The Value of Hydrograph Partitioning Curves for Calibrating Hydrological Models in Glacierized Basins

Abstract

This study refines the method for calibrating a glacio‐hydrological model based on Hydrograph Partitioning Curves (HPCs), and evaluates its value in comparison to multidata set optimization approaches which use glacier mass balance, satellite snow cover images, and discharge. The HPCs are extracted from the observed flow hydrograph using catchment precipitation and temperature gradients. They indicate the periods when the various runoff processes, such as glacier melt or snow melt, dominate the basin hydrograph. The annual cumulative curve of the difference between average daily temperature and melt threshold temperature over the basin, as well as the annual cumulative curve of average daily snowfall on the glacierized areas are used to identify the starting and end dates of snow and glacier ablation periods. Model parameters characterizing different runoff processes are calibrated on different HPCs in a stepwise and iterative way. Results show that the HPC‐based method (1) delivers model‐internal consistency comparably to the tri‐data set calibration method; (2) improves the stability of calibrated parameter values across various calibration periods; and (3) estimates the contributions of runoff components similarly to the tri‐data set calibration method. Our findings indicate the potential of the HPC‐based approach as an alternative for hydrological model calibration in glacierized basins where other calibration data sets than discharge are often not available or very costly to obtain.

Glacio‐hydrological model calibration and evaluation

“…Multi-signal calibration in combination with stepwise calibration for example was used in the Hydrological Partitioning Curves method from He et al (2015He et al ( , 2018. In this study, the streamflow time series was split based on groundwater, rain, snowmelt, and glacier melt dominance.…”

“…An incorrectly estimated fraction of solid precipitation can be internally compensated by melting additional glacier ice or by wrong storage outflow estimates (e.g., Magnusson et al, 2011). To avoid such internal error compensation effects and to better constrain the parameter search, a multidata or multi-signal calibration is thus highly recommended (He et al, 2018;Huss et al, 2008;Konz & Seibert, 2010;Mayr et al, 2013;Pellicciotti et al, 2012;Schaefli et al, 2005;Tarasova et al, 2016).…”

Multi-scale temporal variability in meltwater contributions in a tropical glacierized watershed

“…Due to the unavailability of high-resolution radiation input measurements as well as intensive source-code modifications required to couple energy balance calculations for ice melt into the Flux-PIHM, we added a separate module to simulate glacier melt using a temperature-index scheme (NRCS, 2009). Although the accuracy of a temperatureindex glacier melt model for tropical glaciers can be uncertain due to uncaptured effects of solar radiation, cloud cover, humidity, topography, and aspect (Hock, 1999(Hock, , 2005Pellicciotti et al, 2005;Sicart et al, 2008;Huss et al, 2009;Gabbi et al, 2014;Fernández and Mark, 2016), it remains the most feasible approach in poorly instrumented watersheds given its simplicity and limited field data requirement compared to an energy balance approach (Hock, 2005;Fernández and Mark, 2016;Reveillet et al, 2017). The temperature-index glacier melt model includes…”

Comparing Bayesian and traditional end-member mixing approaches for hydrograph separation in a glacierized basin

“…The generation of snow and glacier melt runoff generally show the largest effect on the runoff seasonality (Aizen et al, 2000;Aizen et al, 2007). In particular, the snowmelt runoff mainly occurs in the warm period from early March to middle September, and the glacier melt typically generates from the high-elevation areas during July to September (Aizen et al, 1996;He et al, 2018;He et al, 2019). We subsequently defined three runoff generation seasons as follows.…”

“…In contrast, the Bayesian_4_Cor and Bayesian_4 estimated the shares of glacier melt and snowmelt as 25-24% and 21-25%, respectively. Considering the significant snow cover area in September in the study basin (He et al 2018;He et al 2019), the contribution of snowmelt in the glacier melt season should be much higher than zero. Again, the Bayesian_4_Cor produced smaller uncertainty ranges for the contributions of groundwater and meltwater compared to the Bayesian_4 and TEMMA_4 (Table 4).…”