Search Results (57)

Accurate precipitation nowcasting is essential for short-term forecasting, but it remains challenging due to the dynamic nature of rainfall mechanisms. This study implements and evaluates two schemes for improving precipitation nowcasting: (1) an advection–diffusion scheme and (2) an advection–diffusion scheme integrated with the linear theory of orographic precipitation. These schemes are implemented into the Local Analysis and Prediction System (LAPS) to produce short-term precipitation forecasts and applied to a case study involving a rainfall event over the Athens metropolitan area in Greece. These schemes are compared against the default LAPS nowcasting module based on a first-order advection scheme (control). The first-order advection scheme, while computationally efficient, lacks the ability to simulate rainfall field evolution due to its exclusion of diffusion processes and orographic effects, leading to inaccurate nowcasts. To address these limitations, the advection–diffusion scheme is introduced to capture the precipitation evolution, and the third scheme integrates the linear theory of orographic precipitation to account for the influence of topography. Preliminary results show improvements in the spatiotemporal distribution of the nowcasted precipitation. These findings suggest that incorporating diffusion and orographic effects can enhance the accuracy of short-term precipitation forecasts, though further evaluation across diverse meteorological events is needed to confirm general applicability. Full article

Gopher tortoises (Gopherus polyphemus) are threatened burrowing keystone ecosystem engineers indigenous to open uplands in the Southeastern United States. Perils to the species include habitat degradation and fragmentation, anthropogenic disturbances, predation, parasites, and disease. Problems are severe in the SE Florida study area due to coastal urban sprawl, confining the tortoises in small, scattered, unnatural pockets subject to novel stresses. The annual South Florida February to ca. late May dry season became a severe drought in 2025. The present project centered on the broad question of foodplant resilience through the drought. The tortoise-grazed areas host three dominant groundcover species, in order of abundance: non-native Richardia grandiflora, native grass Paspalum setaceum, and non-native sedge Fimbristylis cymosa. Key findings were as follows: 1. The most abundant and most-often grazed species, Richardia grandiflora, when tortoises were excluded, expanded despite the drought (from 39% to 49.5% mean coverage). Under combined drought and grazing, that species cover decreased slightly (42.5% to 39.4%). Tortoise-free, Paspalum setaceum declined slightly during the drought (32.7% to 27.1% mean coverage), and showed mixed results with little net effect exposed to drought and to grazing. Never observed to be grazed during the study, Fimbristylis cymosa formed a nearly monospecific lawn in a sizeable portion of the study area. During the drought, it mostly browned, retaining green rosette centers, and tortoise exclusion showed no discernable effect. With transition to late spring, however, with increased rainfall, tortoise exclusion allowed rapid competition from grasses among the Fimbristylis rosettes. Adjacent unenclosed grazing, by contrast, maintained the Fimbristylis lawn without increase in grass coverage. Conclusions are that the two chief “fodder” species, Richardia grandiflora and Paspalum setaceum, were robust to drought and grazing. The introduced Fimbristylis cymosa appears to be facilitated by selective grazing-suppressing grass competitors. Full article

This study employs k-means clustering to analyze local organizations responsible for land improvement in Greece, identifying four distinct groups with consistent geographic patterns but divergent financial and operational characteristics. By integrating unsupervised machine learning with spatial analysis, the research offers a novel perspective on irrigation water pricing and cost recovery. The findings reveal that organizations located on islands, despite high water costs due to limited rainfall and geographic isolation, tend to achieve relatively strong financial performance, indicating the presence of adaptive mechanisms that could inform broader policy strategies. In contrast, organizations managing extensive irrigable land or large volumes of water frequently show poor cost recovery, challenging assumptions about economies of scale and revealing inefficiencies in pricing or governance structures. The spatial coherence of the clusters underscores the importance of geography in shaping institutional outcomes, reaffirming that environmental and locational factors can offer greater explanatory power than algorithmic models alone. This highlights the need for water management policies that move beyond uniform national strategies and instead reflect regional climatic, infrastructural, and economic variability. The study suggests several policy directions, including targeted infrastructure investment, locally calibrated water pricing models, and performance benchmarking based on successful organizational practices. Although grounded in the Greek context, the methodology and insights are transferable to other European and Mediterranean regions facing similar water governance challenges. Recognizing the limitations of the current analysis—including gaps in data consistency and the exclusion of socio-environmental indicators—the study advocates for future research incorporating broader variables and international comparative approaches. Ultimately, it supports a hybrid policy framework that combines data-driven analysis with spatial intelligence to promote sustainability, equity, and financial viability in agricultural water management. Full article

From the urban sustainability perspective and from the steps essential for regulating/balancing the microclimate features, the creation and maintenance of urban green spaces (UGS) are vital. The UGS include vegetation of any kind in urban areas such as parks, gardens, vertical gardens, trees, hedge plants, and roadside plants. This “urban green infrastructure” is a cost-effective and energy-saving means for ensuring sustainable development. The relationship between urban landscape patterns and microclimate needs to be sufficiently understood to make urban living ecologically, economically, and ergonomically justifiable. In this regard, information on diverse patterns of land use intensity or spatial growth is essential to delineate both beneficial and adverse impacts on the urban environment. With this background, the present study aimed to address water requirements of UGS plants and trees during the non-rainy months from Panaji city (Koppen classification: Am) situated on the west coast of India, which receives over 2750 mm of rainfall, almost exclusively during June–September. During the remaining eight months, irrigating the plants in the UGS becomes a serious necessity. In this regard, the daily water requirements (DWR) of 34 tree species, several species of hedge plants, and lawn areas were estimated using standard methods that included primary (field survey-based) and secondary (inputs from key-informant survey questionnaires) data collection to address water requirement of the UGS vegetation. Monthly evapotranspiration rates (ET_o) were derived in this study and were used for calculating the water requirement of the UGS. The day–night average ET_o was over 8 mm, which means that there appears to be an imminent water stress in most UGS of the city in particular during the January–May period. The DWR in seven gardens of Panaji city were ~25 L/tree, 6.77 L/m² hedge plants, and 4.57 L/m² groundcover (=lawns). The water requirements for the entire UGS in Panaji city were calculated. Using this information, the estimated total daily volume of water required for the entire UGS of 1.86 km² in Panaji city is 7.10 million liters. The current supply from borewells of 64,200 L vis a vis means that the ET_o-based DWR of 184,086 L is at a shortage of over 2.88 times and is far inadequate for meeting the daily demand of hedge plants and lawn/groundcover. Full article

Urban green spaces are important components of city spaces that are vulnerable to degradation in soil–water–climate processes. This vulnerability is exacerbated by current climate change and park usage density. This study examines the dynamics of soil water repellency in the topsoils of selected urban parks in Berlin, aiming to assess the relationships between weather conditions, soil water content, and soil water repellency. This study is based on monthly sampled soils from spots originating from three selected parks—Fischtal Park, Stadtpark Steglitz, and Rudolph-Wilde Park—between September 2022 and October 2023; two of the parks are exclusively rainwater fed, and one is irrigated during summer months. For each sample soil, water repellency persistence and severity were analyzed. Time series analysis was conducted including soil water content. In addition, the total organic carbon content (TOC) and sample texture were analyzed. The results show that the rainfall amount, number of dry days, and maximum temperature during different time intervals prior to the sampling date predominantly control the variation in the soil water repellency via the soil water content. Soil water repellency variations observed appear more event-related than monthly or seasonal, as rainfall is evenly distributed through the years without a distinct dry or wet season in Berlin. The non-repellency of the soil samples was usually observed when the associated water content was increased, which is linked to high cumulative rainfall and short dry periods. Low rainfall amounts and long dry periods in summer result in the re-establishment of the soil water repellency, possibly affecting increased runoff generation and soil erosion risk. Spatially, the repellency properties were observed at locations under healthy vegetation cover, while soils located on the upper slope locations and on the pathways lacked repellency characteristics. Full article

Accurately selecting an appropriate probability distribution model is a critical challenge when predicting extreme rainfall in arid and semi-arid regions, especially in countries with diverse climatic conditions. This study presents a comprehensive methodology for evaluating rainfall probability distributions across Pakistan, and aims to create a probabilistic zoning map that could serve as a valuable resource to inform the development of strategies for efficient water resource management and improved flood resilience in diverse climatic and geographic conditions. Precipitation data from the Pakistan Meteorological Department (PMD) over 42 years were compared with CHIRPS, confirming their accuracy. Nine probability distributions were assessed, with five models—log Pearson type-III (LP3), Weibull (W2), log normal (LN2), Generalized Extreme Value (GEV), and gamma (GAM)—deemed most suitable for the region’s climatic variability. The spatial applicability of these distributions was identified as follows: LP3 (30%), LN2 (30%), W2 (15%), GEV (10%), and GAM (15%). The central and southern regions of Punjab were predominantly characterized by LN2, while GAM was prevalent in the coastal areas of Sindh. Balochistan exhibited a heterogeneous distribution of W2, LP3, and LN2, while the mountainous Gilgit-Baltistan region was exclusively associated with GEV. Khyber Pakhtunkhwa demonstrated a mix of GEV and LP3 distributions. Beyond provincial variations, distinct patterns emerged: GEV dominated high-altitude, cold-temperate areas; LP3 was common in mountainous regions with variable temperature profiles; and W2 was prevalent along the flood-prone Indus River. This study provides a robust framework for region-specific disaster preparedness and contributes to sustainable development initiatives by offering tailored strategies for managing extreme rainfall events across Pakistan’s diverse climatic zones. Full article

Rhamdiopsis krugi is a highly specialized troglobitic (exclusively subterranean) catfish endemic to the phreatic water bodies of twelve caves located within two separated metasedimentary basins in the region of Chapada Diamantina, Bahia state, Brazil. This species is included in the List of Endangered Fauna of Brazil, under the Vulnerable category—VU. In general, troglobites have different strategies for searching for food and reproductive partners, as well as unique behaviors. Knowledge of the reproductive periods, as well as its feeding habits, provides fundamental data for effective protection and species conservation. Biological aspects related to feeding habits and reproduction of R. krugi were addressed across six annual cycles, considering both dry and rainy seasons. For this, stomach content analysis, using the frequency of occurrence and volumetric index methods, as well as observation of the sex ratio and stage of maturation of the gonads were carried out for 148 individuals of R. krugi sampled in eight caves in Chapada Diamantina. Stomach volumes correlated with reproduction aspects across the dry and rainy seasons. These populations showed opportunistic carnivorous feeding habits, consuming both autochthonous and allochthonous items, with a preference for foraging in submerged guano deposits, which demonstrates the catfish’s strong dependence on bats. Regarding sex ratios, there was no marked seasonality; however, in rainy seasons, there was a higher proportion of maturing females, showing a reproductive tendency. During these periods, there was also a significantly higher number of stomachs with contents, showing seasonality in the diet. Specialized diet and dependence on rainy periods, especially in diet, corroborate the fragility of R. krugi, especially considering the changes in rainfall regimes in Brazil, with dry seasons exceeding eight months per year in the last ten years. Full article

Extreme precipitation events often present significant risks to human life and property, making their accurate prediction an essential focus of current research. Recent studies have primarily concentrated on exploring the formation mechanisms of extreme precipitation. Existing prediction methods do not adequately account for the combined terrain and atmospheric effects, resulting in shortcomings in extreme precipitation forecasting accuracy. Additionally, the satellite data resolution used in prior studies fails to precisely capture nuanced details of abrupt changes in extreme precipitation. To address these shortcomings, this study introduces an innovative approach for accurately predicting extreme precipitation: the multimodal attention ConvLSTM-GAN for extreme rainfall nowcasting (ER-MACG). This model employs high-resolution Fengyun-4A(FY4A) satellite precipitation products, as well as terrain and atmospheric datasets as inputs. The ER-MACG model enhances the ConvLSTM-GAN framework by optimizing the generator structure with an attention module to improve the focus on critical areas and time steps. This model can alleviate the problem of information loss in the spatial–temporal convolutional long short-term memory network (ConvLSTM) and, compared with the standard ConvLSTM-GAN model, can better handle the detailed changes in time and space in extreme precipitation events to achieve more refined predictions. The main findings include the following: (a) The ER-MACG model demonstrated significantly greater predictive accuracy and overall performance than other existing approaches. (b) The exclusive consideration of DEM and LPW data did not significantly enhance the ability to predict extreme precipitation events in Zhejiang Province. (c) The ER-MACG model significantly improved in identifying and predicting extreme precipitation events of different intensity levels. Full article

This study presents a GIS-based Multi-Criteria Decision Analysis (MCDA) spatial model to assess land suitability for oil palm (OP) cultivation in rainfed conditions. Initially, twelve parameters, viz., rainfall, number of rainy days, mean temperature, RH, ground water level, soil pH, salinity, soil depth, surface texture, stoniness, slope, and drainage, were selected for assessing OP suitability in one of the states (Kerala). However, subsequent ground verification revealed significant discrepancies, which prompted refining the model by focusing on key parameters with greater accuracy and relevance. Accordingly, only five the most critical parameters affecting OP cultivation under rainfed conditions were selected through the rank sum method, and weights were assigned ac-cording to their significance. This study was aimed at creating a comprehensive tool for informed decision making in agricultural planning. District-level spatial data from reliable sources were utilized for Multi-Criteria Decision Analysis. Thematic rasters, representing key factors influencing land suitability, were created in a GIS. Utilizing MCDA techniques, a digital suitability map was generated in ArcGIS 10.3, delineating three distinct classes over an extensive area of 10.5 million hectares. Further, with an aim to focus on actual locations that can be readily planted with oil palm, the suitable locations identified were restricted to eight selected land use/land cover (LULC) classes. This strategic limitation aimed to facilitate the expansion of OP cultivation exclusively to areas deemed most suitable based on the identified criteria. The validation of this developed model involved comparing the suitability map generated with the performance of existing oil palm plantations across diverse locations. The reasonable similarity between the model’s predictions and real-world plantation outcomes validated the effectiveness of this MCDA spatial model. This model not only helps identify suitable locations for rainfed oil palm cultivation but also serves as a valuable tool for strategic decision making in agricultural land use planning. Full article

The mesic-origin species Robinia pseudoacacia L. (black locust) is widely planted in the semiarid and sub-humid areas of the Loess Plateau for the reforestation of vegetation-degraded land. Under the scenario of changing precipitation patterns, exploring the response of photosynthesis to drought allows us to assess the risk to sustainable development of these plantations. In this study, paired plots were established including the control and a treatment of 30% exclusion of throughfall (since 2018). The photosynthetic characteristics were investigated using a portable photosynthesis system for four periods in the full-leaf growing season of 2021–2022, the fourth and fifth years, on both treated and controlled sampling trees. Leaf gas exchange parameters derived from diurnal changing patterns, light response curves, and CO₂ response curves showed significant differences except for period II (9–11 September 2021) between the two plots. The photosynthetic midday depression was observed in 2022 in the treated plot. Meanwhile, the decline of net photosynthetic rate in the treated plot was converted from stomatal limitation to non-stomatal limitation. Furthermore, we observed that black locust adapted to long-term water deficiency by reducing stomatal conductance, increasing water use efficiency and intrinsic water use efficiency. The results demonstrate that reduction in precipitation would cause photosynthesis decrease, weaken the response sensitivity to light and CO₂, and potentially impair photosynthetic resilience of the plantations. They also provide insights into the changes in photosynthetic functions under global climate change and a reference for management of plantations. Full article

Plastic-based contamination has become a major cause of concern as it pervades many environments such as air, water, sediments, and soils. This study sought to examine the presence of microplastics (MPs) and nanoplastics (NPs) in freshwater mussels placed at rainfall/street runoff overflows, downstream (15 km) of the city centre of Montréal, and 8 km downstream of a municipal effluent dispersion plume. MPs and NPs were determined using flow cytometry and size exclusion chromatography using fluorescence detection. Following 3 months of exposure during the summer season, mussels contained elevated amounts of both MPs and NPs. The rainfall overflow and downstream of the city centre were the most contaminated sites. Lipid peroxidation, metallothioneins, and protein aggregates (amyloids) were significantly increased at the most contaminated sites and were significantly correlated with NPs in tissues. Based on the levels of MPs and NPs in mussels exposed to municipal effluent, wastewater treatment plants appear to mitigate plastic contamination albeit not completely. In conclusion, the data support the hypothesis that mussels placed in urbanized areas are more contaminated by plastics, which are associated with oxidative damage. The highest responses observed at the overflow site suggest that tire wear and/or asphalt (road) erosion MPs/NPs represent important sources of contamination for the aquatic biota. Full article

The scope of this study is an attempt to develop an automated visualization module to monitor rainfall and hail estimations in real-time, highlighting areas with potential risk from extreme weather phenomena. The rainfall/hail products are provided by a known satellite-based algorithm that uses exclusively Meteosat multispectral images. The application is fully automated, written in the Python programming environment using open-source libraries, and provides colored graphs about the spatial variation of the examined parameters with the same temporal resolution as the Meteosat imagery. Additional functions of this application include warnings for extreme situations each time predefined threshold values are exceeded, as well as geographical areas that are vulnerable to heavy rainfall and/or hail occurrences. This application is a pilot operating over the Greek periphery. Also, there is a capability to create small video animations for the spatiotemporal evolution of the rainfall and hail estimations up to 6 h before the latest available satellite images. Full article

Agriculture is a fundamental aspect of our society, providing food and resources for a growing population. However, climate change is putting this sector at risk through rising temperatures, changing rainfall patterns and an increase in the frequency and intensity of extreme weather events. Our study highlights the need to address climate change in a differentiated way, taking into account the specificities of each agricultural sector, and therefore aims not only to organise and summarise current research but also to fill an important gap in the existing literature by focusing on the impact of climate change on vegetable crops. The topic was researched using the Web of Science and Scopus databases, where 219 publications were thoroughly reviewed and only those that fully addressed the impact of climate change on vegetable crops were selected. Of the 219 publications reviewed, only 53 focused exclusively on the effects of climate change on vegetable crops, indicating the need for more specialised research in this area, especially given the complex challenges that climate change poses not only in terms of yield but also non-trivial quality and food safety, and can be considered a future research prospect. Full article

Scapania apiculata, Scapania carinthiaca, and Scapania scapanioides are rare deadwood-dwelling liverworts threatened across Europe. Scapania carinthiaca is thus listed in the Habitats Directive. However, their distribution data are scattered, and their ecologic demands are insufficiently studied. Here, we present new locations and data on the ecology of the species, which resulted from a targeted search in selected regions of Austria. We found ten new sites each for Scapania apiculata and Scapania scapanioides and twenty for Scapania carinthiaca. Reproduction was exclusively asexual. The macroclimates of all known locations in Austria did not differ significantly between the three species. It was consistently wet, with a mean annual precipitation of 1615.3 mm, a high evenness of rainfall, and a low desiccation risk. The mean temperature averaged 7.4 °C. The habitat was shaded dead wood of Picea abies, Abies alba, and Fagus sylvatica of all decay stages at a median distance of 2.5 m from streams or springy areas in semi-natural forests of montane and submontane regions. Thus, high deadwood volumes under a suitable climate are a prerequisite for the occurrences of the species. The number of locations of new finds has more than doubled in Austria and thus in Europe. Full article

High-quality rainfall data are crucial for accurately forecasting flash floods and runoff simulations. However, traditional correction methods often overlook errors in rainfall-monitoring data. We established a screening system to identify anomalous stations using the Hampel method, Grubbs criterion, analysis of surrounding measurement stations, and radar-assisted verification. Three rainfall data-fusion methods were used to fuse rainfall station data with radar quantitative precipitation estimation data; the accuracies of the fused data products with and without anomalous data identification were compared. Validation was performed using four 2012 rainfall events in Hebei Province. The 08:00–19:00 July 3 rainfall event had the highest number of anomalous stations (11.5% of the total), while the 01:00–17:00 August 9 event had the lowest number (7.8%). By comparing stations deemed to be anomalous with stations that were actually anomalous, we determined that the accuracy of reference station determination using Hampel’s method and Grubbs’ test was 94.2%. Radar-assisted validation improved the average accuracy of anomalous station identification during the four typical rainfall events from 89.7 to 93.7%. Excluding anomalous data also significantly impacted the efficacy of rainfall-data fusion, as it improved the quality of the rainfall station data. Among the performance indicators, 95% improved after the exclusion of anomalous data for all four rainfall events. Full article