Forest impact on floods due to extreme rainfall and snowmelt in four Latin American environments 2: Model analysis

Bathurst, James C.; Birkinshaw, Stephen; Cisneros, Félipe; Fallas, Jorge A.; Iroumé, Andrés; Iturraspe, Rodolfo Javier; Novillo, Marcelo Gaviño; Urciuolo, Adriana Beatriz; Alvarado, Andrés; Coello, Cristian; Huber, Anton; Miranda, Miriam; Ramírez, Marco; Sarandón, Ramiro

doi:10.1016/j.jhydrol.2010.09.001

Cited by 58 publications

(53 citation statements)

References 37 publications

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“…However, transpiration depends on the actual/potential evapotranspiration (or crop coefficient) and this value was calibrated by taking into account differences between land-use types from previous simulations (e.g. Bathurst et al, 2011;Birkinshaw et al, 2014).…”

Section: Shetranmentioning

confidence: 99%

Climate change impacts on Yangtze River discharge at the Three Gorges Dam

Birkinshaw

Guerreiro

Nicholson

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. The Yangtze River basin is home to more than 400 million people and contributes to nearly half of China's food production. Therefore, planning for climate change impacts on water resource discharges is essential. We used a physically based distributed hydrological model, Shetran, to simulate discharge in the Yangtze River just below the Three Gorges Dam at Yichang (1 007 200 km 2 ), obtaining an excellent match between simulated and measured daily discharge, with Nash-Sutcliffe efficiencies of 0.95 for the calibration period (1996)(1997)(1998)(1999)(2000) and 0.92 for the validation period (2001)(2002)(2003)(2004)(2005). We then used a simple monthly delta change approach for 78 climate model projections (35 different general circulation models -GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5) to examine the effect of climate change on river discharge for 2041-2070 for Representative Concentration Pathway 8.5. Projected changes to the basin's annual precipitation varied between −3.6 and +14.8 % but increases in temperature and consequently evapotranspiration (calculated using the Thornthwaite equation) were projected by all CMIP5 models, resulting in projected changes in the basin's annual discharge from −29.8 to +16.0 %. These large differences were mainly due to the predicted expansion of the summer monsoon north and west into the Yangtze Basin in some CMIP5 models, e.g. CanESM2, but not in others, e.g. CSIRO-Mk3-6-0. This was despite both models being able to simulate current climate well. Until projections of the strength and location of the monsoon under a future climate improve, large uncertainties in the direction and magnitude of future change in discharge for the Yangtze will remain.

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Section: Shetranmentioning

confidence: 99%

Climate change impacts on Yangtze River discharge at the Three Gorges Dam

Birkinshaw

Guerreiro

Nicholson

et al. 2017

Hydrol. Earth Syst. Sci.

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“…Although other parameters such as K sat or soil hydrological parameters are reported to be important, a choice was made based on previous studies [34,39,40,85,86] in order to limit run time. Based on the sum of R 2 , KGE, and NSE, several parameter sets gave satisfactory to good quality measures according to the equifinality concept introduced by Beven and Freer [16].…”

Section: Hydrological Modellingmentioning

confidence: 99%

Applying SHETRAN in a Tropical West African Catchment (Dano, Burkina Faso)—Calibration, Validation, Uncertainty Assessment

Hipt

Diekkrüger

Steup

et al. 2017

Water

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This study presents the calibration and validation of the physically based spatially distributed hydrological and soil erosion model SHETRAN for the Dano catchment, Burkina Faso. A sensitivity analysis of six model parameters was performed to assess the model response and to reduce the number of parameters for calibration. The hydrological component was calibrated and validated using observed discharge data of two years. Statistical quality measures (R 2 , NSE, KGE) ranged from 0.79 to 0.66 during calibration and validation. The calibrated hydrological component was used to feed the erosion modeling. The simulated suspended sediment load (SSL) was compared with turbidity-based measurements of SSL of two years. Achieved quality measures are comparable to other SHETRAN studies. Uncertainties of measured discharge and suspended sediment concentration were determined to assess the propagated uncertainty of SSL. The comparison of measurement uncertainties of discharge and SSL with parameter uncertainty of the corresponding model output showed that simulated discharge and SSL were frequently outside the large measured uncertainty bands. A modified NSE was used to incorporate measurement and parameter uncertainty into the efficiency evaluation of the model. The analyses of simulated erosion sources and spatial patterns showed the importance of river erosion contributing more than 60% to the total simulated sediment loss.

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“…The parameters like Strickler overland flow resistance coefficient, Actual Evapotranspiration/Potential Evapotranspiration ratio and soil parameters namely soil depth, saturated hydraulic conductivity, soil water retention and hydraulic conductivity functions were identified as key parameters required to be specified using field or calibrated data for flow simulations from studies conducted by Parkin [14], Bathurst et al [5,6] and Birkinshaw et al [18]. A sensitivity analysis of the above six parameters is performed to arrive at the final values.…”

Section: Model Calibration and Validationmentioning

confidence: 99%

“…The spatial heterogeneity of rainfall in the model is accounted by using various spatial interpolation methods. The impact of different spatiotemporal resolution of rainfall input on simulated runoff, using hydrological models other than SHETRAN, was examined by many studies [4,5,6,7]. Dirks et al, compared four interpolation methods namely the Inverse distance weighted method, Theissen polygon, Kriging and Areal mean method using rainfall data from a network of thirteen rain gauges in Norfolk Island [8].…”

Section: Introductionmentioning

confidence: 99%