Reducing hydrological modelling uncertainty by using MODIS snow cover data and a topography-based distribution function snowmelt model

Abstract. The dataset Future Flows Hydrology was developed as part of the project "Future Flows and Groundwater Levels" to provide a consistent set of transient daily river flow and monthly groundwater levels projections across England, Wales and Scotland to enable the investigation of the role of climate variability on river flow and groundwater levels nationally and how this may change in the future. Future Flows Hydrology is derived from Future Flows Climate, a national ensemble projection derived from the Hadley Centre's ensemble projection HadRM3-PPE to provide a consistent set of climate change projections for the whole of Great Britain at both space and time resolutions appropriate for hydrological applications. Three hydrological models and one groundwater level model were used to derive Future Flows Hydrology, with 30 river sites simulated by two hydrological models to enable assessment of hydrological modelling uncertainty in studying the impact of climate change on the hydrology. Future Flows Hydrology contains an 11-member ensemble of transient projections from January 1951 to December 2098, each associated with a single realisation from a different variant of HadRM3 and a single hydrological model. Daily river flows are provided for 281 river catchments and monthly groundwater levels at 24 boreholes as .csv files containing all 11 ensemble members. When separate simulations are done with two hydrological models, two separate .csv files are provided. Because of potential biases in the climate-hydrology modelling chain, catchment fact sheets are associated with each ensemble. These contain information on the uncertainty associated with the hydrological modelling when driven using observed climate and Future Flows Climate for a period representative of the reference time slice 1961–1990 as described by key hydrological statistics. Graphs of projected changes for selected hydrological indicators are also provided for the 2050s time slice. Limitations associated with the dataset are provided, along with practical recommendation of use. Future Flows Hydrology is freely available for non-commercial use under certain licensing conditions. For each study site, catchment averages of daily precipitation and monthly potential evapotranspiration, used to drive the hydrological models, are made available, so that hydrological modelling uncertainty under climate change conditions can be explored further. doi:10.5285/f3723162-4fed-4d9d-92c6-dd17412fa37b.

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Impact of Hydrological Modellers’ Decisions and Attitude on the Performance of a Calibrated Conceptual Catchment Model: Results from a ‘Modelling Contest’

Hydrology ◽

10.3390/hydrology5040064 ◽

2018 ◽

Vol 5 (4) ◽

pp. 64

Author(s):

Helge Bormann ◽

Mariana de Brito ◽

Despoina Charchousi ◽

Dimitris Chatzistratis ◽

Amrei David ◽

...

Keyword(s):

Hydrological Model ◽

Hydrological Modelling ◽

Model Code ◽

Modelling Uncertainty ◽

Code Calibration ◽

Model Ensembles ◽

The Individual ◽

Catchment Model ◽

Good Calibration

In this study, 17 hydrologists with different experience in hydrological modelling applied the same conceptual catchment model (HBV) to a Greek catchment, using identical data and model code. Calibration was performed manually. Subsequently, the modellers were asked for their experience, their calibration strategy, and whether they enjoyed the exercise. The exercise revealed that there is considerable modellers’ uncertainty even among the experienced modellers. It seemed to be equally important whether the modellers followed a good calibration strategy, and whether they enjoyed modelling. The exercise confirmed previous studies about the benefit of model ensembles: Different combinations of the simulation results (median, mean) outperformed the individual model simulations, while filtering the simulations even improved the quality of the model ensembles. Modellers’ experience, decisions, and attitude, therefore, have an impact on the hydrological model application and should be considered as part of hydrological modelling uncertainty.

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Diffuse groundwater recharge estimation confronting hydrological modelling uncertainty

Journal of Hydrology ◽

10.1016/j.jhydrol.2020.124642 ◽

2020 ◽

Vol 584 ◽

pp. 124642 ◽

Cited By ~ 2

Author(s):

Ruirui Zhu ◽

Barry F.W. Croke ◽

Anthony J. Jakeman

Keyword(s):

Groundwater Recharge ◽

Hydrological Modelling ◽

Modelling Uncertainty ◽

Recharge Estimation

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Correcting basin-scale snowfall in a mountainous basin using a distributed snowmelt model and remote-sensing data

Hydrology and Earth System Sciences ◽

10.5194/hess-18-747-2014 ◽

2014 ◽

Vol 18 (2) ◽

pp. 747-761 ◽

Cited By ~ 24

Author(s):

M. Shrestha ◽

L. Wang ◽

T. Koike ◽

H. Tsutsui ◽

Y. Xue ◽

...

Keyword(s):

Spatial Distribution ◽

Snow Cover ◽

Search Algorithm ◽

Relative Volume ◽

Shortwave Radiation ◽

Topographic Effects ◽

Hydrologic Modelling ◽

Study Region ◽

Basin Scale ◽

Snowmelt Model

Abstract. Adequate estimation of the spatial distribution of snowfall is critical in hydrologic modelling. However, this is a well-known problem in estimating basin-scale snowfall, especially in mountainous basins with data scarcity. This study focuses on correction and estimation of this spatial distribution, which considers topographic effects within the basin. A method is proposed that optimises an altitude-based snowfall correction factor (Cfsnow). This is done through multi-objective calibration of a spatially distributed, multilayer energy and water balance-based snowmelt model (WEB-DHM-S) with observed discharge and remotely sensed snow cover data from the Moderate Resolution Imaging Spectroradiometer (MODIS). The Shuffled Complex Evolution–University of Arizona (SCE–UA) automatic search algorithm is used to obtain the optimal value of Cfsnow for minimum cumulative error in discharge and snow cover simulations. Discharge error is quantified by Nash–Sutcliffe efficiency and relative volume deviation, and snow cover error was estimated by pixel-by-pixel analysis. The study region is the heavily snow-fed Yagisawa Basin of the Upper Tone River in northeast Japan. First, the system was applied to one snow season (2002–2003), obtaining an optimised Cfsnow of 0.0007 m−1. For validation purposes, the optimised Cfsnow was implemented to correct snowfall in 2004, 2002 and 2001. Overall, the system was effective, implying improvements in correlation of simulated versus observed discharge and snow cover. The 4 yr mean of basin-average snowfall for the corrected spatial snowfall distribution was 1160 mm (780 mm before correction). Execution of sensitivity runs against other model input and parameters indicated that Cfsnow could be affected by uncertainty in shortwave radiation and setting of the threshold air temperature parameter. Our approach is suitable to correct snowfall and estimate its distribution in poorly gauged basins, where elevation dependence of snowfall amount is strong.

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Comparing Simulated and Measured Sensible and Latent Heat Fluxes over Snow under a Pine Canopy to Improve an Energy Balance Snowmelt Model

Journal of Hydrometeorology ◽

10.1175/2008jhm874.1 ◽

2008 ◽

Vol 9 (6) ◽

pp. 1506-1522 ◽

Cited By ~ 61

Author(s):

D. Marks ◽

A. Winstral ◽

G. Flerchinger ◽

M. Reba ◽

J. Pomeroy ◽

...

Keyword(s):

Energy Balance ◽

Snow Cover ◽

Eddy Covariance ◽

Latent Heat ◽

Energy Content ◽

Heat Fluxes ◽

Balance Model ◽

Mass Fluxes ◽

Model Efficiency ◽

Snowmelt Model

Abstract During the second year of the NASA Cold Land Processes Experiment (CLPX), an eddy covariance (EC) system was deployed at the Local Scale Observation Site (LSOS) from mid-February to June 2003. The EC system was located beneath a uniform pine canopy, where the trees are regularly spaced and are of similar age and height. In an effort to evaluate the turbulent flux calculations of an energy balance snowmelt model (SNOBAL), modeled and EC-measured sensible and latent heat fluxes between the snow cover and the atmosphere during this period are presented and compared. Turbulent fluxes comprise a large component of the snow cover energy balance in the premelt and ripening period (March–early May) and therefore control the internal energy content of the snow cover as melt accelerates in late spring. Simulated snow cover depth closely matched measured values (RMS difference 8.3 cm; Nash–Sutcliff model efficiency 0.90), whereas simulated snow cover mass closely matched the few measured values taken during the season. Over the 927-h comparison period using the default model configuration, simulated sensible heat H was within 1 W m−2, latent heat LυE within 4 W m−2, and cumulative sublimation within 3 mm of that measured by the EC system. Differences between EC-measured and simulated fluxes occurred primarily at night. The reduction of the surface layer specification in the model from 25 to 10 cm reduced flux differences between EC-measured and modeled fluxes to 0 W m−2 for H, 2 W m−2 for LυE, and 1 mm for sublimation. When only daytime fluxes were compared, differences were further reduced to 1 W m−2 for LυE and <1 mm for sublimation. This experiment shows that in addition to traditional mass balance methods, EC-measured fluxes can be used to diagnose the performance of a snow cover energy balance model. It also demonstrates the use of eddy covariance methods for measuring heat and mass fluxes from snow covers at a low-wind, below-canopy site.

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