Publications
NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.
2020
Abstract
Various studies investigated the fate of evaporation and the origin of precipitation. The more recent studies among them were often carried out with the help of numerical moisture tracking. Many research questions could be answered within this context, such as dependencies of atmospheric moisture transfers between different regions, impacts of land cover changes on the hydrological cycle, sustainability-related questions, and questions regarding the seasonal and interannual variability of precipitation. In order to facilitate future applications, global datasets on the fate of evaporation and the sources of precipitation are needed. Since most studies are on a regional level and focus more on the sources of precipitation, the goal of this study is to provide a readily available global dataset on the fate of evaporation for a fine-meshed grid of source and receptor cells. The dataset was created through a global run of the numerical moisture tracking model Water Accounting Model-2layers (WAM-2layers) and focused on the fate of land evaporation. The tracking was conducted on a 1.5∘×1.5∘ grid and was based on reanalysis data from the ERA-Interim database. Climatic input data were incorporated in 3- to 6-hourly time steps and represent the time period from 2001 to 2018. Atmospheric moisture was tracked forward in time and the geographical borders of the model were located at ±79.5∘ latitude. As a result of the model run, the annual, the monthly and the interannual average fate of evaporation were determined for 8684 land grid cells (all land cells except those located within Greenland and Antarctica) and provided via source–receptor matrices. The gained dataset was complemented via an aggregation to country and basin scales in order to highlight possible usages for areas of interest larger than grid cells. This resulted in data for 265 countries and 8223 basins. Finally, five types of source–receptor matrices for average moisture transfers were chosen to build the core of the dataset: land grid cell to grid cell, country to grid cell, basin to grid cell, country to country, basin to basin. The dataset is, to our knowledge, the first ready-to-download dataset providing the overall fate of evaporation for land cells of a global fine-meshed grid in monthly resolution. At the same time, information on the sources of precipitation can be extracted from it. It could be used for investigations into average annual, seasonal, and interannual sink and source regions of atmospheric moisture from land masses for most of the regions in the world and shows various application possibilities for studying interactions between people and water, such as land cover changes or human water consumption patterns. The dataset is accessible under https://doi.org/10.1594/PANGAEA.908705 (Link et al., 2019a) and comes along with example scripts for reading and plotting the data.
Abstract
No abstract has been registered
Authors
Constantin M. Zohner Lidong Mo Susanne S. Renner Jens-Christian Svenning Yann Vitasse Blas Manuel Benito De Pando Alejandro Ordonez Frederik Baumgarten Jean-François Bastin Veronica Sebald Peter B. Reich Jingjing Liang Gert-Jan Nabuurs Sergio de-Miguel Giorgio Alberti Clara Antón Fernández Radomir Balazy Urs-Beat Brändli Han Y. H. Chen Chelsea Chisholm Emil Cienciala Selvadurai Dayanandan Tom M. Fayle Lorenzo Frizzera Damiano Gianelle Andrzej M. Jagodzinski Bogdan Jaroszewicz Tommaso Jucker Sebastian Kepfer-Rojas Mohammed Latif Khan Hyun Seok Kim Henn Korjus Vivian Kvist Johannsen Diana Laarmann Mait Lang Tomasz Zawila-Niedzwiecki Pascal A. Niklaus Alain Paquette Hans Pretzsch Purabi Saikia Peter Schall Vladimír Šebeň Miroslav Svoboda Elena Tikhonova Helder Viana Chunyu Zhang Xiuhai Zhao Thomas W. CrowtherAbstract
Late-spring frosts (LSFs) affect the performance of plants and animals across the world’s temperate and boreal zones, but despite their ecological and economic impact on agriculture and forestry, the geographic distribution and evolutionary impact of these frost events are poorly understood. Here, we analyze LSFs between 1959 and 2017 and the resistance strategies of Northern Hemisphere woody species to infer trees’ adaptations for minimizing frost damage to their leaves and to forecast forest vulnerability under the ongoing changes in frost frequencies. Trait values on leaf-out and leaf-freezing resistance come from up to 1,500 temperate and boreal woody species cultivated in common gardens. We find that areas in which LSFs are common, such as eastern North America, harbor tree species with cautious (late-leafing) leaf-out strategies. Areas in which LSFs used to be unlikely, such as broad-leaved forests and shrublands in Europe and Asia, instead harbor opportunistic tree species (quickly reacting to warming air temperatures). LSFs in the latter regions are currently increasing, and given species’ innate resistance strategies, we estimate that ∼35% of the European and ∼26% of the Asian temperate forest area, but only ∼10% of the North American, will experience increasing late-frost damage in the future. Our findings reveal region-specific changes in the spring-frost risk that can inform decision-making in land management, forestry, agriculture, and insurance policy.
Authors
Jouni Siipilehto Micky Allen Urban Nilsson Andreas Brunner Saija Huuskonen Soili Haikarainen Narayanan Subramanian Clara Antón Fernández Emma Holmström Kjell Andreassen Jari HynynenAbstract
New mortality models were developed for the purpose of improving long-term growth and yield simulations in Finland, Norway, and Sweden and were based on permanent national forest inventory plots from Sweden and Norway. Mortality was modelled in two steps. The first model predicts the probability of survival, while the second model predicts the proportion of basal area in surviving trees for plots where mortality has occurred. In both models, the logistic function was used. The models incorporate the variation in prediction period length and in plot size. Validation of both models indicated unbiased mortality rates with respect to various stand characteristics such as stand density, average tree diameter, stand age, and the proportion of different tree species, Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) Karst.), and broadleaves. When testing against an independent dataset of unmanaged spruce-dominated stands in Finland, the models provided unbiased prediction with respect to stand age.
Authors
L. Genesio Ryan Bright G. Alberti A. Peressotti G. Delle Vedove G. Incerti P. Toscano M. Rinaldi O. Muller F. MigliettaAbstract
Sunlight absorbed at the Earth’s surface is re-emitted as longwave radiation. Increasing atmospheric concentrations of CO2 and other greenhouse gases trap an increasing fraction of such heat, leading to global climate change. Here we show that when a chlorophyll (Chl)-deficient soybean mutant is grown in the field, the fraction of solar-irradiance which is reflected, rather than absorbed, is consistently higher than in commercial varieties. But, while the effect on radiative forcing during the crop cycle at the scale of the individual experimental plot was found to be large (−4.1± 0.6 W m−2 ), global substitution of the current varieties with this genotype would cause a small increase in global surface albedo, resulting in a global shortwave radiative forcing of −0.003 W m−2 , corresponding to 4.4 Gt CO2eq. At present, this offsetting effect would come at the expense of reductions to yields, probably associated with different dynamic of photosynthetic response in the Chl-deficient mutant. The idea of reducing surface-driven radiative forcing by means of Chl-deficient crops therefore requires that novel high-yielding and high-albedo crops are made available soon.
Authors
Ryan BrightAbstract
No abstract has been registered
Abstract
In the Nordic countries Finland, Norway and Sweden, the most common regeneration method is planting after clearcutting and, often, mechanical site preparation (MSP). The main focus of this study is to review quantitative effects that have been reported for the five main MSP methods in terms of survival and growth of manually planted coniferous seedlings of Norway spruce (Picea abies (L.) Karst.), Scots pine (Pinus sylvestris L.) and lodgepole pine (Pinus contorta var. latifolia Engelm.) in clearcuts in these three countries. Meta analyses are used to compare the effects of MSP methods to control areas where there was no MSP and identify any relationships with temperature sum and number of years after planting. In addition, the area of disturbed soil surface and the emergence of naturally regenerated seedlings are evaluated. The MSP methods considered are patch scarification, disc trenching, mounding, soil inversion and ploughing. Studies performed at sites with predominately mineral soils (with an organic topsoil no thicker than 0.30 m), in boreal, nemo-boreal and nemoral vegetation zones in the three Fenno-Scandinavian countries are included in the review. Data from 26 experimental and five survey studies in total were compiled and evaluated. The results show that survival rates of planted conifers at sites where seedlings are not strongly affected by pine weevil (Hylobius abietis L.) are generally 80–90% after MSP, and 15–20 percent units higher than after planting in non-prepared sites. The experimental data indicated that soil inversion and potentially ploughing (few studies) give marginally greater rates than the other methods in this respect. The effects of MSP on survival seem to be independent of the temperature sum. Below 800 degree days, however, the reported survival rates are more variable. MSP generally results in trees 10–25% taller 10–15 years after planting compared to no MSP. The strength of the growth effect appears to be inversely related to the temperature sum. The compiled data may assist in the design, evaluation and comparison of possible regeneration chains, i.e. analyses of the efficiency and cost-effectiveness of multiple combinations of reforestation measures.
Abstract
No abstract has been registered
Authors
Lorène Julia Marchand Inge Dox Jožica Gričar Peter Prislan Sebastien Leys Jan Van den Bulcke Patrick Fonti Holger Lange Erik Matthysen Josep Peñuelas Paolo Zuccarini Matteo CampioliAbstract
We explored the inter-individual variability in bud-burst and its potential drivers, in homogeneous mature stands of temperate deciduous trees. Phenological observations of leaves and wood formation were performed weekly from summer 2017 to summer 2018 for pedunculate oak, European beech and silver birch in Belgium. The variability of bud-burst was correlated to previous’ year autumn phenology (i.e. the onset of leaf senescence and the cessation of wood formation) and tree size but with important differences among species. In fact, variability of bud-burst was primarily related to onset of leaf senescence, cessation of wood formation and tree height for oak, beech and birch, respectively. The inter-individual variability of onset of leaf senescence was not related to the tree characteristics considered and was much larger than the inter-individual variability in bud-burst. Multi-species multivariate models could explain up to 66% of the bud-burst variability. These findings represent an important advance in our fundamental understanding and modelling of phenology and tree functioning of deciduous tree species.
Abstract
The rapidly expanding field of machine learning (ML) provides many methodological opportunities which match very well with the needs and challenges of hydrological research. Due to extended measurement networks, more frequent automatic measurements of hydrological variables, and not the least increasing use of remote sensing products, the era of big data surely has arrived in hydrology. Process-based models are usually developed for certain spatiotemporal scales, not fitting easily to the scope of the new datasets. Automatic methods that learn patterns and generalizations have been demonstrated to be superior in many applications. The chapter provides an overview of some of the most important machine learning algorithms which have been used in the hydrological literature. It will be shown that there is no single best method among them, but instead a spectrum of methods should be utilized, from highly flexible ones to more parsimonious learning methods, depending on the specific hydrological application, research question, and data availability. Most machine learning techniques require a calibration and a validation dataset for training. As these data are usually correlated in time and space, the problem of bias-variance tradeoff arises will be discussed as a simple example. The presentation of ML algorithms, roughly following chronological order, is discussed starting with artificial neural networks through support vector machines to gradient boosting machines. As data streams increase, these and other machine learning techniques will play an ever more important role in hydrology.