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To expand the field of harmony theory research, new paths for sustainable development of agricultural regions are explored. The uncertainty problem is selected to determine the optimal parameters of the random forest (RF) model. This paper uses the Dragonfly algorithm (DA) to calibrate the RF model parameters, proposes an improved model (DA-RF), and applies it to measure the agricultural water and soil resources composite system harmony (AWSRCSH) of the Jiansanjiang Branch of the Heilongjiang Great Northern Wilderness Agribusiness Group Corporation. The results show that the DA-RF model can effectively improve the simulation ability and stability with a running time that is longer than that of an empirical method. The annual variation in the AWSRCSH of the Jiansanjiang Branch presents an “N-type” trend. From 1997 to 2005, the branch had not yet entered a peak period of resource and agricultural development, while the AWSRCSH had steadily improved during this period. From 2005 to 2008, due to predatory operations, the AWSRCSH decreased, and the speed of this decrease increased. From 2008 to 2016, since local water and soil resource development was strictly controlled and agricultural modernization increased, the AWSRCSH gradually increased. Due to the advantages of water resources and topography, the AWSRCSH in the farms adjacent to the river was greater than that in inland farms. To address the slow growth rate of the AWSRCSH, some adaptive regulation strategies were proposed according to key driving factors, and the effect of harmonious regulation was predicted.
Xuesong Li; Jilong Liu; Dong Liu; Qiang Fu; Mo Li; Muhammad Abrar Faiz; Shoaib Ali; Tianxiao Li; Song Cui; Muhammad Imran Khan. Measurement and analysis of regional agricultural water and soil resource composite system harmony with an improved random forest model based on a dragonfly algorithm. Journal of Cleaner Production 2021, 305, 127217 .
AMA StyleXuesong Li, Jilong Liu, Dong Liu, Qiang Fu, Mo Li, Muhammad Abrar Faiz, Shoaib Ali, Tianxiao Li, Song Cui, Muhammad Imran Khan. Measurement and analysis of regional agricultural water and soil resource composite system harmony with an improved random forest model based on a dragonfly algorithm. Journal of Cleaner Production. 2021; 305 ():127217.
Chicago/Turabian StyleXuesong Li; Jilong Liu; Dong Liu; Qiang Fu; Mo Li; Muhammad Abrar Faiz; Shoaib Ali; Tianxiao Li; Song Cui; Muhammad Imran Khan. 2021. "Measurement and analysis of regional agricultural water and soil resource composite system harmony with an improved random forest model based on a dragonfly algorithm." Journal of Cleaner Production 305, no. : 127217.
Ganga-Brahmaputra-Meghna (GBM) river basin is the third-largest and one of the most populated river basins in the world. As climate change is affecting most of the hydrometeorological variables across the globe, this study investigated the existence of climate change signal in all four climatological seasons in the GBM river basin and assessed the contribution of anthropogenic activities, i.e., Greenhouse Gases (GHGs) emission in the change. Significant decreasing trends in the monsoon and a small increase in pre-monsoon precipitation were observed. Negligible change was detected in post-monsoon and winter season precipitation. CMIP5 GCMs were used for climate change detection, change point estimation, and attribution studies. Support Vector Machine (SVM) regression method was adopted to downscale GCM variables at the local scale. Monte-Carlo simulation approach was used to detect changes in different seasons. The climate change ‘signals’ were detectable after the year 1980 using Signal to Noise ratio (SNR) method in the majority of central and north-western regions. The change point was detectable only in annual monsoon precipitation at the basin level. Attribution analysis indicated >50% contribution of anthropogenic activities (GHGs) to annual monsoon precipitation changes. So, there is high confidence that monsoon precipitation in GBM has significantly changed due to anthropogenic activities. Different mitigation and adaption measures are also suggested, which may be adopted to manage the growing demand and water availability in the basin.
Chetan Sharma; Anoop Kumar Shukla; Yongqiang Zhang. Climate change detection and attribution in the Ganga-Brahmaputra-Meghna river basins. Geoscience Frontiers 2021, 12, 101186 .
AMA StyleChetan Sharma, Anoop Kumar Shukla, Yongqiang Zhang. Climate change detection and attribution in the Ganga-Brahmaputra-Meghna river basins. Geoscience Frontiers. 2021; 12 (5):101186.
Chicago/Turabian StyleChetan Sharma; Anoop Kumar Shukla; Yongqiang Zhang. 2021. "Climate change detection and attribution in the Ganga-Brahmaputra-Meghna river basins." Geoscience Frontiers 12, no. 5: 101186.
Resilience is an important indicator for measuring regional sustainable development capacity. The construction of a suitable evaluation indicator system is the premise of evaluating regional sustainable development. In this study, taking the Jiansanjiang Administration of Heilongjiang Province in China as an example, a preliminary selection library of the evaluation indicator system for the resilience of a regional agricultural soil–water resource composite system covering seven subsystems and 59 indicators was established. Selection criteria such as the Dale indicator criteria, subjective and objective combination weighting and principal component analysis were introduced to construct an optimization model for the resilience evaluation indicator system for the ASWRS. First, 14 indicators that were incomplete or incapable were removed. Then, the Dale indicator selection criteria were used to ensure that 14 indicators were selected. The binary fuzzy comparison method and criteria importance through interference correlation method were used to calculate the combination weight. Finally, an indicator system optimization model was established. The indicator system was optimized from 59 to 35 indicators, and the completeness of the indicator system reached 85.75%. The proposed method had obvious advantages in terms of indicator identification and elimination, and it may truly achieve the goal of indicator optimization.
Dan Xu; Jilong Liu; Dong Liu; Qiang Fu; Mo Li; Muhammad Abrar Faiz; Sicheng Liu; Tianxiao Li; Song Cui; Ge Yan. Indicator system optimization model for evaluating resilience of regional agricultural soil–water resource composite system. Water Supply 2021, 1 .
AMA StyleDan Xu, Jilong Liu, Dong Liu, Qiang Fu, Mo Li, Muhammad Abrar Faiz, Sicheng Liu, Tianxiao Li, Song Cui, Ge Yan. Indicator system optimization model for evaluating resilience of regional agricultural soil–water resource composite system. Water Supply. 2021; ():1.
Chicago/Turabian StyleDan Xu; Jilong Liu; Dong Liu; Qiang Fu; Mo Li; Muhammad Abrar Faiz; Sicheng Liu; Tianxiao Li; Song Cui; Ge Yan. 2021. "Indicator system optimization model for evaluating resilience of regional agricultural soil–water resource composite system." Water Supply , no. : 1.
The reliability of long‐term precipitation estimates is vital for climatology and hydrometeorology applications. Different climatic zones and high rain gauge network (more than 800) of China are a suitable topography for performance evaluation of different long‐term precipitation datasets. In this study, seven long‐term precipitation datasets are tested against in situ observations at different time scales (1981‐2016) at 813 grid points. Well‐known statistical indicators and FFt (Fast‐frugal tree) decision model are employed to identify the best long‐term datasets. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Climate Data Record is the only datasets that did not perform well in the study area. Asian Precipitation‐Highly‐Resolved Observational Data Integration Towards Evaluation (APHRODITE), Global Precipitation Climatology Centre (GPCC), and Climate Prediction Center (CPC‐Global) estimates are comparable with in situ observations. Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), National Centers for Environmental Prediction (NCEP2) overestimate the precipitation extremes in the region. There exists a difference of 100 to 250 mm among precipitation datasets at an annual scale. All of the seven long‐term datasets underestimate the rxdays across China. The minimum range of rxdays (maximum precipitation amount in under defined days: one day or five days) captured by datasets is comparable except PERSIANN‐CDR. The maximum range calculated with PERSIANN‐CDR is 55.01 (rx1day), and 129.67 (rx5day), much less than the in situ rxdays. This analysis shows that datasets failed to capture maximum precipitation intensity in the region as well. FFt decision model results show that APHRODITE ranked first based on calculated consecutive dry days among all six other datasets in the most climatic zones. Overall, results indicate that data assimilation, the spatial coverage of ground stations, and interpolation techniques used to develop the datasets may limit the reliability of precipitation datasets in the study area. This article is protected by copyright. All rights reserved.
Muhammad Abrar Faiz; Yongqiang Zhang; Faisal Baig; Dariusz WrzesiŃski; Farah Naz. Identification and inter‐comparison of appropriate long‐term precipitation datasets using decision tree model and statistical matrix over China. International Journal of Climatology 2021, 1 .
AMA StyleMuhammad Abrar Faiz, Yongqiang Zhang, Faisal Baig, Dariusz WrzesiŃski, Farah Naz. Identification and inter‐comparison of appropriate long‐term precipitation datasets using decision tree model and statistical matrix over China. International Journal of Climatology. 2021; ():1.
Chicago/Turabian StyleMuhammad Abrar Faiz; Yongqiang Zhang; Faisal Baig; Dariusz WrzesiŃski; Farah Naz. 2021. "Identification and inter‐comparison of appropriate long‐term precipitation datasets using decision tree model and statistical matrix over China." International Journal of Climatology , no. : 1.
Dong Liu; Tingqi Yan; Yi Ji; Qiang Fu; Mo Li; Muhammad Abrar Faiz; Shoaib Ali; Tianxiao Li; Song Cui; Muhammad Imran Khan. Corrigendum to ‘Novel method for measuring regional precipitation complexity characteristics based on multiscale permutation entropy combined with CMFO-PPTTE model’ [J. Hydrol. 592 (2021) 125801]. Journal of Hydrology 2021, 596, 126108 .
AMA StyleDong Liu, Tingqi Yan, Yi Ji, Qiang Fu, Mo Li, Muhammad Abrar Faiz, Shoaib Ali, Tianxiao Li, Song Cui, Muhammad Imran Khan. Corrigendum to ‘Novel method for measuring regional precipitation complexity characteristics based on multiscale permutation entropy combined with CMFO-PPTTE model’ [J. Hydrol. 592 (2021) 125801]. Journal of Hydrology. 2021; 596 ():126108.
Chicago/Turabian StyleDong Liu; Tingqi Yan; Yi Ji; Qiang Fu; Mo Li; Muhammad Abrar Faiz; Shoaib Ali; Tianxiao Li; Song Cui; Muhammad Imran Khan. 2021. "Corrigendum to ‘Novel method for measuring regional precipitation complexity characteristics based on multiscale permutation entropy combined with CMFO-PPTTE model’ [J. Hydrol. 592 (2021) 125801]." Journal of Hydrology 596, no. : 126108.
Affected by climate change and high‐intensity human activities, the precipitation series is characterized by nonlinear complex fluctuations. In order to accurately quantify the precipitation complexity, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method was introduced to address the shortcomings of the traditional multifractal detrended fluctuation analysis (MFDFA), and the CEEMDAN‐MFFA method was proposed to explore the spatiotemporal variability and possible causes of precipitation complexity in Northeast China (NEC). The results showed that the traditional MFDFA method generally overestimated precipitation complexity. However, the CEEMDAN‐MFFA method improved the accuracy and reliability of precipitation complexity analysis. Daily precipitation series in NEC were characterized by multifractal with high uncertainty and anti‐persistence. The lowest precipitation complexity was 0.573, which appeared in Qian'an, Jilin Province, and the highest precipitation complexity was 1.108, which appeared in the Boketu, Inner Mongolia. The cities with high complexity of daily precipitation are sporadically distributed in the three provinces of Heilongjiang, Jilin and Liaoning. The average complexity is ~0.892, accounting for 22.5% of the total administrative districts. The complexity of daily precipitation showed a statistically insignificant downward trend with time. The precipitation dynamics structure in the southeast and northwest of the study area was complex, and the precipitation was less predictable than other regions. However, the precipitation dynamics structure was relatively simple and the precipitation was easily predictable in the northeastern, central and southwestern regions. Elevation, topographic relief, changes in water area, forest area and agricultural production were the main influencing factors for the differences in precipitation complexity. Results also revealed that increasing the area of forestland by means of protecting and breeding forests, via regulation of the forest ecosystems to alleviate the complex precipitation process will become a reliable approach to cope with nonstationary precipitation.
Liangliang Zhang; Heng Li; Dong Liu; Qiang Fu; Mo Li; Muhammad Abrar Faiz; Shoaib Ali; Muhammad Imran Khan; Tianxiao Li. Application of an improved multifractal detrended fluctuation analysis approach for estimation of the complexity of daily precipitation. International Journal of Climatology 2021, 1 .
AMA StyleLiangliang Zhang, Heng Li, Dong Liu, Qiang Fu, Mo Li, Muhammad Abrar Faiz, Shoaib Ali, Muhammad Imran Khan, Tianxiao Li. Application of an improved multifractal detrended fluctuation analysis approach for estimation of the complexity of daily precipitation. International Journal of Climatology. 2021; ():1.
Chicago/Turabian StyleLiangliang Zhang; Heng Li; Dong Liu; Qiang Fu; Mo Li; Muhammad Abrar Faiz; Shoaib Ali; Muhammad Imran Khan; Tianxiao Li. 2021. "Application of an improved multifractal detrended fluctuation analysis approach for estimation of the complexity of daily precipitation." International Journal of Climatology , no. : 1.
Evidence revealed that climate change has a significant impact on grain production in China. Northeast China has abundant agricultural resources which can make the maximum contribution to national food security. This study examines the effects of climate variability and price anomalies on grain yield and land use in Northeast China. The analysis showed that different climate variability phase combinations based on Pacific Decadal Oscillation and North Atlantic Oscillation present variations in signals and different magnitude of effects over the study area. The results revealed that land use by total grain crop negatively responds to the increase in price anomalies in Heilongjiang and Jilin Provinces. To assess the impact of climate change on crop yield model, the yield models under dynamically downscaled regional climate models revealed that climate variables significantly contribute to total grain yields. In the near future, minimum temperature (− 0.26 °C under CanESM2-4.5, − 4.42 °C under HadGEM2-ES), maximum temperature (− 2.82 °C under CanESM2-4.5, − 0.84 under HadGEM2-ES), and precipitation (ranged from 3.59 to 11.10%) positively contribute to total grain yields under both models. Overall, analysis showed that climate change has a significant contribution to grain production. In conclusion, the implications for future research and policymakers have been addressed. Particularly, the importance of considering regional differences in adaptation planning in agricultural regions was also considered.
Trinh Thi Viet Ha; Muhammad Abrar Faiz; Li Shuang. Assessment of the response of climate variability and price anomalies to grain yield and land use in Northeast China. Environmental Science and Pollution Research 2021, 1 -14.
AMA StyleTrinh Thi Viet Ha, Muhammad Abrar Faiz, Li Shuang. Assessment of the response of climate variability and price anomalies to grain yield and land use in Northeast China. Environmental Science and Pollution Research. 2021; ():1-14.
Chicago/Turabian StyleTrinh Thi Viet Ha; Muhammad Abrar Faiz; Li Shuang. 2021. "Assessment of the response of climate variability and price anomalies to grain yield and land use in Northeast China." Environmental Science and Pollution Research , no. : 1-14.
Using optical remote sensing data to downscale microwave soil moisture is currently a crucial technique for producing fine‐resolution soil moisture dataset at large scales. Conventional soil moisture downscaling models are mainly developed and evaluated in arid or semi‐arid regions, but have serious limitation in humid regions. To improve soil moisture estimates under a wide range of climate regimes, this study developed a nonlinear enhanced soil moisture downscaling model and evaluated it at a typical microwave resolution over the contiguous China. The enhanced model clearly outperforms the conventional approach in estimating surface soil moisture by reducing the Root Mean Square Error (RMSE) from 0.175 vol/vol to 0.075 vol/vol under wet climate. Finally, soil moisture downscaling is conducted by applying such models (built at the 25‐km scale) at a finer (1‐km) resolution. Validation result of downscaled soil moisture against in situ dataset demonstrates the advantage of the enhanced model.
Peilin Song; Yongqiang Zhang; Jing Tian. Improving Surface Soil Moisture Estimates in Humid Regions by an Enhanced Remote Sensing Technique. Geophysical Research Letters 2021, 48, 1 .
AMA StylePeilin Song, Yongqiang Zhang, Jing Tian. Improving Surface Soil Moisture Estimates in Humid Regions by an Enhanced Remote Sensing Technique. Geophysical Research Letters. 2021; 48 (5):1.
Chicago/Turabian StylePeilin Song; Yongqiang Zhang; Jing Tian. 2021. "Improving Surface Soil Moisture Estimates in Humid Regions by an Enhanced Remote Sensing Technique." Geophysical Research Letters 48, no. 5: 1.
Comprehensive consideration of physical forms of meteorological and agricultural drought is necessary for the development of robust monitoring and assessment of droughts. This consideration facilitated the development and analyze an integrated weighted drought index (IWDI) by using the AHP (analytical hierarchy process) technique that takes into account all possible variables relevant to different types of drought such as meteorological, agriculture, and soil moisture drought indices. Heilongjiang northeastern province of China had suffered frequent droughts due to the changing climate. Droughts affected agricultural production and caused a decrease in total yield due to less water availability, particularly in summer which is the only growing season in the area. In this study, an integrated index was developed to examine its applicability and to assess the impact of drought on rice yield using eight different drought indices included meteorological, remote sensing multi-sensor, soil moisture conditions, and climate variables. The results showed that IWDI had a significant correlation with meteorological and agricultural drought indices. Detailed analysis revealed that, compared to a single meteorological or an agricultural drought index, IWDI achieved good results for agriculture drought monitoring. Results showed that IWDI captured a significant impact of drought on rice yield and its variation in the area which was from 17 to 75% at different stations. This study also concluded that integration of different indices may be a better option for policymakers and economists in understanding and monitoring agricultural droughts losses.
Muhammad Ahmad Niaz; Muhammad Abrar Faiz; Wei Yongxia. Development of an integrated weighted drought index and its application for agricultural drought monitoring. Arabian Journal of Geosciences 2021, 14, 1 -12.
AMA StyleMuhammad Ahmad Niaz, Muhammad Abrar Faiz, Wei Yongxia. Development of an integrated weighted drought index and its application for agricultural drought monitoring. Arabian Journal of Geosciences. 2021; 14 (6):1-12.
Chicago/Turabian StyleMuhammad Ahmad Niaz; Muhammad Abrar Faiz; Wei Yongxia. 2021. "Development of an integrated weighted drought index and its application for agricultural drought monitoring." Arabian Journal of Geosciences 14, no. 6: 1-12.
Flash flood disaster ranks top among all the natural hazards across the world due to its high frequency, severity and fatality. However, flash flood simulation is still challenging in small and medium-sized catchments with complex orography, flashy hydrological responses and poor observations. Three distributed hydrological models, i.e., TOPModel, HEC and CNFF, are selected to simulate flash floods in seven humid and six semi-humid catchments in China, with consideration of water balance (RER), peak flow rate (REQ) and its occurrence time (TP), hydrograph variation (SNSE) and model uncertainty. Influences of five catchment attributes are further investigated on individual model performances. All three models perform satisfactorily in humid catchments, but less satisfactorily in semi-humid catchments. Water balance is well obtained by CNFF, followed by HEC and TOPModel. Peak flow rate and its occurrence time are most accurately captured by CNFF and HEC, respectively. Hydrograph variations are well reproduced by HEC and CNFF. TOPModel performs well for picking peak flow and hydrograph variation in humid catchments. Uncertainty interval is narrowest for HEC with average relative interval length at 95% confidence level being 0.78 ~ 2.53. Most observations are bracketed by uncertainty intervals for TOPModel (64.79% ~ 91.91% of total). Three model performance indices (i.e., RER, REQ, and SNSE) are mainly affected by drainage area and forest ratio across humid and semi-humid catchments, while TP performance is mainly affected by mean slope in humid catchments.
Xiaoyan Zhai; Liang Guo; Ronghua Liu; Yongyong Zhang; Yongqiang Zhang. Comparing Three Hydrological Models for Flash Flood Simulations in 13 Humid and Semi-humid Mountainous Catchments. Water Resources Management 2021, 35, 1547 -1571.
AMA StyleXiaoyan Zhai, Liang Guo, Ronghua Liu, Yongyong Zhang, Yongqiang Zhang. Comparing Three Hydrological Models for Flash Flood Simulations in 13 Humid and Semi-humid Mountainous Catchments. Water Resources Management. 2021; 35 (5):1547-1571.
Chicago/Turabian StyleXiaoyan Zhai; Liang Guo; Ronghua Liu; Yongyong Zhang; Yongqiang Zhang. 2021. "Comparing Three Hydrological Models for Flash Flood Simulations in 13 Humid and Semi-humid Mountainous Catchments." Water Resources Management 35, no. 5: 1547-1571.
Yuting Yang; Tim R. McVicar; Dawen Yang; Yongqiang Zhang; Shilong Piao; Shushi Peng; Hylke E. Beck. Supplementary material to "Low and contrasting impacts of vegetation CO2 fertilization on terrestrial runoff over the past three decades: Accounting for above- and below-ground vegetation-CO2 effects". 2020, 1 .
AMA StyleYuting Yang, Tim R. McVicar, Dawen Yang, Yongqiang Zhang, Shilong Piao, Shushi Peng, Hylke E. Beck. Supplementary material to "Low and contrasting impacts of vegetation CO2 fertilization on terrestrial runoff over the past three decades: Accounting for above- and below-ground vegetation-CO2 effects". . 2020; ():1.
Chicago/Turabian StyleYuting Yang; Tim R. McVicar; Dawen Yang; Yongqiang Zhang; Shilong Piao; Shushi Peng; Hylke E. Beck. 2020. "Supplementary material to "Low and contrasting impacts of vegetation CO2 fertilization on terrestrial runoff over the past three decades: Accounting for above- and below-ground vegetation-CO2 effects"." , no. : 1.
The influence of entropy values obtained under different multiscale permutation entropy scales on the complexity and unconstrained extreme value problem of traditional bionics algorithms was assessed. The multiscale permutation entropy (MSPE) eigenvalue calculation method based on the chaotic moth-flame optimization-based projection pursuit threat target evaluation (CMFO-PPTTE) model is proposed (CMFO-PPTTE-MSPE). The CMFO-PPTTE-MSPE not only solves the problems of rough calculation and information loss in traditional eigenvalue calculation but also improves the shortcomings of slow convergence and local optimality in the intelligent bionic algorithm. For this purpose, the monthly precipitation of 13 administrative regions in Heilongjiang Province under the Global Precipitation Climatology Centre dataset (GPCC) from 1967 to 2017 was evaluated to improve the precipitation complexity entropy accuracy. The main influencing factors were altitude (p < 0.05), water area (p < 0.05), urban construction area (p < 0.05) and forestland area (p < 0.01). Radial basis function (RBF) neural networks were used to forecast the precipitation in Heilongjiang Province over 36 months, and a better forecast was obtained. To verify the rationality of CMFO-PPTTE-MSPE under GPCC data, compared with the situ data, it is found that GPCC data can more accurately classify the complexity grade of Daxing’anling region. At the same time, the administrative discrimination of GPCC data (1.107) is significantly higher than the situ data (1.023). To verify the rationality of the CMFO-PPTTE-MSPE, the partial mean of MSPE (MSPE-PM), whale optimization algorithm PPTTE (WOA-PPTTE) and ω-particle swarm optimization PPTTE (ω-PSO-PPTTE) models were also used to calculate the MSPE eigenvalues under GPCC data. Comparisons and evaluations were performed after dividing the grade based on geographical discrimination (CMFO-PPTTE-MSPE (1.107) > WOA-PPTTE-MSPE (1.094) > W-PSO-PPTTE-MSPE (1.090) > MSPE-PM (1.063)) and administrative discrimination (CMFO-PPTTE-MSPE (1.146) > W-PSO-PPTTE-MSPE (1.002) > MSPE-PM (1.012) > WOA-PPTTE-MSPE (1.002)). The CMFO-PPTTE-MSPE had a significantly higher distinguishing capabilities than the other algorithms and thus could better distinguish the complexity of precipitation in different regions and geographic locations. In summary, the CMFO-PPTTE-MSPE is beneficial for analyzing precipitation complexity and represents a novel approach for mining the fine structural features of regional hydrological series. However, determining how to apply artificial intelligence algorithms to reasonably calibrate key parameters of the MSPE algorithm to further improve the accuracy of complexity diagnosis for hydrological series will be an important avenue of research.
Dong Liu; Tingqi Yan; Yi Ji; Qiang Fu; Mo Li; Muhammad Abrar Faiz; Shoaib Ali; Tianxiao Li; Song Cui; Muhammad Imran Khan. Novel method for measuring regional precipitation complexity characteristics based on multiscale permutation entropy combined with CMFO-PPTTE model. Journal of Hydrology 2020, 592, 125801 .
AMA StyleDong Liu, Tingqi Yan, Yi Ji, Qiang Fu, Mo Li, Muhammad Abrar Faiz, Shoaib Ali, Tianxiao Li, Song Cui, Muhammad Imran Khan. Novel method for measuring regional precipitation complexity characteristics based on multiscale permutation entropy combined with CMFO-PPTTE model. Journal of Hydrology. 2020; 592 ():125801.
Chicago/Turabian StyleDong Liu; Tingqi Yan; Yi Ji; Qiang Fu; Mo Li; Muhammad Abrar Faiz; Shoaib Ali; Tianxiao Li; Song Cui; Muhammad Imran Khan. 2020. "Novel method for measuring regional precipitation complexity characteristics based on multiscale permutation entropy combined with CMFO-PPTTE model." Journal of Hydrology 592, no. : 125801.
The Qin Mountains region is one of the most important climatic boundaries that divide the North and South of China. This study investigates vegetation covers changes across the Qin Mountains region over the past three decades based on the Landsat-derived Normalized Difference Vegetation Index (NDVI), which were extracted from the Google Earth Engine (GEE). Our results show that the NDVI across the Qin Mountains have increased from 0.624 to 0.776 with annual change rates of 0.0053/a over the past 32 years. Besides, its abrupt point occurred in 2006 and the change rates after this point increased by 0.0094/a (R2 = 0.8159, p < 0.01) (2006–2018), which is higher than that in 1987–1999 and 1999–2006. The mean NDVI have changed in different elevation ranges. The NDVI in the areas below 3300 m increased, such increased is especially most obviously in the cropland. Most of the forest and grassland locate above 3300 m with higher increased rate. Before 2006, the temperature and reference evapotranspiration (PET) were the important driven factors of NDVI change below 3300 m. After afforestation, human activities become important factors that influenced NDVI changes in the low elevation area, but hydro-climatic factors still play an important role in NDVI increase in the higher elevations area.
Chenlu Huang; Qinke Yang; Yuhan Guo; Yongqiang Zhang; Linan Guo. The pattern, change and driven factors of vegetation cover in the Qin Mountains region. Scientific Reports 2020, 10, 1 -11.
AMA StyleChenlu Huang, Qinke Yang, Yuhan Guo, Yongqiang Zhang, Linan Guo. The pattern, change and driven factors of vegetation cover in the Qin Mountains region. Scientific Reports. 2020; 10 (1):1-11.
Chicago/Turabian StyleChenlu Huang; Qinke Yang; Yuhan Guo; Yongqiang Zhang; Linan Guo. 2020. "The pattern, change and driven factors of vegetation cover in the Qin Mountains region." Scientific Reports 10, no. 1: 1-11.
This study aims to address a series of problems with agricultural irrigation water shortages and the poor efficiency of irrigation water use in severely colder irrigation areas in China. For this purpose, a support vector machine model based on the improved gray wolf optimization algorithm (IGWO-SVM) was proposed to improve the accuracy of the evaluation of the resilience of the water resource system in the irrigation areas. The results showed that the overall resilience of the selected irrigation areas was U-shaped from 2007 to 2016. From a spatial perspective, the results revealed that the resilience level of the western Songnen Plain irrigation area was less robust than that of the eastern Sanjiang Plain irrigation area. A comparison with the SVM model and SVM models optimized by the gray wolf optimization algorithm (GWO-SVM) and the gravity search algorithm (GSA-SVM) showed that the mean square error of the IGWO-SVM model was reduced by 7.69%, 12.19%, and 25%; the R2 was 0.33%, 1.11% and 2.73%; and the accuracy was 2.32%, 4.74% and 16.03%, respectively. The running time of IGWO-SVM was 278.42 s, 498.63 s faster than those of GWO-SVM and GSA-SVM on average, respectively. The improvement in the results suggested that the IGWO-SVM model was stable and could be used to evaluate water resource system resilience.
Dong Liu; Maoxun Li; Yi Ji; Qiang Fu; Mo Li; Muhammad Abrar Faiz; Shoaib Ali; Tianxiao Li; Song Cui; Muhammad Imran Khan. Spatial-temporal characteristics analysis of water resource system resilience in irrigation areas based on a support vector machine model optimized by the modified gray wolf algorithm. Journal of Hydrology 2020, 597, 125758 .
AMA StyleDong Liu, Maoxun Li, Yi Ji, Qiang Fu, Mo Li, Muhammad Abrar Faiz, Shoaib Ali, Tianxiao Li, Song Cui, Muhammad Imran Khan. Spatial-temporal characteristics analysis of water resource system resilience in irrigation areas based on a support vector machine model optimized by the modified gray wolf algorithm. Journal of Hydrology. 2020; 597 ():125758.
Chicago/Turabian StyleDong Liu; Maoxun Li; Yi Ji; Qiang Fu; Mo Li; Muhammad Abrar Faiz; Shoaib Ali; Tianxiao Li; Song Cui; Muhammad Imran Khan. 2020. "Spatial-temporal characteristics analysis of water resource system resilience in irrigation areas based on a support vector machine model optimized by the modified gray wolf algorithm." Journal of Hydrology 597, no. : 125758.
Runoff prediction in ungauged and scarcely gauged catchments is a key research field in surface water hydrology. There have been numerous studies before and since the launch of the predictions in ungauged basins (PUB) initiative by the International Association of Hydrological Sciences in 2003. This study critically reviews and assesses the decadal progress in the regionalization of hydrological modeling, which is the major tool for PUB, from 2000 to 2019. This paper found that the journal publications have noticeably increased in terms of PUB in the past 7 years, and research countries have been expanded dramatically since 2013. The regionalization methods are grouped into three categories including similarity‐based, regression‐based, and hydrological signature‐based. There are more detailed researches focusing on the interdisciplinary and profound improvement of each regionalization method. Namely, tremendous efforts have been made and lots of improvements have been carried out in the parameterization domain for the post‐PUB period. However, there is still plenty of room to improve the prediction capability in data‐sparse regions (e.g., further verification and proof of multi‐modeling adaptation and uncertainties description). This paper also discusses possible research directions in the future, including PUB in a changing environment and better utilization of multi‐source remote‐sensing information. This article is categorized under: Science of Water > Science of Water
Yuhan Guo; Yongqiang Zhang; Lu Zhang; ZhongGen Wang. Regionalization of hydrological modeling for predicting streamflow in ungauged catchments: A comprehensive review. WIREs Water 2020, 8, 1 .
AMA StyleYuhan Guo, Yongqiang Zhang, Lu Zhang, ZhongGen Wang. Regionalization of hydrological modeling for predicting streamflow in ungauged catchments: A comprehensive review. WIREs Water. 2020; 8 (1):1.
Chicago/Turabian StyleYuhan Guo; Yongqiang Zhang; Lu Zhang; ZhongGen Wang. 2020. "Regionalization of hydrological modeling for predicting streamflow in ungauged catchments: A comprehensive review." WIREs Water 8, no. 1: 1.
Evaluation and quantification of possible sources of uncertainty and their influence on water resource planning and extreme management is very important for risk modeling and extreme hydrological management. The main objective of this research work is to combine statistical climate ensembles, multiple parameter sets for three conceptual hydrological model structure and five flood frequency distribution models to investigate the interplay among the associated uncertainty in flood and low flow modelling. Uncertainty in the modeling of extreme high flow frequency mainly comes from the quality of the input data, while in the modeling of low flow frequency, the main contributor to the total uncertainty is from model parameterization. This result is also confirmed by using the Analysis Of Variance Analysis (ANOVA) that considers additional information about the interaction impact of the main factors. The total uncertainty of QT90 (extreme peak flow quantile at 90-year return period) quantile shows the interaction of input data and extreme frequency models has significant influence on the total uncertainty. In contrast, in the QT10 (extreme low flow quantile at 10-year return period) estimation, the hydrological models and hydrological parameters have significant impact on the total uncertainty. This implies that the four factors and their interactions may cause significant risk in water resource management and flood and drought risk management, and neglecting of these four factors and their interaction in disaster risk management, water resource planning and evaluation of environmental impact assessment is not feasible and may lead to big risk.
Hadush Kidane Meresa; Yongqiang Zhang. Contrasting uncertainties in estimating floods and extreme low flow. 2020, 1 .
AMA StyleHadush Kidane Meresa, Yongqiang Zhang. Contrasting uncertainties in estimating floods and extreme low flow. . 2020; ():1.
Chicago/Turabian StyleHadush Kidane Meresa; Yongqiang Zhang. 2020. "Contrasting uncertainties in estimating floods and extreme low flow." , no. : 1.
Continuous Contour Trench (CCT) is usually used in semi-arid watersheds to conserve water and reduce soil erosion. There is still a lack of understanding of how the CCT influences hydrological processes at a watershed scale. Therefore, CCT is used with arbitrary dimensions. However, standardisation of CCT dimensions may improve its performance from the user perspective. Therefore, this study investigated the mechanism of standardised CCT in influencing hydrological behaviour in two paired semi-arid micro-watersheds (treated with the CCT system versus untreated) by using a physically-based distributed hydrological modelling system MIKE SHE. The physically-based model performed satisfactorily in simulating surface runoff, groundwater levels and soil moisture during both calibration and validation periods. Subsequently, a stand-alone software, namely CCT_designer, was developed to make the standardisation technique user friendly. The CCT system was found to be useful in soil and water conservation in the treated micro-watershed, leading to considerably higher groundwater recharge and evapotranspiration (ET) than in the untreated micro-watershed. The MIKE SHE simulations showed that the CCT system resulted in 87.6 % average reduction in surface runoff, 42.4 % average increase in groundwater recharge, and 27 % average increase in the plant ET in comparison to the untreated micro-watershed. Using CCT_designer, the CCT depths for Ber, Custard apple and Anola are estimated as 0.27, 0.21 and 0.25 m, respectively, which showed a considerable reduction in the arbitrarily selected value of 0.30 m. Thus, CCT_designer may be a useful tool for soil and water management.
Mahendra Nagdeve; Pranesh Kumar Paul; Yongqiang Zhang; Rajendra Singh. Continuous Contour Trench (CCT): Understandings of hydrological processes after standardisation of dimensions and development of a user-friendly software. Soil and Tillage Research 2020, 205, 104792 .
AMA StyleMahendra Nagdeve, Pranesh Kumar Paul, Yongqiang Zhang, Rajendra Singh. Continuous Contour Trench (CCT): Understandings of hydrological processes after standardisation of dimensions and development of a user-friendly software. Soil and Tillage Research. 2020; 205 ():104792.
Chicago/Turabian StyleMahendra Nagdeve; Pranesh Kumar Paul; Yongqiang Zhang; Rajendra Singh. 2020. "Continuous Contour Trench (CCT): Understandings of hydrological processes after standardisation of dimensions and development of a user-friendly software." Soil and Tillage Research 205, no. : 104792.
Mapping floods is important for policy makers to make timely decisions in regards to emergency responses and future planning. It is therefore crucial to develop a rapid inundation modelling framework to map flood inundation. This study develops an airborne scanning laser altimetry (LiDAR) digital elevation model (DEM) based Rapid flood Inundation Modelling framework (LiDAR-RIM) for assessment of inundation extent, depth, volume and duration for flood events. The modelling framework has been applied to the mid-Murrumbidgee region in the southeast Murray-Darling Basin, Australia for two flood events occurred in December 2010 and March 2012. The inundation extents estimated using this methodology compared well to those obtained from two Landsat ETM+ images, demonstrating suitability and applicability of this method. For testing possibility of larger area application, the framework also uses 30-m resolution shuttle radar topography mission (SRTM)-DEM to replace LiDAR-DEM for the same modelling. The inundation extents obtained by using the SRTM-DEM are smaller than those obtained using the LiDAR-DEM, especially for large flood events. A possible reason is that the river cross sections obtained from the SRTM-DEM are not accurate enough for inundation modelling. The LiDAR-RIM has an advantage for process modelling and scenario modelling under future climatic conditions.
Yongqiang Zhang. Using LiDAR-DEM based rapid flood inundation modelling framework to map floodplain inundation extent and depth. Journal of Geographical Sciences 2020, 30, 1649 -1663.
AMA StyleYongqiang Zhang. Using LiDAR-DEM based rapid flood inundation modelling framework to map floodplain inundation extent and depth. Journal of Geographical Sciences. 2020; 30 (10):1649-1663.
Chicago/Turabian StyleYongqiang Zhang. 2020. "Using LiDAR-DEM based rapid flood inundation modelling framework to map floodplain inundation extent and depth." Journal of Geographical Sciences 30, no. 10: 1649-1663.
Accurate understanding of snow cover phenology and its changes is important to hydrological processes and climate system. Having recognized the potential uncertainties in remote sensing snow cover products, we used daily snow depth observations from 514 meteorological stations across China to investigate the spatiotemporal variations in snow cover phenology during 1970–2014. Climatologically, the snow cover onset date (Do) and end date (De) as well as the number of snow cover days (Ds) depended on latitude at most stations outside of the Tibetan Plateau (TP). For the high-elevation stations, which were mainly in the TP, multiple snow-free breaks (SFBs) during the cold season made Ds insensitive to Do and De. Furthermore, the number of SFBs (Db) increased significantly with the rise in elevation, explaining why higher altitudes in TP did not necessarily have greater Ds values despite the earlier Do and later De values. From 1970 to 2014, most stations in China exhibited delayed Do and advanced De due mainly to the increased temperature, but such trends were significant at only 10.5% and 15.4% of the stations, respectively. During the same period, shortened Ds primarily occurred south of ~ 40° N, whereas the opposite ones dominated north of ~ 40° N. Most stations (except those in Hexi Corridor) with significant growth in Ds were characterized by delayed Do and advanced De. Such a phenomenon of “increased snow cover days during shortened cold season” was due to the significant shrinkage in Db values. The spatial pattern of the trends in annual total snow depth overall follows that of Ds, suggesting that the Ds, when takes SFBs into consideration, could be an indicator of variations of snow water resources in China. The trends in Do, De and Ds were not elevation dependent in TP.
Ning Ma; Kunlun Yu; Yinsheng Zhang; Jianqing Zhai; Yongqiang Zhang; Hongbo Zhang. Ground observed climatology and trend in snow cover phenology across China with consideration of snow-free breaks. Climate Dynamics 2020, 55, 1 -21.
AMA StyleNing Ma, Kunlun Yu, Yinsheng Zhang, Jianqing Zhai, Yongqiang Zhang, Hongbo Zhang. Ground observed climatology and trend in snow cover phenology across China with consideration of snow-free breaks. Climate Dynamics. 2020; 55 (9-10):1-21.
Chicago/Turabian StyleNing Ma; Kunlun Yu; Yinsheng Zhang; Jianqing Zhai; Yongqiang Zhang; Hongbo Zhang. 2020. "Ground observed climatology and trend in snow cover phenology across China with consideration of snow-free breaks." Climate Dynamics 55, no. 9-10: 1-21.
Because remote sensing (RS) data are spatially and temporally explicit and available across the globe, they have the potential to be used for predicting runoff in ungauged catchments and poorly gauged regions, a challenging area of research in hydrology. There is potential to use remotely sensed data for calibrating hydrological models in regions with limited streamflow gauges. This study conducts a comprehensive investigation on how to incorporate gridded remotely sensed evapotranspiration (AET) and water storage data for constraining hydrological model calibration in order to predict daily and monthly runoff in 30 catchments in the Yalong River basin in China. To this end, seven RS data calibration schemes are explored, and compared to direct calibration against observed runoff and traditional regionalization using spatial proximity to predict runoff in ungauged catchments. The results show that using bias-corrected remotely sensed AET (bias-corrected PML-AET data) for constraining model calibration performs much better than using the raw remotely sensed AET data (non-bias-corrected AET obtained from PML model estimate). Using the bias-corrected PML-AET data in a gridded way is much better than using lumped data, and outperforms the traditional regionalization approach especially in headwater and large catchments. Combining the bias-corrected PML-AET and GRACE water storage data performs similarly to using the bias-corrected PML-AET data only. This study demonstrates that there is great potential in using bias-corrected RS-AET data to calibrating hydrological models (without the need for gauged streamflow data) to estimate daily and monthly runoff time series in ungauged catchments and sparsely gauged regions.
Qi Huang; Guanghua Qin; Yongqiang Zhang; Qiuhong Tang; Changming Liu; Jun Xia; Francis Hock Soon Chiew; David A. Post. Using Remote Sensing Data-based Hydrological Model Calibrations for Predicting Runoff in Ungauged or Poorly Gauged Catchments. 2020, 1 .
AMA StyleQi Huang, Guanghua Qin, Yongqiang Zhang, Qiuhong Tang, Changming Liu, Jun Xia, Francis Hock Soon Chiew, David A. Post. Using Remote Sensing Data-based Hydrological Model Calibrations for Predicting Runoff in Ungauged or Poorly Gauged Catchments. . 2020; ():1.
Chicago/Turabian StyleQi Huang; Guanghua Qin; Yongqiang Zhang; Qiuhong Tang; Changming Liu; Jun Xia; Francis Hock Soon Chiew; David A. Post. 2020. "Using Remote Sensing Data-based Hydrological Model Calibrations for Predicting Runoff in Ungauged or Poorly Gauged Catchments." , no. : 1.