Continued decline of global soil moisture content, with obvious soil stratification and regional difference

Projected increase in droughts over the Arabian Peninsula and associated uncertainties

The Arabian Peninsula (AP) has been reported to experience increasing drought in recent decades. With this background, this study evaluates best performing Climate Model Intercomparison Project 6 (CMIP6) Global Climate Models (GCMs) for historical (1985-2014) simulations and future drought projections across the AP until 2100, using the standardized precipitation index (SPI) and standardized precipitation-evapotranspiration index (SPEI). We assess uncertainties from model differences, scenarios, timescales, and methods. Our findings reveal the limitations of most models in reproducing precipitation, leading to uncertainties in SPI projections. Nonetheless, CMIP6-GCMs better capture the increase in the current-day potential evapotranspiration (PET) and therefore the SPEI, which is dominated by PET. The Hargreaves based PET is identified as the most suitable method for SPEI projections. The rate of increase in PET surpasses that of precipitation in all scenarios by fivefold. Consequently, SPEI indicates projected increase in future droughts with greater changes in SSP585 and SSP370 scenario compared to SSP245 and SSP126. In general, drought will exacerbate in the AP despite uncertainties from indices selection, scenarios, and inter-model variability, followed by methods and timescales which predominantly impacts drought magnitude. Over findings emphasize the need for more reliable representation of the AP precipitation in climate models for improved drought projection over the AP to enhance planning and adaptation strategies.

Automatic Cleaning of Time Series Data in Rural Internet of Things Ecosystems That Use Nomadic Gateways

A serious limitation to the deployment of IoT solutions in rural areas may be the lack of available telecommunications infrastructure enabling the continuous collection of measurement data. A nomadic computing system, using a UAV carrying an on-board gateway, can handle this; it leads, however, to a number of technical challenges. One is the intermittent collection of data from ground sensors governed by weather conditions for the UAV measurement missions. Therefore, each sensor should be equipped with software that allows for the cleaning of collected data before transmission to the fly-over nomadic gateway from erroneous, misleading, or otherwise redundant data-to minimize their volume and fit them in the limited transmission window. This task, however, may be a barrier for end devices constrained in several ways, such as limited energy reserve, insufficient computational capability of their MCUs, and short transmission range of their RAT modules. In this paper, a comprehensive approach to these problems is proposed, which enables the implementation of an anomaly detector in time series data with low computational demand. The proposed solution uses the analysis of the physics of the measured signals and is based on a simple anomaly model whose parameters can be optimized using popular AI techniques. It was validated during a full 10-month vegetation period in a real Rural IoT system deployed by Gdańsk Tech.

The role of large reservoirs in drought and flood disaster risk mitigation: A case of the Yellow River Basin

The acceleration of water cycle processes in the context of global warming will exacerbate the frequency and intensity of extreme events and predispose to drought and flood disasters (DFD). The Yellow River Basin (YRB) is one of the basins with significant and sensitive impacts of climate change, comprehensive assessment and prediction of its DFD risk are of great significance for ecological protection and high-quality development. This study first constructed an evaluation index system for drought disaster risk and flood disaster risk based on hazard, vulnerability, exposure and the role of large reservoirs. Secondly, the weights of each evaluation index are established by the analytic hierarchy process. Finally, based on the four-factor theory of disasters, an evaluation model of DFD risk indicators is established. The impact of large reservoirs on DFD risk in the YRB is analyzed with emphasis. The results show that from 1990 to 2020, the drought disaster risk in the YRB is mainly distributed in the source area of the Yellow River and the northwest region (11.26-15.79 %), and the flood disaster risk is mainly distributed in the middle and lower reaches (30.04-31.29 %). Compared to scenarios without considering large reservoirs, the area at risk of high drought and high flood is reduced by 45.45 %, 44.22 % and 31.29 % in 2000, 2010 and 2020, respectively. Large reservoirs in the YRB play an important role in mitigating DFD risk, but their role is weakened with the enhancement of the emission scenario. Under the influence of different scenario models, the DFD risk in the YRB in 2030 and 2060 will increase, and the area of high drought and high flood risk in the middle and upper reaches of the basin will increase by 0.26-25.15 %. Therefore, the YRB should play the role of large reservoirs in DFD risk defense in its actions to cope with future climate change, while improving non-engineering measures such as early warning and emergency management systems to mitigate the impacts of disasters.

Metal nanoparticles and pesticides under global climate change: Assessing the combined effects of multiple abiotic stressors on soil microbial ecosystems

The soil is a vital resource that hosts many microorganisms crucial in biogeochemical cycles and ecosystem health. However, human activities such as the use of metal nanoparticles (MNPs), pesticides and the impacts of global climate change (GCCh) can significantly affect soil microbial communities (SMC). For many years, pesticides and, more recently, nanoparticles have contributed to sustainable agriculture to ensure continuous food production to sustain the significant growth of the world population and, therefore, the demand for food. Pesticides have a recognized pest control capacity. On the other hand, nanoparticles have demonstrated a high ability to improve water and nutrient retention, promote plant growth, and control pests. However, it has been reported that their accumulation in agricultural soils can also adversely affect the environment and soil microbial health. In addition, climate change, with its variations in temperature and extreme water conditions, can lead to drought and increased soil salinity, modifying both soil conditions and the composition and function of microbial communities. Abiotic stressors can interact and synergistically or additively affect soil microorganisms, significantly impacting soil functioning and the capacity to provide ecosystem services. Therefore, this work reviewed the current scientific literature to understand how multiple stressors interact and affect the SMC. In addition, the importance of molecular tools such as metagenomics, metatranscriptomics, proteomics, or metabolomics in the study of the responses of SMC to exposure to multiple abiotic stressors was examined. Future research directions were also proposed, focusing on exploring the complex interactions between stressors and their long-term effects and developing strategies for sustainable soil management. These efforts will contribute to the preservation of soil health and the promotion of sustainable agricultural practices.

Rapid summer Russian Arctic sea-ice loss enhances the risk of recent Eastern Siberian wildfires

In recent decades boreal wildfires have occurred frequently over eastern Siberia, leading to increased emissions of carbon dioxide and pollutants. However, it is unclear what factors have contributed to recent increases in these wildfires. Here, using the data we show that background eastern Siberian Arctic warming (BAW) related to summer Russian Arctic sea-ice decline accounts for ~79% of the increase in summer vapor pressure deficit (VPD) that controls wildfires over eastern Siberia over 2004-2021 with the remaining ~21% related to internal atmospheric variability associated with changes in Siberian blocking events. We further demonstrate that Siberian blocking events are occurring at higher latitudes, are more persistent and have larger zonal scales and slower decay due to smaller meridional potential vorticity gradients caused by stronger BAW under lower sea-ice. These changes lead to more persistent, widespread and intense high-latitude warming and VPD, thus contributing to recent increases in eastern Siberian high-latitude wildfires.

Reviewing the essential roles of remote phenotyping, GWAS and explainable AI in practical marker-assisted selection for drought-tolerant winter wheat breeding

Marker-assisted selection (MAS) plays a crucial role in crop breeding improving the speed and precision of conventional breeding programmes by quickly and reliably identifying and selecting plants with desired traits. However, the efficacy of MAS depends on several prerequisites, with precise phenotyping being a key aspect of any plant breeding programme. Recent advancements in high-throughput remote phenotyping, facilitated by unmanned aerial vehicles coupled to machine learning, offer a non-destructive and efficient alternative to traditional, time-consuming, and labour-intensive methods. Furthermore, MAS relies on knowledge of marker-trait associations, commonly obtained through genome-wide association studies (GWAS), to understand complex traits such as drought tolerance, including yield components and phenology. However, GWAS has limitations that artificial intelligence (AI) has been shown to partially overcome. Additionally, AI and its explainable variants, which ensure transparency and interpretability, are increasingly being used as recognised problem-solving tools throughout the breeding process. Given these rapid technological advancements, this review provides an overview of state-of-the-art methods and processes underlying each MAS, from phenotyping, genotyping and association analyses to the integration of explainable AI along the entire workflow. In this context, we specifically address the challenges and importance of breeding winter wheat for greater drought tolerance with stable yields, as regional droughts during critical developmental stages pose a threat to winter wheat production. Finally, we explore the transition from scientific progress to practical implementation and discuss ways to bridge the gap between cutting-edge developments and breeders, expediting MAS-based winter wheat breeding for drought tolerance.

Estimation of spatial distribution of soil moisture on steep hillslopes by state-space approach (SSA)

Soil moisture is a critical variable that quantifies precipitation, floods, droughts, irrigation, and other factors with regard to decision-making and risk evaluation. An accurate prediction of soil moisture dynamics is important for soil and environmental management. However, the complex topographic condition and land use in hilly and mountainous areas make it a challenge to monitor and predict soil moisture dynamics in these areas. In this study, the determinants of soil moisture variability were determined by structural equation modeling, and then an attempt was made to estimate the spatial distribution of soil moisture content on steep hillslope using the state-space method. Herein, soil moisture at different depths (0-10, 10-20, and 20-30 cm) was monitored by portable time-domain reflectometer (TDR) along this hillslope (100 m × 180 m). It showed that the spatial variability of soil moisture decreased with increasing soil wetness, primarily in the topsoil (0-10 cm). Soil moisture was correlated with elevation (r = 0.28, 0.50, and 0.28), capillary porosity (r = 0.06, 0.37, and 0.28), soil texture (r for Clay: 0.20, 0.24, and 0.16; r for Sand: -0.25, -0.18, and -0.28), organic carbon (r = -0.31, -0.08, and 0.10) and land use (r = -0.01, 0.28, and 0.24) under different conditions (dry, moderate, and wet). Among these determinants, elevation made direct contributions to soil moisture variation, especially under moderate conditions, while land use made its impacts by altering soil texture. It is encouraging that the state-space approach yielded precise and cost-effective predictions of soil moisture dynamics along this steep hillslope since it gives the minimum root-mean-square error (RMSE) and Akaike information criterion (AIC). Moreover, soil organic carbon (AIC = -4.497, RMSE = 0.104, R2 = 0.899), rock fragment contents (AIC = -4.366, RMSE = 0.111, R2 = 0.878), and elevation (AIC = -3.693, RMSE = 0.156, R2 = 0.629) effectively anticipated the spatial distribution of soil moisture under dry, moderate, and wet conditions, respectively. This study confirms the efficacy of the state-space approach as a valuable tool for soil moisture prediction in areas characterized by complex and spatially heterogeneous conditions.

Amazon savannization and climate change are projected to increase dry season length and temperature extremes over Brazil

Land use change and atmospheric composition, two drivers of climate change, can interact to affect both local and remote climate regimes. Previous works have considered the effects of greenhouse gas buildup in the atmosphere and the effects of Amazon deforestation in atmospheric general circulation models. In this study, we investigate the impacts of the Brazilian Amazon savannization and global warming in a fully coupled ocean-land-sea ice-atmosphere model simulation. We find that both savannization and global warming individually lengthen the dry season and reduce annual rainfall over large tracts of South America. The combined effects of land use change and global warming resulted in a mean annual rainfall reduction of 44% and a dry season length increase of 69%, when averaged over the Amazon basin, relative to the control run. Modulation of inland moisture transport due to savannization shows the largest signal to explain the rainfall reduction and increase in dry season length over the Amazon and Central-West. The combined effects of savannization and global warming resulted in maximum daily temperature anomalies, reaching values of up to 14 °C above the current climatic conditions over the Amazon. Also, as a consequence of both climate drivers, both soil moisture and surface runoff decrease over most of the country, suggesting cascading negative future impacts on both agriculture production and hydroelectricity generation.