Catchment-Scale Natural Water Balance in Chile

The El Niño-Southern Oscillation (ENSO) is the main driver of interannual climate variability globally, manifesting as pseudo-oscillatory oceanic/atmospheric anomaly patterns in the tropical Pacific (e.g., Grothe et al., 2019;Timmermann et al., 2018), and affecting the climate of vast regions, notably across the Pacific Rim. In this region, ENSO exhibits a well-known average pattern of sea surface temperature (SST) and other variables, but also an important degree of heterogeneity across episodes. The phases of ENSO are not fully symmetrical: warm (El Niño) episodes may reach SST anomalies of larger amplitude than cold (La Niña) events, while La Niña episodes show greater temporal persistence than El Niño (Timmermann et al., 2018). Further, the spatial ENSO patterns may vary substantially, in particular regarding the location of warm SST anomalies during an El Niño episode (e.g.,

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“…For example, Vásquez et al. (2021) suggested that snow sublimation is a relevant evaporation component along the 29°–36°S region. Jara et al.…”

Section: Resultsmentioning

confidence: 99%

Hydrologic Sensitivities and ENSO Variability Across Hydrological Regimes in Central Chile (28°–41°S)

Hernandez

Mendoza

Boisier

et al. 2022

Water Resources Research

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“…While the model was originally designed as a land surface scheme for coupled simulations within Earth system models (Liang et al, 1994), most applications have involved uncoupled simulations for hydrological characterizations; accordingly, the literature reports many attempts to improve process representations (e.g., Liang et al, 1996Liang et al, , 1999Cherkauer et al, 2003;Andreadis et al, 2009). VIC is predominantly used in the USA (Addor and Melsen, 2019), with many studies focused on water and energy balances (e.g., Andreadis and Lettenmaier, 2006;Cayan et al, 2010); however, its use has expanded to other geographical domains, including China (e.g., Zhao et al, 2013;Gou et al, 2021), Chile (e.g., Vásquez et al, 2021;Vicuña et al, 2021), Europe (e.g., Lohmann et al, 1998;Roudier et al, 2016) and globally (e.g., Shukla et al, 2013;Yang et al, 2021). Ongoing community efforts using the VIC model include the NASA Land Information System (LIS; https://lis.gsfc.nasa.…”

Section: Hydrological Modelmentioning

confidence: 99%

“…This dataset was developed by running the VIC model at the same grid discretization as employed here (i.e., 0.05 • × 0.05 • ) and using a combination of CR2MET version 2.0, ERA-Interim and ERA5 output as meteorological forcings. The spatially distributed parameter fields for our reference simulation were developed via parameter regionalization, based on the similarity between possible donor catchments -whose parameters were calibrated individually (Vásquez et al, 2021) -and each grid cell across the domain, following Beck et al (2016). The reader is referred to Vásquez et al (2021) and DGA (2018DGA ( , 2019a in Spanish) for more details on individual model calibration and parameter regionalization procedures used to generate the reference simulation.…”

Section: Performance Metricsmentioning

confidence: 99%

“…The VIC model (Liang et al, 1994;Hamman et al, 2018) has been used for myriad applications all over the world, including snow modeling (Andreadis et al, 2009;Chen et al, 2014), streamflow forecasting (Wood et al, 2005;DeChant and Moradkhani, 2014), water balance studies Vásquez et al, 2021), extreme event characterization (Melsen et al, 2019), land use change impacts (Chawla and Mujumdar, 2015) and climate change impact assessments (e.g., Vano and Lettenmaier, 2014;Chegwidden et al, 2019). Despite the large number of parameters contained in VIC -either "free" (e.g., the infiltration shape parameter "INFILT", the exponent in baseflow curve) or "observable" (e.g., leaf area index) -many studies have relied on the calibration of only two or three soil-related parameters (Huang and Liang, 2006;Chawla and Mujumdar, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Revisiting parameter sensitivities in the variable infiltration capacity model across a hydroclimatic gradient

Sepúlveda

Mendoza

Mizukami

et al. 2022

Hydrol. Earth Syst. Sci.

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Abstract. Despite the Variable Infiltration Capacity (VIC) model being used for decades in the hydrology community, there are still model parameters whose sensitivities remain unknown. Additionally, understanding the factors that control spatial variations in parameter sensitivities is crucial given the increasing interest in obtaining spatially coherent parameter fields over large domains. In this study, we investigate the sensitivities of 43 soil, vegetation and snow parameters in the VIC model for 101 catchments spanning the diverse hydroclimates of continental Chile. We implement a hybrid local–global sensitivity analysis approach, using eight model evaluation metrics to quantify sensitivities, with four of them formulated from runoff time series, two characterizing snow processes, and the remaining two based on evaporation processes. Our results confirm an overparameterization for the processes analyzed here, with only 12 (i.e., 28 %) parameters found to be sensitive, distributed among soil (7), vegetation (2) and snow (3) model components. Correlation analyses show that climate variables – in particular, mean annual precipitation and the aridity index – are the main controls on parameter sensitivities. Additionally, our results highlight the influence of the leaf area index on simulated hydrologic processes – regardless of the dominant climate types – and the relevance of hard-coded snow parameters. Based on correlation results and the interpretation of spatial sensitivity patterns, we provide guidance on the most relevant parameters for model calibration according to the target processes and the prevailing climate type. Overall, the results presented here contribute to an improved understanding of model behavior across watersheds with diverse physical characteristics that encompass a wide hydroclimatic gradient from hyperarid to humid systems.

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“…Such dataset was developed by running the VIC model at the same grid discretization employed here (i.e., 0.05°x0.05°), using a combination of CR2MET version 2.0, ERA-Interim and ERA5 output as meteorological forcings. The spatially distributed parameter fields for our reference simulation were developed via parameter regionalization, based on the similarity between possible donor catchments -whose parameters were calibrated individually (Vásquez et al, 2021) -and each grid cell across the domain, following Beck et al (2016). The reader is referred to Vásquez et al, (2021) and DGA (2018, 2019, 2020; in spanish) for more details on individual model calibration and parameter regionalization procedures used to generate the reference simulation.…”

Section: Performance Metricsmentioning

confidence: 99%

Revisiting parameter sensitivities in the Variable Infiltration Capacity model

Sepúlveda

Mendoza

Mizukami

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Abstract. Despite the Variable Infiltration Capacity (VIC) model being used for decades in the hydrology community, there are still model parameters whose sensitivities remain unknown. Additionally, understanding the factors that control spatial variations in parameter sensitivities is crucial given the increasing interest to obtain spatially coherent parameter fields over large domains. In this study, we investigate the sensitivities of 43 soil, vegetation and snow parameters in the VIC model for 101 catchments spanning the diverse hydroclimates of continental Chile. We implement a hybrid local-global sensitivity analysis approach, using eight model evaluation metrics to quantify sensitivities, with four of them formulated from runoff time series; two characterizing snow processes, and the remaining two based on evaporation processes. Our results confirm an over-parameterization for the processes analysed here, with only 12 (i.e., 28 %) parameters found as sensitive, distributed among soil (7), vegetation (2) and snow (3) model components. Correlation analyses show that climate variables – in particular, mean annual precipitation and aridity index – are the main controls on parameter sensitivities. Additionally, our results highlight the influence of the leaf area index on simulated hydrologic processes – regardless on the dominant climate types – and the relevance of hard-coded snow parameters. Based on correlation results and the interpretation of spatial sensitivity patterns, we provide guidance on the most relevant parameters for model calibration according to the target processes and the prevailing climate type. Overall, the results presented here contribute to improved understanding of model behaviour across watersheds with diverse physical characteristics that encompass a wide hydroclimatic gradient from hyper-arid to humid systems.

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Catchment-Scale Natural Water Balance in Chile

Cited by 11 publications

References 54 publications

Hydrologic Sensitivities and ENSO Variability Across Hydrological Regimes in Central Chile (28°–41°S)

Hydrologic Sensitivities and ENSO Variability Across Hydrological Regimes in Central Chile (28°–41°S)

Revisiting parameter sensitivities in the variable infiltration capacity model across a hydroclimatic gradient

Revisiting parameter sensitivities in the Variable Infiltration Capacity model

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