Assessing the impact of land use change on hydrology by ensemble modelling (LUCHEM) II: Ensemble combinations and predictions

Viney, Neil R.; Bormann, Helge; Breuer, Lutz; Bronstert, Axel; Croke, Barry; Frede, H. G.; Gräff, Thomas; Hubrechts, Lode; Huisman, Johan Alexander; Jakeman, Anthony J.; Kite, Geoff; Lanini, J; Leavesley, George H.; Lettenmaier, Dennis P.; Lindström, Göran; Seibert, Jan; Sivapalan, Murugesu; Willems, Patrick

doi:10.1016/j.advwatres.2008.05.006

Cited by 134 publications

(124 citation statements)

References 25 publications

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“…These range from small Canadian wetland basins of less than 1 km 2 (Su, 2000), through catchments of hundreds of square kilometres with very different climatologies including studies in Canada (Armstrong and Martz, 2008), Germany (Viney et al, 2009), Turkey (Apaydin et al, 2006) and South Korea (Kim et al, 2007;Park et al, 2009), to major river basins including upper tributaries of the Indus and Yangtze (Jain et al, 1998;Woo et al, 2009).…”

Section: The Slurp Hydrological Modelmentioning

confidence: 99%

Uncertainty in climate change projections of discharge for the Mekong River Basin

Kingston

Thompson

Kite³

2011

Hydrol. Earth Syst. Sci.

158

140

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Abstract. The Mekong River Basin is a key regional resource in Southeast Asia for sectors that include agriculture, fisheries and electricity production. Here we explore the potential impacts of climate change on freshwater resources within the river basin. We quantify uncertainty in these projections associated with GCM structure and climate sensitivity, as well as from hydrological model parameter specification. This is achieved by running patternscaled GCM scenarios through a semi-distributed hydrological model (SLURP) of the basin. Pattern-scaling allows investigation of specific thresholds of global climate change including the postulated 2 • C threshold of "dangerous" climate change. Impacts of a 2 • C rise in global mean temperature are investigated using seven different GCMs, providing an implicit analysis of uncertainty associated with GCM structure. Analysis of progressive changes in global mean temperature from 0.5 to 6 • C above the 1961-1990 baseline (using the HadCM3 GCM) reveals a relatively small but non-linear response of annual river discharge to increasing global mean temperature, ranging from a 5.4 % decrease to 4.5 % increase. Changes in mean monthly river discharge are greater (from −16 % to +55 %, with greatest decreases in July and August, greatest increases in May and June) and result from complex and contrasting intra-basin changes in precipitation, evaporation and snow storage/melt. Whilst overall results are highly GCM dependent (in both direction and magnitude), this uncertainty is primarily driven by differences in GCM projections of future precipitation. In contrast, there is strong consistency between GCMs in terms of both increased potential evapotranspiration and a shift to an earlier

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Section: The Slurp Hydrological Modelmentioning

confidence: 99%

Uncertainty in climate change projections of discharge for the Mekong River Basin

Kingston

Thompson

Kite³

2011

Hydrol. Earth Syst. Sci.

158

140

View full text Add to dashboard Cite

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“…PRMS has been applied successfully in several regions with varying climate and land use (Bae et al, 2008a;Clark et al, 2008;Hay et al, 2006;Qi et al, 2009;Viney et al, 2009). To better understand the wide array of individual and combined factors that can affect the hydrologic response in a watershed system, Risley et al (2010) employed PRMS, driven by GCM outputs, in 14 watersheds across the US, and conducted a comparative statistical analysis on the outputs.…”

Section: A Process Of Flood Frequency Uncertainty Analysismentioning

confidence: 99%

Quantifying uncertainty in urban flooding analysis considering hydro-climatic projection and urban development effects

Jung

Chang

Moradkhani

2011

Hydrol. Earth Syst. Sci.

106

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Abstract. How will the combined impacts of land use change, climate change, and hydrologic modeling influence changes in urban flood frequency and what is the main uncertainty source of the results? Will such changes differ by catchment with different degrees of current and future urban development? We attempt to answer these questions in two catchments with different degrees of urbanization, the Fanno catchment with 84% urban land use and the Johnson catchment with 36% urban land use, both located in the Pacific Northwest of the US. Five uncertainty sources -general circulation model (GCM) structures, future greenhouse gas (GHG) emission scenarios, land use change scenarios, natural variability, and hydrologic model parameters -are considered to compare the relative source of uncertainty in flood frequency projections. Two land use change scenarios, conservation and development, representing possible future land use changes are used for analysis. Results show the highest increase in flood frequency under the combination of medium high GHG emission (A1B) and development scenarios, and the lowest increase under the combination of low GHG emission (B1) and conservation scenarios. Although the combined impact is more significant to flood frequency change than individual scenarios, it does not linearly increase flood frequency. Changes in flood frequency are more sensitive to climate change than land use change in the two catchments for 2050s . Shorter term flood frequency change, 2 and 5 year floods, is highly affected by GCM structure, while longer term flood frequency change above 25 year floods is dominated by natural variability. Projected flood frequency changes more significantly in Johnson creek than Fanno creek. This result indicates that, under expected Correspondence to: H. Chang (changh@pdx.edu) climate change conditions, adaptive urban planning based on the conservation scenario could be more effective in less developed Johnson catchment than in the already developed Fanno catchment.

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“…Since then there have been several more studies which have dealt with multi-model combination of hydrological models (e.g. (Abrahart and See 2002, Ajami, et al 2006, Coulibaly, et al 2005, Hsu, et al 2009, See and Openshaw 2000, Shamseldin, et al 2007, Viney, et al 2009, Xiong, et al 2001). As the nature of the combination function is unknown and no theory exists to analytically derive the combination function from a hydrological or physical point of view, previous studies have used empirical data-driven modeling to derive the combination function and such use is very appropriate.…”

Section: Introductionmentioning

confidence: 99%

Use of Gene Expression Programming for Multimodel Combination of Rainfall-Runoff Models

2012

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This paper deals with the application of an innovative method for combining estimated outputs from a number of rainfall-runoff models using Gene Expression Programming (GEP) to perform symbolic regression. The GEP multi-model combination method uses the synchronous simulated river flows from four conventional rainfall-runoff models to produce a set of combined river flow estimates for four different catchments.The four selected models for the multi-model combinations are the Linear Perturbation Model (LPM), the Linearly Varying Gain Factor Model (LVGFM), the Soil Moisture Accounting and Routing (SMAR) Model, and the Probability-Distributed Interacting Storage Capacity (PDISC) model. The first two of these models are 'black-box' models, the LPM exploiting seasonality and the LVGFM employing a storage-based coefficient of runoff. The remaining two are conceptual models. The data of four catchments with different geographical location, hydrological and climatic conditions, are used to test the performance of the GEP combination method.The results of the model using GEP method are compared against original forecasts obtained from the individual models that contributed to the development of the combined model by means of a few global statistics. The findings show that a GEP approach can successfully used as a multi-model combination method. In addition, the GEP combination method also has benefit over other hitherto tested approaches such as an artificial neural network combination method in that its formulation is transparent, can be expressed as a simple mathematical function, and therefore is useable by people who are unfamiliar with such advanced techniques. The GEP combination method is able to combine model outcomes from less accurate individual models and produce a superior river flow forecast.

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Assessing the impact of land use change on hydrology by ensemble modelling (LUCHEM) II: Ensemble combinations and predictions

Cited by 134 publications

References 25 publications

Uncertainty in climate change projections of discharge for the Mekong River Basin

Uncertainty in climate change projections of discharge for the Mekong River Basin

Quantifying uncertainty in urban flooding analysis considering hydro-climatic projection and urban development effects

Use of Gene Expression Programming for Multimodel Combination of Rainfall-Runoff Models

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