scholarly journals Testing a spatially distributed tracer-aided runoff model in a snow-influenced catchment: Effects of multicriteria calibration on streamwater ages

2018 ◽  
Vol 32 (20) ◽  
pp. 3089-3107 ◽  
Author(s):  
Thea I. Piovano ◽  
Doerthe Tetzlaff ◽  
Pertti Ala-aho ◽  
Jim Buttle ◽  
Carl P. J. Mitchell ◽  
...  
Keyword(s):  
Spatially Distributed ◽  
Multicriteria Calibration ◽  
Runoff Model

Development and application of a spatially distributed snowmelt runoff model for limited data condition

2019 ◽  
Vol 12 (15) ◽  
Author(s):  
S. Rajkumari ◽  
N. Chiphang ◽  
Liza G. Kiba ◽  
A. Bandyopadhyay ◽  
A. Bhadra
Keyword(s):  
Snowmelt Runoff ◽  
Limited Data ◽  
Spatially Distributed ◽  
Runoff Model

scholarly journals Combining an R-Based Evolutionary Algorithm and Hydrological Model for Effective Parameter Calibration

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1339 ◽  
Author(s):  
Mun-Ju Shin ◽  
Yun Choi
Keyword(s):  
Hydrological Model ◽  
Model Parameters ◽  
Model Assessment ◽  
Hydrological Models ◽  
Shuffled Complex Evolution ◽  
Physically Based ◽  
Spatially Distributed ◽  
Rainfall Runoff Model ◽  
Time Required ◽  
Runoff Model

The hydrological model assessment and development (hydromad) modeling package is an R-based package that can be applied to simulate hydrological models and optimize parameters. As the hydromad package is incompatible with hydrological models outside the package, the parameters of such models cannot be directly optimized. Hence, we proposed a method of optimizing the hydrological-model parameters by combining the executable (EXE) file of the hydrological model with the shuffled complex evolution (SCE) algorithm provided by the hydromad package. A physically based, spatially distributed, grid-based rainfall–runoff model (GRM) was employed. By calibrating the parameters of the GRM, the performance of the model was found to be reasonable. Thus, the hydromad can be used to optimize the hydrological-model parameters outside the package if the EXE file of the hydrological model is available. The time required to optimize the parameters depends on the type of event, even for the same catchment area.

scholarly journals Inferring the flood frequency distribution for an ungauged basin using a spatially distributed rainfall-runoff model

2008 ◽  
Vol 5 (1) ◽  
pp. 1-26 ◽  
Author(s):  
G. Moretti ◽  
A. Montanari
Keyword(s):  
Frequency Distribution ◽  
River Flow ◽  
Spatial Scales ◽  
Flood Frequency ◽  
Rainfall Runoff ◽  
Ungauged Catchments ◽  
Wide Range ◽  
Spatially Distributed ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract. The estimation of the peak river flow for ungauged river sections is a topical issue in applied hydrology. Spatially distributed rainfall-runoff models can be a useful tool to this end, since they are potentially able to simulate the river flow at any location of the watershed drainage network. However, it is not fully clear to what extent these models can provide reliable simulations over a wide range of spatial scales. This issue is investigated here by applying a spatially distributed, continuous simulation rainfall-runoff model to infer the flood frequency distribution of the Riarbero Torrent. This is an ungauged mountain creek located in northern Italy, whose drainage area is 17 km2. The results were checked by using estimates of the peak river flow obtained by applying a classical procedure based on hydrological similarity principles. The analysis highlights interesting perspectives for the application of spatially distributed models to ungauged catchments.

scholarly journals Using isotopes to constrain water flux and age estimates in snow-influenced catchments using the STARR (Spatially distributed Tracer-Aided Rainfall-Runoff) model

2017 ◽  
Author(s):  
Pertti Ala-aho ◽  
Doerthe Tetzlaff ◽  
James P. McNamara ◽  
Hjalmar Laudon ◽  
Chris Soulsby
Keyword(s):  
Water Isotopes ◽  
Major Influence ◽  
Variable Degree ◽  
Rainfall Runoff ◽  
Stable Water Isotopes ◽  
Water Age ◽  
Landscape Characteristics ◽  
Spatially Distributed ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract. Tracer-aided hydrological models are increasingly used to reveal fundamentals of runoff generation processes and water travel times in catchments. Modelling studies integrating stable water isotopes as tracers are mostly based in temperate and warm climates, leaving catchments with strong snow-influences catchments underrepresented in the literature. Such catchments are challenging, as the isotopic tracer signals in water entering the catchments as snowmelt are typically distorted from incoming precipitation due to fractionation processes in seasonal snowpack. We used the Spatially Distributed Tracer-Aided Rainfall-Runoff model (STARR) to simulate fluxes, storage and mixing of water and tracers, as well as estimating water ages in three long-term experimental catchments with varying degrees of snow influence and contrasting landscape characteristics. The sites have exceptionally long and rich datasets of hydrometric data and - most importantly - stable water isotopes for both rain and snow conditions. To adapt the STARR model for sites with strong snow-influence, we developed a novel parsimonious calculation scheme that takes into account the isotopic fractionation through snow evaporation and snow melt. The modified STARR setup simulated the stream flows, isotope ratios and snow pack dynamics quite well in all three catchments. From this, our simulations indicated contrasting median water ages and water age distributions between catchments brought about mainly by differences in topography, soils and geology. However, the variable degree of snow influence in catchments also had a major influence on the stream hydrograph, storage dynamics and water age distributions, which was captured by the model. Our study demonstrated the importance of including snow evaporative fractionation processes in tracer-aided modelling for catchments with seasonal snowpack, while the influence of fractionation during snowmelt could not be unequivocally shown. Our work shows the utility of isotopes to provide a proof of concept for our modelling framework in snow influenced catchments.

scholarly journals Inferring the flood frequency distribution for an ungauged basin using a spatially distributed rainfall-runoff model

2008 ◽  
Vol 12 (4) ◽  
pp. 1141-1152 ◽  
Author(s):  
G. Moretti ◽  
A. Montanari
Keyword(s):  
Frequency Distribution ◽  
River Flow ◽  
Spatial Scales ◽  
Flood Frequency ◽  
Rainfall Runoff ◽  
River Flows ◽  
Wide Range ◽  
Spatially Distributed ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract. The estimation of the peak river flow for ungauged river sections is a topical issue in applied hydrology. Spatially distributed rainfall-runoff models can be a useful tool to this end, since they are potentially able to simulate the river flow at any location of the watershed drainage network. However, it is not fully clear to what extent these models can provide reliable simulations over a wide range of spatial scales. This issue is investigated here by applying a spatially distributed, continuous simulation rainfall-runoff model to infer the flood frequency distribution of the Riarbero River. This is an ungauged mountain creek located in northern Italy, whose drainage area is 17 km2. The hydrological model is first calibrated by using a 1-year record of hourly meteorological data and river flows observed at the outlet of the 1294 km2 wide Secchia River basin, of which the Riarbero is a tributary. The model is then validated by performing a 100-year long simulation of synthetic river flow data, which allowed us to compare the simulated and observed flood frequency distributions at the Secchia River outlet and the internal cross river section of Cavola Bridge, where the basin area is 337 km2. Finally, another simulation of hourly river flows was performed by referring to the outlet of the Riarbero River, therefore allowing us to estimate the related flood frequency distribution. The results were validated by using estimates of peak river flow obtained by applying hydrological similarity principles and a regional method. The results show that the flood flow estimated through the application of the distributed model is consistent with the estimate provided by the regional procedure as well as the behaviors of the river banks. Conversely, the method based on hydrological similarity delivers an estimate that seems to be not as reliable. The analysis highlights interesting perspectives for the application of spatially distributed models to ungauged catchments.

scholarly journals Effects of Spatially-Distributed Rainfall on Runoff for a Conceptual Catchment

1991 ◽  
Vol 22 (1) ◽  
pp. 1-14 ◽  
Author(s):  
L.G. Watts ◽  
A. Calver
Keyword(s):  
Overland Flow ◽  
Rainfall Runoff ◽  
Peak Response ◽  
Physically Based ◽  
Spatially Distributed ◽  
Channel Velocity ◽  
Peak Runoff ◽  
Rainfall Runoff Model ◽  
Definition Of ◽  
Runoff Model

A physically-based rainfall-runoff model is used to investigate effects of moving storms on the runoff hydrograph of throughflow dominated idealised catchments. Simulations are undertaken varying the storm speed, direction, intensity, the part of the catchment affected by rainfall, and the spatial definition of rainfall zones. For a 100 km2 catchment, under the circumstances investigated, an efficient spatial resolution of rainfall data is around 2.5 km along the path of the storm. Storms moving downstream produce earlier, higher peaks than do storms moving upstream. Error is most likely to be introduced into lumped-rainfall predictions for slower storm speeds, and the likely direction of this error can be specified. Differences in magnitude of peak response between downstream and upstream storm directions reach a maximum at a storm speed and direction similar to the average peak channel velocity. These results are qualitatively similar to those reported for overland flow dominated catchments, but differences in peak runoff between downstream and upstream storm directions are much smaller where rainfall inputs are modified by a period of hillslope throughflow.

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