NEOPRENE v1.0.1: a Python library for generating spatial rainfall based on the Neyman–Scott process

Diez-Sierra, Javier; Navas, Salvador; del Jesus, Manuel

doi:10.5194/gmd-16-5035-2023

Geosci. Model Dev.

2023

DOI: 10.5194/gmd-16-5035-2023

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NEOPRENE v1.0.1: a Python library for generating spatial rainfall based on the Neyman–Scott process

Javier Diez-Sierra,

Salvador Navas,

Manuel del Jesus

Abstract: Abstract. Long time series of rainfall at different levels of aggregation (daily or hourly in most cases) constitute the basic input for hydrological, hydraulic and climate studies. However, oftentimes the length, completeness, time resolution or spatial coverage of the available records falls short of the minimum requirements to build robust estimations. Here, we introduce NEOPRENE, a Python library to generate synthetic time series of rainfall. NEOPRENE simulates multi-site synthetic rainfall that reproduces… Show more

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Cited by 2 publications

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Geographical Transferability of Pretrained K-Means Clustering–Artificial Neural Network Model for Disaggregation of Rainfall Data in an Indian Monsoon Climate

Bhattacharyya,

Saha

2023

J. Hydrol. Eng.

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Geographical Transferability of Pretrained K-Means Clustering–Artificial Neural Network Model for Disaggregation of Rainfall Data in an Indian Monsoon Climate

Bhattacharyya,

Saha

2023

J. Hydrol. Eng.

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STORM v.2: A simple, stochastic rainfall model for exploring the impacts of climate and climate change at and near the land surface in gauged watersheds

Rios Gaona,

Michaelides,

Singer

2024

Geosci. Model Dev.

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Abstract. Climate change is expected to have major impacts on land surface and subsurface processes through its expression in the hydrological cycle, but the impacts to any particular basin or region are highly uncertain. Non-stationarities in the frequency, magnitude, duration, and timing of rainfall events have important implications for human societies, water resources, and ecosystems. The conventional approach for assessing the impacts of climate change is to downscale global climate model output and use it to drive regional and local models that express the climate within hydrology near the land surface. While this approach may be useful for linking global general circulation models to the regional hydrological cycle, it is limited for examining the details of hydrological response to climate forcing for a specific location over timescales relevant to decision-makers. For example, the management of a flood or a drought hazard requires detailed information that includes uncertainty based on the variability in storm characteristics rather than on the differences between models within an ensemble. To fill this gap, we present the second version of our STOchastic Rainfall Model (STORM), an open-source and user-friendly modelling framework for simulating a climatic expression as rainfall fields over a basin. This work showcases the use of STORM in simulating ensembles of realistic sequences, and spatial patterns of rainstorms for current climate conditions, and bespoke climate change scenarios that are likely to affect the water balance near the Earth's surface. We outline and detail STORM's new approaches as follows: one copula for linking marginal distributions of storm intensity and duration; orographic stratification of rainfall using the copula approach; a radial decay rate for rainfall intensity which takes into consideration potential, but unrecorded, maximum storm intensities; an optional component to simulate storm start dates and times via circular/directional statistics; and a simple implementation for modelling future climate scenarios. We also introduce a new pre-processing module that facilitates the generation of model input in the form of probability density functions (PDFs) from historical data for subsequent stochastic sampling. Independent validation showed that the average performance of STORM falls within 5.5 % of the historical seasonal total rainfall in the Walnut Gulch Experimental Watershed (Arizona, USA) that occurred in the current century.

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NEOPRENE v1.0.1: a Python library for generating spatial rainfall based on the Neyman–Scott process

Cited by 2 publications

References 45 publications

Geographical Transferability of Pretrained K-Means Clustering–Artificial Neural Network Model for Disaggregation of Rainfall Data in an Indian Monsoon Climate

Geographical Transferability of Pretrained K-Means Clustering–Artificial Neural Network Model for Disaggregation of Rainfall Data in an Indian Monsoon Climate

STORM v.2: A simple, stochastic rainfall model for exploring the impacts of climate and climate change at and near the land surface in gauged watersheds

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