Application of parameter estimation and regionalization methodologies to ungauged basins of the Upper Blue Nile River Basin, Ethiopia

Kim, Ungtae; Kaluarachchi, Jagath J.

doi:10.1016/j.jhydrol.2008.08.016

Cited by 105 publications

(94 citation statements)

References 53 publications

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“…Other studies have provided parameter estimations for water balance models (e.g. Kim and Kaluarachchi, 2008) and the analysis of runoff and sediment fluxes (e.g. Gebremicael et al, 2013;Steenhuis et al, 2009) in the upper Blue Nile.…”

Section: Introductionmentioning

confidence: 99%

Upper Blue Nile basin water budget from a multi-model perspective

Jung

Getirana

Policelli

et al. 2017

Journal of Hydrology

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a b s t r a c tImproved understanding of the water balance in the Blue Nile is of critical importance because of increasingly frequent hydroclimatic extremes under a changing climate. The intercomparison and evaluation of multiple land surface models (LSMs) associated with different meteorological forcing and precipitation datasets can offer a moderate range of water budget variable estimates. In this context, two LSMs, Noah version 3.3 (Noah3.3) and Catchment LSM version Fortuna 2.5 (CLSMF2.5) coupled with the Hydrological Modeling and Analysis Platform (HyMAP) river routing scheme are used to produce hydrological estimates over the region. The two LSMs were forced with different combinations of two reanalysis-based meteorological datasets from the Modern-Era Retrospective analysis for Research and Applications datasets (i.e., MERRA-Land and MERRA-2) and three observation-based precipitation datasets, generating a total of 16 experiments. Modeled evapotranspiration (ET), streamflow, and terrestrial water storage estimates were evaluated against the Atmosphere-Land Exchange Inverse (ALEXI) ET, insitu streamflow observations, and NASA Gravity Recovery and Climate Experiment (GRACE) products, respectively. Results show that CLSMF2.5 provided better representation of the water budget variables than Noah3.3 in terms of Nash-Sutcliffe coefficient when considering all meteorological forcing datasets and precipitation datasets. The model experiments forced with observation-based products, the Climate Hazards group Infrared Precipitation with Stations (CHIRPS) and the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA), outperform those run with MERRA-Land and MERRA-2 precipitation. The results presented in this paper would suggest that the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System incorporate CLSMF2.5 and HyMAP routing scheme to better represent the water balance in this region.

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Section: Introductionmentioning

confidence: 99%

Upper Blue Nile basin water budget from a multi-model perspective

Jung

Getirana

Policelli

et al. 2017

Journal of Hydrology

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“…Kokkonen et al, 2003, Oudin et al, 2008Kim and Kaluarachchi, 2008), using averages of streamflow that are simulated from parameter sets transferred from the donor catchments or "output averaging" (e.g. Oudin et al, 2008) and ensemble simulations.…”

Section: Regionalization Methodsmentioning

confidence: 99%

“…Littlewood and Croke (2013) indicated the importance of a fine temporal resolution also for extraction of the information content of the data for accuracy of calibrated parameters, which would allow more reliable parameter regionalization. Uncertainties related to model structure, parameter calibration and input data affect the performance of the regionalization of precipitation-runoff models for continuous simulation of streamflow (see Wagener and Wheater, 2006;Oudin et al, 2008;Oudin et al, 2010;Kim and Kaluarachchi, 2008;Gupta et al, 2008). Engeland and Gottschalk (2002) noted that structural errors in the model are more important than parameter uncertainties.…”

Section: Factors That Influence Regionalization Performancementioning

confidence: 99%

“…Several previous studies (e.g. Croke and McIntyre, 2013;Yadav et al, 2007;Oudin et al, 2008;Samuel et al, 2011;Kim and Kaluarachchi, 2008) noted the importance of considering the representativeness or quality of input climate data for PUB.…”

Section: Factors That Influence Regionalization Performancementioning

confidence: 99%

“…multi-donor median of parameter set) and then transferring homogeneous parameter sets for the whole region for prediction. The only difference between this method and the "parameter averaging" pooling option by Kokkonen et al (2003), Oudin et al (2008) and Kim and Kaluarachchi (2008) is that the median rather than the mean values of parameters were used. However, a limitation of transferring either median or mean parameters is that the method transfers the regional median or mean of each parameter rather than a set of optimal parameters and hence does not keep the correlation structure of the calibrated model parameters.…”

Section: Regional Median Parametersmentioning

confidence: 99%

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Evaluation of Regionalization Methods for Hourly Continuous Streamflow Simulation Using Distributed Models in Boreal Catchments

et al. 2015

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Regionalization for prediction in ungauged basins at hourly resolution is important for water resources management (e.g. floods and hydropeaking). In this paper, calibration of 26 catchments (39-3090 km 2 ) in mid-Norway was performed using hourly records and three spatially distributed (1x1 km 2 ) precipitation-runoff models: a first-order nonlinear system model (hereafter Kirchmod), the HBV model and the Basic-Grid-Model (BGM). Four regionalization methods for each model namely parameter set yielding maximum regional weighted average (MRWA) performance measures (PM), regional median of optimal parameters (RMedP), nearest neighbor (NN) and physical similarity (PS) were evaluated and compared with three benchmarks. Parameter transfer from best regional donor (BRD) and from an ideal best arbitrary single-donor (BASD), and local calibration (LC) were as benchmarks. The physical similarity attributes include hypsometric curves (PSH), land use (PSL), drainage density (PSD), catchment area (PSA), terrain slope (PSS), bedrock geology (PSR), soil type (PSSO) and combination of all (PSC).Comprehensive evaluation of single-and multi-donors, simple benchmarks and more advanced regionalization methods using multi-models, two PM and their statistical evaluation indicate that the identification of regionalization methods is dependent on the models, the PM and their statistical evaluation. In general, the PSH, PSC and BRD methods performed better for the Nash-Sutcliffe efficiency (NSE) based on boxplots and regional median values of both the NSE and relative deterioration or improvement of the NSE from the local calibration due to the regionalization. The methods also performed better for the individual catchments. The PSS, RMedP, MRWA and BRD methods performed better for the log-transformed streamflow 2 (NSEln) based on the same evaluation criteria. Similar performance to the more advanced regionalization methods of transfer of homogeneous parameter sets across the whole region from the BRD for both NSE and NSEln indicate the potential of the simple regionalization approach for predicting runoff response in the region.

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Improving the surface‐ground water interactions in the Community Land Model: Case study in the Blue Nile Basin

Anagnostou

Wang

et al. 2014

Water Resources Research

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Soil moisture is a key water cycle parameter known to interact with atmospheric processes.Arguably, land surface models that simulate land surface processes and surface fluxes to the atmosphere do not capture adequately the spatial variability of soil moisture, particularly over areas with complex topography. In this study, version 3.5 of the Community Land Model (CLM3.5) is applied with a new parameterization in an effort to correct the spatial bias of soil moisture and understand the consequential effects on the simulated water cycle fluxes and states in the Blue Nile Basin. This parameterization accounts for a groundwater recharge term from surface water, a process that is not included in CLM, providing an effective two-way interaction scheme between rivers and groundwater. Using satellite soil moisture data, this parameterized term is shown to have a positive correlation to contributing area, defined at each model grid cell and representing the number of grid cells that drain to that local grid cell. With the new parameterization applied to CLM, soil moisture, soil temperature, evapotranspiration flux, water table depth, and vegetation water content all showed significant differences from the control CLM run (without the parameterization) at or above the 95% confidence level. The differences in the spatial distribution of these variables are expected to affect precipitation simulations from regional climate modeling. As the Blue Nile is a region that has one of the greatest interannual and seasonal precipitation variability globally, the ability to predict this variability is essential for optimal reservoir operations including buffering of water resources during times of drought.

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Application of parameter estimation and regionalization methodologies to ungauged basins of the Upper Blue Nile River Basin, Ethiopia

Cited by 105 publications

References 53 publications

Upper Blue Nile basin water budget from a multi-model perspective

Upper Blue Nile basin water budget from a multi-model perspective

Evaluation of Regionalization Methods for Hourly Continuous Streamflow Simulation Using Distributed Models in Boreal Catchments

Improving the surface‐ground water interactions in the Community Land Model: Case study in the Blue Nile Basin

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