scholarly journals Spatial Heterogeneity in the Occurrence Probability of Rainstorms over China

Entropy ◽  
2018 ◽  
Vol 20 (12) ◽  
pp. 958
Author(s):  
Yan-Fang Sang
Keyword(s):  
Spatial Heterogeneity ◽  
River Basin ◽  
Yangtze River Basin ◽  
Daily Rainfall ◽  
Aridity Index ◽  
Rainfall Data ◽  
Occurrence Probability ◽  
Pearl River Basin ◽  
Entropy Index ◽  
Huai River

Detecting the spatial heterogeneity in the potential occurrence probability of water disasters is a foremost and critical issue for the prevention and mitigation of water disasters. However, it is also a challenging task due to the lack of effective approaches. In the article, the entropy index was employed and those daily rainfall data at 520 stations were used to investigate the occurrences of rainstorms in China. Results indicated that the entropy results were mainly determined by statistical characters (mean value and standard deviation) of rainfall data, and can categorically describe the spatial heterogeneity in the occurrence of rainstorms by considering both their occurrence frequencies and magnitudes. Smaller entropy values mean that rainstorm events with bigger magnitudes were more likely to occur. Moreover, the spatial distribution of entropy values kept a good relationship with the hydroclimate conditions, described by the aridity index. In China, rainstorms are more to likely occur in the Pearl River basin, Southeast River basin, lower-reach of the Yangtze River basin, Huai River basin, and southwest corner of China. In summary, the entropy index can be an effective alternative for quantifying the potential occurrence probability of rainstorms. Four thresholds of entropy value were given to distinguish the occurrence frequency of rainstorms as five levels: very high, high, mid, low and very low, which can be a helpful reference for the study of daily rainstorms in other basins and regions.

scholarly journals Carbon Storage Change Analysis and Emission Reduction Suggestions under Land Use Transition: A Case Study of Henan Province, China

2021 ◽  
Vol 18 (4) ◽  
pp. 1844
Author(s):  
Dongyang Xiao ◽  
Haipeng Niu ◽  
Jin Guo ◽  
Suxia Zhao ◽  
Liangxin Fan
Keyword(s):  
Land Use ◽  
River Basin ◽  
Yangtze River ◽  
Carbon Emission ◽  
Yellow River ◽  
Yangtze River Basin ◽  
Yellow River Basin ◽  
River Basins ◽  
Henan Province ◽  
Huai River

The significant spatial heterogeneity among river basin ecosystems makes it difficult for local governments to carry out comprehensive governance for different river basins in a special administrative region spanning multi-river basins. However, there are few studies on the construction of a comprehensive governance mechanism for multi-river basins at the provincial level. To fill this gap, this paper took Henan Province of China, which straddles four river basins, as the study region. The chord diagram, overlay analysis, and carbon emission models were applied to the remote sensing data of land use to analyze the temporal and spatial patterns of carbon storage caused by land-use changes in Henan Province from 1990 to 2018 to reflect the heterogeneity of the contribution of the four basins to human activities and economic development. The results revealed that food security land in the four basins decreased, while production and living land increased. Ecological conservation land was increased over time in the Yangtze River Basin. In addition, the conversion from food security land to production and living land was the common characteristic for the four basins. Carbon emission in Henan increased from 134.46 million tons in 1990 to 553.58 million tons in 2018, while its carbon absorption was relatively stable (1.67–1.69 million tons between 1990 and 2018). The carbon emitted in the Huai River Basin was the main contributor to Henan Province’s total carbon emission. The carbon absorption in Yellow River Basin and Yangtze River Basin had an obvious spatial agglomeration effect. Finally, considering the current need of land spatial planning in China and the goal of carbon neutrality by 2060 set by the Chinese government, we suggested that carbon sequestration capacity should be further strengthened in Yellow River Basin and Yangtze River Basin based on their respective ecological resource advantages. For future development in Hai River Basin and Huai River Basin, coordinating the spatial allocation of urban scale and urban green space to build an ecological city is a key direction to embark upon.

scholarly journals Attribution Analysis of Long-Term Trends of Aridity Index in the Huai River Basin, Eastern China

2020 ◽  
Vol 12 (5) ◽  
pp. 1743
Author(s):  
Meng Li ◽  
Ronghao Chu ◽  
Abu Reza Md. Towfiqul Islam ◽  
Yuelin Jiang ◽  
Shuanghe Shen
Keyword(s):  
River Basin ◽  
Time Scale ◽  
Aridity Index ◽  
Growing Season ◽  
Dominant Factor ◽  
Annual Time Scale ◽  
Huai River ◽  
Autumn And Winter ◽  
Annual Time ◽  
The Huai River Basin

This paper aims to combinedly investigate the spatiotemporal trends of precipitation (Pre), reference evapotranspiration (ET0), and aridity index (AI) by employing nonparametric methods based on daily datasets from 137 meteorological stations during 1961–2014 in the Huai River Basin (HRB). The dominant factors influencing ET0 and AI trends were also explored using the detrended and differential equation methods. Results show that (1) Pre, ET0, and AI were much larger in summer than in other seasons, and AI had a nonsignificant increasing trend in annual time scale, while Pre and ET0 exhibited decreasing trends, but AI showed a downward trend in spring and autumn (becoming drier) and an upward trend during summer and winter due to increased Pre (becoming wetter); (2) lower AI values were identified in north and higher in south, and lower ET0 was identified in south and higher in north in annual time scale, growing season and spring, while ET0 decreased from west to east in summer and winter, the spatial distribution of Pre was similar to that of AI; (3) for ET0 trends, in general, wind speed at two-meter height (u2) was the dominant factor in spring, autumn, winter, and annual time scale, while in other seasons, solar radiation (Rs) played a dominant role; (4) for AI trends, AI was mostly contributed by Pre in spring, autumn, and winter, the Rs contributed the most to AI trend in growing season and summer, then in annual time scale, u2 was the dominant factor; (5) overall, the contribution of Pre to AI trends was much larger than that of ET0 in spring, autumn, and winter, while AI was mostly contributed by ET0 in annual time scale, growing season and summer. The outcomes of the study may improve our scientific understanding of recent climate change effects on dry–wet variations in the HRB; moreover, this information may be utilized in other climatic regions for comparison analyses.

scholarly journals Rainfall Prediction with AMSR-E Soil Moisture Products Using SM2RAIN and Nonlinear Autoregressive Networks with Exogenous Input (NARX) for Poorly Gauged Basins: Application to the Karkheh River Basin, Iran

2018 ◽  
Author(s):  
Majid Fereidoon ◽  
Manfred Koch
Keyword(s):  
Soil Moisture ◽  
River Basin ◽  
Land Surface ◽  
Daily Rainfall ◽  
Rainfall Data ◽  
Rainfall Prediction ◽  
Nonlinear Autoregressive ◽  
Karkheh River Basin ◽  
The Karkheh River ◽  
Narx Model

Accurate estimates of daily rainfall are essential for understanding and modeling the physical processes involved in the interaction between the land surface and the atmosphere. In this study, daily satellite soil moisture observations from the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) generated by implementing the standard NASA- algorithm are employed for estimating rainfall, firstly, through the use of recently developed approach, SM2RAIN (Brocca et al., 2013) and, secondly, the nonlinear autoregressive network with exogenous inputs (NARX) neural modelling at five climate stations in the Karkheh river basin (KRB), located in southwest Iran. In the SM2RAIN method, the period 1 January 2003 to 31 December 2005 is used for the calibration of algorithm and the remaining 9 months from 1 January 2006 to 30 September 2006 is used for the validation of the rainfall estimates. In the NARX model, the full study period is split into a training (1 January 2003 to 31 September 2005) and a testing (1 September 2005 to 30 September 2006) stage. For the prediction of the rainfall as the desired target (output), relative soil moisture changes from AMSR-E and measured air temperature time series are chosen as exogenous (external) inputs in NARX. The quality of the estimated rainfall data is evaluated by comparing it with observed rainfall data at the five rain gauges in terms of the correlation coefficient R, the RMSE and the statistical bias. For the SM2RAIN method, R ranges between 0.44 and 0.9 for all stations, whereas for the NARX- model the values are generally slightly lower. Moreover, the values of the bias for each station indicate that although SM2RAIN is likely to underestimate large rainfall intensities, due to the known effect of soil moisture saturation, its biases are somewhat lower than those of NARX. In conclusion, the results of the present study show that with the use of AMSR-E soil moisture products in the physically based SM2RAIN- algorithm as well as in the NARX neural network, rainfall for poorly gauged regions can be fairly predicted.

scholarly journals Multifractal analyses of daily rainfall time series in Pearl River basin of China

2014 ◽  
Vol 405 ◽  
pp. 193-202 ◽  
Author(s):  
Zu-Guo Yu ◽  
Yee Leung ◽  
Yongqin David Chen ◽  
Qiang Zhang ◽  
Vo Anh ◽  
...  
Keyword(s):  
Time Series ◽  
River Basin ◽  
Daily Rainfall ◽  
Pearl River ◽  
Rainfall Time Series ◽  
Pearl River Basin

scholarly journals Impacts of land use/cover change and reforestation on summer rainfall for the Yangtze River Basin

2020 ◽  
Author(s):  
Wei Li ◽  
Lu Li ◽  
Jie Chen ◽  
Qian Lin ◽  
Hua Chen
Keyword(s):  
Land Use ◽  
River Basin ◽  
Yangtze River ◽  
Yangtze River Basin ◽  
Daily Rainfall ◽  
Summer Rainfall ◽  
Rainfall Amount ◽  
The Yangtze River ◽  
The Yangtze River Basin ◽  
Land Use And Cover

Abstract. Land use and cover has been significantly changed all around the world during the last decade. In particular, the Returning Farmland to Forest Program (RFFP) have resulted in significant changes in regional land use and cover, especially in China. The land use and cover change (LUCC) may lead to the change in regional climate. In this study, we take the Yangtze river basin as a case study and analyze the impacts of LUCC and reforestation on summer rainfall amount and extremes based on the Weather Research and Forecasting model. Firstly, two observed land use and cover scenarios (1990 and 2010) were chosen to investigate the impacts of LUCC on the summer rainfall during the last decade. Secondly, two hypothetical reforestation scenarios (i.e., scenarios of 20 % and 50 % cropland changed to be forest) were taken based on the control year of 2010 to test the sensitivity of summer rainfall (amount and extremes) to reforestation. The results showed that LUCC between 1990 and 2010 decreased average summer rainfall, while increased extreme summer daily rainfall in the Yangtze River basin. The extreme summer daily rainfall increased up to 50 mm, which was mainly observed in the midstream and downstream. Reforestation could increase summer rainfall amount and extremes, and the effects were more pronounced at the local scale where suffered reforestation than at the whole basin. Moreover, the effects of reforestation were influenced by the reforestation proportion. In this study, the average summer rainfall increased more for the scenario of 20 % croplands changed to forests than that for the scenario of 50 %, while the high-intensity short-duration rainfall increased more for the scenario of 50 % croplands changed to forests than that for the scenario of 20 %. Although a comprehensive assessment of the impacts of LUCC on summer rainfall amount and extremes was conducted, further studies are needed to better investigate the uncertainty.

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