f6cec7d4-edee-44b8-8f44-86d4f12ac72d English ISO/IEC 8859-1 (also known as Latin 1) dataset dataset NERC EDS Environmental Information Data Centre Lancaster Environment Centre, Library Avenue, Bailrigg Lancaster LA1 4AP UK info@eidc.ac.uk https://eidc.ac.uk/ EIDC website The Environmental Information Data Centre (EIDC) is the UK's national data centre for terrestrial and freshwater sciences. information pointOfContact 2025-11-13T16:22:33 UK GEMINI 2.3 WGS 84 / Pseudo-Mercator Parameters for rainfall-runoff modelling in subcatchments of the Citarum basin, Java, Indonesia 2022-09-02 publication https://catalogue.ceh.ac.uk/id/f6cec7d4-edee-44b8-8f44-86d4f12ac72d 10.5285/f6cec7d4-edee-44b8-8f44-86d4f12ac72d doi: Vesuviano, G., Mathias, S.A., Rahayu, R., Reaney, S.M. (2022). Parameters for rainfall-runoff modelling in subcatchments of the Citarum basin, Java, Indonesia. NERC EDS Environmental Information Data Centre 10.5285/f6cec7d4-edee-44b8-8f44-86d4f12ac72d The dataset contains parameter values that maximize revised Kling Gupta Efficiency (KGE’) between modelled and observed daily mean river flows when running one of 24 different hydrological models with one of 21 different climatic input datasets in one of 33 different catchments across the Citarum basin or 5 catchments across the Ciliwung basin, both in Java island, Indonesia. This dataset was created as part of a study on the advantages and disadvantages of using existing hydrological models, primarily developed for temperate and cold climates, in a tropical volcanic region. The hydrological models were based on those created for MARRMoT v1.2 (10.5194/gmd-12-2463-2019), recoded as sequential models in the R programming language. This work was supported by the Natural Environment Research Council (Grants NE/S00310X/1 and NE/S002790/1). Full details about this dataset can be found at https://doi.org/10.5285/f6cec7d4-edee-44b8-8f44-86d4f12ac72d Vesuviano, G. UK Centre for Ecology & Hydrology enquiries@ceh.ac.uk https://orcid.org/0000-0003-2157-8875 ORCID record ORCID is an open, non-profit, community-driven effort to create and maintain a registry of unique researcher identifiers and a transparent method of linking research activities and outputs to these identifiers. information author Mathias, S.A. Durham University s.a.mathias@durham.ac.uk https://orcid.org/0000-0003-3054-9056 ORCID record ORCID is an open, non-profit, community-driven effort to create and maintain a registry of unique researcher identifiers and a transparent method of linking research activities and outputs to these identifiers. information author Rahayu, R. Durham University rahmawati.rahayu@durham.ac.uk author Reaney, S.M. Durham University sim.reaney@durham.ac.uk https://orcid.org/0000-0003-3063-2044 ORCID record ORCID is an open, non-profit, community-driven effort to create and maintain a registry of unique researcher identifiers and a transparent method of linking research activities and outputs to these identifiers. information author Vesuviano, G. UK Centre for Ecology & Hydrology enquiries@ceh.ac.uk pointOfContact NERC EDS Environmental Information Data Centre info@eidc.ac.uk custodian NERC EDS Environmental Information Data Centre info@eidc.ac.uk publisher Vesuviano, G. UK Centre for Ecology & Hydrology enquiries@ceh.ac.uk owner Hydrology Indonesia Rainfall-runoff model Model parameters otherRestrictions Registration is required to access this data otherRestrictions This resource is available under the terms of the Open Government Licence otherRestrictions If you reuse this data, you should cite: Vesuviano, G., Mathias, S.A., Rahayu, R., Reaney, S.M. (2022). Parameters for rainfall-runoff modelling in subcatchments of the Citarum basin, Java, Indonesia. NERC EDS Environmental Information Data Centre https://doi.org/10.5285/f6cec7d4-edee-44b8-8f44-86d4f12ac72d textTable English utf8 environment 106.758 108.005 -7.191 -5.967 Comma-separated values (CSV) NERC EDS Environmental Information Data Centre info@eidc.ac.uk distributor https://data-package.ceh.ac.uk/data/f6cec7d4-edee-44b8-8f44-86d4f12ac72d Download the data Download a copy of this data download https://data-package.ceh.ac.uk/sd/f6cec7d4-edee-44b8-8f44-86d4f12ac72d.zip Supporting information Supporting information available to assist in re-use of this dataset information dataset dataset Commission Regulation (EU) No 1089/2010 of 23 November 2010 implementing Directive 2007/2/EC of the European Parliament and of the Council as regards interoperability of spatial data sets and services 2010-12-08 The parameter set in each row of each file was selected from 100,000 candidate sets – being the one set that maximized KGE’ between modelled and observed river flow. First, 100,000 candidate parameter sets were generated by sampling the parameter values from uniform distributions with upper and lower limits suggested for each model parameter in the documentation of MARRMoT v1.2 (10.5194/gmd-12-2463-2019). Each model was input with one of 21 combinations of seven precipitation datasets of daily time resolution (AgMERRA, CHIRPS v2, CPC, MSWEP v2, PERSIANN-CDR, SA-OBS, Yanto (10.1038/sdata.2017.72)) and three evapotranspiration datasets of daily time resolution (AgMERRA, SA-OBS, Yanto) as climatic input data – for each catchment separately, catchment-average values were produced by identifying the catchment outline from a hydrologically corrected Digital Elevation Model (HydroSHEDS v1.1 CON 15-arcsecond) and overlaying this into the gridded precipitation and evapotranspiration products. Daily mean runoff (the model outflow) was obtained for each of the 38 catchments for 2,100,000 cases (100,000 parameter sets with 7 precipitation and 3 evapotranspiration datasets). Then, modified Kling-Gupta Efficency (KGE’) between modelled and observed daily mean flow was found, ignoring the first three years of modelled flow as “spin-up”. Finally, the parameter set resulting in the highest KGE’ was saved for each catchment with each combination of input climatic data (38 catchments × 7 precipitation datasets × 3 evapotranspiration datasets = 798 parameter sets), for each model, resulting in the 24 files of 798 rows each that comprise this dataset.