Characteristics of extreme rainfall in South China during the late rice growth period

2020 ◽  
Vol 112 (6) ◽  
pp. 5105-5114
Author(s):  
Liji Wu ◽  
Fei Hu ◽  
Shenggang Pan
Keyword(s):  
South China ◽  
Growth Period ◽  
Extreme Rainfall ◽  
Rice Growth ◽  
Late Rice

Single rice growth period was prolonged by cultivars shifts, but yield was damaged by climate change during 1981-2009 in China, and late rice was just opposite

2013 ◽  
Vol 19 (10) ◽  
pp. 3200-3209 ◽  
Author(s):  
Fulu Tao ◽  
Zhao Zhang ◽  
Wenjiao Shi ◽  
Yujie Liu ◽  
Dengpan Xiao ◽  
...  
Keyword(s):  
Climate Change ◽  
Growth Period ◽  
Rice Growth ◽  
Single Rice ◽  
Late Rice

Cadmium and arsenic accumulation during the rice growth period under in situ remediation

2019 ◽  
Vol 171 ◽  
pp. 451-459 ◽  
Author(s):  
Jiao-feng Gu ◽  
Hang Zhou ◽  
Hui-ling Tang ◽  
Wen-tao Yang ◽  
Min Zeng ◽  
...  
Keyword(s):  
Growth Period ◽  
In Situ Remediation ◽  
Rice Growth ◽  
Arsenic Accumulation

On the Localized Extreme Rainfall over the Great Bay Area in South China with Complex Topography and Strong UHI Effects

2021 ◽  
Author(s):  
Xiaoyan Sun ◽  
Yali Luo ◽  
Xiaoyu Gao ◽  
Mengwen Wu ◽  
Mingxin Li ◽  
...  
Keyword(s):  
South China ◽  
Global Analysis ◽  
Urban Canopy ◽  
Extreme Rainfall ◽  
Urban Agglomeration ◽  
Convective Initiation ◽  
Sea Breezes ◽  
Bay Area ◽  
Great Bay ◽  
Hourly Rainfall

AbstractIn this study, high-resolution surface and radar observations are used to analyze 24 localized extreme hourly rainfall (EXHR, > 60mm/h) events with strong urban heat island (UHI) effects over the Great Bay Area (GBA) in South China during 2011-2016 warm seasons. Quasi-idealized, convection-permitting ensemble simulations driven by diurnally varying lateral boundary conditions, which are extracted from the composite global analysis of 3-5 June 2013, are then conducted with a multi-layer urban canopy model to unravel the influences of the UHI and various surface properties nearby on the EXHR generation in a complex geographical environment with sea-land contrast, topography, and vegetation variation. Results show that EXHR is mostly distributed over the urban agglomeration and within about 40 km on its downwind side, and produced during the afternoon-to-evening hours by short-lived meso-γ to β-scale storms. On the EXHR days, the GBA is featured by a weak-gradient environment with abundant moisture, and a weak southwesterly flow prevailing in the boundary layer (BL). The UHI effects lead to the development of a deep mixed layer with “warm bubbles” over the urban agglomeration, in which the lower-BL convergence and BL-top divergence is developed, assisting in convective initiation. Such urban BL processes and associated convective development with moisture supply by the synoptic low-level southwesterly flow are enhanced by orographically increased horizontal winds and sea breezes under the influence of the herringbone coastline, thereby increasing the inhomogeneity and intensity of rainfall production over the “Π-shaped” urban clusters. Vegetation variations are not found to be an important factor in determining the EXHR production over the region.

scholarly journals Impacts of climate warming, cultivar shifts, and phenological dates on rice growth period length in China after correction for seasonal shift effects

2019 ◽  
Vol 155 (1) ◽  
pp. 127-143 ◽  
Author(s):  
Tao Ye ◽  
Shuo Zong ◽  
Axel Kleidon ◽  
Wenping Yuan ◽  
Yao Wang ◽  
...  
Keyword(s):  
Climate Warming ◽  
Growth Period ◽  
Period Length ◽  
Rice Growth ◽  
Seasonal Shift ◽  
Phenological Dates

scholarly journals Assessment of Rice Developmental Stage Using Time Series UAV Imagery for Variable Irrigation Management

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5354
Author(s):  
Chin-Ying Yang ◽  
Ming-Der Yang ◽  
Wei-Cheng Tseng ◽  
Yu-Chun Hsu ◽  
Guan-Sin Li ◽  
...  
Keyword(s):  
Irrigation System ◽  
Irrigation Management ◽  
Growth Period ◽  
Water Shortage ◽  
Water Saving ◽  
Growth Stages ◽  
Rice Growth ◽  
Final Yield ◽  
Uav Images ◽  
Sequential Images

Rice is one of the three major crops in the world and is the major crop in Asia. Climate change and water resource shortages may result in decreases in rice yields and possible food shortage crises. In this study, water-saving farming management was tested, and IOT field water level monitoring was used to regulate water inflow automatically. Plant height (PH) is an important phenotype to be used to determine difference in rice growth periods and yields using water-saving irrigation. An unmanned aerial vehicle (UAV) with an RGB camera captured sequential images of rice fields to estimate rice PH compared with PH measured on site for estimating rice growth stages. The test results, with two crop harvests in 2019, revealed that with adequate image calibration, the correlation coefficient between UAV-PH and field-PH was higher than 0.98, indicating that UAV images can accurately determine rice PH in the field and rice growth phase. The study demonstrated that water-saving farming is effective, decreasing water usage for the first and second crops of 2019 by 53.5% and 21.7%, respectively, without influencing the growth period and final yield. Coupled with an automated irrigation system, rice farming can be adaptive to water shortage situations.

scholarly journals Observed Link of Extreme Hourly Precipitation Changes to Urbanization over Coastal South China

2019 ◽  
Vol 58 (8) ◽  
pp. 1799-1819 ◽  
Author(s):  
Mengwen Wu ◽  
Yali Luo ◽  
Fei Chen ◽  
Wai Kin Wong
Keyword(s):  
South China ◽  
Extreme Rainfall ◽  
Rapid Urbanization ◽  
Extreme Rainfall Events ◽  
Convective Initiation ◽  
Rainfall Events ◽  
Hourly Precipitation ◽  
Urban Cluster ◽  
Wide Range ◽  
Extreme Rain

AbstractUnderstanding changes in subdaily rainfall extremes is critical to urban planners for building more sustainable and resilient cities. In this study, the hourly precipitation data in 1971–2016 from 61 rain gauges are combined with historical land-use change data to investigate changes in extreme hourly precipitation (EXHP) in the Pearl River delta (PRD) region of South China. Also, 120 extreme rainfall events (EXREs) during 2011–16 are analyzed using observations collected at densely distributed automatic weather stations and radar network. Statistically significant increase of hourly precipitation intensity leads to higher annual amounts of both total and extreme precipitation over the PRD urban cluster in the rapid urbanization period (about 1994–2016) than during the preurbanization era (1971 to about 1993), suggesting a possible link between the enhanced rainfall and the rapid urbanization. Those urbanization-related positive trends are closely related to more frequent occurrence of abrupt rainfall events with short duration (≤6 h) than the continuous or growing rainfall events with longer duration. The 120 EXREs in 2011–16 are categorized into six types according to the originating location and movement of the extreme-rain-producing storms. Despite the wide range of synoptic backgrounds and seasons, rainfall intensification by the strong urban heat island (UHI) effect is a clear signal in all the six types, especially over the inland urban cluster with prominent UHIs. The UHI thermal perturbation probably plays an important role in the convective initiation and intensification of the locally developed extreme-rain-producing storms during the daytime.

scholarly journals Rice (Oryza sativa L.) Growth Modeling Based on Growth Degree Day (GDD) and Artificial Intelligence Algorithms

Agriculture ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 59
Author(s):  
Li-Wei Liu ◽  
Chun-Tang Lu ◽  
Yu-Min Wang ◽  
Kuan-Hui Lin ◽  
Xing-Mao Ma ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Oryza Sativa ◽  
Oryza Sativa L ◽  
Gene Expression Programming ◽  
Model Performance ◽  
Growth Period ◽  
Rice Production ◽  
Growth Prediction ◽  
Rice Growth ◽  
Degree Day

Rice (Oryza sativa L.) growth prediction is key for precise rice production. However, the traditional linear rice growth forecasting model is ineffective under rapidly changing climate conditions. Here we show that growth rate (Gr) can be well-predicted by artificial intelligence (AI)-based artificial neural networks (ANN) and gene-expression programming (GEP), with accumulated air temperatures based on growth degree day (GDD). In total, 10,246 Gr from 95 cultivations were obtained with three cultivars, TK9, TNG71, and KH147, in Central and Southern Taiwan. The model performance was evaluated by the Pearson correlation coefficient (r), root mean square error (RMSE), and relative RMSE (r-RMSE) in the whole growth period (lifecycle), as well as the average and specific key stages (transplanting, 50% initial tillering, panicle initiation, 50% heading, and physiological maturity). The results in lifecycle Gr modeling showed that ANN and GEP models had comparable r (0.9893), but the GEP model had the lowest RMSE (3.83 days) and r-RMSE (7.24%). In stage average and specific key stages, each model has its own best-fit growth period. Overall, GEP model is recommended for rice growth prediction considering the model performance, applicability, and routine farming work. This study may lead to smart rice production due to the enhanced capacity to predict rice growth in the field.

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