DOI: 10.5281/zenodo.7986017
title: Archive for Master's thesis

description: This archive contains model forcing and output for the Shyft model, along with scripts of the related data processing. The structure of the archive (folders) is as follows: "lalm" and "elverum: contain the meteorological forcing data for the two catchments Lalm and Elverum, respectively. "model forcing for historical periods": contains the bias corrected climate model data representing the three historical periods. "model simulation output": contains the model output data for the simulations of the three historical periods. "shyft workspace": contains the data processing scripts. 1. This dataset consists of the folders: "senorge", "era5" and "hysn5". The included data variables are: temperature, precipitation, wind speed, relative humidity and radiation. Temperature and precipitation are found in "senorge". Wind speed is found in "era5". Lastly, relative humidity and radiation are found in "hysn5". The dataset is of the netCDF-format. The folders contain data that was downloaded from the sources: SeNorge2018 (The Norwegian Meteorological institute, 2022), ERA5-land (Muñoz, 2019; Muñoz, 2021) and HYSN5 (Haddeland, 2022). The data is described as follows: temperature: Description: daily mean air temperature Unit: degrees Celsius Spatial resolution: 1x1 km Grid mapping: UTM Zone 33 Dimension: time, latitude and longitude precipitation: Description: daily mean precipitation Unit: mm/day Spatial resolution: 1x1 km Grid mapping: UTM Zone 33 Dimension: time, latitude and longitude wind speed: Description: daily mean wind speed Unit: m/s Spatial resolution: 0.1x0.1 degree (native resolution of 9 km) Grid mapping: EPSG:4326 Dimension: time, latitude and longitude relative humidity: Description: daily mean near-surface relative humidity Unit: % Spatial resolution: 1x1 km Grid mapping: UTM Zone 33 Dimension: time, latitude and longitude radiation: Description: daily mean surface downwelling shortwave radiation Unit: W/m<sup>2 </sup> Spatial resolution: 1x1 km Grid mapping: UTM Zone 33 Dimension: time, latitude and longitude 2. This dataset contains climate model data for the three historical periods: Medieval Warm Period (MWP; 1000-1150 AD), Little Ice Age (LIA; 1600-1750 AD) and Industrial Time (IT; 1800-1950 AD). The data covers the two catchments Lalm (L) and Elverum (E) for simulations using both low solar variability (Solar 1; S1) and high solar variability (Solar 2; S2). The data consists of the variables: temperature (temp), precipitation (prec), wind speed (wind), relative humidity (humi) and radiation (radi). The dataset is of the netCDF-format. The related source data is not published here, due to licences. Contact Lu Li at the NORCE research centre regarding data accessibility. The data is described as follows: temperature: Description: daily mean air temperature Unit: degrees Celsius Spatial resolution: 1x1 km Grid mapping: UTM Zone 33 Dimension: time, latitude and longitude precipitation: Description: daily mean precipitation Unit: mm/hour Spatial resolution: 1x1 km Grid mapping: UTM Zone 33 Dimension: time, latitude and longitude wind speed: Description: daily mean wind speed Unit: m/s Spatial resolution: 0.1x0.1 degree (native resolution of 9 km) Grid mapping: UTM Zone 33 Dimension: time, latitude and longitude relative humidity: Description: daily mean near-surface relative humidity Unit: - Spatial resolution: 1x1 km Grid mapping: UTM Zone 33 Dimension: time, latitude and longitude radiation: Description: daily mean surface downwelling shortwave radiation Unit: W/m<sup>2 </sup> Spatial resolution: 1x1 km Grid mapping: UTM Zone 33 Dimension: time, latitude and longitude 3. This dataset contains time series data for the three historical periods: Medieval Warm Period (MWP; 1000-1150 AD), Little Ice Age (LIA; 1600-1750 AD) and Industrial Time (IT; 1800-1950 AD), which are output from the Shyft model. The data covers the two catchments Lalm (L) and Elverum (E) for simulations using both low solar variability (Solar 1; S1) and high solar variability (Solar 2; S2). The data consists of the variables: discharge, temperature, precipitation, wind_speed, relative_humidity and radiation, snow water equivalent (SWE) and snow covered area (SCA). The dataset is of the csv-format. NB: the datetime index of the data suggests that the data covers the period of 1700-1850, however this is only true for IT. This inconsistency is caused by a limitation of datetime64 in pandas, which does not handle dates prior to the year 1678. The data is described as follows: discharge: Description: daily mean air temperature Unit: degrees Celsius Dimension: time temperature: Description: daily mean air temperature Unit: degrees Celsius Dimension: time precipitation: Description: daily mean precipitation Unit: mm/hour Dimension: time wind_speed: Description: daily mean wind speed Unit: m/s Dimension: time relative_humidity: Description: daily mean near-surface relative humidity Unit: - Dimension: time radiation: Description: daily mean surface downwelling shortwave radiation Unit: W/m<sup>2 </sup> Dimension: time SWE: Description: daily mean snow water equivalent Unit: mm Dimension: time SCA: Description: daily mean snow covered area (% of total catchment area) Unit: - Dimension: time 4. The scripts make up the workflow of the thesis. In order to reproduce the results, the first script has to be run firstly, then the second script is applied on the output from the first etc. Keep in mind that manual adjustments inside the scripts are required in order to obtain some of the results. The scripts are described as follows: "Subsetting_data.ipynb": This script subsets forcing data (temperature, precipitation, wind speed, relative humidity and radiation) from the sources (SeNorge2018, ERA5-Land and HySN5) to the catchments of Lalm and Elverum. "convert_netcdf.ipynb": This script converts netCDF-files of temperature, precipitation, wind speed, relative humidity and radiation to fit as model forcing to the Shyft modeling framework. It also creates a cell data file containing information about the catchments (Lalm and Elverum) forest, lake and glacier fraction, which are required in Shyft. "QDM_lalm.ipynb" and "QDM_elverum.ipynb": These scripts perform the bias correction approach, Quantile Delta Mapping (QDM), on the climate model data (temperature, precipitation, wind speed, relative humidity and radiation). "extract_historical_periods.ipynb": This script extracts the three historical periods of 1000-1150 (Medieval Warm Period), 1600-1750 (Little Ice Age) and 1800-1950 (Industrial Time) from the climate model data (temperature, precipitation, wind speed, relative humidity and radiation). "calibration_lalm.ipynb" and "calibration_elverum.ipynb": Scripts that runs the calibration of Lalm and Elverum catchment using the Shyft model, respectively.* "simulation_lalm.ipynb" and "simulation_elverum.ipynb": Scripts that runs the simulation of Lalm and Elverum catchment using the Shyft model, respectively.* "data_analysis.ipynb": This script contains the data analysis performed on the Shyft model simulation output. The analysis includes: calculations of mean monthly values of the climate variables (discharge, temperature, precipitation, snow water equivalent and snow covered area), decadal time series of the climate variables, calculations of mean floods and 100-year floods, flood and extreme precipitation frequency analysis, calculation of season index, estimation of flood generating processes, plotting of flood roses and estimation of Standardised Precipitation Index. *For the Shyft model configuration, simulation and calibration files (yaml-files) are included in the folder "yaml_lalm" and "yaml_elverum" for the two catchments. These yaml-files are described as follows: simulation.yaml: is used for configuration of the model simulation calibration.yaml: is used for configuration of the model calibration calibrated_model.yaml: contains the calibrated model parameters datasets.yaml: contains the paths to the data variables interpolation.yaml: contains the interpolation methods and parameters region.yaml: contains the modeling domain References: Haddeland, I. (2022). HySN2018v2005ERA5 (Version 1) [Data set]. Zenodo. (Accessed on: 19-09-2022). doi: https://doi.org/10.5281/zenodo.5947547. Muñoz Sabater, J. (2019). ERA5-Land hourly data from 1981 to present [Dataset]. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on: 19-09-2022). doi: https://doi.org/10.24381/cds.e2161bac. Muñoz Sabater, J. (2021). ERA5-Land hourly data from 1950 to 1980 [Data set]. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). (Accessed on: 19-09-2022). doi: https://doi.org/10.24381/cds.e2161bac. The Norwegian Meteorological institute, MET Norway (2022). Norwegian observational gridded climate datasets [Data set]. Thredds.met. (Accessed on: 05-09-2022). url: https://thredds.met.no/thredds/catalog/senorge/seNorge_ 2018/Archive/catalog.html.