A Simple Technique for Obtaining Future Climate Data Inputs for Natural Resource Models

doi:10.13031/aea.32.10993

Cited by 9 publications

(1 citation statement)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CLIGEN is a stochastic weather generator established by the United States Department of Agriculture (USDA) to provide a climate input for Water Erosion Prediction Project(WEPP) model [14,15], which includes daily precipitation, precipitation pattern, maximum and minimum temperature, dew temperature, wind direction, wind speed and solar radiation [16]. It has been used widely to downscale the monthly data of GCM outputs [17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Multi-Site Statistical Downscaling Method Using GCM-Based Monthly Data for Daily Precipitation Generation

Shao

Liu

et al. 2020

Water

View full text Add to dashboard Cite

Global Climate Models (GCMs) can provide essential meteorological data as inputs for simulating and assessing the impact of climate change on catchment hydrology. However, downscaling of GCM outputs is often required due to their coarse spatial and temporal resolution. As an effective downscaling method, stochastic weather generators can reproduce daily sequences with statistically similar statistical characteristics. Most weather generators can only simulate single-site meteorological data, which are spatially uncorrelated. Therefore, this study introduces a method for multi-site precipitation downscaling based on a combination of a single-site stochastic weather generator, CLIGEN (CLImate GENerator), and a modified shuffle procedure constrained with multi-model ensemble GCM monthly precipitation outputs. The applicability of the downscaling method is demonstrated in the Huangfuchuan Basin (arid to semi-arid climate) for a historical period (1976–2005) and a projection period (2021–2070, historical, the representative concentration path (RCP) 2.6, RCP4.5, RCP4.8 scenarios) to generate spatially correlated daily precipitation. The results show that the proposed downscaling method can accurately simulate the mean of daily, monthly and annual precipitation and the wet spell lengths, and the inter-station correlation among 10 sites in the basin. In addition, this combination method generated the projected precipitation and showed an increasing trend for future years. These findings could help us better cope with the potential risks of climate change.

show abstract

Section: Introductionmentioning

confidence: 99%

Multi-Site Statistical Downscaling Method Using GCM-Based Monthly Data for Daily Precipitation Generation

Shao

Liu

et al. 2020

Water

View full text Add to dashboard Cite

show abstract

Prediction of the karstic spring flow rates under climate change by climatic variables based on the artificial neural network: a case study of Iran

Zeydalinejad

Nassery

Shakiba

et al. 2020

Environ Monit Assess

View full text Add to dashboard Cite

Modelling climate change impact on soil erosion in a watershed of north-western Lesser Himalayan region

Sooryamol

Kumar

Regina

et al. 2022

J. Sediment. Environ.

View full text Add to dashboard Cite

A Simple Technique for Obtaining Future Climate Data Inputs for Natural Resource Models

Abstract: ABSTRACT.Those conducting impact studies using natural resource models need to be able to quickly and easily obtain downscaled future climate data from multiple General Circulation Models (GCMs)

Cited by 9 publications

References 24 publications

Multi-Site Statistical Downscaling Method Using GCM-Based Monthly Data for Daily Precipitation Generation

Multi-Site Statistical Downscaling Method Using GCM-Based Monthly Data for Daily Precipitation Generation

Prediction of the karstic spring flow rates under climate change by climatic variables based on the artificial neural network: a case study of Iran

Modelling climate change impact on soil erosion in a watershed of north-western Lesser Himalayan region

Contact Info

Product

Resources

About