Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan

This study aimed to develop and evaluate data driven models for prediction of forest yield under different climate change scenarios in the Gallies forest division of district Abbottabad, Pakistan. The Random Forest (RF) and Kernel Ridge Regression (KRR) models were developed and evaluated using yield data of two species (Blue pine and Silver fir) as an objective variable and climate data (temperature, humidity, rainfall and wind speed) as predictive variables. Prediction accuracy of both the models were assessed by means of root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (r), relative root mean squared error (RRMSE), Legates-McCabe’s (LM), Willmott’s index (WI) and Nash-Sutcliffe (NSE) metrics. Overall, the RF model outperformed the KRR model due to its higher accuracy in forecasting of forest yield. The study strongly recommends that RF model should be applied in other regions of the country for prediction of forest growth and yield, which may help in the management and future planning of forest productivity in Pakistan.

Selecting the optimal fine-scale historical climate data for assessing current and future hydrological conditions

High-resolution historical climate grids are readily available and frequently used as inputs for a wide range of regional management and risk assessments including water supply, ecological processes, and as baseline for climate change impact studies that compare them to future projected conditions. Because historical gridded climates are produced using various methods, their portrayal of landscape conditions differ, which becomes a source of uncertainty when they are applied to subsequent analyses. Here we tested the range of values from five gridded climate datasets. We compared their values to observations from 1,231 weather stations, first using each dataset’s native scale, and then after each was rescaled to 270-meter resolution. We inputted the downscaled grids to a mechanistic hydrology model and assessed the spatial results of six hydrological variables across California, in 10 ecoregions and 11 large watersheds in the Sierra Nevada. PRISM was most accurate for precipitation, ClimateNA for maximum temperature, and TopoWx for minimum temperature. The single most accurate dataset overall was PRISM due to the best performance for precipitation and low air temperature errors. Hydrological differences ranged up to 70% of the average monthly streamflow with an average of 35% disagreement for all months derived from different historical climate maps. Large differences in minimum air temperature data produced differences in modeled actual evapotranspiration, snowpack, and streamflow. Areas with the highest variability in climate data, including the Sierra Nevada and Klamath Mountains ecoregions, also had the largest spread for Snow Water Equivalent (SWE), recharge and runoff.

Assessment of changes in Grenache grapevine maturity in a Mediterranean context over the last half-century

This study aims to i) evaluate some descriptive variables for Grenache berry composition over the last 50 years in the southern Rhône Valley wine-growing region and ii) analyse the impacts of climate on the main annual developmental phases of the Grenache berry to understand recent changes observed in the vineyard. A large and spatialised historical, open database from the Rhône Valley grape maturity network (1969–2020) was used to explore trends in grape profile during maturity and at harvest. Then, gridded climate data was used for processing phenological stages and ecoclimatic indicators. Significant changes in grapevine phenology and maturity dynamics were found and linked with changes to ecoclimatic indicators by carrying out a correlation analysis. Depending on the phenological phases, a limited number of ecoclimatic indicators had a significant effect on the maturity profile. The results highlight direct climate impacts on different maturity and yield variables over the last 50 years. These results provide important information about future issues in grape production and the implications for managing viticulture adaptation strategies and thus serve as a basis for assessing, prioritising and optimising technical means of maintaining current grape quality and yield.This study uses an ecoclimatic approach for examining in detail the effects of climate change on the Grenache grape variety in a Mediterranean context. The open database provides the latest information from a large network of plots and over a long period of time, making it possible to validate many results recorded in the literature. This is the first study to use this open database and we wish this database could lead to further explorations and results in viticulture and climate change issues.

Climate change extreme and seasonal toxic metal occurrence in Romanian freshwaters in the last two decades—case study and critical review

The relationship between metal levels in the Olt River ecosystem in southern Romania (measured during 2018‒2019, with 1064 sediment and water samples) and daily climate data were explored to assess the need for targeted source identification and mitigation strategies. In 2018, there was a strong relationship between the sediment Pb, As, Cd, and Hg contents and temperature (r > 0.8, p < 0.001). Mercury in sediments had a positive correlation with precipitation, and Hg in the water correlated with minimum temperature in May 2018 (p < 0.01). In July 2019, heavy metals were positively correlated with precipitation and negatively correlated with temperature. According to nonsymmetrical correspondence analysis, the four climate parameters analyzed were linearly correlated with the frequency of metal detection (p < 0.001) in both years. The statistical analysis showed strong relationships between heavy metal levels and climatic factors and attributed the discrepancies in elemental concentrations between 2018 and 2019 to climate warming.

Radiative closure and cloud effects on the radiation budget based on satellite and ship-borne observations during the Arctic summer research cruise PS106

For understanding Arctic climate change, it is critical to quantify and address uncertainties in climate data records on clouds and radiative fluxes derived from long-term passive satellite observations. A unique set of observations collected during the research vessel Polarstern PS106 expedition (28 May to 16 July 2017) by the OCEANET facility is exploited here for this purpose and compared with the CERES SYN1deg Ed. 4.1 satellite remote sensing products. Mean cloud fraction (CF) of 86.7 % for CERES and 76.1 % for OCEANET were found for the entire cruise. The difference of CF between both data sets is due to different spatial resolution and momentary data gaps due to technical limitations of the set of ship-borne instruments. A comparison of radiative fluxes during clear-sky conditions enables radiative closure for CERES products by means of independent radiative transfer simulations. Several challenges were encountered to accurately represent clouds in radiative transfer under cloudy conditions, especially for ice-containing clouds and low-level stratus (LLS) clouds. During LLS conditions, the OCEANET retrievals were in particular compromised by the altitude detection limit of 155 m of the cloud radar. Radiative fluxes from CERES show a good agreement with ship observations, having a bias (standard deviation) of −6.0 (14.6) W m−2 and 23.1 (59.3) W m−2 for the downward longwave (LW) and shortwave (SW) fluxes, respectively. Based on CERES products, mean values of the radiation budget and the cloud radiative effect (CRE) were determined for the PS106 cruise track and the central Arctic region (70°–90° N). For the period of study, the results indicate a strong influence of the SW flux in the radiation budget, which is reduced by clouds leading to a net surface CRE of −8.8 W m−2 and −9.3 W m−2 along the PS106 cruise and for the entire Arctic, respectively. The similarity of local and regional CRE supports that the PS106 cloud observations can be considered to be representative of Arctic cloudiness during early summer.

Assessment of Flood Risk Map under Climate Change RCP8.5 Scenarios in Taiwan

Climate change has exerted a significant global impact in recent years, and extreme weather-related hazards and incidents have become the new normal. For Taiwan in particular, the corresponding increase in disaster risk threatens not only the environment but also the lives, safety, and property of people. This highlights the need to develop a methodology for mapping disaster risk under climate change and delineating those regions that are potentially high-risk areas requiring adaptation to a changing climate in the future. This study provides a framework of flood risk map assessment under the RCP8.5 scenario by using different spatial scales to integrate the projection climate data of high resolution, inundation potential maps, and indicator-based approach at the end of the 21st century in Taiwan. The reference period was 1979–2003, and the future projection period was 2075–2099. High-resolution climate data developed by dynamic downscaling of the MRI-JMA-AGCM model was used to assess extreme rainfall events. The flood risk maps were constructed using two different spatial scales: the township level and the 5 km × 5 km grid. As to hazard-vulnerability(H-V) maps, users can overlay maps of their choice—such as those for land use distribution, district planning, agricultural crop distribution, or industrial distribution. Mapping flood risk under climate change can support better informed decision-making and policy-making processes in planning and preparing to intervene and control flood risks. The elderly population distribution is applied as an exposure indicator in order to guide advance preparation of evacuation plans for high-risk areas. This study found that higher risk areas are distributed mainly in northern and southern parts of Taiwan and the hazard indicators significantly increase in the northern, north-eastern, and southern regions under the RCP8.5 scenario. Moreover, the near-riparian and coastal townships of central and southern Taiwan have higher vulnerability levels. Approximately 14% of townships have a higher risk level of flooding disaster and another 3% of townships will become higher risk. For higher-risk townships, adaptation measures or strategies are suggested to prioritize improving flood preparation and protecting people and property. Such a flood risk map can be a communication tool to effectively inform decision- makers, citizens, and stakeholders about the variability of flood risk under climate change. Such maps enable decision-makers and national spatial planners to compare the relative flood risk of individual townships countrywide in order to determine and prioritize risk adaptation areas for planning spatial development policies.

Determination of ecological conditions and geographical distribution of vegetation in the Goksu basin

On the ecological conditions and distribution of vegetation in any geographical area; The mutual interaction of factors such as climate (temperature-precipitation), topography (altitude-mountain extent), soil plays an important role. In addition, these factors also determine the ecological and geographical distribution of vegetation at micro and macro levels. In this study, geographic information systems (GIS) are used as a method and here; Digital elevation model of the basin (30x30), multi-year climate data (precipitation, temperature), Erinc climate type results, soil distribution, stand distribution, plant profiles and field photographs are the materials used in the study. By processing these data, the type and distribution of vegetation in the Goksu basin were determined. According to these findings, physical factors such as altitude and the extent of the mountains have created significant differences in the precipitation and temperature distribution of the basin. This difference was clearly observed in the Erinc climate classification results, and the south of the basin presented humid and semihumid climate characteristics, and the north presented semi-arid climate characteristics. These climatic conditions also affected the soil formation and type,causing a wide distribution of non-calcareous brown soils and non-calcareous brown forest soils in the field. As a result of all these conditions, plant species showed different vertical and spatial distribution. In the part from the south of the basin to Saimbeyli, plant species such maquis, pinus brutia, pinus nigra, Cedrus libani, Abies, Juniperus are distributed, while in the north, oak species such as oak, Bromus torhentallus, Astragalus, Thymus have been distributed. Thus, factors such as climate, topography and soil played an important role in the spread of vegetation and species in the Goksu Basin.

AgMIP Regional Integrated Assessment of Agricultural Systems in Nioro, Senegal

The Agricultural Model Intercomparison and Improvement Project (AgMIP) developed protocol-based methods for Regional Integrated Assessment (RIA) of agricultural systems. These methods have been applied by teams of scientists working with regional and national stakeholders across Sub-Saharan Africa and South Asia. This paper describes the data sets that were used to implement the AgMIP RIA methods for the Nioro region of Senegal. The goal of the RIA is to assess the potential impacts of climate change on the principal agricultural system in the Senegal peanut basin comprised of peanut, millet, maize and other minor crops and livestock, and to assess adaptations of that system to climate change, under current as well as future climate and socio-economic conditions. The data sets include: the Representative Agricultural Pathways (RAPs) developed for Nioro from 2000-2050; climate data used to implement crop yield simulations; the data used to parameterize the Agricultural Production Systems sIMulator (APSIM) and the Decision Support System for Agrotechnology Transfer (DSSAT) crop models, which include historical climate data and future climate scenarios; and the data used to parameterize the Tradeoff Analysis Model for Multi-dimensional Impact Assessment (TOA-MD) economic simulation model. The analysis is structured around four AgMIP “core questions'' of climate impact assessment.