Future Changes of Precipitation over the Han River Basin Using NEX-GDDP Dataset and the SVR_QM Method

Xu, Ran; Chen, Yumin; Chen, Zeqiang

doi:10.3390/atmos10110688

Cited by 13 publications

(10 citation statements)

References 46 publications

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“…To project the future meteorological droughts (i.e., SPI and SPEI), the future projections of meteorological datasets (precipitation, maximum ( T max ) and minimum ( T min ) temperature) are obtained from NEX‐GDDP data, which is released in June 2015 by NASA (Xu et al ., 2019). The spatial resolution of the datasets is 0.25° longitude and 0.25° latitude and available at daily time scale.…”

Section: Study Area and Data Usedmentioning

confidence: 99%

Identification of future meteorological drought hotspots over Indian region: A study based on NEX‐GDDP data

Das

Umamahesh

2021

Intl Journal of Climatology

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The present study aims to answer the following two research questions using the future outputs from 19 GCMs of the novel NASA Earth Exchange Global Daily Downscaled Projections (NEX‐GDDP) under two Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios. (a) What will be the possible variability of future meteorological drought properties during the 21st century? (b) What will be the possible influence of temperature in modulating the drought properties? In order to answer the above questions, five different drought properties namely frequency, severity, duration, peak, and areal spread are analysed for future (2022–2099) using two meteorological drought indices such as Standardized Precipitation Index (SPI) and Standardized Precipitation‐Evapotranspiration Index (SPEI) for short (3‐month scale) and long (12‐month scale) term drought conditions. The essential outcomes from the study indicate a warming and wetting climate over Maharashtra during the 21st century. With an increase in monthly precipitation, severity and duration of future meteorological droughts are likely to decrease in long‐term drought condition. Conversely, in the short‐term condition, the properties are expected to increase. The drought properties like peak and areal spread are expected to increase for both short and long‐term drought conditions in most of the regions. Temperature plays a vital role in modulating the meteorological droughts, and the present study notices the same in most of the cases in all the drought properties, especially in drought severity, duration, and peak. All the division are identified as hotspot regions during the mid and later part of the 21st century for short‐term drought condition. The number of hotspot regions decreases in long‐term drought condition as compared to short‐term drought condition.

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Section: Study Area and Data Usedmentioning

confidence: 99%

Identification of future meteorological drought hotspots over Indian region: A study based on NEX‐GDDP data

Das

Umamahesh

2021

Intl Journal of Climatology

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“…Perbandingan MKT, RKTP, SVR-RBF, dan RFR telah banyak dikaji dalam berbagai penelitian (Farahani et al, 2010;Jing et al, 2016;Smith et al, 2013;Xu et al, 2019). Perbandingan tersebut dilakukan dengan menggunakan data empiris maupun simulasi.…”

Section: Pendahuluanunclassified

“…, dan * merupakan slack variables yang menyatakan batas atas dan batas bawah galat dengan batas toleransi , dan adalah konstanta positif sebagai penentu derajat penalized loss saat ada galat. SVM menggunakan penalized loss Vapnik'sinsensitive loss function (Xu et al, 2019).…”

Section: Svr-rbfunclassified

Kajian Simulasi Perbandingan Metode Regresi Kuadrat Terkecil Parsial, Support Vector Machine, Dan Random Forest

Fauzi

Soleh

Djuraidah

2020

IJSA

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Highly correlated predictors and nonlinear relationships between response and predictors potentially affected the performance of predictive modeling, especially when using the ordinary least square (OLS) method. The simple technique to solve this problem is by using another method such as Partial Least Square Regression (PLSR), Support Vector Regression with kernel Radial Basis Function (SVR-RBF), and Random Forest Regression (RFR). The purpose of this study is to compare OLS, PLSR, SVR-RBF, and RFR using simulation data. The methods were evaluated by the root mean square error prediction (RMSEP). The result showed that in the linear model, SVR-RBF and RFR have large RMSEP; OLS and PLSR are better than SVR-RBF and RFR, and PLSR provides much more stable prediction than OLS in case of highly correlated predictors and small sample size. In nonlinear data, RFR produced the smallest RMSEP when data contains high correlated predictors.

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“…This dataset is publicly available and some studies have already used it in different sectors and regions [12][13][14]. Nonetheless, very few studies have focused on the application of NEX-GDDP data for streamflow prediction, especially in Pakistan.…”

Section: Introductionmentioning

confidence: 99%

Impacts of Climate Change on the Hydrometeorological Characteristics of the Soan River Basin, Pakistan

Usman¹,

Ndehedehe

Ahmad³

et al. 2021

Atmosphere

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The global hydrological cycle is vulnerable to changing climatic conditions, especially in developing regions, which lack abundant resources and management of freshwater resources. This study evaluates the impacts of climate change on the hydrological regime of the Chirah and Dhoke Pathan sub catchments of the Soan River Basin (SRB), in Pakistan, by using the climate models included in the NEX-GDDP dataset and the hydrological model HBV-light. After proper calibration and validation, the latter is forced with NEX-GDDP inputs to simulate a historic and a future (under the RCP 4.5 and RCP 8.5 emission scenarios) streamflow. Multiple evaluation criteria were employed to find the best performing NEX-GDDP models. A different ensemble was produced for each sub catchment by including the five best performing NEX-GDDP GCMs (ACCESS1-0, CCSM4, CESM1-BGC, MIROC5, and MRI-CGCM3 for Chirah and BNU-ESM, CCSM4, GFDL-CM3. IPSL-CM5A-LR and NorESM1-M for Dhoke Pathan). Our results show that the streamflow is projected to decrease significantly for the two sub catchments, highlighting the vulnerability of the SRB to climate change.

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Future Changes of Precipitation over the Han River Basin Using NEX-GDDP Dataset and the SVR_QM Method

Cited by 13 publications

References 46 publications

Identification of future meteorological drought hotspots over Indian region: A study based on NEX‐GDDP data

Identification of future meteorological drought hotspots over Indian region: A study based on NEX‐GDDP data

Kajian Simulasi Perbandingan Metode Regresi Kuadrat Terkecil Parsial, Support Vector Machine, Dan Random Forest

Impacts of Climate Change on the Hydrometeorological Characteristics of the Soan River Basin, Pakistan

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