# Upper Murray, Australia, 300 Year Stochastic Rainfall Reconstructions #----------------------------------------------------------------------- # World Data Center for Paleoclimatology, Boulder # and # NOAA Paleoclimatology Program #----------------------------------------------------------------------- # NOTE: Please cite Publication, and Online_Resource and date accessed when using these data. # If there is no publication information, please cite Investigators, Title, and Online_Resource and date accessed. # # # Online_Resource: https://www.ncdc.noaa.gov/paleo/study/18795 # # Original_Source_URL: http://www1.ncdc.noaa.gov/pub/data/paleo/contributions_by_author/ho2015/readme-ho2015.txt # # Description/Documentation lines begin with # # Data lines have no # # # Archive: Climate Reconstructions #-------------------- # Contribution_Date # Date: 2015-10-01 #-------------------- # Title # Study_Name: Upper Murray, Australia, 300 Year Stochastic Rainfall Reconstructions #-------------------- # Investigators # Investigators: Ho, M; Kiem, A.S.; Verdon-Kidd, D.C. #-------------------- # Description_Notes_and_Keywords # Description: Stochastic reconstruction of upper Murray rainfall using paleoclimate rainfall proxies of Queensland corals (see Lough, 2011, Paleoceanography), # Lake Tay tree-rings (see Cullen and Grierson, 2009, Climate Dynamics), and moisture sensitive speleothem record from Wombeyan Caves (see McDonald, 2005, # University of Newcastle thesis). Upper Murray rainfall reconstruction developed using an AR1 Monte Carlo model of rainfall at the Wombeyan Caves # and a reduced space objective analysis to model upper Murray rainfall. # # 10, 000 replicates of the stochastically generated upper Murray rainfall are stored in ten data files, each with 1000 # replicates in the columns. Rows correspond to data in years 1685-1981. Values are comma seperated. # Reconstructions for Deniliquin Post Office, Upper Murray catchment, Murray Darling Basin, Bureau of Meteorology Station Number: 074128 #-------------------- # Publication # Authors: Michelle Ho, Anthony S. Kiem, Danielle C. Verdon‐Kidd # Published_Date_or_Year: 2015-10-01 # Published_Title: A paleoclimate rainfall reconstruction in the Murray-Darling Basin (MDB), Australia: 2. Assessing hydroclimatic risk using paleoclimate records of wet and dry epochs # Journal_Name: Water Resources Research # Volume: # Edition: # Issue: # Pages: # DOI: 10.1002/2015WR017059 # Online_Resource: http://onlinelibrary.wiley.com/doi/10.1002/2015WR017059/full # Full_Citation: # Abstract: Estimates of hydrological risk are crucial to enable adequate planning and preparation for extreme events. However, the accurate estimation of hydrological risk is hampered by relatively short instrumental records in many parts of the world. Information derived from climate-sensitive paleoclimate proxies provide an opportunity to resolve hydroclimatic variability, but many regions, such as Australia's Murray-Darling Basin (MDB), currently lack the suitable in situ proxies necessary to do this. Here, new MDB rainfall reconstructions are presented based on a novel method using paleoclimate rainfall proxies in the Australasian region spanning from 749 BCE to 1980 CE. Our results emphasize the need to develop additional reconstructions and, with the companion paper, demonstrate how this information can be used to benefit water resource management. This study shows that prior to the 20th century both dry and wet epochs have persisted for longer periods than observed in the instrumental record - with the probability of both dry and wet periods exceeding a decade at least 10 times more likely prior to 1883 than suggested by the instrumental records. Some reconstructed rainfalls exceeded the instrumental range (i.e. drier dry epochs and wetter wet spells) despite a systematic underestimation of extremes due to a combination of proxy quality and model bias. Importantly, the results demonstrate that the instrumental record does not cover the full range of hydroclimatic variability possible in the MDB. Therefore hydroclimatic risk assessments based on the instrumental record likely underestimate, or at least misinterpret, the frequency, duration and magnitude of wet and dry epochs. #------------------ # Funding_Agency # Funding_Agency_Name: Australian Department of Education and Training # Grant: #------------------ # Funding_Agency # Funding_Agency_Name: Commonwealth Scientific and Industrial Research Organisation # Grant: #------------------ # Funding_Agency # Funding_Agency_Name: National Climate Change Adaptation Research Facility # Grant: #------------------ # Funding_Agency # Funding_Agency_Name: Australian Postgraduate Award # Grant: #------------------ # Funding_Agency # Funding_Agency_Name: Land and Water Flagship Scholarship # Grant: #------------------ # Funding_Agency # Funding_Agency_Name: Water Network Scholarship # Grant: #------------------ # Site_Information # Site_Name: Deniliquin Post Office # Location: Australia/New Zealand>Australia # Country: Australia # Northernmost_Latitude: -35.5 # Southernmost_Latitude: -35.5 # Easternmost_Longitude: 144.95 # Westernmost_Longitude: 144.95 # Elevation: 93 m #------------------ # Data_Collection # Collection_Name: Murray2015precip # Earliest_Year: 1685 # Most_Recent_Year: 1981 # Time_Unit: AD # Core_Length: # Notes: #------------------ # Chronology: # # #---------------- # Variables # # Data variables follow are preceded by "##" in columns one and two. # Data line variables format: Variables list, one per line, shortname-tab-longname-tab-longname components (9 components: what, material, error, units, seasonality, archive, detail, method, C or N for Character or Numeric data) # ## age_AD age, , , AD, , , , ,N ## precip precipitation, , , mm/year, Annual, reconstruction, , ,N # #---------------- # Data: # Data lines follow (have no #) # Data line format - tab-delimited text, variable short name as header # Missing Values: # # 10,000 replicates of the stochastically generated upper Murray rainfall are stored in ten text data files, # each with 1000 replicates in the columns. Rows correspond to data in years 1685-1981. Values are comma seperated. # See data files MuWQL_*.txt in: http://www1.ncdc.noaa.gov/pub/data/paleo/contributions_by_author/ho2015/ age_AD precip