scholarly journals Five years of land surface phenology in a large-scale flooding and draining manipulation in a coastal Arctic ecosystem

2017 ◽  
Author(s):  
Santonu Goswami ◽  
John A. Gamon ◽  
Sergio Vargas ◽  
Craig E. Tweedie
Keyword(s):  
Surface Water ◽  
Land Surface ◽  
Large Scale ◽  
Ground Level ◽  
The Arctic ◽  
Surface Hydrology ◽  
Experimental Conditions ◽  
Land Surface Phenology ◽  
Growing Period ◽  
Near Ir

AbstractThis study was motivated by the knowledge gap for observing the complex interplay between surface hydrology and plant phenology in arctic landscapes and was conducted as part of a large scale, multi investigator flooding and draining experiment near Barrow, Alaska (71°17’01” N, 156°35’48” W) during 2005 - 2009. Hyperspectral reflectance data were collected in the visible to near IR region of the spectrum using a robotic tram system that operated along a 300m transects during the snow free growing period between June and August, 2005-09. Interannual patterns of land-surface phenology (NDVI) unexpectedly lacked marked differences under experimental conditions. Measurement of NDVI was, however, compromised for presence of surface water. Land-surface phenology and surface water was negatively correlated, which held when scaled to a 2km by 2km MODIS subset of the study area. This result suggested that published findings of ‘greening of the Arctic’ may relate to a ‘drying of the Arctic’ i.e. reduced surface water in vegetated high-latitude landscapes where surface water is close to ground level.

scholarly journals Atmospheric Circulation Effects on Wind Speed Variability at Turbine Height

2007 ◽  
Vol 46 (4) ◽  
pp. 445-456 ◽  
Author(s):  
Katherine Klink
Keyword(s):  
Wind Speed ◽  
Pressure Gradient ◽  
Large Scale ◽  
Ground Level ◽  
The Arctic ◽  
Circulation Index ◽  
Regression Equations ◽  
Change Models ◽  
Wind Speeds ◽  
The North

Abstract Mean monthly wind speed at 70 m above ground level is investigated for 11 sites in Minnesota for the period 1995–2003. Wind speeds at these sites show significant spatial and temporal coherence, with prolonged periods of above- and below-normal values that can persist for as long as 12 months. Monthly variation in wind speed primarily is determined by the north–south pressure gradient, which captures between 22% and 47% of the variability (depending on the site). Regression on wind speed residuals (pressure gradient effects removed) shows that an additional 6%–15% of the variation can be related to the Arctic Oscillation (AO) and Niño-3.4 sea surface temperature (SST) anomalies. Wind speeds showed little correspondence with variation in the Pacific–North American (PNA) circulation index. The effect of the strong El Niño of 1997/98 on the wind speed time series was investigated by recomputing the regression equations with this period excluded. The north–south pressure gradient remains the primary determinant of mean monthly 70-m wind speeds, but with 1997/98 removed the influence of the AO increases at nearly all stations while the importance of the Niño-3.4 SSTs generally decreases. Relationships with the PNA remain small. These results suggest that long-term patterns of low-frequency wind speed (and thus wind power) variability can be estimated using large-scale circulation features as represented by large-scale climatic datasets and by climate-change models.

scholarly journals Modifying a dynamic global vegetation model for simulating large spatial scale land surface water balances

2012 ◽  
Vol 16 (8) ◽  
pp. 2547-2565 ◽  
Author(s):  
G. Tang ◽  
P. J. Bartlein
Keyword(s):  
Soil Moisture ◽  
Surface Water ◽  
Surface Runoff ◽  
Land Surface ◽  
Surface Hydrology ◽  
Large Spatial Scale ◽  
Land Surface Water ◽  
Land Surface Hydrology ◽  
Water Balances ◽  
Global Vegetation

Abstract. Satellite-based data, such as vegetation type and fractional vegetation cover, are widely used in hydrologic models to prescribe the vegetation state in a study region. Dynamic global vegetation models (DGVM) simulate land surface hydrology. Incorporation of satellite-based data into a DGVM may enhance a model's ability to simulate land surface hydrology by reducing the task of model parameterization and providing distributed information on land characteristics. The objectives of this study are to (i) modify a DGVM for simulating land surface water balances; (ii) evaluate the modified model in simulating actual evapotranspiration (ET), soil moisture, and surface runoff at regional or watershed scales; and (iii) gain insight into the ability of both the original and modified model to simulate large spatial scale land surface hydrology. To achieve these objectives, we introduce the "LPJ-hydrology" (LH) model which incorporates satellite-based data into the Lund-Potsdam-Jena (LPJ) DGVM. To evaluate the model we ran LH using historical (1981–2006) climate data and satellite-based land covers at 2.5 arc-min grid cells for the conterminous US and for the entire world using coarser climate and land cover data. We evaluated the simulated ET, soil moisture, and surface runoff using a set of observed or simulated data at different spatial scales. Our results demonstrate that spatial patterns of LH-simulated annual ET and surface runoff are in accordance with previously published data for the US; LH-modeled monthly stream flow for 12 major rivers in the US was consistent with observed values respectively during the years 1981–2006 (R2 > 0.46, p < 0.01; Nash-Sutcliffe Coefficient > 0.52). The modeled mean annual discharges for 10 major rivers worldwide also agreed well (differences < 15%) with observed values for these rivers. Compared to a degree-day method for snowmelt computation, the addition of the solar radiation effect on snowmelt enabled LH to better simulate monthly stream flow in winter and early spring for rivers located at mid-to-high latitudes. In addition, LH-modeled monthly soil moisture for the state of Illinois (US) agreed well (R2 = 0.79, p < 0.01) with observed data for the years 1984–2001. Overall, this study justifies both the feasibility of incorporating satellite-based land covers into a DGVM and the reliability of LH to simulate land-surface water balances. To better estimate surface/river runoff at mid-to-high latitudes, we recommended that LPJ-DGVM considers the effects of solar radiation on snowmelt.

scholarly journals Modeling large-scale human alteration of land surface hydrology and climate

2017 ◽  
Vol 4 (1) ◽  
Author(s):  
Yadu N. Pokhrel ◽  
Farshid Felfelani ◽  
Sanghoon Shin ◽  
Tomohito J. Yamada ◽  
Yusuke Satoh
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Surface Hydrology ◽  
Land Surface Hydrology

Remote sensing: hydrology

2009 ◽  
Vol 33 (4) ◽  
pp. 490-509 ◽  
Author(s):  
Qiuhong Tang ◽  
Huilin Gao ◽  
Hui Lu ◽  
Dennis P. Lettenmaier
Keyword(s):  
Remote Sensing ◽  
Surface Water ◽  
Land Surface ◽  
Water Budget ◽  
Current Status ◽  
State Variables ◽  
Surface Hydrology ◽  
Earth Observing System ◽  
Land Surface Water ◽  
Terrestrial Water

Satellite remote sensing is a viable source of observations of land surface hydrologic fluxes and state variables, particularly in regions where in situ networks are sparse. Over the last 10 years, the study of land surface hydrology using remote sensing techniques has advanced greatly with the launch of NASA’s Earth Observing System (EOS) and other research satellite platforms, and with the development of more sophisticated retrieval algorithms. Most of the constituent variables in the land surface water balance (eg, precipitation, evapotranspiration, snow and ice, soil moisture, and terrestrial water storage variations) are now observable at varying spatial and temporal resolutions and accuracy via remote sensing. We evaluate the current status of estimates of each of these variables, as well as river discharge, the direct estimation of which is not yet possible. Although most of the constituent variables are observable by remote sensing, attempts to close the surface water budget from remote sensing alone have generally been unsuccessful, suggesting that current generation sensors and platforms are not yet able to provide hydrologically consistent observations of the land surface water budget at any spatial scale.

scholarly journals Remote Sensing of Large River Basins

2020 ◽  
Author(s):  
Soner Uereyen ◽  
Felix Bachofer ◽  
Juliane Huth ◽  
Igor Klein ◽  
Claudia Kuenzer
Keyword(s):  
Surface Water ◽  
Spatial Scale ◽  
Land Surface ◽  
Large Scale ◽  
River Basins ◽  
Large River ◽  
Research Articles ◽  
Human Society ◽  
Large River Basins

&lt;p&gt;Irrespective of administrative boundaries, river basins are natural spatial units covering the entire land area. They provide many resources, including freshwater, which is essential for the environment and human society, as well as irrigation water and hydropower. At the same time, river basins are highly pressured i.e. by human induced environmental changes, such as deforestation, urban expansion, dam construction, as well as climate change induced sea level rise at estuarine regions and extreme events such as droughts and flooding. Therefore, monitoring of river basins is of high importance to understand their current and future state, in particular for researchers, stake holders and decision makers. However, land surface and surface water variables of many large river basins remain mostly unmonitored at basin scale. Currently, only a few inventories characterizing large scale river basins exist. Here, spatially and temporally consistent databases describing the evolution and status of large river basins are lacking. In this context, Earth observation (EO) is a potential source of spatial information providing large scale data at global scale. In this study, we provide a comprehensive overview of research articles focusing on EO-based characterization of large river basins and corresponding land surface and surface water parameters, we summarize the spatial distribution and spatial scale of investigated study areas, we analyze used sensor types and their temporal resolution, and we identify how EO can further contribute to characterization of large river basins. The results reveal that most of the reviewed research articles focus on mapping of vegetation, surface water, as well as land cover and land use properties. In addition, we found that research articles related to EO applications hardly investigate study areas at the spatial scale of large river basins. Overall, the findings of our review contribute to a better understanding of the potentials and limitations of EO-based analyses of large river basins.&lt;/p&gt;

scholarly journals Constraints and Opportunities for Detecting Land Surface Phenology in Drylands

2021 ◽  
Author(s):  
Shawn D Taylor ◽  
Dawn M Browning ◽  
Ruben A Baca ◽  
Feng Gao
Keyword(s):  
Remote Sensing ◽  
Vegetation Cover ◽  
Land Surface ◽  
Satellite Remote Sensing ◽  
Large Scale ◽  
Vegetation Index ◽  
Scale Effects ◽  
Simulated Data ◽  
Area Index ◽  
Land Surface Phenology

Land surface phenology, the tracking of seasonal productivity via satellite remote sensing, enables global scale tracking of ecosystem processes, but its utility is limited in some areas. In dryland ecosystems low vegetation cover can cause the growing season vegetation index (VI) to be indistinguishable from the dormant season VI, making phenology extraction impossible. Here, using simulated data and multi-temporal UAV imagery of a desert shrubland, we explore the feasibility of detecting LSP with respect to fractional vegetation cover, plant functional types, and VI uncertainty. We found that plants with distinct VI signals, such as deciduous shrubs with a high leaf area index, require at least 30-40\% fractional cover on the landscape to consistently detect pixel level phenology with satellite remote sensing. Evergreen plants, which have lower VI amplitude between dormant and growing seasons, require considerably higher cover and can have undetectable phenology even with 100\% vegetation cover. We also found that even with adequate cover, biases in phenological metrics can still exceed 20 days, and can never be 100\% accurate due to VI uncertainty from shadows, sensor view angle, and atmospheric interference. Many dryland areas do not have detectable LSP with the current suite of satellite based sensors. Our results showed the feasibility of dryland LSP studies using high-resolution UAV imagery, and highlighted important scale effects due to within canopy VI variation. Future sensors with sub-meter resolution will allow for identification of individual plants and are the best path forward for studying large scale phenological trends in drylands.

scholarly journals A New Lightweight Convolutional Neural Network for Multi-Scale Land Surface Water Extraction from GaoFen-1D Satellite Images

2021 ◽  
Vol 13 (22) ◽  
pp. 4576
Author(s):  
Yueming Duan ◽  
Wenyi Zhang ◽  
Peng Huang ◽  
Guojin He ◽  
Hongxiang Guo
Keyword(s):  
Neural Network ◽  
Surface Water ◽  
Spatial Resolution ◽  
Land Surface ◽  
Large Scale ◽  
The Other ◽  
Water Extraction ◽  
Mapping Accuracy ◽  
Multi Scale ◽  
Land Surface Water

Mapping land surface water automatically and accurately is closely related to human activity, biological reproduction, and the ecological environment. High spatial resolution remote sensing image (HSRRSI) data provide extensive details for land surface water and gives reliable data support for the accurate extraction of land surface water information. The convolutional neural network (CNN), widely applied in semantic segmentation, provides an automatic extraction method in land surface water information. This paper proposes a new lightweight CNN named Lightweight Multi-Scale Land Surface Water Extraction Network (LMSWENet) to extract the land surface water information based on GaoFen-1D satellite data of Wuhan, Hubei Province, China. To verify the superiority of LMSWENet, we compared the efficiency and water extraction accuracy with four mainstream CNNs (DeeplabV3+, FCN, PSPNet, and UNet) using quantitative comparison and visual comparison. Furthermore, we used LMSWENet to extract land surface water information of Wuhan on a large scale and produced the land surface water map of Wuhan for 2020 (LSWMWH-2020) with 2m spatial resolution. Random and equidistant validation points verified the mapping accuracy of LSWMWH-2020. The results are summarized as follows: (1) Compared with the other four CNNs, LMSWENet has a lightweight structure, significantly reducing the algorithm complexity and training time. (2) LMSWENet has a good performance in extracting various types of water bodies and suppressing noises because it introduces channel and spatial attention mechanisms and combines features from multiple scales. The result of land surface water extraction demonstrates that the performance of LMSWENet exceeds that of the other four CNNs. (3) LMSWENet can meet the requirement of high-precision mapping on a large scale. LSWMWH-2020 can clearly show the significant lakes, river networks, and small ponds in Wuhan with high mapping accuracy.

scholarly journals Large scale climate oscillation impacts on temperature, precipitation and land surface phenology in Central Asia

2018 ◽  
Vol 13 (6) ◽  
pp. 065018 ◽  
Author(s):  
Kirsten M de Beurs ◽  
Geoffrey M Henebry ◽  
Braden C Owsley ◽  
Irina N Sokolik
Keyword(s):  
Central Asia ◽  
Land Surface ◽  
Large Scale ◽  
Land Surface Phenology ◽  
Climate Oscillation

Defining Homogeneous Drought Zones Based on Soil Moisture across Japan and Teleconnections with Large-Scale Climate Signals

2021 ◽  
Author(s):  
Ke Shi ◽  
Yoshiya Touge ◽  
So Kazama
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Large Scale ◽  
Southern Oscillation ◽  
Wavelet Coherence ◽  
The Arctic ◽  
Surface Model ◽  
Drought Prediction ◽  
Climate Signals ◽  
Wavelet Coherence Analysis

Abstract Droughts are widespread disasters worldwide and are concurrently influenced by multiple large-scale climate signals. This is particularly true over Japan, where drought has strong heterogeneity due to multiple factors such as monsoon, topography, and ocean circulations. Regional heterogeneity poses challenges for drought prediction and management. To overcome this difficulty, this study provides a comprehensive analysis of teleconnection between climate signals and homogeneous drought zones over Japan. First, droughts are characterized by simulated soil moisture from land surface model during 1958-2012. The Mclust toolkit, distinct empirical orthogonal function, and wavelet coherence analysis are used, respectively, to investigate the homogeneous drought zone, principal component of each homogeneous zone, and teleconnection between climate signals and drought. Results indicate that nine homogeneous drought zones with different characteristics are defined and quantified. Among these nine zones, zone-1 is dominated by extreme drought events. Zone-2 and zone-6 are typical representatives of spring droughts, while zone-7 is wet for most of the period. The Hokkaido region is divided into wetter zone-4 and drier zone-9. Zone-3, zone-5 and zone-8 are distinguished by the topography. The analyses also reveal almost nine zones have a high level of homogeneity, with more than 60% explained variance. Also, these nine zones are dominated by different large-scale climate signals: the Arctic Oscillation has the strongest impact on zone-1, zone-7, and zone-8; the influence of the North Atlantic Oscillation on zone-3, zone-4, and zone-6 is significant; zone-2 and zone-9 are both dominated by the Pacific Decadal Oscillation; El Niño-Southern Oscillation dominates zone-5. The results will be valuable for drought management and drought prevention.

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