Filtyp
Drought is a natural disaster that occurs globally and can damage the environment, disrupt agricultural production and cause large economic losses. The accurate prediction of drought can effectively reduce the impacts of droughts. Deep learning methods have shown promise in drought prediction, with convolutional neural networks (CNNs) being particularly effective in handling spatial information. I
Global warming has largely advanced spring vegetation phenology, which has subsequently affected terrestrial carbon and water cycles. However, further shifts in vegetation phenology under future climate change remain unclear. We estimated the start of the growing season (SOS) by applying multiple extraction methods based on the NDVI3g data set, and then parameterized and evaluated 11 spring vegeta
The ice phenology of alpine lakes on the Tibetan Plateau (TP) is a rapid and direct responder to climate changes, and the variations in lake ice exhibit high temporal frequency characteristics. MODIS and passive microwave data are widely used to monitor lake ice changes with high temporal resolution. However, the low spatial resolutions make it difficult to effectively quantify the freeze-melt dyn
The past evolution of precipitation and atmospheric convection in the Western Pacific Warm Pool (WPWP) is critical for global climate changes but is under debate because of its forcing mechanisms. Here, we present a high temporal resolution (∼156 years) grain-size record of core MD01–2385 over the last 140 kyr, in offshore northern New Guinea to reveal sediment dynamics as a proxy for precipitatio
Remote sensing precipitation data have the characteristics of wide coverage and revealing spatiotemporal information, but their spatial resolution is low. The accuracy of the data is obviously different in different study areas and hydrometeorological conditions. This study evaluated four precipitation products in the Yellow River basin from 2001 to 2019, constructed the optimal combined product,
Incomplete wildfire combustion in boreal forests leaves behind legacy plant-soil feedbacks known to restrict plant biodiversity. These restrictions can inhibit carbon recapture after fire by limiting ecosystem transition to vegetation growth patterns that are capable of offsetting warmth-enhanced soil decomposition under climate change. Here, we field-surveyed plant regrowth conditions 2 years aft
It has been widely reported that isoprene emissions from the Arctic ecosystem have a strong temperature response. Here we identify sedges (Carex spp. and Eriophorum spp.) as key contributors to this high sensitivity using plant chamber experiments. We observe that sedges exhibit a markedly stronger temperature response compared to that of other isoprene emitters and predictions by the widely accep
Bark beetle infestations pose a significant threat to forest ecosystems, necessitating timely intervention for effective management and conservation. This study focuses on an 8,100 km2 area in southern Sweden, heavily impacted by a bark beetle outbreak triggered by a severe drought in 2018. Using spectral indices derived from Sentinel-2 data, it explores the potential of a window-based approach fo
Modern hydrological modeling frequently incorporates global remote sensing or reanalysis products for multivariate calibration. Although these datasets significantly contribute to model accuracy, the inherent uncertainties in the datasets and multivariate calibration present challenges in the modeling process. To address this issue, this study introduces an adaptive, process-wise fitting framework
Accurately representing and predicting hydrological processes in terrestrial ecosystems is crucial for understanding the impacts of global environmental change, particularly climate change. This doctoral thesis aims to advance the understanding of vegetation-hydrology interactions and provides new modelling approaches to enhance hydrological prediction abilities. The thesis is grounded in the eval
Fog makes a significant contribution to the hydrology of a wide range of important terrestrial ecosystems. The amount and frequency of fog immersion are affected by rapid ongoing anthropogenic changes but the impacts of these changes remain relatively poorly understood compared with changes in rainfall. Here, we present the design and performance of a novel experiment to actively manipulate low ly
Background: In the Sahel, one of the largest semi-arid areas in the world, pastoral livestock is the main source of protein for the local population. The quantification of herbaceous biomass in the Sahelian rangelands is of major importance since it provides food for the livestock. The main method used to monitor the biomass consists of cutting, drying, and weighting it. However, indirect methods
Drought is a multifaceted phenomenon driven by both natural climate fluctuations and human activities. Unlike traditional, slow-developing droughts, flash droughts (FDs) emerge suddenly and intensify rapidly, posing significant challenges for natural resource management. The lack of effective tools to quantify FDs exacerbates stakeholders' concerns, particularly due to the risks they pose to water
Increased global vegetation gross primary productivity (GPP) over the past decades has led to an enhanced terrestrial carbon sink, an important factor in mitigating global warming. However, the global spatiotemporal evolution of GPP trends is still under debate, largely limiting our understanding of the sustainability in terrestrial carbon sink. Here in this study, based on a dozen of long-term gl
The color of lakes is an essential indicator of the local ecological state, and the corresponding changes can reflect the physical and biochemical processes of lakes. However, worldwide changes in lake color and their drivers remain largely unknown. Here, we analyze the long-term color distributions and changes of 67,579 lakes worldwide from 1984 to 2021 by utilizing 32 million consistent satellit
This study investigates seasonal changes in near-surface wind speeds in the Arctic using the regional climate model (RCM) simulations with RCA4 driven by four global climate models (GCMs) CMIP5 under Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios. In addition, the RCM RCA-GUESS (RCA4 with interactive vegetation dynamics) is used to investigate the role of biogeophysical feedback
This study investigates the requirements for estimating CO2 emissions at the country scale using observational data from the Integrated Carbon Observation System (ICOS) atmosphere network, taking Italy as a case study. In particular, we explore the potential expansion of Italy’s current atmospheric ICOS network by identifying additional existing and future stations that would most effectively impr
Ocean evaporation, represented by latent heat flux (LE), plays a crucial role in global precipitation patterns, water cycle dynamics, and energy exchange processes. However, existing bulk methods for quantifying ocean evaporation are associated with considerable uncertainties. The maximum entropy production (MEP) theory provides a novel framework for estimating surface heat fluxes, but its applica
