scholarly journals CAMELS-AUS: hydrometeorological time series and landscape attributes for 222 catchments in Australia

2021 ◽  
Vol 13 (8) ◽  
pp. 3847-3867
Author(s):  
Keirnan J. A. Fowler ◽  
Suwash Chandra Acharya ◽  
Nans Addor ◽  
Chihchung Chou ◽  
Murray C. Peel
Keyword(s):  
Land Use ◽  
Time Series ◽  
Land Cover ◽  
Large Scale ◽  
Arid Zone ◽  
Anthropogenic Influence ◽  
Rating Curve ◽  
Uncertainty Estimates ◽  
The Usa ◽  
Landscape Attributes

Abstract. This paper presents the Australian edition of the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) series of datasets. CAMELS-AUS (Australia) comprises data for 222 unregulated catchments, combining hydrometeorological time series (streamflow and 18 climatic variables) with 134 attributes related to geology, soil, topography, land cover, anthropogenic influence and hydroclimatology. The CAMELS-AUS catchments have been monitored for decades (more than 85 % have streamflow records longer than 40 years) and are relatively free of large-scale changes, such as significant changes in land use. Rating curve uncertainty estimates are provided for most (75 %) of the catchments, and multiple atmospheric datasets are included, offering insights into forcing uncertainty. This dataset allows users globally to freely access catchment data drawn from Australia's unique hydroclimatology, particularly notable for its large interannual variability. Combined with arid catchment data from the CAMELS datasets for the USA and Chile, CAMELS-AUS constitutes an unprecedented resource for the study of arid-zone hydrology. CAMELS-AUS is freely downloadable from https://doi.org/10.1594/PANGAEA.921850 (Fowler et al., 2020a).

scholarly journals CAMELS-AUS: Hydrometeorological time series and landscape attributes for 222 catchments in Australia

2021 ◽  
Author(s):  
Keirnan J. A. Fowler ◽  
Suwash Chandra Acharya ◽  
Nans Addor ◽  
Chihchung Chou ◽  
Murray C. Peel
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Interannual Variability ◽  
Large Scale ◽  
Arid Zone ◽  
Anthropogenic Influence ◽  
Rating Curve ◽  
Uncertainty Estimates ◽  
The Usa ◽  
Landscape Attributes

Abstract. This paper presents the Australian edition of the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) series of datasets. CAMELS-AUS comprises data for 222 unregulated catchments, combining hydrometeorological timeseries (streamflow and 18 climatic variables) with 134 attributes related to geology, soil, topography, land cover, anthropogenic influence, and hydroclimatology. The CAMELS-AUS catchments have been monitored for decades (more than 85 % have streamflow records longer than 40 years) and are relatively free of large scale changes, such as significant changes in landuse. Rating curve uncertainty estimates are provided for most (75 %) of the catchments and multiple atmospheric datasets are included, offering insights into forcing uncertainty. This dataset, the first of its kind in Australia, allows users globally to freely access catchment data drawn from Australia's unique hydroclimatology, particularly notable for its large interannual variability. Combined with arid catchment data from the CAMELS datasets for the USA and Chile, CAMELS-AUS constitutes an unprecedented resource for the study of arid-zone hydrology. CAMELS-AUS is freely downloadable from https://doi.pangaea.de/10.1594/PANGAEA.921850 (Fowler et al., 2020a).

scholarly journals CAMELS-AUS: Hydrometeorological time series and landscape attributes for 222 catchments in Australia

2021 ◽  
Author(s):  
Keirnan Fowler ◽  
Suwash Chandra Acharya ◽  
Nans Addor ◽  
Chihchung Chou ◽  
Murray Peel
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Arid Zone ◽  
Anthropogenic Influence ◽  
Rating Curve ◽  
Large Sample ◽  
Large Samples ◽  
Uncertainty Estimates ◽  
The Usa ◽  
Landscape Attributes

<p>Large samples of catchments are becoming increasingly important to gain generalisable insights from hydrological research.  Such insights are facilitated by freely available large sample hydrology datasets, with one example being the CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) series of datasets.  Here we present CAMELS-AUS, the Australian edition of CAMELS. CAMELS-AUS comprises data for 222 unregulated catchments, combining hydrometeorological timeseries (streamflow and 18 climatic variables) with 134 attributes related to geology, soil, topography, land cover, anthropogenic influence, and hydroclimatology. The CAMELS-AUS catchments have been monitored for decades (more than 85 % have streamflow records longer than 40 years) and are relatively free of large scale changes, such as significant changes in landuse. Rating curve uncertainty estimates are provided for most (75 %) of the catchments and multiple atmospheric datasets are included, offering insights into forcing uncertainty. This dataset, the first of its kind in Australia, allows users globally to freely access catchment data drawn from Australia's unique hydroclimatology, particularly notable for its large interannual variability. Combined with arid catchment data from the CAMELS datasets for the USA and Chile, CAMELS-AUS constitutes an unprecedented resource for the study of arid-zone hydrology. CAMELS-AUS is freely downloadable from and the corresponding paper is available at https://essd.copernicus.org/preprints/essd-2020-228/.</p>

Spatio-temporal comparison of different approaches to derive land use and land cover change emissions by models

2021 ◽  
Author(s):  
Wolfgang Obermeier ◽  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Large Scale ◽  
Agricultural Land ◽  
Land Cover Changes ◽  
Legacy Effects ◽  
Equatorial Africa ◽  
The Usa ◽  
Spatio Temporal ◽  
Global Carbon

<p>The quantification of the net carbon flux from land use and land cover changes (f<sub>LULCC</sub>) is essential to understand the global carbon cycle, and consequently, to support climate change mitigation. However, large-scale f<sub>LULCC</sub> is not directly measurable, and can only be inferred by models, such as semi-empirical bookkeeping models, and process-based dynamic global vegetation models (DGVMs). By definition, f<sub>LULCC</sub> estimates between these two model types are not directly comparable. For example, transient DGVM-based f<sub>LULCC</sub> of the annual global carbon budget includes the so-called Loss of Additional Sink Capacity (LASC). The latter accounts for environmental impacts on the land carbon storage capacities of managed land compared to potential vegetation which is not included in bookkeeping models. Additionally, estimates of transient DGVM-based f<sub>LULCC</sub> differ from bookkeeping model estimates, since they depend on arbitrarily chosen simulation time periods and the timing of land use and land cover changes within the historic period (which includes different accumulation periods for legacy effects). However, DGVMs enable a f<sub>LULCC</sub> approximation independent of the timing of land use and land cover changes and their legacy effects by simulations run under constant pre-industrial or present-day environmental forcings.</p><p>In this study, we analyze these different DGVM-derived f<sub>LULCC</sub> definitions, under transiently changing environmental conditions and fixed pre-industrial and fixed present-day conditions, within 18 regions for twelve DGVMs and quantify their differences as well as climate- and CO<sub>2</sub>-induced components. The multi model mean under transient conditions reveals a global f<sub>LULCC</sub> of 2.0±0.6 PgC yr<sup>-1</sup> for 2009-2018, with ~40% stemming from the LASC (0.8±0.3 PgC yr<sup>-1</sup>). Within the industrial period (1850 onward), cumulative f<sub>LULCC</sub> reached 189±56 PgC with 40±15 PgC from the LASC.</p><p>Regional hotspots of high LASC values exist in the USA, China, Brazil, Equatorial Africa and Southeast Asia, which we mainly relate to deforestation for cropland. Distinct negative LASC estimates were observed in Europe (early reforestation) and from 2000 onward in the Ukraine (recultivation of post-Soviet abandoned agricultural land). Negative LASC estimates indicate that fLULCC estimates in these regions are lower in transient DGVM simulations compared to bookkeeping-approaches. By unraveling the spatio-temporal variability of the different DGVM-derived f<sub>LULCC</sub> estimates, our study calls for a harmonized attribution of model-derived f<sub>LULCC</sub>. We propose an approach that bridges bookkeeping and DGVM approaches for f<sub>LULCC</sub> estimation by adopting a mean DGVM-ensemble LASC for a defined reference period.</p>

scholarly journals Reconstructing Three Decades of Land Use and Land Cover Changes in Brazilian Biomes with Landsat Archive and Earth Engine

2020 ◽  
Vol 12 (17) ◽  
pp. 2735 ◽  
Author(s):  
Carlos M. Souza ◽  
Julia Z. Shimbo ◽  
Marcos R. Rosa ◽  
Leandro L. Parente ◽  
Ane A. Alencar ◽  
...  
Keyword(s):  
Land Use ◽  
Time Series ◽  
Land Cover ◽  
Time Series Data ◽  
Google Earth ◽  
Series Data ◽  
Data Series ◽  
Forest Conversion ◽  
Pixel Resolution ◽  
Historical Land Use

Brazil has a monitoring system to track annual forest conversion in the Amazon and most recently to monitor the Cerrado biome. However, there is still a gap of annual land use and land cover (LULC) information in all Brazilian biomes in the country. Existing countrywide efforts to map land use and land cover lack regularly updates and high spatial resolution time-series data to better understand historical land use and land cover dynamics, and the subsequent impacts in the country biomes. In this study, we described a novel approach and the results achieved by a multi-disciplinary network called MapBiomas to reconstruct annual land use and land cover information between 1985 and 2017 for Brazil, based on random forest applied to Landsat archive using Google Earth Engine. We mapped five major classes: forest, non-forest natural formation, farming, non-vegetated areas, and water. These classes were broken into two sub-classification levels leading to the most comprehensive and detailed mapping for the country at a 30 m pixel resolution. The average overall accuracy of the land use and land cover time-series, based on a stratified random sample of 75,000 pixel locations, was 89% ranging from 73 to 95% in the biomes. The 33 years of LULC change data series revealed that Brazil lost 71 Mha of natural vegetation, mostly to cattle ranching and agriculture activities. Pasture expanded by 46% from 1985 to 2017, and agriculture by 172%, mostly replacing old pasture fields. We also identified that 86 Mha of the converted native vegetation was undergoing some level of regrowth. Several applications of the MapBiomas dataset are underway, suggesting that reconstructing historical land use and land cover change maps is useful for advancing the science and to guide social, economic and environmental policy decision-making processes in Brazil.

scholarly journals Human and Climate Effects on the Hamoun Wetlands

2019 ◽  
Vol 11 (3) ◽  
pp. 609-622 ◽  
Author(s):  
Saeideh Maleki ◽  
Saeid Soltani Koupaei ◽  
Alireza Soffianian ◽  
Sassan Saatchi ◽  
Saeid Pourmanafi ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Time Series ◽  
Water Stress ◽  
Rural Areas ◽  
Human Activities ◽  
Large Scale ◽  
Upstream Region ◽  
Ecosystem Recovery ◽  
Climate Effects

Abstract Negative impacts of climate change on ecosystems have been increasing, and both the intensification and the mitigation of these impacts are strongly linked with human activities. Management and reduction of human-induced disturbances on ecosystems can mitigate the effects of climate change and enhance the ecosystem recovery process. Here, we investigate coupled human and climate effects on the wetland ecosystem of the lower Helmand basin from 1977 to 2014. Using time series climate-variable data and land-use changes from Landsat time series imagery, we compared changes in ecosystem status between the upstream and downstream regions. Results show that despite a strong and prolonged drought in the region, the upstream region of the lower Helmand basin remained dominated by agriculture, causing severe water stress on the Hamoun wetlands downstream. The loss of available water in wetlands was followed by large-scale land abandonment in rural areas, migration to the cities, and increasing unemployment and economic hardship. Our results suggest that unsustainable land-use policies in the upstream region, combined with synergistic effects of human activities and climate in lower Helmand basin, have exacerbated the effects of water stress on local inhabitants in the downstream region.

scholarly journals Characterizing Land Use/Land Cover Using Multi-Sensor Time Series from the Perspective of Land Surface Phenology

2019 ◽  
Vol 11 (14) ◽  
pp. 1677 ◽  
Author(s):  
Lan H. Nguyen ◽  
Geoffrey M. Henebry
Keyword(s):  
Land Use ◽  
Time Series ◽  
Land Cover ◽  
Land Surface ◽  
Quadratic Model ◽  
Observation Data ◽  
Land Use Land Cover ◽  
Land Surface Phenology ◽  
Land Cover Map ◽  
Phenological Metrics

Due to a rapid increase in accessible Earth observation data coupled with high computing and storage capabilities, multiple efforts over the past few years have aimed to map land use/land cover using image time series with promising outcomes. Here, we evaluate the comparative performance of alternative land cover classifications generated by using only (1) phenological metrics derived from either of two land surface phenology models, or (2) a suite of spectral band percentiles and normalized ratios (spectral variables), or (3) a combination of phenological metrics and spectral variables. First, several annual time series of remotely sensed data were assembled: Accumulated growing degree-days (AGDD) from the MODerate resolution Imaging Spectroradiometer (MODIS) 8-day land surface temperature products, 2-band Enhanced Vegetation Index (EVI2), and the spectral variables from the Harmonized Landsat Sentinel-2, as well as from the U.S. Landsat Analysis Ready Data surface reflectance products. Then, at each pixel, EVI2 time series were fitted using two different land surface phenology models: The Convex Quadratic model (CxQ), in which EVI2 = f(AGDD) and the Hybrid Piecewise Logistic Model (HPLM), in which EVI2 = f(day of year). Phenometrics and spectral variables were submitted separately and together to Random Forest Classifiers (RFC) to depict land use/land cover in Roberts County, South Dakota. HPLM RFC models showed slightly better accuracy than CxQ RFC models (about 1% relative higher in overall accuracy). Compared to phenometrically-based RFC models, spectrally-based RFC models yielded more accurate land cover maps, especially for non-crop cover types. However, the RFC models built from spectral variables could not accurately classify the wheat class, which contained mostly spring wheat with some fields in durum or winter varieties. The most accurate RFC models were obtained when using both phenometrics and spectral variables as inputs. The combined-variable RFC models overcame weaknesses of both phenometrically-based classification (low accuracy for non-vegetated covers) and spectrally-based classification (low accuracy for wheat). The analysis of important variables indicated that land cover classification for this study area was strongly driven by variables related to the initial green-up phase of seasonal growth and maximum fitted EVI2. For a deeper evaluation of RFC performance, RFC classifications were also executed with several alternative sampling scenarios, including different spatiotemporal filters to improve accuracy of sample pools and different sample sizes. Results indicated that a sample pool with less filtering yielded the most accurate predicted land cover map and a stratified random sample dataset covering approximately 0.25% or more of the study area were required to achieve an accurate land cover map. In case of data scarcity, a smaller dataset might be acceptable, but should not smaller than 0.05% of the study area.

scholarly journals Discriminating Land Use and Land Cover Classes in Brazil Based on the Annual PROBA-V 100 m Time Series

2020 ◽  
Vol 13 ◽  
pp. 3409-3420 ◽  
Author(s):  
Yosio Edemir Shimabukuro ◽  
Egidio Arai ◽  
Valdete Duarte ◽  
Andeise Cerqueira Dutra ◽  
Henrique Luis Godinho Cassol ◽  
...  
Keyword(s):  
Land Use ◽  
Time Series ◽  
Land Cover
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