Articles | Volume 17, issue 4
https://doi.org/10.5194/hess-17-1445-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-17-1445-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
17 Apr 2013
Research article |
| 17 Apr 2013
Statistical analysis of error propagation from radar rainfall to hydrological models
D. Zhu
Department of Civil Engineering, Swansea University, Swansea, UK
D. Z. Peng
College of Water Sciences, Beijing Normal University, Beijing, China
I. D. Cluckie
Department of Civil Engineering, Swansea University, Swansea, UK
Related authors
No articles found.
Xiaowan Liu, Zongxue Xu, Hong Yang, Xiuping Li, and Dingzhi Peng
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2020-71, https://doi.org/10.5194/essd-2020-71, 2020
Revised manuscript not accepted
Short summary
Short summary
The retreat of glaciers over the QTP is intensifying. To understand changes in glaciers, the two inventories (RGI 4.0 and GIC-Ⅱ) provide potential, but glacier volumes are not convincing. The study recalculated and compared glacier volumes in RGI 4.0 and GIC-Ⅱ for the QTP. The results indicate the slope-dependent algorithm performs better than area-volume-based equations. The northern QTP has a larger degree of fragmentation. An obvious offset of glacier volumes in different aspects is observed.
Zongxue Xu, Dingzhi Peng, Wenchao Sun, Bo Pang, Depeng Zuo, Andreas Schumann, and Yangbo Chen
Proc. IAHS, 379, 463–464, https://doi.org/10.5194/piahs-379-463-2018, https://doi.org/10.5194/piahs-379-463-2018, 2018
Dehua Zhu, Shirley Echendu, Yunqing Xuan, Mike Webster, and Ian Cluckie
Hydrol. Earth Syst. Sci., 20, 4707–4715, https://doi.org/10.5194/hess-20-4707-2016, https://doi.org/10.5194/hess-20-4707-2016, 2016
Short summary
Short summary
The study in the paper is utilizing and maximizing high-performance computing (HPC) power resources to support the study on extreme weather impact due to climate change, which for the first time allows modellers to simulate the entire system, ranging from the global circulation to a target catchment for impact study on a single platform, where both NWP and the hydrological model are executed so that more effective interaction and communication can be achieved and maintained between the model.
D. Zhu, Y. Xuan, and I. Cluckie
Hydrol. Earth Syst. Sci., 18, 257–272, https://doi.org/10.5194/hess-18-257-2014, https://doi.org/10.5194/hess-18-257-2014, 2014
D. Zhu, Q. Ren, Y. Xuan, Y. Chen, and I. D. Cluckie
Hydrol. Earth Syst. Sci., 17, 495–505, https://doi.org/10.5194/hess-17-495-2013, https://doi.org/10.5194/hess-17-495-2013, 2013
Related subject area
Subject: Water Resources Management | Techniques and Approaches: Uncertainty analysis
Robust multi-objective optimization under multiple uncertainties using the CM-ROPAR approach: case study of water resources allocation in the Huaihe River basin
Actionable human-water systems modeling under uncertainty
Evaluating the impact of post-processing medium-range ensemble streamflow forecasts from the European Flood Awareness System
Coupled effects of observation and parameter uncertainty on urban groundwater infrastructure decisions
Disentangling sources of future uncertainties for water management in sub-Saharan river basins
Possibilistic response surfaces: incorporating fuzzy thresholds into bottom-up flood vulnerability analysis
Future hot-spots for hydro-hazards in Great Britain: a probabilistic assessment
Evaluation of impacts of future climate change and water use scenarios on regional hydrology
Planning for climate change impacts on hydropower in the Far North
Describing the interannual variability of precipitation with the derived distribution approach: effects of record length and resolution
Dissolved oxygen prediction using a possibility theory based fuzzy neural network
Projected changes in US rainfall erosivity
Approximating uncertainty of annual runoff and reservoir yield using stochastic replicates of global climate model data
Assessment of precipitation and temperature data from CMIP3 global climate models for hydrologic simulation
Robust global sensitivity analysis of a river management model to assess nonlinear and interaction effects
Sensitivity and uncertainty in crop water footprint accounting: a case study for the Yellow River basin
Irrigation efficiency and water-policy implications for river basin resilience
On an improved sub-regional water resources management representation for integration into earth system models
The implications of climate change scenario selection for future streamflow projection in the Upper Colorado River Basin
Prioritization of water management under climate change and urbanization using multi-criteria decision making methods
Crop yields response to water pressures in the Ebro basin in Spain: risk and water policy implications
Jitao Zhang, Dimitri Solomatine, and Zengchuan Dong
Hydrol. Earth Syst. Sci., 28, 3739–3753, https://doi.org/10.5194/hess-28-3739-2024, https://doi.org/10.5194/hess-28-3739-2024, 2024
Short summary
Short summary
Faced with the problem of uncertainty in the field of water resources management, this paper proposes the Copula Multi-objective Robust Optimization and Probabilistic Analysis of Robustness (CM-ROPAR) approach to obtain robust water allocation schemes based on the uncertainty of drought and wet encounters and the uncertainty of inflow. We believe that this research article not only highlights the significance of the CM-ROPAR approach but also provides a new concept for uncertainty analysis.
Laura Gil-García, Nazaret M. Montilla-López, Carlos Gutiérrez-Martín, Ángel Sánchez-Daniel, Pablo Saiz-Santiago, Josué M. Polanco-Martínez, Julio Pindado, and C. Dionisio Pérez-Blanco
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-61, https://doi.org/10.5194/hess-2024-61, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
This paper presents an interdisciplinary model for quantifying uncertainties in water allocation under climate change. It combines climate, hydrological, and microeconomic experiments with a decision support system. Multi-model analyses reveal potential futures for water management policies, emphasizing nonlinear climate responses. As illustrated in the Douro River Basin, minor water allocation changes have significant economic impacts, stresssing the need for adaptation strategies.
Gwyneth Matthews, Christopher Barnard, Hannah Cloke, Sarah L. Dance, Toni Jurlina, Cinzia Mazzetti, and Christel Prudhomme
Hydrol. Earth Syst. Sci., 26, 2939–2968, https://doi.org/10.5194/hess-26-2939-2022, https://doi.org/10.5194/hess-26-2939-2022, 2022
Short summary
Short summary
The European Flood Awareness System creates flood forecasts for up to 15 d in the future for the whole of Europe which are made available to local authorities. These forecasts can be erroneous because the weather forecasts include errors or because the hydrological model used does not represent the flow in the rivers correctly. We found that, by using recent observations and a model trained with past observations and forecasts, the real-time forecast can be corrected, thus becoming more useful.
Marina R. L. Mautner, Laura Foglia, and Jonathan D. Herman
Hydrol. Earth Syst. Sci., 26, 1319–1340, https://doi.org/10.5194/hess-26-1319-2022, https://doi.org/10.5194/hess-26-1319-2022, 2022
Short summary
Short summary
Sensitivity analysis can be harnessed to evaluate effects of model uncertainties on planning outcomes. This study explores how observation and parameter uncertainty propagate through a hydrogeologic model to influence the ranking of decision alternatives. Using global sensitivity analysis and evaluation of aquifer management objectives, we evaluate how physical properties of the model and choice of observations for calibration can lead to variations in decision-relevant model outputs.
Alessandro Amaranto, Dinis Juizo, and Andrea Castelletti
Hydrol. Earth Syst. Sci., 26, 245–263, https://doi.org/10.5194/hess-26-245-2022, https://doi.org/10.5194/hess-26-245-2022, 2022
Short summary
Short summary
This study aims at designing water supply strategies that are robust against climate, social, and land use changes in a sub-Saharan river basin. We found that robustness analysis supports the discovery of policies enhancing the resilience of water resources systems, benefiting the agricultural, energy, and urban sectors. We show how energy sustainability is affected by water availability, while urban and irrigation resilience also depends on infrastructural interventions and land use changes.
Thibaut Lachaut and Amaury Tilmant
Hydrol. Earth Syst. Sci., 25, 6421–6435, https://doi.org/10.5194/hess-25-6421-2021, https://doi.org/10.5194/hess-25-6421-2021, 2021
Short summary
Short summary
Response surfaces are increasingly used to identify the hydroclimatic conditions leading to a water resources system's failure. Partitioning the surface usually requires performance thresholds that are not necessarily crisp. We propose a methodology that combines the inherent uncertainty of response surfaces with the ambiguity of performance thresholds. The proposed methodology is illustrated with a multireservoir system in Canada for which some performance thresholds are imprecise.
Lila Collet, Shaun Harrigan, Christel Prudhomme, Giuseppe Formetta, and Lindsay Beevers
Hydrol. Earth Syst. Sci., 22, 5387–5401, https://doi.org/10.5194/hess-22-5387-2018, https://doi.org/10.5194/hess-22-5387-2018, 2018
Short summary
Short summary
Floods and droughts cause significant damages and pose risks to lives worldwide. In a climate change context this work identifies hotspots across Great Britain, i.e. places expected to be impacted by an increase in floods and droughts. By the 2080s the western coast of England and Wales and northeastern Scotland would experience more floods in winter and droughts in autumn, with a higher increase in drought hazard, showing a need to adapt water management policies in light of climate change.
Seungwoo Chang, Wendy Graham, Jeffrey Geurink, Nisai Wanakule, and Tirusew Asefa
Hydrol. Earth Syst. Sci., 22, 4793–4813, https://doi.org/10.5194/hess-22-4793-2018, https://doi.org/10.5194/hess-22-4793-2018, 2018
Short summary
Short summary
It is important to understand potential impacts of climate change and human water use on streamflow and groundwater levels. This study used climate models with an integrated hydrologic model to project future streamflow and groundwater level in Tampa Bay for a variety of future water use scenarios. Impacts of different climate projections on streamflow were found to be much stronger than the impacts of different human water use scenarios, but both were significant for groundwater projection.
Jessica E. Cherry, Corrie Knapp, Sarah Trainor, Andrea J. Ray, Molly Tedesche, and Susan Walker
Hydrol. Earth Syst. Sci., 21, 133–151, https://doi.org/10.5194/hess-21-133-2017, https://doi.org/10.5194/hess-21-133-2017, 2017
Short summary
Short summary
We know that climate is changing quickly in the Far North (the Arctic and sub-Arctic). Hydropower continues to grow in this region because water resources are perceived to be plentiful. However, with changes in glacier extent and permafrost, and more extreme events, will those resources prove reliable into the future? This study amasses the evidence that quantitative hydrology modeling and uncertainty assessment have matured to the point where they should be used in water resource planning.
Claudio I. Meier, Jorge Sebastián Moraga, Geri Pranzini, and Peter Molnar
Hydrol. Earth Syst. Sci., 20, 4177–4190, https://doi.org/10.5194/hess-20-4177-2016, https://doi.org/10.5194/hess-20-4177-2016, 2016
Short summary
Short summary
We show that the derived distribution approach is able to characterize the interannual variability of precipitation much better than fitting a probabilistic model to annual rainfall totals, as long as continuously gauged data are available. The method is a useful tool for describing temporal changes in the distribution of annual rainfall, as it works for records as short as 5 years, and therefore does not require any stationarity assumption over long periods.
Usman T. Khan and Caterina Valeo
Hydrol. Earth Syst. Sci., 20, 2267–2293, https://doi.org/10.5194/hess-20-2267-2016, https://doi.org/10.5194/hess-20-2267-2016, 2016
Short summary
Short summary
This paper contains a new two-step method to construct fuzzy numbers using observational data. In addition an existing fuzzy neural network is modified to account for fuzzy number inputs. This is combined with possibility-theory based intervals to train the network. Furthermore, model output and a defuzzification technique is used to estimate the risk of low Dissolved Oxygen so that water resource managers can implement strategies to prevent the occurrence of low Dissolved Oxygen.
M. Biasutti and R. Seager
Hydrol. Earth Syst. Sci., 19, 2945–2961, https://doi.org/10.5194/hess-19-2945-2015, https://doi.org/10.5194/hess-19-2945-2015, 2015
Short summary
Short summary
We estimate future changes in US erosivity from the most recent ensemble projections of daily and monthly rainfall accumulation. The expectation of overall increase in erosivity is confirmed by these calculations, but a quantitative assessment is marred by large uncertainties. Specifically, the uncertainty in the method of estimation of erosivity is more consequential than that deriving from the spread in climate simulations, and leads to changes of uncertain sign in parts of the south.
M. C. Peel, R. Srikanthan, T. A. McMahon, and D. J. Karoly
Hydrol. Earth Syst. Sci., 19, 1615–1639, https://doi.org/10.5194/hess-19-1615-2015, https://doi.org/10.5194/hess-19-1615-2015, 2015
Short summary
Short summary
We present a proof-of-concept approximation of within-GCM uncertainty using non-stationary stochastic replicates of monthly precipitation and temperature projections and investigate the impact of within-GCM uncertainty on projected runoff and reservoir yield. Amplification of within-GCM variability from precipitation to runoff to reservoir yield suggests climate change impact assessments ignoring within-GCM uncertainty would provide water resources managers with an unjustified sense of certainty
T. A. McMahon, M. C. Peel, and D. J. Karoly
Hydrol. Earth Syst. Sci., 19, 361–377, https://doi.org/10.5194/hess-19-361-2015, https://doi.org/10.5194/hess-19-361-2015, 2015
Short summary
Short summary
Here we assess GCM performance from a hydrologic perspective. We identify five better performing CMIP3 GCMs that reproduce grid-scale climatological statistics of observed precipitation and temperature over global land regions for future hydrologic simulation. GCM performance in reproducing observed mean and standard deviation of annual precipitation, mean annual temperature and mean monthly precipitation and temperature was assessed and ranked, and five better performing GCMs were identified.
L. J. M. Peeters, G. M. Podger, T. Smith, T. Pickett, R. H. Bark, and S. M. Cuddy
Hydrol. Earth Syst. Sci., 18, 3777–3785, https://doi.org/10.5194/hess-18-3777-2014, https://doi.org/10.5194/hess-18-3777-2014, 2014
L. Zhuo, M. M. Mekonnen, and A. Y. Hoekstra
Hydrol. Earth Syst. Sci., 18, 2219–2234, https://doi.org/10.5194/hess-18-2219-2014, https://doi.org/10.5194/hess-18-2219-2014, 2014
C. A. Scott, S. Vicuña, I. Blanco-Gutiérrez, F. Meza, and C. Varela-Ortega
Hydrol. Earth Syst. Sci., 18, 1339–1348, https://doi.org/10.5194/hess-18-1339-2014, https://doi.org/10.5194/hess-18-1339-2014, 2014
N. Voisin, H. Li, D. Ward, M. Huang, M. Wigmosta, and L. R. Leung
Hydrol. Earth Syst. Sci., 17, 3605–3622, https://doi.org/10.5194/hess-17-3605-2013, https://doi.org/10.5194/hess-17-3605-2013, 2013
B. L. Harding, A. W. Wood, and J. R. Prairie
Hydrol. Earth Syst. Sci., 16, 3989–4007, https://doi.org/10.5194/hess-16-3989-2012, https://doi.org/10.5194/hess-16-3989-2012, 2012
J.-S. Yang, E.-S. Chung, S.-U. Kim, and T.-W. Kim
Hydrol. Earth Syst. Sci., 16, 801–814, https://doi.org/10.5194/hess-16-801-2012, https://doi.org/10.5194/hess-16-801-2012, 2012
S. Quiroga, Z. Fernández-Haddad, and A. Iglesias
Hydrol. Earth Syst. Sci., 15, 505–518, https://doi.org/10.5194/hess-15-505-2011, https://doi.org/10.5194/hess-15-505-2011, 2011
Cited articles
Abbott, M. B., Bathurst, J. C., Cunge, J. A., O'Connell, P. E., and Rasmussen, J.: An introduction to the European Hydrological System – Système Hydrologique Européen, "SHE", 1: History and philosophy of a physically–based, distributed modeling system, J. Hydrol., 87, 45–59, 1986a.
Abbott, M. B., Bathurst, J. C., Cunge, J. A., O'Connell, P. E., and Rasmussen, J.: An introduction to the European Hydrological System – Système Hydrologique Européen, "SHE", 2: Structure of a physically–based, distributed modeling system, J. Hydrol., 87, 61–77, 1986b.
Anagnostou, E. N. and Krajewski, W. F.: Simulation of radar reflectivity fields: algorithm formulation and evaluation, Water Resour. Res., 33, 1419–1429, 1997.
Battan, L. J.: Radar observation of the atmosphere, The University of Chicago Press, 1973.
Bell, V. A. and Moore, R. J.: A grid-based distributed flood forecasting model for use with weather radar data: Part 1. Formulation, Hydrol. Earth Syst. Sci., 2, 265–281, https://doi.org/10.5194/hess-2-265-1998, 1998a.
Bell, V. A. and Moore, R. J.: A grid-based distributed flood forecasting model for use with weather radar data: Part 2. Case studies, Hydrol. Earth Syst. Sci., 2, 283–298, https://doi.org/10.5194/hess-2-283-1998, 1998b.
Beven, K. J. and Freer, J.: A dynamic TOPMODEL, Hydrol. Process., 15, 1993–2011, 2001.
Beven, K. J. and Kirkby, M. J.: A physically based variable contributing area model of basin hydrology, Hydrol. Sci. B., 24, 43–69, 1979.
Borga, M.: Use of radar rainfall estimates in rainfall-runoff modeling: an assessment of predictive uncertainty. Fifth International Symposium on Hydrological Applications of Weather Radar-Radar Hydrology, Japan, 451–456, 2001.
Borga, M.: Accuracy of radar rainfall estimates for stream flow simulation, J. Hydrol., 267, 26–39, 2002.
Bringi, V. N., Huang, G. J., Chandrasekar, V., and Keenan, T. D.: An areal rainfall estimator using differential propagation phase: evaluation using a c-band radar and a dense gauge network in the tropics, J. Atmos. Oceanic Technol., 18, 1810–1818, 2001.
Browning, K. A.: Meteorological applications of radar, Rep. Prog. Phys., 41, 763–801, 1978.
Carpenter, T. M., Georgakakos, K. P., and Sperfslagea, J. A.: On the parametric and nexrad-radar sensitivities of a distributed hydrologic model suitable for operational use, J. Hydrol., 253, 169–193, 2001.
Ciach, G. J., Krajewski, W. F., and Villarini, G.: Product-error-driven uncertainty model for probabilistic quantitative precipitation estimation with NEXRAD data, J. Hydrometeorol., 8, 1325–1347, 2007.
Cluckie, I. D. and Owens, M. D.: Real-time rainfall-runoff models and use of weather radar information. Weather Radar and Flood Forecasting, John Wiley & Sons, 1987.
Cluckie, I. D., Yu, P., and Tilford, K.: Real-time forecasting: model structure and data resolution, Weather Radar Networking, Brussels, Belgium, 1989.
Collier, C. G.: Applications of weather radar systems, Praxis Publishing Ltd, 1996.
Collier, C. G. and Knowles, J. M.: Accuracy of rainfall estimates by radar, part iii: application for short-term flood forecasting, J. Hydrol., 83, 237–249, 1986.
Collier, C. G., Larke, P. R., and May, B. R.: A weather radar correction procedure for real-time estimation of surface rainfall, Q. J. Roy. Meteorol. Soc., 109, 589–608, 1983.
Duncan, M. R., Austin, B., Fabry, F., and Austin, G. L.: The effect of gauge sampling density on the accuracy of streamflow prediction for rural catchments, J. Hydrol., 142, 445–476, 1993.
Fabry, F., Austin, G. L., and Tees, D.: The accuracy of rainfall estimates by radar as a function of range, Q. J. Roy. Meteorol. Soc., 118, 435–453, 1992.
Fabry, F., Bellon, A., Duncan, M. R., and Austin, G. L.: High resolution rainfall measurements by radar for very small basins: the sampling problem re-examined, J. Hydrol., 161, 415–428, 1994.
Fulton, R. A., Breidenbach, J. P., Seo, D. J., Miller, D. A., and Bannon, T.: The wsr-88d rainfall algorithm, Weather Forecast., 13, 377–395, 1998.
Germann, U., Berenguer, M., Sempere-Torres, D., and Zappa, M.: REAL – Ensemble radar precipitation estimation for hydrology in a mountainous region, Q. J. Roy. Meteorol. Soc., 135, 445–456, 2009.
Habib, E., Aduvala, A. V., and Meselhe, E. A.: Analysis of radar-rainfall error characteristics and implications for streamflow simulation uncertainty, Hydrolog. Sci. J., 53, 568–587, 2008.
Hardaker, P. J., Holt, A. R., and Collier, C. G.: A melting-layer model and its use in correcting for the bright band in single-polarization radar echoes, Q. J. Roy. Meteorol. Soc., 121, 495–525, 1995.
Harrison, D. L., Driscoll, S. J., and Kitchen, M.: Improving precipitation estimates from weather radar using quality control and correction techniques, Meteorol. Appl., 7, 135–144, 2000.
Harrold, T. W., English, E. J., and Nicholass, C. A.: The accuracy of radar derived rainfall measurements in hilly terrain, Q. J. Roy. Meteorol. Soc., 100, 331–350, 1974.
Hossain, F., Anagnostou, E. N., Dinku, T., and Borga, M.: Hydrological model sensitivity to parameter and radar rainfall estimation uncertainty, Hydrol. Process., 18, 3277–3291, 2004.
Kitchen, M.: Towards improved radar estimates of surface precipitation rate at long range, Q. J. Roy. Meteorol. Soc., 123, 145–163, 1997.
Krajewski, W. F. and Smith, J. A.: Radar hydrology: rainfall estimation, Adv. Water. Resour., 25, 1387–1394, 2002.
Krajewski, W. F., Raghavan, R., and Chandrasekar, V.: Physically based simulation of radar rainfall data using a space-time rainfall model, J. Appl. Meteor., 32, 268–283, 1993.
Lukacs, E. and King, E. P.: A property of normal distribution, The Annals of Mathematical Statistics, 25, 389–394, 1954.
Morin, E., Maddox, R. A., Goodrich, D., and Sorooshian, S.: Radar Z-R relationship for summer monsoon storms in Arizona, Weather Forecast., 20, 672–679, 2005.
National Soil Resources Institute: NSRI soil data structures and relationships, Cranfield University, 2006.
Owens, M. D.: Real-time flood forecasting using weather radar data, Ph. D. Thesis, University of Salford, UK, 1986.
Peng, D. Z. and Du, Y.: Comparative analysis of several Lhasa River basin flood forecast models in Yarlung Zangbo River, iCBBE 2010, Chengdu, China, 2010.
Peng, D. Z. and Xu, Z. X.: Simulating the impact of climate change on stream flow in the Tarim River basin by using a modified semi-distributed monthly water balance model, Hydrol. Process., 24, 209–216, 2010.
Pessoa, M. L., Bras, R. L., and Williams, E. R.: Use of weather radar for flood forecasting in the Sieve River basin: a sensitivity analysis, J. Appl. Meteor., 32, 462–475, 1993.
Probert-Jones, J. R.: The radar equation in meteorology, Q. J. Roy. Meteorol. Soc., 88, 485–495, 1962.
Refsgaard, J. C. and Storm, B.: MIKE SHE, in: Computer Models of Watershed Hydrology, edited by: Singh, V. P., Water Resources Publications, Highlands Ranch, 809–846, 1995.
Reichel, F. V., Hans-Reinard, S. K., Cluckie, I. D., and Rico-Ramirez, M. A.: Radar-based flood forecasting for the Upper Medway Catchment in the UK, Proceedings of the Institution of Civil Engineers-Water Management, 2008.
Rico-Ramirez, M. A., Cluckie, I. D., Shepherd, G., and Pallot, A.: A high-resolution radar experiment on the island of Jersey, Meteorol. Appl., 14, 117–129, 2007.
Sharif, H. O., Ogden, F. L., Krajewski, W. F., and Xue, M.: Numerical simulations of radar rainfall error propagation, Water Resour. Res., 38, 1140, https://doi.org/10.1029/2001WR000525, 2002.
Sharif, H. O., Ogden, F. L., Krajewski, W. F., and Xue, M.: Statistical analysis of radar rainfall error propagation, J. Hydrometeorol., 5, 199–212, 2004.
Tachikawa, Y., Vieux, B. E., Georgakakos, K. P., and Nakakita, E.: Weather Radar Information and Distributed Hydrological Model, IAHS Publication, 2002.
Vieux, B. E. and Bedient, P. B.: Estimation of rainfall for flood prediction from WRS-88D reflectivity: a case study, 17–18 October 1994, Weather Forecast., 13, 407–415, 1998.
Vivoni, E. R., Entekhabi, D., and Hoffman, R. N.: Error propagation of radar rainfall nowcasting fields through a fully distributed flood forecasting model, J. Appl. Meteor. Climatol., 46, 932–940, 2007.
Wilson, J. W. and Brandes, E. A.: Radar measurement of rainfall – a summary, B. Am. Meteorol. Soc., 60, 1048–1058, 1979.
Winchell, M., Gupta, H. V., and Sorooshian, S.: On the simulation of infiltration and saturation-excess runoff using radar-based rainfall estimates: effects of algorithm uncertainty and pixel aggregation, Water Resour. Res., 34, 2655–2670, 1998.
Wyss, J., Williams, E. R., and Bras, R. L.: Hydrologic modeling of New England basins using radar rainfall data, J. Geophys. Res., 95, 2143–2152, 1990.
Yang, Z. and Han, D.: Derivation of unit hydrograph using a transfer function approach, Water Resour. Res., 42, W01501, https://doi.org/10.1029/2005WR004227, 2006.
Zhu, D. and Cluckie, I. D.: A preliminary appraisal of Thurnham Dual Polarisation Radar in the context of hydrological modelling structure, Hydrol. Res., 43, 736–752, 2011.
