V. Sasseville, Alexandre Langlois, Ludovic Brucker, Cheryl Ann Johnson
Climate change has a profound effect on Arctic meteorology extreme events, such as rain-on-snow (ROS), which affects surface state variable spatial and temporal variability. Passive microwave satellite images can help detect such events in polar regions where local meteorological and snow information are scarce. In this study, we use a detection algorithm using a high-resolution passive microwave data to monitor spatial and temporal variability of ROS over the Canadian Arctic Archipelago from 1987 to 2019. The method is validated using data from several meteorological stations and atmospheric corrections have been applied to the passive microwave dataset. Our approach to detect ROS is based on two methods: 1) over a fixed time-period (i.e. November 1st to May 31st) throughout the study period and 2) using an a-prior detection for snow presence before applying our ROS algorithm (i.e. length of studied winter varies yearly). Event occurrence is analyzed for each winter and separated by island groups of the Canadian Arctic Archipelago. Results show an increase in absolute ROS occurrence, mainly along the coasts, although no statistically significant trends are observed.
{"title":"Patterns and trend analysis of rain-on-snow events using passive microwave satellite data over the Canadian Arctic Archipelago since 1987","authors":"V. Sasseville, Alexandre Langlois, Ludovic Brucker, Cheryl Ann Johnson","doi":"10.1175/jhm-d-22-0218.1","DOIUrl":"https://doi.org/10.1175/jhm-d-22-0218.1","url":null,"abstract":"\u0000Climate change has a profound effect on Arctic meteorology extreme events, such as rain-on-snow (ROS), which affects surface state variable spatial and temporal variability. Passive microwave satellite images can help detect such events in polar regions where local meteorological and snow information are scarce. In this study, we use a detection algorithm using a high-resolution passive microwave data to monitor spatial and temporal variability of ROS over the Canadian Arctic Archipelago from 1987 to 2019. The method is validated using data from several meteorological stations and atmospheric corrections have been applied to the passive microwave dataset. Our approach to detect ROS is based on two methods: 1) over a fixed time-period (i.e. November 1st to May 31st) throughout the study period and 2) using an a-prior detection for snow presence before applying our ROS algorithm (i.e. length of studied winter varies yearly). Event occurrence is analyzed for each winter and separated by island groups of the Canadian Arctic Archipelago. Results show an increase in absolute ROS occurrence, mainly along the coasts, although no statistically significant trends are observed.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"3 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139444337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lu Li, Yongjiu Dai, Zhongwang Wei, Shangguan Wei, Yonggen Zhang, Nan Wei, Qingliang Li
Accurate prediction of hydrological variables (HVs) is critical for understanding hydrological processes. Deep learning (DL) models have shown excellent forecasting abilities for different HVs. However, most DL models typically predicted HVs independently, without satisfying the principle of water balance. This missed the interactions between different HVs in the hydrological system and the underlying physical rules. In this study, we developed a DL model based on multitask learning and hybrid physically constrained schemes to simultaneously forecast soil moisture, evapotranspiration, and runoff. The models were trained using ERA5-Land data, which have water budget closure. We thoroughly assessed the advantages of the multitask framework and the proposed constrained schemes. Results showed that multitask models with different loss-weighted strategies produced comparable or better performance compared to the single-task model. The multitask model with a scaling factor of 5 achieved the best among all multitask models and performed better than the single-task model over 70.5% of grids. In addition, the hybrid constrained scheme took advantage of both soft and hard constrained models, providing physically consistent predictions with better model performance. The hybrid constrained models performed the best among different constrained models in terms of both general and extreme performance. Moreover, the hybrid model was affected the least as the training data were artificially reduced, and provided better spatiotemporal extrapolation ability under different artificial prediction challenges. These findings suggest that the hybrid model provides better performance compared to previously reported constrained models when facing limited training data and extrapolation challenges.
{"title":"Enforcing Water Balance in Multitask Deep Learning Models for Hydrological Forecasting","authors":"Lu Li, Yongjiu Dai, Zhongwang Wei, Shangguan Wei, Yonggen Zhang, Nan Wei, Qingliang Li","doi":"10.1175/jhm-d-23-0073.1","DOIUrl":"https://doi.org/10.1175/jhm-d-23-0073.1","url":null,"abstract":"Accurate prediction of hydrological variables (HVs) is critical for understanding hydrological processes. Deep learning (DL) models have shown excellent forecasting abilities for different HVs. However, most DL models typically predicted HVs independently, without satisfying the principle of water balance. This missed the interactions between different HVs in the hydrological system and the underlying physical rules. In this study, we developed a DL model based on multitask learning and hybrid physically constrained schemes to simultaneously forecast soil moisture, evapotranspiration, and runoff. The models were trained using ERA5-Land data, which have water budget closure. We thoroughly assessed the advantages of the multitask framework and the proposed constrained schemes. Results showed that multitask models with different loss-weighted strategies produced comparable or better performance compared to the single-task model. The multitask model with a scaling factor of 5 achieved the best among all multitask models and performed better than the single-task model over 70.5% of grids. In addition, the hybrid constrained scheme took advantage of both soft and hard constrained models, providing physically consistent predictions with better model performance. The hybrid constrained models performed the best among different constrained models in terms of both general and extreme performance. Moreover, the hybrid model was affected the least as the training data were artificially reduced, and provided better spatiotemporal extrapolation ability under different artificial prediction challenges. These findings suggest that the hybrid model provides better performance compared to previously reported constrained models when facing limited training data and extrapolation challenges.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"22 9","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139129471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Z. F. Johnson, Jacob Stuivenvolt-Allen, Hayden Mahan, Jonathan D.D. Meyer, Matthew Miksch
The southwestern United States is highly sensitive to drought, prompting efforts to understand and predict its hydroclimate. Oftentimes, the emphasis is on wintertime precipitation variability, yet the southwestern United States exhibits a summertime monsoon where a significant portion of annual precipitation falls through daily convection activities manifested by a midtropospheric ridge of high pressure. Here, we examine synoptic patterns of the southwestern ridge through a k-means clustering analysis and assess how these synoptic patterns translate into streamflow changes in the upper Colorado River basin. A linear perspective suggests ~ 17% of upper Colorado River discharge at Lee’s Ferry, Arizona gauge comes from summertime monsoon rains. The ridge of high pressure exhibits diversity in its intensity, structure, and position, inducing changes in moisture advection and precipitation. A ridge shifted north or east of its climatological center increases moisture and precipitation over the southwestern United States, while a ridge toward the south or northwest inhibits precipitation. A ridge east of its climatological center contributes to increased streamflow, whereas a ridge west or northwest of its climatological center decreases streamflow. Cooling in the central tropical Pacific and the Pacific Meridional Mode region favors an eastward shift of the ridge of high pressure corresponding to wet days. Eastern tropical Pacific warming favors a southward shift of the ridge corresponding to dry days. These results support an intermediate scale between climate forcing and summertime Colorado River discharge through changes in the intensity, structure, and position of the southwestern ridge of high pressure, integral to the Southwest United States hydroclimate
{"title":"Upper Colorado River streamflow dependencies on summertime synoptic circulations and hydroclimate variability","authors":"Z. F. Johnson, Jacob Stuivenvolt-Allen, Hayden Mahan, Jonathan D.D. Meyer, Matthew Miksch","doi":"10.1175/jhm-d-23-0053.1","DOIUrl":"https://doi.org/10.1175/jhm-d-23-0053.1","url":null,"abstract":"\u0000The southwestern United States is highly sensitive to drought, prompting efforts to understand and predict its hydroclimate. Oftentimes, the emphasis is on wintertime precipitation variability, yet the southwestern United States exhibits a summertime monsoon where a significant portion of annual precipitation falls through daily convection activities manifested by a midtropospheric ridge of high pressure. Here, we examine synoptic patterns of the southwestern ridge through a k-means clustering analysis and assess how these synoptic patterns translate into streamflow changes in the upper Colorado River basin. A linear perspective suggests ~ 17% of upper Colorado River discharge at Lee’s Ferry, Arizona gauge comes from summertime monsoon rains. The ridge of high pressure exhibits diversity in its intensity, structure, and position, inducing changes in moisture advection and precipitation. A ridge shifted north or east of its climatological center increases moisture and precipitation over the southwestern United States, while a ridge toward the south or northwest inhibits precipitation. A ridge east of its climatological center contributes to increased streamflow, whereas a ridge west or northwest of its climatological center decreases streamflow. Cooling in the central tropical Pacific and the Pacific Meridional Mode region favors an eastward shift of the ridge of high pressure corresponding to wet days. Eastern tropical Pacific warming favors a southward shift of the ridge corresponding to dry days. These results support an intermediate scale between climate forcing and summertime Colorado River discharge through changes in the intensity, structure, and position of the southwestern ridge of high pressure, integral to the Southwest United States hydroclimate","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"27 2","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138947704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Droughts are becoming more frequent and severe in Southwest China, with Yunnan being the most affected region. Most of the current studies on droughts in Yunnan are limited to individual cases and lack a common determination of the causes of historical drought events. In this study, we analyzed drought characteristics and causes over the last 60 years (1961-2020) in Yunnan Province in two seasons: dry and rainy. There was a clear trend of aridity in Yunnan Province in terms of both temporal and spatial changes, and there was a long-term drought period in approximately 2010; in particular, the frequency of drought in the dry season significantly increased. Due to its unique geographical location, precipitation in Yunnan Province is influenced by various factors, and the main influencing factors differed in different periods. SST variation is the most important factor affecting Yunnan drought, precipitation in Yunnan Province is affected by the warm pool of the Indian Ocean throughout the year, and thermal anomalies in different locations of the Pacific Ocean have different effects on Yunnan Province and often overlap with the effects of SST anomalies in the Indian Ocean.
{"title":"Analysis of drought characteristics and causes in Yunnan Province in the last 60 years (1961-2020)","authors":"Tianqing Lan, Xiaodong Yan","doi":"10.1175/jhm-d-23-0092.1","DOIUrl":"https://doi.org/10.1175/jhm-d-23-0092.1","url":null,"abstract":"\u0000Droughts are becoming more frequent and severe in Southwest China, with Yunnan being the most affected region. Most of the current studies on droughts in Yunnan are limited to individual cases and lack a common determination of the causes of historical drought events. In this study, we analyzed drought characteristics and causes over the last 60 years (1961-2020) in Yunnan Province in two seasons: dry and rainy. There was a clear trend of aridity in Yunnan Province in terms of both temporal and spatial changes, and there was a long-term drought period in approximately 2010; in particular, the frequency of drought in the dry season significantly increased. Due to its unique geographical location, precipitation in Yunnan Province is influenced by various factors, and the main influencing factors differed in different periods. SST variation is the most important factor affecting Yunnan drought, precipitation in Yunnan Province is affected by the warm pool of the Indian Ocean throughout the year, and thermal anomalies in different locations of the Pacific Ocean have different effects on Yunnan Province and often overlap with the effects of SST anomalies in the Indian Ocean.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"103 42","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138958843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Joseph W. Lockwood, T. Loridan, Ning Lin, Michael Oppenheimer, Nic Hannah
Extreme rainfall found in tropical-cyclones (TCs) is a risk for human life and property in many low to mid latitude regions. Probabilistic modeling of TC rainfall in risk assessment and forecasting can be computational expensive, and existing models are largely unable to model key rainfall asymmetries such as rain-bands and extra-tropical transition. Here, a machine learning-based framework is developed to model over-water TC rainfall for the North Atlantic basin. First, a catalog of high-resolution TC precipitation simulations for 26 historical events is assembled for the North Atlantic basin using the Weather Research and Forecasting (WRF) Model. The simulated spatial distribution of rainfall for these historical events are then decomposed via principal component analysis (PCA), and quantile regression forest models (QRF) are trained to predict the conditional distributions of the first five principal component (PC) weights. Conditional distributions of rain rate levels are estimated separately using historical satellite data and a QRF model. With these models, probabilistic predictions of rainfall maps can be made given a set of storm characteristics and local environmental conditions. The model is able to capture storm total rainfall compared to satellite observations with a correlation coefficient of 0.96 and r-squared value of 0.93. Additionally, the model shows good accuracy in modeling hourly total rainfall compared to satellite observations. Rain rate maps predicted by the model are also compared to historical satellite observations and to the WRF simulations during cross-validation, and the spatial distribution of estimates captures rainfall variability consistent with TC rain-bands, wavenumber asymmetries and possibly extra-tropical transition.
{"title":"A machine learning approach to model over ocean tropical cyclone precipitation","authors":"Joseph W. Lockwood, T. Loridan, Ning Lin, Michael Oppenheimer, Nic Hannah","doi":"10.1175/jhm-d-23-0065.1","DOIUrl":"https://doi.org/10.1175/jhm-d-23-0065.1","url":null,"abstract":"Extreme rainfall found in tropical-cyclones (TCs) is a risk for human life and property in many low to mid latitude regions. Probabilistic modeling of TC rainfall in risk assessment and forecasting can be computational expensive, and existing models are largely unable to model key rainfall asymmetries such as rain-bands and extra-tropical transition. Here, a machine learning-based framework is developed to model over-water TC rainfall for the North Atlantic basin. First, a catalog of high-resolution TC precipitation simulations for 26 historical events is assembled for the North Atlantic basin using the Weather Research and Forecasting (WRF) Model. The simulated spatial distribution of rainfall for these historical events are then decomposed via principal component analysis (PCA), and quantile regression forest models (QRF) are trained to predict the conditional distributions of the first five principal component (PC) weights. Conditional distributions of rain rate levels are estimated separately using historical satellite data and a QRF model. With these models, probabilistic predictions of rainfall maps can be made given a set of storm characteristics and local environmental conditions. The model is able to capture storm total rainfall compared to satellite observations with a correlation coefficient of 0.96 and r-squared value of 0.93. Additionally, the model shows good accuracy in modeling hourly total rainfall compared to satellite observations. Rain rate maps predicted by the model are also compared to historical satellite observations and to the WRF simulations during cross-validation, and the spatial distribution of estimates captures rainfall variability consistent with TC rain-bands, wavenumber asymmetries and possibly extra-tropical transition.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"33 5","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139176194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Widespread floods affecting multiple subbasins in a river basin have implications for infrastructure, agriculture, environment, and groundwater recharge. However, the crucial linkage between widespread floods and their drivers remains unexplored for Indian sub-continental river basins. Here, we examine the occurrence and drivers of widespread flooding in seven Indian sub-continental river basins during the observed climate (1959-2020). The peninsular river basins have a high probability of widespread flooding, compared to the transboundary basins of Ganga and Brahmaputra. Favorable antecedent baseflow and soil moisture conditions, uniform precipitation distribution, and precipitation seasonality determine the probability of widespread floods in Indian river basins. The widespread floods are associated with large atmospheric circulations that cause precipitation in a large part of a river basin. Our findings highlight the prominent drivers and mechanisms of widespread floods with implications for flood mitigation in India.
{"title":"Drivers of widespread floods in Indian river basins","authors":"N. J S, Vimal Mishra","doi":"10.1175/jhm-d-23-0168.1","DOIUrl":"https://doi.org/10.1175/jhm-d-23-0168.1","url":null,"abstract":"\u0000Widespread floods affecting multiple subbasins in a river basin have implications for infrastructure, agriculture, environment, and groundwater recharge. However, the crucial linkage between widespread floods and their drivers remains unexplored for Indian sub-continental river basins. Here, we examine the occurrence and drivers of widespread flooding in seven Indian sub-continental river basins during the observed climate (1959-2020). The peninsular river basins have a high probability of widespread flooding, compared to the transboundary basins of Ganga and Brahmaputra. Favorable antecedent baseflow and soil moisture conditions, uniform precipitation distribution, and precipitation seasonality determine the probability of widespread floods in Indian river basins. The widespread floods are associated with large atmospheric circulations that cause precipitation in a large part of a river basin. Our findings highlight the prominent drivers and mechanisms of widespread floods with implications for flood mitigation in India.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"101 5","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139005515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhi-Weng Chua, Yuriy Kuleshov, Andrew B. Watkins, S. Choy, Chayn Sun
Satellites provide a useful way of estimating rainfall where the availability of in situ data is low but their indirect nature of estimation means there can be substantial biases. Consequently, the assimilation of in situ data is an important step in improving the accuracy of the satellite rainfall analysis. The effectiveness of this step varies with gauge density, and this study investigated the effectiveness of statistical interpolation (SI), also known as optimal interpolation (OI), on a monthly time scale when gauge density is extremely low using Papua New Guinea (PNG) as a study region. The topography of the region presented an additional challenge to the algorithm. An open-source implementation of SI was developed on Python 3 and confirmed to be consistent with an existing implementation, addressing a lack of open-source implementation for this classical algorithm. The effectiveness of the analysis produced by this algorithm was then compared to the pure satellite analysis over PNG from 2001 to 2014. When performance over the entire study domain was considered, the improvement from using SI was close to imperceptible because of the small number of stations available for assimilation and the small radius of influence of each station (imposed by the topography present in the domain). However, there was still value in using OI as performance around each of the stations was noticeably improved, with the error consistently being reduced along with a general increase in the correlation metric. Furthermore, in an operational context, the use of OI provides an important function of ensuring consistency between in situ data and the gridded analysis. The blending of satellite and gauge rainfall data through a process known as statistical interpolation (SI) is known to be capable of producing a more accurate dataset that facilitates better estimation of rainfall. However, the performance of this algorithm over a domain such as Papua New Guinea, where gauge density is extremely low, is not often explored. This study reveals that, although an improvement over the entire Papua New Guinea domain was slight, the algorithm is still valuable as there was a consistent improvement around the stations. Additionally, an adaptable and open-source version of the algorithm is provided, allowing users to blend their own satellite and gauge data and create better geospatial datasets for their own purposes.
{"title":"A Statistical Interpolation of Satellite Data with Rain Gauge Data over Papua New Guinea","authors":"Zhi-Weng Chua, Yuriy Kuleshov, Andrew B. Watkins, S. Choy, Chayn Sun","doi":"10.1175/jhm-d-23-0035.1","DOIUrl":"https://doi.org/10.1175/jhm-d-23-0035.1","url":null,"abstract":"\u0000Satellites provide a useful way of estimating rainfall where the availability of in situ data is low but their indirect nature of estimation means there can be substantial biases. Consequently, the assimilation of in situ data is an important step in improving the accuracy of the satellite rainfall analysis. The effectiveness of this step varies with gauge density, and this study investigated the effectiveness of statistical interpolation (SI), also known as optimal interpolation (OI), on a monthly time scale when gauge density is extremely low using Papua New Guinea (PNG) as a study region. The topography of the region presented an additional challenge to the algorithm. An open-source implementation of SI was developed on Python 3 and confirmed to be consistent with an existing implementation, addressing a lack of open-source implementation for this classical algorithm. The effectiveness of the analysis produced by this algorithm was then compared to the pure satellite analysis over PNG from 2001 to 2014. When performance over the entire study domain was considered, the improvement from using SI was close to imperceptible because of the small number of stations available for assimilation and the small radius of influence of each station (imposed by the topography present in the domain). However, there was still value in using OI as performance around each of the stations was noticeably improved, with the error consistently being reduced along with a general increase in the correlation metric. Furthermore, in an operational context, the use of OI provides an important function of ensuring consistency between in situ data and the gridded analysis.\u0000\u0000\u0000The blending of satellite and gauge rainfall data through a process known as statistical interpolation (SI) is known to be capable of producing a more accurate dataset that facilitates better estimation of rainfall. However, the performance of this algorithm over a domain such as Papua New Guinea, where gauge density is extremely low, is not often explored. This study reveals that, although an improvement over the entire Papua New Guinea domain was slight, the algorithm is still valuable as there was a consistent improvement around the stations. Additionally, an adaptable and open-source version of the algorithm is provided, allowing users to blend their own satellite and gauge data and create better geospatial datasets for their own purposes.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":" 9","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138618637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Benjamin I Cook, Weston Anderson, K. Slinski, S. Shukla, Amy McNally
The state of the El Niño Southern Oscillation (ENSO) is critical for seasonal climate forecasts, but recent events diverged substantially from expectations in many regions, including Sub-Saharan Africa where seasonal forecasts are critical tools for addressing food security. Here, we evaluate 39 years (1982–2020) of data on hydroclimate, leaf area index, and maize yields to investigate the strength of ENSO teleconnections in southern and East Africa. Teleconnections to precipitation, soil moisture, and leaf area index are generally stronger during ENSO phases that cause drought conditions (El Niño in southern Africa and La Niña in East Africa), with seasonality that aligns well with the maize growing seasons. Within maize growing areas, however, ENSO teleconnections to hydroclimate and vegetation are generally weaker compared to the broader geographic regions, especially in East Africa. There is also little evidence that the magnitude of the ENSO event affects the hydroclimate or vegetation response in these maize regions. Maize yields in Kenya, Malawi, South Africa, and Zimbabwe all correlate significantly with hydroclimate and leaf area index, with South Africa and Zimbabwe showing the strongest and most consistent yield responses to ENSO events. Our results highlight the chain of causality from El Niño and La Niña forcing of regional anomalies in hydroclimate to vegetation health and maize yields in southern and East Africa. The large spread across individual ENSO events, however, underscores the limitations of this climate mode for seasonal climate prediction in the region, and the importance of finding additional sources of skill for improving climate and yield forecasts.
{"title":"Investigating the strength and variability of El Niño Southern Oscillation teleconnections to hydroclimate and maize yields in southern and East Africa","authors":"Benjamin I Cook, Weston Anderson, K. Slinski, S. Shukla, Amy McNally","doi":"10.1175/jhm-d-23-0098.1","DOIUrl":"https://doi.org/10.1175/jhm-d-23-0098.1","url":null,"abstract":"The state of the El Niño Southern Oscillation (ENSO) is critical for seasonal climate forecasts, but recent events diverged substantially from expectations in many regions, including Sub-Saharan Africa where seasonal forecasts are critical tools for addressing food security. Here, we evaluate 39 years (1982–2020) of data on hydroclimate, leaf area index, and maize yields to investigate the strength of ENSO teleconnections in southern and East Africa. Teleconnections to precipitation, soil moisture, and leaf area index are generally stronger during ENSO phases that cause drought conditions (El Niño in southern Africa and La Niña in East Africa), with seasonality that aligns well with the maize growing seasons. Within maize growing areas, however, ENSO teleconnections to hydroclimate and vegetation are generally weaker compared to the broader geographic regions, especially in East Africa. There is also little evidence that the magnitude of the ENSO event affects the hydroclimate or vegetation response in these maize regions. Maize yields in Kenya, Malawi, South Africa, and Zimbabwe all correlate significantly with hydroclimate and leaf area index, with South Africa and Zimbabwe showing the strongest and most consistent yield responses to ENSO events. Our results highlight the chain of causality from El Niño and La Niña forcing of regional anomalies in hydroclimate to vegetation health and maize yields in southern and East Africa. The large spread across individual ENSO events, however, underscores the limitations of this climate mode for seasonal climate prediction in the region, and the importance of finding additional sources of skill for improving climate and yield forecasts.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"7 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139221160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
H. Titley, H. Cloke, E. Stephens, F. Pappenberger, E. Zsoter
Fluvial flooding is a major cause of death and damages from tropical cyclones (TCs), so it is important to understand the predictability of river flooding in TC cases, and the potential of global ensemble flood forecast systems to inform warning and preparedness activities. This paper demonstrates a methodology using ensemble forecasts to follow predictability and uncertainty through the forecast chain in the Global Flood Awareness System (GloFAS), to explore the connections between the skill of the TC track, intensity, precipitation and river discharge forecasts. Using the case of Hurricane Iota, which brought severe flooding to Central America in November 2020, we assess the performance of each ensemble member at each stage of the forecast, along with the overall spread and change between forecast runs, and analyse the connections between each forecast component. Strong relationships are found between track, precipitation and river discharge skill. Changes in TC intensity skill only result in significant improvements in discharge skill in river catchments close to the landfall location that are impacted by the heavy rains around the eye wall. The rainfall from the wider storm circulation is crucial to flood impacts in most of the affected river basins, with a stronger relationship with the post-landfall track error rather than the precise landfall location. We recommend the wider application of this technique in TC cases, to investigate how this cascade of predictability varies with different forecast and geographical contexts, to help inform flood early warning in TCs.
{"title":"Using ensembles to analyse predictability links in the tropical cyclone flood forecast chain","authors":"H. Titley, H. Cloke, E. Stephens, F. Pappenberger, E. Zsoter","doi":"10.1175/jhm-d-23-0022.1","DOIUrl":"https://doi.org/10.1175/jhm-d-23-0022.1","url":null,"abstract":"Fluvial flooding is a major cause of death and damages from tropical cyclones (TCs), so it is important to understand the predictability of river flooding in TC cases, and the potential of global ensemble flood forecast systems to inform warning and preparedness activities. This paper demonstrates a methodology using ensemble forecasts to follow predictability and uncertainty through the forecast chain in the Global Flood Awareness System (GloFAS), to explore the connections between the skill of the TC track, intensity, precipitation and river discharge forecasts. Using the case of Hurricane Iota, which brought severe flooding to Central America in November 2020, we assess the performance of each ensemble member at each stage of the forecast, along with the overall spread and change between forecast runs, and analyse the connections between each forecast component. Strong relationships are found between track, precipitation and river discharge skill. Changes in TC intensity skill only result in significant improvements in discharge skill in river catchments close to the landfall location that are impacted by the heavy rains around the eye wall. The rainfall from the wider storm circulation is crucial to flood impacts in most of the affected river basins, with a stronger relationship with the post-landfall track error rather than the precise landfall location. We recommend the wider application of this technique in TC cases, to investigate how this cascade of predictability varies with different forecast and geographical contexts, to help inform flood early warning in TCs.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"25 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139249259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Extreme hydrological events (including droughts and floods) produce severe social and economic impacts. Monitoring hydrological processes from remote sensing is necessary to improve understanding and preparedness for these events, with current missions focusing on a range of hydrological variables (i.e. SWOT, SMAP, GRACE). This study uses output from three state-of-the-art land surface assimilation models and an event clustering algorithm to identify the characteristic spatial and temporal scales of large-scale extreme dry and wet events in the contiguous United States for three major hydrological processes: precipitation, runoff and soil moisture. We also examine the sensitivity of extreme event characteristics to model resolution, and assess inter-model differences. We find that models generally agree in terms of the mean characteristics of events: precipitation dry events are shorter duration compared to soil moisture and runoff, and more intense events tend to be smaller in area. We also find that mean spatial and temporal characteristics are highly dependent on model resolution; important in the context of detecting and monitoring these events. Results from this study can be used to inform land surface model development, extreme hydrology event detection, and sampling requirements of upcoming remote sensing missions in hydrology.
{"title":"An investigation of the spatial and temporal characteristics of extreme dry and wet events across NLDAS-2 models","authors":"M. Pascolini‐Campbell, J. Reager","doi":"10.1175/jhm-d-23-0038.1","DOIUrl":"https://doi.org/10.1175/jhm-d-23-0038.1","url":null,"abstract":"Extreme hydrological events (including droughts and floods) produce severe social and economic impacts. Monitoring hydrological processes from remote sensing is necessary to improve understanding and preparedness for these events, with current missions focusing on a range of hydrological variables (i.e. SWOT, SMAP, GRACE). This study uses output from three state-of-the-art land surface assimilation models and an event clustering algorithm to identify the characteristic spatial and temporal scales of large-scale extreme dry and wet events in the contiguous United States for three major hydrological processes: precipitation, runoff and soil moisture. We also examine the sensitivity of extreme event characteristics to model resolution, and assess inter-model differences. We find that models generally agree in terms of the mean characteristics of events: precipitation dry events are shorter duration compared to soil moisture and runoff, and more intense events tend to be smaller in area. We also find that mean spatial and temporal characteristics are highly dependent on model resolution; important in the context of detecting and monitoring these events. Results from this study can be used to inform land surface model development, extreme hydrology event detection, and sampling requirements of upcoming remote sensing missions in hydrology.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"51 3","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139251734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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