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Remote Sensing of the Water Cycle in Mountain Regions

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 (15 January 2022) | Viewed by 14972

Special Issue Editors


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Guest Editor
Consiglio Nazionale delle Ricerche, Istituto di Scienze dell’Atmosfera e del Clima, corso Fiume 4, 10133 Torino, Italy
Interests: climate variability and change in mountain regions; elevation-dependent warming; precipitation; climate extremes; climate model evaluation/validation

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Guest Editor
Faculty of Geography, Philipps-Universität Marburg, Deutschhausstr. 12, 35032 Marburg, Germany
Interests: sustainable development; climate change; numerical modeling; remote sensing; environment; spatial analysis; satellite image analysis; mapping; biodiversity; geographic information system

Special Issue Information

Dear Colleagues,

Mountains exist in many regions of the world and are home to a significant fraction of the world population and to half of global biodiversity hotspots. They safeguard essential nature contributions to people (NCPs). Most important is their role as “water towers” for lowland regions, providing freshwater for domestic use, irrigation, hydropower, or industry. They also harbor unique flora and fauna, critical habitat for rare, endemic, and endangered species, wood, snow-based recreation, and other essential ecosystem services. However, mountains are also particularly vulnerable to climate change, where impacts are already extensive and observable, the implications of which go far beyond mountain regions themselves. Monitoring and understanding climate and environmental changes in mountain regions is therefore essential.

There is growing evidence of elevation-dependent warming (Pepin et al., 2015) and climate change, which has important implications for the mass balance of the high-altitude cryosphere and associated runoff, for mountain hydrological cycles, for ecosystems and farming communities, and for species residing in restricted altitudinal ranges (Dainese et al. 2017, Immerzeel et al. 2020). However, because of the sparseness or even the lack of high-elevation observations, there is a high likelihood that we cannot monitor some of the most vulnerable regions threatened by climate and environmental change. Satellite Earth observation (EO) data can be considered as an appropriate source to complement scattered in situ measurements, even in mountain regions, and to validate or evaluate climate and hydrological model simulations.

Only by increasing integrated observational (in situ, remote sensing and modeling) efforts in mountains can efficient adaptation and mitigation measures for sustainable development strategies under environmental changes be developed.

Given the global relevance of mountain regions, the proposed Special Issue intends to compile remote sensing studies aiming at a better understanding by quantifying the ongoing changes in mountain environments, particularly in the water cycle and its components. Furthermore, papers dealing with the impacts of these changes on ecosystems, biodiversity, and downstream areas are welcome, too. Further, we encourage the submission of papers discussing future needs to increase the knowledge of the mountain water cycle and the mechanisms of change through satellite-based remote sensing.

Dr. Elisa Palazzi
Prof. Dr. Joerg Bendix
Guest Editors

Related References:

Pepin, N., Bradley, R.S., Diaz, H.F., Baraer, M., Caceres, E.B., Forsythe, N., Fowler, H., Greenwood, G., Hashmi, M.Z., Liu, X.D., Miller, J.R., Ning, L., Ohmura, A., Palazzi, E., Rangwala, I., Schöner, W., Severskiy, I., Shahgedanova, M., Wang, M.B., Williamson, S.N., Yang, D.Q., Elevation- dependent warming in mountain regions of the world (2015) Nature Climate Change, 5 (5), pp. 424-430. DOI: 10.1038/nclimate2563, 2015.

Dainese, M., Aikio, S., Hulme, P. E., Bertolli, A., Prosser, F., & Marini, L. (2017). Human disturbance and upward expansion of plants in a warming climate. Nature Climate Change, 7(8), 577-580.

Immerzeel, W. W. et al. (2020). Importance and vulnerability of the world’s water towers. Nature, 577(7790), 364-369.

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 submissions that pass pre-check are 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 2700 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
  • Remote sensing
  • Elevation dependent warming
  • Cloud and rainfall changes in mountains
  • Elevation dependent climate change, including extremes
  • Mountain ecosystems
  • Biodiversity loss
  • Ecosystem services, nature contributions to people (NCP)

Published Papers (5 papers)

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13 pages, 4228 KiB  
Communication
Observations of Winter Ablation on Glaciers in the Mount Everest Region in 2020–2021
by Mauri Pelto, Prajjwal Panday, Tom Matthews, Jon Maurer and L. Baker Perry
Remote Sens. 2021, 13(14), 2692; https://doi.org/10.3390/rs13142692 - 8 Jul 2021
Cited by 9 | Viewed by 4750
Abstract
Recent observations of rising snow lines and reduced snow-covered areas on glaciers during the October 2020–January 2021 period in the Nepal–China region of Mount Everest in Landsat and Sentinel imagery highlight observations that significant ablation has occurred in recent years on many Himalayan [...] Read more.
Recent observations of rising snow lines and reduced snow-covered areas on glaciers during the October 2020–January 2021 period in the Nepal–China region of Mount Everest in Landsat and Sentinel imagery highlight observations that significant ablation has occurred in recent years on many Himalayan glaciers in the post-monsoon and early winter periods. For the first time, we now have weather stations providing real-time data in the Mount Everest region that may sufficiently transect the post-monsoon snow line elevation region. These sensors have been placed by the Rolex National Geographic Perpetual Planet expedition. Combining in situ weather records and remote sensing data provides a unique opportunity to examine the impact of the warm and dry conditions during the 2020 post-monsoon period through to the 2020/2021 winter on glaciers in the Mount Everest region. The ablation season extended through January 2021. Winter (DJF) ERA5 reanalysis temperature reconstructions for Everest Base Camp (5315 m) for the 1950–February 2021 period indicate that six days in the January 10–15 period in 2021 fell in the top 1% of all winter days since 1950, with January 13, January 14, and January 12, being the first, second, and third warmest winter days in the 72-year period. This has also led to the highest freezing levels in winter for the 1950–2021 period, with the January 12–14 period being the only period in winter with a freezing level above 6000 m. Full article
(This article belongs to the Special Issue Remote Sensing of the Water Cycle in Mountain Regions)
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22 pages, 6111 KiB  
Article
Clustering of Rainfall Types Using Micro Rain Radar and Laser Disdrometer Observations in the Tropical Andes
by Gabriela Urgilés, Rolando Célleri, Katja Trachte, Jörg Bendix and Johanna Orellana-Alvear
Remote Sens. 2021, 13(5), 991; https://doi.org/10.3390/rs13050991 - 5 Mar 2021
Cited by 3 | Viewed by 2692
Abstract
Lack of rainfall information at high temporal resolution in areas with a complex topography as the Tropical Andes is one of the main obstacles to study its rainfall dynamics. Furthermore, rainfall types (e.g., stratiform, convective) are usually defined by using thresholds of some [...] Read more.
Lack of rainfall information at high temporal resolution in areas with a complex topography as the Tropical Andes is one of the main obstacles to study its rainfall dynamics. Furthermore, rainfall types (e.g., stratiform, convective) are usually defined by using thresholds of some rainfall characteristics such as intensity and velocity. However, these thresholds highly depend on the local climate and the study area. In consequence, these thresholds are a constraining factor for the rainfall class definitions because they cannot be generalized. Thus, this study aims to analyze rainfall-event types by using a data-driven clustering approach based on the k-means algorithm that allows accounting for the similarities of rainfall characteristics of each rainfall type. It was carried out using three years of data retrieved from a vertically pointing Micro Rain Radar (MRR) and a laser disdrometer. The results show two main rainfall types (convective and stratiform) in the area which highly differ in their rainfall features. In addition, a mixed type was found as a subgroup of the stratiform type. The stratiform type was found more frequently throughout the year. Furthermore, rainfall events of short duration (less than 70 min) were prevalent in the study area. This study will contribute to analyze the rainfall formation processes and the vertical profile. Full article
(This article belongs to the Special Issue Remote Sensing of the Water Cycle in Mountain Regions)
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19 pages, 5467 KiB  
Article
Using Remotely Sensed Information to Improve Vegetation Parameterization in a Semi-Distributed Hydrological Model (SMART) for Upland Catchments in Australia
by Seokhyeon Kim, Hoori Ajami and Ashish Sharma
Remote Sens. 2020, 12(18), 3051; https://doi.org/10.3390/rs12183051 - 18 Sep 2020
Cited by 2 | Viewed by 2848
Abstract
Appropriate representation of the vegetation dynamics is crucial in hydrological modelling. To improve an existing limited vegetation parameterization in a semi-distributed hydrologic model, called the Soil Moisture and Runoff simulation Toolkit (SMART), this study proposed a simple method to incorporate daily leaf area [...] Read more.
Appropriate representation of the vegetation dynamics is crucial in hydrological modelling. To improve an existing limited vegetation parameterization in a semi-distributed hydrologic model, called the Soil Moisture and Runoff simulation Toolkit (SMART), this study proposed a simple method to incorporate daily leaf area index (LAI) dynamics into the model using mean monthly LAI climatology and mean rainfall. The LAI-rainfall sensitivity is governed by a parameter that is optimized by maximizing the Pearson correlation coefficient (R) between the estimated and satellite-derived LAI time series. As a result, the LAI-rainfall sensitivity is smallest for forest, shrub, and woodland regions across Australia, and increases for grasslands and croplands. The impact of the proposed method on catchment-scale simulations of soil moisture (SM), evapotranspiration (ET) and discharge (Q) in SMART was examined across six eco-hydrologically contrasted upland catchments in Australia. Results showed that the proposed method produces almost identical results compared to simulations by the satellite-derived LAI time series. In addition, the simulation results were considerably improved in nutrient/light limited catchments compared to the cases with the default vegetation parameterization. The results showed promise, with possibilities of extension to other hydrologic models that need similar specifications for inbuilt vegetation dynamics. Full article
(This article belongs to the Special Issue Remote Sensing of the Water Cycle in Mountain Regions)
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23 pages, 60666 KiB  
Article
Precipitation Retrieval over the Tibetan Plateau from the Geostationary Orbit—Part 2: Precipitation Rates with Elektro-L2 and Insat-3D
by Christine Kolbe, Boris Thies, Nazli Turini, Zhiyu Liu and Jörg Bendix
Remote Sens. 2020, 12(13), 2114; https://doi.org/10.3390/rs12132114 - 1 Jul 2020
Cited by 4 | Viewed by 2501 | Correction
Abstract
We present the new Precipitation REtrieval covering the TIbetan Plateau (PRETIP) as a feasibility study using the two geostationary (GEO) satellites Elektro-L2 and Insat-3D with reference to the GPM (Global Precipitation Measurement Mission) IMERG (Integrated Multi-satellitE Retrievals for GPM) product. The present study [...] Read more.
We present the new Precipitation REtrieval covering the TIbetan Plateau (PRETIP) as a feasibility study using the two geostationary (GEO) satellites Elektro-L2 and Insat-3D with reference to the GPM (Global Precipitation Measurement Mission) IMERG (Integrated Multi-satellitE Retrievals for GPM) product. The present study deals with the assignment of the rainfall rate. For precipitation rate assignment, the best-quality precipitation estimates from the gauge calibrated microwave (MW) within the IMERG product were combined with the GEO data by Random Forest (RF) regression. PRETIP was validated with independent MW precipitation information not considered for model training and revealed a good performance on 30 min and 11 km spatio-temporal resolution with a correlation coefficient of R = 0.59 and outperforms the validation of the independent MW precipitation with IMERG’s IR only product (R = 0.18). A comparison of PRETIP precipitation rates in 4 km resolution with daily rain gauge measurements from the Chinese Ministry of Water Resources revealed a correlation of R = 0.49. No differences in the performance of PRETIP for various elevation ranges or between the rainy (July, August) and the dry (May, September) season could be found. Full article
(This article belongs to the Special Issue Remote Sensing of the Water Cycle in Mountain Regions)
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3 pages, 2331 KiB  
Correction
Correction: Kolbe, C., et al. Precipitation Retrieval over the Tibetan Plateau from the Geostationary Orbit—Part 2: Precipitation Rates with Elektro-L2 and Insat-3D. Remote Sensing 2020, 12, 2114
by Christine Kolbe, Boris Thies, Nazli Turini, Zhiyu Liu and Jörg Bendix
Remote Sens. 2020, 12(21), 3594; https://doi.org/10.3390/rs12213594 - 2 Nov 2020
Cited by 1 | Viewed by 1356
Abstract
The authors wish to make the following corrections to this paper [...] Full article
(This article belongs to the Special Issue Remote Sensing of the Water Cycle in Mountain Regions)
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