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14 results for “drought indices”
Harmonized Palmer Drought Severity Indices throughout the Contiguous United States for HydroBASINS basins
In times of a changing hydroclimate and growing human population, there is a need to assess how various climatic and demand conditions influence water availability on the landscape. Tendency for drought conditions is a prime example of a key hydroclimatic metric that is useful for understanding water retention in the surrounding landscape. However, merging drought climatological data with co-located aquatic data is challenging. To facilitate national-scale analyses of basin-level drought conditions (i.e., Palmer Drought Severity Index; PDSI) with co-located water quality data, we present aggregated PDSI data for the contiguous United States. Data are aggregated using the HydroBASINS basin shapefiles. HYBAS_ID is retained to enable merging with HydroBASINS parent datasets.
COM-DROUGHTS Drought Indices
<h3><strong>Data Description:</strong></h3> <p>The COM-DROUGHTS Drought Indices dataset consists of daily time series of three drought indicators for meteorological, agricultural, and hydrological drought for 52 lower lying catchments (mean altitude <1500 m asl) in Switzerland for the period 1991-2099. The time series were derived from the newest transiently simulated climate and hydrological scenarios (CH2018, Hydro-CH2018) for Switzerland. Time series are provided on catchment-scale (aggregated).</p> <p>Following Stagge et al. 2015, all years have been harmonized to 365 days by excluding the last day of the year in case of leap years.</p> <p>The time series data is available for 3 representative concentration paths (RCPs, see e.g., Moss et al. 2010 / van Vuuren et al. 2011):<br>- RCP2.6: 8 GCM-RCM model chains<br>- RCP4.5: 16 GCM-RCM model chains<br>- RCP8.5: 20 GCM-RCM model chains</p> <p>Descriptive information is available for:<br>- catchment and climate characteristics (<em>catchment_climate_characteristics.csv</em>)<br>- model calibration and validation statistics (<em>catchment_calibration_validation.csv</em>)<br>- GCM-modelchains and RCP-scenarios (<em>models_scenario_info.csv</em>)</p> <h3><strong>Drought Indices:</strong></h3> <p><strong>1) Meteorological drought: SPI3</strong><br>- represented by the Standardized Precipitation Index (McKee et al. 1993) on a three-monthly basis<br>- derived from the CH2018 climate scenarios for Switzerland (daily mean precipitation aggregated on catchment-area).<br><strong>2) Agricultural drought: ET/PET</strong><br>- represented by the relative evapo(transpi)ration (ET/PET; see e.g., Fuhrer and Jasper 2009) <br>- derived from daily time series of evapotranspiration (ET) and potential evapotranspiration (PET) (output of the Hydro-CH2018 Runoff ensemble / hydrological model simulations; see Muelchi et al. 2021)).<br><strong>3) Hydrological drought: M7Q</strong><br>- represented by the 7-day average streamflow (M7Q)<br>- derived from daily runoff time series (Hydro-CH2018 Runoff ensemble / hydrological model simulations; see Muelchi et al. 2021).</p> <h3><strong>Data Organization:</strong></h3> <p>- Dataset provided as .zip-File<br>- Data files are provided in <em>.csv</em>-Format<br>- A <em>README.txt</em> describes folder, data structure and file-naming convention.<br>- info-files with keys and information on catchments and GCM-RCM chains</p> <p><strong>Drought indices data is structured as follows:</strong><br><strong>> <em><strong>F</strong>older:</em></strong> INDEX_NAME<br>--> containing all data-files: <br>1 File per GCM_RCM model chain containing daily time series for all 52 catchments and index (variations)</p> <p>File-Naming Convention: [INDEX_NAME]_[SCENARIO]_[GCM_RCM_RESOLUTION_SCENARIO].csv<br><br>Example: <em>SPI/SPI_RCP26_KNMI-RACMO_HADGEM_EUR44_RCP26.csv</em></p> <h3><strong>Further reading & methodological details:</strong></h3> <p>The following paper based on this dataset provides more detailed information on the datasets, the underlying methods and associated datasets and literature for further reading. </p> <div> <div>von Matt, C. N., Muelchi, R., Gudmundsson, L., and Martius, O.: Compound droughts under climate change in Switzerland, Nat. Hazards Earth Syst. Sci., 24, 1975–2001, https://doi.org/10.5194/nhess-24-1975-2024, 2024.</div> <div><a href="https://nhess.copernicus.org/articles/24/1975/2024/nhess-24-1975-2024.html">https://nhess.copernicus.org/articles/24/1975/2024/nhess-24-1975-2024.html</a></div> </div> <h3><strong>Funding:</strong></h3> <p>The COM-DROUGHTS project was funded by the Swiss Federal Office for the Environment (FOEN).</p> <h3><strong>References:</strong></h3> <p><strong>1</strong><strong>)</strong> CH2018 Project Team, 2018. CH2018 - Climate Scenarios for Switzerland. National Centre for Climate Services. https://doi.org/doi: 10.18751/Climate/Scenarios/CH2018/1.0<br><strong>2)</strong> Fuhrer, J., Jasper, K., 2009. Bewässerungsbedürftigkeit von Acker- und Grasland im heutigen Klima. Agrarforschung 10, 396–401.<br><strong>3)</strong> McKee, T., Doesken, N., Kleist, J., 1993. THE RELATIONSHIP OF DROUGHT FREQUENCY AND DURATION TO TIME SCALES. Eight conference on applied climatology, 17-22 January 1993, Anaheim, California.<br><strong>4)</strong> Moss, R. H., Edmonds, J. A., Hibbard, K. A., Manning, M. R., Rose, S. K., van Vuuren, D. P., Carter, T. R., Emori, S., Kainuma, M., Kram, T., Meehl, G. A., Mitchell, J. F. B., Nakicenovic, N., Riahi, K., Smith, S. J., Stouffer, R. J., Thomson, A. M., Weyant, J. P., and Wilbanks, T. J.: The next generation of scenarios for climate change research and assessment, Nature, 463, 747–756, https://doi.org/10.1038/nature08823, 2010.<br><strong>5)</strong> Muelchi, R., Rössler, O., Schwanbeck, J., Weingartner, R., Martius, O., 2021. An ensemble of daily simulated runoff data (1981–2099) under climate change conditions for 93 catchments in Switzerland (Hydro-CH2018-Runoff ensemble). Geoscience Data Journal 9, 46–57. https://doi.org/10.1002/gdj3.117<br><strong>6) </strong>Stagge, J. H., Tallaksen, L. M., Gudmundsson, L., Van Loon, A. F., and Stahl, K.: Candidate Distributions for Climatological Drought Indices (SPI and SPEI), International Journal of Climatology, 35, 4027–4040, https://doi.org/10.1002/joc.4267, 2015.<br><strong>7)</strong> van Vuuren, D. P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard, K., Hurtt, G. C., Kram, T., Krey, V., Lamarque, J.-F., Masui, T., Meinshausen, M., Nakicenovic, N., Smith, S. J., and Rose, S. K.: The representative concentration pathways: an overview, Climatic Change, 109, 5, https://doi.org/10.1007/s10584-011-0148-z, 2011.</p>
Streamflow drought hazard indicators for monitoring drought risk for human water supply and river ecosystems at the global scale
<p><strong>1) Indicators of streamflow drought hazard (SDHI) as computed by WaterGAP 2.2d (climate data WFDEI-GPCC) for the whole globe except Antarctica, spatial resolution: 0.5°, monthly data for the reference period 1986-2015:</strong></p> <p><strong>Indicators of drought magnitude: </strong>SPI12, SPEI12, SSI1, SSI12, EP1, RDQI1</p> <p><strong>Indicators of drought severity: </strong>CDQI1-Q50, CDQI1-Q80, CDQI1-Q80-HS, CDQI1-WUs, CDQI1-WUs-EFR, CDQI1-Q50_f, CDQI1-Q80_f, CDQI1-WUs_f, CDQI1-WUs-EFR_f, CEP1(20%)_f, CRDQI1(-50%)_f</p> <p><strong>2) WaterGAP grid cell IDs ("arcid") with longitude and latitude </strong>(WaterGAP_ArcID_lon_lat.txt)</p> <p><strong>3) Streamflow observations and SDHI (based on observations) for two GRDC gauging stations: </strong>Input_Figure_2_Little_Colorado_River.txt (station near Cameron) and Input_Figure_2_Danube_River.txt (station at Hofkirchen)</p> <p><strong>4) WaterGAP output: Mean monthly surface water abstractions in km3 per month: </strong>Mean_monthly_WUs_km3_per_month_WFDEI_GPCC_ant_22d_1986_2015.txt</p> <p><strong>5) Input data for computing Pearson correlation between SSI1 based on observations and each of the five indicators SSI1 (simulated), SPI3, SPI6, SPI9, and SPI12.</strong> Indicators computed for 218 out of 220 GRDC gauging stations with continuous streamflow observations between 1986 and 2015. The folder also contains a list of the 220 GRDC station numbers and the related WaterGAP grid cell ID ("arcid").</p> <p> </p>
Long-term daily hydrometeorological drought indices, soil moisture, and evapotranspiration for ICOS ecosystem sites
<p>Standardized drought indices to support research at ICOS ecosystem sites. Dataset to Nature Scientific Data submission.</p> <p>"The dataset comprises four files for each of the 101 sites: "[site_name]_input" contains the observational data extracted from E-OBS, PET estimates as well as the simulated soil water storage and actual evapotranspiration from mHM; and threemore files for each of the standardized drought indices ("SSMI_[site_name]", "SPI_[site_name]", "SPEI_[site_name]"). Details on the variables, their units and their origin are given in Tab. 1. For the SPI and SPEI, the file of each site contains the estimates for various aggregation times, ranging from 5 to 730 days in steps of 5 days from 5 to 365 and steps of 10 days from 370 to 730. Each data file has a daily temporal resolution and covers the time span from 1950 to 2021."</p>
Streamflow drought hazard indicators for monitoring drought hazard for human water supply and river ecosystems at the global scale (WaterGAP 2.2d, WFDEI-GPCC)
<p><strong>1) Streamflow drought hazard indicators (SDHIs) as computed by WaterGAP 2.2d (climate data WFDEI-GPCC) for the whole globe except Antarctica, spatial resolution: 0.5°, monthly data for the reference period 1986-2015:</strong></p> <p><strong>Indicators of drought magnitude: </strong>SSI1, SSI12, EP1, RDQI1</p> <p><strong>Indicators of drought severity: </strong></p> <p>CDQI1-Q50, CDQI1-Q50_f, CDQI1-Q80, CDQI1-Q80_f, CDQI1-Q80-HS, CDQI6-Q80, CDQI6-Q80_f,</p> <p>CDQI1-WUs, CDQI1-WUs-EFR, CDQI1-WUs-EFR_f,</p> <p>CEP1(20%), CEP1(20%)_f, CRDQI1(-50%), CRDQI1(-50%)_f</p> <p><strong>2) WaterGAP grid cell IDs ("arcid") with longitude and latitude:</strong></p> <p>(WaterGAP_ArcID_lon_lat.txt)</p> <p><strong>3) Streamflow observations at 220 GRDC gauging stations and list of the 220 GRDC station numbers with related WaterGAP grid cell ID ("arcid"):</strong></p> <p>Observed_monthly_streamflow_km3month_220_calstations_1986_2015.txt</p> <p>GRDC_number_WaterGAP_ArcID_220_stations.txt</p> <p><strong>4) SDHIs for four GRDC gauging stations:</strong></p> <p>time_series_danube_river.txt, time_series_angara_river.txt, time_series_white_river.txt, time_series_orange_river.txt</p> <p><strong>5) WaterGAP output: Mean monthly surface water abstractions in km3 per month: </strong>Mean_monthly_WUs_km3_per_month_WFDEI_GPCC_ant_22d_1986_2015.txt</p> <p> </p>
Applying RGB- and Thermal-Based Vegetation Indices from UAVs for High-Throughput Field Phenotyping of Drought Tolerance in Forage Grasses
<p>Basic data from publication <a href="https://doi.org/10.3390/rs13010147">https://doi.org/10.3390/rs13010147</a></p> <p><strong>All_TDRdata.csv</strong> contains the data from 48 TDR sensors (30 cm) installed in the three rainout shelters.</p> <ul> <li>Sensors 1 - 18 were installed vertically to obtain soil moisture content averaged over the 10 - 40 cm profile, on 6 locations per shelter</li> <li>Sensors 19-21 were installed diagonally to obtain soil moisture content averaged over the 20 - 40 cm profile on one location per shelter</li> <li>Sensors 22-24 were installed diagonally to obtain soil moisture content averaged over the 40 - 60 cm profile on one location per shelter</li> <li>Sensors 25-27 were installed horizontally to obtain soil moisture content at 10 cm depth on one location per shelter</li> <li>Sensors 28-30 were installed horizontally to obtain soil moisture content at 20 cm depth on one location per shelter</li> <li>Sensors 31-33 were installed horizontally to obtain soil moisture content at 30 cm depth on one location per shelter</li> <li>Sensors 34-36 were installed horizontally to obtain soil moisture content at 40 cm depth on one location per shelter</li> <li>Sensors 37-39 were installed horizontally to obtain soil moisture content at 50 cm depth on one location per shelter</li> <li>Sensors 40-42 were installed horizontally to obtain soil moisture content at 60 cm depth on one location per shelter</li> <li>Sensors 43-45 were installed horizontally to obtain soil moisture content at 70 cm depth on one location per shelter</li> <li>Sensors 46-48 were installed horizontally to obtain soil moisture content at 80 cm depth on one location per shelter</li> </ul> <p>Climate.txt contains the daily averaged microclimatic data</p> <p>PhenotypingData.csv contains the phenotypic data from the UAV flights and the breeder scores</p> <p> </p>
Data of monthly climate variables and drought indices within continental Chile for 1981-2023
<p>The dataset contains derived climatic data from ERA-5 at monthly frequency for continental Chile. The variables are:</p><ul><li>Precipitation (pre)</li><li>Minimum temperature (tas_min)</li><li>Mean temperature (tas)</li><li>Maximum temperature (tas_max)</li><li>Reference evapotranspiration (pet)</li><li>Snow water eqivalent (swe)</li><li>Soil volumetric water content at 1m depth (sm)</li></ul><p>Besides, the dataset contains the follwoing derived drough indices:</p><ul><li>Standardized Precipitation Index (SPI) for 1, 3, 6, 12, 24, and 36 months (spi_1<i> to </i>spi<i>_</i>36)</li><li>Standardized Precipitation Evapotranspiration Index (SPEI) for 1, 3, 6, 12, 24, and 36 months (spei_1<i> to </i>spei<i>_</i>36)</li><li>Evaporative Demand Drought Index (EDDI) for 1, 3, 6, 12, 24, and 36 months (eddi_1<i> to eddi_</i>36)</li><li>Anomaly of cumulative soil moisture at 1m depth (zcSM) for 1, 3, 6, 12, 24, and 36 months (zcsm_1<i> to zcsm_</i>36)</li><li>Anomaly of cumulative NDVI (zcNDVI) for 1, 3, and 6 months (zcndvi<i>1 to zcndvi</i>6)</li><li>Snow Water Equivalent Index (SWEI)</li></ul><p> </p>
IMPREX - TUC - Local scale seasonal hydrological and drought indices forecasts
<p>The compressed files are seasonal forecasts of discharge based on ECMWF System4 (1981-2003) and GLOSEA5 (1996-2003) for the Koutsoulidis catchment, Crete, Greece. The location of the Koutsoulidis catchment as well as the methodology of data development and validation are described in the open access publication by Grillakis et al., 2018 (<a href="https://www.mdpi.com/2073-4441/10/11/1593">https://www.mdpi.com/2073-4441/10/11/1593</a>). Seasonal forecasts of Standardized Precipitation Index (SPI) based on ECMWF System4 (1981-2009) and GLOSEA5 (1996-2009) for the Messara catchment are also included. A description of the study site and methodology is included in the poster attached in the compressed file (EGU201712072-Koutroulis).</p>
Multi-locus genome-wide association study for grain yield and drought tolerance indices in sorghum accessions
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Data set of resilience indices to drought and prescribed burning of Pinus nigra ssp. salzmannii and P. sylvestris L. trees
<p>Data on resilience and resistance indices to drought and burning inferred from tree ring width and carbon 13 isotopes for burned and unburned <em>P. nigra</em> spp. <em>salzmannii</em> and <em>P. sylvestris </em>trees<em>.</em>The dataset contains two files:</p> <p>TreeGrowth.txt: Data on resilience and resistance indices to drought and prescribed burning as well as the ratio of latewood to earlywood. Indices are inferred from total tree-ring, earlywood, and latewood widths. Included variables:</p> <ul> <li> Idsite (factor): code to identify uniquely each locality. Two levels: Miravé (1) and Lloreda (2).</li> <li> Idplot (factor): code to identify uniquely burned plots. Four levels: Miravé-Fall (1), Mirave-Spring (2), Lloreda-Fall (3) and Lloreda-Spring (4).</li> <li>Treatment (factor): whether the plot was burned or not. Two levels: control or left unburned (C) or burned (B)</li> <li>BurningSeason (factor): season of the burn. Three levels: control or left unburned (C), fall burn (F) or spring burn (S)</li> <li>Sp (factor): species. Two levels: <em>Pinus sylvestris</em> (ps) or<em> Pinus nigra </em>(pn)</li> <li>TreeCode (numeric): code to identify trees in the field</li> <li>dbh (numeric): diameter at breast height (cm)</li> <li>c12 (numeric): competition index before burning</li> <li>rci15 (numeric): release from tree competition 2 years post-burning calculated as the difference between pre (CI12) and post-burning competition (CI15) indices relative to pre-burning levels </li> <li>bchmin (numeric): minimum bole scorch height (cm)</li> <li>bchmax (numeric): maximum bole scorch height (cm)</li> <li>whiteAshes (numeric): white ashes after burning (%, in 1m radius from tree center)</li> <li>Resistance (numeric): Average basal area increment (BAI) during the stress period (drought 2012 and prescribed burning 2013) divided by the average BAI of the three years preceding the stress period (2009 to 2011)</li> <li>resilTTR (numeric): Average total tree ring BAI of the two years after the stress period (2014 and 2015) divided by average total tree ring BAI of the three years preceding the stress period (2009 to 2011)</li> <li>resilTTR14 (numeric): Total tree ring BAI of the first year after the stress period (2014) divided by average total tree ring BAI of the three years preceding the stress period (2009 to 2011)</li> <li>resilTTR15 (numeric): Total tree ring BAI of the second year after the stress period (2015) divided by average total tree ring BAI of the three years preceding the stress period (2009 to 2011)</li> <li>resilEW (numeric): Average earlywood BAI of the two years after the stress period (2014 and 2015) divided by average earlywood BAI of the three years preceding the stress period (2009 to 2011)</li> <li>resilEW14 (numeric): Earlywood BAI of the first year after the stress period (2014) divided by average earlywood BAI of the three years preceding the stress period (2009 to 2011)</li> <li>resilEW15 (numeric): Earlywood BAI of the second year after the stress period (2015) divided by average earlywood BAI of the three years preceding the stress period (2009 to 2011)</li> <li>resilLW (numeric): Average latewood BAI of the two years after the stress period (2014 and 2015) divided by average latewood BAI of the three years preceding the stress period (2009 to 2011)</li> <li>resilLW14 (numeric): Latewood BAI of the first year after the stress period (2014) divided by average latewood BAI of the three years preceding the stress period (2009 to 2011)</li> <li>resilLW15 (numeric): Latewood BAI of the second year after the stress period (2015) divided by average latewood BAI of the three years preceding the stress period (2009 to 2011)</li> <li>preLWEW (numeric): mean pre-stress ratio of latewood to earlywood calculated for the period 2006-2011</li> <li>difLWEW (numeric): mean post-stress latewood:earlywood (2014-2015) minus mean pre-stress latewood:earlywood (2006-2011)</li> </ul> <p>d13c.txt: Early and late-wood carbon 13 isotope from 2011 to 2015 for <em>P. nigra</em> spp. <em>salzmannii</em> and <em>P. sylvestris</em> burned in spring and fall in year 2013 at two sites. </p> <ul> <li>Idsite (factor): code to identify uniquely each locality. Two levels: Miravé (1) and Lloreda (2).</li> <li>Sp (factor): species. Two levels: Pinus sylvestris (ps) or Pinus nigra (pn)</li> <li>BurningSeason (factor): season of the burn. Three levels: control or left unburned (C), fall (F) or spring (S)</li> <li>TreeCode (factor): code to identify trees in the field."Pool" means that a pool of 5 individuals was used to determine carbon 13 isotope.</li> <li>SeasonalGrowth (factor): seasonal wood growth. Two levels: earlywood (E) and latewood (L).</li> <li>year (numeric): calendar year.</li> <li>d13c (numeric): carbon 13 isotope<br> </li> </ul>
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>
Transcriptome analyses indicate changes in seed oil content under drought stress in Brassica napus L.
GEO Series GSE160342. Brassica napus. 6 samples. Type: Expression profiling by high throughput sequencing.
Genome Profiling of Two Black Poplar (Populus nigra L.) Genotypes Differentially Adapted To Drought Stress Indicated Adaptation-Specific Transcripts
GEO Series GSE69875. Populus sp.; Populus nigra. 16 samples. Type: Expression profiling by array.
Data for Krishna river basin to develop integrated drought indices
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