A nested multisite daily rainfall stochastic generation model

By R. Srikanthan, G. Pegram. 2009

126 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Ocean Engineering, Soil Science

View Paper: https://www.semanticscholar.org/paper/ec5d5c79fb9dc79090e8540fdbd5fe8adbfa194e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Global and Planetary Change

1. Balaji Rajagopalan
2. David E. Robertson
3. Quan J. Wang
4. András Bárdossy
5. Anne-Catherine Favre
6. Guillaume Évin
7. William Kleiber
8. R. Katz

In Ocean Engineering

1. David E. Robertson
2. Quan J. Wang
3. Gregoire Mariethoz
4. Julien Straubhaar
5. András Bárdossy
6. William Kleiber
7. Balaji Rajagopalan
8. R. Katz

In Soil Science

1. Anne-Catherine Favre
2. David E. Robertson
3. András Bárdossy
4. Quan J. Wang
5. William Kleiber
6. Balaji Rajagopalan
7. R. Katz
8. Fabio Oriani

Citations in the corpus

• Simulation of rainfall time series from different climatic regions using the direct sampling technique (2014, methodology): “An alternative proposed method is model nesting (Wang and Nathan, 2002; Srikanthan, 2004, 2005; Srikanthan and Pegram, 2009), that implies the correction of the generated daily rainfall using a multiplicative factor to compensate the bias in the higher-scale statistics.”
• Coupled stochastic weather generation using spatial and generalized linear models (2015, methodology): “Variations of this have been used in subsequent multi-site rainfall generators (Srikanthan and Pegram 2009; Brissette et al. 2007) and weather generators (Qian et al. 2002; Baigorria and Jones 2010; Khalili et al. 2009).”
• Multisite probabilistic forecasting of seasonal flows for streams with zero value occurrences (2011, methodology): “The most commonly used approach is to model rainfall occurrence and amounts separately [e.g., Wilks, 1998; Charles et al., 1999; Srikanthan and Pegram, 2009].”
• Daily spatiotemporal precipitation simulation using latent and transformed Gaussian processes (2012, background): “Indeed, simulating model parameters is a straightforward and easy way to reduce overdispersion, whereas other authors have needed to postprocess their daily simulations to align with monthly and annual scales [Srikanthan and Pegram, 2009].”
• Stochastic generation of multi-site daily precipitation focusing on extreme events (2018, background): “As indicated above, Srikanthan and Pegram (2009) show that a fourth-order Markov chain improves the reproduction of dry/wet period lengths.”
• Copula based multisite model for daily precipitation simulation (2009, methodology): “As a point of departure, the normal score (or quantile) transform (NQT) has been used in multi-site stochastic simulation in hydrology for the last 40 or more years, starting with the work of Thomas and Fiering (1962) and Matalas(1967).”