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      Forecasting dryland vegetation condition months in advance through satellite data assimilation

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          Abstract

          Dryland ecosystems are characterised by rainfall variability and strong vegetation response to changes in water availability over a range of timescales. Forecasting dryland vegetation condition can be of great value in planning agricultural decisions, drought relief, land management and fire preparedness. At monthly to seasonal time scales, knowledge of water stored in the system contributes more to predictability than knowledge of the climate system state. However, realising forecast skill requires knowledge of the vertical distribution of moisture below the surface and the capacity of the vegetation to access this moisture. Here, we demonstrate that contrasting satellite observations of water presence over different vertical domains can be assimilated into an eco-hydrological model and combined with vegetation observations to infer an apparent vegetation-accessible water storage (hereafter called accessible storage). Provided this variable is considered explicitly, skilful forecasts of vegetation condition are achievable several months in advance for most of the world’s drylands.

          Abstract

          Forecasting drought and its impact on agriculture and ecosystems is challenged by a lack of knowledge of vegetation access to deep moisture. Here the authors show that combining vegetation and water storage remote sensing can be used to infer this knowledge, allowing drought impact forecasts months in advance.

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          A global analysis of root distributions for terrestrial biomes

          Understanding and predicting ecosystem functioning (e.g., carbon and water fluxes) and the role of soils in carbon storage requires an accurate assessment of plant rooting distributions. Here, in a comprehensive literature synthesis, we analyze rooting patterns for terrestrial biomes and compare distributions for various plant functional groups. We compiled a database of 250 root studies, subdividing suitable results into 11 biomes, and fitted the depth coefficient β to the data for each biome (Gale and Grigal 1987). β is a simple numerical index of rooting distribution based on the asymptotic equation Y=1-βd, where d = depth and Y = the proportion of roots from the surface to depth d. High values of β correspond to a greater proportion of roots with depth. Tundra, boreal forest, and temperate grasslands showed the shallowest rooting profiles (β=0.913, 0.943, and 0.943, respectively), with 80-90% of roots in the top 30 cm of soil; deserts and temperate coniferous forests showed the deepest profiles (β=0.975 and 0.976, respectively) and had only 50% of their roots in the upper 30 cm. Standing root biomass varied by over an order of magnitude across biomes, from approximately 0.2 to 5 kg m-2. Tropical evergreen forests had the highest root biomass (5 kg m-2), but other forest biomes and sclerophyllous shrublands were of similar magnitude. Root biomass for croplands, deserts, tundra and grasslands was below 1.5 kg m-2. Root/shoot (R/S) ratios were highest for tundra, grasslands, and cold deserts (ranging from 4 to 7); forest ecosystems and croplands had the lowest R/S ratios (approximately 0.1 to 0.5). Comparing data across biomes for plant functional groups, grasses had 44% of their roots in the top 10 cm of soil. (β=0.952), while shrubs had only 21% in the same depth increment (β=0.978). The rooting distribution of all temperate and tropical trees was β=0.970 with 26% of roots in the top 10 cm and 60% in the top 30 cm. Overall, the globally averaged root distribution for all ecosystems was β=0.966 (r 2=0.89) with approximately 30%, 50%, and 75% of roots in the top 10 cm, 20 cm, and 40 cm, respectively. We discuss the merits and possible shortcomings of our analysis in the context of root biomass and root functioning.
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            Increasing drought under global warming in observations and models

            Aiguo Dai (2013)
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              The gravity recovery and climate experiment: Mission overview and early results

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                Author and article information

                Contributors
                siyuan.tian@anu.edu.au
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                28 January 2019
                28 January 2019
                2019
                : 10
                : 469
                Affiliations
                [1 ]ISNI 0000 0001 2180 7477, GRID grid.1001.0, Research School of Earth Sciences, , Australian National University, ; Canberra, 2601 ACT Australia
                [2 ]ISNI 0000 0001 2180 7477, GRID grid.1001.0, Fenner School of Environment & Society, , Australian National University, ; Canberra, 2601 ACT Australia
                Author information
                http://orcid.org/0000-0002-7688-0249
                http://orcid.org/0000-0002-6508-7480
                http://orcid.org/0000-0001-7192-5391
                http://orcid.org/0000-0003-3056-4109
                Article
                8403
                10.1038/s41467-019-08403-x
                6349931
                30692539
                5f898920-16c9-4782-b223-22edbf29f071
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 26 April 2018
                : 8 January 2019
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