A review of freely accessible global datasets for the study of floods, droughts and their interactions with human societies
TL;DR: The availability of planetary-scale geospatial datasets that can support the study of water-related disasters in the Anthropocene is rapidly growing as mentioned in this paper, and they review 124 global and free datasets allowing...
Abstract: The availability of planetary-scale geospatial datasets that can support the study of water-related disasters in the Anthropocene is rapidly growing. We review 124 global and free datasets allowing ...
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01 Apr 2015
Abstract: In the last decade record-breaking rainfall events have occurred in many places around the world causing severe impacts to human society and the environment including agricultural losses and floodings. There is now medium confidence that human-induced greenhouse gases have contributed to changes in heavy precipitation events at the global scale. Here, we present the first analysis of record-breaking daily rainfall events using observational data. We show that over the last three decades the number of record-breaking events has significantly increased in the global mean. Globally, this increase has led to 12 % more record-breaking rainfall events over 1981–2010 compared to those expected in stationary time series. The number of record-breaking rainfall events peaked in 2010 with an estimated 26 % chance that a new rainfall record is due to long-term climate change. This increase in record-breaking rainfall is explained by a statistical model which accounts for the warming of air and associated increasing water holding capacity only. Our results suggest that whilst the number of rainfall record-breaking events can be related to natural multi-decadal variability over the period from 1901 to 1980, observed record-breaking rainfall events significantly increased afterwards consistent with rising temperatures.
181 citations
TL;DR: In this article, the authors apply a bibliometric analysis using the Web of Science (WoS) database to assess the historic evolution and future prospects (emerging fields of application) of FRA.
Abstract: Studies looking at flood risk analysis and assessment (FRA) reviews are not customary, and they usually approach to methodological and spatial scale issues, uncertainty, mapping or economic damage topics. However, most of these reviews provide a snapshot of the scientific state of the art of FRA that shows only a partial view, focused on a limited number of selected methods and approaches. In this paper, we apply a bibliometric analysis using the Web of Science (WoS) database to assess the historic evolution and future prospects (emerging fields of application) of FRA. The scientific production of FRA has increased considerably in the past decade. At the beginning, US researchers dominated the field, but now they have been overtaken by the Chinese. The Netherlands and Germany may be highlighted for their more complete analyses and assessments (e.g., including an uncertainty analysis of FRA results), and this can be related to the presence of competitive research groups focused on FRA. Regarding FRA fields of application, resilience analysis shows some growth in recent years while land planning, risk perception and risk warning show a slight decrease in the number of papers published. Global warming appears to dominate part of future FRA production, which affects both fluvial and coastal floods. This, together with the improvement of economic evaluation and psycho-social analysis, appear to be the main trends for the future evolution of FRA. Finally, we cannot ignore the increase in analysis using big data analysis, machine learning techniques, and remote sensing data (particularly in the case of UAV sensors data).
33 citations
TL;DR: In this article, the authors present a survey of the work of the UK's Department of Civil Engineering, University of Bristol, Bristol, UK Cabot Institute for the Environment and the UK Institute for Environmental Science and Geography.
Abstract: Department of Civil Engineering, University of Bristol, Bristol, UK Cabot Institute for the Environment, University of Bristol, Bristol, UK Institute for Environmental Science and Geography, University of Potsdam, Potsdam, Germany Department of Civil Engineering, University of Victoria, Victoria, British Columbia, Canada School of Earth and Ocean Sciences, University of Victoria, Victoria, British Columbia, Canada School of Geographical Sciences, University of Bristol, Bristol, UK Institute of Earth and Environmental Sciences, University of Freiburg, Freiburg, Germany
27 citations
Cites background from "A review of freely accessible globa..."
...Equally, we see the emergence of large-scale and even global data sets which add new dimensions to our ability to analyze global hydrology (Beck, van Dijk, et al., 2019; Ghiggi et al., 2019; Lindersson et al., 2020)....
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TL;DR: In this article , the authors review the achievements of more than three decades of advances using remote sensing to study surface waters in Africa, highlighting the current benefits and difficulties, and discuss future opportunities and how the use of remote sensing could benefit scientific and societal applications, such as water resource management, flood risk prevention and environment monitoring under current global change.
Abstract: The African continent hosts some of the largest freshwater systems worldwide, characterized by a large distribution and variability of surface waters that play a key role in the water, energy and carbon cycles and are of major importance to the global climate and water resources. Freshwater availability in Africa has now become of major concern under the combined effect of climate change, environmental alterations and anthropogenic pressure. However, the hydrology of the African river basins remains one of the least studied worldwide and a better monitoring and understanding of the hydrological processes across the continent become fundamental. Earth Observation, that offers a cost-effective means for monitoring the terrestrial water cycle, plays a major role in supporting surface hydrology investigations. Remote sensing advances are therefore a game changer to develop comprehensive observing systems to monitor Africa's land water and manage its water resources. Here, we review the achievements of more than three decades of advances using remote sensing to study surface waters in Africa, highlighting the current benefits and difficulties. We show how the availability of a large number of sensors and observations, coupled with models, offers new possibilities to monitor a continent with scarce gauged stations. In the context of upcoming satellite missions dedicated to surface hydrology, such as the Surface Water and Ocean Topography (SWOT), we discuss future opportunities and how the use of remote sensing could benefit scientific and societal applications, such as water resource management, flood risk prevention and environment monitoring under current global change.The hydrology of African surface water is of global importance, yet it remains poorly monitored and understoodComprehensive review of remote sensing and modeling advances to monitor Africa's surface water and water resourcesFuture opportunities with upcoming satellite missions and to translate scientific advances into societal applications.
26 citations
01 Dec 2019
TL;DR: The first study assessing the feasibility of forecasting drought impacts using machine-learning to relate forecasted hydro-meteorological drought indices to reported drought impacts shows that models, which were built with sufficient amount of reported drought impact in a certain sector, are able to forecast drought impacts a few months ahead.
Abstract: Present-day drought early warning systems provide the end-users information on the ongoing and forecasted drought hazard (e.g. river flow deficit). However, information on the forecasted drought impacts, which is a prerequisite for drought management, is still missing. Here we present the first study assessing the feasibility of forecasting drought impacts, using machine-learning to relate forecasted hydro-meteorological drought indices to reported drought impacts. Results show that models, which were built with more than 50 months of reported drought impacts, are able to forecast drought impacts a few months ahead. This study highlights the importance of drought impact databases for developing drought impact functions. Our findings recommend that institutions that provide operational drought early warnings should not only forecast drought hazard, but also impacts after developing an impact database. There still lacks a forecast system that inform end-users regarding the drought impacts, which will be however important for drought management. Here the authors assess the feasibility of forecasting drought impacts using machine-learning and confirm that models, which were built with sufficient amount of reported drought impacts in a certain sector, are able to forecast drought impacts a few months ahead.
25 citations
References
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Technical University of Madrid1, Stanford University2, Elsevier3, VU University Amsterdam4, National Institutes of Health5, University of Leicester6, Harvard University7, Beijing Genomics Institute8, Maastricht University9, Wageningen University and Research Centre10, University of Oxford11, Heriot-Watt University12, University of Manchester13, University of California, San Diego14, Leiden University Medical Center15, Leiden University16, Federal University of São Paulo17, Science for Life Laboratory18, Bayer19, Swiss Institute of Bioinformatics20, Cray21, University Medical Center Groningen22, Erasmus University Rotterdam23
TL;DR: The FAIR Data Principles as mentioned in this paper are a set of data reuse principles that focus on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals.
Abstract: There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.
7,602 citations
TL;DR: Google Earth Engine is a cloud-based platform for planetary-scale geospatial analysis that brings Google's massive computational capabilities to bear on a variety of high-impact societal issues including deforestation, drought, disaster, disease, food security, water management, climate monitoring and environmental protection.
6,262 citations
"A review of freely accessible globa..." refers background in this paper
...including Google Earth Engine (Gorelick et al., 2017), NASA Earth Exchange (Nemani, Votava, Michaelis, Melton, & Milesi, 2011), and Open Data Cube (Killough, 2018)....
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...Cloud computation platforms are virtual servers that provide access to data, processing power and analytical scripts, including Google Earth Engine (Gorelick et al., 2017), NASA Earth Exchange (Nemani, Votava, Michaelis, Melton, & Milesi, 2011), and Open Data Cube (Killough, 2018)....
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TL;DR: In this paper, an updated gridded climate dataset (referred to as CRU TS3.10) from monthly observations at meteorological stations across the world's land areas is presented.
Abstract: This paper describes the construction of an updated gridded climate dataset (referred to as CRU TS3.10) from monthly observations at meteorological stations across the world's land areas. Station anomalies (from 1961 to 1990 means) were interpolated into 0.5° latitude/longitude grid cells covering the global land surface (excluding Antarctica), and combined with an existing climatology to obtain absolute monthly values. The dataset includes six mostly independent climate variables (mean temperature, diurnal temperature range, precipitation, wet-day frequency, vapour pressure and cloud cover). Maximum and minimum temperatures have been arithmetically derived from these. Secondary variables (frost day frequency and potential evapotranspiration) have been estimated from the six primary variables using well-known formulae. Time series for hemispheric averages and 20 large sub-continental scale regions were calculated (for mean, maximum and minimum temperature and precipitation totals) and compared to a number of similar gridded products. The new dataset compares very favourably, with the major deviations mostly in regions and/or time periods with sparser observational data. CRU TS3.10 includes diagnostics associated with each interpolated value that indicates the number of stations used in the interpolation, allowing determination of the reliability of values in an objective way. This gridded product will be publicly available, including the input station series (http://www.cru.uea.ac.uk/ and http://badc.nerc.ac.uk/data/cru/). © 2013 Royal Meteorological Society
5,552 citations
01 May 2017
TL;DR: ROZA was developed under the umbrella of LTER-France (Long Term Ecological Research) in order to facilitate the re-use of data and samples and will favor to use of paleodata by non-paleodata scientists, in particular ecologists.
Abstract: Managing paleoscience data is highly challenging to the multiplicity of actors in play, types of sampling, analysis, post-analysis treatments, statistics etc. However, a well-structured curating of data would permit innovative developments based on data and/or sample re-use, such as meta-analysis or the development of new proxies on previously studied cores. In this paper, we will present two recent initiatives that allowed us tackling this objective at a French national level: the “National Cyber Core Repository” (NCCR) and the “LTER-France retro-observatory” (ROZA).NCCR was developed under the umbrella of the French National Center fo Coring and Drilling (C2FN) thanks to the national excellence equipment project CLIMCOR. It aims at gathering on a unique website the locations and metadata of any scientific coring/drilling performed by French teams or using French facilities, whatever the type of archive it is (lake/marine sediment; ice etc.). It uses international standard, notably IGSN (for samples), ORCID (for persons) and DOI (for campaigns). NCC follows the INSPIRE ISO 19115 standard in order to catalogue the data. For continental sediment, NCCR may be fed directly on the field through a specifically developed mobile application.Based on NCCR, further initiatives may be led. In particular, under the umbrella of LTER-France (Long Term Ecological Research), we developed ROZA in order to facilitate the re-use of data and samples. Here the idea is to capitalise the knowledge on a given lake from which several sediment cores can be taken through time. In that aim we selected at least one lake from each of the 13 areas composing the network LTER-France. To enter the database, a set of mandatory data must be provided under a pre-determined format. In that case, the insertion of ROZA within the network LTER will favor to use of paleodata by non-paleodata scientists, in particular ecologists.
3,648 citations
"A review of freely accessible globa..." refers methods in this paper
...The data principles of FAIR have guided our selection of datasets, encouraging findability, accessibility, interoperability and reusability (Wilkinson et al., 2016)....
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TL;DR: Using three million Landsat satellite images, this globally consistent, validated data set shows that impacts of climate change and climate oscillations on surface water occurrence can be measured and that evidence can be gathered to show how surface water is altered by human activities.
Abstract: A freely available dataset produced from three million Landsat satellite images reveals substantial changes in the distribution of global surface water over the past 32 years and their causes, from climate change to human actions. The distribution of surface water has been mapped globally, and local-to-regional studies have tracked changes over time. But to date, there has been no global and methodologically consistent quantification of changes in surface water over time. Jean-Francois Pekel and colleagues have analysed more than three million Landsat images to quantify month-to-month changes in surface water at a resolution of 30 metres and over a 32-year period. They find that surface waters have declined by almost 90,000 square kilometres—largely in the Middle East and Central Asia—but that surface waters equivalent to about twice that area have been created elsewhere. Drought, reservoir creation and water extraction appear to have driven most of the changes in surface water over the past decades. The location and persistence of surface water (inland and coastal) is both affected by climate and human activity1 and affects climate2,3, biological diversity4 and human wellbeing5,6. Global data sets documenting surface water location and seasonality have been produced from inventories and national descriptions7, statistical extrapolation of regional data8 and satellite imagery9,10,11,12, but measuring long-term changes at high resolution remains a challenge. Here, using three million Landsat satellite images13, we quantify changes in global surface water over the past 32 years at 30-metre resolution. We record the months and years when water was present, where occurrence changed and what form changes took in terms of seasonality and persistence. Between 1984 and 2015 permanent surface water has disappeared from an area of almost 90,000 square kilometres, roughly equivalent to that of Lake Superior, though new permanent bodies of surface water covering 184,000 square kilometres have formed elsewhere. All continental regions show a net increase in permanent water, except Oceania, which has a fractional (one per cent) net loss. Much of the increase is from reservoir filling, although climate change14 is also implicated. Loss is more geographically concentrated than gain. Over 70 per cent of global net permanent water loss occurred in the Middle East and Central Asia, linked to drought and human actions including river diversion or damming and unregulated withdrawal15,16. Losses in Australia17 and the USA18 linked to long-term droughts are also evident. This globally consistent, validated data set shows that impacts of climate change and climate oscillations on surface water occurrence can be measured and that evidence can be gathered to show how surface water is altered by human activities. We anticipate that this freely available data will improve the modelling of surface forcing, provide evidence of state and change in wetland ecotones (the transition areas between biomes), and inform water-management decision-making.
2,469 citations
"A review of freely accessible globa..." refers background in this paper
...Near-polar areas are also periodically missing in datasets from optical satellite imagery, due to low solar zenith angles (Pekel et al., 2016)....
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...Many remotely sensed datasets are burdened by data gaps and inhomogeneities (Carroll et al., 2017; W. Dorigo et al., 2017; NOAA, 2017; Pekel et al., 2016; Sheffield et al., 2014), due to technical variability along the temporal records and/or natural conditions such as cloud obscuration....
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...Heavy vegetation tends to interfere with soil moisture retrievals and surface water detection (W. Dorigo et al., 2017; Pekel et al., 2016)....
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