A comparison of the reproducibility of regional precipitation properties simulated respectively by weather generators and stochastic simulation methods

Li, Yang; Zhong, Ping‐an; Zhu, Feilin; Ma, Yufei; Han, Wang; Li, Jieyu; Xu, Chengjing

doi:10.1007/s00477-021-02053-6

Cited by 8 publications

(4 citation statements)

References 37 publications

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“…The LARS-WG has many features that make it more suitable for weather projections. Together with the SWAT model, these features include an assessment of risk in hydrological or agricultural applications, the creation of multiple-year climate change scenarios at the daily time scale, better simulation of monthly precipitation extremes, risk analysis of extreme precipitation, easy in transformed data format with the SWAT, and simulation of temperature and precipitation at single climate stations under RCPs2.6, 4.5, 6, and 8.5 scenarios of greenhouse emissions (Yang et al 2021).…”

Section: Lars-wgmentioning

confidence: 99%

Hydrologic response of arid and semi-arid river basins in Iraq under a changing climate

Saeed

Al-Khafaji

Al-Faraj

2022

Journal of Water and Climate Change

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An assessment of the total hydrologic response of arid and semi-arid river basins to various scenarios of climate change by considering evapotranspiration, streamflow, and snowmelt is essential for sustainable management of water resources. The Diyala River Basin in Iraq has been chosen as a typical case study of dozens of river basins in arid and semi-arid regions. Here, the LASRS-WG, the Soil and Water Assessment Tool (SWAT), and the SWAT Calibration and Uncertainty Program (CUP) were used to evaluate the total response by considering three Representative Concentration Pathways (RCPs); RCPs 2.6, 4.5, and 8.5 over three periods, 2021–2040, 2041–2061, and 2061–2080, were considered. The results indicate that by the year 2080, the basin will experience a temperature increase by 6.6, 10.1, and 16.6% for RCP 2.6, RCP 4.5, and RCP 8.5, respectively. The corresponding reduction in precipitation will be 3.2, 6.4, and 8.7%, resulting in 38.8, 47.9, and 52.8% fall in streamflow for RCPs 2.6, 4.5, and 8.5, respectively. Due to the increase in temperature, an earlier and less contribution of snowmelt is expected in the projected streamflow. Our findings provide a useful reference and a guide to decision makers for developing adaption plans to sustainably manage water resources in the Diyala River Basin and other similar basins in arid and semi-arid regions.

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Section: Lars-wgmentioning

confidence: 99%

Hydrologic response of arid and semi-arid river basins in Iraq under a changing climate

Saeed

Al-Khafaji

Al-Faraj

2022

Journal of Water and Climate Change

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“…The sixth version of the LARS-WG model includes phase five of Coupled Model Inter-comparison Project (CMIP5) and the capability to project climate data into the year 2100 under the RCPs 2.6, 4.5, 6, and 8.5. The model is characterized by the ability to simulate extreme hydrologic events, possess the noises and errors in time series, fill the gaps in time series, interpolate the observation values located outside of the feasible range, and optionally to used specific GCMs [44,45].…”

Section: Climate Change Prediction Modelmentioning

confidence: 99%

Potential Impact of Global Warming on Climate and Streamflow of Adhaim River Basin, Iraq

Saeed¹,

Al-Khafaji²,

Al-Faraj³

2022

ETJ

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 Connection between LARS-WG and SWAT models presented successful simulation for current and future climate and hydrological systems in ARB.  ARB tend to become hotter and drier by 2080 due to global warming.  Adaptation strategies should be applied in water resources management of ARB.Global warming induces to increase of greenhouse gases in the atmosphere and plays a crucial role in determining the future trend in climatology and hydrology of a watershed. This paper aims to investigate the implications of global warming on future climate and its consequents on streamflow of the Adhaim River Basin (ARB). For this purpose, the Long Ashton Research Station-Weather Generator (LARS-WG) and Soil and Water Assessment Tool (SWAT)-Based models were implemented. The climate and hydrologic records for the period 1990-2019 were used as a Reference Period (RP) and projected to 2080 under Representative Concentration Pathways (RCPs 2.6, 4.5, and 8.5) and five Global Climate Models (GCMs). The results show that the region of ARB tends to become hotter and drier with an increase in mean temperature by 1.2, 2.9, and 4.6 °C under the considered RCPs, respectively. However, precipitation tends to decrease from 366 mm/y in RP to 320.2, 302, and 300.5 mm/y by 2080 under the considered RCPs. Consequently, the streamflow will decrease to about 28, 26, and 24 m3/s by 2080 under the considered RCPs, respectively, compared with 28.96 m3/s in RP. Therefore, adaptation strategies are highly recommended to alleviate the negative impacts of climate change, and the implications of climate change on groundwater, water demand, and adaptation plans should be investigated in future studies.

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“…Water generators are divided into two categories of parametric and nonparametric methods. The parametric method models weather variables using parametric probability distributions while nonparametric generators do not rely on distribution assumptions and parameters (Yang et al 2022a).…”

Section: Introductionmentioning

confidence: 99%

“…Both parametric and nonparametric methods are capable of generating average weather variables in an acceptable quality, however, synthesising extreme weather variables is challenging for parametric models especially extreme rainfalls (Chen and Brissette 2014; Agilan and Umamahesh 2019a; Yang et al 2022b).…”

Section: Introductionmentioning

confidence: 99%

A Novel Framework for Nonparametric Rainfall Generator Based on Deep Convolutional Wasserstein Generative Networks (DC-WGANs)

Mirzaei

Dehghani

2022

Preprint

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A novel rainfall generator based on Deep convolutional Wasserstein Generative Adversarial Networks (DC-WGANs) is implemented to generate spatial-temporal hourly rainfall for the Kelantan River basin in Malaysia. DC-WGAN as a deep generative model consists of two deep neural networks, generator and discriminator, which compete against each other in a min-max game. The final output of this contest is a generator capable of generating unlimited realistic samples from the dataset. The novelty of our generator is converting time series rainfall data of multi-station to images, then building realistic images based on them, and finally converting back to multivariate time series rainfall. Models' outcomes were reasonable in terms of occurrence, extremes, and amount of rainfall, in hourly, daily, and yearly time resolutions. The model's ability to cover a wide range of rainfall variability can be used in uncertainty analysis and dataset augmentation. In our limited number of stations, a high correlation between synthetic data and original data was observed, ranging R from 0.97 to 0.99; also, despite other nonparametric methods, the proposed generator was able to generate extreme events in good quality and quantity. The conclusion is drawn from a case study in Malaysia but can be generalized worldwide by using global data.

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A comparison of the reproducibility of regional precipitation properties simulated respectively by weather generators and stochastic simulation methods

Cited by 8 publications

References 37 publications

Hydrologic response of arid and semi-arid river basins in Iraq under a changing climate

Hydrologic response of arid and semi-arid river basins in Iraq under a changing climate

Potential Impact of Global Warming on Climate and Streamflow of Adhaim River Basin, Iraq

A Novel Framework for Nonparametric Rainfall Generator Based on Deep Convolutional Wasserstein Generative Networks (DC-WGANs)

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