Special Issue "Remote Sensing of Water Cycle Essential Climate Variables and Their Applications"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (31 March 2021).

Special Issue Editors

Dr. Elsa Cattani
E-Mail Website
Guest Editor
Consiglio Nazionale delle Ricerche, Istituto di Scienze dell’Atmosfera e del Clima, via Gobetti 101, I-40129 Bologna, Italy
Interests: remote sensing of precipitation from satellite; climatology of precipitation
Prof. Dr. Ali Behrangi
E-Mail Website
Guest Editor
Department of Hydrology & Atmospheric Sciences, The University of Arizona, 1133 E. James E. Rogers Way, PO Box 210011, Tucson, AZ 85721-0011, USA
Interests: Remote sensing of precipitation and cloud; high latitude and mountainous rain and snow retrievals and analysis; weather and climatic extremes (drought, flood, fire, tropical storms) and societal interactions global water and energy budget analysis; hydrologic/watershed modeling and optimization; developing high resolution precipitation products; representation of precipitation in climate models; evaluation of precipitation products using ground validation data
Dr. Geert Sterk
E-Mail Website
Guest Editor
Dept. of Physical Geography, Universiteit Utrecht, Vening Meineszgebouw A, Princetonlaan 8a, 3584 CB Utrecht, The Netherlands
Interests: Hydrology and land degradation; physics and modelling of hydrology; wind erosion and water erosion; remote sensing application to landscape studies

Special Issue Information

Dear Colleagues,

Fifty-four Essential Climate Variables (ECV) are currently identified by the Global Climate Observing System (GCOS) (https://gcos.wmo.int/en/essential-climate-variables). They represent key variables for long-term monitoring of the state of the global climate system in support of the activities of the United Nations Framework Convention on Climate Change (UNFCCC) and the Intergovernmental Panel on Climate Change (IPCC). Recently, many initiatives have promoted the generation and collection of ECV Climate Data Records (CDR) as a time series of measurements of sufficient length, consistency, and continuity to determine climate variability and change (NRC, 2004). Among them, it is worth mentioning the European Space Agency—Climate Change Initiative (ESA-CCI; Hollmann et al, 2013), the Copernicus Climate Change Services (C3S), and the NOAA National Centers for Environmental Information (NCEI).

Water cycle studies are greatly benefiting from the ECV and CDR concepts, as many fundamental variables from the atmospheric, terrestrial, and oceanic water cycle components are included in the ECV list and regularly monitored through the generation of CDRs. The water cycle is the most important chain of processes supporting life on our planet and controlling weather and climate. Nevertheless, it is still poorly understood. Evaporation, evapotranspiration, sublimation, water vapor transport, condensation, precipitation, runoff, infiltration and percolation, groundwater flow, and plant uptake are essential pieces of the mosaic and require remote sensing observations with a global perspective for a correct closure of the cycle (Levizzani and Cattani, 2019). Moreover, the modification of the Earth’s water cycle in a frame of climate change is a prominent issue that requires the attention of the scientific community. The answers should point not only to increasing the knowledge of Earth’s climate system, but also to influencing environmental policies and decision making. The increasing availability of CDRs provides precious information for multiple application fields, such as water resource management, agriculture and food security, public health, and energy production.

The focus of this Remote Sensing of the Water Cycle Special Issue is thus on remote sensing-related ECV CDRs of all water cycle components and their applications. Submitted manuscripts will preferably report scientific advances in the following topics, but other topics related to the scope of the SI will also be considered:

  • New water cycle CDRs: development and generation procedures;
  • Validation, capability assessment, and intercomparisons of water cycle CDRs;
  • CDR exploitation in long-term analyses: regional climatology, variability and trends, extreme event projections;
  • Droughts and floods: climatology and climatic driver identification;
  • Capability of CDRs to capture extremes;
  • Water cycle CDR exploitation in climate services for societal benefits;
  • Exploratory studies of the connections among the water cycle, agriculture and food, public health, and energy.

References

Hollman, R. et al. The ESA Climate Change Initiative. Bull. Amer. Meteorol. Soc. 2013, 94, 1541, doi:10.1175/BAMS-D-11-00254.1

Levizzani, V.; Cattani, E. Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate. Remote Sens. 2019, 11, 2301, doi:10.3390/rs11192301.

US National Research Council (NRC), 2004. Climate Data Records from Environmental Satellites: Interim Report (2004). Washington, DC: Committee on Climate Data Records from NOAA Operational Satellites/National Research Council/National Academies Press. 150 p.

Dr. Elsa Cattani
Prof. Ali Behrangi
Dr. Geert Sterk
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • water cycle
  • essential climate variables
  • climate data record
  • remote sensing
  • climate variability
  • societal applications

Published Papers (3 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Other

Open AccessArticle
Evaluation the Performance of Several Gridded Precipitation Products over the Highland Region of Yemen for Water Resources Management
Remote Sens. 2020, 12(18), 2984; https://doi.org/10.3390/rs12182984 - 14 Sep 2020
Cited by 1 | Viewed by 897
Abstract
Management of water resources under climate change is one of the most challenging tasks in many arid and semiarid regions. A major challenge in countries, such as Yemen, is the lack of sufficient and long-term climate data required to drive hydrological models for [...] Read more.
Management of water resources under climate change is one of the most challenging tasks in many arid and semiarid regions. A major challenge in countries, such as Yemen, is the lack of sufficient and long-term climate data required to drive hydrological models for better management of water resources. In this study, we evaluated the accuracy of accessible satellite and reanalysis-based precipitation products against observed data from Al Mahwit governorate (highland region, Yemen) during 1998–2007. Here, we evaluated the accuracy of the Climate Hazards Group Infrared Precipitation with Station (CHIRPS) data, National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR), Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), Tropical Rainfall Measuring Mission (TRMM 3B42), Unified Gauge-Based Analysis of Global Daily Precipitation (CPC), and European Atmospheric Reanalysis (ERA-5). The evaluation was performed on daily, monthly, and annual time steps by directly comparing the data from each single station with the data from the nearest grid box for each product. At a daily timescale, CHIRPS captures the daily rainfall characteristics best, such as the number of wet days, with average deviation from wet durations around 11.53%. TRMM 3B42 is the second-best performing product for a daily estimate with an average deviation of around 34.7%. However, CFSR (85.3%) and PERSIANN-CDR (103%) and ERA-5 (−81.13%) show an overestimation and underestimation of wet days and do not reflect rainfall variability of the study area. Moreover, CHIRPS is the most accurate gridded product on a monthly basis with high correlation and lower bias. The average monthly correlation between the observed and CHIRPS, TRMM 3B42, PERSIANN-CDR, CPC, ERA-5, and CFSR is 0.78, 0.56, 0.53, 0.15, 0.20, and 0.51, respectively. The average monthly bias is −2.9, −5.25, 7.35, −25.29, −24.96, and 16.68 mm for CHIRPS, TRMM 3B42, PERSIANN-CDR, CPC, ERA-5, and CFSR, respectively. CHIRPS displays the spatial distribution of annual rainfall pattern well with percent bias (Pbias) of around −8.68% at the five validation points, whereas TRMM 3B42, PERSIANN-CDR, and CFSR show a deviation of greater than 15.30, 22.90, and 66.21%, respectively. CPC and ERA-5 show Pbias of about −88.6% from observed data. Overall, in absence of better data, CHIRPS data can be used for hydrological and climate change studies on the highland region of Yemen where precipitation is often episodical and measurement records are spatially and temporally limited. Full article
Show Figures

Graphical abstract

Open AccessArticle
Enhancing Precipitation Estimates Through the Fusion of Weather Radar, Satellite Retrievals, and Surface Parameters
Remote Sens. 2020, 12(8), 1342; https://doi.org/10.3390/rs12081342 - 23 Apr 2020
Cited by 8 | Viewed by 1775
Abstract
Accurate and timely monitoring of precipitation remains a challenge, particularly in hyper-arid regions such as the United Arab Emirates (UAE). The aim of this study is to improve the accuracy of the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission’s latest [...] Read more.
Accurate and timely monitoring of precipitation remains a challenge, particularly in hyper-arid regions such as the United Arab Emirates (UAE). The aim of this study is to improve the accuracy of the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission’s latest product release (IMERG V06B) locally over the UAE. Two distinct approaches, namely, geographically weighted regression (GWR), and artificial neural networks (ANNs) are tested. Daily soil moisture retrievals from the Soil Moisture Active Passive (SMAP) mission (9 km), terrain elevations from the Advanced Spaceborne Thermal Emission and Reflection digital elevation model (ASTER DEM, 30 m) and precipitation estimates (0.5 km) from a weather radar network are incorporated as explanatory variables in the proposed GWR and ANN model frameworks. First, the performances of the daily GPM and weather radar estimates are assessed using a network of 65 rain gauges from 1 January 2015 to 31 December 2018. Next, the GWR and ANN models are developed with 52 gauges used for training and 13 gauges reserved for model testing and seasonal inter-comparisons. GPM estimates record higher Pearson correlation coefficients (PCC) at rain gauges with increasing elevation (z) and higher rainfall amounts (PCC = 0.29 z0.12), while weather radar estimates perform better for lower elevations and light rain conditions (PCC = 0.81 z−0.18). Taylor diagrams indicate that both the GWR- and the ANN-adjusted precipitation products outperform the original GPM and radar estimates, with the poorest correction obtained by GWR during the summer period. The incorporation of soil moisture resulted in improved corrections by the ANN model compared to the GWR, with relative increases in Nash–Sutcliffe efficiency (NSE) coefficients of 56% (and 25%) for GPM estimates, and 34% (and 53%) for radar estimates during summer (and winter) periods. The ANN-derived precipitation estimates can be used to force hydrological models over ungauged areas across the UAE. The methodology is expandable to other arid and hyper-arid regions requiring improved precipitation monitoring. Full article
Show Figures

Graphical abstract

Other

Jump to: Research

Open AccessTechnical Note
A New Satellite-Based Retrieval of Low-Cloud Liquid-Water Path Using Machine Learning and Meteosat SEVIRI Data
Remote Sens. 2020, 12(21), 3475; https://doi.org/10.3390/rs12213475 - 22 Oct 2020
Viewed by 597
Abstract
Clouds are one of the major uncertainties of the climate system. The study of cloud processes requires information on cloud physical properties, in particular liquid water path (LWP). This parameter is commonly retrieved from satellite data using look-up table approaches. However, existing LWP [...] Read more.
Clouds are one of the major uncertainties of the climate system. The study of cloud processes requires information on cloud physical properties, in particular liquid water path (LWP). This parameter is commonly retrieved from satellite data using look-up table approaches. However, existing LWP retrievals come with uncertainties related to assumptions inherent in physical retrievals. Here, we present a new retrieval technique for cloud LWP based on a statistical machine learning model. The approach utilizes spectral information from geostationary satellite channels of Meteosat Spinning-Enhanced Visible and Infrared Imager (SEVIRI), as well as satellite viewing geometry. As ground truth, data from CloudNet stations were used to train the model. We found that LWP predicted by the machine-learning model agrees substantially better with CloudNet observations than a current physics-based product, the Climate Monitoring Satellite Application Facility (CM SAF) CLoud property dAtAset using SEVIRI, edition 2 (CLAAS-2), highlighting the potential of such approaches for future retrieval developments. Full article
Show Figures

Graphical abstract

Back to TopTop