The current COVID-19 pandemic raises concerns worldwide, leading to serious health, economic, and social challenges. The rapid spread of the virus at a global scale highlights the need for a more harmonized, less privacy-concerning, easily accessible approach to monitoring the human mobility that has proven to be associated with viral transmission. In this study, we analyzed over 580 million tweets worldwide to see how global collaborative efforts in reducing human mobility are reflected from the user-generated information at the global, country, and U.S. state scale. Considering the multifaceted nature of mobility, we propose two types of distance: the single-day distance and the cross-day distance. To quantify the responsiveness in certain geographic regions, we further propose a mobility-based responsive index (MRI) that captures the overall degree of mobility changes within a time window. The results suggest that mobility patterns obtained from Twitter data are amenable to quantitatively reflect the mobility dynamics. Globally, the proposed two distances had greatly deviated from their baselines after March 11, 2020, when WHO declared COVID-19 as a pandemic. The considerably less periodicity after the declaration suggests that the protection measures have obviously affected people’s travel routines. The country scale comparisons reveal the discrepancies in responsiveness, evidenced by the contrasting mobility patterns in different epidemic phases. We find that the triggers of mobility changes correspond well with the national announcements of mitigation measures, proving that Twitter-based mobility implies the effectiveness of those measures. In the U.S., the influence of the COVID-19 pandemic on mobility is distinct. However, the impacts vary substantially among states.

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A high-resolution global dataset of topographic index values for use in large-scale hydrological modelling

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-11-6139-2014 ◽

2014 ◽

Vol 11 (6) ◽

pp. 6139-6166 ◽

Cited By ~ 2

Author(s):

T. R. Marthews ◽

S. J. Dadson ◽

B. Lehner ◽

S. Abele ◽

N. Gedney

Keyword(s):

High Resolution ◽

Land Surface ◽

Large Scale ◽

Global Scale ◽

Surface Fluxes ◽

Data Sets ◽

Topographic Index ◽

Surface Water Flow ◽

Land Surface Water ◽

Compound Topographic Index

Abstract. Modelling land surface water flow is of critical importance for simulating land-surface fluxes, predicting runoff and water table dynamics and for many other applications of Land Surface Models. Many approaches are based on the popular hydrology model TOPMODEL, and the most important parameter of this model is the well-knowntopographic index. Here we present new, high-resolution parameter maps of the topographic index for all ice-free land pixels calculated from hydrologically-conditioned HydroSHEDS data sets using the GA2 algorithm. At 15 arcsec resolution, these layers are 4× finer than the resolution of the previously best-available topographic index layers, the Compound Topographic Index of HYDRO1k (CTI). In terms of the largest river catchments occurring on each continent, we found that in comparison to our revised values, CTI values were up to 20% higher in e.g. the Amazon. We found the highest catchment means were for the Murray-Darling and Nelson-Saskatchewan rather than for the Amazon and St. Lawrence as found from the CTI. We believe these new index layers represent the most robust existing global-scale topographic index values and hope that they will be widely used in land surface modelling applications in the future.

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The CIMR mission and its unique capabilities for soil moisture sensing

10.5194/egusphere-egu21-9484 ◽

2021 ◽

Author(s):

Maria Piles ◽

Roberto Fernandez-Moran ◽

Luis Gómez-Chova ◽

Gustau Camps-Valls ◽

Dara Entekhabi ◽

...

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Spatial Scales ◽

Microwave Radiometer ◽

Critical Time ◽

Global Scale ◽

The Arctic ◽

Temporal Perspective ◽

Microwave Radiometers ◽

Moisture Sensing

The Copernicus Imaging Microwave Radiometer (CIMR) mission is currently being developed as a High Priority Copernicus Mission to support the Integrated European Policy for the Arctic. Due to its measurement characteristics, CIMR has exciting capabilities to enable a unique set of land surface products and science applications at a global scale. These characteristics go beyond what previous microwave radiometers (e.g. AMSR series, SMAP and SMOS) provide, and therefore allow for entirely new approaches to the estimation of bio-geophysical products from brightness temperature observations. Most notably, CIMR channels (L-,C-,X-,Ka-,Ku-bands) are very well fit for the simultaneous retrieval of soil moisture and vegetation properties, like biomass and moisture of different plant components such as leaves, stems or trunks. Also, the distinct spatial resolution of each frequency band allows for the development of approaches to cascade information and obtain these properties at multiple spatial scales. From a temporal perspective, CIMR has a higher revisit time than previous L-band missions dedicated to soil moisture monitoring (about 1 day global, sub-daily at the poles). This improved temporal resolution could allow resolving critical time scales of water processes, which is relevant to better model and understand land-atmosphere exchanges and feedbacks. In this presentation, new opportunities for soil moisture remote sensing made possible by the CIMR mission, as well as synergies and cross-sensor opportunities will be discussed.&#160;&#160;

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Evaluation of key parameters controlling phenology-induced variability of surface fluxes in land surface models

10.5194/egusphere-egu21-11445 ◽

2021 ◽

Author(s):

Jan De Pue ◽

José Miguel Barrios ◽

Liyang Liu ◽

Philippe Ciais ◽

Alirio Arboleda ◽

...

Keyword(s):

Land Surface ◽

Gross Primary Production ◽

Global Scale ◽

Plant Phenology ◽

Surface Fluxes ◽

Terrestrial Vegetation ◽

Land Surface Models ◽

Area Index ◽

Wide Range ◽

Surface Models

Over the past decades, land surface models have evolved into advanced tools which comprise detailed process descriptions and interactions at a broad range of scales. One of the challenges in these models is the accurate simulation of plant phenology. It is a key element at the nexus of the simulated hydrological and carbon cycle, where the leaf area index (LAI) plays a major role in flux partitioning, water balance and gross primary production. In this study, three well-established models are used to simulate the intrinsically coupled fluxes of water, energy and carbon from terrestrial vegetation. ORCHIDEE, ISBA-CC and the LSA-SAF algorithm each have a different approach to represent plant phenology. Whereas ISBA-CC has a fairly simple biomass allocation scheme to represent the phenological cycle, ORCHIDEE relies on a dedicated phenology module, and LSA-SAF is driven by remote-sensed forcing variables, such as LAI. Simulations were performed for a wide range of hydro-climatic biomes and plant functional types at field scale. The simulated fluxes were validated using eddy-covariance measurements, and the simulated phenology was compared to remote-sensed observations. These models are tools to extrapolate leaf-level processes to global scale climate predictions. The origin of the parameters controlling phenology-induced variability in these models ranges from plant-scale lab experiments to global-scale calibration. The aim of this study is to investigate the key parameters controlling phenology-induced variability in these models.

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Global long-term land surface temperature for NOAA AVHRR: extension from 1981-2000 to 1981-2020

10.5194/egusphere-egu21-14373 ◽

2021 ◽

Author(s):

Jin Ma ◽

Ji Zhou

Keyword(s):

Climate Change ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Global Scale ◽

Mean Bias Error ◽

Bias Error ◽

Important Indicator ◽

Noaa Avhrr

As an important indicator of land-atmosphere energy interaction, land surface temperature (LST) plays an important role in the research of climate change, hydrology, and various land surface processes. Compared with traditional ground-based observation, satellite remote sensing provides the possibility to retrieve LST more efficiently over a global scale. Since the lack of global LST before, Ma et al., (2020) released a global 0.05 &#215;0.05&#160; long-term (1981-2000) LST based on NOAA-7/9/11/14 AVHRR. The dataset includes three layers: (1) instantaneous LST, a product generated based on an ensemble of several split-window algorithms with a random forest (RF-SWA); (2) orbital-drift-corrected (ODC) LST, a drift-corrected version of RF-SWA LST at 14:30 solar time; and (3) monthly averages of ODC LST. To meet the requirement of the long-term application, e.g. climate change, the period of the LST is extended from 1981-2000 to 1981-2020 in this study. The LST from 2001 to 2020 are retrieved from NOAA-16/18/19 AVHRR with the same algorithm for NOAA-7/8/11/14 AVHRR. The train and test results based on the simulation data from SeeBor and TIGR atmospheric profiles show that the accuracy of the RF-SWA method for the three sensors is consistent with the previous four sensors, i.e. the mean bias error and standard deviation less than 0.10 K and 1.10 K, respectively, under the assumption that the maximum emissivity and water vapor content uncertainties are 0.04 and 1.0 g/cm2, respectively. The preliminary validation against in-situ LST also shows a similar accuracy, indicating that the accuracy of LST from 1981 to 2020 are consistent with each other. In the generation code, the new LST has been improved in terms of land surface emissivity estimation, identification of cloud pixel, and the ODC method in order to generate a more reliable LST dataset. Up to now, the new version LST product (1981-2020) is under generating and will be released soon in support of the scientific research community.

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Investigating uncertainties in global gridded datasets of climate extremes

Climate of the Past ◽

10.5194/cp-10-2171-2014 ◽

2014 ◽

Vol 10 (6) ◽

pp. 2171-2199 ◽

Cited By ~ 21

Author(s):

R. J. H. Dunn ◽

M. G. Donat ◽

L. V. Alexander

Keyword(s):

Land Surface ◽

Structural Changes ◽

Climate Extremes ◽

Global Scale ◽

Data Sets ◽

Spatially Correlated ◽

Related Data ◽

Methodological Choices ◽

Surface Time ◽

Station Density

Abstract. We assess the effects of different methodological choices made during the construction of gridded data sets of climate extremes, focusing primarily on HadEX2. Using global land-surface time series of the indices and their coverage, as well as uncertainty maps, we show that the choices which have the greatest effect are those relating to the station network used or that drastically change the values for individual grid boxes. The latter are most affected by the number of stations required in or around a grid box and the gridding method used. Most parametric changes have a small impact, on global and on grid box scales, whereas structural changes to the methods or input station networks may have large effects. On grid box scales, trends in temperature indices are very robust to most choices, especially in areas which have high station density (e.g. North America, Europe and Asia). The precipitation indices, being less spatially correlated, can be more susceptible to methodological choices, but coherent changes are still clear in regions of high station density. Regional trends from all indices derived from areas with few stations should be treated with care. On a global scale, the linear trends over 1951–2010 from almost all choices fall within the 5–95th percentile range of trends from HadEX2. This demonstrates the robust nature of HadEX2 and related data sets to choices in the creation method.

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Ability of the land surface model ISBA-A-gs to simulate leaf area index at the global scale: Comparison with satellites products

Journal of Geophysical Research Atmospheres ◽

10.1029/2005jd006691 ◽

2006 ◽

Vol 111 (D18) ◽

Cited By ~ 94

Author(s):

Anne-Laure Gibelin ◽

Jean-Christophe Calvet ◽

Jean-Louis Roujean ◽

Lionel Jarlan ◽

Sietse O. Los

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Land Surface ◽

Land Surface Model ◽

Global Scale ◽

Surface Model ◽

Area Index

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A Review of Earth Observation-Based Analyses for Major River Basins

Remote Sensing ◽

10.3390/rs11242951 ◽

2019 ◽

Vol 11 (24) ◽

pp. 2951 ◽

Cited By ~ 1

Author(s):

Soner Uereyen ◽

Claudia Kuenzer

Keyword(s):

Land Surface ◽

Spatial Databases ◽

Spatial Information ◽

Environmental Changes ◽

River Basins ◽

Global Scale ◽

Earth Observation ◽

Basin Scale ◽

The Status ◽

Major River

Regardless of political boundaries, river basins are a functional unit of the Earth’s land surface and provide an abundance of resources for the environment and humans. They supply livelihoods supported by the typical characteristics of large river basins, such as the provision of freshwater, irrigation water, and transport opportunities. At the same time, they are impacted i.e., by human-induced environmental changes, boundary conflicts, and upstream–downstream inequalities. In the framework of water resource management, monitoring of river basins is therefore of high importance, in particular for researchers, stake-holders and decision-makers. However, land surface and surface water properties of many major river basins remain largely unmonitored at basin scale. Several inventories exist, yet consistent spatial databases describing the status of major river basins at global scale are lacking. Here, Earth observation (EO) is a potential source of spatial information providing large-scale data on the status of land surface properties. This review provides a comprehensive overview of existing research articles analyzing major river basins primarily using EO. Furthermore, this review proposes to exploit EO data together with relevant open global-scale geodata to establish a database and to enable consistent spatial analyses and evaluate past and current states of major river basins.

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