Forest fluxes and mortality response to drought: model description (ORCHIDEE-CAN-NHA r7236) and evaluation at the Caxiuanã drought experiment

Extreme drought events in Amazon forests are expected to become more frequent and more intense with climate change, threatening ecosystem function and carbon balance. Yet large uncertainties exist on the resilience of this ecosystem to drought. A better quantification of tree hydraulics and mortality processes is needed to anticipate future drought effects on Amazon forests. Most state-of-the-art dynamic global vegetation models are relatively poor in their mechanistic description of these complex processes. Here, we implement a mechanistic plant hydraulic module within the ORCHIDEE-CAN-NHA r7236 land surface model to simulate the percentage loss of conductance (PLC) and changes in water storage among organs via a representation of the water potentials and vertical water flows along the continuum from soil to roots, stems and leaves. The model was evaluated against observed seasonal variability in stand-scale sap flow, soil moisture and productivity under both control and drought setups at the Caxiuanã throughfall exclusion field experiment in eastern Amazonia between 2001 and 2008. A relationship between PLC and tree mortality is built in the model from two empirical parameters, the cumulated duration of drought exposure that triggers mortality, and the mortality fraction in each day exceeding the exposure. Our model captures the large biomass drop in the year 2005 observed 4 years after throughfall reduction, and produces comparable annual tree mortality rates with observation over the study period. Our hydraulic architecture module provides promising avenues for future research in assimilating experimental data to parameterize mortality due to drought-induced xylem dysfunction. We also highlight that species-based (isohydric or anisohydric) hydraulic traits should be further tested to generalize the model performance in predicting the drought risks.</p

Investigating patterns of pond and lake distributions to enhance the modeling of future Arctic surface inundation

Permafrost acts as an impermeable subsurface in Arctic lowland landscapes. This hydrological barrier results in carbon-rich, water-saturated soils as well as many ponds and lakes. The rapidly warming Arctic climate very likely will affect the surface inundation in Arctic lowlands due to changes in precipitation, evapotranspiration, and permafrost degradation. Drying and wetting of the surface may occur in different regions and potentially alter the exchange of energy and carbon between the surface and the atmosphere. With increased permafrost thaw, for example, water may drain to deeper soil layers or drainage maybe enhanced due to newly forming drainage networks. Melting ground ice and subsequent inundation, on the other hand, may enhance formation of new ponds and wet areas. The current distribution of ponds and lakes in the Arctic is the result of complex interactions between climate, ground ice volume, topography, age and sediment characteristics. Because lake formation and growth processes occur at spatial scales orders of magnitude below those of the resolution for global or pan-arctic models land surface models, statistical representations of lake size distributions and other properties to inform such processes in future models are needed that can be related to macroscopic landcape properties. This study proposes basic observationally-constrained relationships to enhance the modeling of future Arctic surface inundation. We mapped ponds and lakes in 21 circum-arctic sites representing different permafrost-soil landscapes, i.e., physiographic regions with similar surface geology, regional climate, and biomes. We used high-resolution optical and radar satellite imagery with spatial resolutions of 4 m or better to create detailed water body maps and derive representative probability density functions (PDF). PDFs of ponds and lakes vary little within the same ecoregion. Significant differences, however, do occur between landscapes. We used regional permafrost-soil landscape maps of Alaska, Canada, and Siberia to upscale the water body distributions to the circum-arctic. We here present regional distribution parameters, i.e. pond and lake fractions as well as PDF moments (mean surface area, standard deviation, and skewness) and their uncertainties. Younger landscapes, that developed in the early Holocene exhibit very skewed water body distributions. These landscapes are dominated by many ponds and feature only very few large lakes. Older landscapes, on the other hand, show more larger lakes but also a higher variability in pond and lake size. For lakes smaller than 5*10⁵ m², PDFs change in a regular fashion across all sites: Relationships between mean surface area and standard deviation show a linear behaviour whereas the correlation between mean and skewness log-normal. We hypothesize that these relationships are an expression of pond and lake growth and/or lake formation in the landscapes and discuss the potential of the observed patterns to improve predictions of future distributions of Arctic ponds and lakes

Size Distributions of Arctic Waterbodies Reveal Consistent Relations in Their Statistical Moments in Space and Time

Arctic lowlands are characterized by large numbers of small waterbodies, which are known to affect surface energy budgets and the global carbon cycle. Statistical analysis of their size distributions has been hindered by the shortage of observations at sufficiently high spatial resolutions. This situation has now changed with the high-resolution (<5 m) circum-Arctic Permafrost Region Pond and Lake (PeRL) database recently becoming available. We have used this database to make the first consistent, high-resolution estimation of Arctic waterbody size distributions, with surface areas ranging from 0.0001 km2 (100 m2) to 1 km2. We found that the size distributions varied greatly across the thirty study regions investigated and that there was no single universal size distribution function (including power-law distribution functions) appropriate across all of the study regions. We did, however, find close relationships between the statistical moments (mean, variance, and skewness) of the waterbody size distributions from different study regions. Specifically, we found that the spatial variance increased linearly with mean waterbody size (R2 = 0.97, p < 2.2e-16) and that the skewness decreased approximately hyperbolically. We have demonstrated that these relationships (1) hold across the 30 Arctic study regions covering a variety of (bio)climatic and permafrost zones, (2) hold over time in two of these study regions for which multi-decadal satellite imagery is available, and (3) can be reproduced by simulating rising water levels in a high-resolution digital elevation model. The consistent spatial and temporal relationships between the statistical moments of the waterbody size distributions underscore the dominance of topographic controls in lowland permafrost areas. These results provide motivation for further analyses of the factors involved in waterbody development and spatial distribution and for investigations into the possibility of using statistical moments to predict future hydrologic dynamics in the Arctic

Size Distributions of Arctic Waterbodies Reveal Consistent Relations in Their Statistical Moments in Space and Time

Arctic lowlands are characterized by large numbers of small waterbodies, which are known to affect surface energy budgets and the global carbon cycle. Statistical analysis of their size distributions has been hindered by the shortage of observations at sufficiently high spatial resolutions. This situation has now changed with the high-resolution (&lt;5 m) circum-Arctic Permafrost Region Pond and Lake (PeRL) database recently becoming available. We have used this database to make the first consistent, high-resolution estimation of Arctic waterbody size distributions, with surface areas ranging from 0.0001 km2 (100 m2) to 1 km2. We found that the size distributions varied greatly across the thirty study regions investigated and that there was no single universal size distribution function (including power-law distribution functions) appropriate across all of the study regions. We did, however, find close relationships between the statistical moments (mean, variance, and skewness) of the waterbody size distributions from different study regions. Specifically, we found that the spatial variance increased linearly with mean waterbody size (R2 = 0.97, p &lt; 2.2e-16) and that the skewness decreased approximately hyperbolically. We have demonstrated that these relationships (1) hold across the 30 Arctic study regions covering a variety of (bio)climatic and permafrost zones, (2) hold over time in two of these study regions for which multi-decadal satellite imagery is available, and (3) can be reproduced by simulating rising water levels in a high-resolution digital elevation model. The consistent spatial and temporal relationships between the statistical moments of the waterbody size distributions underscore the dominance of topographic controls in lowland permafrost areas. These results provide motivation for further analyses of the factors involved in waterbody development and spatial distribution and for investigations into the possibility of using statistical moments to predict future hydrologic dynamics in the Arctic

PeRL:A circum-Arctic Permafrost Region Pond and Lake database

Ponds and lakes are abundant in Arctic permafrost lowlands. They play an important role in Arctic wetland ecosystems by regulating carbon, water, and energy fluxes and providing freshwater habitats. However, ponds, i.e., waterbodies with surface areas smaller than 1. 0 × 104ĝ€m2, have not been inventoried on global and regional scales. The Permafrost Region Pond and Lake (PeRL) database presents the results of a circum-Arctic effort to map ponds and lakes from modern (2002-2013) high-resolution aerial and satellite imagery with a resolution of 5ĝ€m or better. The database also includes historical imagery from 1948 to 1965 with a resolution of 6ĝ€m or better. PeRL includes 69 maps covering a wide range of environmental conditions from tundra to boreal regions and from continuous to discontinuous permafrost zones. Waterbody maps are linked to regional permafrost landscape maps which provide information on permafrost extent, ground ice volume, geology, and lithology. This paper describes waterbody classification and accuracy, and presents statistics of waterbody distribution for each site. Maps of permafrost landscapes in Alaska, Canada, and Russia are used to extrapolate waterbody statistics from the site level to regional landscape units. PeRL presents pond and lake estimates for a total area of 1. 4 × 106ĝ€km2 across the Arctic, about 17ĝ€% of the Arctic lowland ( &lt; ĝ€300ĝ€mĝ€a.s.l.) land surface area. PeRL waterbodies with sizes of 1. 0 × 106ĝ€m2 down to 1. 0 × 102ĝ€m2 contributed up to 21ĝ€% to the total water fraction. Waterbody density ranged from 1. 0 × 10 to 9. 4 × 101ĝ€kmĝ'2. Ponds are the dominant waterbody type by number in all landscapes representing 45-99ĝ€% of the total waterbody number. The implementation of PeRL size distributions in land surface models will greatly improve the investigation and projection of surface inundation and carbon fluxes in permafrost lowlands. Waterbody maps, study area boundaries, and maps of regional permafrost landscapes including detailed metadata are available at https://doi.pangaea.de/10.1594/PANGAEA.868349

Multiple equilibria on planet Dune: climate-vegetation dynamics on a sandy planet

International audienceWe study the interaction between climate and vegetation on an ideal water-limited planet, focussing on the influence of vegetation on the global water cycle. We introduce a simple mechanistic box model consisting in a two-layer representation of the atmosphere and a two-layer soil scheme. The model includes the dynamics of vegetation cover, and the main physical processes of energy and water exchange among the different components. With a realistic choice of parameters, this model displays three stable equilibria, depending on the initial conditions of soil water and vegetation cover. The system reaches a hot and dry state for low values of initial water content and/or vegetation cover, while we observe a wet, vegetated state with mild surface temperature when the system starts from larger initial values of both variables. The third state is a cold desert, where plants transfer enough water to the atmosphere to start a weaker, evaporation-dominated water cycle before they wilt. These results indicate that in this system vegetation plays a central role in transferring water from the soil to the atmosphere and trigger a hydrologic cycle. The model adopted here can also be used to conceptually illustrate processes and feedbacks affecting the water cycle in water-limited continental areas on Earth

A stochastic model for the polygonal tundra based on Poisson-Voronoi Diagrams

Sub-grid and small scale processes occur in various ecosystems and landscapes (e.g., periglacial ecosystems, peatlands and vegetation patterns). These local heterogeneities are often important or even fundamental to better understand general and large scale properties of the system, but they are either ignored or poorly parameterized in regional and global models. Because of their small scale, the underlying generating processes can be well explained and resolved only by local mechanistic models, which, on the other hand, fail to consider the regional or global influences of those features. A challenging problem is then how to deal with these interactions across different spatial scales, and how to improve our understanding of the role played by local soil heterogeneities in the climate system. This is of particular interest in the northern peatlands, because of the huge amount of carbon stored in these regions. Land-atmosphere greenhouse gas fluxes vary dramatically within these environments. Therefore, to correctly estimate the fluxes a description of the small scale soil variability is needed. Applications of statistical physics methods could be useful tools to upscale local features of the landscape, relating them to large-scale properties. To test this approach we considered a case study: the polygonal tundra. Cryogenic polygons, consisting mainly of elevated dry rims and wet low centers, pattern the terrain of many subartic regions and are generated by complex crack-and-growth processes. Methane, carbon dioxide and water vapor fluxes vary largely within the environment, as an effect of the small scale processes that characterize the landscape. It is then essential to consider the local heterogeneous behavior of the system components, such as the water table level inside the polygon wet centers, or the depth at which frozen soil thaws. We developed a stochastic model for this environment using Poisson-Voronoi diagrams, which is able to upscale statistical large scale properties of the system taking into account the main processes within the single polygons. We compare the results with available recent field studies and demonstrate that the model captures the main statistical characteristics of the landscape and describes its dynamical behavior under climatic forcings (e.g., precipitation and evapotranspiration). We analyze seasonal dynamics of water table variations and the landscape response under different scenarios of precipitation income. We upscale methane fluxes by using a simple idealized model for methane emission. We also investigate hydraulic interconnectivities and large-scale drainage through percolation properties and thresholds in the Voronoi-Deleaunay graph. The model captures the main statistical characteristics of the landscape topography, such as polygon area and surface properties as well as the water balance. This approach enables us to statistically relate large-scale properties of the system taking into account the main small-scale processes within the single polygons. Overall, the general agreement between field measurements and model results suggests that such statistical methods and simple parameterizations, if accurately tuned with field data, could be a powerful way to consider spatial scale interactions in such heterogenous and complex environments. http://www.earth-syst-dynam-discuss.net/3/453/2012/esdd-3-453-2012.htm

It’s all about water: from small scale hydrologic processes in ice wedge polygonal tundra and thermokarst lakes to larger scale river runoff (Lena River Delta, Siberia)

The Lena River Delta in Northern Yakutia forms one of the largest deltas in the Arctic and its catchment area (2 430 000 km2) is one of the largest in the whole of Eurasia. The Lena River distributes water and sediment in four main channels before discharging in total about 30 km3 of water through the delta into the Arctic Ocean every year and its discharge has been observed to be increasing. The goal of this presentation is to characterize the hydrologic processes that are strongly affected by a transient climate component- the permafrost. Permafrost plays a major role for storage and release of water to rivers and surface and subsurface water bodies. Conversely, the coupled water and heat fluxes in the atmosphere and below ground have a marked influence on the permafrost’s thermal regime. Our study site, the Lena River Delta, is also one of the coldest and driest places on Earth, with mean annual air temperatures of about -13 °C, a large annual air temperature range of about 44 °C and summer precipitation usually less than 150 mm. Very cold continuous permafrost of about −8.6 °C (11 m depth) underlays the area between about 400 and 600 m below surface and since 2006 the permafrost has warmed than 1 °C at 10.7 m. Roughly half of the land surface is dominated by wet surfaces, such as polygons, ponds and thermokarst lakes. This contribution summarizes past and ongoing research on hydrologic processes across spatial scales, from microtopographic processes of polygonal tundra to regional scale deltaic processes to assess short and long term changes in water fluxes. We quantify unfrozen water in soils, streams and river discharges and water bodies’ storage at larger scales. Water bodies were mapped using optical and radar satellite data with resolutions of 4 m or better, Landsat-5 TM at 30 m and the MODIS water mask at 250 m resolution. Ponds, i. e. water bodies with surface are smaller than 104 m, make over 95 % of the total number of water bodies and are not resolved in Landsat-scale land cover classifications. Ponds are generally well mixed and experience high water temperatures up to 23 °C during the summer and are, therefore, hotspots for biological activity and CO2 emission. The ponds in the study area freeze completely in winter, whereas the deeper thermokarst lakes do not freeze to the bottom, with implications for coupling of the permafrost to the atmosphere. These deep thermokarst lakes are thermally stratified during winter and reach maximum water temperatures of up to 19 °C during summer. The summer water balance at the catchment scale was found to be mainly controlled by vertical fluxes (precipitation and evapotranspiration). On the other hand, redistribution of storage water due to lateral fluxes takes place within the microtopography of polygonal tundra. The long-term summer storage (precipitation minus evapotranspiration) from 1958-2011 indicates a reasonably balance on the polygonal tundra with an average surplus of 5 mm, but it is also characterized by high interannual variability due to precipitation input. During negative water balance years where evapotranspiration exceeds precipitation, shallower water bodies dry out. The extent of wetlands and water bodies will shift with changes in vertical water fluxes as well as permafrost warming and thaw. Thus, water bodies can serve as sentinels of environmental change and we present applicable remote-sensing observations and upscaling method

Poisson-Voronoi Diagrams and the Polygonal Tundra

Sub-grid and small scale processes occur in various ecosystems and landscapes (e.g., periglacial ecosystems, peatlands and vegetation patterns). These local heterogeneities are often important or even fundamental to better understand general and large scale properties of the system, but they are either ignored or poorly parameterized in regional and global models. Because of their small scale, the underlying generating processes can be well explained and resolved only by local mechanistic models, which, on the other hand, fail to consider the regional or global influences of those features. A challenging problem is then how to deal with these interactions across different spatial scales, and how to improve our understanding of the role played by local soil heterogeneities in the climate system. This is of particular interest in the northern peatlands, because of the huge amount of carbon stored in these regions. Land-atmosphere greenhouse gas fluxes vary dramatically within these environments. Therefore, to correctly estimate the fluxes a description of the small scale soil variability is needed. Applications of statistical physics methods could provide useful tools to upscale local features of the landscape, relating them to large-scale properties. To test this approach we considered a case study: the polygonal tundra. Cryogenic polygons, consisting mainly of elevated dry rims and wet low centers, pattern the terrain of many subartic regions and are generated by complex crack-and-growth processes. Methane, carbon dioxide and water vapor fluxes vary largely within the environment, as an effect of the small scale processes that characterize the landscape. It is then essential to consider the local heterogeneous behavior of the system components, such as the water table level inside the polygon wet centers, or the depth at which frozen soil thaws. We developed a stochastic model for this environment using Poisson-Voronoi diagrams, which are able to upscale statistical large scale properties of the system taking into account the main processes within the single polygons. We then compare the results with available recent field studies and demonstrate that the model captures the main statistical characteristics of the landscape and describes its dynamical behavior under climatic forcings (e.g., precipitation and evapotranspiration). In particular, we model and analyze water table dynamics, which directly influences greenhouse gas emissions and changes in the system. Hydraulic interconnectivities and large-scale drainage may also be investigated through percolation properties and thresholds in the Voronoi-Deleaunay graph

Upscaling microtopography in high-latitude peatlands

A challenging problem in climate modeling is how to deal with interactions and feedbacks across a multiplicity of spatial scales, and how to improve our understanding of the role played by local soil heterogeneities in the climate system. This is of particular interest in northern peatlands, because of the large amount of carbon stored in the soil. Greenhouse gas (GHG) fluxes, such as methane, carbon dioxide and water vapor, vary largely within the environment, as an effect of the small scale processes that characterize the landscape. It is then essential to consider the local heterogeneous behavior of the system components in order to properly estimate water and carbon balances. We propose a novel method to fill the scaling gap from local mechanistic models to large scale mean field approximations. We developed a surface model for peatlands working at the landscape scale, which is able to show the impact of surface microtopography in modeling greenhouse gas fluxes. We tuned our landscape-scale model with data from a peatland site in the Komi Republic of Russia. We simulate surface microtopography and hydrology, and we couple it to a process-based model for methane emissions from the soil (Walter and Heiman, 1996). By partitioning the space in smaller subunits and then analyzing the statistical properties of the tiling, we are able to resolve the small scale processes and investigate their effects at larger scales. We not only investigate the influence of the hummocky surface on GHG emissions, but we are also able to simulate how complex hydrological interactions happening within the system at a subgrid scale affect the landscape-scale land-atmosphere GHG fluxes. We force our model climatology with data from the CMIP5 experiments. Future projections use forcing from the RCP 8.5 scenario, in order to investigate the impact of microrelieves on the future carbon cycle. We also explore potential dynamical feedbacks with the atmospheric water cycle and energy balance by coupling the surface model with an idealized box model of the atmosphere