The impact of land use change on catchment hydrology in large catchments: The Comet River, Central Queensland, Australia

Siriwardena, Lionel; Finlayson, Brian; McMahon, TA

doi:10.1016/j.jhydrol.2005.10.030

Cited by 233 publications

(149 citation statements)

References 11 publications

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“…experimental studies of CO 2 effects on plants (Ainsworth and Long, 2005), or the effects of land-use changes on catchment hydrology (e.g. Siriwardena et al, 2006). Increased confidence in model performance can be achieved through the evaluation of specific assumptions embedded in models against experimental data (Medlyn et al, 2015).…”

Section: Model Comparisons Evaluations and The Need For Benchmarkingmentioning

confidence: 99%

Reliable, robust and realistic: the three R's of next-generation land-surface modelling

et al. 2015

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Abstract. Land-surface models (LSMs) are increasingly called upon to represent not only the exchanges of energy, water and momentum across the land-atmosphere interface (their original purpose in climate models), but also how ecosystems and water resources respond to climate, atmospheric environment, land-use and land-use change, and how these responses in turn influence land-atmosphere fluxes of carbon dioxide (CO 2 ), trace gases and other species that affect the composition and chemistry of the atmosphere. However, the LSMs embedded in state-of-the-art climate models differ in how they represent fundamental aspects of the hydrological and carbon cycles, resulting in large inter-model differences and sometimes faulty predictions. These "thirdgeneration" LSMs respect the close coupling of the carbon and water cycles through plants, but otherwise tend to be under-constrained, and have not taken full advantage of robust hydrological parameterizations that were independently developed in offline models. Benchmarking, combining multiple sources of atmospheric, biospheric and hydrological data, should be a required component of LSM development, but this field has been relatively poorly supported and intermittently pursued. Moreover, benchmarking alone is not sufficient to ensure that models improve. Increasing complexity may increase realism but decrease reliability and robustness, by increasing the number of poorly known model parameters. In contrast, simplifying the representation of complex processes by stochastic parameterization (the representation of unresolved processes by statistical distributions of values) has been shown to improve model reliability and realism in both atmospheric and land-surface modelling contexts. We provide examples for important processes in hydrology (the generation of runoff and flow routing in heterogeneous catchments) and biology (carbon uptake by speciesdiverse ecosystems). We propose that the way forward for next-generation complex LSMs will include: (a) representations of biological and hydrological processes based on the implementation of multiple internal constraints; (b) systematic application of benchmarking and data assimilation techniques to optimize parameter values and thereby test the structural adequacy of models; and (c) stochastic parameterization of unresolved variability, applied in both the hydrological and the biological domains.

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Section: Model Comparisons Evaluations and The Need For Benchmarkingmentioning

confidence: 99%

Reliable, robust and realistic: the three R's of next-generation land-surface modelling

et al. 2015

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“…Because point measurement is fraught with complications, it is important to examine the quality of precipitation data before undertaking hydrologic analyses [41,42]. Therefore, double-mass plots were created to check for undercatch due to obstructions at each gauging site where the precipitation was measured [43]. The results from the double-mass curve analyses indicated undercatch precipitation at Site #1 presumably attributable to a tree line induced fetch issue.…”

Section: Map Modelingmentioning

confidence: 99%

An Assessment of Mean Areal Precipitation Methods on Simulated Stream Flow: A SWAT Model Performance Assessment

Zeiger

Hubbart

2017

Water

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Accurate mean areal precipitation (MAP) estimates are essential input forcings for hydrologic models. However, the selection of the most accurate method to estimate MAP can be daunting because there are numerous methods to choose from (e.g., proximate gauge, direct weighted average, surface-fitting, and remotely sensed methods). Multiple methods (n = 19) were used to estimate MAP with precipitation data from 11 distributed monitoring sites, and 4 remotely sensed data sets. Each method was validated against the hydrologic model simulated stream flow using the Soil and Water Assessment Tool (SWAT). SWAT was validated using a split-site method and the observed stream flow data from five nested-scale gauging sites in a mixed-land-use watershed of the central USA. Cross-validation results showed the error associated with surface-fitting and remotely sensed methods ranging from −4.5% to −5.1%, and −9.8% to −14.7%, respectively. Split-site validation results showed the percent bias (PBIAS) values that ranged from −4.5% to −160%. Second order polynomial functions especially overestimated precipitation and subsequent stream flow simulations (PBIAS = −160) in the headwaters. The results indicated that using an inverse-distance weighted, linear polynomial interpolation or multiquadric function method to estimate MAP may improve SWAT model simulations. Collectively, the results highlight the importance of spatially distributed observed hydroclimate data for precipitation and subsequent steam flow estimations. The MAP methods demonstrated in the current work can be used to reduce hydrologic model uncertainty caused by watershed physiographic differences.

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“…Land-use or land-cover (LULC) changes are typically more gradual for larger catchments and often occur over only a portion of the total catchment area. As a result it is usually impossible to find suitable control catchments beyond the headwater scale (Siriwardena et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

“…It is generally accepted that different parameter sets might perform equally well for a certain simulation period, but might give varying predictions when used for a different period. One notable exception is Siriwardena et al (2006), who examined eight different parameter sets derived for a conceptual runoff model applied in Australia using different calibration strategies. Clearly, it is important to consider more than just one single "optimal" parameter set.…”

Section: Introductionmentioning

confidence: 99%

Land-cover impacts on streamflow: a change-detection modelling approach that incorporates parameter uncertainty

Seibert

McDonnell

2010

Hydrological Sciences Journal

103

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Land-cover impacts on streamflow: a change-detection modelling approach that incorporates parameter uncertainty. Hydrol. Sci. J. 55(3), 316-332. AbstractThe effect of land-use or land-cover change on stream runoff dynamics is not fully understood. In many parts of the world, forest management is the major land-cover change agent. While the paired catchment approach has been the primary methodology used to quantify such effects, it is only possible for small headwater catchments where there is uniformity in precipitation inputs and catchment characteristics between the treatment and control catchments. This paper presents a model-based change-detection approach that includes model and parameter uncertainty as an alternative to the traditional paired-catchment method for larger catchments. We use the HBV model and data from the HJ Andrews Experimental Forest in Oregon, USA, to develop and test the approach on two small (<1 km 2 ) headwater catchments (a 100% clear-cut and a control) and then apply the technique to the larger 62 km 2 Lookout catchment. Three different approaches are used to detect changes in stream peak flows using: (a) calibration for a period before (or after) change and simulation of runoff that would have been observed without land-cover changes (reconstruction of runoff series); (b) comparison of calibrated parameter values for periods before and after a land-cover change; and (c) comparison of runoff predicted with parameter sets calibrated for periods before and after a land-cover change. Our proof-of-concept change detection modelling showed that peak flows increased in the clear-cut headwater catchment, relative to the headwater control catchment, and several parameter values in the model changed after the clear-cutting. Some minor changes were also detected in the control, illustrating the problem of false detections. For the larger Lookout catchment, moderately increased peak flows were detected. Monte Carlo techniques used to quantify parameter uncertainty and compute confidence intervals in model results and parameter ranges showed rather wide distributions of model simulations. While this makes change detection more difficult, it also demonstrated the need to explicitly consider parameter uncertainty in the modelling approach to obtain reliable results.Key words change detection; forest hydrology; forest harvesting; HJ Andrews; HBV model Impacts de l'occupation du sol sur les écoulements en rivière: une approche de détection de changement par modélisation qui inclut les incertitudes sur les paramètres Résumé L'effet de la modification de l'occupation et de la couverture du sol sur la dynamique des écoulements reste mal compris. Dans la plupart des cas, la gestion des forêts est le facteur le plus important quant à la modification de l'occupation du sol. L'approche d'analyse des bassins versants par appariement est la principale méthode pour quantifier de tels effets. Cependant, cette méthode n'est adaptée que dans le cas de petits bassins versants de tête, où les précipitations incide...

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The impact of land use change on catchment hydrology in large catchments: The Comet River, Central Queensland, Australia

Cited by 233 publications

References 11 publications

Reliable, robust and realistic: the three R's of next-generation land-surface modelling

Reliable, robust and realistic: the three R's of next-generation land-surface modelling

An Assessment of Mean Areal Precipitation Methods on Simulated Stream Flow: A SWAT Model Performance Assessment

Land-cover impacts on streamflow: a change-detection modelling approach that incorporates parameter uncertainty

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