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El Niño‐Southern Oscillation (ENSO) is an important driver of interannual climate variability with increasing attention for its impacts on water and flood management. The impact of ENSO on basin‐scale floods during the TRMM period (1998–2013) is examined by using the streamflow outputs from the Dominant river Routing Integrated with VIC Environment model (DRIVE). Significant simultaneous correlations between flood indices and Niño 3.4 appear in many flood‐prone river basins during peak flood months across both the tropics and midlatitudes especially for flood frequency and flood duration. Gauged by significant lag‐correlations between floods and Niño 3.4, significant ENSO‐leading‐floods relations are found as well in many river basins in South America, south and southeastern Asia, and northern Africa. These ENSO‐floods‐relations can greatly enhance understanding of physical mechanisms relevant to the ENSO impact and may also improve the skills of basin‐scale monthly‐to‐seasonal flood forecast, thus allowing for better preparedness and management of flood risks.
Yan Yan; Huan Wu; Guojun Gu; Philip J. Ward; Lifeng Luo; Xiaomeng Li; Zhijun Huang; Jing Tao. Exploring the ENSO Impact on Basin‐Scale Floods Using Hydrological Simulations and TRMM Precipitation. Geophysical Research Letters 2020, 47, 1 .
AMA StyleYan Yan, Huan Wu, Guojun Gu, Philip J. Ward, Lifeng Luo, Xiaomeng Li, Zhijun Huang, Jing Tao. Exploring the ENSO Impact on Basin‐Scale Floods Using Hydrological Simulations and TRMM Precipitation. Geophysical Research Letters. 2020; 47 (22):1.
Chicago/Turabian StyleYan Yan; Huan Wu; Guojun Gu; Philip J. Ward; Lifeng Luo; Xiaomeng Li; Zhijun Huang; Jing Tao. 2020. "Exploring the ENSO Impact on Basin‐Scale Floods Using Hydrological Simulations and TRMM Precipitation." Geophysical Research Letters 47, no. 22: 1.
Seasonal forecasts from dynamical models are expected to be useful for drought predictions in many regions. This study investigated the usefulness of the Climate Forecast System version 2 (CFSv2) in improving meteorological drought prediction in China based on its 25-year reforecast. The six-month standard precipitation index (SPI6) was used as the drought indicator, and its persistence forecast served as the benchmark against which CFSv2 forecasts were evaluated. The analysis found that the SPI6 persistence forecast shows good skills in all regions at short lead times, and CFSv2 forecast can further improve those skills in most regions. The improvement is particularly pronounced at longer lead times and over the humid regions in the southeast. This study also examined the seasonality and regionality of persistence forecast skills and CFSv2 contributions, and reveals regions where CFSv2 forecast shows no or sometimes even negative contributions.
Yang Lang; Lifeng Luo; Aizhong Ye; Qingyun Duan. Do CFSv2 Seasonal Forecasts Help Improve the Forecast of Meteorological Drought over Mainland China? Water 2020, 12, 2010 .
AMA StyleYang Lang, Lifeng Luo, Aizhong Ye, Qingyun Duan. Do CFSv2 Seasonal Forecasts Help Improve the Forecast of Meteorological Drought over Mainland China? Water. 2020; 12 (7):2010.
Chicago/Turabian StyleYang Lang; Lifeng Luo; Aizhong Ye; Qingyun Duan. 2020. "Do CFSv2 Seasonal Forecasts Help Improve the Forecast of Meteorological Drought over Mainland China?" Water 12, no. 7: 2010.
It is critical important to accurately evaluate hydrological drought evolutions based on a composite index for the sustainable development of water resources. In this study, a Nonlinear Joint Hydrological Drought Index (NJHDI) is constructed by combining surface water and groundwater to better reflect the multivariate phenomenon in characterizing hydrological drought. The bi-dimensional copula function is applied to describe the complicated and nonlinear relationship between surface water and groundwater due to its dependence structure and flexibility. To reflect the changes in underlying surface, the time-varying parameters are applied in the Soil and Water Assessment Tool (SWAT) model to simulate the surface water and groundwater more accurately. Based on the NJHDI, the spatio-temporal hydrological drought evolutions as well as their potential influencing factors are analyzed in a case study of the Yellow River Basin (YRB). Results indicate that: 1) compared to the single hydrological index, the NJHDI is more sensitive and effective to capture historically droughts; 2) hydrological droughts became increasingly serious during the last few decades, especially in parts of the basin located in the Loess Plateau areas where the ecosystem is extremely fragile; 3) climate and LULC changes have both contributed to the changes in hydrological droughts but their contributions vary significantly spatially. In general, the NJHDI-based hydrological drought evolution can provide more reliable information in hydrological drought decision-making owing to considering changes in underlying land surface, and combining the surface water and groundwater with nonlinear combination method.
Yunyun Li; Lifeng Luo; Jianxia Chang; Yimin Wang; Aijun Guo; Jingjing Fan; Quan Liu. Hydrological drought evolution with a nonlinear joint index in regions with significant changes in underlying surface. Journal of Hydrology 2020, 585, 124794 .
AMA StyleYunyun Li, Lifeng Luo, Jianxia Chang, Yimin Wang, Aijun Guo, Jingjing Fan, Quan Liu. Hydrological drought evolution with a nonlinear joint index in regions with significant changes in underlying surface. Journal of Hydrology. 2020; 585 ():124794.
Chicago/Turabian StyleYunyun Li; Lifeng Luo; Jianxia Chang; Yimin Wang; Aijun Guo; Jingjing Fan; Quan Liu. 2020. "Hydrological drought evolution with a nonlinear joint index in regions with significant changes in underlying surface." Journal of Hydrology 585, no. : 124794.
The climatology of shallow foehn, also known locally as elevated south-easterly gales (ESEGs), on the northern lee side of the central Tianshan Mountains is analysed using radiosonde data recorded over a period of 10 years at Urumqi, the capital of Xinjiang Uygur Autonomous Region in north-western China. ESEGs are a frequent weather phenomenon in the region, occurring on average 128.5 days/yr and most often in winter (47.0 days), particularly January (17.5 days), and least in summer (18 days). The vertical ESEG structures also undergo seasonal variations, with the height of the maximum ESEG wind and the base and top of the ESEG layer more elevated in the warm half (late spring through early fall) of the year than the cold half. However, the monthly mean maximum wind speeds, which vary around 10 m/s, appear to be independent of season. A typical synoptic pattern accompanying ESEGs is characterized by opposite air masses to the east (cold) and west (warm) of Xinjiang. A strong low-level west-east pressure gradient helps push cold air to enter Xinjiang from the east, which subsequently induces a north-south pressure gradient across the Tianshan Mountains. This regional pressure gradient combined with terrain channelling leads to the development of ESEGs. A comparison of boundary layer structure between days with/without ESEGs in winter shows that the ESEG days are accompanied by deeper (average of 900 m) and more intense (average 6 °C) inversion, but there is little difference in the cold air pool Froude Number between the two types of days.
Xia Li; Xiangao Xia; Shiyuan Zhong; Lifeng Luo; Xiaojing Yu; Jian Jia; Keming Zhao; Na Li; Yan Liu; Quan Ren. Shallow foehn on the northern leeside of Tianshan Mountains and its influence on atmospheric boundary layer over Urumqi, China — A climatological study. Atmospheric Research 2020, 240, 104940 .
AMA StyleXia Li, Xiangao Xia, Shiyuan Zhong, Lifeng Luo, Xiaojing Yu, Jian Jia, Keming Zhao, Na Li, Yan Liu, Quan Ren. Shallow foehn on the northern leeside of Tianshan Mountains and its influence on atmospheric boundary layer over Urumqi, China — A climatological study. Atmospheric Research. 2020; 240 ():104940.
Chicago/Turabian StyleXia Li; Xiangao Xia; Shiyuan Zhong; Lifeng Luo; Xiaojing Yu; Jian Jia; Keming Zhao; Na Li; Yan Liu; Quan Ren. 2020. "Shallow foehn on the northern leeside of Tianshan Mountains and its influence on atmospheric boundary layer over Urumqi, China — A climatological study." Atmospheric Research 240, no. : 104940.
Convolutional methods are useful for modeling geospatial data as they enable the extraction of broad-scale spatial patterns from the local attributes observed at each location. The weighted aggregation performed by the convolutional operator also helps to smoothen the noisy data collected at the given locations. However, current convolutional methods are primarily designed to learn the spatial dependencies of the input (predictor) variables only. The recent success in applying multi-task learning to various geospatial prediction problems shows that the model parameters themselves may also be spatially related. This suggests the possibility of employing convolutional methods to learn the spatial dependencies among the model parameters at different locations, especially in situations where there are limited training data available to fit accurate local models. In this paper, we investigate three different ways to incorporate convolutions into geospatial prediction models—convolutions on the predictors, model parameters, or a hybrid of both. We provide guidance on when convolution of each type can be fruitfully applied and verify their effectiveness using both synthetic and real-world datasets.
Tyler Wilson; Pang-Ning Tan; Lifeng Luo. Convolutional Methods for Predictive Modeling of Geospatial Data. Proceedings of the 2020 SIAM International Conference on Data Mining 2020, 28 -36.
AMA StyleTyler Wilson, Pang-Ning Tan, Lifeng Luo. Convolutional Methods for Predictive Modeling of Geospatial Data. Proceedings of the 2020 SIAM International Conference on Data Mining. 2020; ():28-36.
Chicago/Turabian StyleTyler Wilson; Pang-Ning Tan; Lifeng Luo. 2020. "Convolutional Methods for Predictive Modeling of Geospatial Data." Proceedings of the 2020 SIAM International Conference on Data Mining , no. : 28-36.
A new data assimilation technique, unscented weighted ensemble Kalman filter (UWEnKF) was developed based on the scaled unscented transformation and ensemble Kalman filter (EnKF). In UWEnKF, the individual members selected are unequally weighted and symmetric about the expectation. To investigate the performance of UWEnKF, nine assimilation experiments with different ensemble sizes (161, 1601, 16001) and different assimilation frequencies (every 6 h, every 12 h, every 24 h) were designed to assimilate soil surface (5 cm) moisture data observed at station HY in the upper reaches of the Yellow River, in the northeastern of Tibetan plateau, China into the Richards equation. The results showed that the performance of the filter was greatly affected by random noise, and the filter was sensitive to ensemble size and assimilation frequency. Increasing the ensemble size reduced the effects of random noise on filter performance in several independent assimilation runs (i.e., it decreased the differences between the results of the several independent assimilation runs). Reducing the assimilation frequency also reduced the effects of random noise on filter performance. UWEnKF gave more accurate soil moisture model results than EnKF for all ensemble sizes and assimilation frequencies at all soil depths. Additionally, EnKF may have different performances according to different initial conditions, but not for UWEnKF. Precipitation and soil properties uncertainties had some impact on filter performance. Thus, UWEnKF is a better choice than EnKF, while it is more computationally demanding, for improving soil moisture predictions by assimilating data from many sources, such as satellite-observed soil moisture data, at a low assimilation frequency (e.g., every 24 h).
Xiaolei Fu; Zhongbo Yu; Yongjian Ding; Yu Qin; Lifeng Luo; Chuancheng Zhao; Haishen Lü; Xiaolei Jiang; Qin Ju; Chuanguo Yang. Unscented weighted ensemble Kalman filter for soil moisture assimilation. Journal of Hydrology 2019, 580, 124352 .
AMA StyleXiaolei Fu, Zhongbo Yu, Yongjian Ding, Yu Qin, Lifeng Luo, Chuancheng Zhao, Haishen Lü, Xiaolei Jiang, Qin Ju, Chuanguo Yang. Unscented weighted ensemble Kalman filter for soil moisture assimilation. Journal of Hydrology. 2019; 580 ():124352.
Chicago/Turabian StyleXiaolei Fu; Zhongbo Yu; Yongjian Ding; Yu Qin; Lifeng Luo; Chuancheng Zhao; Haishen Lü; Xiaolei Jiang; Qin Ju; Chuanguo Yang. 2019. "Unscented weighted ensemble Kalman filter for soil moisture assimilation." Journal of Hydrology 580, no. : 124352.
Huan Wu; John S. Kimball; Naijun Zhou; Lorenzo Alfieri; Lifeng Luo; Jinyang Du; Zhijun Huang. Evaluation of real-time global flood modeling with satellite surface inundation observations from SMAP. Remote Sensing of Environment 2019, 233, 1 .
AMA StyleHuan Wu, John S. Kimball, Naijun Zhou, Lorenzo Alfieri, Lifeng Luo, Jinyang Du, Zhijun Huang. Evaluation of real-time global flood modeling with satellite surface inundation observations from SMAP. Remote Sensing of Environment. 2019; 233 ():1.
Chicago/Turabian StyleHuan Wu; John S. Kimball; Naijun Zhou; Lorenzo Alfieri; Lifeng Luo; Jinyang Du; Zhijun Huang. 2019. "Evaluation of real-time global flood modeling with satellite surface inundation observations from SMAP." Remote Sensing of Environment 233, no. : 1.
Multi-step prediction of sea surface temperature (SST) is a challenging problem because small errors in its shortrange forecasts can be compounded to create large errors at longer ranges. In this paper, we propose a hierarchical LSTM framework to improve the accuracy for long-term SST prediction. Our framework alleviates the error accumulation problem in multi-step prediction by leveraging outputs from an ensemble of physically-based dynamical models. Unlike previous methods, which simply take a linear combination of the outputs to produce a single deterministic forecast, our framework learns a nonlinear relationship among the ensemble member forecasts. In addition, its multi-level structure is designed to capture the temporal autocorrelation between forecasts generated for the same lead time as well as those generated for different lead times. Experiments performed using SST data from the tropical Pacific ocean region show that the proposed framework outperforms various baseline methods in more than 70% of the grid cells located in the study region.
Xi Liu; Tyler Wilson; Pang-Ning Tan; Lifeng Luo. Hierarchical LSTM Framework for Long-Term Sea Surface Temperature Forecasting. 2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA) 2019, 41 -50.
AMA StyleXi Liu, Tyler Wilson, Pang-Ning Tan, Lifeng Luo. Hierarchical LSTM Framework for Long-Term Sea Surface Temperature Forecasting. 2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA). 2019; ():41-50.
Chicago/Turabian StyleXi Liu; Tyler Wilson; Pang-Ning Tan; Lifeng Luo. 2019. "Hierarchical LSTM Framework for Long-Term Sea Surface Temperature Forecasting." 2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA) , no. : 41-50.
Low‐level jets (LLJs) are relatively fast‐moving streams of air that form in the lower troposphere and are a common phenomenon across the Great Plains (GP) of the United States. LLJs play an important role in moisture transport and the development of nocturnal convection in the spring and summer. Alterations to surface moisture and energy fluxes can influence the planetary boundary layer (PBL) development and thus LLJs. One important anthropogenic process that has been shown to affect the surface energy budget is irrigation. In this study, we investigate the effects of irrigation on LLJ development across the GP by incorporating a dynamic and realistic irrigation scheme into the Weather Research and Forecasting (WRF) model. WRF simulations were conducted with and without the irrigation scheme for the exceptionally dry summer of 2012 over the GP. The results show irrigation‐introduced changes to LLJ features both over and downstream of the most heavily irrigated regions in the GP. There were statistically significant increases to LLJ speeds in the simulation with the irrigation parameterization. Decreases to the mean jet core height on the order of 50 m during the overnight hours were also simulated when irrigation was on. The overall frequency of jet occurrences increased over the irrigated regions by 5–10%; however, these differences were not statistically significant. These changes were weaker than those reported in earlier studies based on simple representations of irrigation that unrealistically saturate the soil columns over large areas over a long period of time, which highlights the importance and necessity to represent human activity more accurately in modeling studies.
S. Arcand; L. Luo; S. Zhong; L. Pei; X. Bian; J.A. Winkler. Modeled changes to the Great Plains low‐level jet under a realistic irrigation application. Atmospheric Science Letters 2019, 20, e888 .
AMA StyleS. Arcand, L. Luo, S. Zhong, L. Pei, X. Bian, J.A. Winkler. Modeled changes to the Great Plains low‐level jet under a realistic irrigation application. Atmospheric Science Letters. 2019; 20 (3):e888.
Chicago/Turabian StyleS. Arcand; L. Luo; S. Zhong; L. Pei; X. Bian; J.A. Winkler. 2019. "Modeled changes to the Great Plains low‐level jet under a realistic irrigation application." Atmospheric Science Letters 20, no. 3: e888.
Drought is a slowly developing process and usually begins to impact a region without much warning once the water deficit reaches a certain threshold. Predicting the drought a few months in advance will benefit a variety of sectors for drought planning and preparedness. In response to the National Integrated Drought Information System (NIDIS), the Princeton land surface hydrology group has been working on drought monitoring and forecasting for over 10 years and has developed a seasonal drought forecasting system based on global climate forecast models and a large-scale land surface hydrology model. This chapter will showcase the performances of the system in predicting soil moisture drought area, frequency, and severity over the Conterminous United States (CONUS) at seasonal scales; discuss about the challenges in forecasting streamflow for hydrologic drought; and provide an outlook for future developments and applications.
Eric F. Wood; Xing Yuan; Joshua K. Roundy; Ming Pan; Lifeng Luo. Seasonal Drought Forecasting on the Example of the USA. Handbook of Hydrometeorological Ensemble Forecasting 2019, 1279 -1287.
AMA StyleEric F. Wood, Xing Yuan, Joshua K. Roundy, Ming Pan, Lifeng Luo. Seasonal Drought Forecasting on the Example of the USA. Handbook of Hydrometeorological Ensemble Forecasting. 2019; ():1279-1287.
Chicago/Turabian StyleEric F. Wood; Xing Yuan; Joshua K. Roundy; Ming Pan; Lifeng Luo. 2019. "Seasonal Drought Forecasting on the Example of the USA." Handbook of Hydrometeorological Ensemble Forecasting , no. : 1279-1287.
Meteorological and hydrological droughts can bring different socioeconomic impacts. In this study, we investigated meteorological and hydrological drought characteristics and propagation using the standardized precipitation index (SPI) and standardized streamflow index (SSI), over the upstream and midstream of the Heihe River basin (UHRB and MHRB, respectively). The correlation analysis and cross-wavelet transform were adopted to explore the relationship between meteorological and hydrological droughts in the basin. Three modeling experiments were performed to quantitatively understand how climate change and human activities influence hydrological drought and propagation. Results showed that meteorological drought characteristics presented little difference between UHRB and MHRB, while hydrological drought events are more frequent in the MHRB. In the UHRB, there were positive relationships between meteorological and hydrological droughts, whereas drought events became less frequent but longer when meteorological drought propagated into hydrological drought. Human activities have obviously changed the positive correlation to negative in the MHRB, especially during warm and irrigation seasons. The propagation time varied with seasonal climate characteristics and human activities, showing shorter values due to higher evapotranspiration, reservoir filling, and irrigation. Quantitative evaluation showed that climate change was inclined to increase streamflow and propagation time, contributing from −57% to 63%. However, more hydrological droughts and shorter propagation time were detected in the MHRB because human activities play a dominant role in water consumption with contribution rate greater than (−)89%. This study provides a basis for understanding the mechanism of hydrological drought and for the development of improved hydrological drought warning and forecasting system in the HRB.
Feng Ma; Lifeng Luo; Aizhong Ye; Qingyun Duan. Drought Characteristics and Propagation in the Semiarid Heihe River Basin in Northwestern China. Journal of Hydrometeorology 2019, 20, 59 -77.
AMA StyleFeng Ma, Lifeng Luo, Aizhong Ye, Qingyun Duan. Drought Characteristics and Propagation in the Semiarid Heihe River Basin in Northwestern China. Journal of Hydrometeorology. 2019; 20 (1):59-77.
Chicago/Turabian StyleFeng Ma; Lifeng Luo; Aizhong Ye; Qingyun Duan. 2019. "Drought Characteristics and Propagation in the Semiarid Heihe River Basin in Northwestern China." Journal of Hydrometeorology 20, no. 1: 59-77.
Endorheic and arid regions around the world are suffering from serious drought problems. In this study, a drought forecasting system based on eight state-of-the-art climate models from the North American Multi-Model Ensemble (NMME) and a Distributed Time-Variant Gain Hydrological Model (DTVGM) was established and assessed over the upstream and midstream of Heihe River basin (UHRB and MHRB), a typical arid endorheic basin. The 3-month Standardized Precipitation Index (SPI3) and 1-month Standardized Streamflow Index (SSI1) were used to capture meteorological and hydrological drought, and values below −1 indicate drought events. The skill of the forecasting systems was evaluated in terms of anomaly correlation (AC) and Brier score (BS) or Brier skill score (BSS). The predictability for meteorological drought was quantified using AC and BS with a “perfect model” assumption, referring to the upper limit of forecast skill. The hydrological predictability was to distinguish the role of initial hydrological conditions (ICs) and meteorological forcings, which was quantified by root-mean-square error (RMSE) within the ESP (Ensemble Streamflow Prediction) and reverse ESP framework. The UHRB and MHRB showed season-dependent meteorological drought predictability and forecast skill, with higher values during winter and autumn than that during spring. For hydrological forecasts, the forecast skill in the UHRB was higher than that in MHRB. Predicting meteorological droughts more than 2 months in advance became difficult because of complex climate mechanisms. However, the hydrological drought forecasts could show some skills up to 3–6 lead months due to memory of ICs during cold and dry seasons. During wet seasons, there are no skillful hydrological predictions from lead month 2 onwards because of the dominant role of meteorological forcings. During spring, the improvement of hydrological drought predictions was the most significant as more streamflow was generated by seasonal snowmelt. Besides meteorological forcings and ICs, human activities have reduced the hydrological variability and increased hydrological drought predictability during the wet seasons in the MHRB.
Feng Ma; Lifeng Luo; Aizhong Ye; Qingyun Duan. Seasonal drought predictability and forecast skill in the semi-arid endorheic Heihe River basin in northwestern China. Hydrology and Earth System Sciences 2018, 22, 5697 -5709.
AMA StyleFeng Ma, Lifeng Luo, Aizhong Ye, Qingyun Duan. Seasonal drought predictability and forecast skill in the semi-arid endorheic Heihe River basin in northwestern China. Hydrology and Earth System Sciences. 2018; 22 (11):5697-5709.
Chicago/Turabian StyleFeng Ma; Lifeng Luo; Aizhong Ye; Qingyun Duan. 2018. "Seasonal drought predictability and forecast skill in the semi-arid endorheic Heihe River basin in northwestern China." Hydrology and Earth System Sciences 22, no. 11: 5697-5709.
For many spatio-temporal applications, building regression models that can reproduce the true data distribution is often as important as building models with high prediction accuracy. For example, knowing the future distribution of daily temperature and precipitation can help scientists determine their long-term trends and assess their potential impact on human and natural systems. As conventional methods are designed to minimize residual errors, the shape of their predicted distribution may not be consistent with their actual distribution. To overcome this challenge, this paper presents a novel, distribution-preserving multi-task learning framework for multi-location prediction of spatio-temporal data. The framework employs a non-parametric density estimation approach with L2-distance to measure the divergence between the predicted and true distribution of the data. Experimental results using climate data from more than 1500 weather stations in the United States show that the proposed framework reduces the distribution error for more than 78% of the stations without degrading the prediction accuracy significantly.
Xi Liu; Pang-Ning Tan; Zubin Abraham; Lifeng Luo; Pouyan Hatami. Distribution Preserving Multi-task Regression for Spatio-Temporal Data. 2018 IEEE International Conference on Data Mining (ICDM) 2018, 1134 -1139.
AMA StyleXi Liu, Pang-Ning Tan, Zubin Abraham, Lifeng Luo, Pouyan Hatami. Distribution Preserving Multi-task Regression for Spatio-Temporal Data. 2018 IEEE International Conference on Data Mining (ICDM). 2018; ():1134-1139.
Chicago/Turabian StyleXi Liu; Pang-Ning Tan; Zubin Abraham; Lifeng Luo; Pouyan Hatami. 2018. "Distribution Preserving Multi-task Regression for Spatio-Temporal Data." 2018 IEEE International Conference on Data Mining (ICDM) , no. : 1134-1139.
Endorheic and arid regions around the world are suffering from serious drought problems. In this study, a drought forecasting system based on eight state-of-the-art climate models from North American Multi-Model Ensemble (NMME) and a Distributed Time-Variant Gain Hydrological Model (DTVGM) was established and assessed over the upstream and midstream of Heihe River basin (UHRB and MHRB), a typical arid endorheic basin. The 3-month Standardized Precipitation Index (SPI3) and 1-month Standardized Streamflow Index (SSI1) were used to capture meteorological and hydrological drought, and values below -1 indicate drought events. The skill of the forecasting systems was evaluated in terms of Anomaly Correlation (AC) and Brier skill score (BSS). The UHRB and MHRB showed season-dependent meteorological drought predictability and forecast skill, with higher values during winter and autumn than that during spring. For hydrological forecasts, the forecast skill in the UHRB was higher than that in MHRB. Predicting meteorological droughts more than 2 months in advance became difficult because of complex climate mechanism. However, the hydrological drought forecasts could show some skills up to 3–6 lead months due to memory of initial hydrologic conditions (ICs) during cold and dry seasons. During wet seasons, there's no skillful hydrological predictions since lead-2 month because the dominant role of meteorological forcings. During spring, the improvement of hydrological drought predictions is the most significant as more streamflow was generated by seasonal snowmelt. Besides meteorological forcings and ICs, human activities have reduced the hydrological variability and increased hydrological predictability during the wet seasons in the MHRB.
Feng Ma; Lifeng Luo; Aizhong Ye; Qingyun Duan. Seasonal drought predictability and forecast skill in the semi-arid endorheic Heihe River basin in Northwestern China. 2018, 2018, 1 -28.
AMA StyleFeng Ma, Lifeng Luo, Aizhong Ye, Qingyun Duan. Seasonal drought predictability and forecast skill in the semi-arid endorheic Heihe River basin in Northwestern China. . 2018; 2018 ():1-28.
Chicago/Turabian StyleFeng Ma; Lifeng Luo; Aizhong Ye; Qingyun Duan. 2018. "Seasonal drought predictability and forecast skill in the semi-arid endorheic Heihe River basin in Northwestern China." 2018, no. : 1-28.
In climate and environmental sciences, vast amount of spatio-temporal data have been generated at varying spatial resolutions from satellite observations and computer models. Integrating such diverse sources of data has proven to be useful for building prediction models as the multi-scale data may capture different aspects of the Earth system. In this paper, we present a novel framework called MUSCAT for predictive modeling of multi-scale, spatio-temporal data. MUSCAT performs a joint decomposition of multiple tensors from different spatial scales, taking into account the relationships between the variables. The latent factors derived from the joint tensor decomposition are used to train the spatial and temporal prediction models at different scales for each location. The outputs from these ensemble of spatial and temporal models will be aggregated to generate future predictions. An incremental learning algorithm is also proposed to handle the massive size of the tensors. Experimental results on real-world data from the United States Historical Climate Network (USHCN) showed that MUSCAT outperformed other competing methods in more than 70\% of the locations.
Jianpeng Xu; Xi Liu; Tyler Wilson; Pang-Ning Tan; Pouyan Hatami; Lifeng Luo. MUSCAT: Multi-Scale Spatio-Temporal Learning with Application to Climate Modeling. Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence 2018, 2912 -2918.
AMA StyleJianpeng Xu, Xi Liu, Tyler Wilson, Pang-Ning Tan, Pouyan Hatami, Lifeng Luo. MUSCAT: Multi-Scale Spatio-Temporal Learning with Application to Climate Modeling. Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence. 2018; ():2912-2918.
Chicago/Turabian StyleJianpeng Xu; Xi Liu; Tyler Wilson; Pang-Ning Tan; Pouyan Hatami; Lifeng Luo. 2018. "MUSCAT: Multi-Scale Spatio-Temporal Learning with Application to Climate Modeling." Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence , no. : 2912-2918.
Better quantification of the spatiotemporal distribution of soil moisture across different spatial scales contributes significantly to the understanding of land surface processes on the Earth as an integrated system. While observational data for root-zone soil moisture (RZSM) often have sparse spatial coverage, model-simulated soil moisture may provide a useful alternative. TOPMODEL-Based Land Surface–Atmosphere Transfer Scheme (TOPLATS) has been widely studied and actively modified in recent years, while a detailed regional application with evaluation currently is still lacking. Thus, TOPLATS was used to generate high-resolution (30 arc s) RZSM based on coarse-scale (0.125°) forcing data over part of the Arkansas–Red River basin. First, the simulated RZSM was resampled to coarse scale to compare with the results of Mosaic, Noah, and VIC from NLDAS. Second, TOPLATS performance was assessed based on the spatial absolute difference among the models. The comparison shows that TOPLATS performance is similar to VIC, but different from Mosaic and Noah. Last, the simulated RZSM was compared with in situ observations of 16 stations in the study area. The results suggest that the simulated spatial distribution of RZSM is largely consistent with the distribution of topographic index (TI) in most instances, as topography was traditionally considered a major, but not the only, factor in horizontal redistribution of soil moisture. In addition, the finer-resolution RZSM can reflect the in situ soil moisture change at most local sites to a certain degree. The evaluation confirms that TOPLATS is a useful tool to estimate high-resolution soil moisture and has great potential to provide regional soil moisture estimates.
Xiaolei Fu; Lifeng Luo; Ming Pan; Zhongbo Yu; Ying Tang; Yongjian Ding. Evaluation of TOPMODEL-Based Land Surface–Atmosphere Transfer Scheme (TOPLATS) through a Soil Moisture Simulation. Earth Interactions 2018, 22, 1 -19.
AMA StyleXiaolei Fu, Lifeng Luo, Ming Pan, Zhongbo Yu, Ying Tang, Yongjian Ding. Evaluation of TOPMODEL-Based Land Surface–Atmosphere Transfer Scheme (TOPLATS) through a Soil Moisture Simulation. Earth Interactions. 2018; 22 (15):1-19.
Chicago/Turabian StyleXiaolei Fu; Lifeng Luo; Ming Pan; Zhongbo Yu; Ying Tang; Yongjian Ding. 2018. "Evaluation of TOPMODEL-Based Land Surface–Atmosphere Transfer Scheme (TOPLATS) through a Soil Moisture Simulation." Earth Interactions 22, no. 15: 1-19.
Accurate and timely precipitation forecasts are a key factor for improving hydrological forecasts. Therefore, it is fundamental to evaluate the skill of Numerical Weather Prediction (NWP) for precipitation forecasting. In this study, the Global Environmental Multi-scale (GEM) model, which is widely used around Canada, was chosen as the high-resolution medium-term prediction model. Based on the forecast precipitation with the resolution of 0.24° and taking regional differences into consideration, the study explored the forecasting skill of GEM in nine drought sub-regions around China. Spatially, GEM performs better in East and South China than in the inland areas. Temporally, the model is able to produce more precise precipitation during flood periods (summer and autumn) compared with the non-flood season (winter and spring). The forecasting skill variability differs with regions, lead time and season. For different precipitation categories, GEM for trace rainfall and little rainfall performs much better than moderate rainfall and above. Overall, compared with other prediction systems, GEM is applicable for the 0–96 h forecast, especially for the East and South China in flood season, but improvement for the prediction of heavy and storm rainfall and for the inland areas should be focused on as well.
Huating Xu; Zhiyong Wu; Lifeng Luo; Hai He. Verification of High-Resolution Medium-Range Precipitation Forecasts from Global Environmental Multiscale Model over China during 2009–2013. Atmosphere 2018, 9, 104 .
AMA StyleHuating Xu, Zhiyong Wu, Lifeng Luo, Hai He. Verification of High-Resolution Medium-Range Precipitation Forecasts from Global Environmental Multiscale Model over China during 2009–2013. Atmosphere. 2018; 9 (3):104.
Chicago/Turabian StyleHuating Xu; Zhiyong Wu; Lifeng Luo; Hai He. 2018. "Verification of High-Resolution Medium-Range Precipitation Forecasts from Global Environmental Multiscale Model over China during 2009–2013." Atmosphere 9, no. 3: 104.
Mapping irrigated area, frequency, timing, and amount is important for sustainable management of water resources in semi-arid and arid regions. Various studies exist on the mapping of irrigation using remote sensing and census statistics, but they mainly focus on the mapping of irrigation extent without taking frequency and timing into account. In this study, we proposed a new approach to extract irrigation attributes including irrigation extent, frequency and timing using multi-source data—moderate resolution imaging spectroradiometer (MODIS), Landsat, and ancillary data. A time-series dataset with 30-m spatial resolution was generated by fusing 480-m time-series MODIS and Landsat imagery. We used the greenness index (the ratio of NIR and green spectral bands) to detect irrigation events during the first half of the growing season. Rainfall events were assumed as water supplement events along with irrigation events. The number of water supplement stages were then recorded cumulatively when a water supplement event was detected using a threshold-based model. To estimate the possible dates of each water supplement stage, Gaussian process regression and linear regression models were applied. The new framework was applied to the Hexi Corridor in northwestern China, an intensively irrigated region with a semi-arid climate. Results show that the overall accuracy of water supplement stage using the proposed method is 87%. Validation of the number of water supplement stages and possible dates of water supply by GRP model with a “strict” (or “loose”) assessment method shows an overall accuracy of 55% (94%) and 59% (89%), respectively. The good accuracy of the additional independent validations for different years and sites demonstrates the robustness of the proposed method, suggesting the general applicability to other regions. Overall, this research demonstrates that the proposed method is promising in detecting irrigation attributes such as frequency and timing which have not been explored in previous research.
Yaoliang Chen; Dengsheng Lu; Lifeng Luo; Yadu Pokhrel; Kalyanmoy Deb; Jingfeng Huang; Youhua Ran. Detecting irrigation extent, frequency, and timing in a heterogeneous arid agricultural region using MODIS time series, Landsat imagery, and ancillary data. Remote Sensing of Environment 2017, 204, 197 -211.
AMA StyleYaoliang Chen, Dengsheng Lu, Lifeng Luo, Yadu Pokhrel, Kalyanmoy Deb, Jingfeng Huang, Youhua Ran. Detecting irrigation extent, frequency, and timing in a heterogeneous arid agricultural region using MODIS time series, Landsat imagery, and ancillary data. Remote Sensing of Environment. 2017; 204 ():197-211.
Chicago/Turabian StyleYaoliang Chen; Dengsheng Lu; Lifeng Luo; Yadu Pokhrel; Kalyanmoy Deb; Jingfeng Huang; Youhua Ran. 2017. "Detecting irrigation extent, frequency, and timing in a heterogeneous arid agricultural region using MODIS time series, Landsat imagery, and ancillary data." Remote Sensing of Environment 204, no. : 197-211.
Water sustainability in megacities is a growing challenge with far‐reaching effects. Addressing sustainability requires an integrated, multidisciplinary approach able to capture interactions among hydrology, population growth, and socioeconomic factors and to reflect changes due to climate variability and land use. We developed a new systems modeling framework to quantify the influence of changes in land use, crop growth, and urbanization on groundwater storage for Beijing, China. This framework was then used to understand and quantify causes of observed decreases in groundwater storage from 1993 to 2006, revealing that the expansion of Beijing's urban areas at the expense of croplands has enhanced recharge while reducing water lost to evapotranspiration, partially ameliorating groundwater declines. The results demonstrate the efficacy of such a systems approach to quantify the impacts of changes in climate and land use on water sustainability for megacities, while providing a quantitative framework to improve mitigation and adaptation strategies that can help address future water challenges.
D. W. Hyndman; T. Xu; J. M. Deines; G. Cao; R. Nagelkirk; A. Viña; W. McConnell; B. Basso; A. D. Kendall; S. Li; L. Luo; F. Lupi; D. Ma; J. A. Winkler; W. Yang; Chunmiao Zheng; J. Liu. Quantifying changes in water use and groundwater availability in a megacity using novel integrated systems modeling. Geophysical Research Letters 2017, 44, 8359 -8368.
AMA StyleD. W. Hyndman, T. Xu, J. M. Deines, G. Cao, R. Nagelkirk, A. Viña, W. McConnell, B. Basso, A. D. Kendall, S. Li, L. Luo, F. Lupi, D. Ma, J. A. Winkler, W. Yang, Chunmiao Zheng, J. Liu. Quantifying changes in water use and groundwater availability in a megacity using novel integrated systems modeling. Geophysical Research Letters. 2017; 44 (16):8359-8368.
Chicago/Turabian StyleD. W. Hyndman; T. Xu; J. M. Deines; G. Cao; R. Nagelkirk; A. Viña; W. McConnell; B. Basso; A. D. Kendall; S. Li; L. Luo; F. Lupi; D. Ma; J. A. Winkler; W. Yang; Chunmiao Zheng; J. Liu. 2017. "Quantifying changes in water use and groundwater availability in a megacity using novel integrated systems modeling." Geophysical Research Letters 44, no. 16: 8359-8368.
Zengchao Hao; Youlong Xia; Lifeng Luo; Vijay P. Singh; Wei Ouyang; FangHua Hao. Toward a categorical drought prediction system based on U.S. Drought Monitor (USDM) and climate forecast. Journal of Hydrology 2017, 551, 300 -305.
AMA StyleZengchao Hao, Youlong Xia, Lifeng Luo, Vijay P. Singh, Wei Ouyang, FangHua Hao. Toward a categorical drought prediction system based on U.S. Drought Monitor (USDM) and climate forecast. Journal of Hydrology. 2017; 551 ():300-305.
Chicago/Turabian StyleZengchao Hao; Youlong Xia; Lifeng Luo; Vijay P. Singh; Wei Ouyang; FangHua Hao. 2017. "Toward a categorical drought prediction system based on U.S. Drought Monitor (USDM) and climate forecast." Journal of Hydrology 551, no. : 300-305.