147 citations as recorded by crossref.

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51. The impact of tipping‐bucket raingauge measurement errors on design rainfall for urban‐scale applications A. Molini et al. https://doi.org/10.1002/hyp.5646
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54. Assessing the Impact of Urban Development and On-Site Stormwater Detention on Regional Hydrology Using Monte Carlo Simulated Rainfall R. Ronalds & H. Zhang https://doi.org/10.1007/s11269-019-02275-y
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116. Incorporating daily rainfalls to derive rainfall DDF relationships at ungauged sites in Hong Kong and quantifying their uncertainty P. Jiang & Y. Tung https://doi.org/10.1007/s00477-014-0915-2
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124. Future Projection of Extreme Rainfall for Flood Management due to Climate Change in an Urban Area S. Halder & U. Saha https://doi.org/10.1061/JSWBAY.0000954
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128. Generating hourly mean areal precipitation times series with an at-site weather generator in Switzerland K. Maloku et al. https://doi.org/10.1007/s00477-024-02757-5
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130. Multisite multivariate disaggregation of climate parameters using multiplicative random cascades A. Gaur & M. Lacasse https://doi.org/10.1016/j.uclim.2018.08.010
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133. Hydrological model calibration for derived flood frequency analysis using stochastic rainfall and probability distributions of peak flows U. Haberlandt & I. Radtke https://doi.org/10.5194/hess-18-353-2014
134. A new discrete multiplicative random cascade model for downscaling intermittent rainfall fields M. Schleiss https://doi.org/10.5194/hess-24-3699-2020
135. Introduction of k-means clustering into random cascade model for disaggregation of rainfall from daily to 1-hour resolution with improved preservation of extreme rainfall P. Deka & U. Saha https://doi.org/10.1016/j.jhydrol.2023.129478
136. A multisite daily rainfall generator driven by bivariate copula‐based mixed distributions F. Serinaldi https://doi.org/10.1029/2008JD011258
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139. Spatial disaggregation of bias-corrected GCM precipitation for improved hydrologic simulation: Ping River Basin, Thailand D. Sharma et al. https://doi.org/10.5194/hess-11-1373-2007
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141. Improvement of extreme rainfall characteristics for disaggregation of rainfall using MMRC with machine learning based DBSCAN clustering algorithm D. Chowdhury & U. Saha https://doi.org/10.1007/s12145-024-01309-3
142. Disaggregating daily precipitations into hourly values with a transformed censored latent Gaussian process D. Allard & M. Bourotte https://doi.org/10.1007/s00477-014-0913-4
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144. Spatial Disaggregation of Areal Rainfall Using Two Different Artificial Neural Networks Models S. Kim & V. Singh https://doi.org/10.3390/w7062707
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