Impact of climate change on rainfall in Northwestern Bangladesh using multi‐<scp>GCM</scp> ensembles

Kumar, D. Preveen; Arya, D. S.; Murumkar, Asmita; Rahman, M. M.

doi:10.1002/joc.3770

Cited by 20 publications

(8 citation statements)

References 25 publications

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“…Similarly, in September, the error in the mean values between the observed and modeled series was only 1.2%, but the difference in standard deviations was large. The standard deviation of the modeled series was 0.455 • C, much lower than the value for the observed series of 0.833 • C. The model precision here was acceptable overall within the same level as other model applications [42][43][44], thus indicating the dependability of LARS-WG in downscaling analysis to this study area. These series of synthetic daily weather data were then used as input data for the verified ReNuMa to estimate further changes in the watershed hydrochemical processes, as described in detail in Section 3.3.…”

Section: Lars-wg Modelsupporting

confidence: 47%

Estimation of the Source Apportionment of Phosphorus and Its Responses to Future Climate Changes Using Multi-Model Applications

Sha

Wang

Lü

et al. 2018

Water

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The eutrophication issue in the Yangtze Basin was considered, and the phosphorus loads from its tributary, the Modaoxi River, were estimated. The phosphorus flux and source apportionment of the Modaoxi River watershed were modeled and quantified, and their changes with respect to future projected climate scenarios were simulated with multiple model applications. The Regional Nutrient Management (ReNuMa) model based on Generalized Watershed Loading Functions (GWLF) was employed as a tool to model the hydrochemical processes of the watershed and thereby estimate the monthly streamflow and the phosphorus flux as well as its source apportionment. The Long Ashton Research Station Weather Generator (LARS-WG) was used to predict future daily weather data through the statistical downscaling of the general circulation model (GCM) outputs based on projected climate scenarios. The synthetic time series of daily precipitation and temperatures generated by LARS-WG were further used as input data for ReNuMa to estimate the responses of the watershed hydrochemical processes to future changed climate conditions. The results showed that both models could be successfully applied and that the future wetter and warmer climate trends would have generally positive impacts on the watershed phosphorus yields, with greater contributions coming from runoff. These results could provide valuable support for local water environmental management.

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Section: Lars-wg Modelsupporting

confidence: 47%

Estimation of the Source Apportionment of Phosphorus and Its Responses to Future Climate Changes Using Multi-Model Applications

Sha

Wang

Lü

et al. 2018

Water

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“…Kumar et al . () used the Long Ashton Research Station Weather Generator (LARS‐WG) (Semenov and Barrow, ) to simulate daily rainfall data for 10 rainfall stations located in the Jamuneswari catchment of Teesta River basin. The model was found to well‐reproduce the monthly mean and variance for most of the months, but could not satisfactorily reproduce the wet and dry spells for non‐monsoon months (December to January).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of spatio‐temporal rainfall variability and performance of a stochastic rainfall model in Bangladesh

Chowdhury

Kar²,

Shahid

et al. 2019

Intl Journal of Climatology

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Rainfall in Bangladesh exhibits persistent wet and dry anomalies associated with occurrence of floods and droughts. Assessing inter-annual variability of rainfall is vital to account these hydrological extremes in the design and operations of water systems. However, the inter-annual variability obtained from short record rainfall data might be misleading as it does not contain whole climate variability which signifies the utmost importance of stochastic rainfall models. Since the inter-annual variability and stochastic models have not been explored adequately for rainfall in Bangladesh, this study evaluated (a) the spatio-temporal variability of rainfall focusing on inter-annual variability, and (b) applicability of a stochastic daily rainfall model, referred as the Decadal and Hierarchical Markov Chain (DHMC) model. Daily rainfall data of 1973-2012 for 18 stations across Bangladesh were used to investigate the probability distributions and autocorrelations of rainfall, and the model performances. Results show a higher magnitude of inter-annual variabilities of rainfall depth (standard deviation 80-250 mm) and wet spells (standard deviation 4-6 days) in wetter months (June to September) across rainfall stations in the east region of the country. In contrast, higher rates of inter-annual variabilities (i.e., coefficients of variations) were observed in drier months across the west region. Spatially, the dry spells were observed consistent across the country. Monthly rainfall showed decreasing trend over the region from west to the middle part of the country, whereas monthly number of wet days showed increasing trend over the eastern part. The DHMC was found to preserve the observed variabilities of rainfall at daily to multiyear resolutions at all stations, except a tendency to underestimate the autocorrelation of monthly rainfall depth. Despite this limitation, DHMC can be considered as a suitable stochastic rainfall simulator for a tropical monsoon climate like Bangladesh.

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“…LARS-WG is a well-known daily WG frequently employed in climate change impact assessment studies. This model has been used worldwide for a diverse set of climatic conditions and has been shown capable of reproducing various statistics of weather variables on daily timescale as well as their extreme values (Semenov et al, 1998;Semenov, 2008;Semenov et al, 2013;Kumar et al, 2014;Lu et al, 2015;Agarwal et al, 2016;Bian et al, 2017). However, this model performs poorly with regards to reproducing the LFV of variables in its outputs (Semenov et al, 1998;Khazaei et al, 2013).…”

Section: The Basic Wgmentioning

confidence: 99%

A new method for improving the performance of weather generators in reproducing low‐frequency variability and in downscaling

Khazaei

Zahabiyoun

Hasirchian

2020

Intl Journal of Climatology

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Weather generators (WGs) are models fitted to a series of observed weather data in order to generate long synthetic series of these data, which is important for risk assessment. WGs are also among the main downscaling tools for climate change impact assessments. Despite their many capabilities, WGs cannot properly reproduce low-frequency variability (LFV). Moreover, when using WGs for the downscaling of future climate scenarios, a change in rainfall occurrence will have unintended effects on secondary variables such as minimum temperature (T min) and maximum temperature (T max). This paper presents an approach based on the Quantile Perturbation Method for correcting the LFV of rainfall outputs pertaining to WGs by perturbing the monthly means of the daily generated series. A multivariate monthly generator was used to improve LFV in T min and T max. An advantage of the proposed method is the preservation of the correlation between daily and monthly timescales as well as the cross-correlation between variables during the LFV correction. This method further reduces the unintended effects of changes in rainfall occurrence on the secondary variables during downscaling. After applying the method to the LARS-WG model, the performance of the modified model was tested both directly and indirectly. Concerning the direct validation, the statistical characteristics of the simulated and observed weather data related to the four stations were compared over a diverse range of climatic conditions. Regarding the indirect validation, a comparison was made between the statistical characteristics of the runoffs simulated with a daily hydrologic model fed with the generated and observed weather data. The results showed that the method improved the performance of LARS-WG regarding the reproduction of some of the observed weather characteristics, particularly LFV. The method was also shown to be well capable of reducing the unintended effects of the changes in rainfall occurrence on secondary variables during downscaling.

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Impact of climate change on rainfall in Northwestern Bangladesh using multi‐GCM ensembles

Cited by 20 publications

References 25 publications

Estimation of the Source Apportionment of Phosphorus and Its Responses to Future Climate Changes Using Multi-Model Applications

Estimation of the Source Apportionment of Phosphorus and Its Responses to Future Climate Changes Using Multi-Model Applications

Evaluation of spatio‐temporal rainfall variability and performance of a stochastic rainfall model in Bangladesh

A new method for improving the performance of weather generators in reproducing low‐frequency variability and in downscaling

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