Issue |
Natl Sci Open
Volume 3, Number 1, 2024
Special Topic: Climate Change Impacts and Adaptation
|
|
---|---|---|
Article Number | 20230027 | |
Number of page(s) | 21 | |
Section | Earth and Environmental Sciences | |
DOI | https://doi.org/10.1360/nso/20230027 | |
Published online | 11 January 2024 |
- Dottori F, Szewczyk W, Ciscar JC, et al. Increased human and economic losses from river flooding with anthropogenic warming. Nat Clim Change 2018; 8: 781-786. [Article] [CrossRef] [Google Scholar]
- Götzinger J, Bárdossy A. Generic error model for calibration and uncertainty estimation of hydrological models. Water Resour Res 2008; 44: W00B07. [Article] [Google Scholar]
- Renard B, Kavetski D, Leblois E, et al. Toward a reliable decomposition of predictive uncertainty in hydrological modeling: Characterizing rainfall errors using conditional simulation. Water Resour Res 2011; 47: W11516. [Article] [NASA ADS] [Google Scholar]
- Fan YR, Yu L, Shi X, et al. Tracing uncertainty contributors in the multi-hazard risk analysis for compound extremes. Earths Future 2021; 9: e2021EF002280. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Hirabayashi Y, Mahendran R, Koirala S, et al. Global flood risk under climate change. Nat Clim Change 2013; 3: 816-821. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Lavery S, Donovan B. Flood risk management in the Thames Estuary looking ahead 100 years. Phil Trans R Soc A 2005; 363: 1455-1474. [Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
- Sanders R,Tabuchi S. Decision support system for flood risk analysis for the River Thames, United Kingdom. Photogramm Eng Remote Sens 2000; 66:1185-1193. [Google Scholar]
- Miller JD, Hutchins M. The impacts of urbanisation and climate change on urban flooding and urban water quality: A review of the evidence concerning the United Kingdom. J Hydrol-Regional Studies 2017; 12: 345-362. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Collet L, Harrigan S, Prudhomme C, et al. Future hot-spots for hydro-hazards in Great Britain: A probabilistic assessment. Hydrol Earth Syst Sci 2018; 22: 5387-5401. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Visser-Quinn A, Beevers L, Collet L, et al. Spatio-temporal analysis of compound hydro-hazard extremes across the UK. Adv Water Resour 2019; 130: 77-90. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Kay AL, Rudd AC, Fry M, et al. Climate change impacts on peak river flows: Combining national-scale hydrological modelling and probabilistic projections. Clim Risk Manage 2021; 31: 100263. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Duan Q, Sorooshian S, Gupta VK. Optimal use of the SCE-UA global optimization method for calibrating watershed models. J Hydrol 1994; 158: 265-284. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Gupta HV, Kling H, Yilmaz KK, et al. Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. J Hydrol 2009; 377: 80-91. [Article] [CrossRef] [Google Scholar]
- Gabriel RK, Fan Y. Multivariate hydrologic risk analysis for river thames. Water 2022; 14: 384. [Article] [CrossRef] [Google Scholar]
- Bussi G, Dadson SJ, Prudhomme C, et al. Modelling the future impacts of climate and land-use change on suspended sediment transport in the River Thames (UK). J Hydrol 2016; 542: 357-372. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Lu Q, Futter MN, Nizzetto L, et al. Fate and transport of polychlorinated biphenyls (PCBs) in the River Thames catchment – Insights from a coupled multimedia fate and hydrobiogeochemical transport model. Sci Total Environ 2016; 572: 1461-1470. [Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
- Bell VA, Kay AL, Cole SJ, et al. How might climate change affect river flows across the Thames Basin? An area-wide analysis using the UKCP09 Regional Climate Model ensemble. J Hydrol 2012; 442-443: 89-104. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Stevens AJ, Clarke D, Nicholls RJ. Trends in reported flooding in the UK: 1884–2013. Hydrol Sci J 2016; 61: 50-63. [Article] [Google Scholar]
- Tanguy M, Prudhomme C, Smith K, et al. Historic gridded potential evapotranspiration (PET) based on temperature-based equation McGuinness-Bordne calibrated for the UK (1891–2015). NERC Environmental Information Data Centre, 2017. [Article]. [Google Scholar]
- Thrasher B, Wang W, Michaelis A, et al. NASA global daily downscaled projections, CMIP6. Sci Data 2022; 9: 262. [Article] [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
- Ziehn T, Chamberlain MA, Law RM, et al. The Australian Earth System Model: ACCESS-ESM1.5. J South Hemisph Earth Syst Sci 2020; 70: 193-214. [Article] [CrossRef] [Google Scholar]
- Wu T, Yu R, Lu Y, et al. BCC-CSM2-HR: A high-resolution version of the Beijing Climate Center Climate System Model. Geosci Model Dev 2021; 14: 2977-3006. [Article] [CrossRef] [Google Scholar]
- Droogers P, Allen RG. Estimating reference evapotranspiration under inaccurate data conditions. Irrigation Drainage Syst 2002; 16: 33-45. [Article] [CrossRef] [Google Scholar]
- Moore RJ. The probability-distributed principle and runoff production at point and basin scales. Hydrol Sci J 1985; 30: 273-297. [Article] [Google Scholar]
- Fan YR, Huang GH, Baetz BW, et al. Development of integrated approaches for hydrological data assimilation through combination of ensemble Kalman filter and particle filter methods. J Hydrol 2017; 550: 412-426. [Article] [CrossRef] [Google Scholar]
- Moradkhani H, Hsu KL, Gupta H, et al. Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter. Water Resour Res 2005; 41: W05012. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Perrin C, Michel C, Andréassian V. Improvement of a parsimonious model for streamflow simulation. J Hydrol 2003; 279: 275-289. [Article] [CrossRef] [Google Scholar]
- Westra S, Thyer M, Leonard M, et al. A strategy for diagnosing and interpreting hydrological model nonstationarity. Water Resour Res 2014; 50: 5090-5113. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Smith KA, Barker LJ, Tanguy M, et al. A multi-objective ensemble approach to hydrological modelling in the UK: An application to historic drought reconstruction. Hydrol Earth Syst Sci 2019; 23: 3247-3268. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Jakeman AJ, Hornberger GM. How much complexity is warranted in a rainfall-runoff model?. Water Resour Res 1993; 29: 2637-2649. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Jakeman AJ, Littlewood IG, Whitehead PG. Computation of the instantaneous unit hydrograph and identifiable component flows with application to two small upland catchments. J Hydrol 1990; 117: 275-300. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Vaze J, Post DA, Chiew FHS, et al. Climate non-stationarity – Validity of calibrated rainfall-runoff models for use in climate change studies. J Hydrol 2010; 394: 447-457. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Viney NR, Bormann H, Breuer L, et al. Assessing the impact of land use change on hydrology by ensemble modelling (LUCHEM) II: Ensemble combinations and predictions. Adv Water Resour 2009; 32: 147-158. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Shin MJ, Guillaume JHA, Croke BFW, et al. Addressing ten questions about conceptual rainfall-runoff models with global sensitivity analyses in R. J Hydrol 2013; 503: 135-152. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Shin MJ, Guillaume JHA, Croke BFW, et al. A review of foundational methods for checking the structural identifiability of models: Results for rainfall-runoff. J Hydrol 2015; 520: 1-16. [Article] [CrossRef] [Google Scholar]
- Shin MJ, Kim CS. Assessment of the suitability of rainfall-runoff models by coupling performance statistics and sensitivity analysis. Hydrol Res 2017; 48: 1192-1213. [Article] [Google Scholar]
- Croke B, Jakeman A. Use of the IHACRES rainfall-runoff model in arid and semi-arid regions. In: Wheater H, Sorooshian S, Sharma K (Eds.). Hydrological Modelling in Arid and Semi-Arid Areas (International Hydrology Series). Cambridge: Cambridge University Press, 2007, 41–48 [CrossRef] [Google Scholar]
- Fan YR, Shi X, Duan QY, et al. Towards reliable uncertainty quantification for hydrologic predictions. Part I: Development of a particle copula Metropolis Hastings method. J Hydrol 2022; 612: 128163. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Fan YR, Shi X, Duan QY, et al. Towards reliable uncertainty quantification for hydrologic predictions. Part II: Characterizing impacts of uncertain factors through an iterative factorial data assimilation framework. J Hydrol 2022; 612: 128136. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Duan Q, Pappenberger F, Wood A, et al. Handbook of Hydrometeorological Ensemble Forecasting. Berlin, Heidelberg: Springer, 2019 [CrossRef] [Google Scholar]
- Liu Z, Cheng L, Lin K, et al. A hybrid bayesian vine model for water level prediction. Environ Model Software 2021; 142: 105075. [Article] [CrossRef] [Google Scholar]
- Raftery AE, Gneiting T, Balabdaoui F, et al. Using bayesian model averaging to calibrate forecast ensembles. Mon Weather Rev 2005; 133: 1155-1174. [Article] [NASA ADS] [CrossRef] [Google Scholar]
- Wu H, Su X, Singh VP, et al. Bayesian vine copulas improve agricultural drought prediction for long lead times. Agric For Meteor 2023; 331: 109326. [Article] [CrossRef] [Google Scholar]
- Yang T, Liu X, Wang L, et al. Simulating hydropower discharge using multiple decision tree methods and a dynamical model merging technique. J Water Resour Plann Manage 2020; 146: 04019072. [Article] [CrossRef] [Google Scholar]
- Yang T, Tao Y, Li J, et al. Multi-criterion model ensemble of CMIP5 surface air temperature over China. Theor Appl Climatol 2018; 132: 1057-1072. [Article] [CrossRef] [Google Scholar]
- Duan Q, Ajami NK, Gao X, et al. Multi-model ensemble hydrologic prediction using Bayesian model averaging. Adv Water Resour 2007; 30: 1371-1386. [Article] [NASA ADS] [CrossRef] [Google Scholar]
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