STORM 1.0: a simple, flexible, and parsimonious stochastic rainfall generator for simulating climate and climate change

Singer, Michael Bliss; Michaelides, Katerina; Hobley, Daniel E. J.

doi:10.5194/gmd-11-3713-2018

Cited by 28 publications

(23 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Changes in heavy rainfall in recent decades, such as extremely long wet spells and rainfall intensification, have been reported in different regions (Alexander et al, 2006;Fischer and Knutti, 2016;Peterson et al, 2013;Singh et al, 2014;Westra et al, 2013). The intensity of heavy rainstorms is sensitive to warming (e.g.…”

Section: Introductionmentioning

confidence: 99%

Temperature effects on the spatial structure of heavy rainfall modify catchment hydro-morphological response

et al. 2020

View full text Add to dashboard Cite

Abstract. Heavy rainfall is expected to intensify with increasing temperatures, which will likely affect rainfall spatial characteristics. The spatial variability of rainfall can affect streamflow and sediment transport volumes and peaks. Yet, the effect of climate change on the small-scale spatial structure of heavy rainfall and subsequent impacts on hydrology and geomorphology remain largely unexplored. In this study, the sensitivity of the hydro-morphological response to heavy rainfall at the small-scale resolution of minutes and hundreds of metres was investigated. A numerical experiment was conducted in which synthetic rainfall fields representing heavy rainfall events of two types, stratiform and convective, were simulated using a space-time rainfall generator model. The rainfall fields were modified to follow different spatial rainfall scenarios associated with increasing temperatures and used as inputs into a landscape evolution model. The experiment was conducted over a complex topography, a medium-sized (477 km2) Alpine catchment in central Switzerland. It was found that the responses of the streamflow and sediment yields are highly sensitive to changes in total rainfall volume and to a lesser extent to changes in local peak rainfall intensities. The results highlight that the morphological components are more sensitive to changes in rainfall spatial structure in comparison to the hydrological components. The hydro-morphological features were found to respond more to convective rainfall than stratiform rainfall because of localized runoff and erosion production. It is further shown that assuming heavy rainfall to intensify with increasing temperatures without introducing changes in the rainfall spatial structure might lead to overestimation of future climate impacts on basin hydro-morphology.

show abstract

Section: Introductionmentioning

confidence: 99%

Temperature effects on the spatial structure of heavy rainfall modify catchment hydro-morphological response

et al. 2020

View full text Add to dashboard Cite

show abstract

“…9), (d) the evolution of post-glacial drainage networks (Lai and Anders, 2018, reproduction of their Fig. 4), (e) estimates of landslide probability (Strauch et al, 2018, reproduction modified from their Fig. 9), (f) and coevolution of vegetation and erosion (Schmid et al, 2018, reproduction of their Fig.…”

Section: The Three Landlab Audiencesmentioning

confidence: 99%

Short communication: Landlab v2.0: a software package for Earth surface dynamics

et al. 2020

Self Cite

View full text Add to dashboard Cite

Abstract. Numerical simulation of the form and characteristics of Earth's surface provides insight into its evolution. Landlab is an open-source Python package that contains modularized elements of numerical models for Earth's surface, thus reducing time required for researchers to create new or reimplement existing models. Landlab contains a gridding engine which represents the model domain as a dual graph of structured quadrilaterals (e.g., raster) or irregular Voronoi polygon–Delaunay triangle mesh (e.g., regular hexagons, radially symmetric meshes, and fully irregular meshes). Landlab also contains components – modular implementations of single physical processes – and a suite of utilities that support numerical methods, input/output, and visualization. This contribution describes package development since version 1.0 and backward-compatibility-breaking changes that necessitate the new major release, version 2.0. Substantial changes include refactoring the grid, improving the component standard interface, dropping Python 2 support, and creating 31 new components – for a total of 58 components in the Landlab package. We describe reasons why many changes were made in order to provide insight for designers of future packages. We conclude by discussing lessons about the dynamics of scientific software development gained from the experience of using, developing, maintaining, and teaching with Landlab.

show abstract

“…The SBM is based on the linear theory of threedimensional (3-D) stratified, hydrostatic flow over mountains with uniform incoming horizontal wind speed and stability (Smith, 1980(Smith, , 1989. It explicitly considers linear flow effects evolving over mountains, such as upstream-tilted gravity waves or a flow that goes around rather than over an obstacle in the case of low wind speed, high static stability, and/or large mountains (i.e., small Froude numbers).…”

Section: Orographic Precipitationmentioning

confidence: 99%

Flood-related extreme precipitation in southwestern Germany: development of a two-dimensional stochastic precipitation model

Ehmele

Kunz

2019

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. Various fields of application, such as risk assessments of the insurance industry or the design of flood protection systems, require reliable precipitation statistics in high spatial resolution, including estimates for events with high return periods. Observations from point stations, however, lack of spatial representativeness, especially over complex terrain. Current numerical weather models are not capable of running simulations over thousands of years. This paper presents a new method for the stochastic simulation of widespread precipitation based on a linear theory describing orographic precipitation and additional functions that consider synoptically driven rainfall and embedded convection in a simplified way. The model is initialized by various statistical distribution functions describing prevailing atmospheric conditions such as wind vector, moisture content, or stability, estimated from radiosonde observations for a limited sample of observed heavy rainfall events. The model is applied for the stochastic simulation of heavy rainfall over the complex terrain of southwestern Germany. It is shown that the model provides reliable precipitation fields despite its simplicity. The differences between observed and simulated rainfall statistics are small, being of the order of only ±10 % for return periods of up to 1000 years.

show abstract

STORM 1.0: a simple, flexible, and parsimonious stochastic rainfall generator for simulating climate and climate change

Cited by 28 publications

References 59 publications

Temperature effects on the spatial structure of heavy rainfall modify catchment hydro-morphological response

Temperature effects on the spatial structure of heavy rainfall modify catchment hydro-morphological response

Short communication: Landlab v2.0: a software package for Earth surface dynamics

Flood-related extreme precipitation in southwestern Germany: development of a two-dimensional stochastic precipitation model

Contact Info

Product

Resources

About