Articles | Volume 19, issue 6
https://doi.org/10.5194/hess-19-2881-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-19-2881-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Review article
| 
22 Jun 2015
Review article |
| 22 Jun 2015
Laser vision: lidar as a transformative tool to advance critical zone science
University of Nevada, Department of Natural Resources and Environmental Science, Reno, Nevada, USA
J. A. Marshall
University of Oregon, Department of Geological Sciences, Eugene, Oregon, USA
S. W. Lyon
Stockholm University, Department of Physical Geography, Stockholm, Sweden
T. B. Barnhart
University of Colorado, Geography Department and Institute for Arctic and Alpine Research, Boulder, Colorado, USA
B. A. Fisher
University of Minnesota, Land and Atmospheric Science, Minnesota, St. Paul, Minnesota,USA
M. Donovan
University of Maryland, Geography and Environmental Systems Department, Baltimore County, Baltimore, Maryland, USA
K. M. Brubaker
Hobart and William Smith Colleges, Environmental Studies, Geneva, New York, USA
C. J. Crosby
UNAVCO, Boulder, Colorado, USA
N. F. Glenn
Boise State University, Department of Geosciences, Boise, Idaho, USA
C. L. Glennie
University of Houston, Department of Civil and Environmental Engineering, Houston, Texas, USA
P. B. Kirchner
University of California, Joint Institute for Regional Earth System Science and Engineering, Los Angeles, California, USA
N. Lam
Stockholm University, Department of Physical Geography, Stockholm, Sweden
K. D. Mankoff
Woods Hole Oceanographic Institute, Department of Physical Oceanography, Woods Hole, Massachusetts, USA
J. L. McCreight
National Center for Atmospheric Research, Boulder, Colorado, USA
N. P. Molotch
University of Colorado, Geography Department and Institute for Arctic and Alpine Research, Boulder, Colorado, USA
K. N. Musselman
University of Saskatchewan, Centre for Hydrology, Saskatchewan, Canada
J. Pelletier
University of Arizona, Department of Geosciences, Tucson, Arizona, USA
T. Russo
Pennsylvania State University, State College, Department of Geosciences, Pennsylvania, USA
H. Sangireddy
University of Texas, Department of Civil Engineering, Austin, Texas, USA
Y. Sjöberg
Stockholm University, Department of Physical Geography, Stockholm, Sweden
T. Swetnam
University of Arizona, Department of Geosciences, Tucson, Arizona, USA
N. West
Pennsylvania State University, State College, Department of Geosciences, Pennsylvania, USA
Related authors
Gary Sterle, Julia Perdrial, Dustin W. Kincaid, Kristen L. Underwood, Donna M. Rizzo, Ijaz Ul Haq, Li Li, Byung Suk Lee, Thomas Adler, Hang Wen, Helena Middleton, and Adrian A. Harpold
Hydrol. Earth Syst. Sci., 28, 611–630, https://doi.org/10.5194/hess-28-611-2024, https://doi.org/10.5194/hess-28-611-2024, 2024
Short summary
Short summary
We develop stream water chemistry to pair with the existing CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) dataset. The newly developed dataset, termed CAMELS-Chem, includes common stream water chemistry constituents and wet deposition chemistry in 516 catchments. Examples show the value of CAMELS-Chem to trend and spatial analyses, as well as its limitations in sampling length and consistency.
Sebastian A. Krogh, Lucia Scaff, James W. Kirchner, Beatrice Gordon, Gary Sterle, and Adrian Harpold
Hydrol. Earth Syst. Sci., 26, 3393–3417, https://doi.org/10.5194/hess-26-3393-2022, https://doi.org/10.5194/hess-26-3393-2022, 2022
Short summary
Short summary
We present a new way to detect snowmelt using daily cycles in streamflow driven by solar radiation. Results show that warmer sites have earlier and more intermittent snowmelt than colder sites, and the timing of early snowmelt events is strongly correlated with the timing of streamflow volume. A space-for-time substitution shows greater sensitivity of streamflow timing to climate change in colder rather than in warmer places, which is then contrasted with land surface simulations.
Hang Wen, Julia Perdrial, Benjamin W. Abbott, Susana Bernal, Rémi Dupas, Sarah E. Godsey, Adrian Harpold, Donna Rizzo, Kristen Underwood, Thomas Adler, Gary Sterle, and Li Li
Hydrol. Earth Syst. Sci., 24, 945–966, https://doi.org/10.5194/hess-24-945-2020, https://doi.org/10.5194/hess-24-945-2020, 2020
Short summary
Short summary
Lateral carbon fluxes from terrestrial to aquatic systems remain central uncertainties in determining ecosystem carbon balance. This work explores how temperature and hydrology control production and export of dissolved organic carbon (DOC) at the catchment scale. Results illustrate the asynchrony of DOC production, controlled by temperature, and export, governed by flow paths; concentration–discharge relationships are determined by the relative contribution of shallow versus groundwater flow.
Rose Petersky and Adrian Harpold
Hydrol. Earth Syst. Sci., 22, 4891–4906, https://doi.org/10.5194/hess-22-4891-2018, https://doi.org/10.5194/hess-22-4891-2018, 2018
Short summary
Short summary
Ephemeral snowpacks are snowpacks that persist for less than 2 months. We show that ephemeral snowpacks melt earlier and provide less soil water input in the spring. Elevation is strongly correlated with whether snowpacks are ephemeral or seasonal. Snowpacks were also more likely to be ephemeral on south-facing slopes than north-facing slopes at high elevations. In warm years, the Great Basin shifts to ephemerally dominant as rain becomes more prevalent at increasing elevations.
Adrian A. Harpold, Michael L. Kaplan, P. Zion Klos, Timothy Link, James P. McNamara, Seshadri Rajagopal, Rina Schumer, and Caitriana M. Steele
Hydrol. Earth Syst. Sci., 21, 1–22, https://doi.org/10.5194/hess-21-1-2017, https://doi.org/10.5194/hess-21-1-2017, 2017
Short summary
Short summary
The phase of precipitation as rain or snow is fundamental to hydrological processes and water resources. Despite its importance, the methods used to predict precipitation phase are inconsistent and often overly simplified. We review these methods and underlying mechanisms that control phase. We present a vision to meet important research gaps needed to improve prediction, including new field-based and remote measurements, validating new and existing methods, and expanding regional prediction.
Mai Winstrup, Heidi Ranndal, Signe Hillerup Larsen, Sebastian B. Simonsen, Kenneth D. Mankoff, Robert S. Fausto, and Louise Sandberg Sørensen
Earth Syst. Sci. Data, 16, 5405–5428, https://doi.org/10.5194/essd-16-5405-2024, https://doi.org/10.5194/essd-16-5405-2024, 2024
Short summary
Short summary
Surface topography across the marginal zone of the Greenland Ice Sheet is constantly evolving. Here we present an annual series (2019–2022) of summer digital elevation models (PRODEMs) for the Greenland Ice Sheet margin, covering all outlet glaciers from the ice sheet. The PRODEMs are based on fusion of CryoSat-2 radar altimetry and ICESat-2 laser altimetry. With their high spatial and temporal resolution, the PRODEMs will enable detailed studies of the changes in marginal ice sheet elevations.
Shfaqat A. Khan, Helene Seroussi, Mathieu Morlighem, William Colgan, Veit Helm, Gong Cheng, Danjal Berg, Valentina R. Barletta, Nicolaj K. Larsen, William Kochtitzky, Michiel van den Broeke, Kurt H. Kjær, Andy Aschwanden, Brice Noël, Jason E. Box, Joseph A. MacGregor, Robert S. Fausto, Kenneth D. Mankoff, Ian M. Howat, Kuba Oniszk, Dominik Fahrner, Anja Løkkegaard, Eigil Y. H. Lippert, and Javed Hassan
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-348, https://doi.org/10.5194/essd-2024-348, 2024
Preprint under review for ESSD
Short summary
Short summary
The surface elevation of the Greenland Ice Sheet is changing due to surface mass balance processes and ice dynamics, each exhibiting distinct spatiotemporal patterns. Here, we employ satellite and airborne altimetry data with fine spatial (1 km) and temporal (monthly) resolutions to document this spatiotemporal evolution from 2003 to 2023. This dataset of fine-resolution altimetry data in both space and time will support studies of ice mass loss and useful for GIS ice sheet modelling.
Brenton A. Wilder, Joachim Meyer, Josh Enterkine, and Nancy F. Glenn
The Cryosphere, 18, 5015–5029, https://doi.org/10.5194/tc-18-5015-2024, https://doi.org/10.5194/tc-18-5015-2024, 2024
Short summary
Short summary
Remotely sensed properties of snow are dependent on accurate terrain information, which for a lot of the cryosphere and seasonal snow zones is often insufficient in accuracy. However, as we show in this paper, we can bypass this issue by optimally solving for the terrain by utilizing the raw radiance data returned to the sensor. This method performed well when compared to validation datasets and has the potential to be used across a variety of different snow climates.
Gary Sterle, Julia Perdrial, Dustin W. Kincaid, Kristen L. Underwood, Donna M. Rizzo, Ijaz Ul Haq, Li Li, Byung Suk Lee, Thomas Adler, Hang Wen, Helena Middleton, and Adrian A. Harpold
Hydrol. Earth Syst. Sci., 28, 611–630, https://doi.org/10.5194/hess-28-611-2024, https://doi.org/10.5194/hess-28-611-2024, 2024
Short summary
Short summary
We develop stream water chemistry to pair with the existing CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) dataset. The newly developed dataset, termed CAMELS-Chem, includes common stream water chemistry constituents and wet deposition chemistry in 516 catchments. Examples show the value of CAMELS-Chem to trend and spatial analyses, as well as its limitations in sampling length and consistency.
Baptiste Vandecrux, Jason E. Box, Andreas P. Ahlstrøm, Signe B. Andersen, Nicolas Bayou, William T. Colgan, Nicolas J. Cullen, Robert S. Fausto, Dominik Haas-Artho, Achim Heilig, Derek A. Houtz, Penelope How, Ionut Iosifescu Enescu, Nanna B. Karlsson, Rebecca Kurup Buchholz, Kenneth D. Mankoff, Daniel McGrath, Noah P. Molotch, Bianca Perren, Maiken K. Revheim, Anja Rutishauser, Kevin Sampson, Martin Schneebeli, Sandy Starkweather, Simon Steffen, Jeff Weber, Patrick J. Wright, Henry Jay Zwally, and Konrad Steffen
Earth Syst. Sci. Data, 15, 5467–5489, https://doi.org/10.5194/essd-15-5467-2023, https://doi.org/10.5194/essd-15-5467-2023, 2023
Short summary
Short summary
The Greenland Climate Network (GC-Net) comprises stations that have been monitoring the weather on the Greenland Ice Sheet for over 30 years. These stations are being replaced by newer ones maintained by the Geological Survey of Denmark and Greenland (GEUS). The historical data were reprocessed to improve their quality, and key information about the weather stations has been compiled. This augmented dataset is available at https://doi.org/10.22008/FK2/VVXGUT (Steffen et al., 2022).
Alexa Marion Hinzman, Ylva Sjöberg, Steve W. Lyon, Wouter R. Berghuijs, and Ype van der Velde
EGUsphere, https://doi.org/10.5194/egusphere-2023-2391, https://doi.org/10.5194/egusphere-2023-2391, 2023
Preprint archived
Short summary
Short summary
An Arctic catchment with permafrost responds in a linear fashion: water in=water out. As permafrost thaws, 9 of 10 nested catchments become more non-linear over time. We find upstream catchments have stronger streamflow seasonality and exhibit the most nonlinear storage-discharge relationships. Downstream catchments have the greatest increases in non-linearity over time. These long-term shifts in the storage-discharge relationship are not typically seen in current hydrological models.
Anja Løkkegaard, Kenneth D. Mankoff, Christian Zdanowicz, Gary D. Clow, Martin P. Lüthi, Samuel H. Doyle, Henrik H. Thomsen, David Fisher, Joel Harper, Andy Aschwanden, Bo M. Vinther, Dorthe Dahl-Jensen, Harry Zekollari, Toby Meierbachtol, Ian McDowell, Neil Humphrey, Anne Solgaard, Nanna B. Karlsson, Shfaqat A. Khan, Benjamin Hills, Robert Law, Bryn Hubbard, Poul Christoffersen, Mylène Jacquemart, Julien Seguinot, Robert S. Fausto, and William T. Colgan
The Cryosphere, 17, 3829–3845, https://doi.org/10.5194/tc-17-3829-2023, https://doi.org/10.5194/tc-17-3829-2023, 2023
Short summary
Short summary
This study presents a database compiling 95 ice temperature profiles from the Greenland ice sheet and peripheral ice caps. Ice viscosity and hence ice flow are highly sensitive to ice temperature. To highlight the value of the database in evaluating ice flow simulations, profiles from the Greenland ice sheet are compared to a modeled temperature field. Reoccurring discrepancies between modeled and observed temperatures provide insight on the difficulties faced when simulating ice temperatures.
William Colgan, Christopher Shields, Pavel Talalay, Xiaopeng Fan, Austin P. Lines, Joshua Elliott, Harihar Rajaram, Kenneth Mankoff, Morten Jensen, Mira Backes, Yunchen Liu, Xianzhe Wei, Nanna B. Karlsson, Henrik Spanggård, and Allan Ø. Pedersen
Geosci. Instrum. Method. Data Syst., 12, 121–140, https://doi.org/10.5194/gi-12-121-2023, https://doi.org/10.5194/gi-12-121-2023, 2023
Short summary
Short summary
We describe a new drill for glaciers and ice sheets. Instead of drilling down into the ice, via mechanical action, our drill melts into the ice. Our goal is simply to pull a cable of temperature sensors on a one-way trip down to the ice–bed interface. Here, we describe the design and testing of our drill. Under laboratory conditions, our melt-tip drill has an efficiency of ∼ 35 % with a theoretical maximum penetration rate of ∼ 12 m h−1. Under field conditions, our efficiency is just ∼ 15 %.
Oliver Wigmore and Noah P. Molotch
Earth Syst. Sci. Data, 15, 1733–1747, https://doi.org/10.5194/essd-15-1733-2023, https://doi.org/10.5194/essd-15-1733-2023, 2023
Short summary
Short summary
We flew a custom-built drone fitted with visible, near-infrared and thermal cameras every week over a summer season at Niwot Ridge in Colorado's alpine tundra. We processed these images into seamless orthomosaics that record changes in snow cover, vegetation health and the movement of water over the land surface. These novel datasets provide a unique centimetre resolution snapshot of ecohydrologic processes, connectivity and spatial and temporal heterogeneity in the alpine zone.
Eva Friis Møller, Asbjørn Christensen, Janus Larsen, Kenneth D. Mankoff, Mads Hvid Ribergaard, Mikael Sejr, Philip Wallhead, and Marie Maar
Ocean Sci., 19, 403–420, https://doi.org/10.5194/os-19-403-2023, https://doi.org/10.5194/os-19-403-2023, 2023
Short summary
Short summary
Melt from the Greenland ice sheet and sea ice both influence light and nutrient availability in the Arctic coastal ocean. We use a 3D coupled hydrodynamic–biogeochemical model to evaluate the relative importance of these processes for timing, distribution, and magnitude of phytoplankton production in Disko Bay, west Greenland. Our study indicates that decreasing sea ice and more freshwater discharge can work synergistically and increase primary productivity of the coastal ocean around Greenland.
Sebastian A. Krogh, Lucia Scaff, James W. Kirchner, Beatrice Gordon, Gary Sterle, and Adrian Harpold
Hydrol. Earth Syst. Sci., 26, 3393–3417, https://doi.org/10.5194/hess-26-3393-2022, https://doi.org/10.5194/hess-26-3393-2022, 2022
Short summary
Short summary
We present a new way to detect snowmelt using daily cycles in streamflow driven by solar radiation. Results show that warmer sites have earlier and more intermittent snowmelt than colder sites, and the timing of early snowmelt events is strongly correlated with the timing of streamflow volume. A space-for-time substitution shows greater sensitivity of streamflow timing to climate change in colder rather than in warmer places, which is then contrasted with land surface simulations.
Mimmi Oksman, Anna Bang Kvorning, Signe Hillerup Larsen, Kristian Kjellerup Kjeldsen, Kenneth David Mankoff, William Colgan, Thorbjørn Joest Andersen, Niels Nørgaard-Pedersen, Marit-Solveig Seidenkrantz, Naja Mikkelsen, and Sofia Ribeiro
The Cryosphere, 16, 2471–2491, https://doi.org/10.5194/tc-16-2471-2022, https://doi.org/10.5194/tc-16-2471-2022, 2022
Short summary
Short summary
One of the questions facing the cryosphere community today is how increasing runoff from the Greenland Ice Sheet impacts marine ecosystems. To address this, long-term data are essential. Here, we present multi-site records of fjord productivity for SW Greenland back to the 19th century. We show a link between historical freshwater runoff and productivity, which is strongest in the inner fjord – influenced by marine-terminating glaciers – where productivity has increased since the late 1990s.
William Colgan, Agnes Wansing, Kenneth Mankoff, Mareen Lösing, John Hopper, Keith Louden, Jörg Ebbing, Flemming G. Christiansen, Thomas Ingeman-Nielsen, Lillemor Claesson Liljedahl, Joseph A. MacGregor, Árni Hjartarson, Stefan Bernstein, Nanna B. Karlsson, Sven Fuchs, Juha Hartikainen, Johan Liakka, Robert S. Fausto, Dorthe Dahl-Jensen, Anders Bjørk, Jens-Ove Naslund, Finn Mørk, Yasmina Martos, Niels Balling, Thomas Funck, Kristian K. Kjeldsen, Dorthe Petersen, Ulrik Gregersen, Gregers Dam, Tove Nielsen, Shfaqat A. Khan, and Anja Løkkegaard
Earth Syst. Sci. Data, 14, 2209–2238, https://doi.org/10.5194/essd-14-2209-2022, https://doi.org/10.5194/essd-14-2209-2022, 2022
Short summary
Short summary
We assemble all available geothermal heat flow measurements collected in and around Greenland into a new database. We use this database of point measurements, in combination with other geophysical datasets, to model geothermal heat flow in and around Greenland. Our geothermal heat flow model is generally cooler than previous models of Greenland, especially in southern Greenland. It does not suggest any high geothermal heat flows resulting from Icelandic plume activity over 50 million years ago.
Kenneth D. Mankoff, Xavier Fettweis, Peter L. Langen, Martin Stendel, Kristian K. Kjeldsen, Nanna B. Karlsson, Brice Noël, Michiel R. van den Broeke, Anne Solgaard, William Colgan, Jason E. Box, Sebastian B. Simonsen, Michalea D. King, Andreas P. Ahlstrøm, Signe Bech Andersen, and Robert S. Fausto
Earth Syst. Sci. Data, 13, 5001–5025, https://doi.org/10.5194/essd-13-5001-2021, https://doi.org/10.5194/essd-13-5001-2021, 2021
Short summary
Short summary
We estimate the daily mass balance and its components (surface, marine, and basal mass balance) for the Greenland ice sheet. Our time series begins in 1840 and has annual resolution through 1985 and then daily from 1986 through next week. Results are operational (updated daily) and provided for the entire ice sheet or by commonly used regions or sectors. This is the first input–output mass balance estimate to include the basal mass balance.
Robert S. Fausto, Dirk van As, Kenneth D. Mankoff, Baptiste Vandecrux, Michele Citterio, Andreas P. Ahlstrøm, Signe B. Andersen, William Colgan, Nanna B. Karlsson, Kristian K. Kjeldsen, Niels J. Korsgaard, Signe H. Larsen, Søren Nielsen, Allan Ø. Pedersen, Christopher L. Shields, Anne M. Solgaard, and Jason E. Box
Earth Syst. Sci. Data, 13, 3819–3845, https://doi.org/10.5194/essd-13-3819-2021, https://doi.org/10.5194/essd-13-3819-2021, 2021
Short summary
Short summary
The Programme for Monitoring of the Greenland Ice Sheet (PROMICE) has been measuring climate and ice sheet properties since 2007. Here, we present our data product from weather and ice sheet measurements from a network of automatic weather stations mainly located in the melt area of the ice sheet. Currently the PROMICE automatic weather station network includes 25 instrumented sites in Greenland.
Anne Solgaard, Anders Kusk, John Peter Merryman Boncori, Jørgen Dall, Kenneth D. Mankoff, Andreas P. Ahlstrøm, Signe B. Andersen, Michele Citterio, Nanna B. Karlsson, Kristian K. Kjeldsen, Niels J. Korsgaard, Signe H. Larsen, and Robert S. Fausto
Earth Syst. Sci. Data, 13, 3491–3512, https://doi.org/10.5194/essd-13-3491-2021, https://doi.org/10.5194/essd-13-3491-2021, 2021
Short summary
Short summary
The PROMICE Ice Velocity product is a time series of Greenland Ice Sheet ice velocity mosaics spanning September 2016 to present. It is derived from Sentinel-1 SAR data and has a spatial resolution of 500 m. Each mosaic spans 24 d (two Sentinel-1 cycles), and a new one is posted every 12 d (every Sentinel-1A cycle). The spatial comprehensiveness and temporal consistency make the product ideal for monitoring and studying ice-sheet-wide ice discharge and dynamics of glaciers.
Ahmad Hojatimalekshah, Zachary Uhlmann, Nancy F. Glenn, Christopher A. Hiemstra, Christopher J. Tennant, Jake D. Graham, Lucas Spaete, Arthur Gelvin, Hans-Peter Marshall, James P. McNamara, and Josh Enterkine
The Cryosphere, 15, 2187–2209, https://doi.org/10.5194/tc-15-2187-2021, https://doi.org/10.5194/tc-15-2187-2021, 2021
Short summary
Short summary
We describe the relationships between snow depth, vegetation canopy, and local-scale processes during the snow accumulation period using terrestrial laser scanning (TLS). In addition to topography and wind, our findings suggest the importance of fine-scale tree structure, species type, and distributions on snow depth. Snow depth increases from the canopy edge toward the open areas, but wind and topographic controls may affect this trend. TLS data are complementary to wide-area lidar surveys.
Elin Jutebring Sterte, Fredrik Lidman, Emma Lindborg, Ylva Sjöberg, and Hjalmar Laudon
Hydrol. Earth Syst. Sci., 25, 2133–2158, https://doi.org/10.5194/hess-25-2133-2021, https://doi.org/10.5194/hess-25-2133-2021, 2021
Short summary
Short summary
A numerical model was used to estimate annual and seasonal mean travel times across 14 long-term nested monitored catchments in the boreal region. The estimated travel times and young water fractions were consistent with observed variations of base cation concentration and stable water isotopes, δ18O. Soil type was the most important factor regulating the variation in mean travel times among sub-catchments, while the areal coverage of mires increased the young water fraction.
Karun Pandit, Hamid Dashti, Andrew T. Hudak, Nancy F. Glenn, Alejandro N. Flores, and Douglas J. Shinneman
Biogeosciences, 18, 2027–2045, https://doi.org/10.5194/bg-18-2027-2021, https://doi.org/10.5194/bg-18-2027-2021, 2021
Short summary
Short summary
A dynamic global vegetation model, Ecosystem Demography (EDv2.2), is used to understand spatiotemporal dynamics of a semi-arid shrub ecosystem under alternative fire regimes. Multi-decadal point simulations suggest shrub dominance for a non-fire scenario and a contrasting phase of shrub and C3 grass growth for a fire scenario. Regional gross primary productivity (GPP) simulations indicate moderate agreement with MODIS GPP and a GPP reduction in fire-affected areas before showing some recovery.
Kenneth D. Mankoff, Brice Noël, Xavier Fettweis, Andreas P. Ahlstrøm, William Colgan, Ken Kondo, Kirsty Langley, Shin Sugiyama, Dirk van As, and Robert S. Fausto
Earth Syst. Sci. Data, 12, 2811–2841, https://doi.org/10.5194/essd-12-2811-2020, https://doi.org/10.5194/essd-12-2811-2020, 2020
Short summary
Short summary
This work partitions regional climate model (RCM) runoff from the MAR and RACMO RCMs to hydrologic outlets at the ice margin and coast. Temporal resolution is daily from 1959 through 2019. Spatial grid is ~ 100 m, resolving individual streams. In addition to discharge at outlets, we also provide the streams, outlets, and basin geospatial data, as well as a script to query and access the geospatial or time series discharge data from the data files.
Stefano Manzoni, Giorgos Maneas, Anna Scaini, Basil E. Psiloglou, Georgia Destouni, and Steve W. Lyon
Hydrol. Earth Syst. Sci., 24, 3557–3571, https://doi.org/10.5194/hess-24-3557-2020, https://doi.org/10.5194/hess-24-3557-2020, 2020
Short summary
Short summary
A modeling tool is developed to assess the vulnerability of coastal wetlands to climatic and water management changes. Applied to the case study of the Gialova lagoon (Greece), this tool highlights the reliance of the lagoon functionality on scarce freshwater sources already under high demand from agriculture. Climatic changes will likely increase lagoon salinity, despite efforts to improve water management.
Kenneth D. Mankoff, Anne Solgaard, William Colgan, Andreas P. Ahlstrøm, Shfaqat Abbas Khan, and Robert S. Fausto
Earth Syst. Sci. Data, 12, 1367–1383, https://doi.org/10.5194/essd-12-1367-2020, https://doi.org/10.5194/essd-12-1367-2020, 2020
Short summary
Short summary
We have produced an open and reproducible estimate of Greenland Ice Sheet solid ice discharge from 1986 to 2020. Our results show three modes at the the total ice sheet scale: steady discharge from 1986 through 2000, increasing discharge from 2000 through 2005, and steady discharge from 2005 through 2019. The behavior of individual sectors and glaciers is more complicated. This work was done to provide a 100 % reproducible estimate to help constrain mass balance and sea-level-rise estimates.
Navid Ghajarnia, Georgia Destouni, Josefin Thorslund, Zahra Kalantari, Imenne Åhlén, Jesús A. Anaya-Acevedo, Juan F. Blanco-Libreros, Sonia Borja, Sergey Chalov, Aleksandra Chalova, Kwok P. Chun, Nicola Clerici, Amanda Desormeaux, Bethany B. Garfield, Pierre Girard, Olga Gorelits, Amy Hansen, Fernando Jaramillo, Jerker Jarsjö, Adnane Labbaci, John Livsey, Giorgos Maneas, Kathryn McCurley Pisarello, Sebastián Palomino-Ángel, Jan Pietroń, René M. Price, Victor H. Rivera-Monroy, Jorge Salgado, A. Britta K. Sannel, Samaneh Seifollahi-Aghmiuni, Ylva Sjöberg, Pavel Terskii, Guillaume Vigouroux, Lucia Licero-Villanueva, and David Zamora
Earth Syst. Sci. Data, 12, 1083–1100, https://doi.org/10.5194/essd-12-1083-2020, https://doi.org/10.5194/essd-12-1083-2020, 2020
Short summary
Short summary
Hydroclimate and land-use conditions determine the dynamics of wetlands and their ecosystem services. However, knowledge and data for conditions and changes over entire wetlandscapes are scarce. This paper presents a novel database for 27 wetlandscapes around the world, combining survey-based local information and hydroclimatic and land-use datasets. The developed database can enhance our capacity to understand and manage critical wetland ecosystems and their services under global change.
Hang Wen, Julia Perdrial, Benjamin W. Abbott, Susana Bernal, Rémi Dupas, Sarah E. Godsey, Adrian Harpold, Donna Rizzo, Kristen Underwood, Thomas Adler, Gary Sterle, and Li Li
Hydrol. Earth Syst. Sci., 24, 945–966, https://doi.org/10.5194/hess-24-945-2020, https://doi.org/10.5194/hess-24-945-2020, 2020
Short summary
Short summary
Lateral carbon fluxes from terrestrial to aquatic systems remain central uncertainties in determining ecosystem carbon balance. This work explores how temperature and hydrology control production and export of dissolved organic carbon (DOC) at the catchment scale. Results illustrate the asynchrony of DOC production, controlled by temperature, and export, governed by flow paths; concentration–discharge relationships are determined by the relative contribution of shallow versus groundwater flow.
Karun Pandit, Hamid Dashti, Nancy F. Glenn, Alejandro N. Flores, Kaitlin C. Maguire, Douglas J. Shinneman, Gerald N. Flerchinger, and Aaron W. Fellows
Geosci. Model Dev., 12, 4585–4601, https://doi.org/10.5194/gmd-12-4585-2019, https://doi.org/10.5194/gmd-12-4585-2019, 2019
Short summary
Short summary
We explored shrub parameters representing sagebrush ecosystems within a dynamic vegetation model and estimated gross primary production (GPP) for two sagebrush sites in the northern Great Basin. Comparison with observations from eddy covariance (EC) tower data showed our modeled results were encouraging, although some seasonal underestimates were apparent. We believe our findings on preliminary parameterization of shrub PFT is an important step towards subsequent studies on shrubland ecosystems.
Keith S. Jennings and Noah P. Molotch
Hydrol. Earth Syst. Sci., 23, 3765–3786, https://doi.org/10.5194/hess-23-3765-2019, https://doi.org/10.5194/hess-23-3765-2019, 2019
Short summary
Short summary
There is a wide variety of modeling methods to designate precipitation as rain, snow, or a mix of the two. Here we show that method choice introduces marked uncertainty to simulated snowpack water storage (> 200 mm) and snow cover duration (> 1 month) in areas that receive significant winter and spring precipitation at air temperatures at and near freezing. This marked uncertainty has implications for water resources management as well as simulations of past and future hydroclimatic states.
Kenneth D. Mankoff, William Colgan, Anne Solgaard, Nanna B. Karlsson, Andreas P. Ahlstrøm, Dirk van As, Jason E. Box, Shfaqat Abbas Khan, Kristian K. Kjeldsen, Jeremie Mouginot, and Robert S. Fausto
Earth Syst. Sci. Data, 11, 769–786, https://doi.org/10.5194/essd-11-769-2019, https://doi.org/10.5194/essd-11-769-2019, 2019
Short summary
Short summary
We have produced an open and reproducible estimate of Greenland Ice Sheet solid ice discharge from 1986 through 2017. Our results show three modes at the total ice-sheet scale: steady discharge from 1986 through 2000, increasing discharge from 2000 through 2005, and steady discharge from 2005 through 2017. The behavior of individual sectors and glaciers is more complicated. This work was done to provide a 100 % reproducible estimate to help constrain mass balance and sea-level rise estimates.
Arvid Bring and Steve W. Lyon
Hydrol. Earth Syst. Sci., 23, 2369–2378, https://doi.org/10.5194/hess-23-2369-2019, https://doi.org/10.5194/hess-23-2369-2019, 2019
Short summary
Short summary
Hydrology education strives to teach students both quantitative ability and complex professional skills. Our research shows that role-play simulations are useful to make students able to integrate various analytical skills in complicated settings while not interfering with traditional teaching that fosters their ability to solve mathematical problems. Despite this there are several potential challenging areas in using role-plays, and we therefore suggest ways around these potential roadblocks.
Roger C. Bales, Erin M. Stacy, Xiande Meng, Martha H. Conklin, Peter B. Kirchner, and Zeshi Zheng
Earth Syst. Sci. Data, 10, 2115–2122, https://doi.org/10.5194/essd-10-2115-2018, https://doi.org/10.5194/essd-10-2115-2018, 2018
Short summary
Short summary
This 2006–2016 record of snow depth, soil moisture and soil temperature, and meteorological data quantifies hydrologic inputs and storage in the mostly undeveloped Wolverton catchment (2180–2750 m) in Sequoia National Park. Two meteorological stations were installed, along with clustered sensors that recorded differences in snow and soil moisture across the landscape with regard to aspect and canopy cover at elevations of 2250 and 2625 m, just above the current rain–snow transition elevation.
Stefano Manzoni, Petr Čapek, Philipp Porada, Martin Thurner, Mattias Winterdahl, Christian Beer, Volker Brüchert, Jan Frouz, Anke M. Herrmann, Björn D. Lindahl, Steve W. Lyon, Hana Šantrůčková, Giulia Vico, and Danielle Way
Biogeosciences, 15, 5929–5949, https://doi.org/10.5194/bg-15-5929-2018, https://doi.org/10.5194/bg-15-5929-2018, 2018
Short summary
Short summary
Carbon fixed by plants and phytoplankton through photosynthesis is ultimately stored in soils and sediments or released to the atmosphere during decomposition of dead biomass. Carbon-use efficiency is a useful metric to quantify the fate of carbon – higher efficiency means higher storage and lower release to the atmosphere. Here we summarize many definitions of carbon-use efficiency and study how this metric changes from organisms to ecosystems and from terrestrial to aquatic environments.
Rose Petersky and Adrian Harpold
Hydrol. Earth Syst. Sci., 22, 4891–4906, https://doi.org/10.5194/hess-22-4891-2018, https://doi.org/10.5194/hess-22-4891-2018, 2018
Short summary
Short summary
Ephemeral snowpacks are snowpacks that persist for less than 2 months. We show that ephemeral snowpacks melt earlier and provide less soil water input in the spring. Elevation is strongly correlated with whether snowpacks are ephemeral or seasonal. Snowpacks were also more likely to be ephemeral on south-facing slopes than north-facing slopes at high elevations. In warm years, the Great Basin shifts to ephemerally dominant as rain becomes more prevalent at increasing elevations.
Keith S. Jennings, Timothy G. F. Kittel, and Noah P. Molotch
The Cryosphere, 12, 1595–1614, https://doi.org/10.5194/tc-12-1595-2018, https://doi.org/10.5194/tc-12-1595-2018, 2018
Short summary
Short summary
We show through observations and simulations that cold content, a key part of the snowpack energy budget, develops primarily through new snowfall. We also note that cold content damps snowmelt rate and timing at sub-seasonal timescales, while seasonal melt onset is controlled by the timing of peak cold content and total spring precipitation. This work has implications for how cold content is represented in snow models and improves our understanding of its effect on snowmelt processes.
Keith N. Musselman, Noah P. Molotch, and Steven A. Margulis
The Cryosphere, 11, 2847–2866, https://doi.org/10.5194/tc-11-2847-2017, https://doi.org/10.5194/tc-11-2847-2017, 2017
Short summary
Short summary
We present a study of how melt rates in the California Sierra Nevada respond to a range of warming projected for this century. Snowfall and melt were simulated for historical and modified (warmer) snow seasons. Winter melt occurs more frequently and more intensely, causing an increase in extreme winter melt. In a warmer climate, less snow persists into the spring, causing spring melt to be substantially lower. The results offer insight into how snow water resources may respond to climate change.
Dominik Schneider, Noah P. Molotch, and Jeffrey S. Deems
The Cryosphere Discuss., https://doi.org/10.5194/tc-2017-167, https://doi.org/10.5194/tc-2017-167, 2017
Revised manuscript not accepted
Short summary
Short summary
New data from the ongoing Airborne Snow Observatory (ASO) provides an unprecedented look at the spatial and temporal patterns of snow water content (SWE) over multiple years in California, USA. We found that relationships between SWE, snow covered area, and topography transfer between years at accuracy levels equivalent to those from models generated from ASO data collected on the day of interest. This research provides a first attempt at extending the value of ASO beyond the observations.
Sina Muster, Kurt Roth, Moritz Langer, Stephan Lange, Fabio Cresto Aleina, Annett Bartsch, Anne Morgenstern, Guido Grosse, Benjamin Jones, A. Britta K. Sannel, Ylva Sjöberg, Frank Günther, Christian Andresen, Alexandra Veremeeva, Prajna R. Lindgren, Frédéric Bouchard, Mark J. Lara, Daniel Fortier, Simon Charbonneau, Tarmo A. Virtanen, Gustaf Hugelius, Juri Palmtag, Matthias B. Siewert, William J. Riley, Charles D. Koven, and Julia Boike
Earth Syst. Sci. Data, 9, 317–348, https://doi.org/10.5194/essd-9-317-2017, https://doi.org/10.5194/essd-9-317-2017, 2017
Short summary
Short summary
Waterbodies are abundant in Arctic permafrost lowlands. Most waterbodies are ponds with a surface area smaller than 100 x 100 m. The Permafrost Region Pond and Lake Database (PeRL) for the first time maps ponds as small as 10 x 10 m. PeRL maps can be used to document changes both by comparing them to historical and future imagery. The distribution of waterbodies in the Arctic is important to know in order to manage resources in the Arctic and to improve climate predictions in the Arctic.
Kenneth D. Mankoff and Slawek M. Tulaczyk
The Cryosphere, 11, 303–317, https://doi.org/10.5194/tc-11-303-2017, https://doi.org/10.5194/tc-11-303-2017, 2017
Short summary
Short summary
There may be a ~ 7-fold increases in heat at the bed of Greenland by the end of the century due to increased runoff. The impact this will have on the ice is uncertain, but recent results indicate more heat may reduced glacier velocity near the margin, and accelerate it in the interior. We used existing model output of Greenland surface melt, ice sheet surface, and basal topography. All code needed to recreate the results, using free software, is included.
Adrian A. Harpold, Michael L. Kaplan, P. Zion Klos, Timothy Link, James P. McNamara, Seshadri Rajagopal, Rina Schumer, and Caitriana M. Steele
Hydrol. Earth Syst. Sci., 21, 1–22, https://doi.org/10.5194/hess-21-1-2017, https://doi.org/10.5194/hess-21-1-2017, 2017
Short summary
Short summary
The phase of precipitation as rain or snow is fundamental to hydrological processes and water resources. Despite its importance, the methods used to predict precipitation phase are inconsistent and often overly simplified. We review these methods and underlying mechanisms that control phase. We present a vision to meet important research gaps needed to improve prediction, including new field-based and remote measurements, validating new and existing methods, and expanding regional prediction.
Felix C. Seidel, Karl Rittger, S. McKenzie Skiles, Noah P. Molotch, and Thomas H. Painter
The Cryosphere, 10, 1229–1244, https://doi.org/10.5194/tc-10-1229-2016, https://doi.org/10.5194/tc-10-1229-2016, 2016
Short summary
Short summary
Quantifying the snow albedo effect is an important step to predict water availability as well as changes in climate and sea level. We use imaging spectroscopy to determine optical properties of mountain snow. We find an inverse relationship between snow albedo and grain size as well as between elevation and grain size. Under strong melt conditions, however, we show that the optical-equivalent snow grain size increases slower than expected at lower elevations and we explain possible reasons.
Patrick W. Bogaart, Ype van der Velde, Steve W. Lyon, and Stefan C. Dekker
Hydrol. Earth Syst. Sci., 20, 1413–1432, https://doi.org/10.5194/hess-20-1413-2016, https://doi.org/10.5194/hess-20-1413-2016, 2016
Short summary
Short summary
We analyse how stream discharge declines after rain storms. This "recession" behaviour contains information about the capacity of the catchment to hold or release water. Looking at many rivers in Sweden, we were able to link distinct recession regimes to land use and catchment characteristics. Trends in recession behaviour are found to correspond to intensifying agriculture and extensive reforestation. We conclude that both humans and nature reorganizes the soil in order to enhance efficiency.
Susan L. Brantley, Roman A. DiBiase, Tess A. Russo, Yuning Shi, Henry Lin, Kenneth J. Davis, Margot Kaye, Lillian Hill, Jason Kaye, David M. Eissenstat, Beth Hoagland, Ashlee L. Dere, Andrew L. Neal, Kristen M. Brubaker, and Dan K. Arthur
Earth Surf. Dynam., 4, 211–235, https://doi.org/10.5194/esurf-4-211-2016, https://doi.org/10.5194/esurf-4-211-2016, 2016
Short summary
Short summary
In order to better understand and forecast the evolution of the environment from the top of the vegetation canopy down to bedrock, numerous types of intensive measurements have been made over several years in a small watershed. The ability to expand such a study to larger areas and different environments requiring fewer measurements is essential. This study presents one possible approach to such an expansion, to collect necessary and sufficient measurements in order to forecast this evolution.
Z. Zheng, P. B. Kirchner, and R. C. Bales
The Cryosphere, 10, 257–269, https://doi.org/10.5194/tc-10-257-2016, https://doi.org/10.5194/tc-10-257-2016, 2016
Short summary
Short summary
By analyzing high-resolution lidar products and using statistical methods, we quantified the snow depth dependency on elevation, slope and aspect of the terrain and also the surrounding vegetation in four catchment size sites in the southern Sierra Nevada during snow peak season. The relative importance of topographic and vegetation attributes varies with elevation and canopy, but all these attributes were found significant in affecting snow distribution in mountain basins.
M. Fritz, B. N. Deshpande, F. Bouchard, E. Högström, J. Malenfant-Lepage, A. Morgenstern, A. Nieuwendam, M. Oliva, M. Paquette, A. C. A. Rudy, M. B. Siewert, Y. Sjöberg, and S. Weege
The Cryosphere, 9, 1715–1720, https://doi.org/10.5194/tc-9-1715-2015, https://doi.org/10.5194/tc-9-1715-2015, 2015
Short summary
Short summary
This is a contribution about the future of permafrost research to the 3rd International Conference on Arctic Research Planning 2015 (ICARP III).
We summarize the top five research questions for the next decade of permafrost science from the perspective of early career researchers (ECRs).
We highlight the pathways and structural preconditions to address these research priorities.
This manuscript is an outcome of a community consultation conducted for and by ECRs on a global level.
Y. Sjöberg, P. Marklund, R. Pettersson, and S. W. Lyon
The Cryosphere, 9, 465–478, https://doi.org/10.5194/tc-9-465-2015, https://doi.org/10.5194/tc-9-465-2015, 2015
Short summary
Short summary
Permafrost peatlands are hydrological and biogeochemical hotspots in discontinuous permafrost areas. We estimate the depths to the permafrost table surface and base across a peatland in northern Sweden using ground penetrating radar and electrical resistivity tomography. Seasonal frost tables, taliks, and the permafrost base could be detected. The results highlight the added value of combining techniques for assessing distributions of permafrost in the rapidly changing sporadic permafrost zone.
P. B. Kirchner, R. C. Bales, N. P. Molotch, J. Flanagan, and Q. Guo
Hydrol. Earth Syst. Sci., 18, 4261–4275, https://doi.org/10.5194/hess-18-4261-2014, https://doi.org/10.5194/hess-18-4261-2014, 2014
Short summary
Short summary
In this study we present results from LiDAR snow depth measurements made over 53 sq km and a 1600 m elevation gradient. We found a lapse rate of 15 cm accumulated snow depth and 6 cm SWE per 100 m in elevation until 3300 m, where depth sharply decreased. Residuals from this trend revealed the role of aspect and highlighted the importance of solar radiation and wind for snow distribution. Lastly, we compared LiDAR SWE estimations with four model estimates of SWE and total precipitation.
J. L. McCreight and E. E. Small
The Cryosphere, 8, 521–536, https://doi.org/10.5194/tc-8-521-2014, https://doi.org/10.5194/tc-8-521-2014, 2014
R. Giesler, S. W. Lyon, C.-M. Mörth, J. Karlsson, E. M. Karlsson, E. J. Jantze, G. Destouni, and C. Humborg
Biogeosciences, 11, 525–537, https://doi.org/10.5194/bg-11-525-2014, https://doi.org/10.5194/bg-11-525-2014, 2014
E. J. Jantze, S. W. Lyon, and G. Destouni
Hydrol. Earth Syst. Sci., 17, 3827–3839, https://doi.org/10.5194/hess-17-3827-2013, https://doi.org/10.5194/hess-17-3827-2013, 2013
S. W. Lyon, M. T. Walter, E. J. Jantze, and J. A. Archibald
Hydrol. Earth Syst. Sci., 17, 269–279, https://doi.org/10.5194/hess-17-269-2013, https://doi.org/10.5194/hess-17-269-2013, 2013
Related subject area
Subject: Ecohydrology | Techniques and Approaches: Remote Sensing and GIS
Revealing joint evolutions and causal interactions in complex eco-hydrological systems by a network-based framework
Circumarctic land cover diversity considering wetness gradients
Multi-decadal floodplain classification and trend analysis in the Upper Columbia River valley, British Columbia
Estimating leaf moisture content at global scale from passive microwave satellite observations of vegetation optical depth
Simulating carbon and water fluxes using a coupled process-based terrestrial biosphere model and joint assimilation of leaf area index and surface soil moisture
Untangling irrigation effects on maize water and heat stress alleviation using satellite data
Information-based uncertainty decomposition in dual-channel microwave remote sensing of soil moisture
Assessing the large-scale plant–water relations in the humid, subtropical Pearl River basin of China
Technical note: Accounting for snow in the estimation of root zone water storage capacity from precipitation and evapotranspiration fluxes
Long-term water stress and drought assessment of Mediterranean oak savanna vegetation using thermal remote sensing
Temporal interpolation of land surface fluxes derived from remote sensing – results with an unmanned aerial system
Pattern and structure of microtopography implies autogenic origins in forested wetlands
The influence of water table depth on evapotranspiration in the Amazon arc of deforestation
Does the Normalized Difference Vegetation Index explain spatial and temporal variability in sap velocity in temperate forest ecosystems?
Comparison of MODIS and SWAT evapotranspiration over a complex terrain at different spatial scales
Evolution of the vegetation system in the Heihe River basin in the last 2000 years
Attribution of satellite-observed vegetation trends in a hyper-arid region of the Heihe River basin, Western China
Evapotranspiration and water yield over China's landmass from 2000 to 2010
Satellite-based analysis of recent trends in the ecohydrology of a semi-arid region
Soil moisture controls on patterns of grass green-up in Inner Mongolia: an index based approach
Groundwater surface water interactions and the role of phreatophytes in identifying recharge zones
Quantifying the performance of automated GIS-based geomorphological approaches for riparian zone delineation using digital elevation models
Climate change, growing season water deficit and vegetation activity along the north–south transect of eastern China from 1982 through 2006
Hydrological differentiation and spatial distribution of high altitude wetlands in a semi-arid Andean region derived from satellite data
The impact of in-canopy wind profile formulations on heat flux estimation in an open orchard using the remote sensing-based two-source model
The use of remote sensing to quantify wetland loss in the Choke Mountain range, Upper Blue Nile basin, Ethiopia
Lu Wang, Yue-Ping Xu, Haiting Gu, Li Liu, Xiao Liang, and Siwei Chen
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-226, https://doi.org/10.5194/hess-2024-226, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
To understand how eco-hydrological variables evolve jointly and why, this study develops a framework using correlation and causality to construct complex relationships between variables at the system level. Causality provides more detailed information that the compound causes of evolutions regarding any variable can be traced. Joint evolution is controlled by the combination of external drivers and direct causality. Overall, the study facilitates the comprehension of eco-hydrological processes.
Annett Bartsch, Aleksandra Efimova, Barbara Widhalm, Xaver Muri, Clemens von Baeckmann, Helena Bergstedt, Ksenia Ermokhina, Gustaf Hugelius, Birgit Heim, and Marina Leibman
Hydrol. Earth Syst. Sci., 28, 2421–2481, https://doi.org/10.5194/hess-28-2421-2024, https://doi.org/10.5194/hess-28-2421-2024, 2024
Short summary
Short summary
Wetness gradients and landcover diversity for the entire Arctic tundra have been assessed using a novel satellite-data-based map. Patterns of lakes, wetlands, general soil moisture conditions and vegetation physiognomy are represented at 10 m. About 40 % of the area north of the treeline falls into three units of dry types, with limited shrub growth. Wetter regions have higher landcover diversity than drier regions.
Italo Sampaio Rodrigues, Christopher Hopkinson, Laura Chasmer, Ryan J. MacDonald, Suzanne E. Bayley, and Brian Brisco
Hydrol. Earth Syst. Sci., 28, 2203–2221, https://doi.org/10.5194/hess-28-2203-2024, https://doi.org/10.5194/hess-28-2203-2024, 2024
Short summary
Short summary
The research evaluated the trends and changes in land cover and river discharge in the Upper Columbia River Wetlands using remote sensing and hydroclimatic data. The river discharge increased during the peak flow season, resulting in a positive trend in the open-water extent in the same period, whereas open-water area declined on an annual basis. Furthermore, since 2003 the peak flow has occurred 11 d earlier than during 1903–1928, which has led to larger discharges in a shorter time.
Matthias Forkel, Luisa Schmidt, Ruxandra-Maria Zotta, Wouter Dorigo, and Marta Yebra
Hydrol. Earth Syst. Sci., 27, 39–68, https://doi.org/10.5194/hess-27-39-2023, https://doi.org/10.5194/hess-27-39-2023, 2023
Short summary
Short summary
The live fuel moisture content (LFMC) of vegetation canopies is a driver of wildfires. We investigate the relation between LFMC and passive microwave satellite observations of vegetation optical depth (VOD) and develop a method to estimate LFMC from VOD globally. Our global VOD-based estimates of LFMC can be used to investigate drought effects on vegetation and fire risks.
Sinan Li, Li Zhang, Jingfeng Xiao, Rui Ma, Xiangjun Tian, and Min Yan
Hydrol. Earth Syst. Sci., 26, 6311–6337, https://doi.org/10.5194/hess-26-6311-2022, https://doi.org/10.5194/hess-26-6311-2022, 2022
Short summary
Short summary
Accurate estimation for global GPP and ET is important in climate change studies. In this study, the GLASS LAI, SMOS, and SMAP datasets were assimilated jointly and separately in a coupled model. The results show that the performance of joint assimilation for GPP and ET is better than that of separate assimilation. The joint assimilation in water-limited regions performed better than in humid regions, and the global assimilation results had higher accuracy than other products.
Peng Zhu and Jennifer Burney
Hydrol. Earth Syst. Sci., 26, 827–840, https://doi.org/10.5194/hess-26-827-2022, https://doi.org/10.5194/hess-26-827-2022, 2022
Short summary
Short summary
Satellite data were used to disentangle water and heat stress alleviation due to irrigation. Our findings are as follows. (1) Irrigation-induced cooling was captured by satellite LST but air temperature failed. (2) Irrigation extended maize growing season duration, especially during grain filling. (3) Water and heat stress alleviation constitutes 65 % and 35 % of the irrigation benefit. (4) The crop model simulating canopy temperature better captures the irrigation benefit.
Bonan Li and Stephen P. Good
Hydrol. Earth Syst. Sci., 25, 5029–5045, https://doi.org/10.5194/hess-25-5029-2021, https://doi.org/10.5194/hess-25-5029-2021, 2021
Short summary
Short summary
We found that satellite retrieved soil moisture has large uncertainty, with uncertainty caused by the algorithm being closely related to the satellite soil moisture quality. The information provided by the two main inputs is mainly redundant. Such redundant components and synergy components provided by two main inputs to the satellite soil moisture are related to how the satellite algorithm performs. The satellite remote sensing algorithms may be improved by performing such analysis.
Hailong Wang, Kai Duan, Bingjun Liu, and Xiaohong Chen
Hydrol. Earth Syst. Sci., 25, 4741–4758, https://doi.org/10.5194/hess-25-4741-2021, https://doi.org/10.5194/hess-25-4741-2021, 2021
Short summary
Short summary
Using remote sensing and reanalysis data, we examined the relationships between vegetation development and water resource availability in a humid subtropical basin. We found overall increases in total water storage and surface greenness and vegetation production, and the changes were particularly profound in cropland-dominated regions. Correlation analysis implies water availability leads the variations in greenness and production, and irrigation may improve production during dry periods.
David N. Dralle, W. Jesse Hahm, K. Dana Chadwick, Erica McCormick, and Daniella M. Rempe
Hydrol. Earth Syst. Sci., 25, 2861–2867, https://doi.org/10.5194/hess-25-2861-2021, https://doi.org/10.5194/hess-25-2861-2021, 2021
Short summary
Short summary
Root zone water storage capacity determines how much water can be stored belowground to support plants during periods without precipitation. Here, we develop a satellite remote sensing method to estimate this key variable at large scales that matter for management. Importantly, our method builds on previous approaches by accounting for snowpack, which may bias estimates from existing approaches. Ultimately, our method will improve large-scale understanding of plant access to subsurface water.
María P. González-Dugo, Xuelong Chen, Ana Andreu, Elisabet Carpintero, Pedro J. Gómez-Giraldez, Arnaud Carrara, and Zhongbo Su
Hydrol. Earth Syst. Sci., 25, 755–768, https://doi.org/10.5194/hess-25-755-2021, https://doi.org/10.5194/hess-25-755-2021, 2021
Short summary
Short summary
Drought is a devastating natural hazard and difficult to define, detect and quantify. Global meteorological data and remote-sensing products present new opportunities to characterize drought in an objective way. In this paper, we applied the surface energy balance model SEBS to estimate monthly evapotranspiration (ET) from 2001 to 2018 over the dehesa area of the Iberian Peninsula. ET anomalies were used to identify the main drought events and analyze their impacts on dehesa vegetation.
Sheng Wang, Monica Garcia, Andreas Ibrom, and Peter Bauer-Gottwein
Hydrol. Earth Syst. Sci., 24, 3643–3661, https://doi.org/10.5194/hess-24-3643-2020, https://doi.org/10.5194/hess-24-3643-2020, 2020
Short summary
Short summary
Remote sensing only provides snapshots of rapidly changing land surface variables; this limits its application for water resources and ecosystem management. To obtain continuous estimates of surface temperature, soil moisture, evapotranspiration, and ecosystem productivity, a simple and operational modelling scheme is presented. We demonstrate it with temporally sparse optical and thermal remote sensing data from an unmanned aerial system at a Danish bioenergy plantation eddy covariance site.
Jacob S. Diamond, Daniel L. McLaughlin, Robert A. Slesak, and Atticus Stovall
Hydrol. Earth Syst. Sci., 23, 5069–5088, https://doi.org/10.5194/hess-23-5069-2019, https://doi.org/10.5194/hess-23-5069-2019, 2019
Short summary
Short summary
We found evidence for spatial patterning of soil elevation in forested wetlands that was well explained by hydrology. The patterns that we found were strongest at wetter sites, and were weakest at drier sites. When a site was wet, soil elevations typically only belonged to two groups: tall "hummocks" and low "hollows. The tall, hummock groups were spaced equally apart from each other and were a similar size. We believe this is evidence for a biota–hydrology feedback that creates hummocks.
John O'Connor, Maria J. Santos, Karin T. Rebel, and Stefan C. Dekker
Hydrol. Earth Syst. Sci., 23, 3917–3931, https://doi.org/10.5194/hess-23-3917-2019, https://doi.org/10.5194/hess-23-3917-2019, 2019
Short summary
Short summary
The Amazon rainforest has undergone extensive land use change, which greatly reduces the rate of evapotranspiration. Forest with deep roots is replaced by agriculture with shallow roots. The difference in rooting depth can greatly reduce access to water, especially during the dry season. However, large areas of the Amazon have a sufficiently shallow water table that may provide access for agriculture. We used remote sensing observations to compare the impact of deep and shallow water tables.
Anne J. Hoek van Dijke, Kaniska Mallick, Adriaan J. Teuling, Martin Schlerf, Miriam Machwitz, Sibylle K. Hassler, Theresa Blume, and Martin Herold
Hydrol. Earth Syst. Sci., 23, 2077–2091, https://doi.org/10.5194/hess-23-2077-2019, https://doi.org/10.5194/hess-23-2077-2019, 2019
Short summary
Short summary
Satellite images are often used to estimate land water fluxes over a larger area. In this study, we investigate the link between a well-known vegetation index derived from satellite data and sap velocity, in a temperate forest in Luxembourg. We show that the link between the vegetation index and transpiration is not constant. Therefore we suggest that the use of vegetation indices to predict transpiration should be limited to ecosystems and scales where the link has been confirmed.
Olanrewaju O. Abiodun, Huade Guan, Vincent E. A. Post, and Okke Batelaan
Hydrol. Earth Syst. Sci., 22, 2775–2794, https://doi.org/10.5194/hess-22-2775-2018, https://doi.org/10.5194/hess-22-2775-2018, 2018
Short summary
Short summary
In recent decades, evapotranspiration estimation has been improved by remote sensing methods as well as by hydrological models. However, comparing these methods shows differences of up to 31 % at a spatial resolution of 1 km2. Land cover differences and catchment averaged climate data in the hydrological model were identified as the principal causes of the differences in results. The implication is that water management will have to deal with large uncertainty in estimated water balances.
Shoubo Li, Yan Zhao, Yongping Wei, and Hang Zheng
Hydrol. Earth Syst. Sci., 21, 4233–4244, https://doi.org/10.5194/hess-21-4233-2017, https://doi.org/10.5194/hess-21-4233-2017, 2017
Short summary
Short summary
This study aims to investigate the evolution of natural and crop vegetation systems over the past 2000 years accommodated with the changes in water regimes at the basin scale. It is based on remote-sensing data and previous historical research. The methods developed and the findings obtained from this study could assist in understanding how current ecosystem problems were created in the past and what their implications for future river basin management are.
Y. Wang, M. L. Roderick, Y. Shen, and F. Sun
Hydrol. Earth Syst. Sci., 18, 3499–3509, https://doi.org/10.5194/hess-18-3499-2014, https://doi.org/10.5194/hess-18-3499-2014, 2014
Y. Liu, Y. Zhou, W. Ju, J. Chen, S. Wang, H. He, H. Wang, D. Guan, F. Zhao, Y. Li, and Y. Hao
Hydrol. Earth Syst. Sci., 17, 4957–4980, https://doi.org/10.5194/hess-17-4957-2013, https://doi.org/10.5194/hess-17-4957-2013, 2013
M. Gokmen, Z. Vekerdy, W. Verhoef, and O. Batelaan
Hydrol. Earth Syst. Sci., 17, 3779–3794, https://doi.org/10.5194/hess-17-3779-2013, https://doi.org/10.5194/hess-17-3779-2013, 2013
H. Liu, F. Tian, H. C. Hu, H. P. Hu, and M. Sivapalan
Hydrol. Earth Syst. Sci., 17, 805–815, https://doi.org/10.5194/hess-17-805-2013, https://doi.org/10.5194/hess-17-805-2013, 2013
T. S. Ahring and D. R. Steward
Hydrol. Earth Syst. Sci., 16, 4133–4142, https://doi.org/10.5194/hess-16-4133-2012, https://doi.org/10.5194/hess-16-4133-2012, 2012
D. Fernández, J. Barquín, M. Álvarez-Cabria, and F. J. Peñas
Hydrol. Earth Syst. Sci., 16, 3851–3862, https://doi.org/10.5194/hess-16-3851-2012, https://doi.org/10.5194/hess-16-3851-2012, 2012
P. Sun, Z. Yu, S. Liu, X. Wei, J. Wang, N. Zegre, and N. Liu
Hydrol. Earth Syst. Sci., 16, 3835–3850, https://doi.org/10.5194/hess-16-3835-2012, https://doi.org/10.5194/hess-16-3835-2012, 2012
M. Otto, D. Scherer, and J. Richters
Hydrol. Earth Syst. Sci., 15, 1713–1727, https://doi.org/10.5194/hess-15-1713-2011, https://doi.org/10.5194/hess-15-1713-2011, 2011
C. Cammalleri, M. C. Anderson, G. Ciraolo, G. D'Urso, W. P. Kustas, G. La Loggia, and M. Minacapilli
Hydrol. Earth Syst. Sci., 14, 2643–2659, https://doi.org/10.5194/hess-14-2643-2010, https://doi.org/10.5194/hess-14-2643-2010, 2010
E. Teferi, S. Uhlenbrook, W. Bewket, J. Wenninger, and B. Simane
Hydrol. Earth Syst. Sci., 14, 2415–2428, https://doi.org/10.5194/hess-14-2415-2010, https://doi.org/10.5194/hess-14-2415-2010, 2010
Cited articles
Abdalati, W., Zwally, H. J., Bindschadler, R., Csatho, B., Farrell, S. L., Fricker, H. A., Harding, D., Kwok, R., Lefsky, M., Markus, T., Marshak, A., Neumann, T., Palm, S., Schutz, B., Smith, B., Spinhirne, J., and Webb, C.: The ICESat-2 laser altimetry mission, Proc. IEEE, 98, 735–751, https://doi.org/10.1109/JPROC.2009.2034765, 2010.
Ahmed, O. S., Franklin, S. E. and Wulder, M. A.: Interpretation of forest disturbance using a time series of Landsat imagery and canopy structure from airborne lidar, Can. J. Remote Sens., 39, 521–542, https://doi.org/10.5589/m14-004, 2014.
Anderson, S. and Pitlick, J.: Using repeat lidar to estimate sediment transport in a steep stream, J. Geophys. Res.-Earth, 119, 621–643, https://doi.org/10.1002/2013JF002933, 2014.
Antonarakis, A. S., Richards, K. S., Brasington, J., and Muller, E.: Determining leaf area index and leafy tree roughness using terrestrial laser scanning, Water Resour. Res., 46, W06510, https://doi.org/10.1029/2009WR008318, 2010.
Antonarakis, A. S., Munger, J. W., and Moorcroft, P. R.: Imaging spectroscopy- and lidar-derived estimates of canopy composition and structure to improve predictions of forest carbon fluxes and ecosystem dynamics, Geophys. Res. Lett., 41, 2013GL058373, https://doi.org/10.1002/2013GL058373, 2014.
Arnold, N. S., Rees, W. G., Devereux, B. J., and Amable, G. S.: Evaluating the potential of high-resolution airborne LiDAR data in glaciology, Int. J. Remote Sens., 27, 1233–1251, https://doi.org/10.1080/01431160500353817, 2006.
Aryal, A., Brooks, B. A., Reid, M. E., Bawden, G. W., and Pawlak, G. R.: Displacement fields from point cloud data: Application of particle imaging velocimetry to landslide geodesy, J. Geophys. Res.-Atmos., 117, F01029, https://doi.org/10.1029/2011JF002161, 2012.
Asner, G. P., Knapp, D. E., Boardman, J., Green, R. O., Kennedy-Bowdoin, T., Eastwood, M., Martin, R. E., Anderson, C., and Field, C. B.: Carnegie Airborne Observatory-2: Increasing science data dimensionality via high-fidelity multi-sensor fusion, Remote Sens. Environ., 124, 454–465, https://doi.org/10.1016/j.rse.2012.06.012, 2012a.
Asner, G. P., Mascaro, J., Muller-Landau, H., Vieilledent, G., Vaudry, R., Rasamoelina, M., Hall, J., and van Breugel, M.: A universal airborne LiDAR approach for tropical forest carbon mapping, Oecologia, 168, 1147–1160, https://doi.org/10.1007/s00442-011-2165-z, 2012b.
Avitabile, V., Baccini, A., Friedl, M. A., and Schmullius, C.: Capabilities and limitations of Landsat and land cover data for aboveground woody biomass estimation of Uganda, Remote Sens. Environ., 117, 366–380, https://doi.org/10.1016/j.rse.2011.10.012, 2012.
Baccini, A., Goetz, S. J., Walker, W. S., Laporte, N. T., Sun, M., Sulla-Menashe, D., Hackler, J., Beck, P. S. A., Dubayah, R., Friedl, M. A., Samanta, S., and Houghton, R. A.: Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps, Nature Climate Change, 2, 182–185, https://doi.org/10.1038/nclimate1354, 2012.
Barnhart, T. B. and Crosby, B. T.: Comparing Two Methods of Surface Change Detection on an Evolving Thermokarst Using High-Temporal-Frequency Terrestrial Laser Scanning, Selawik River, Alaska, Remote Sens., 5, 2813–2837, https://doi.org/10.3390/rs5062813, 2013.
Bates, P. D.: Integrating remote sensing data with flood inundation models: how far have we got?, Hydrol. Process., 26, 2515–2521, https://doi.org/10.1002/hyp.9374, 2012.
Belmont, P., Gran, K. B., Schottler, S. P., Wilcock, P. R., Day, S. S., Jennings, C., Lauer, J. W., Viparelli, E., Willenbring, J. K., Engstrom, D. R., and Parker, G.: Large Shift in Source of Fine Sediment in the Upper Mississippi River, Environ. Sci. Technol., 45, 8804–8810, https://doi.org/10.1021/es2019109, 2011.
Bittner, S., Gayler, S., Biernath, C., Winkler, J. B., Seifert, S., Pretzsch, H. and Priesack, E.: Evaluation of a ray-tracing canopy light model based on terrestrial laser scans, Can. J. Remote Sens., 38, 619–628, https://doi.org/10.5589/m12-050, 2014.
Bode, C. A., Limm, M. P., Power, M. E., and Finlay, J. C.: Subcanopy Solar Radiation model: Predicting solar radiation across a heavily vegetated landscape using LiDAR and GIS solar radiation models, Remote Sens. Environ., 154, 387–397, https://doi.org/10.1016/j.rse.2014.01.028, 2014.
Booth, A. M., Roering, J. J., and Perron, J. T.: Automated landslide mapping using spectral analysis and high-resolution topographic data: Puget Sound lowlands, Washington, and Portland Hills, Oregon, Geomorphology, 109, 132–147, https://doi.org/10.1016/j.geomorph.2009.02.027, 2009.
Bork, E. W. and Su, J. G.: Integrating LIDAR data and multispectral imagery for enhanced classification of rangeland vegetation: A meta analysis, Remote Sens. Environ., 111, 11–24, https://doi.org/10.1016/j.rse.2007.03.011, 2007.
Boutet, J. C., Jr and Weishampel, J. F.: Spatial pattern analysis of pre- and post-hurricane forest canopy structure in North Carolina, USA, Landscape Ecol., 18, 553–559, https://doi.org/10.1023/A:1026058312853, 2003.
Brasington, J., Vericat, D., and Rychkov, I.: Modeling river bed morphology, roughness, and surface sedimentology using high resolution terrestrial laser scanning, Water Resour. Res., 48, W11519, https://doi.org/10.1029/2012WR012223, 2012.
Bright, B. C., Hudak, A. T., McGaughey, R., Andersen, H.-E., and Negrón, J.: Predicting live and dead tree basal area of bark beetle affected forests from discrete-return lidar, Can. J. Remote Sens., 39, S99–S111, https://doi.org/10.5589/m13-027, 2014.
Brooks, B. A., Glennie, C., Hudnut, K. W., Ericksen, T., and Hauser, D.: Mobile Laser Scanning Applied to the Earth Sciences, Eos, Transactions American Geophysical Union, 94, 313–315, https://doi.org/10.1002/2013EO360002, 2013.
Broxton, P. D., Harpold, A. A., Brooks, P. D., Troch, P. A., and Molotch, N. P.: Quantifying the effects of vegetation structure on wintertime vapor losses from snow in mixed-conifer forests, Ecohydrol., https://doi.org/10.1002/eco.1565, in press, 2014.
Brubaker, K. M., Myers, W. L., Drohan, P. J., Miller, D. A., and Boyer, E. W.: The Use of LiDAR Terrain Data in Characterizing Surface Roughness and Microtopography, Appl. Environ. Soil Sci., 13, 891534, https://doi.org/10.1155/2013/891534, 2013.
Cho, M. A., Mathieu, R., Asner, G. P., Naidoo, L., van Aardt, J., Ramoelo, A., Debba, P., Wessels, K., Main, R., Smit, I. P. J., and Erasmus, B.: Mapping tree species composition in South African savannas using an integrated airborne spectral and LiDAR system, Remote Sens. Environ., 125, 214–226, https://doi.org/10.1016/j.rse.2012.07.010, 2012.
Chorover, J., Troch, P. A., Rasmussen, C., Brooks, P. D., Pelletier, J. D., Breshars, D. D., Huxman, T. E., Kurc, S. A., Lohse, K. A., McIntosh, J. C., Meixner, T., Schaap, M. G., Litvak, M. E., Perdrial, J., Harpold, A., and Durcik, M.: How Water, Carbon, and Energy Drive Critical Zone Evolution: The Jemez-Santa Catalina Critical Zone Observatory, Vadose Zone Journal, 10, 884–899, 2011.
Coops, N. C., Morsdorf, F., Schaepman, M. E., and Zimmermann, N. E.: Characterization of an alpine tree line using airborne LiDAR data and physiological modeling, Glob. Change Biol., 19, 3808–3821, https://doi.org/10.1111/gcb.12319, 2013.
Crosby, C., Arrowsmith, R., Nandigam, V., and Chaitanya, B, J. R. A. and Crosby et al, C. J.: Online access and processing of LiDAR topography data, Cambridge University Press, 251–265, 2011.
Day, S. S., Gran, K. B., Belmont, P., and Wawrzyniec, T.: Measuring bluff erosion part 1: terrestrial laser scanning methods for change detection, Earth Surf. Proc. Land., 38, 1055–1067, https://doi.org/10.1002/esp.3353, 2013.
Deems, J. S., Fassnacht, S. R., and Elder, K. J.: Fractal Distribution of Snow Depth from Lidar Data, J. Hydrometeor, 7, 285–297, https://doi.org/10.1175/JHM487.1, 2006.
Deems, J. S., Fassnacht, S. R., and Elder, K. J.: Interannual Consistency in Fractal Snow Depth Patterns at Two Colorado Mountain Sites, J. Hydrometeor, 9, 977–988, https://doi.org/10.1175/2008JHM901.1, 2008.
Deems, J. S., Painter, T. H., and Finnegan, D. C.: LiDAR measurement of snow depth: a review, J. Glaciol., 59, 467–479, https://doi.org/10.3189/2013JoG12J154, 2013.
DeLong, S. B., Prentice, C. S., Hilley, G. E., and Ebert, Y.: Multitemporal ALSM change detection, sediment delivery, and process mapping at an active earthflow, Earth Surf. Proc. Land., 37, 262–272, https://doi.org/10.1002/esp.2234, 2011.
Dickinson, Y., Zenner, E. K., and Miller, D.: Examining the effect of diverse management strategies on landscape scale patterns of forest structure in Pennsylvania using novel remote sensing techniques, Can. J. For. Res., 44, 301–312, https://doi.org/10.1139/cjfr-2013-0315, 2014.
Drake, J. B. and Weishampel, J. F.: Multifractal analysis of canopy height measures in a longleaf pine savanna, Forest Ecol. Manage., 128, 121–127, https://doi.org/10.1016/S0378-1127(99)00279-0, 2000.
Egli, L., Jonas, T., Grünewald, T., Schirmer, M., and Burlando, P.: Dynamics of snow ablation in a small Alpine catchment observed by repeated terrestrial laser scans, Hydrol. Process., 26, 1574–1585, https://doi.org/10.1002/hyp.8244, 2011.
Essery, R., Bunting, P., Rowlands, A., Rutter, N., Hardy, J., Melloh, R., Link, T., Marks, D., and Pomeroy, J.: Radiative Transfer Modeling of a Coniferous Canopy Characterized by Airborne Remote Sens., J. Hydrometeor, 9, 228–241, https://doi.org/10.1175/2007JHM870.1, 2008.
Fassnacht, S. R. and Deems, J. S.: Measurement sampling and scaling for deep montane snow depth data, Hydrol. Process., 20, 829–838, https://doi.org/10.1002/hyp.6119, 2006.
Fernandez-Diaz, J. C., Glennie, C. L., Carter, W. E., Shrestha, R. L., Sartori, M. P., Singhania, A., Legleiter, C. J., and Overstreet, B. T.: Early Results of Simultaneous Terrain and Shallow Water Bathymetry Mapping Using a Single-Wavelength Airborne LiDAR Sensor, Selected Topics in Applied Earth Observations and Remote Sens., IEEE Geosci. Remote S., 7, 623–635, https://doi.org/10.1109/JSTARS.2013.2265255, 2014.
Fewtrell, T. J., Bates, P. D., Horritt, M., and Hunter, N. M.: Evaluating the effect of scale in flood inundation modelling in urban environments, Hydrol. Process., 22, 5107–5118, https://doi.org/10.1002/hyp.7148, 2008.
Féret, J.-B. and Asner, G. P.: Semi-Supervised Methods to Identify Individual Crowns of Lowland Tropical Canopy Species Using Imaging Spectroscopy and LiDAR, Remote Sens., 4, 2457–2476, https://doi.org/10.3390/rs4082457, 2012.
Flener, C., Vaaja, M., Jaakkola, A., Krooks, A., Kaartinen, H., Kukko, A., Kasvi, E., Hyyppä, H., Hyyppä, J. and Alho, P.: Seamless Mapping of River Channels at High Resolution Using Mobile LiDAR and UAV-Photography, Remote Sens., 5, 6382–6407, https://doi.org/10.3390/rs5126382, 2013.
Fonstad, M. A., Dietrich, J. T., Courville, B. C., Jensen, J. L., and Carbonneau P. E.: Topographic structure from motion: a new development in photogrammetric measurement, Earth Surf. Proc. Land., 38, 421–430, https://doi.org/10.1002/esp.3366, 2013.
Forzieri, G., Moser, G., Vivoni, E. R., Castelli, F. and Canovaro, F.: Riparian Vegetation Mapping for Hydraulic Roughness Estimation Using Very High Resolution Remote Sens. Data Fusion, J. Hydraul. Eng., 136, 855–867, https://doi.org/10.1061/(ASCE)HY.1943-7900.0000254, 2010.
Frazer, G. W., Wulder, M. A., and Niemann, K. O.: Simulation and quantification of the fine-scale spatial pattern and heterogeneity of forest canopy structure: A lacunarity-based method designed for analysis of continuous canopy heights, Forest Ecol. Manage., 214, 65–90, https://doi.org/10.1016/j.foreco.2005.03.056, 2005.
French, J. R.: Airborne LiDAR in support of geomorphological and hydraulic modelling, Earth Surf. Proc. Land., 28, 321–335, https://doi.org/10.1002/esp.484, 2003.
Glenn, N. F., Streutker, D. R., Chadwick, D. J., Thackray, G. D., and Dorsch, S. J.: Analysis of LiDAR-derived topographic information for characterizing and differentiating landslide morphology and activity, Geomorphology, 73, 131–148, https://doi.org/10.1016/j.geomorph.2005.07.006, 2006.
Glennie, C. L., Carter, W. E., Shrestha, R. L., and Dietrich, W. E.: Geodetic imaging with airborne LiDAR: the Earth's surface revealed, Rep. Prog. Phys., 76, 1–24, 2013.
Glennie, C. L., Hinojosa-Corona, A., Nissen, E., Kusari, A., Oskin, M. E., Arrowsmith, J. R., and Borsa, A.: Optimization of legacy lidar data sets for measuring near-field earthquake displacements, Geophys. Res. Lett., 41, 2014GL059919, https://doi.org/10.1002/2014GL059919, 2014.
Guzzetti, F., Mondini, A. C., Cardinali, M., Fiorucci, F., Santangelo, M., and Chang, K.-T.: Landslide inventory maps: New tools for an old problem, Earth Sci. Rev., 112, 42–66, https://doi.org/10.1002/2014GL059919, 2012.
Grünewald, T., Schirmer, M., Mott, R., and Lehning, M.: Spatial and temporal variability of snow depth and ablation rates in a small mountain catchment, The Cryosphere, 4, 215–225, https://doi.org/10.5194/tc-4-215-2010, 2010.
Hakala, T., Suomalainen, J., Kaasalainen, S., and Chen, Y.: Full waveform hyperspectral LiDAR for terrestrial laser scanning, Opt. Express, 20, 7119–7127, https://doi.org/10.1364/OE.20.007119, 2012.
Harman, C. J., Lohse, K. A., Troch, P. A., and Sivapalan, M.: Spatial patterns of vegetation, soils, and microtopography from terrestrial laser scanning on two semiarid hillslopes of contrasting lithology, J. Geophys. Res.-Biogeo., 119, 163–180, https://doi.org/10.1002/2013JG002507, 2014.
Harpold, A. A., Biederman, J. A., Condon, K., Merino, M., Korgaonkar, Y., Nan, T., Sloat, L. L., Ross, M., and Brooks, P. D.: Changes in snow accumulation and ablation following the Las Conchas Forest Fire, New Mexico, USA, Ecohydrol., 7, 440–452, https://doi.org/10.1002/eco.1363, 2014a.
Harpold, A. A., Guo, Q., Molotch, N., Brooks, P. D., Bales, R., Fernandez-Diaz, J. C., Musselman, K. N., Swetnam, T. L., Kirchner, P., Meadows, M. W., Flanagan, J., and Lucas, R.: LiDAR?derived snowpack data sets from mixed conifer forests across the Western United States, Water Resour. Res., 50, 2749–2755, https://doi.org/10.1002/2013WR013935, 2014b.
Hartzell, P., Glennie, C., Biber, K., and Khan, S.: Application of multispectral LiDAR to automated virtual outcrop geology, ISPRS J. Photogramm., 88, 147–155, https://doi.org/10.1016/j.isprsjprs.2013.12.004, 2014.
Hedrick, A., Marshall, H.-P., Winstral, A., Elder, K., Yueh, S., and Cline, D.: Independent evaluation of the SNODAS snow depth product using regional-scale lidar-derived measurements, The Cryosphere, 9, 13–23, https://doi.org/10.5194/tc-9-13-2015, 2015.
Hopkinson, C. and Demuth, M. N.: Using airborne lidar to assess the influence of glacier downwasting on water resources in the Canadian Rocky Mountains, Can. J. Remote Sens., 32, 212–222, https://doi.org/10.5589/m06-012, 2006.
Hopkinson, C., Collins, T., Anderson, A., Pomeroy, J., and Spooner, I.: Spatial Snow Depth Assessment Using LiDAR Transect Samples and Public GIS Data Layers in the Elbow River Watershed, Alberta, Can. Water Resour. J., 37, 69–87, https://doi.org/10.4296/cwrj3702893, 2012.
Hopkinson, C., Lovell, J., Chasmer, L., Jupp, D., Kljun, N., and van Gorsel, E.: Integrating terrestrial and airborne lidar to calibrate a 3D canopy model of effective leaf area index, Remote Sens. Environ., 136, 301–314, https://doi.org/10.1016/j.rse.2013.05.012, 2013.
Hudak, A. T., Strand, E. K., Vierling, L. A., Byrne, J. C., Eitel, J. U. H., Martinuzzi, S., and Falkowski, M. J.: Quantifying aboveground forest carbon pools and fluxes from repeat LiDAR surveys, Remote Sens. Environ., 123, 25–40, https://doi.org/10.1016/j.rse.2012.02.023, 2012.
Hurst, M. D., Mudd, S. M., Walcott, R., Attal, M., and Yoo, K.: Using hilltop curvature to derive the spatial distribution of erosion rates, J. Geophys. Res.-Atmos., 117, F02017, https://doi.org/10.1029/2011JF002057, 2012.
Hurst, M. D., Mudd, S. M., Yoo, K., Attal, M., and Walcott, R.: Influence of lithology on hillslope morphology and response to tectonic forcing in the northern Sierra Nevada of California, J. Geophys. Res.-Earth, 118, 832–851, \https://doi.org/10.1002/jgrf.20049, 2013.
Hyde, P., Dubayah, R., Peterson, B., BLAIR, J., Hofton, M., Hunsaker, C., Knox, R., and Walker, W.: Mapping forest structure for wildlife habitat analysis using waveform lidar: Validation of montane ecosystems, Remote Sens. Environ., 96, 427–437, https://doi.org/10.1016/j.rse.2005.03.005, 2005.
Hyyppä, J., Holopainen, M., and Olsson, H.: Laser Scanning in Forests, Remote Sens., 4, 2919–2922, https://doi.org/10.3390/rs4102919, 2012.
James, L. A., Singer, M. B., Ghoshal, S., and Megison, M.: Historical channel changes in the lower Yuba and Feather Rivers, California: Long-term effects of contrasting river-management strategies, in Management and Restoration of Fluvial Systems with Broad Historical Changes and Human Impacts, Geological Society of America, 451, 57–81, https://doi.org/10.1130/2009.2451(04), 2009.
James, L. A., Hodgson, M. E., Ghoshal, S., and Latiolais, M. M.: Geomorphic change detection using historic maps and DEM differencing: The temporal dimension of geospatial analysis, Geomorphology, 137, 181–198, https://doi.org/10.1016/j.geomorph.2010.10.039, 2012.
James, M. R. and Robson, S.: Straightforward reconstruction of 3D surfaces and topography with a camera: Accuracy and geoscience application, J. Geophys. Res., 117, F03017, https://doi.org/10.1029/2011JF002289, 2010.
Javernick, L., Brasington, J., and Caruso, B.: Modeling the topography of shallow braided rivers using Structure-from-Motion photogrammetry, Geomorphology, 213, 166–182, https://doi.org/10.1016/j.geomorph.2014.01.006, 2014.
Jencso, K. G., McGlynn, B. L., Gooseff, M. N., Wondzell, S. M., Bencala, K. E., and Marshall, L. A.: Hydrologic connectivity between landscapes and streams: Transferring reach?and plot?scale understanding to the catchment scale, Water Resour. Res., 45, W04428, https://doi.org/10.1029/2008WR007225, 2009.
Jones, A. F., Brewer, P. A., Johnstone, E., and Macklin, M. G.: High-resolution interpretative geomorphological mapping of river valley environments using airborne LiDAR data, Earth Surf. Proc. Land., 32, 1574–1592, https://doi.org/10.1002/esp.1505, 2007.
Kaasalainen, S., Lindroos, T., and Hyyppä, J.: Toward Hyperspectral Lidar: Measurement of Spectral Backscatter Intensity With a Supercontinuum Laser Source, IEEE T. Geosci. Remote, 4, 211–215, https://doi.org/10.1109/LGRS.2006.888848, 2007.
Kampe, T.U., Johnson, B. R., Kuester, M., and Keller, M.: NEON: the first continental-scale ecological observatory with airborne remote sensing of vegetation canopy biochemistry and structure, J. Appl. Remote Sens., 4, 043510–043510, https://doi.org/10.1117/1.3361375, 2010
Kessler, A. C., Gupta, S. C., Dolliver, H. A. S., and Thoma, D. P.: Lidar Quantification of Bank Erosion in Blue Earth County, Minnesota, Journal of Environmental Quality, 41, 197–207, https://doi.org/10.2134/jeq2011.0181, 2012.
Kinzel, P. J., Wright, C. W., Nelson, J. M., and Burman, A. R.: Evaluation of an Experimental LiDAR for Surveying a Shallow, Braided, Sand-Bedded River, J. Hydraul. Eng., 133, 838–842, https://doi.org/10.1061/(ASCE)0733-9429(2007)133:7(838), 2007.
Kinzel, P. J., Legleiter, C. J., and Nelson, J. M.: Mapping River Bathymetry With a Small Footprint Green LiDAR: Applications and Challenges1, JAWRA J. Am. Water Resour. As., 49, 183–204, https://doi.org/10.1111/jawr.12008, 2013.
Kirchner, P. B., Bales, R. C., Molotch, N. P., Flanagan, J., and Guo, Q.: LiDAR measurement of seasonal snow accumulation along an elevation gradient in the southern Sierra Nevada, California, Hydrol. Earth Syst. Sci. Discuss., 11, 5327–5365, https://doi.org/10.5194/hessd-11-5327-2014, 2014.
Kohler, J., Neumann, T. A., Robbins, J. W., Tronstad, S., and Melland, G.: ICESat Elevations in Antarctica Along the 2007–2009 Norway-USA Traverse: Validation With Ground-Based GPS, IEEE T. Geosci. Remote, 51, 1578–1587, https://doi.org/10.1109/TGRS.2012.2207963, 2013.
Krishnan, S., Crosby, C., Nandigam, V., Phan, M., Cowart, C., Baru, C., and Arrowsmith, R.: OpenTopography: A Services Oriented Architecture for Community Access to LIDAR Topography, 7:1–7:8, ACM, New York, NY, USA, 2011.
Kurz, T. H., Buckley, S. J., Howell, J. A., and Schneider, D.: Integration of panoramic hyperspectral imaging with terrestrial lidar data, The Photogrammetric Record, 26, 212–228, https://doi.org/10.1111/j.1477-9730.2011.00632.x, 2011.
Lague, D., Brodu, N., and leroux, J.: Accurate 3D comparison of complex topography with terrestrial laser scanner: Application to the Rangitikei canyon (N-Z), ISPRS J. Photogramm., 82, 10–26, https://doi.org/10.1016/j.isprsjprs.2013.04.009, 2013.
Lam, N., Nathanson, M., Lundgren, N., Rehnström, R., and Lyon, S. W.: A Cost-Effective Laser Scanning Method for Mapping Stream Channel Geometry and Roughness, J. Am. Water Resour. As., 1–10, https://doi.org/10.1111/1752-1688.12299, 2015.
Lane, C. and D'Amico, E.: Calculating the Ecosystem Service of Water Storage in Isolated Wetlands using LiDAR in North Central Florida, USA, Wetlands, 30, 967–977, https://doi.org/10.1007/s13157-010-0085-z, 2010.
Lane, S. N., Brookes, C. J., Kirkby, M. J., and Holden, J.: A network index based version of TOPMODEL for use with high resolution digital topographic data, Hydrol. Proces., 18, 191–201, https://doi.org/10.1002/hyp.5208, 2004.
Lee, H., Slatton, K. C., Roth, B. E., and Cropper, W. P.: Prediction of forest canopy light interception using three dimensional airborne LiDAR data, Int. J. Remote Sens., 30, 189–207, https://doi.org/10.1080/01431160802261171, 2009.
Lefsky, M. A., Cohen, W. B., Parker, G. G., and Harding, D. J.: Lidar Remote Sensing for Ecosystem Studies, BioScience, 52, 19–30, 2002.
Legleiter, C. J., Kyriakidis, P. C., McDonald, R. R., and Nelson, J. M.: Effects of uncertain topographic input data on two-dimensional flow modeling in a gravel-bed river, Water Resour. Res., 47, W03518, https://doi.org/10.1029/2010WR009618, 2011.
Levy, J. S., Fountain, A. G., Dickson, J. L., Head, J. W., Okal, M., Marchant, D. R., and Watters, J.: Accelerated thermokarst formation in the McMurdo Dry Valleys, Antarctica, Sci. Rep., 3, 2269, https://doi.org/10.1038/srep02269, 2013.
Li, S., MacMillan, R. A., Lobb, D. A., McConkey, B. G., Moulin, A., and Fraser, W. R.: Lidar DEM error analyses and topographic depression identification in a hummocky landscape in the prairie region of Canada, Geomorphology, 129, 263–275, https://doi.org/10.1016/j.geomorph.2011.02.020, 2011.
Lin, Y., Hyyppä, J., and Jaakkola, A.: Mini-UAV-Borne LIDAR for Fine-Scale Mapping, IEEE T. Geosci. Remote, 8, 426–430, https://doi.org/10.1109/LGRS.2010.2079913, 2011.
Lotsari, E., Vaaja, M., Flener, C., Kaartinen, H., Kukko, A., Kasvi, E., Hyyppä, H., Hyyppä, J., and Alho P.: Annual bank and point bar morphodynamics of a meandering river determined by high-accuracy multitemporal laser scanning and flow data, Water Resour. Res., 50, 5532–5559, https://doi.org/10.1002/2013WR014106, 2014.
Lyon, S. W., Nathanson, M., Lam, N., Dahlke, H. E., Rutzinger, M., Kean, J. W., and Laudon, H.: Can Low-Resolution Airborne Laser Scanning Data Be Used to Model Stream Rating Curves?, Water, 7, 1324–1339, https://doi.org/10.3390/w7041324, 2015.
Mallet, C. and Bretar, F.: Full-waveform topographic LiDAR: State-of-the-art, ISPRS J. Photogramm., 64, 1–16, https://doi.org/10.1016/j.isprsjprs.2008.09.007, 2009.
Maltamo, M., Næsset, E., and Vauhkonen, J. (Eds.): Forestry Applications of Airborne Laser Scanning, Springer, Netherlands, 269–292, 2014.
Mandlburger, G., Hauer, C., Höfle, B., Habersack, H., and Pfeifer, N.: Optimisation of LiDAR derived terrain models for river flow modelling, Hydrol. Earth Syst. Sci., 13, 1453–1466, https://doi.org/10.5194/hess-13-1453-2009, 2009.
Mankoff, K. D. and Russo, T. A.: The Kinect: a low-cost, high-resolution, short-range 3D camera, Earth Surf. Proc. Land., 38, 926–936, https://doi.org/10.1002/esp.3332, 2013.
Marks, K. and Bates, P.: Integration of high-resolution topographic data with floodplain flow models, Hydrol. Process., 14, 2109–2122, https://doi.org/10.1002/1099-1085(20000815/30)14:11/12<2109::AID-HYP58>3.0.CO;2-1, 2000.
Marshall, J. A. and Roering, J. J.: Diagenetic variation in the Oregon Coast Range: Implications for rock strength, soil production, hillslope form, and landscape evolution, J. Geophys. Res.-Earth, 119, 2013JF003004, https://doi.org/10.1002/2013JF003004, 2014.
Martinuzzi, S., Vierling, L. A., Gould, W. A., Falkowski, M. J., Evans, J. S., Hudak, A. T., and Vierling, K. T.: Mapping snags and understory shrubs for a LiDAR-based assessment of wildlife habitat suitability, Remote Sens. Environ., 113, 2533–2546, https://doi.org/10.1016/j.rse.2009.07.002, 2009.
Mascaro, J., Detto, M., Asner, G. P., and Muller-Landau, H. C.: Evaluating uncertainty in mapping forest carbon with airborne LiDAR, Remote Sens. Environ., 115, 3770–3774, https://doi.org/10.1016/j.rse.2011.07.019, 2011.
Mason, D. C., Cobby, D. M., Horritt, M. S., and Bates, P. D.: Floodplain friction parameterization in two-dimensional river flood models using vegetation heights derived from airborne scanning laser altimetry, Hydrol. Process., 17, 1711–1732, https://doi.org/10.1002/hyp.1270, 2003.
Mattmann, C. A.: Computing: A vision for data science, Nature, 493, 473–475, https://doi.org/10.1038/493473a, 2013.
McCreight, J. L., Slater, A. G., Marshall, H. P., and Rajagopalan, B.: Inference and uncertainty of snow depth spatial distribution at the kilometre scale in the Colorado Rocky Mountains: the effects of sample size, random sampling, predictor quality, and validation procedures, Hydrol. Process., 28, 933–957, https://doi.org/10.1002/hyp.9618, 2014.
McKean, J., Isaak, D., and Wright, W.: Improving Stream Studies With a Small-Footprint Green Lidar, Eos, Transactions American Geophysical Union, 90, 341–342, https://doi.org/10.1029/2009EO390002, 2009.
McKean, J., McKean, J., Tonina, D., Tonina, D., Bohn, C., Bohn, C., Wright, C. W., and Wright, C. W.: Effects of bathymetric lidar errors on flow properties predicted with a multi-dimensional hydraulic model, J. Geophys. Res.-Earth, 119, 644–664, https://doi.org/10.1002/2013JF002897, 2014.
McKean, J. A., Isaak, D. J., and Wright, C. W.: Geomorphic controls on salmon nesting patterns described by a new, narrow-beam terrestrial-aquatic lidar, Front. Ecol. Environ., 6, 125–130, https://doi.org/10.1890/070109, 2008.
Meyer, V., Saatchi, S. S., Chave, J., Dalling, J. W., Bohlman, S., Fricker, G. A., Robinson, C., Neumann, M., and Hubbell, S.: Detecting tropical forest biomass dynamics from repeated airborne lidar measurements, Biogeosciences, 10, 5421–5438, https://doi.org/10.5194/bg-10-5421-2013, 2013.
Mitchell, P. J., Lane, P. N. J., and Benyon, R. G.: Capturing within catchment variation in evapotranspiration from montane forests using LiDAR canopy profiles with measured and modelled fluxes of water, Ecohydrol., 5, 708–720, https://doi.org/10.1002/eco.255, 2011.
Moeser, D., Roubinek, J., Schleppi, P., Morsdorf, F., and Jonas, T.: Canopy closure, LAI and radiation transfer from airborne LiDAR synthetic images, Agr. Forest Meteorol., 197, 158–168, https://doi.org/10.1016/j.agrformet.2014.06.008, 2014.
Mora, B., Wulder, M. A., Hobart, G. W., White, J. C., Bater, C. W., Gougeon, F. A., Varhola, A., and Coops, N. C.: Forest inventory stand height estimates from very high spatial resolution satellite imagery calibrated with lidar plots, Int. J. Remote Sens., 34, 4406–4424, https://doi.org/10.1080/01431161.2013.779041, 2013.
Mundt, J. T., Streutker, D. R., and Glenn, N. F.: Mapping Sagebrush Distribution Using Fusion of Hyperspectral and Lidar Classifications, Photogramm. Eng. Rem. S., 72, 47–54, https://doi.org/10.14358/PERS.72.1.47, 2006.
Musselman, K. N., Margulis, S. A., and Molotch, N. P.: Estimation of solar direct beam transmittance of conifer canopies from airborne LiDAR, Remote Sens. Environ., 136, 402–415, https://doi.org/10.1016/j.rse.2013.05.021, 2013.
Næsset, E. and Gobakken, T.: Estimating forest growth using canopy metrics derived from airborne laser scanner data, Remote Sens. Environ., 96, 453–465, https://doi.org/10.1016/j.rse.2005.04.001, 2005.
Nathanson, M., Kean, J. W., Grabs, T. J., Seibert, J., Laudon, H., and Lyon, S. W.: Modelling rating curves using remotely sensed LiDAR data, Hydrol. Process., 26, 1427–1434, https://doi.org/10.1002/hyp.9225, 2012.
National Research Council: New Research Opportunities in the Earth Sciences, National Academy Press, Washington D.C., ailable at: http://www.nap.edu/openbook.php?record_id=13236 (last access 31 October 2014), 2012.
Nissen, E., Krishnan, A. K., Arrowsmith, J. R., and Saripalli, S.: Three-dimensional surface displacements and rotations from differencing pre- and post-earthquake LiDAR point clouds, Geophys. Res. Lett., 39, L16301, https://doi.org/10.1029/2012GL052460, 2012.
Nissen, E., Maruyama, T., Arrowsmith, J. R., Elliott, J. R., Krishnan, A. K., Oskin, M. E., and Saripalli, S.: Coseismic fault zone deformation revealed with differential lidar: examples from Japanese Mw 7 intraplate earthquakes, Earth Planet. Sci. Lett., 405, 244–256, https://doi.org/10.1016/j.epsl.2014.08.031, 2014.
Olsoy, P. J., Glenn, N. F., Clark, P. E., and Derryberry, D. R.: Aboveground total and green biomass of dryland shrub derived from terrestrial laser scanning, ISPRS J. Photogramm., 88, 166–173, https://doi.org/10.1016/j.isprsjprs.2013.12.006, 2014.
Oskin, M. E., Arrowsmith, J. R., Corona, A. H., Elliott, A. J., Fletcher, J. M., Fielding, E. J., Gold, P. O., Garcia, J. J. G., Hudnut, K. W., Liu-Zeng, J., and Teran, O. J.: Near-Field Deformation from the El Mayor-Cucapah Earthquake Revealed by Differential LIDAR, Science, 335, 702–705, https://doi.org/10.1126/science.1213778, 2012.
Palminteri, S., Powell, G. V. N., Asner, G. P., and Peres, C. A.: LiDAR measurements of canopy structure predict spatial distribution of a tropical mature forest primate, Remote Sens. Environ., 127, 98–105, https://doi.org/10.1016/j.rse.2012.08.014, 2012.
Passalacqua, P., Tarolli, P., and Foufoula-Georgiou, E.: Testing space-scale methodologies for automatic geomorphic feature extraction from lidar in a complex mountainous landscape, Water Resour. Res., 46, W11535, https://doi.org/10.1029/2009WR008812, 2010.
Pelletier, J. D.: Deviations from self-similarity in barchan form and flux: The case of the Salton Sea dunes, California, J. Geophys. Res.-Earth, 118, 2013JF002867, https://doi.org/10.1002/2013JF002867, 2013.
Pelletier, J. D. and Perron, J. T.: Analytic solution for the morphology of a soil-mantled valley undergoing steady headward growth: Validation using case studies in southeastern Arizona, J. Geophys. Res.-Earth, 117, F02018, https://doi.org/10.1029/2011JF002281, 2012.
Pelletier, J. D. and Orem, C. A.: How do sediment yields from post-wildfire debris-laden flows depend on terrain slope, soil burn severity class, and drainage basin area? Insights from airborne-LiDAR change detection, Earth Surf. Proc. Land., 39, 1822–1832, https://doi.org/10.1002/esp.3570, 2014.
Pelletier, J. D., DeLong, S. B., Orem, C. A., Becerra, P., Compton, K., Gressett, K., Lyons-Baral, J., McGuire, L. A., Molaro, J. L., and Spinler, J. C.: How do vegetation bands form in dry lands? Insights from numerical modeling and field studies in southern Nevada, USA, J. Geophys. Res.-Atmos., 117, F04026, https://doi.org/10.1029/2012JF002465, 2012.
Pelletier, J. D., Barron-Gafford, G. A., Breshears, D. D., Brooks, P. D., Chorover, J., Durcik, M., Harman, C. J., Huxman, T. E., Lohse, K. A., Lybrand, R., Meixner, T., McIntosh, J. C., Papuga, S. A., Rasmussen, C., Schaap, M., Swetnam, T. L., and Troch, P. A.: Coevolution of nonlinear trends in vegetation, soils, and topography with elevation and slope aspect: A case study in the sky islands of southern Arizona, J. Geophys. Res.-Earth, 118, 741–758, https://doi.org/10.1002/jgrf.20046, 2013.
Perignon, M. C., Tucker, G. E., Griffin, E. R., and Friedman, J. M.: Effects of riparian vegetation on topographic change during a large flood event, Rio Puerco, New Mexico, USA, J. Geophys. Res.-Earth, 118, 1193–1209, https://doi.org/10.1002/jgrf.20073, 2013.
Perron, J. T. and Hamon, J. L.: Equilibrium form of horizontally retreating, soil-mantled hillslopes: Model development and application to a groundwater sapping landscape, J. Geophys. Res.-Atmos., 117, F01027, https://doi.org/10.1029/2011JF002139, 2012.
Perron, J. T., Kirchner, J. W., and Dietrich, W. E.: Spectral signatures of characteristic spatial scales and nonfractal structure in landscapes, J. Geophys. Res.-Atmos., 113, F04003, https://doi.org/10.1029/2007JF000866, 2008.
Persson, A., Hasan, A., Tang, J., and Pilesjö, P.: Modelling Flow Routing in Permafrost Landscapes with TWI: An Evaluation against Site-Specific Wetness Measurements, Transactions in GIS, 16, 701–713, https://doi.org/10.1111/j.1467-9671.2012.01338.x, 2012.
Plotnick, R., Gardner, R., Hargrove, W., Prestegaard, K., and Perlmutter, M.: Lacunarity analysis: A general technique for the analysis of spatial patterns, Phys. Rev. E, 53, 5461–5468, https://doi.org/10.1103/PhysRevE.53.5461, 1996.
Rengers, F. K. and Tucker, G. E.: Analysis and modeling of gully headcut dynamics, North American high plains, J. Geophys. Res.-Earth, 119, 983–1003, https://doi.org/10.1002/2013JF002962, 2014.
Rhoades, E. L., O'Neal, M. A., and Pizzuto, J. E.: Quantifying bank erosion on the South River from 1937 to 2005, and its importance in assessing Hg contamination, Appl. Geogr., 29, 125–134, https://doi.org/10.1016/j.apgeog.2008.08.005, 2009.
Riaño, D., Valladares, F., Condés, S., and Chuvieco, E.: Estimation of leaf area index and covered ground from airborne laser scanner (Lidar) in two contrasting forests, Agr. Forest Meteorol., 124, 269–275, https://doi.org/10.1016/j.agrformet.2004.02.005, 2004.
Richardson, J. J., Moskal, L. M., and Kim, S.-H.: Modeling approaches to estimate effective leaf area index from aerial discrete-return LIDAR, Agr. Forest Meteorol., 149, 1152–1160, https://doi.org/10.1016/j.agrformet.2009.02.007, 2009.
Roering, J. J.: How well can hillslope evolution models "explain" topography? Simulating soil transport and production with high-resolution topographic data, Geol. Soc. Am. Bull., 120, 1248–1262, https://doi.org/10.1130/B26283.1, 2008.
Roering, J. J., Stimely, L. L., Mackey, B. H., and Schmidt, D. A.: Using DInSAR, airborne LiDAR, and archival air photos to quantify landsliding and sediment transport, Geophys. Res. Lett., 36, L19402, https://doi.org/10.1029/2009GL040374, 2009.
Roering, J. J., Marshall, J., Booth, A. M., Mort, M., and Jin, Q.: Evidence for biotic controls on topography and soil production, Earth Planet. Sci. Lett., 298, 183–190, https://doi.org/10.1016/j.epsl.2010.07.040, 2010.
Roering, J. J., Mackey, B. H., Marshall, J. A., Sweeney, K. E., Deligne, N. I., Booth, A. M., Handwerger, A. L., and Cerovski-Darriau, C.: "You are HERE": Connecting the dots with airborne lidar for geomorphic fieldwork, Geomorphology, 200, 172–183, https://doi.org/10.1016/j.geomorph.2013.04.009, 2013.
Sankey, J. B., Glenn, N. F., Germino, M. J., Gironella, A. I. N., and Thackray, G. D.: Relationships of aeolian erosion and deposition with LiDAR-derived landscape surface roughness following wildfire, Geomorphology, 119, 135–145, https://doi.org/10.1016/j.geomorph.2010.03.013, 2010.
Sankey, J. B., Law, D. J., Breshears, D. D., Munson, S. M., and Webb, R. H.: Employing lidar to detail vegetation canopy architecture for prediction of aeolian transport, Geophys. Res. Lett., 40, 1724–1728, https://doi.org/10.1002/grl.50356, 2013.
Schimel, D. S., Asner, G. P., and Moorcroft, P.: Observing changing ecological diversity in the Anthropocene, Front. Ecol. Environ., 11, 129–137, https://doi.org/10.1890/120111, 2013.
Seidel, D., Fleck, S., Leuschner, C., and Hammett, T.: Review of ground-based methods to measure the distribution of biomass in forest canopies, Ann. For. Sci., 68, 225–244, https://doi.org/10.1007/s13595-011-0040-z, 2011.
Shook, K., Pomeroy, J. W., Spence, C., and Boychuk, L.: Storage dynamics simulations in prairie wetland hydrology models: evaluation and parameterization, Hydrol. Process., 27, 1875–1889, https://doi.org/10.1002/hyp.9867, 2013.
Simard, M., Pinto, N., Fisher, J. B., and Baccini, A.: Mapping forest canopy height globally with spaceborne lidar, J. Geophys. Res.-Atmos., 116, G04021, https://doi.org/10.1029/2011JG001708, 2011.
Snyder, N. P.: Studying Stream Morphology With Airborne Laser Elevation Data, Eos, Transactions American Geophysical Union, 90, 45–46, https://doi.org/10.1029/2009EO060001, 2009.
Sørensen, R. and Seibert, J.: Effects of DEM resolution on the calculation of topographical indices: TWI and its components, J. Hydrol., 347, 79–89, https://doi.org/10.1016/j.jhydrol.2007.09.001, 2007.
Staley, D. M., Wasklewicz, T. A., and Kean, J. W.: Characterizing the primary material sources and dominant erosional processes for post-fire debris-flow initiation in a headwater basin using multi-temporal terrestrial laser scanning data, Geomorphology, 214, 324–338, https://doi.org/10.1016/j.geomorph.2014.02.015, 2014.
Stennett, T. A.: Lidar: Strap in tight, and prepare to go vertical, Photogramm. Eng. Rem. S., 70, 545–548, 2004.
Stout, J. C. and Belmont, P.: TerEx Toolbox for semi-automated selection of fluvial terrace and floodplain features from lidar, Earth Surf. Proc. Land., 39, 569–580, https://doi.org/10.1002/esp.3464, 2014.
Streutker, D. R. and Glenn, N. F.: LiDAR measurement of sagebrush steppe vegetation heights, Remote Sens. Environ., 102, 135–145, https://doi.org/10.1016/j.rse.2006.02.011, 2006.
Tarolli, P.: High-resolution topography for understanding Earth surface processes: Opportunities and challenges, Geomorphology, 216, 295–312, https://doi.org/10.1016/j.geomorph.2014.03.008, 2014.
Tenenbaum, D. E., Band, L. E., Kenworthy, S. T., and Tague, C. L.: Analysis of soil moisture patterns in forested and suburban catchments in Baltimore, Maryland, using high-resolution photogrammetric and LIDAR digital elevation datasets, Hydrol. Process., 20, 219–240, https://doi.org/10.1002/hyp.5895, 2006.
Thoma, D. P., Gupta, S. C., Bauer, M. E., and Kirchoff, C. E.: Airborne laser scanning for riverbank erosion assessment, Remote Sens. Environ., 95, 493–501, https://doi.org/10.1016/j.rse.2005.01.012, 2005.
Tinkham, W. T., Smith, A. M. S., Marshall, H.-P., Link, T. E., Falkowski, M. J., and Winstral, A. H.: Quantifying spatial distribution of snow depth errors from LiDAR using Random Forest, Remote Sens. Environ., 141, 105–115, https://doi.org/10.1016/j.rse.2013.10.021, 2014.
Trujillo, E., Ramírez, J. A., and Elder, K. J.: Scaling properties and spatial organization of snow depth fields in sub-alpine forest and alpine tundra, Hydrol. Process., 23, 1575–1590, https://doi.org/10.1002/hyp.7270, 2009.
Varhola, A. and Coops, N. C.: Estimation of watershed-level distributed forest structure metrics relevant to hydrologic modeling using LiDAR and Landsat, J. Hydrol., 487, 70–86, https://doi.org/10.1016/j.jhydrol.2013.02.032, 2013.
Varhola, A., Coops, N. C., Alila, Y., and Weiler, M.: Exploration of remotely sensed forest structure and ultrasonic range sensor metrics to improve empirical snow models, Hydrol. Process., 28, 4433–4448, https://doi.org/10.1002/hyp.9952, 2014.
Vierling, K. T., Vierling, L. A., Gould, W. A., Martinuzzi, S., and Clawges, R. M.: Lidar: shedding new light on habitat characterization and modeling, Front. Ecol. Environ., 6, 90–98, https://doi.org/10.1890/070001, 2008.
Vosselman, G. and Maas, H.-G.: Airborne and terrestrial laser scanning, CRC Press, London, 213–235, 2010.
Wagner, W., Hollaus, M., Briese, C., and Ducic, V.: 3D vegetation mapping using small footprint full?waveform airborne laser scanners, Int. J. Remote Sens., 29, 1433–1452, https://doi.org/10.1080/01431160701736398, 2008.
Wallace, L., Lucieer, A., Watson, C., and Turner, D.: Development of a UAV-LiDAR System with Application to Forest Inventory, Remote Sens., 4, 1519–1543, https://doi.org/10.3390/rs4061519, 2012.
Wedding, L. M., Friedlander, A. M., McGranaghan, M., Yost, R. S., and Monaco, M. E.: Using bathymetric lidar to define nearshore benthic habitat complexity: Implications for management of reef fish assemblages in Hawaii, Remote Sens. Environ., 112, 4159–4165, https://doi.org/10.1016/j.rse.2008.01.025, 2008.
West, N., Kirby, E., Bierman, P., and Clarke, B. A.: Aspect-dependent variations in regolith creep revealed by meteoric 10Be, Geology, 42, 507–510, https://doi.org/10.1130/G35357.1, 2014.
Westoby, M. J., Brasington, J., Glasser, N. F., Hambrey, M. J., and Reynolds, J. M.: "Structure-from-Motion" photogrammetry: A low-cost, effective tool for geoscience applications, Geomorphology, 179, 300–314, https://doi.org/10.1016/j.geomorph.2012.08.021, 2012.
Wheaton, J. M., Brasington, J., Darby, S. E., and Sear, D. A.: Accounting for uncertainty in DEMs from repeat topographic surveys: improved sediment budgets, Earth Surf. Proc. Land., 35, 136–156, https://doi.org/10.1002/esp.1886, 2010a.
Wheaton, J. M., Brasington, J., Darby, S. E., Merz, J., Pasternack, G. B., Sear, D., and Vericat, D.: Linking geomorphic changes to salmonid habitat at a scale relevant to fish, River Res. Applic., 26, 469–486, https://doi.org/10.1002/rra.1305, 2010b.
Williams, G. D., Maksym, T., Kunz, C., Kimball, P., Singh, H., Wilkinson, J., Lachlan-Cope, T., Trujillo, E., Steer, A., Massom, R., Meiners, K., Heil, P., Lieser, J., and Leonard, K.: Beyond Point Measurements: Sea Ice Floes Characterized in 3-D, Eos, Transactions American Geophysical Union, 94, 69–70, https://doi.org/10.1002/2013EO070002, 2013.
Wulder, M. A., White, J. C., Nelson, R. F., Næsset, E., Ørka, H. O., Coops, N. C., Hilker, T., Bater, C. W., and Gobakken, T.: Lidar sampling for large-area forest characterization: A review, Remote Sens. Environ., 121, 196–209, https://doi.org/10.1016/j.rse.2012.02.001, 2012.
Young, A. P., Olsen, M. J., Driscoll, N., Flick, R. E., Gutierrez, R., Guza, R. T., Johnstone, E., and Kuester, F.: Comparison of Airborne and Terrestrial Lidar Estimates of Seacliff Erosion in Southern California, Photogramm. Eng. Rem. S., 76, 421–427, https://doi.org/10.14358/PERS.76.4.421, 2010.
Yu, X., Hyyppä, J., Kaartinen, H., and Maltamo, M.: Automatic detection of harvested trees and determination of forest growth using airborne laser scanning, Remote Sens. Environ., 90, 451–462, https://doi.org/10.1016/j.rse.2004.02.001, 2004.
Zellweger, F., Morsdorf, F., Purves, R., Braunisch, V., and Bollmann, K.: Improved methods for measuring forest landscape structure: LiDAR complements field-based habitat assessment, Biodivers. Conserv., 23, 289–307, https://doi.org/10.1007/s10531-013-0600-7, 2014.
Zhao, K., Popescu, S., and Nelson, R.: Lidar remote sensing of forest biomass: A scale-invariant estimation approach using airborne lasers, Remote Sens. Environ., 113, 182–196, https://doi.org/10.1016/j.rse.2008.09.009, 2009.
Download
The requested paper has a corresponding corrigendum published. Please read the corrigendum first before downloading the article.
- Article
(1728 KB) - Full-text XML
Short summary
This review's objective is to demonstrate the transformative potential of lidar by critically assessing both challenges and opportunities for transdisciplinary lidar applications in geomorphology, hydrology, and ecology. We find that using lidar to its full potential will require numerous advances, including more powerful open-source processing tools, new lidar acquisition technologies, and improved integration with physically based models and complementary observations.
This review's objective is to demonstrate the transformative potential of lidar by critically...
