Effect of temporal resolution of NDVI on potential evapotranspiration estimation and hydrological model performance

The assimilation of different satellite and in situ products generally improves the hydrological model predictive skill. Most studies have focused on assimilating a single product at a time with the ensemble size subjectively chosen by the modeller. In this study, we used the European-scale Hydrological Predictions for the Environment hydrological model in the Umeälven catchment in northern Sweden with the stream discharge and local reservoir inflow as target variables to objectively choose an ensemble size that optimised model performance when the ensemble Kalman filter method is used. We further assessed the effect of assimilating different satellite products; namely, snow water equivalent, fractional snow cover, and actual and potential evapotranspiration, as well as in situ measurements of river discharge and local reservoir inflows. We finally investigated the combinations of those products that improved model predictions of the target variables and how the model performance varied through the year for those combinations. We found that an ensemble size of 50 was sufficient for all products except the reservoir inflow, which required 100 members and that in situ products outperform satellite products when assimilated. In particular, potential evapotranspiration alone or as combinations with other products did not generally improve predictions of our target variables. However, assimilating combinations of the snow products, discharge and local reservoir without evapotranspiration products improved the model performance.

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Impact of satellite and in situ data assimilation on hydrological predictions

10.5194/egusphere-egu2020-9305 ◽

2020 ◽

Author(s):

Jude Lubega Musuuza ◽

Louise Crochemore ◽

David Gustafsson ◽

Rafael Pimentel ◽

Ilias Pechlivanidis

Keyword(s):

Hydrological Model ◽

Snow Water Equivalent ◽

Potential Evapotranspiration ◽

Model Performance ◽

Ensemble Size ◽

In Situ Data ◽

Predictive Skill ◽

Improve Model ◽

Reservoir Inflow

The assimilation of different satellite and in-situ products generally improves the hydrological model predictive skill. Most studies have focused on assimilating a single product at a time with the ensemble size subjectively chosen by the modeller. In this study, we use the European-scale Hydrological Predictions for the Environment hydrological model in the Ume&#228;lven catchment in northern Sweden with the stream discharge and local reservoir inflow as target variables to objectively choose an ensemble size that optimises model performance. We further assess the effect of assimilating different satellite products namely snow water equivalent, fractional snow cover, and actual and potential evapotranspiration; as well as in situ measurements of river discharge and local reservoir inflows. We finally investigate the combinations of those products that improve model predictions of the target variables and how the model performance varies through the year for those combinations. We found that an ensemble size of 50 was sufficient for all products except the reservoir inflow, which required 100 members and that in situ products outperform satellite products when assimilated. In particular, potential evapotranspiration alone or as combinations with other products did not generally improve predictions of our target variables. However, assimilating combinations of the snow products, discharge and local reservoir without ET products improves the model performance.

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Hydrological Model Calibration in Data-Limited Catchments Using Non-Continuous Data Series with Different Lengths

10.29007/5hv1 ◽

2018 ◽

Author(s):

Chuanzhe Li ◽

Jia Liu ◽

Fuliang Yu ◽

Jiyang Tian ◽

Yang Wang ◽

...

Keyword(s):

Model Calibration ◽

Hydrological Model ◽

Potential Evapotranspiration ◽

Model Performance ◽

Data Series ◽

Observation Data ◽

Calibration Data ◽

Series Length ◽

Streamflow Data ◽

The Impact

This paper evaluates the effects of calibration data series length on the performance of a hydrological model in data-limited catchments where data are non-continuous and fragmental. Non-continuous calibration periods were used for more independent streamflow data for SIMHYD model calibration. Nash-Sutcliffe efficiency and percentage water balance error were used as performance measures. The particle swarm optimization method was used to calibrate the rainfall-runoff models. Different lengths of data series ranging from one year to ten years were used to study the impact of calibration data series length. Fifty-five relatively unimpaired catchments located all over Australia with daily precipitation, potential evapotranspiration, and streamflow data were tested to obtain more general conclusions. The results show that longer calibration data series do not necessarily result in better model performance. Our results may have useful and interesting implications for the efficiency of using limited observation data for hydrological model calibration in different climates.

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Impact of temporal resolution of inputs on hydrological model performance: An analysis based on 2400 flood events

Journal of Hydrology ◽

10.1016/j.jhydrol.2016.04.016 ◽

2016 ◽

Vol 538 ◽

pp. 454-470 ◽

Cited By ~ 26

Author(s):

Andrea Ficchì ◽

Charles Perrin ◽

Vazken Andréassian

Keyword(s):

Temporal Resolution ◽

Hydrological Model ◽

Model Performance ◽

Flood Events

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Correction: Li, Z., et al. Study on the Applicability of the Hargreaves Potential Evapotranspiration Estimation Method in CREST-Distributed Hydrological Model (Version 3.0) Applications. Water 2018, 10, 1882

Water ◽

10.3390/w11102099 ◽

2019 ◽

Vol 11 (10) ◽

pp. 2099

Author(s):

Zhansheng Li ◽

Yuan Yang ◽

Guangyuan Kan ◽

Yang Hong

Keyword(s):

Environmental Science ◽

Hydrological Model ◽

Civil Engineering ◽

Potential Evapotranspiration ◽

Estimation Method ◽

Distributed Hydrological Model ◽

Model Version ◽

University Of Oklahoma ◽

Email Address ◽

Evapotranspiration Estimation

In the published article [1], the authors realized some errors in the affiliation of Yang Hong and thus wish to make the revisions as below: Add the missed Affiliation 3 “School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK 73019, USA” for Yang Hong; Correct the email address of Yang Hong into yanghong@ou [...]

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Sensitivity and Performance Analyses of the Distributed Hydrology–Soil–Vegetation Model Using Geomorphons for Landform Mapping

Water ◽

10.3390/w13152032 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2032

Author(s):

Pâmela A. Melo ◽

Lívia A. Alvarenga ◽

Javier Tomasella ◽

Carlos R. Mello ◽

Minella A. Martins ◽

...

Keyword(s):

Complex Terrain ◽

Overland Flow ◽

Hydrological Model ◽

Model Performance ◽

Soil Parameters ◽

Vegetation Model ◽

Landform Classification ◽

Physically Based ◽

And Performance ◽

Streamflow Simulation

Landform classification is important for representing soil physical properties varying continuously across the landscape and for understanding many hydrological processes in watersheds. Considering it, this study aims to use a geomorphology map (Geomorphons) as an input to a physically based hydrological model (Distributed Hydrology Soil Vegetation Model (DHSVM)) in a mountainous headwater watershed. A sensitivity analysis of five soil parameters was evaluated for streamflow simulation in each Geomorphons feature. As infiltration and saturation excess overland flow are important mechanisms for streamflow generation in complex terrain watersheds, the model’s input soil parameters were most sensitive in the “slope”, “hollow”, and “valley” features. Thus, the simulated streamflow was compared with observed data for calibration and validation. The model performance was satisfactory and equivalent to previous simulations in the same watershed using pedological survey and moisture zone maps. Therefore, the results from this study indicate that a geomorphologically based map is applicable and representative for spatially distributing hydrological parameters in the DHSVM.

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SHETRAN and HEC HMS Model Evaluation for Runoff and Soil Moisture Simulation in the Jičinka River Catchment (Czech Republic)

Water ◽

10.3390/w13060872 ◽

2021 ◽

Vol 13 (6) ◽

pp. 872

Author(s):

Vesna Đukić ◽

Ranka Erić

Keyword(s):

Soil Moisture ◽

Czech Republic ◽

Hydrological Model ◽

Flash Flood ◽

Model Performance ◽

Rainfall Runoff ◽

The Czech Republic ◽

River Catchment ◽

Hydrological Variable ◽

Model Structures

Due to the improvement of computation power, in recent decades considerable progress has been made in the development of complex hydrological models. On the other hand, simple conceptual models have also been advanced. Previous studies on rainfall–runoff models have shown that model performance depends very much on the model structure. The purpose of this study is to determine whether the use of a complex hydrological model leads to more accurate results or not and to analyze whether some model structures are more efficient than others. Different configurations of the two models of different complexity, the Système Hydrologique Européen TRANsport (SHETRAN) and Hydrologic Modeling System (HEC-HMS), were compared and evaluated in simulating flash flood runoff for the small (75.9 km2) Jičinka River catchment in the Czech Republic. The two models were compared with respect to runoff simulations at the catchment outlet and soil moisture simulations within the catchment. The results indicate that the more complex SHETRAN model outperforms the simpler HEC HMS model in case of runoff, but not for soil moisture. It can be concluded that the models with higher complexity do not necessarily provide better model performance, and that the reliability of hydrological model simulations can vary depending on the hydrological variable under consideration.

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Development of a Stepped Calibration Approach for XAJ Hydrological Model

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.405-408.2222 ◽

2013 ◽

Vol 405-408 ◽

pp. 2222-2225

Author(s):

Qian Li ◽

Wei Min Bao ◽

Jing Lin Qian

Keyword(s):

Root Mean Square Error ◽

Objective Function ◽

Traditional Method ◽

Hydrological Model ◽

Model Performance ◽

Layer By Layer ◽

Mean Square ◽

Objective Functions ◽

Single Objective ◽

Calibration Approach

This paper discusses the conceptual stepped calibration approach (SCA) which has been developed for the Xinanjiang (XAJ) model. Multi-layer and multi-objective functions which can make optimization work simpler and more effective are introduced in this procedure. In all eight parameters were considered, they were divided into four layers according to the structure of XAJ model, and then calibrated layer by layer. The SCA procedure tends to improve the performance of the traditional method of calibration (thus, using a single objective function, such as root mean square error RMSE). The compared results demonstrate that the SCA yield better model performance than RMSE.

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Parameter transferability between multiple gridded input datasets challenges hydrological model performance under changing climate

10.5194/egusphere-egu21-8782 ◽

2021 ◽

Author(s):

Moctar Dembélé ◽

Bettina Schaefli ◽

Grégoire Mariéthoz

Keyword(s):

West Africa ◽

Large Scale ◽

Hydrological Model ◽

Meteorological Data ◽

Model Performance ◽

Hydrologic Model ◽

Hydrological Modelling ◽

Climate Change Scenarios ◽

Depth Analysis ◽

Input Dataset

The diversity of remotely sensed or reanalysis-based rainfall data steadily increases, which on one hand opens new perspectives for large scale hydrological modelling in data scarce regions, but on the other hand poses challenging question regarding parameter identification and transferability under multiple input datasets. This study analyzes the variability of hydrological model performance when (1) a set of parameters is transferred from the calibration input dataset to a different meteorological datasets and reversely, when (2) an input dataset is used with a parameter set, originally calibrated for a different input dataset.The research objective is to highlight the uncertainties related to input data and the limitations of hydrological model parameter transferability across input datasets. An ensemble of 17 rainfall datasets and 6 temperature datasets from satellite and reanalysis sources (Demb&#233;l&#233; et al., 2020), corresponding to 102 combinations of meteorological data, is used to force the fully distributed mesoscale Hydrologic Model (mHM). The mHM model is calibrated for each combination of meteorological datasets, thereby resulting in 102 calibrated parameter sets, which almost all give similar model performance. Each of the 102 parameter sets is used to run the mHM model with each of the 102 input datasets, yielding 10404 scenarios to that serve for the transferability tests. The experiment is carried out for a decade from 2003 to 2012 in the large and data-scarce Volta River basin (415600 km2) in West Africa.The results show that there is a high variability in model performance for streamflow (mean CV=105%) when the parameters are transferred from the original input dataset to other input datasets (test 1 above). Moreover, the model performance is in general lower and can drop considerably when parameters obtained under all other input datasets are transferred to a selected input dataset (test 2 above). This underlines the need for model performance evaluation when different input datasets and parameter sets than those used during calibration are used to run a model. Our results represent a first step to tackle the question of parameter transferability to climate change scenarios. An in-depth analysis of the results at a later stage will shed light on which model parameterizations might be the main source of performance variability.Demb&#233;l&#233;, M., Schaefli, B., van de Giesen, N., & Mari&#233;thoz, G. (2020). Suitability of 17 rainfall and temperature gridded datasets for large-scale hydrological modelling in West Africa. Hydrology and Earth System Sciences (HESS). https://doi.org/10.5194/hess-24-5379-2020

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