Sensitivity to calibration data of simulated soil moisture-related drought indices

Bargaoui, Zoubeïda; Houcine, Ahmed

doi:10.1684/sec.2010.0263

Cited by 4 publications

(4 citation statements)

References 12 publications

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“…The model has seven parameters which are parameter (a) linked to the field capacity, parameter (b) linked to the recession of soil humidity, (c) representing capillary rise pressure, (p) representing soil porosity, (D) for the thickness of the active soil layer (mm), (η) the inverse of efficient porosity, and (σ ) the resistance of vegetation to evapotranspiration. Bargaoui and Houcine (2010) reformulated parameters (a, b, c) using soil texture parameters: field capacity (SFC), saturated hydraulic conductivity (K s ), effective soil porosity (p), and the soil water retention curve shape parameter (B) as well as the thickness of the active soil layer (D). Rawls et al (1982) and Cosby et al (1984) models are adopted to estimate K s and SFC respectively.…”

Section: Bucket Bottom Hole (Bbh) Modelmentioning

confidence: 99%

Comparison of Actual Evapotranspiration assessment by satellite-based model SEBS and hydrological model BBH in northern Tunisia

Abid,

Bargaoui,

Ben Jaafar

et al. 2024

Proc. IAHS

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Abstract. Estimating actual evapotranspiration (AET) in agricultural semi-arid regions is important for crop yield and drought assessment. The Surface Energy Balance System (SEBS) model, a physically based energy balance model using satellite information is used to estimate AET at the 10 d scale, with a 3 km resolution. The bucket bottom hole (BBH) model, a conceptual daily water balance model is calibrated using the equifinality approach and run for simulating daily AET. Five watersheds located in northern Tunisia with areas varying between 56 and 448 km2 were calibrated using daily rainfall and potential evapotranspiration data as entry and river discharge as output data. Sets of model parameters fulfilling both absolute relative errors of simulated discharge less than 20 % and Nash–Sutcliffe coefficients greater than 0.75 were selected. Three years were selected for the comparison (2010, 2017, and 2018). For every year, six subperiods of 10 d are considered belonging to January, March, April, May, July, and September. Boxplots of AET-BBH estimations are plotted to achieve a comparison with AET-SEBS estimates. It is found that AET comparisons are well favorable for January, March, and April while less satisfactory for May and September. They do not match for July. AET-SEBS are much higher in comparison with AET-BBH estimates with an RMSE and MAE equal respectively to 17 and 19 mm 10 d−1. These results may help stakeholders to assess AET coming from different data sources and models.

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Section: Bucket Bottom Hole (Bbh) Modelmentioning

confidence: 99%

Comparison of Actual Evapotranspiration assessment by satellite-based model SEBS and hydrological model BBH in northern Tunisia

Abid,

Bargaoui,

Ben Jaafar

et al. 2024

Proc. IAHS

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“…This case study is presented in Bargaoui et al (2008) and Bargaoui & Houcine (2010). It is aimed to calibrate the H.C. model using climatic, rainfall and runoff data from gauged watersheds.…”

Section: Fitting Empirical Models Of Regional Evapotranspirationmentioning

confidence: 99%

“…In the period September 1985 to August 1989, meteorological data are missing and the used E 0 values are the daily long term average computed for September 1989-August 1999. The H.C. model results in an average annual evapotranspiration E m = 213 mm/year (Bargaoui & Houcine, 2010). BBH model inputs are precipitation and potential evapotranspiration and seven parameters are to be calibrated.…”

Section: Multicriteria Calibration Of Bbh Model Using Regional Evapotmentioning

confidence: 99%

“…where s is the degree of saturation (unit less); K S saturated hydraulic conductivity at soil surface (mm/day); B is the soil water retention curve shape parameter; S FC (unit less) is the field capacity; W max = nZ a (W max is the total water-holding capacity in mm). Coupling this expression with pedo-transfer functions it makes it possible after Bargaoui & Houcine ( 2010), to derive the parameters (a, b, c) as following using pedo-transfer parameters K S , B and S FC :…”

Section: Multicriteria Calibration Of Bbh Model Using Regional Evapotmentioning

confidence: 99%

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Estimation of Evapotranspiration Using Soil Water Balance Modelling

Bargaoui¹

2012

Evapotranspiration - Remote Sensing and Modeling

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Hydrological Modeling in Northern Tunisia with Regional Climate Model Outputs: Performance Evaluation and Bias-Correction in Present Climate Conditions

Foughali

Tramblay

Bargaoui

et al. 2015

Climate

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This work aims to evaluate the performance of a hydrological balance model in a watershed located in northern Tunisia (wadi Sejnane, 378 km 2 ) in present climate conditions using input variables provided by four regional climate models. A modified version (MBBH) of the lumped and single layer surface model BBH (Bucket with Bottom Hole model, in which pedo-transfer parameters estimated using watershed physiographic characteristics are introduced) is adopted to simulate the water balance components. Only two parameters representing respectively the water retention capacity of the soil and the vegetation resistance to evapotranspiration are calibrated using rainfall-runoff data. The evaluation criterions for the MBBH model calibration are: relative bias, mean square error and the ratio of mean actual evapotranspiration to mean potential evapotranspiration. Daily air temperature, rainfall and runoff observations are available from 1960 to 1984. The period 1960-1971 is selected for calibration while the period 1972-1984 is chosen for validation. Air temperature and precipitation series are provided by four regional climate models (DMI, ARP, SMH and ICT) from the European program ENSEMBLES, forced by two global climate models (GCM): ECHAM and ARPEGE. The regional climate model outputs (precipitation and air temperature) are compared to the observations in terms of statistical distribution. The analysis was performed at the seasonal scale for precipitation. We found out that RCM OPEN ACCESSClimate 2015, 3 460 precipitation must be corrected before being introduced as MBBH inputs. Thus, a nonparametric quantile-quantile bias correction method together with a dry day correction is employed. Finally, simulated runoff generated using corrected precipitation from the regional climate model SMH is found the most acceptable by comparison with runoff simulated using observed precipitation data, to reproduce the temporal variability of mean monthly runoff. The SMH model is the most accurate to reproduce the occurrence of dry days but still underestimates them. From the statistical distribution point of view, corrected SMH precipitation data introduced into the MBBH model were not able to reproduce extreme runoff values generated by observed precipitation data during validation (larger than 80 mm/month). This may be due to the SMH weakness in reproducing moderate and high rainfall levels even after bias correction. This approach may be considered as a way to use regional climate models (RCM) model outputs for studying hydrological impacts.

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Sensitivity to calibration data of simulated soil moisture-related drought indices

Cited by 4 publications

References 12 publications

Comparison of Actual Evapotranspiration assessment by satellite-based model SEBS and hydrological model BBH in northern Tunisia

Comparison of Actual Evapotranspiration assessment by satellite-based model SEBS and hydrological model BBH in northern Tunisia

Estimation of Evapotranspiration Using Soil Water Balance Modelling

Hydrological Modeling in Northern Tunisia with Regional Climate Model Outputs: Performance Evaluation and Bias-Correction in Present Climate Conditions

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