DOI: 10.5061/dryad.qz612jmrq
title: The dilemma of objective function selection for sensitivity and uncertainty analyses of semi-distributed hydrologic models across spatial and temporal scales

description: Semi-distributed hydrologic models have been extensively utilized for
 simulating watershed-scale hydrologic processes, given their fast
 execution time and flexibility in implementing management practices.
 However, they have many parameters, several of which cannot be accurately
 measured, leading to equifinality. Sensitivity analysis (SA) is often used
 to identify the most important parameters and reduce the dimensionality of
 calibration. Here, we applied a global SA method based on the Variogram
 Analysis of Response Surfaces (VARS) to investigate parameter importance
 and the influence of parameter identifiability on streamflow prediction
 uncertainty. In our experiments, we considered the impacts of space and
 time scales of interest, the choice of objective functions (single- versus
 multi-objective), and the direct significance of parameters in driving
 models, independent of observed data on model outputs (regardless of
 objective functions). We set up the Soil and Water Assessment Tool (SWAT)
 for three catchments (&lt; 200 km2) with different physiographic
 characteristics in the United States to simulate daily streamflow at three
 spatial discretization scales. SA was performed at daily, monthly, and
 yearly aggregation scales. Our findings, across all experiments, indicate
 that the runoff curve number is the most important parameter in SWAT.
 Results also reveal that parameter importance is largely dependent on the
 temporal aggregation scale of outputs but almost independent of spatial
 discretization. Further, we show that the choice of objective function and
 performance threshold significantly influences the number and
 configuration of important parameters, highlighting the benefit of
 incorporating multiple objectives in SA, and their trade-off between
 improving sharpness and reliability.
description: # The dilemma of objective function selection for sensitivity and
 uncertainty analyses of semi-distributed hydrologic models across spatial
 and temporal scales
 [https://doi.org/10.5061/dryad.qz612jmrq](https://doi.org/10.5061/dryad.qz612jmrq) ## Description of the data This dataset contains the data required to replicate the analyses in Acero et al. (2024). The data cover three catchments in the USA: Mission Creek (MC) in California, Little Washita River (LWR) in Oklahoma, and Willow Creek (WC) in Illinois. Geospatial data include DEM, land use/land cover, and soil class, as well as daily streamflow, precipitation, and temperature records from 1980 to 2020. this data can be used for catchment delineation using tools such as ArcSWAT. Factor space data is also provided to generate parameter sets using Progressive Latin Hypercube Sampling (PLHS) in VARS-TOOL. Normalized IVARS50 values are also provided for each catchment scenario, considering different spatiotemporal scales. ### Files and variables # File: Data.zip **Folder structure and description:** 1. SWAT 1. Input_data: geospatial data required to delineate LWR, MC, and WC catchments using ArcSWAT * Raster digital elevation model (DEM) data in meters at 30-m resolution: DEM_30m_NAD83_UTM{UTM zone}.tif  * Raster land use/land cover data at 30-m resolution: LUC_30m_NAD83_UTM{UTM zone}.tif  * USDA-NASS land cover codes and corresponding SWAT counterpart: luc.csv * Shapefile with SSURGO soil class data: SSURGO_NAD83_UTM{UTM zone}.shp  * Shapefile with gauging station location for each catchment: stream_gage_NAD83_UTM{UTM zone}.shp * Text files with coordinates, elevation (mamsl), and records of daily precipitation (mm) and temperature (°C) stations for the 1980-2020 period: pcp.txt, pcp{station number}.txt, tmp.txt, tmp{station number}.txt 2. Streamflow_records: text files with raw streamflow records (cfs) of USGS gauging stations 10257600, 07327447, and 05591550 up to 2020; {station code}-{two-letter state abbreviation}.txt 2. VARS-TOOL: input and output data required to generate parameter value combinations and sensitivity metrics for the LWR, MC, and WC catchments using VARS-TOOL 1. Input_data: text file with the factor space to generate parameter sets for the LWR, MC, and WC catchments using PLHS in VARS-TOOL; factorSpace.txt. the file includes four columns with the factor number, factor lower and upper bounds, and factor name. 2. Output_data: CSV files with normalized IVARS50 values for each of the 31-35 SWAT parameters (columns - see Supplemental Information SWAT_parameters.xlsx) considered for the sensitivity analysis under 16 objective functions (rows) and nine spatiotemporal scale scenarios for each study catchment; VARS_TO_{two-letter state abbreviation}_{spatial discretization scale}km2 \_{one-letter temporal aggregation scale}.csv.  ## Access information Raw input data was derived from the following sources: * DWR-CIMIS, 2020. Weather station records [WWW Document]. URL [https://cimis.water.ca.gov/Default.aspx](https://cimis.water.ca.gov/Default.aspx) (accessed 9.24.20) * Menne, M.J., Durre, I., Korzeniewski, B., McNeal, S., Thomas, K., Yin, X., Anthony, S., Ray, R., Vose, R.S., Gleason, B.E., Houston, T.G., 2012a. Global historical climatology network - daily (GHCN-Daily) [WWW Document]. [https://doi.org/10.7289/V5D21VHZ](https://doi.org/10.7289/V5D21VHZ) * Starks, P.J., Fiebrich, C.A., Grimsley, D.L., Garbrecht, J.D., Steiner, J.L., Guzman, J.A., Moriasi, D.N., 2014. Upper Washita River Experimental Watersheds: Meteorologic and Soil Climate Measurement Networks. J Environ Qual 43, 1239–1249. [https://doi.org/10.2134/jeq2013.08.0312](https://doi.org/10.2134/jeq2013.08.0312) * USDA-NASS, 2009. CropScape - Cropland Data Layer [WWW Document]. URL [https://nassgeodata.gmu.edu/CropScape/](https://nassgeodata.gmu.edu/CropScape/) (accessed 9.19.20). * USDA-NRCS, 2003. Soil Survey Geographic (SSURGO) Database [WWW Document]. URL [https://sdmdataaccess.sc.egov.usda.gov](https://sdmdataaccess.sc.egov.usda.gov) (accessed 1.14.21). * USGS, 2019. 1 arc-second resolution Digital Elevation Model (published 2019-2020) [WWW Document]. URL [https://apps.nationalmap.gov/downloader/#/](https://apps.nationalmap.gov/downloader/#/) (accessed 8.31.20).