This statistic shows the ten major U.S. cities with the most rainy days per year between 1981 and 2010. Rochester, New York, had an average of about 167 days per year with precipitation. The sunniest city in the U.S. was Phoenix, Arizona, with an average of 85 percent of sunshine per day.

The majority of the wettest cities in the United States are located in the Southeast. The major city with the most precipitation is New Orleans, Louisiana, which receives an average of 1592 millimeters (62.7 inches) of precipitation every year, based on an average between 1981 and 2010.

Historical changes of annual temperature and precipitation indices at selected 210 U.S. cities

This dataset provide:

Annual average temperature, total precipitation, and temperature and precipitation extremes calculations for 210 U.S. cities.

Historical rates of changes in annual temperature, precipitation, and the selected temperature and precipitation extreme indices in the 210 U.S. cities.

Estimated thresholds (reference levels) for the calculations of annual extreme indices including warm and cold days, warm and cold nights, and precipitation amount from very wet days in the 210 cities.

Annual average of daily mean temperature, Tmax, and Tmin are included for annual average temperature calculations. Calculations were based on the compiled daily temperature and precipitation records at individual cities.

Temperature and precipitation extreme indices include: warmest daily Tmax and Tmin, coldest daily Tmax and Tmin , warm days and nights, cold days and nights, maximum 1-day precipitation, maximum consecutive 5-day precipitation, precipitation amounts from very wet days.

Number of missing daily Tmax, Tmin, and precipitation values are included for each city.

Rates of change were calculated using linear regression, with some climate indices applied with the Box-Cox transformation prior to the linear regression.

The historical observations from ACIS belong to Global Historical Climatological Network - daily (GHCN-D) datasets. The included stations were based on NRCC’s “ThreadEx” project, which combined daily temperature and precipitation extremes at 255 NOAA Local Climatological Locations, representing all large and medium size cities in U.S. (See Owen et al. (2006) Accessing NOAA Daily Temperature and Precipitation Extremes Based on Combined/Threaded Station Records).

Resources:

See included README file for more information.

Additional technical details and analyses can be found in: Lai, Y., & Dzombak, D. A. (2019). Use of historical data to assess regional climate change. Journal of climate, 32(14), 4299-4320. https://doi.org/10.1175/JCLI-D-18-0630.1

Other datasets from the same project can be accessed at: https://kilthub.cmu.edu/projects/Use_of_historical_data_to_assess_regional_climate_change/61538

ACIS database for historical observations: http://scacis.rcc-acis.org/

GHCN-D datasets can also be accessed at: https://www.ncei.noaa.gov/data/global-historical-climatology-network-daily/

Station information for each city can be accessed at: http://threadex.rcc-acis.org/

• 2024 August updated -

  Annual calculations for 2022 and 2023 were added.

  Linear regression results and thresholds for extremes were updated because of the addition of 2022 and 2023 data.

  Note that future updates may be infrequent.

• 2022 January updated -

  Annual calculations for 2021 were added.

  Linear regression results and thresholds for extremes were updated because of the addition of 2021 data.

• 2021 January updated -

  Annual calculations for 2020 were added.

  Linear regression results and thresholds for extremes were updated because of the addition of 2020 data.

• 2020 January updated -

  Annual calculations for 2019 were added.

  Linear regression results and thresholds for extremes were updated because of the addition of 2019 data.

  Thresholds for all 210 cities were combined into one single file – Thresholds.csv.

• 2019 June updated -

  Baltimore was updated with the 2018 data (previously version shows NA for 2018) and new ID to reflect the GCHN ID of Baltimore-Washington International AP. city_info file was updated accordingly.

  README file was updated to reflect the use of "wet days" index in this study. The 95% thresholds for calculation of wet days utilized all daily precipitation data from the reference period and can be different from the same index from some other studies, where only days with at least 1 mm of precipitation were utilized to calculate the thresholds. Thus the thresholds in this study can be lower than the ones that would've be calculated from the 95% percentiles from wet days (i.e., with at least 1 mm of precipitation).

Oberstdorf, located in Bavaria, recorded around ***** liters of precipitation per square meter in 2024. Kiel in the north, on the other hand, recorded annual precipitation of around *** liters per square meter.

In 2024, Louisiana recorded ***** inches of precipitation. This was the highest precipitation within the 48 contiguous U.S. states that year. On the other hand, Nevada was the driest state, with only **** inches of precipitation recorded. Precipitation across the United States Not only did Louisiana record the largest precipitation volume in 2024, but it also registered the highest precipitation anomaly that year, around 14.36 inches above the 1901-2000 annual average. In fact, over the last decade, rainfall across the United States was generally higher than the average recorded for the 20th century. Meanwhile, the driest states were located in the country's southwestern region, an area which – according to experts – will become even drier and warmer in the future. How does global warming affect precipitation patterns? Rising temperatures on Earth lead to increased evaporation which – ultimately – results in more precipitation. Since 1900, the volume of precipitation in the United States has increased at an average rate of **** inches per decade. Nevertheless, the effects of climate change on precipitation can vary depending on the location. For instance, climate change can alter wind patterns and ocean currents, causing certain areas to experience reduced precipitation. Furthermore, even if precipitation increases, it does not necessarily increase the water availability for human consumption, which might eventually lead to drought conditions.

These daily weather records were compiled from a subset of stations in the Global Historical Climatological Network (GHCN)-Daily dataset. A weather record is considered broken if the value exceeds the maximum (or minimum) value recorded for an eligible station. A weather record is considered tied if the value is the same as the maximum (or minimum) value recorded for an eligible station. Daily weather parameters include Highest Min/Max Temperature, Lowest Min/Max Temperature, Highest Precipitation, Highest Snowfall and Highest Snow Depth. All stations meet defined eligibility criteria. For this application, a station is defined as the complete daily weather records at a particular location, having a unique identifier in the GHCN-Daily dataset. For a station to be considered for any weather parameter, it must have a minimum of 30 years of data with more than 182 days complete in each year. This is effectively a 30-year record of service requirement, but allows for inclusion of some stations which routinely shut down during certain seasons. Small station moves, such as a move from one property to an adjacent property, may occur within a station history. However, larger moves, such as a station moving from downtown to the city airport, generally result in the commissioning of a new station identifier. This tool treats each of these histories as a different station. In this way, it does not thread the separate histories into one record for a city. Records Timescales are characterized in three ways. In order of increasing noteworthiness, they are Daily Records, Monthly Records and All Time Records. For a given station, Daily Records refers to the specific calendar day: (e.g., the value recorded on March 7th compared to every other March 7th). Monthly Records exceed all values observed within the specified month (e.g., the value recorded on March 7th compared to all values recorded in every March). All-Time Records exceed the record of all observations, for any date, in a station's period of record. The Date Range and Location features are used to define the time and location ranges which are of interest to the user. For example, selecting a date range of March 1, 2012 through March 15, 2012 will return a list of records broken or tied on those 15 days. The Location Category and Country menus allow the user to define the geographic extent of the records of interest. For example, selecting Oklahoma will narrow the returned list of records to those that occurred in the state of Oklahoma, USA. The number of records broken for several recent periods is summarized in the table and updated daily. Due to late-arriving data, the number of recent records is likely underrepresented in all categories, but the ratio of records (warm to cold, for example) should be a fairly strong estimate of a final outcome. There are many more precipitation stations than temperature stations, so the raw number of precipitation records will likely exceed the number of temperature records in most climatic situations.

In 2011, Buffalo, New York was the major city in the United States with the most partial to heavy cloud cover, with 311 days of clouds in that year. Seattle, Pittsburg, Rochester, and Cleveland rounded out the top five cities.

Buffalo’s climate

Buffalo, New York, is located on the eastern end of Lake Erie and is the origin point of the Niagara River, and its location on Lake Erie helps to regulate the city’s climate. However, between 1981 and 2010, it had an average of 167 days with more than 0.01 inches of rainfall per year, and also had an average wind speed of 11.8 miles per hour.

The second largest city in New York

Buffalo is the second largest city in New York state, with a metro area population of 1.13 million in 2017. The city is not far from Niagara Falls, which was listed as one of the most expensive summer destinations in the state of New York.

The Baltimore radar rainfall dataset was developed from a multi-sensor analysis combining radar rainfall estimates from the Sterling, VA WSR88D radar (KLWX) with measurements from a collection of ground based rain gages. The archived data have a 15-minute time resolution and a grid resolution of 0.01 degree latitude/longitude (approximately 1 km x 1 km); 15-minute rainfall accumulations for each grid are in mm. The dataset spans 22 years, 2000-2021, and covers an area of approximately 4,900 km^2 (70 by 70 grids, each with approximate area of 1 km^2) surrounding the Baltimore, MD metropolitan area (Figure 1). The rainfall data cover the six months from April to September of each year. This is the period with most intense sub-daily rainfall and the period for which radar measurements are most accurate. Figure 1 illustrates the climatological analyses of mean annual frequency of days with at least 1 hour rainfall exceeding 25 mm. The striking spatial variability of convective rainfall is illustrated in Figure 2 by the April-September climatology of annual lightning strikes.

As with many long-term environmental data sets, sensor technology has changed during the time period of the archive. The Sterling, VA WSR88D radar underwent a hardware upgrade from single polarization to dual polarization in 2012. Prior to the upgrade, rainfall was estimated using a conventional radar-reflectivity algorithm (HydroNEXRAD) which converts reflectivity measurements in polar coordinates from the lowest sweep to rainfall estimates on a 0.01 degree latitude-longitude grid at the surface (see Seo et al. 2010 and Smith et al. 2012 for details on the algorithm). The polarimetric upgrade introduced new measurements into the radar-rainfall algorithm. In addition to reflectivity, the operational rainfall product, Digital Precipitation Rate (DPR), directly uses differential reflectivity and specific differential phase shift measurements to estimate rainfall (https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00708; see also Giangrande and Ryzhkov 2008). Details of the algorithm structure and parameterization for the DPR radar-rainfall estimates have been modified during the 10-year period of the data set.

A storm-based (daily) multiplicative mean field bias has been applied to both datasets. The mean field bias is computed as the ratio of daily rain gage rainfall at a point to daily radar rainfall for the bin that contains the gage. The rain gage dataset is compiled from rain gages in the Baltimore metropolitan region and surrounding areas and includes gages acquired from both Baltimore City and Baltimore County, and the Global Historical Climatology Network daily (GHCNd). Mean field bias improves rainfall estimates and diminishes the impacts of changing measurement procedures.

The dataset has been archived in 2 formats: netCDF gridded rainfall, 1 file for each 15-minute time period, and csv or excel format point rainfall (1 point at the center of each grid) in a timeseries format with 1 file per calendar month covering the entire 70x70 domain. The csv files are in folders organized by calendar year. The first five columns in each file represent year, month, day, hour, and minute and can be combined to generate a unique date-time value for each time step. Each additional column is a complete time series for the month and represents data from one of the 1-km2 grid cells in the original data set.

The latitude and longitude coordinates for each pixel in the grid are provided. The latitude and longitude represent the centroid of the cell, which is square when represented in latitude and longitude coordinates and rectangular when represented in other distance-based coordinate systems such as State Plane or Universal Transverse Mercator. There are 4900 pixels in the domain. In order to visualize the data using GIS or other software, the user needs to associate each column in the annual rainfall file with the latitude and longitude values for that grid cell number.

These data may be subject to modest revision or reformatting in future versions. The current version is version 2.0 and is being offered to users who wish to explore the data. We will revise this document as needed.

The annual number of rain days in the UK has fluctuated over the past three decades. In 2024, there were *** days in which * mm or more of rain fell. The year with the greatest number of rain days was 2000 when ***** days had at least * mm of rain. England is the driest country in the UK England is on average the driest country in the United Kingdom. In 2024, the country recorded an annual rainfall of **** mm. After England, Northern Ireland is the country that receives the least amount of rainfall across the UK. Wettest regions in Britain Despite Cardiff being the wettest city in the United Kingdom according to the Met Office, Scotland had received on average the largest volume of annual rainfall in the past 10 years. The northern and western regions of the UK – where rainfall is arriving from the Atlantic – tend to be the wettest in the country.

Berlin (Germany) has one of the longest climate records in the world (Cubasch and Kadow, 2011). In the 17th century mainly the family of the astronomer Kirch started measuring the temperature and wrote down general weather patterns. In the beginning of the 18th century the measurements became more regular including multiple measures per day. Over time, more inner city stations appeared but also disappeared. Therefore, this Berlin Climate Record is a moving station, but representable for the inner city of Berlin. With these datasets we reactivate the long inner city climate record in several variables. In this study, we digitized, analyzed, corrected, reconstructed, and provide the datasets on the very rare daily time frequency.

U.S. 15 Minute Precipitation Data is digital data set DSI-3260, archived at the National Climatic Data Center (NCDC). This is precipitation data. The primary source of data for this file is approximately 2,000 mostly U.S. weather stations operated or managed by the U.S. National Weather Service. Stations are primary, secondary, or cooperative observer sites that have the capability to measure precipitation at 15 minute intervals. This dataset contains 15-minute precipitation data (reported 4 times per hour, if precip occurs) for U.S. stations along with selected non-U.S. stations in U.S. territories and associated nations. It includes major city locations and many small town locations. Daily total precipitation is also included as part of the data record. NCDC has in archive data from most states as far back as 1970 or 1971, and continuing to the present day. The major parameter is precipitation amounts at 15 minute intervals, when precipitation actually occurs.

Dataset Title: Urban Air Quality and Health Impact Dataset: A Comprehensive Overview of U.S. Cities

Description:

This dataset provides an extensive collection of synthetic data related to urban air quality and its potential health impacts across major U.S. cities. The data has been augmented to include a wide range of features, making it a valuable resource for research and analysis in the fields of environmental science, public health, and urban studies.

Features:

• DateTime: Timestamp of the recorded data.
• City: The U.S. city where the data was recorded (e.g., Phoenix, San Diego, New York City).
• Temp_Max: Maximum temperature for the day (°F).
• Temp_Min: Minimum temperature for the day (°F).
• Temp_Avg: Average temperature for the day (°F).
• Feels_Like_Max: Maximum "feels like" temperature for the day (°F).
• Feels_Like_Min: Minimum "feels like" temperature for the day (°F).
• Feels_Like_Avg: Average "feels like" temperature for the day (°F).
• Dew_Point: Dew point temperature (°F).
• Humidity: Relative humidity percentage.
• Precipitation: Total precipitation for the day (inches).
• Precip_Prob: Probability of precipitation (percentage).
• Precip_Cover: Coverage of precipitation (percentage).
• Precip_Type: Type of precipitation (e.g., rain, snow).
• Snow: Amount of snowfall (inches).
• Snow_Depth: Snow depth (inches).
• Wind_Gust: Maximum wind gust speed (mph).
• Wind_Speed: Average wind speed (mph).
• Wind_Direction: Wind direction (degrees).
• Pressure: Atmospheric pressure (hPa).
• Cloud_Cover: Cloud cover percentage.
• Visibility: Visibility distance (miles).
• Solar_Radiation: Solar radiation (W/m²).
• Solar_Energy: Solar energy received (kWh).
• UV_Index: UV index level.
• Severe_Risk: Risk level of severe weather (e.g., low, moderate, high).
• Sunrise: Sunrise time (HH:MM:SS).
• Sunset: Sunset time (HH:MM:SS).
• Moon_Phase: Phase of the moon (e.g., new moon, full moon).
• Conditions: General weather conditions (e.g., clear, cloudy).
• Description: Detailed description of the weather conditions.
• Icon: Weather icon representation.
• Stations: Weather stations reporting data.
• Source: Data source information.
• Temp_Range: Temperature range for the day (difference between max and min temperatures).
• Heat_Index: Heat index value for the day.
• Severity_Score: Score representing the severity of weather conditions.
• Condition_Code: Code representing specific weather conditions.
• Month: Month of the year.
• Season: Season of the year (e.g., winter, spring).
• Day_of_Week: Day of the week.
• Is_Weekend: Indicator if the day is a weekend.
• Health_Risk_Score: Score representing the potential health risk based on weather and air quality conditions.

Usage:

This dataset is intended for researchers, data scientists, and analysts interested in studying the relationships between air quality, weather conditions, and public health. It can be used for developing predictive models, conducting statistical analyses, and creating visualizations to better understand urban environmental impacts.

Source:

The data is synthesized and augmented based on real-world weather data from major U.S. cities and is intended to serve as a comprehensive resource for urban air quality and health impact studies.

Notes:

• The dataset is synthetic and has been generated to provide a broad range of scenarios for analysis.
• Ensure to validate any findings with real-world data when applying the insights to practical applications. .

The Re-Climate® API delivers the most reliable seasonal climate prediction system in Europe, with its extended precipitation forecasts independently assessed by the National Physical Laboratory. Uniquely, WeatherLogistics® delivers long-range forecasts of 2 to 15 weeks (14 to 90 days) suitable for operations planning.

WeatherLogistics® research and development focusses on reducing model bias and uncertainties at a local scale. It achieves this by blending numerous climate datasets and weather models (ECMWF, Met Office, Météo-France and a jet stream linked statistical model) through its algorithms. This helps organisations to accurately forecast extreme weather, within well-calibrated confidence intervals, calculate their risk exposure and adapt to extreme weather and chronic climate hazards.

Re-Climate®'s unique provision of confidence bounds, and Python Quickstart code allows users to reduce their Data Science workload and calculate accurate likelihoods of occurrence for weather events. Downscaling is both temporal and spatial and based on local variability training (altitude, coastlines, surface land cover, geology, rain shadows, frost basins and other geographical features), with daily precipitation events spatially correlated and consistent with ERA-5 observations. Evidence for its validity is supported through impartial assessment work undertaken by the UK's national measurements standards agency (NPL Management Limited).

Modelling weather events and seasonal climate helps decision-makers communicate losses linked to floods, drought, wind damage, hail, heatwaves or freeze days to within 3.5 kilometres. Forecasts of climate perils covering the next full 3 months.

Supplied as a 100-member 'ensemble', a collection of well-calibrated daily weather time series, Re-Climate® enables firms to better hedge or price their risk. The product also helps clients adapt to acute physical risks posed by extreme weather events such as heatwaves and floods, develop rigorous scenario plans, and protect assets from climate hazards on operational timescales.

WeatherLogistics has a mission to help meet UN Sustainable Development Goals (SDGs) for food, water, and natural resources. Currently the firm is developing a sustainability monitoring platform to advise farmers on their present and future climate exposure and how to reduce both their agricultural inputs and reduce greenhouse gas emissions. This forms part of a 12-month project in partnership with the University of Leicester, the University of Reading and supported and funded by UKRI/ STFC.

Future farmers will be able to build smarter precision farming solutions and benchmarking platforms, integrated pest management systems, and decision-support applications to avoid food supply distribution. Growers can also optimise their operations with better timed preventative spraying, frost protection, and crop quality assurance.

This statistic shows the number of rain days in the months of June, July and August from 1986 to 2016 in selected cities in Germany. During the period of consideration *** rain days were counted in Frankfurt. Rain days are defined as day with more than *** liters precipitation per square meters per day.

ABSTRACT Extreme weather events have emerged as one of the main manifestations of climate change, being that the mitigation of the elapsed impacts demand studies of the magnitude and frequency of their occurrence. This study aims to identify the trends of extreme precipitation events in the Metropolitan Region of Belo Horizonte, especially concerning their frequency. The trends of precipitation were studied with especial regard to the indices set by ETCCDMI (Expert Team on Climate Change Detection Monitoring and Indices), including time series of annual number of rainy days above a certain threshold recorded at fourteen rainfall gauging stations. One stage of this study consisted in surveying the flood occurrence in the area, besides analyzing the precipitation data corresponding to date of flood occurrences, in order to establish a threshold value beyond which an event would entail potential impacts. No regional index pattern could be set based on such results, although the rainfall station located in the city of Belo Horizonte reported a statistically significant increase in daily precipitation events above 10, 20, 30 and 40 mm, in maximum precipitation recorded over five consecutive days, in daily intensity, and in total annual precipitation. Abrupt changes in rainfall series were also recorded. The results have indicated that the city may be potentially impacted by extreme rainfall increase, probably associated to changes in temperatures on regional and local scales.

Anomalous weather resulting in Temperature and Precipitation extremes occurs almost every day somewhere in Canada. For the purpose of identifying and tabulating daily extremes of record for temperature, precipitation and snowfall, the Meteorological Service of Canada has threaded or put together data from closely related stations to compile a long time series of data for about 750 locations in Canada to monitor for record-breaking weather. Virtual Climate stations correspond with the city pages of weather.gc.ca. This data provides the daily extremes of record for Precipitation for each day of the year. Daily elements include: Greatest Precipitation.

Body mass in overwintering waterfowl is an important fitness attribute as it affects winter survival, timing of spring migration, and subsequent reproductive success. Recent research in Europe and the western United States indicates body mass of mallards (Anas platyrhynchos) has increased from the late 1960s to early 2000s. The underlying mechanism is currently unknown; however, researchers hypothesize that increases are due to a more benign winter climate, increased food availability through natural and artificial flooding, introgression of wild mallard populations by game-farm mallards, or shifting of wintering distributions northward. Further investigation of factors related to winter mallard body mass increases and whether this phenomenon is occurring in other major flyways could increase understanding of intrinsic and extrinsic variables influencing waterfowl fitness. We collected and analyzed mallard body mass data in the Lower Mississippi Alluvial Valley from 1979 to 2021 to determine sources of temporal variation. We measured hunter-harvested mallards from private hunting clubs, public hunting areas, and duck-plucking businesses. Mallard body mass increased by approximately 6% among all age-sex classes from 1979 to 2021. We also compiled weather data (rainfall [cm], weather severity index information [WSI], river gage discharge [cfs] and height [m]) to relate to mallard body mass measurements. Methods To analyze age and sex differences among and within years from 1979 to 2021, we categorized each mallard into one of four classes comprised of adult males, adult females, juvenile males, and juvenile females, referred to as AgeSex. To explore how mallard body mass has changed from 1979 to 2021, we used year as a fixed effect within our models. Because study years (or duck hunting seasons) span calendar years (often Nov-Feb), for clarity our use of the term “year” refers to the duck hunting season initiating in November of that year and spanning to February of the next calendar year. Days refer to chronological days within hunting seasons. Because hunting season dates varied among years, we represented days within seasons as modified Julian days, with the earliest date that a mallard’s mass was measured across the study labeled as day 1 (November 19th) and each subsequent day numbered sequentially until day 83 (Feb 13th), the latest date a bird was measured. To assess the relationship of cumulative rainfall and cold weather severity (or Weather Severity Index developed by Schummer et al., 2010; WSI) with mallard body mass, we compiled climate data from representative National Oceanic and Atmospheric Administration (NOAA) weather stations. The variables we used were daily cumulative precipitation (cm) and minimum and maximum daily temperature (°C). We obtained data from Yazoo City, Yazoo County, Mississippi (station name: Yazoo City 5 NNE) for winters 1979–1980 through 1982–1983 and from Arkansas (station names: Stuttgart 9 ESE, Des Arc, Searcy, Georgetown, Pine Bluff, Augusta, Wynne, Alicia, Keiser, Eudora, Monticello Municipal Airport, Marianna, Arkansas Post, Rohwer, Paragould, and Pocahontas) for winters 1990–1991, 1999–2000 through 2003–2004, 2015–2016, 2016–2017, 2019–2020, and 2020–2021 based on proximity of sampling sites and weather stations. We recognize that daily rainfall on a given date may not be the best measure of how precipitation influenced body mass on the date of harvest. Because the known movement of waterfowl before measurement of body mass was unknown, and it can take waterfowl anywhere from 8 to 72 h to digest most food resources (Charalambidou et al., 2005), we calculated a 3-day cumulative rainfall before the dates of mallard measurement to more accurately represent the relationship between precipitation and mallard body mass. Similarly, we did not use in our analysis the daily average temperature from the day that a bird was measured. Instead, we calculated daily average temperatures for each day and season and used these values to calculate a 3-day mean of daily average temperatures before mallards were measured. Finally, we calculated WSI using our 3-day mean temperatures (by modifying the WSI equation from Schummer et al., 2010) to evaluate the relationship of weather severity and mallard body mass. We modified the WSI equation to use three-day means rather than two-week means because we wanted to represent cumulative of temperature experienced by ducks more recently before measurement (See Eq. (1) in Veon et al. 2023). Finally, we used river gage height (m) data as a function of flooding. River gage height values were identified using associated discharge (cfs) values from rate tables obtained from the USGS Lower Mississippi-Gulf Water Science Center for Mississippi winters 1979–1983 (gage name: Big Black River near Bovina) and for Arkansas winters 1990–1991,1999–2000 through 2003–2004, 2015–2016, 2016–2017, and 2019–2020, and 2020–2021 (gage names: Black River near Corning, Black River at Pocahontas, Black River at Black Rock, Cache River at Egypt, White River at Newport, White River at Georgetown, Cache River near Cotton Plant White River at DeValls Bluff, L′Anguille River near Colt, L′Anguille River near Palestine, Bayou Meto near Lonoke, Bayou Bartholomew at Garrett Bridge, and Bayou Bartholomew near McGehee). We collected data from river gages nearest our sample sites to examine the relationship of daily river height to mallard body mass. Similar to rainfall, we expected mallard body mass would be greater when river levels were higher because of increased foraging habitat. Importantly, we found rainfall and river gage height were not highly correlated (Pearson Correlation [r] = 0.25). This result may indicate that rivers can fluctuate from rainfall upstream of areas absent of local rainfall or by human control (e.g., locks, dams, levees) (Junk et al., 1989; MRC, 2007). Additionally, forage availability may reflect different combinations of rainfall and river flooding. Rainfall may be more influential in flooding habitat not connected to or near river systems (e.g., puddling or ponding), whereas river flooding more likely to provides access to foraging habitat in riparian and adjacent overbank habitats (Smith and Callahan, 1983; Galat et al., 1998, Heitmeyer, 2006).

Hurricane Helene's track and wind swath through the Carolinas. Data sources: NOAA National Hurricane Center, ESRI, Henderson CountyThe City of Hendersonville stands at the convergence of several creeks and streams within the French Broad River Basin. While the City is no stranger to localized flooding, the days of rain before Helene reached the area exceeded previous records, breaching levels past the 500-year floodplain. Hendersonville received 21.96 inches of rainfall through 8am Saturday, the 3rd highest recorded in the region. (The National Weather Service)

The city of Tabarka had the highest number of rainy days in Tunisia in 2019. In that agricultural year, the area recorded *** days of rain, registering the highest amount of rainfall in the country. The capital city, Tunis-Carthage, registered ** rainy days in the same period. On the other hand, El-Borma was the driest area.

The Rainfall data consists of daily time series of rainfall data in millimeters. The amount of rainfall is measured using a rain gauge. A rain gauge consists of a cylindrical vessel assembly kept in the open to collect rain. Rainfall collected in the rain gauge is measured at regular intervals such as at around 08:00am in the City. Our Depots collect these readings every day and send the data to Head Office. read more