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5 results for “Adige River”
Figure 3 from: Lencioni V, Moubayed J (2021) Synorthocladius federicoi sp. nov., a new species occurring in the middle basin of the Adige River, northern Italy (Diptera, Chironomidae, Orthocladiinae). ZooKeys 1057: 105-116. https://doi.org/10.3897/zookeys.1057.68175
Figure 3 Type locality of Synorthocladius federicoi sp. nov., Adige River, Verona (northern Italy) (by V. Lencioni).
Figure 1 from: Lencioni V, Moubayed J (2021) Synorthocladius federicoi sp. nov., a new species occurring in the middle basin of the Adige River, northern Italy (Diptera, Chironomidae, Orthocladiinae). ZooKeys 1057: 105-116. https://doi.org/10.3897/zookeys.1057.68175
Figure 1 Male imago of Synorthocladius spp. Head (dorsal, left side) with vertex, coronal area and temporals of AS. federicoi sp. nov. BS. semivirens. Antenna, apex of last flagellomere of C, DS. federicoi sp. nov. ES. semivirens. Palpomere 3 of FS. federicoi sp. nov. GS. semivirens. Clypeus of HS. federicoi sp. nov. IS. semivirens. Lobes of antepronotum and acrostichals of JS. federicoi sp. nov. KS. semivirens. Humeral area of LS. federicoi sp. nov. MS. semivirens. The arrows indicate some distinguishing characters.
Figure 2 from: Lencioni V, Moubayed J (2021) Synorthocladius federicoi sp. nov., a new species occurring in the middle basin of the Adige River, northern Italy (Diptera, Chironomidae, Orthocladiinae). ZooKeys 1057: 105-116. https://doi.org/10.3897/zookeys.1057.68175
Figure 2 Male imago of Synorthocladius spp. Chaetotaxy of tergites II–VI of AS. federicoi sp. nov. BS. semivirens. S. federicoi sp. nov. C hypopygium in dorsal view D tergite IX and anal point in lateral view E megaseta, dorsal F gonostylus, other aspect in ventral view G hypopygium, ventral H inferior volsella I virga. S. semivirensJ tergite IX and anal point in lateral view K anal point, dorsal L, M inferior volsella, two aspects. The arrows indicate some distinguishing characters.
Compound Drought and Heatwave (CDHW) Event Indicators – Adige River Catchment, 1950–2023
<table> <tbody> <tr> <td> <p><strong>Science Case Name </strong></p> </td> <td> <p>Hot and dry compound events in the Adige River Catchment (Eastern Italian Alps)</p> </td> </tr> <tr> <td> <p><strong>Dataset Title </strong></p> </td> <td> <p>Compound Drought and Heatwave (CDHW) event indicators for the Adige River Catchment (1950-2023)</p> </td> </tr> <tr> <td> <p><strong>Dataset Description </strong></p> </td> <td> <p>Occurrence and severity gridded fields of CDHW events over the Adige River Catchment from 1950 to 2023. The dataset includes the event list with duration, extent, total severity, magnitude and ranking of each identified event.</p> </td> </tr> <tr> <td> <p><strong>Key Methodologies</strong></p> </td> <td> <p>A CDHW event is determined by the co-occurrence of drought and heatwave conditions over at least 60% of the Adige River Catchment. Drought and heatwave indicators were derived using daily temperature and precipitation data from the E-OBS gridded dataset.</p> <p>A drought period is identified as a sequence of consecutive months with a negative Standardized Precipitation Index (SPI), starting with the first month where the SPI-6 (6-month timescale) falls below -1. Heatwaves are defined as periods of at least three consecutive days where the daily maximum temperature (TX) exceeds the 90th percentile for that specific calendar day, determined using a 31-day running mean centred on the day under evaluation and considering all values from 1950 to 2023. When two or more periods of consecutive exceedances are separated by one day with TX below the threshold, they are considered as a single heatwave occurrence and the day below the threshold is included in the event duration. </p> <p>In the occurrence grids, for each day in a CDHW event, grid cells where both drought and heatwave conditions are detected are flagged as "1". If the compound condition is not met, the cell is flagged as "0". In the severity fields, the severity (dimensionless) for each day in a CDHW event is calculated as the product of the standardized daily TX over the days of the event and the absolute value of SPI-6 in the corresponding month. The calculation of the severity is similar to the one proposed by Mukherjee and Mishra (2021), but with the percentiles used in the standardization of TX varying with the day of the year. </p> <p>The list of CDHW events affecting the Adige River Catchment over 1950-2023 includes the start and end dates, the percentage of the area affected, the total severity (dimensionless), magnitude (dimensionless) and ranking of the event. The total severity of the event is defined as the average of the CDHW severities of all grid cells in the catchment experiencing the CDHW conditions. The total severity of the CDHW event at each grid cell is calculated as the sum of the daily severity values over days flagged as "1". The magnitude is the product of the total severity and the fraction of area affected. CDHW events are ranked based on their magnitude.</p> </td> </tr> <tr> <td> <p><strong>Temporal Domain</strong></p> </td> <td> <p>1950-2023</p> </td> </tr> <tr> <td> <p><strong>Spatial Domain</strong></p> </td> <td> <p>Extended region centred on the Adige River Catchment (7.10°-15.30°E, 44.10°-49.10°N); Spatial resolution: 0.1°x0.1° (EPGS:4326)</p> </td> </tr> <tr> <td> <p><strong>Key Indicators </strong></p> </td> <td> <p>CDHW occurrence and severity (grid) and CDHW duration, extent, total severity and magnitude (list)</p> </td> </tr> <tr> <td> <p><strong>Data Format</strong></p> </td> <td> <p>netCDF and CSV</p> </td> </tr> <tr> <td> <p><strong>Source Data</strong></p> </td> <td> <p>E-OBS dataset (v29.0e)</p> </td> </tr> <tr> <td> <p><strong>Accessibility</strong></p> </td> <td> <p>Zenodo, https://doi.org/10.5281/zenodo.13839122 </p> </td> </tr> <tr> <td> <p><strong>Stakeholder Relevance</strong></p> </td> <td> <p>The list of hot and dry events for the Adige River Catchment can support regional water managers to identify the most critical meteorological conditions over the past decades and link them to the observed local impacts. The details about event severity, magnitude and spatial and temporal extent provided in the list can be used directly to characterize the impactful hot and dry episodes. Moreover, the gridded fields offer a consistent description of the phenomena throughout the Adige River Catchment and can be used to localize the affected areas and identify the main spatial patterns of hot and dry conditions. </p> </td> </tr> <tr> <td> <p><strong>Limitations/Assumptions</strong></p> </td> <td> <p>Results accuracy can be influenced by potential biases in the original data source over the study area. The 0.1-grid of E-OBS limits a detailed representation of local conditions and spatial patterns, while the continuous temporal coverage enables historical event detection and analysis. Thresholds used in the proposed definitions are based on expert knowledge and other choices are also possible.</p> </td> </tr> <tr> <td> <p><strong>Contact Information</strong></p> </td> <td> <p>Elena Maines (Center for Climate Change and Transformation - Eurac Research) (data curator)</p> <p>Alice Crespi (Center for Climate Change and Transformation - Eurac Research) (data curator)</p> <p>Marc Lemus-Canovas (Center for Climate Change and Transformation - Eurac Research; Universidade de Santiago de Compostela) (data curator)</p> </td> </tr> </tbody> </table>
HDBSCAN Clusters Maize Crop Stress - Adige River-Fed Downstream Irrigated Plain, 2022-2023
<div dir="ltr"> <table style="width: 99.5207%;"><colgroup><col style="width: 27.9117%;"><col style="width: 72.0629%;"></colgroup> <tbody> <tr> <td> <p dir="ltr"><strong>Demonstration Case Name</strong></p> </td> <td> <p dir="ltr">Multi-Hazards in the Downstream Area of the Adige River Basin.</p> </td> </tr> <tr> <td> <p dir="ltr"><strong>Dataset Name/Title</strong></p> </td> <td> <p dir="ltr">HDBSCAN Clusters Maize Crop Stress - Adige River-Fed Downstream Irrigated Plain, 2022-2023</p> </td> </tr> <tr> <td> <p dir="ltr"><strong>Dataset Description</strong></p> </td> <td> <p dir="ltr">The dataset contains HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise) clusters based on a synthetic stress indicator obtained through a PCA (Principal Component Analysis) on NDVI (Normalized Difference Vegetation Index) and NDMI (Normalized Difference Moisture Index) Sentinel-2 based indices. The dataset contains identified vegetation stress clusters on the following dates: 02/07/2022 17/07/2022, 22/07/2022, 01/08/2022, 06/08/2022, 11/08/2022, 16/08/2022, 07/07/2023, 17/07/2023, 27/07/2023, 06/08/2023, 11/08/2023, 16/08/2023, covering portion of 2022 and 2023 maize cropping seasons (July, August) in the selected area. All observations are located in a plain area that relies on the Adige River for cropland irrigation. For each row, the dataset contains the following columns: </p> <ul> <li> <p dir="ltr"><em>date</em>: date of the clustering, in dd/mm/YYYY format</p> </li> <li> <p dir="ltr"><em>cluster</em>: cluster number, -1 identifies the noise (unclassified) cluster</p> </li> <li> <p dir="ltr"><em>mean_ndvi</em>: mean NDVI across all maize fields in the cluster</p> </li> <li> <p dir="ltr"><em>mean_ndmi</em>: mean NDMI across all maize fields in the cluster</p> </li> <li> <p dir="ltr"><em>prevalent_hsg</em>: most frequent Hydrologic Soil Group </p> </li> <li> <p dir="ltr"><em>SPEI90</em>: Standardized Precipitation Evapotranspiration Index (SPEI) calculated on a 90 days time frame (see <em>Key Methodologies </em>for details)</p> </li> <li> <p dir="ltr"><em>SPEI180</em>: Standardized Precipitation Evapotranspiration Index (SPEI) calculated on a 180 days time frame (see <em>Key Methodologies </em>for details)</p> </li> <li> <p dir="ltr"><em>SPEI365</em>: Standardized Precipitation Evapotranspiration Index (SPEI) calculated on a 365 days time frame (see <em>Key Methodologies </em>for details)</p> </li> <li> <p dir="ltr"><em>temp_anom_X</em>: temperature anomaly (° C) X days before the considered date (see <em>Key Methodologies </em>for details)</p> </li> <li> <p dir="ltr"><em>SWI005_X</em>: Soil Water Index (%) with T-value = 5 (indicating the model water infiltration time) X days before the considered date</p> </li> <li> <p dir="ltr"><em>irr_channel_distance_m</em>: average distance (m) of maize fields in the cluster from the closest irrigation channel</p> </li> <li> <p dir="ltr"><em>geometry</em>: polygon geometry of the cluster in EPSG:32632</p> </li> </ul> </td> </tr> <tr> <td> <p dir="ltr"><strong>Key Methodologies</strong></p> </td> <td> <p dir="ltr">The 1981-2023 period was used as reference for the computation of the SPEI index, using the Hargreaves equation (Hargreaves, 1994) to estimate the daily potential evapotranspiration, with the extra-terrestrial radiation evaluated from the latitude and the day of the year. The gamma distribution was used for standardizing the water balance time series.</p> <p dir="ltr">Temperature anomalies were defined for each calendar day as the difference between the mean daily temperature and the 50th percentile of the 1991–2020 calendar day. Percentiles were computed using a centred 15‑day moving window to smooth short-term variability. </p> <p dir="ltr">Maize crop field-level NDVI and NDMI values were calculated by averaging pixel-level NDVI/NDMI derived from Sentinel-2 L2A observations within the irrigated districts fed by the Adige River waters (data courtesy of ANBI Veneto). The observations result from a multi-step data cleaning process. Images were cloud masked using the Sentinel-2 Scene Classification Layer (SCL). Crop field observations were excluded if more than 50% of their pixels were unavailable e.g., due to cloud cover; remaining observations were filtered using a Bare Soil Index (BSI) threshold of 0.08 (Mzid et al., 2021), to exclude non vegetated (bare soil) pixels. Finally, fields associated with same year alternating crops were disentangled by analyzing temporal BSI profiles to detect two green-up periods separated by at least one observation identified as bare soil (BSI > 0.08).</p> <p dir="ltr">A synthetic stress indicator was defined as the first principal component (PC1) derived from a one-component PCA applied to NDVI and NDMI values. HDBSCAN algorithm was used on the synthetic stress indicator to identify clusters following hyperparameters optimization via grid search.</p> <p dir="ltr"> </p> </td> </tr> <tr> <td> <p dir="ltr"><strong>Temporal Domain</strong></p> </td> <td> <p dir="ltr">2022-2023</p> </td> </tr> <tr> <td> <p dir="ltr"><strong>Spatial Domain</strong></p> </td> <td> <p dir="ltr">The dataset is provided over the [10.7, 45.0, 12.3, 45.6] spatial domain (min longitude, min latitude, max longitude, max latitude in WGS84, EPSG:4326).</p> </td> </tr> <tr> <td> <p dir="ltr"><strong>Key Variables/Indicators</strong></p> </td> <td> <p dir="ltr">Date, cluster, mean_ndvi, mean_ndmi, n_fields, prevalent_hsg, SPEI90, SPEI180, SPEI365, temp_anom_1, temp_anom_2, temp_anom_3, temp_anom_4, temp_anom_5, temp_anom_6, SWI005_1, SWI005_2, SWI005_3, irr_channel_distance_m, geometry</p> <p dir="ltr">See <em>Dataset Description</em> section for details on the variables.</p> </td> </tr> <tr> <td> <p dir="ltr"><strong>Data Format</strong></p> </td> <td> <p dir="ltr">csv</p> </td> </tr> <tr> <td> <p dir="ltr"><strong>Source Data</strong></p> </td> <td> <ul> <li> <p dir="ltr">NDVI, NDMI and BSI were obtained from ESA Copernicus Sentinel-2 L2A</p> </li> <li> <p dir="ltr">Crop field level information on farmer declared crop were obtained from the Veneto Agency for Payments in Agriculture (AVEPA, Agenzia Veneta per i Pagamenti, https://www.avepa.it/web/avepa)</p> </li> <li> <p dir="ltr">Hydrologic Soil Group at 1:250000 scale data were obtained from the Agenzia Regionale per la prevenzione e protezione ambientale Veneto (ARPAV, Agenzia Regionale per la Prevenzione e Protezione Ambientale del Veneto - Regional Agency for Environmental Prevention and Protection of Veneto, https://www.arpa.veneto.it/), and follow the classification scheme outlined in USDA National Engineering Handbook (USDA-NRCS, 2009)</p> </li> <li> <p dir="ltr">Irrigated districts were provided by courtesy of ANBI Veneto (Associazione Nazionale Bonifiche Irrigazioni - National Association of Land Reclamation and Irrigation)</p> </li> <li> <p dir="ltr">Temperature and precipitation data for SPEI and temperature anomaly were obtained from the SCIA-ISPRA dataset (ISPRA, <a href="https://scia.isprambiente.it/dati-e-indicatori/">https://scia.isprambiente.it/dati-e-indicatori/</a>)</p> </li> </ul> </td> </tr> <tr> <td> <p dir="ltr"><strong>Accessibility</strong></p> </td> <td> <p dir="ltr">https://doi.org/10.5281/zenodo.15301314</p> </td> </tr> <tr> <td> <p dir="ltr"><strong>Stakeholder Relevance</strong></p> </td> <td> <p dir="ltr">The dataset provides valuable post-disaster information on crop vegetation dynamics during hot and dry events. Impacted maize cultivated areas (clusters) across multiple dates during 2022 and 2023 cropping seasons are reported. The inclusion of two years, one characterized by severe drought (2022) and one by non-drought conditions (2023) provides insights into how the considered cropland area was affected under different abiotic stressor conditions. Additional information is provided by the inclusion of meteorological (SPEI, temperature anomaly), soil and territorial characteristic layers, thus enabling a more detailed analysis of the underlying impact drivers to support the understanding of the root causes of impacts. Moreover, the dataset provides a spatially and temporally explicit representation of maize stress clusters, highlighting areas that were more impacted during the course of the 2022 maize cropping season thus providing a valuable overview of the most vulnerable areas. This approach represents a promising tool to aid adaptation and management strategies, particularly related to water management (e.g., irrigation) and crop suitability under different meteorological and physical conditions.</p> </td> </tr> <tr> <td> <p dir="ltr"><strong>Limitations/Assumptions</strong></p> </td> <td> <p dir="ltr">In cases where a field was associated with more than one crop, a disaggregation technique was applied based on assumptions about crop growth phases. Additionally, NDVI and NDMI are not exclusively influenced by plant responses to drought, extreme heat and their combinations, as other factors (e.g., pests, soil/crop management) can affect plant vigour. However, the spatial extension and intensity of the 2022 drought event, the comparison with 2023 and the large number of fields considered potentially limit these uncertainties.</p> </td> </tr> <tr> <td> <p dir="ltr"><strong>Additional Outputs/Information</strong></p> </td> <td> <p dir="ltr">The dataset access is currently restricted due to pending related publication.</p> </td> </tr> <tr> <td> <p dir="ltr"><strong>Contact Information</strong></p> </td> <td> <p dir="ltr">Furlanetto, Jacopo (CMCC Foundation - Euro-Mediterranean Center on Climate Change, National Biodiversity Future Center) - Data curator</p> <p dir="ltr">Albergo, Edoardo (CMCC Foundation - Euro-Mediterranean Center on Climate Change, National Biodiversity Future Center) - Data curator</p> <p dir="ltr">Masina, Marinella (CMCC Foundation - Euro-Mediterranean Center on Climate Change)- Data curator</p> <p dir="ltr">Maraschini, Margherita (CMCC Foundation - Euro-Mediterranean Center on Climate Change) - Data curator</p> <p dir="ltr">Ferrario, Davide Mauro (CMCC Foundation - Euro-Mediterranean Center on Climate Change) - Data curator</p> <p dir="ltr">Torresan, Silvia (CMCC Foundation - Euro-Mediterranean Center on Climate Change, National Biodiversity Future Center) - Data manager</p> </td> </tr> </tbody> </table> </div>
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