Wetland maps including open water extent dynamics based on ENVISAT ASAR WS for Siberia, 2007 and 2008, links to GeoTIFFs

Wetlands store large amounts of carbon, and depending on their status and type, they release specific amounts of methane gas to the atmosphere. The connection between wetland type and methane emission has been investigated in various studies and utilized in climate change monitoring and modelling. For improved estimation of methane emissions, land surface models require information such as the wetland fraction and its dynamics over large areas. Existing datasets of wetland dynamics present the total amount of wetland (fraction) for each model grid cell, but do not discriminate the different wetland types like permanent lakes, periodically inundated areas or peatlands. Wetland types differently influence methane fluxes and thus their contribution to the total wetland fraction should be quantified. Especially wetlands of permafrost regions are expected to have a strong impact on future climate due to soil thawing. In this study ENIVSAT ASAR Wide Swath data was tested for operational monitoring of the distribution of areas with a long-term SW near 1 (hSW) in northern Russia (SW = degree of saturation with water, 1 = saturated), which is a specific characteristic of peatlands. For the whole northern Russia, areas with hSW were delineated and discriminated from dynamic and open water bodies for the years 2007 and 2008. The area identified with this method amounts to approximately 300,000 km**2 in northern Siberia in 2007. It overlaps with zones of high carbon storage. Comparison with a range of related datasets (static and dynamic) showed that hSW represents not only peatlands but also temporary wetlands associated with post-forest fire conditions in permafrost regions. Annual long-term monitoring of change in boreal and tundra environments is possible with the presented approach. Sentinel-1, the successor of ENVISAT ASAR, will provide data that may allow continuous monitoring of these wetland dynamics in the future complementing global observations of wetland fraction.

ERA-interim reanalysis debiased at FLUXNET sites

(preliminary) Exchanges of carbon, water and energy between the land surface and the atmosphere are monitored by eddy covariance technique at the ecosystem level. Currently, the FLUXNET database contains more than 500 sites registered and up to 250 of them sharing data (Free Fair Use dataset). Many modelling groups use the FLUXNET dataset for evaluating ecosystem model's performances but it requires uninterrupted time series for the meteorological variables used as input. Because original in-situ data often contain gaps, from very short (few hours) up to relatively long (some months), we develop a new and robust method for filling the gaps in meteorological data measured at site level. Our approach has the benefit of making use of continuous data available globally (ERA-interim) and high temporal resolution spanning from 1989 to today. These data are however not measured at site level and for this reason a method to downscale and correct the ERA-interim data is needed. We apply this method on the level 4 data (L4) from the LaThuile collection, freely available after registration under a Fair-Use policy. The performances of the developed method vary across sites and are also function of the meteorological variable. On average overall sites, the bias correction leads to cancel from 10% to 36% of the initial mismatch between in-situ and ERA-interim data, depending of the meteorological variable considered. In comparison to the internal variability of the in-situ data, the root mean square error (RMSE) between the in-situ data and the un-biased ERA-I data remains relatively large (on average overall sites, from 27% to 76% of the standard deviation of in-situ data, depending of the meteorological variable considered). The performance of the method remains low for the Wind Speed field, in particular regarding its capacity to conserve a standard deviation similar to the one measured at FLUXNET stations.

Geochemical composition of surface snow, ice and debris from glaciers in Norway, Nepal and New Zealand

Alpine glacier samples were collected in four contrasting regions to measure supraglacial dust and debris geochemical composition. A total of 70 surface glacier ice, snow and debris samples were collected in 2009 and 2010 in Svalbard, Norway, Nepal and New Zealand. Trace elemental abundances in snow and ice samples were measured via inductively coupled plasma mass spectrometry (ICP-MS). Supraglacial debris mineral, bulk oxide and trace element composition were determined via X-ray diffraction (XRD) and X-ray fluorescence spectroscopy (XRF). A total of 45 elements and 10 oxide compound abundances are reported. The uniform data collection procedure, analytical measurement methods and geochemical comparison techniques are used to evaluate supraglacial dust and debris composition variability in the contrasting glacier study regions. Elemental abundances revealed sea salt aerosol and metal enrichment in Svalbard, low levels of crustal dust and marine influences to southern Norway, high crustal dust and anthropogenic enrichment in the Khumbu Himalayas, and sulfur and metals attributed to quiescent degassing and volcanic activity in northern New Zealand. Rare earth element and Al/Ti elemental ratios demonstrated distinct provenance of particulates in each study region. Ca/S elemental ratio data showed seasonal denudation in Svalbard and Norway. Ablation season atmospheric particulate transport trajectories were mapped in each of the study regions and suggest provenance pathways. The in situ data presented provides first order glacier surface geochemical variability as measured from four diverse alpine glacier regions. This geochemical surface glacier data is relevant to glaciologic ablation rate understanding as well as satellite atmospheric and land-surface mapping techniques currently in development.

Maps of subsoil temperature and active layer depth of Yakutian ASSR (Autonomous Soviet Socialist Republic of the Soviet Union)

Based on the map of landscapes and permafrost conditions in Yakutia (Merzlotno-landshaftnaya karta Yakutskoi0 ASSR, Gosgeodeziya SSSR, 1991), rasterized maps of permafrost temperature and active-layer thickness of Yakutia, East Siberia were derived. The mean and standard deviation at 0.5-degree grid cell size are estimated by assigning a probability density function at 0.001-degree spatial resolution. Spatial pattern of both variables are dominated by a climatic gradient from north to south, and by mountains and the soil type distribution. Uncertainties are highest in mountains and in the sporadic permafrost zone in the south. The maps are best suited as a benchmark for land surface models which include a permafrost module.

Surface temperature measurements near Ny-Ålesund, Svalbard, Norway in 2008 with links to data files

The ground surface temperature is one of the key parameters that determine the thermal regime of permafrost soils in arctic regions. Due to remoteness of most permafrost areas, monitoring of the land surface temperature (LST) through remote sensing is desirable. However, suitable satellite platforms such as MODIS provide spatial resolutions, that cannot resolve the considerable small-scale heterogeneity of the surface conditions characteristic for many permafrost areas. This study investigates the spatial variability of summer surface temperatures of high-arctic tundra on Svalbard, Norway. A thermal imaging system mounted on a mast facilitates continuous monitoring of approximately 100 x 100 m of tundra with a wide variability of different surface covers and soil moisture conditions over the entire summer season from the snow melt until fall. The net radiation is found to be a control parameter for the differences in surface temperature between wet and dry areas. Under clear-sky conditions in July, the differences in surface temperature between wet and dry areas reach up to 10K. The spatial differences reduce strongly in weekly averages of the surface temperature, which are relevant for the soil temperature evolution of deeper layers. Nevertheless, a considerable variability remains, with maximum differences between wet and dry areas of 3 to 4K. Furthermore, the pattern of snow patches and snow-free areas during snow melt in July causes even greater differences of more than 10K in the weekly averages. Towards the end of the summer season, the differences in surface temperature gradually diminish. Due to the pronounced spatial variability in July, the accumulated degree-day totals of the snow-free period can differ by more than 60% throughout the study area. The terrestrial observations from the thermal imaging system are compared to measurements of the land surface temperature from the MODIS sensor. During periods with frequent clear-sky conditions and thus a high density of satellite data, weekly averages calculated from the thermal imaging system and from MODIS LST agree within less than 2K. Larger deviations occur when prolonged cloudy periods prevent satellite measurements. Futhermore, the employed MODIS L2 LST data set contains a number of strongly biased measurements, which suggest an admixing of cloud top temperatures. We conclude that a reliable gap filling procedure to moderate the impact of prolonged cloudy periods would be of high value for a future LST-based permafrost monitoring scheme. The occurrence of sustained subpixel variability of the summer surface temperature is a complicating factor, whose impact needs to be assessed further in conjunction with other spatially variable parameters such as the snow cover and soil properties.

Catchment characteristics and soil and water chemistry of the San Francisco basin and subbasins, Ecuador

In this study, the Mean Transit Time and Mixing Model Analysis methods are combined to unravel the runoff generation process of the San Francisco River basin (73.5 km**2) situated on the Amazonian side of the Cordillera Real in the southernmost Andes of Ecuador. The montane basin is covered with cloud forest, sub-páramo, pasture and ferns. Nested sampling was applied for the collection of streamwater samples and discharge measurements in the main tributaries and outlet of the basin, and for the collection of soil and rock water samples. Weekly to biweekly water grab samples were taken at all stations in the period April 2007-November 2008. Hydrometric data, Mean Transit Time and Mixing Model Analysis allowed preliminary evaluation of the processes controlling the runoff in the San Francisco River basin. Results suggest that flow during dry conditions mainly consists of lateral flow through the C-horizon and cracks in the top weathered bedrock layer, and that all subcatchments have an important contribution of this deep water to runoff, no matter whether pristine or deforested. During normal to low precipitation intensities, when antecedent soil moisture conditions favour water infiltration, vertical flow paths to deeper soil horizons with subsequent lateral subsurface flow contribute most to streamflow. Under wet conditions in forested catchments, streamflow is controlled by near surface lateral flow through the organic horizon. Exceptionally, saturation excess overland flow occurs. By absence of the litter layer in pasture, streamflow under wet conditions originates from the A horizon, and overland flow.

Characteristics of soil profiles from the Ouémé Upper Catchment (HVO), Térou, Benin

Soil degradation threatens agricultural production and food security in Sub-Saharan Africa. In the coming decades, soil degradation, in particular soil erosion, will become worse through the expansion of agriculture into savannah and forest and changes in climate. This study aims to improve the understanding of how land use and climate change affect the hydrological cycle and soil erosion rates at the catchment scale. We used the semi-distributed, time-continuous erosion model SWAT (Soil Water Assessment Tool) to quantify runoff processes and sheet and rill erosion in the Upper Ouémé River catchment (14500 km**2, Central Benin) for the period 1998-2005. We could then evaluate a range of land use and climate change scenarios with the SWAT model for the period 2001-2050 using spatial data from the land use model CLUE-S and the regional climate model REMO. Field investigations were performed to parameterise a soil map, to measure suspended sediment concentrations for model calibration and validation and to characterise erosion forms, degraded agricultural fields and soil conservation practices. Modelling results reveal current "hotspots" of soil erosion in the north-western, eastern and north-eastern parts of the Upper Ouémé catchment. As a consequence of rapid expansion of agricultural areas triggered by high population growth (partially caused by migration) and resulting increases in surface runoff and topsoil erosion, the mean sediment yield in the Upper Ouémé River outlet is expected to increase by 42 to 95% by 2025, depending on the land use scenario. In contrast, changes in climate variables led to decreases in sediment yield of 5 to 14% in 2001-2025 and 17 to 24% in 2026-2050. Combined scenarios showed the dominance of land use change leading to changes in mean sediment yield of -2 to +31% in 2001-2025. Scenario results vary considerably within the catchment. Current "hotspots" of soil erosion will aggravate, and a new "hotspot" will appear in the southern part of the catchment. Although only small parts of the Upper Ouémé catchment belong to the most degraded zones in the country, sustainable soil and plant management practices should be promoted in the entire catchment. The results of this study can support planning of soil conservation activities in Benin.

Macroinvertebrate abundance in montane tropical streams of varying land-use, Ecuador

Despite the importance of tropical montane cloud forest streams, studies investigating aquatic communities in these regions are rare and knowledge on the driving factors of community structure is missing. The objectives of this study therefore were to understand how land-use influences habitat structure and macroinvertebrate communities in cloud forest streams of southern Ecuador. We evaluated these relationships in headwater streams with variable land cover, using multivariate statistics to identify relationships between key habitat variables and assemblage structure, and to resolve differences in composition among sites. Results show that shading intensity, substrate type and pH were the environmental parameters most closely related to variation in community composition observed among sites. In addition, macroinvertebrate density and partly diversity was lower in forested sites, possibly because the pH in forested streams lowered to almost 5 during spates. Standard bioindicator metrics were unable to detect the changes in assemblage structure between disturbed and forested streams. In general, our results indicate that tropical montane headwater streams are complex and heterogeneous ecosystems with low invertebrate densities. We also found that some amount of disturbance, i.e. patchy deforestation, can lead at least initially to an increase in macroinvertebrate taxa richness of these streams.

Baseline characteristics of climate, permafrost, and land cover from Samoylov Island, Lena River Delta, Siberia

Samoylov Island is centrally located within the Lena River Delta at 72° N, 126° E and lies within the Siberian zone of continuous permafrost. The landscape on Samoylov Island consists mainly of late Holocene river terraces with polygonal tundra, ponds and lakes, and an active floodplain. The island has been the focus of numerous multidisciplinary studies since 1993, which have focused on climate, land cover, ecology, hydrology, permafrost and limnology. This paper aims to provide a framework for future studies by describing the characteristics of the island's meteorological parameters (temperature, radiation and snow cover), soil temperature, and soil moisture. The land surface characteristics have been described using high resolution aerial images in combination with data from ground-based observations. Of note is that deeper permafrost temperatures have increased between 0.3 to 1.3 °C over the last five years. However, no clear warming of air and active layer temperatures is detected since 1998, though winter air temperatures during recent years have not been as cold as in earlier years.

Towards convection-resolving, global atmospheric simulations with the Model for Prediction Across Scales (MPAS) v3.1: an extreme scaling experiment, links to sourcecode of model and grides

The Model for Prediction Across Scales (MPAS) is a novel set of Earth system simulation components and consists of an atmospheric model, an ocean model and a land-ice model. Its distinct features are the use of unstructured Voronoi meshes and C-grid discretisation to address shortcomings of global models on regular grids and the use of limited area models nested in a forcing data set, with respect to parallel scalability, numerical accuracy and physical consistency. This concept allows one to include the feedback of regional land use information on weather and climate at local and global scales in a consistent way, which is impossible to achieve with traditional limited area modelling approaches. Here, we present an in-depth evaluation of MPAS with regards to technical aspects of performing model runs and scalability for three medium-size meshes on four different high-performance computing (HPC) sites with different architectures and compilers. We uncover model limitations and identify new aspects for the model optimisation that are introduced by the use of unstructured Voronoi meshes. We further demonstrate the model performance of MPAS in terms of its capability to reproduce the dynamics of the West African monsoon (WAM) and its associated precipitation in a pilot study. Constrained by available computational resources, we compare 11-month runs for two meshes with observations and a reference simulation from the Weather Research and Forecasting (WRF) model. We show that MPAS can reproduce the atmospheric dynamics on global and local scales in this experiment, but identify a precipitation excess for the West African region. Finally, we conduct extreme scaling tests on a global 3?km mesh with more than 65 million horizontal grid cells on up to half a million cores. We discuss necessary modifications of the model code to improve its parallel performance in general and specific to the HPC environment. We confirm good scaling (70?% parallel efficiency or better) of the MPAS model and provide numbers on the computational requirements for experiments with the 3?km mesh. In doing so, we show that global, convection-resolving atmospheric simulations with MPAS are within reach of current and next generations of high-end computing facilities.

Robust trends of landscape dynamics in the Arctic Lena Delta with temporally dense Landsat time-series stacks, with links to GeoTIFFs

Arctic permafrost landscapes are among the most vulnerable and dynamic landscapes globally, but due to their extent and remoteness most of the landscape changes remain unnoticed. In order to detect disturbances in these areas we developed an automated processing chain for the calculation and analysis of robust trends of key land surface indicators based on the full record of available Landsat TM, ETM +, and OLI data. The methodology was applied to the ~ 29,000 km**2 Lena Delta in Northeast Siberia, where robust trend parameters (slope, confidence intervals of the slope, and intercept) were calculated for Tasseled Cap Greenness, Wetness and Brightness, NDVI, and NDWI, and NDMI based on 204 Landsat scenes for the observation period between 1999 and 2014. The resulting datasets revealed regional greening trends within the Lena Delta with several localized hot-spots of change, particularly in the vicinity of the main river channels. With a 30-m spatial resolution various permafrost-thaw related processes and disturbances, such as thermokarst lake expansion and drainage, fluvial erosion, and coastal changes were detected within the Lena Delta region, many of which have not been noticed or described before. Such hotspots of permafrost change exhibit significantly different trend parameters compared to non-disturbed areas. The processed dataset, which is made freely available through the data archive PANGAEA, will be a useful resource for further process specific analysis by researchers and land managers. With the high level of automation and the use of the freely available Landsat archive data, the workflow is scalable and transferrable to other regions, which should enable the comparison of land surface changes in different permafrost affected regions and help to understand and quantify permafrost landscape dynamics.