Tree species identity and diversity drive fungal richness and community composition along an elevational gradient in a Mediterranean ecosystem.

Ecological and taxonomic knowledge is important for conservation and utilization of biodiversity. Biodiversity and ecology of fungi in Mediterranean ecosystems is poorly understood. Here, we examined the diversity and spatial distribution of fungi along an elevational gradient in a Mediterranean ecosystem, using DNA metabarcoding. This study provides novel information about diversity of all ecological and taxonomic groups of fungi along an elevational gradient in a Mediterranean ecosystem. Our analyses revealed that among all biotic and abiotic variables tested, host species identity is the main driver of the fungal richness and fungal community composition. Fungal richness was strongly associated with tree richness and peaked in Quercus-dominated habitats and Cistus-dominated habitats. The highest taxonomic richness of ectomycorrhizal fungi was observed under Quercus ilex, whereas the highest taxonomic richness of saprotrophs was found under Pinus. Our results suggest that the effect of plant diversity on fungal richness and community composition may override that of abiotic variables across environmental gradients.

Precipitation Estimation Using L-Band and C-Band Soil Moisture Retrievals.

An established methodology for estimating precipitation amounts from satellite-based soil moisture retrievals is applied to L-band products from the Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) satellite missions and to a C-band product from the Advanced Scatterometer (ASCAT) mission. The precipitation estimates so obtained are evaluated against in situ (gauge-based) precipitation observations from across the globe. The precipitation estimation skill achieved using the L-band SMAP and SMOS datasets is higher than that obtained with the C-band product, as might be expected given that L-band is sensitive to a thicker layer of soil and thereby provides more information on the response of soil moisture to precipitation. The square of the correlation coefficient between the SMAP-based precipitation estimates and the observations (for aggregations to ~100 km and 5 days) is on average about 0.6 in areas of high rain gauge density. Satellite missions specifically designed to monitor soil moisture thus do provide significant information on precipitation variability, information that could contribute to efforts in global precipitation estimation.

Exploring the actual spatial resolution of 1 km satellite soil moisture products.

High-resolution soil moisture data is crucial in the development of hydrological applications as it provides detailed insights into the spatiotemporal variability of soil moisture. The emergence of advanced remote sensing technologies, alongside the widespread adoption of machine learning, has facilitated the creation of continental and global soil moisture products both at fine spatial (1 km) and temporal (daily) scales. Some of these products rely on several data sources as input (satellite, in situ, modelling), and therefore an evaluation of their actual spatial and temporal resolution is required. Nevertheless, the absence of appropriate ground monitoring networks poses a significant challenge for this assessment. In this study, five high-resolution (1 km) soil moisture products (S1-RT1, S1-COP, SMAP-Planet, SMAP-NSIDC, and ESACCI-Zheng) were analysed and evaluated throughout the Italian territory, together with a coarse resolution (12.5 km) dataset for comparison (ASCAT-HSAF). The main objective is to investigate their actual spatial and temporal resolution, and accuracy. Firstly, a cross-comparison of the products in space and time is carried out, including the use of triple collocation analysis. Secondly, an application-based assessment is implemented, considering irrigation, fire, drought, and precipitation case studies. The results clearly indicate the limitations and the potential of each product. Sentinel-1 based products (S1-COP and S1-RT1) are found able to reproduce high-resolution spatial patterns by detecting localised events for irrigation, fire, and precipitation. Their lower temporal resolution leads to accuracies lower than that of the SMAP-Planet product, and comparable with SMAP-NSIDC and ESACCI-Zheng products. However, SMAP-Planet is found to have an actual spatial resolution coarser than 1 km. The study highlights the need for further research to improve the high-resolution soil moisture products, and particularly to determine accurately the spatial resolution represented in soil moisture products. At the same time, the analysed products are found able to address high-resolution applications for the first time, opening promising activities for their operational use in hydrology and water resources management.

SM2RAIN-Climate, a monthly global long-term rainfall dataset for climatological studies.

A reliable and accurate long-term rainfall dataset is an indispensable resource for climatological studies and crucial for application in water resource management, agriculture, and hydrology. SM2RAIN (Soil Moisture to Rain) derived datasets stand out as a unique and wholly independent global product that estimates rainfall from satellite soil moisture observations. Previous studies have demonstrated the SM2RAIN products' high potential in estimating rainfall around the world. This manuscript describes the SM2RAIN-Climate rainfall product, which uses the European Space Agency (ESA) Climate Change Initiative (CCI) soil moisture v06.1 to provide monthly global rainfall for the 24-year period 1998-2021 at 1-degree spatial resolution. The assessment of the proposed rainfall dataset against different existing state-of-the-art rainfall products exhibits the robust performance of SM2RAIN-Climate in most regions of the world. This performance is indicated by correlation coefficients between SM2RAIN-Climate and state-of-the-art products, consistently exceeding 0.8. Moreover, evaluation results indicate the potential of SM2RAIN-Climate as an independent rainfall product from other satellite rainfall products in capturing the pattern of global rainfall trend.

Detecting characteristics of extreme precipitation events using regional and satellite-based precipitation gridded datasets over a region in Central Europe.

Perception of the spatio-temporal events of extreme precipitation and their variations is essential for diminishing the natural hazards linked with extreme events. In this research, a satellite-based precipitation dataset derived from remotely sensed soil moisture (SM2RAIN-ASCAT, obtained from ASCAT satellite soil moisture data through the Soil Moisture to Rain algorithm) was selected to evaluate the accuracy of daily precipitation and extreme events estimations against a regional gridded weather dataset by employing various performance indicators, and ETCCDI indicators (CDD, and CWD, SDII, R10mm, R20mm, R95p, R99p, Rx1day, and Rx5day). The study area includes entire Poland as well as small parts of Ukraine, Belarus, Slovakia, the Czech Republic, Russia, and Germany. According to PBIAS (~ -3.9 %) and coefficient of correlation (~0.74), SM2RAIN-ASCAT has good accuracy in the study area. Assessments reveal that, in general, over southern, mountainous part SM2RAIN-ASCAT does not have accurate estimations. According to the reference dataset, during the 2007-2019 period, on average, the length of dry days was ~22 days, while SM2RAIN-ASCAT shows ~19.6 consecutive dry days. In contrast, SM2RAIN-ASCAT overestimated (16 days/year) the consecutive wet days compared to the reference dataset (~8.7 days/year). SM2RAIN-ASCAT underestimated the number of heavy precipitation days index (R10mm) over the northern part of the region, close to the Baltic Sea), but the accuracy increased in the southern parts. SM2RAIN-ASCAT underestimated the maximum 1-day rainfall total and relative max 5-day precipitation amount indices. The total precipitation divided by the amount of wet days index shows that SM2RAIN-ASCAT has relatively acceptable accuracy in the center and south of the study area. Our results show that SM2RAIN-ASCAT should be improved for relatively higher extreme indicators.

A comprehensive assessment of SM2RAIN-NWF using ASCAT and a combination of ASCAT and SMAP soil moisture products for rainfall estimation.

Rainfall estimation using remote sensing products is an alternative to in situ measurement rainfall due to their high temporal and spatial resolution. Using satellite soil moisture (SM) observations in the SM to Rain (SM2RAIN) algorithm have a great potential to estimate rainfall. SMA2RAIN-NWF algorithm is a reinforced version of a SMA2RAIN algorithm which was developed to estimate rainfall through the integration of the SM2RAIN algorithm and the net water flux (NWF) model. A new release of SMA2RAIN-NWF algorithm uses the Advanced Microwave Scanning Radiometer 2 (AMSR2) SM dataset as input datasets. The aim here is to assess the SMA2RAIN-NWF by using multiple SM products including ASCAT, and their integration in four aggregations (AGGR) periods (1, 7, 14, and 30 days) by comparing with rainfall observation of 15 stations over the Lake Urmia basin, Iran for the period January 2015 to December 2019. The Discrete Cosine Transform (DCT) method is applied to fill the gap in the satellite SM time series. Moreover, the effect of land cover classes (grasslands, croplands, and urban) on rainfall estimation is investigated. Considering the Kling-Gupta efficiency (KGE) and correlation coefficient (R) values in comparisons of calibration and validation revealed that urban areas experienced a minimum decrement rate (2-5 %). A comparison of three SM products (ASCAT, ASCAT+SMAP, and ASCAT+DCT) show that all products had a high performance on a daily time scale in term of the KGE and R. The results showed that algorithm performance gradually rose via an increase in AGGR levels, reaching KGE and R values of 0.8 and above. Furthermore, the comparison of SM2RAIN-NWF and SM2RAIN show an improvement of SM2RAIN-NWF performance across various AGGRs.

Spatiotemporal drought monitoring using bottom-up precipitation dataset (SM2RAIN-ASCAT) over different regions of Iran.

Drought is a natural phenomenon that can significantly impacts on water resources studies, agricultural and environmental societies around the world, hence, accurate spatio-temporal monitoring of drought is very important. In this research, a comparative analysis of a newly developed precipitation dataset, SM2RAIN-ASCAT (which is based on bottom-up approach), with 40 ground-measured Iranian Meteorological Organization (IMO) precipitation data are performed to estimate the precipitation and monitor the drought events over diverse climate regions of Iran. The SPI index, as a widely used drought index, at the temporal resolution ranging from one month to one year is used to this aim, and the outputs are analyzed based on the statistical and categorical metrics. Results indicated that the highest correlation coefficient (CC) and lowest root mean square error (RMSE) between SM2RAIN-ASCAT and in situ observations are found at 10-day and monthly time scales. Analyzing both datasets using FAR and POD indices in the mid and long-term time scales indicated that the SM2RAIN-ASCAT has a good performance in detecting rainy days. This product overestimate the precipitation values in extra-arid regions, while in humid and per-humid climate areas it tends to underestimation. Moreover, assessing the reliability of this product for drought monitoring showed that the SPI at 1, 3 and 6 month time scales are in good agreement with ground-based observations over different climate regions of Iran. At these temporal resolutions, the CC value between SPIs calculated based on in situ observations and SM2RAIN-ASCAT is higher than 0.7 in more than 75% of the meteorological stations. The efficiency of SM2RAIN-ASCAT in detecting drought periods in extra-arid and arid zones is relatively better than that of in humid and per-humid climates. In addition, the performance of this product for capturing wet periods in extra-arid to semi-arid regions is better than that of in Mediterranean and humid zones. Overall, the outcomes of this study demonstrated that SM2RAIN-ASCAT, despite poor performance in estimating precipitation in some regions, can be considered as a complementary to ground-gauge observations or an appropriate alternative dataset for drought analysis, especially in arid and semi-arid regions which include most parts of the world.

River flow prediction in data scarce regions: soil moisture integrated satellite rainfall products outperform rain gauge observations in West Africa.

Satellite precipitation products have been largely improved in the recent years particularly with the launch of the global precipitation measurement (GPM) core satellite. Moreover, the development of techniques for exploiting the information provided by satellite soil moisture to complement/enhance precipitation products have improved the accuracy of accumulated rainfall estimates over land. Such satellite enhanced precipitation products, available with a short latency (< 1 day), represent an important and new source of information for river flow prediction and water resources management, particularly in developing countries in which ground observations are scarcely available and the access to such data is not always ensured. In this study, three recently developed rainfall products obtained from the integration of GPM rainfall and satellite soil moisture products have been used; namely GPM+SM2RAIN, PRISM-SMOS, and PRISM-SMAP. The prediction of observed daily river discharge at 10 basins located in Europe (4), West Africa (3) and South Africa (3) is carried out. For comparison, we have also considered three rainfall products based on: (1) GPM only, i.e., the Early Run version of the Integrated Multi-Satellite Retrievals for GPM (GPM-ER), (2) rain gauges, i.e., the Global Precipitation Climatology Centre, and (3) the latest European Centre for Medium-Range Weather Forecasts reanalysis, ERA5. Three different conceptual and lumped rainfall-runoff models are employed to obtain robust and reliable results over the 3-year data period 2015-2017. Results indicate that, particularly over scarcely gauged areas (West Africa), the integrated products outperform both ground- and reanalysis-based rainfall estimates. For all basins, the GPM+SM2RAIN product is performing the best among the short latency products with mean Kling-Gupta Efficiency (KGE) equal to 0.87, and significantly better than GPM-ER (mean KGE = 0.77). The integrated products are found to reproduce particularly well the high flows. These results highlight the strong need to disseminate such integrated satellite rainfall products for hydrological (and agricultural) applications in poorly gauged areas such as Africa and South America.

Cultivation Area Affects the Presence of Fungal Communities and Secondary Metabolites in Italian Durum Wheat Grains.

In this study, durum wheat kernels harvested in three climatically different Italian cultivation areas (Emilia Romagna, Umbria and Sardinia) in 2015, were analyzed with a combination of different isolation methods to determine their fungal communities, with a focus on Fusarium head blight (FHB) complex composition, and to detect fungal secondary metabolites in the grains. The genus Alternaria was the main component of durum wheat mycobiota in all investigated regions, with the Central Italian cultivation area showing the highest incidence of this fungal genus and of its secondary metabolites. Fusarium was the second most prevalent genus of the fungal community in all cultivation environments, even if regional differences in species composition were detected. In particular, Northern areas showed the highest Fusarium incidence, followed by Central and then Southern cultivation areas. Focusing on the FHB complex, a predominance of Fusariumpoae, in particular in Northern and Central cultivation areas, was found. Fusariumgraminearum, in the analyzed year, was mainly detected in Emilia Romagna. Because of the highest Fusarium incidence, durum wheat harvested in the Northern cultivation area showed the highest presence of Fusarium secondary metabolites. These results show that durum wheat cultivated in Northern Italy may be subject to a higher FHB infection risk and to Fusarium mycotoxins accumulation.