Assessment of climate change impact on floods using weather generator and continuous rainfall-runoff model

2011 ◽  
Vol 32 (13) ◽  
pp. 1997-2006 ◽  
Author(s):  
Mohammad Reza Khazaei ◽  
Bagher Zahabiyoun ◽  
Bahram Saghafian
Keyword(s):  
Climate Change ◽  
Climate Change Impact ◽  
Weather Generator ◽  
Rainfall Runoff ◽  
Change Impact ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Continuous Rainfall

scholarly journals Assessing Hydrologic Impact of Climate Change with Uncertainty Estimates: Bayesian Neural Network Approach

2010 ◽  
Vol 11 (2) ◽  
pp. 482-495 ◽  
Author(s):  
Mohammad Sajjad Khan ◽  
Paulin Coulibaly
Keyword(s):  
Neural Network ◽  
Climate Change ◽  
Rainfall Runoff ◽  
Bayesian Neural Network ◽  
Uncertainty Estimates ◽  
Change Impact ◽  
Climate Change Impact Study ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Different Sources

Abstract A major challenge in assessing the hydrologic effect of climate change remains the estimation of uncertainties associated with different sources, such as the global climate models, emission scenarios, downscaling methods, and hydrologic models. There is a demand for an efficient and easy-to-use rainfall–runoff modeling tool that can capture the different sources of uncertainties to generate future flow simulations that can be used for decision making. To manage the large range of uncertainties in the climate change impact study on water resources, a neural network–based rainfall–runoff model—namely, Bayesian neural network (BNN)—is proposed. The BNN model is used with Canadian Centre for Climate Modelling and Analysis Coupled GCM, versions 1 and 2 (CGCM1 and CGCM2, respectively) with two emission scenarios, Intergovernmental Panel on Climate Change (IPCC) IS92a and Special Report on Emissions Scenarios (SRES) B2. One widely used statistical downscaling model (SDSM) is used in the analysis. The study is undertaken to simulate daily river flow and daily reservoir inflow in the Serpent and the Chute-du-Diable watersheds, respectively, in northeastern Canada. It is found that the uncertainty bands of the mean ensemble flow (i.e., flow simulated using the mean of the ensemble members of downscaled meteorological variables) is able to mostly encompass all other flows simulated with various individual downscaled meteorological ensemble members whichever CGCM or emission scenario is used. In addition, the uncertainty bands are also able to typically encompass most of the flows simulated with another rainfall–runoff model, namely, Hydrologiska Byråns Vattenbalansavdelning (HBV). The study results suggest that the BNN model could be used as an effective hydrological modeling tool in assessing the hydrologic effect of climate change with uncertainty estimates in the form of confidence intervals. It could be a good alternative method where resources are not available to implement the general multimodel ensembles approach. The BNN approach makes the climate change impact study on water resources with uncertainty estimate relatively simple, cost effective, and time efficient.

scholarly journals Coupling Tank Model and Lars-Weather Generator in Assessments of the Impacts of Climate Change on Water Resources

2019 ◽  
Vol 27 (1) ◽  
pp. 14-24 ◽  
Author(s):  
Naser Mohammadzadeh ◽  
Bahman Jabbarian Amiri ◽  
Leila Eslami Endergoli ◽  
Shirin Karimi
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Weather Generator ◽  
Climate Change Scenarios ◽  
Rainfall Runoff ◽  
The Caspian Sea ◽  
Tank Model ◽  
Rainfall Runoff Model ◽  
The Impact ◽  
Runoff Model

Abstract With the aim of assessing the impact of climate change on surface water resources, a conceptual rainfall-runoff model (the tank model) was coupled with LARS-WG as a weather generator model. The downscaled daily rainfall, temperature, and evaporation from LARS-WG under various IPCC climate change scenarios were used to simulate the runoff through the calibrated Tank model. A catchment (4648 ha) located in the southern basin of the Caspian Sea was chosen for this research study. The results showed that this model has a reasonable predictive capability in simulating minimum and maximum temperatures at a level of 99%, rainfall at a level of 93%, and radiation at a level of 97% under various scenarios in agreement with the observed data. Moreover, the results of the rainfall-runoff model indicated an increase in the flow rate of about 108% under the A1B scenario, 101% under the A2 scenario, and 93% under the B1 scenario over the 30-year time period of the discharge prediction.

scholarly journals Application of a continuous Rainfall-Runoff model to the basin of Kosynthos river using the hydrological software HEC-HMS

2014 ◽  
Vol 16 (1) ◽  
pp. 188-203 ◽  
Keyword(s):  
Soil Conservation ◽  
Curve Number ◽  
Hydrologic Model ◽  
Army Corps ◽  
Soil Conservation Service ◽  
Rainfall Runoff ◽  
Runoff Hydrograph ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Continuous Rainfall

<div> <h1 style="text-align: justify;"><span style="font-size:11px;"><span style="font-family:arial,helvetica,sans-serif;">In this paper, the application of a continuous rainfall-runoff model to the basin of Kosynthos River (district of Xanthi, Thrace, northeastern Greece), as well as the comparison of the computational runoff results with field discharge measurements are presented. The rainfall losses are estimated by the widely known Soil Conservation Service-Curve Number model, while the transformation of rainfall excess into direct runoff hydrograph is made by using the dimensionless unit hydrograph of Soil Conservation Service. The baseflow is computed by applying an exponential recession model. The routing of the total runoff hydrograph from the outlet of a sub-basin to the outlet of the whole basin is achieved by the Muskingum-Cunge model. The application of this complex hydrologic model was elaborated with the HEC-HMS 3.5 Hydrologic Modeling System of the U.S. Army Corps of Engineers. The results of the comparison between computed and measured discharge values are very satisfactory.</span></span></h1> </div> <p>&nbsp;</p>

scholarly journals Estimating the Relative Uncertainties Sourced from GCMs and Hydrological Models in Modeling Climate Change Impact on Runoff

2012 ◽  
Vol 13 (1) ◽  
pp. 122-139 ◽  
Author(s):  
Jin Teng ◽  
Jai Vaze ◽  
Francis H. S. Chiew ◽  
Biao Wang ◽  
Jean-Michel Perraud
Keyword(s):  
Climate Change ◽  
Climate Change Impact ◽  
Daily Rainfall ◽  
Low Flow ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Runoff Modeling ◽  
Change Impact ◽  
The Difference ◽  
Climate Series

Abstract This paper assesses the relative uncertainties from GCMs and from hydrological models in modeling climate change impact on runoff across southeast Australia. Five lumped conceptual daily rainfall–runoff models are used to model runoff using historical daily climate series and using future climate series obtained by empirically scaling the historical climate series informed by simulations from 15 GCMs. The majority of the GCMs project a drier future for this region, particularly in the southern parts, and this is amplified as a bigger reduction in the runoff. The results indicate that the uncertainty sourced from the GCMs is much larger than the uncertainty in the rainfall–runoff models. The variability in the climate change impact on runoff results for one rainfall–runoff model informed by 15 GCMs (an about 28%–35% difference between the minimum and maximum results for mean annual, mean seasonal, and high runoff) is considerably larger than the variability in the results between the five rainfall–runoff models informed by 1 GCM (a less than 7% difference between the minimum and maximum results). The difference between the rainfall–runoff modeling results is larger in the drier regions for scenarios of big declines in future rainfall and in the low-flow characteristics. The rainfall–runoff modeling here considers only the runoff sensitivity to changes in the input climate data (primarily daily rainfall), and the difference between the hydrological modeling results is likely to be greater if potential changes in the climate–runoff relationship in a warmer and higher CO2 environment are modeled.

scholarly journals Climate change model as a decision support tool for water resources management in northern Iraq: a case study of Greater Zab River

2017 ◽  
Vol 10 (1) ◽  
pp. 197-209 ◽  
Author(s):  
Y. Osman ◽  
N. Al-Ansari ◽  
M. Abdellatif
Keyword(s):  
Climate Change ◽  
Water Management ◽  
Daily Rainfall ◽  
Decision Support Tool ◽  
Northern Region ◽  
Future Climate Change ◽  
Rainfall Runoff ◽  
Support Tool ◽  
Rainfall Runoff Model ◽  
Runoff Model

Abstract The northern region of Iraq heavily depends on rivers, such as the Greater Zab, for water supply and irrigation. Thus, river water management in light of future climate change is of paramount importance in the region. In this study, daily rainfall and temperature obtained from the Greater Zab catchment, for 1961–2008, were used in building rainfall and evapotranspiration models using LARS-WG and multiple linear regressions, respectively. A rainfall–runoff model, in the form of autoregressive model with exogenous factors, has been developed using observed flow, rainfall and evapotranspiration data. The calibrated rainfall–runoff model was subsequently used to investigate the impacts of climate change on the Greater Zab flows for the near (2011–2030), medium (2046–2065), and far (2080–2099) futures. Results from the impacts model showed that the catchment is projected to suffer a significant reduction in total annual flow in the far future; with more severe drop during the winter and spring seasons in the range of 25 to 65%. This would have serious ramifications for the current agricultural activities in the catchment. The results could be of significant benefits for water management planners in the catchment as they can be used in allocating water for different users in the catchment.

scholarly journals Parameter Uncertainty of a Snowmelt Runoff Model and Its Impact on Future Projections of Snowmelt Runoff in a Data-Scarce Deglaciating River Basin

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2417 ◽  
Author(s):  
Yiheng Xiang ◽  
Lu Li ◽  
Jie Chen ◽  
Chong-Yu Xu ◽  
Jun Xia ◽  
...  
Keyword(s):  
Climate Change ◽  
Impact Assessment ◽  
Parameter Uncertainty ◽  
Climate Change Impact ◽  
Model Parameters ◽  
Snowmelt Runoff ◽  
Climate Change Impact Assessment ◽  
Sources Of Uncertainty ◽  
Change Impact ◽  
Runoff Model

The impacts of climate change on water resources in snow- and glacier-dominated basins are of great importance for water resource management. The Snowmelt Runoff Model (SRM) was developed to simulate and predict daily streamflow for high mountain basins where snowmelt runoff is a major contributor. However, there are many sources of uncertainty when using an SRM for hydrological simulations, such as low-quality input data, imperfect model structure and model parameters, and uncertainty from climate scenarios. Among these, the identification of model parameters is considered to be one of the major sources of uncertainty. This study evaluates the parameter uncertainty for SRM simulation based on different calibration strategies, as well as its impact on future hydrological projections in a data-scarce deglaciating river basin. The generalized likelihood uncertainty estimation (GLUE) method implemented by Monte Carlo sampling was used to estimate the model uncertainty arising from parameters calibrated by means of different strategies. Future snowmelt runoff projections under climate change impacts in the middle of the century and their uncertainty were assessed using average annual hydrographs, annual discharge and flow duration curves as the evaluation criteria. The results show that: (1) the strategy with a division of one or two sub-period(s) in a hydrological year is more appropriate for SRM calibration, and is also more rational for hydrological climate change impact assessment; (2) the multi-year calibration strategy is also more stable; and (3) the future runoff projection contains a large amount of uncertainty, among which parameter uncertainty plays a significant role. The projections also indicate that the onset of snowmelt runoff is likely to shift earlier in the year, and the discharge over the snowmelt season is projected to increase. Overall, this study emphasizes the importance of considering the parameter uncertainty of time-varying hydrological processes in hydrological modelling and climate change impact assessment.

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