Spatio-temporal prediction of site index based on forest inventories and climate change scenarios

2012 ◽  
Vol 279 ◽  
pp. 97-111 ◽  
Author(s):  
Arne Nothdurft ◽  
Thilo Wolf ◽  
Andre Ringeler ◽  
Jürgen Böhner ◽  
Joachim Saborowski
Keyword(s):  
Climate Change ◽  
Site Index ◽  
Climate Change Scenarios ◽  
Temporal Prediction ◽  
Forest Inventories ◽  
Spatio Temporal

Linking climate, gross primary productivity, and site index across forests of the western United States

2011 ◽  
Vol 41 (8) ◽  
pp. 1710-1721 ◽  
Author(s):  
Aaron R. Weiskittel ◽  
Nicholas L. Crookston ◽  
Philip J. Radtke
Keyword(s):  
Climate Change ◽  
Growth Models ◽  
Gross Primary Production ◽  
Site Index ◽  
Nonparametric Model ◽  
Climate Change Scenarios ◽  
Geographic Scale ◽  
Potential Productivity ◽  
The One ◽  
The Relationship

Assessing forest productivity is important for developing effective management regimes and predicting future growth. Despite some important limitations, the most common means for quantifying forest stand-level potential productivity is site index (SI). Another measure of productivity is gross primary production (GPP). In this paper, SI is compared with GPP estimates obtained from 3-PG and NASA’s MODIS satellite. Models were constructed that predict SI and both measures of GPP from climate variables. Results indicated that a nonparametric model with two climate-related predictor variables explained over 68% and 76% of the variation in SI and GPP, respectively. The relationship between GPP and SI was limited (R2 of 36%–56%), while the relationship between GPP and climate (R2 of 76%–91%) was stronger than the one between SI and climate (R2 of 68%–78%). The developed SI model was used to predict SI under varying expected climate change scenarios. The predominant trend was an increase of 0–5 m in SI, with some sites experiencing reductions of up to 10 m. The developed model can predict SI across a broad geographic scale and into the future, which statistical growth models can use to represent the expected effects of climate change more effectively.

scholarly journals Soil salinization under different climate change scenarios: A global scale analysis

2021 ◽  
Author(s):  
Nima Shokri ◽  
Amirhossein Hassani ◽  
Adisa Azapagic
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Soil Salinity ◽  
Global Scale ◽  
Soil Salinization ◽  
Water Evaporation ◽  
Climate Change Scenarios ◽  
Spatio Temporal ◽  
Salt Affected Soils

<p>Population growth and climate change is projected to increase the pressure on land and water resources, especially in arid and semi-arid regions. This pressure is expected to affect all driving mechanisms of soil salinization comprising alteration in soil hydrological balance, sea salt intrusion, wet/dry deposition of wind-born saline aerosols — leading to an increase in soil salinity. Soil salinity influences soil stability, bio-diversity, ecosystem functioning and soil water evaporation (1). It can be a long-term threat to agricultural activities and food security. To devise sustainable action plan investments and policy interventions, it is crucial to know when and where salt-affected soils occur. However, current estimates on spatio-temporal variability of salt-affected soils are majorly localized and future projections in response to climate change are rare. Using Machine Learning (ML) algorithms, we related the available measured soil salinity values (represented by electrical conductivity of the saturated paste soil extract, EC<sub>e</sub>) to some environmental information (or predictors including outputs of Global Circulation Models, soil, crop, topographic, climatic, vegetative, and landscape properties of the sampling locations) to develop a set of data-driven predictive tools to enable the spatio-temporal predictions of soil salinity. The outputs of these tools helped us to estimate the extent and severity of the soil salinity under current and future climatic patterns at different geographical levels and identify the salinization hotspots by the end of the 21<sup>st</sup> century in response to climate change. Our analysis suggests that a soil area of 11.73 Mkm<sup>2</sup> located in non-frigid zones has been salt-affected in at least three-fourths of the 1980 - 2018 period (2). At the country level, Brazil, Peru, Sudan, Colombia, and Namibia were estimated to have the highest rates of annual increase in the total area of soils with an EC<sub>e</sub> ≥ 4 dS m<sup>-1</sup>. Additionally, the results indicate that by the end of the 21<sup>st</sup> century, drylands of South America, southern and Western Australia, Mexico, southwest United States, and South Africa will be the salinization hotspots (compared to the 1961 - 1990 period). The results of this study could inform decision-making and contribute to attaining the United Nation’s Sustainable Development Goals for land and water resources management.</p><p>1. Shokri-Kuehni, S.M.S., Raaijmakers, B., Kurz, T., Or, D., Helmig, R., Shokri, N. (2020). Water Table Depth and Soil Salinization: From Pore-Scale Processes to Field-Scale Responses. Water Resour. Res., 56, e2019WR026707. https://doi.org/ 10.1029/2019WR026707</p><p>2. Hassani, A., Azapagic, A., Shokri, N. (2020). Predicting Long-term Dynamics of Soil Salinity and Sodicity on a Global Scale, Proc. Nat. Acad. Sci., 117, 52, 33017–33027. https://doi.org/10.1073/pnas.2013771117</p>

Subnational violent conflict forecasts for sub-Saharan Africa, 2015–65, using climate-sensitive models

2017 ◽  
Vol 54 (2) ◽  
pp. 175-192 ◽  
Author(s):  
Frank DW Witmer ◽  
Andrew M Linke ◽  
John O’Loughlin ◽  
Andrew Gettelman ◽  
Arlene Laing
Keyword(s):  
Climate Change ◽  
Violent Conflict ◽  
Sub Saharan Africa ◽  
Population Projections ◽  
High Fertility ◽  
Climate Change Scenarios ◽  
Political Rights ◽  
Monthly Data ◽  
Spatio Temporal ◽  
Sub Saharan

How will local violent conflict patterns in sub-Saharan Africa evolve until the middle of the 21st century? Africa is recognized as a particularly vulnerable continent to environmental and climate change since a large portion of its population is poor and reliant on rain-fed agriculture. We use a climate-sensitive approach to model sub-Saharan African violence in the past (geolocated to the nearest settlements) and then forecast future violence using sociopolitical factors such as population size and political rights (governance), coupled with temperature anomalies. Our baseline model is calibrated using 1° gridded monthly data from 1980 to 2012 at a finer spatio-temporal resolution than existing conflict forecasts. We present multiple forecasts of violence under alternative climate change scenarios (optimistic and current global trajectories), of political rights scenarios (improvement and decline), and population projections (low and high fertility). We evaluate alternate shared socio-economic pathways (SSPs) by plotting violence forecasts over time and by detailed mapping of recent and future levels of violence by decade. The forecasts indicate that a growing population and rising temperatures will lead to higher levels of violence in sub-Saharan Africa if political rights do not improve. If political rights continue to improve at the same rate as observed over the last three decades, there is reason for optimism that overall levels of violence will hold steady or even decline in Africa, in spite of projected population increases and rising temperatures.

Simulating the spatio-temporal variability of streamflow response to climate change scenarios in a mesoscale basin

2004 ◽  
Vol 293 (1-4) ◽  
pp. 255-269 ◽  
Author(s):  
G Drogue ◽  
L Pfister ◽  
T Leviandier ◽  
A El Idrissi ◽  
J.-F Iffly ◽  
...  
Keyword(s):  
Climate Change ◽  
Temporal Variability ◽  
Climate Change Scenarios ◽  
Streamflow Response ◽  
Spatio Temporal

scholarly journals Spatio-Temporal Analysis of Historical and Future Climate Data in the Texas High Plains

2020 ◽  
Vol 12 (15) ◽  
pp. 6036
Author(s):  
Yong Chen ◽  
Gary W. Marek ◽  
Thomas H. Marek ◽  
Dana O. Porter ◽  
Jerry E. Moorhead ◽  
...  
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
General Circulation Models ◽  
Quality Data ◽  
High Plains ◽  
Climate Change Scenarios ◽  
Climate Data ◽  
Air Temperatures ◽  
Texas High Plains ◽  
Spatio Temporal

Agricultural production in the Texas High Plains (THP) relies heavily on irrigation and is susceptible to drought due to the declining availability of groundwater and climate change. Therefore, it is meaningful to perform an overview of possible climate change scenarios to provide appropriate strategies for climate change adaptation in the THP. In this study, spatio-temporal variations of climate data were mapped in the THP during 2000–2009, 2050–2059, and 2090–2099 periods using 14 research-grade meteorological stations and 19 bias-corrected General Circulation Models (GCMs) under representative concentration pathway (RCP) scenarios RCP 4.5 and 8.5. Results indicated different bias correction methods were needed for different climatic parameters and study purposes. For example, using high-quality data from the meteorological stations, the linear scaling method was selected to alter the projected precipitation while air temperatures were bias corrected using the quantile mapping method. At the end of the 21st century (2090–2099) under the severe CO2 emission scenario (RCP 8.5), the maximum and minimum air temperatures could increase from 3.9 to 10.0 °C and 2.8 to 8.4 °C across the entire THP, respectively, while precipitation could decrease by ~7.5% relative to the historical (2000–2009) observed data. However, large uncertainties were found according to 19 GCM projections.

scholarly journals Climate-sensitive site index models for Norway

2016 ◽  
Vol 46 (6) ◽  
pp. 794-803 ◽  
Author(s):  
Clara Antón-Fernández ◽  
Blas Mola-Yudego ◽  
Lise Dalsgaard ◽  
Rasmus Astrup
Keyword(s):  
Climate Change ◽  
Site Index ◽  
National Forest Inventory ◽  
Climatic Conditions ◽  
Site Quality ◽  
Climate Change Scenarios ◽  
Climate Data ◽  
Intergovernmental Panel ◽  
Potential Component ◽  
Using Data

The present study aims to develop biologically sound and parsimonious site index models for Norway to predict changes in site index (SI) under different climatic conditions. The models are constructed using data from the Norwegian National Forest Inventory and climate data from the Norwegian meteorological institute. Site index was modeled using the potential modifier functional form, with a potential component (POT) depending on site quality classes and two modifier components (MOD): temperature and moisture. Each of these modifiers was based on a portfolio of candidate variables. The best model for spruce-dominated stands included temperature as modifier (R2= 0.56). In the case of pine- and deciduous-dominated stands, the best models included both modifiers (R2= 0.40 and 0.54 for temperature and moisture, respectively). We illustrate the use of the models by analyzing the possible shift in SI for year 2100 under one (RCP4.5) of the benchmark scenarios adopted by the Intergovernmental Panel on Climate Change for its fifth assessment report. The models presented can be valuable for evaluating the effect of climate change scenarios in Norwegian forests.

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