Scripts and Data for "Disentangling the Hydrological and Hydraulic Controls on Streamflow Variability in E3SM V2 – A Case Study in the Pantanal Region"

<p>Matlab scripts for processing and showing the coupled ELM-MOSART coupled simulations for Pantanal region.</p> <p>domain_lnd_Pantanal_default.nc, MOSART_Pantanal_default_c211116.nc, and&nbsp;surfdata_Pantanal_default_c220520.nc are the domain file, MOSART input file, and ELM surface dataset, respectively.&nbsp;</p> <p><a href="https://zenodo.org/api/files/6be468b6-cbfa-4ef4-bb10-0f15812be9bd/Pantanal_half_calibration_CLMCRUNCEPv7.sh">Pantanal_half_calibration_CLMCRUNCEPv7.sh</a>&nbsp;is the bash script to run E3SM with coupled ELM-MOSART configuration. ANd detailed instruction of running E3SMV2 can be found at: https://e3sm.org/model/running-e3sm/e3sm-quick-start/ (last access: Aug 2023).</p> <p>Pantanal_GSIM.zip contains the observed streamflow that used in this study, which is downloaded from&nbsp;<a href="https://doi.pangaea.de/10.1594/PANGAEA.887470">https://doi.pangaea.de/10.1594/PANGAEA.887470</a> (last access: Aug 2023). The reference is&nbsp;Gudmundsson, Lukas; Do, Hong Xuan; Leonard, Michael; Westra, Seth (2018): The Global Streamflow Indices and Metadata Archive (GSIM) &ndash; Part 2: Quality control, time-series indices and homogeneity assessment. Earth System Science Data, 10(2), 787-804, https://doi.org/10.5194/essd-10-787-2018.</p> <p>BFI3.mat is the baseflow index from GSCD and processed to the study domain.&nbsp;The GSCD dataset was download from <a href="http://www.gloh2o.org/gscd/">http://www.gloh2o.org/gscd/</a>&nbsp;(last access: Aug 2023).&nbsp;The reference is&nbsp;Beck, H. E., van Dijk, A. I. J. M., Miralles, D. G., de Jeu, R. A. M., Bruijnzeel, L. A., McVicar, T. R., and Schellekens, J.: Global patterns in base flow index and recession based on streamflow observations from 3394 catchments, Water Resour Res, 49, 7843-7863, <a href="https://doi.org/10.1002/2013WR013918">https://doi.org/10.1002/2013WR013918</a>, 2013.</p> <p>runoff_uncertianty.mat contains the annual runoff time series from GRUN, LORA, and GFRF that processed to the study domain.&nbsp;The GRUN runoff dataset was downloaded from <a href="https://doi.org/10.6084/m9.figshare.9228176">https://doi.org/10.6084/m9.figshare.9228176</a>&nbsp;(last access: Aug 2023). The LORA runoff dataset was downloaded from <a href="https://dap.nci.org.au/thredds/remoteCatalogService?catalog=http://dapds00.nci.org.au/thredds/catalog/ks32/ARCCSS_Data/LORA/v1-0/catalog.xml">https://dap.nci.org.au/thredds/remoteCatalogService?catalog=http://dapds00.nci.org.au/thredds/catalog/ks32/ARCCSS_Data/LORA/v1-0/catalog.xml</a>&nbsp;(last access: Aug 2023). The reference is Hobeichi, S., Abramowitz, G., Evans, J., and Beck, H. E.: Linear Optimal Runoff Aggregate (LORA): a global gridded synthesis runoff product, Hydrol. Earth Syst. Sci., 23, 851-870, 10.5194/hess-23-851-2019, 2019. The GRFR runoff was downloaded from <a href="http://hydrology.princeton.edu/data/mpan/GRFR/runoff/monthly_1deg/">http://hydrology.princeton.edu/data/mpan/GRFR/runoff/monthly_1deg/</a>&nbsp;(last access: Aug 2023). The reference is&nbsp;Yang, Y., Pan, M., Lin, P., Beck, H. E., Zeng, Z., Yamazaki, D., David, C. d. H., Lu, H., Yang, K., Hong, Y., and Wood, E. F.: Global Reach-level 3-hourly River Flood Reanalysis (1980-2019), B Am Meteorol Soc, 1-49, 10.1175/BAMS-D-20-0057.1, 2021.</p> <p>GLAD_Pantanal_half.mat, GLAD_Pantanal_8th.mat are the processed surface water fraction from GLAD at half and 8th spatial resoution. Specifically,&nbsp;The GLAD surface water dynamics was downloaded from <a href="https://console.cloud.google.com/storage/browser/earthenginepartners-hansen/water;tab=objects">https://console.cloud.google.com/storage/browser/earthenginepartners-hansen/water;tab=objects</a>&nbsp;(last access: Aug 2023). The reference is&nbsp;Pickens, A. H., Hansen, M. C., Hancher, M., Stehman, S. V., Tyukavina, A., Potapov, P., Marroquin, B., and Sherani, Z.: Mapping and sampling to characterize global inland water dynamics from 1999 to 2018 with full Landsat time-series, Remote Sens Environ, 243, 111792, <a href="https://doi.org/10.1016/j.rse.2020.111792">https://doi.org/10.1016/j.rse.2020.111792</a>, 2020.</p>