Lisa Katz, Gabriel Lewis, Sebastian Krogh, Stephen Drake, Erin Hanan, Benjamin Hatchett, Adrian Harpold
Abstract Predicting winter flooding is critical to protecting people and securing water resources in California’s Sierra Nevada. Rain-on-snow (ROS) events are a common cause of widespread flooding and are expected to increase in both frequency and magnitude with anthropogenic climate change in this region. ROS flood severity depends on terrestrial water input (TWI), the sum of rain and snowmelt that reaches the land surface. However, an incomplete understanding of the processes that control the flow and refreezing of liquid water in the snowpack limits flood prediction by operational and research models. We examine how antecedent snowpack conditions alter TWI during 71 ROS events between water years 1981 and 2019. Observations across a 500-m elevation gradient from the Independence Creek catchment were input into SNOWPACK, a one-dimensional, physically based snow model, initiated with the Richards equation and calibrated with collocated snow pillow observations. We compare observed “historical” and “scenario” ROS events, where we hold meteorologic conditions constant but vary snowpack conditions. Snowpack variables include cold content, snow density, liquid water content, and snow water equivalent. Results indicate that historical events with TWI > rain are associated with the largest observed streamflows. A multiple linear regression analysis of scenario events suggests that TWI is sensitive to interactions between snow density and cold content, with denser (>0.30 g cm −3 ) and colder (<−0.3 MJ of cold content) snowpacks retaining >50 mm of TWI. These results highlight the importance of hydraulic limitations in dense snowpacks and energy limitations in warm snowpacks for retaining liquid water that would otherwise be available as TWI for flooding. Significance Statement The purpose of this study is to understand how the snowpack modulates quantities of water that reach the land surface during rain-on-snow (ROS) events. While the amount of near-term storm rainfall is reasonably predicted by meteorologists, major floods associated with ROS are more difficult to predict and are expected to increase in frequency. Our key findings are that liquid water inputs to the land surface vary with snowpack characteristics, and although many hydrologic models incorporate snowpack cold content and density to some degree, the complexity of ROS events justifies the need for additional observations to improve operational forecasting model results. Our findings suggest additional comparisons between existing forecasting models and those that physically represent the snowpack, as well as field-based observations of cold content and density and liquid water content, would be useful follow-up investigations.
预测冬季洪水对于保护加州内华达山脉的人民和确保水资源至关重要。雨雪(ROS)事件是大范围洪水的常见原因，预计随着该地区人为气候变化，其频率和强度都将增加。ROS洪水的严重程度取决于陆地水输入(TWI)，即到达陆地表面的雨水和融雪的总和。然而，对控制积雪中液态水流动和再冻结过程的不完全理解限制了业务和研究模式对洪水的预测。我们研究了在1981年至2019年水年之间的71次ROS事件中，先前的积雪条件如何改变TWI。独立溪流域500米海拔梯度的观测数据被输入到SNOWPACK中，这是一个一维的、基于物理的雪模型，由理查兹方程启动，并通过搭配雪枕观测进行校准。我们比较了观测到的“历史”和“情景”ROS事件，其中我们保持气象条件不变，但积雪条件不同。积雪变量包括冷含量、雪密度、液态水含量和雪水当量。结果表明，历史事件与TWI >雨与观测到的最大流量有关。情景事件的多元线性回归分析表明，TWI对雪密度和冷含量之间的相互作用很敏感，较密(< 0.30 g cm−3)和较冷(<−0.3 MJ)的积雪保留了>50 mm的TWI。这些结果强调了密集积雪中水力限制和温暖积雪中能量限制对保留液态水的重要性，否则液态水可以作为TWI用于洪水。本研究的目的是了解在雨雪(ROS)事件期间，积雪如何调节到达陆地表面的水量。虽然气象学家可以合理地预测近期风暴降雨量，但与ROS相关的大洪水更难预测，预计频率会增加。我们的主要发现是，地表液态水输入随积雪特征而变化，尽管许多水文模型在一定程度上考虑了积雪冷含量和密度，但ROS事件的复杂性证明需要额外的观测来改进业务预测模型的结果。我们的研究结果表明，将现有的预报模式与那些实际代表积雪的模式进行进一步的比较，以及对冷含量、密度和液态水含量的实地观测，将是有用的后续调查。
{"title":"Antecedent snowpack cold content alters the hydrologic response to extreme rain-on-snow events","authors":"Lisa Katz, Gabriel Lewis, Sebastian Krogh, Stephen Drake, Erin Hanan, Benjamin Hatchett, Adrian Harpold","doi":"10.1175/jhm-d-22-0090.1","DOIUrl":"https://doi.org/10.1175/jhm-d-22-0090.1","url":null,"abstract":"Abstract Predicting winter flooding is critical to protecting people and securing water resources in California’s Sierra Nevada. Rain-on-snow (ROS) events are a common cause of widespread flooding and are expected to increase in both frequency and magnitude with anthropogenic climate change in this region. ROS flood severity depends on terrestrial water input (TWI), the sum of rain and snowmelt that reaches the land surface. However, an incomplete understanding of the processes that control the flow and refreezing of liquid water in the snowpack limits flood prediction by operational and research models. We examine how antecedent snowpack conditions alter TWI during 71 ROS events between water years 1981 and 2019. Observations across a 500-m elevation gradient from the Independence Creek catchment were input into SNOWPACK, a one-dimensional, physically based snow model, initiated with the Richards equation and calibrated with collocated snow pillow observations. We compare observed “historical” and “scenario” ROS events, where we hold meteorologic conditions constant but vary snowpack conditions. Snowpack variables include cold content, snow density, liquid water content, and snow water equivalent. Results indicate that historical events with TWI > rain are associated with the largest observed streamflows. A multiple linear regression analysis of scenario events suggests that TWI is sensitive to interactions between snow density and cold content, with denser (>0.30 g cm −3 ) and colder (<−0.3 MJ of cold content) snowpacks retaining >50 mm of TWI. These results highlight the importance of hydraulic limitations in dense snowpacks and energy limitations in warm snowpacks for retaining liquid water that would otherwise be available as TWI for flooding. Significance Statement The purpose of this study is to understand how the snowpack modulates quantities of water that reach the land surface during rain-on-snow (ROS) events. While the amount of near-term storm rainfall is reasonably predicted by meteorologists, major floods associated with ROS are more difficult to predict and are expected to increase in frequency. Our key findings are that liquid water inputs to the land surface vary with snowpack characteristics, and although many hydrologic models incorporate snowpack cold content and density to some degree, the complexity of ROS events justifies the need for additional observations to improve operational forecasting model results. Our findings suggest additional comparisons between existing forecasting models and those that physically represent the snowpack, as well as field-based observations of cold content and density and liquid water content, would be useful follow-up investigations.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135324581","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}
Abstract Rainfall and temperature extremes have become more frequent and severe in recent times due to changing climate. Since these catastrophic occurrences directly affect a region’s hydrology, it is imperative to develop models that can project and explain the joint behavior of climate variables. Copula functions have been used relatively successfully to capture multivariate processes. With climate being a multifaceted process, there is interdependence between variables, making copula use desirable since traditional bivariate distributions do not account for the dependent structure. In this study, we introduced a bivariate exponentiated Teissier distribution based on a Clayton copula. For parameter estimation, the maximum likelihood and inference functions for margin approaches are used. A simulation study that considered various sets of parameters is also conducted in order to select the most efficient parameter estimation method. Last, the applicability of the proposed model is demonstrated using real-world data from flood and temperature processes. After fitting, the log-likelihood, Akaike information criteria (AIC), and Bayesian information criteria (BIC) values of the proposed model are −145.00, 300.00, and 311.71 for flood data, respectively, and −128.71, 267.42, and 275.98 for temperature data, respectively. Estimated parameters are for flood data and for temperature data. It is concluded that this model may be effectively used for modeling the hydrological processes for calculating the probabilities of flood and extreme temperature events.
{"title":"A New Statistical Distribution Derived from a Clayton Copula for Modeling Bivariate Processes","authors":"Neeraj Poonia, Sarita Azad","doi":"10.1175/jhm-d-23-0011.1","DOIUrl":"https://doi.org/10.1175/jhm-d-23-0011.1","url":null,"abstract":"Abstract Rainfall and temperature extremes have become more frequent and severe in recent times due to changing climate. Since these catastrophic occurrences directly affect a region’s hydrology, it is imperative to develop models that can project and explain the joint behavior of climate variables. Copula functions have been used relatively successfully to capture multivariate processes. With climate being a multifaceted process, there is interdependence between variables, making copula use desirable since traditional bivariate distributions do not account for the dependent structure. In this study, we introduced a bivariate exponentiated Teissier distribution based on a Clayton copula. For parameter estimation, the maximum likelihood and inference functions for margin approaches are used. A simulation study that considered various sets of parameters is also conducted in order to select the most efficient parameter estimation method. Last, the applicability of the proposed model is demonstrated using real-world data from flood and temperature processes. After fitting, the log-likelihood, Akaike information criteria (AIC), and Bayesian information criteria (BIC) values of the proposed model are −145.00, 300.00, and 311.71 for flood data, respectively, and −128.71, 267.42, and 275.98 for temperature data, respectively. Estimated parameters are for flood data and for temperature data. It is concluded that this model may be effectively used for modeling the hydrological processes for calculating the probabilities of flood and extreme temperature events.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135369096","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}
Michel Bechtold, Sara Modanesi, Hans Lievens, Pierre Baguis, Isis Brangers, Alberto Carrassi, Augusto Getirana, Alexander Gruber, Zdenko Heyvaert, Christian Massari, Samuel Scherrer, Stéphane Vannitsem, Gabrielle De Lannoy
Abstract Accurate streamflow simulations rely on good estimates of the catchment-scale soil moisture distribution. Here, we evaluated the potential of Sentinel-1 backscatter data assimilation (DA) to improve soil moisture and streamflow estimates. Our DA system consisted of the Noah-MP land surface model coupled to the HyMAP river routing model and the water cloud model as backscatter observation operator. The DA system was set up at 0.01° resolution for two contrasting catchments in Belgium: i) the Demer catchment dominated by agriculture, and ii) the Ourthe catchment dominated by mixed forests. We present results of two experiments with an ensemble Kalman filter updating either soil moisture only or soil moisture and Leaf Area Index (LAI). The DA experiments covered the period January 2015 through August 2021 and were evaluated with independent rainfall error estimates based on station data, LAI from optical remote sensing, soil moisture retrievals from passive microwave observations, and streamflow measurements. Our results indicate that the assimilation of Sentinel-1 backscatter observations can partly correct errors in surface soil moisture due to rainfall errors and overall improve surface soil moisture estimates. However, updating soil moisture and LAI simultaneously did not bring any benefit over updating soil moisture only. Our results further indicate that streamflow estimates can be improved through Sentinel-1 DA in a catchment with strong soil moisture-runoff coupling, as observed for the Ourthe catchment, suggesting that there is potential for Sentinel-1 DA even for forested catchments.
{"title":"Assimilation of Sentinel-1 backscatter into a land surface model with river routing and its impact on streamflow simulations in two Belgian catchments","authors":"Michel Bechtold, Sara Modanesi, Hans Lievens, Pierre Baguis, Isis Brangers, Alberto Carrassi, Augusto Getirana, Alexander Gruber, Zdenko Heyvaert, Christian Massari, Samuel Scherrer, Stéphane Vannitsem, Gabrielle De Lannoy","doi":"10.1175/jhm-d-22-0198.1","DOIUrl":"https://doi.org/10.1175/jhm-d-22-0198.1","url":null,"abstract":"Abstract Accurate streamflow simulations rely on good estimates of the catchment-scale soil moisture distribution. Here, we evaluated the potential of Sentinel-1 backscatter data assimilation (DA) to improve soil moisture and streamflow estimates. Our DA system consisted of the Noah-MP land surface model coupled to the HyMAP river routing model and the water cloud model as backscatter observation operator. The DA system was set up at 0.01° resolution for two contrasting catchments in Belgium: i) the Demer catchment dominated by agriculture, and ii) the Ourthe catchment dominated by mixed forests. We present results of two experiments with an ensemble Kalman filter updating either soil moisture only or soil moisture and Leaf Area Index (LAI). The DA experiments covered the period January 2015 through August 2021 and were evaluated with independent rainfall error estimates based on station data, LAI from optical remote sensing, soil moisture retrievals from passive microwave observations, and streamflow measurements. Our results indicate that the assimilation of Sentinel-1 backscatter observations can partly correct errors in surface soil moisture due to rainfall errors and overall improve surface soil moisture estimates. However, updating soil moisture and LAI simultaneously did not bring any benefit over updating soil moisture only. Our results further indicate that streamflow estimates can be improved through Sentinel-1 DA in a catchment with strong soil moisture-runoff coupling, as observed for the Ourthe catchment, suggesting that there is potential for Sentinel-1 DA even for forested catchments.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136308502","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}
In this study, the FLEXible PARTicle Dispersion Model (FLEXPART) is applied to analyze the moisture sources of Northeast China precipitation from March 1979 to February 2018. The results show that there is mainly one particle aggregation channel in winter, namely the Eastern Europe–Siberia–Lake Baikal–Northeast Asia channel (the western channel). Compared with winter, there are two extra channels in summer, namely the Indochina Peninsula–South China Sea–East China channel (the southern channel) and the Philippine Sea–Ryukyu Islands channel (the southeastern channel). From the long-term mean, Siberia–Mongolia–Xinjiang (SMX) is the most dominant moisture source of Northeast China precipitation in all seasons. As for the moisture contribution rate of each source region to Northeast China precipitation, there is a seesaw interannual relationship between SMX and other source regions. The moisture from the Central–East China is critical to the interdecadal shift of Northeast China summer precipitation. This interdecadal shift is related to the moisture transport from low latitudes to Northeast China, which is modulated by the positive phase of the Pacific Decadal Oscillation and the negative phase of the Atlantic Multidecadal Oscillation.
{"title":"Lagrangian simulations of moisture sources for Northeast China precipitation during 1979–2018","authors":"Shibo Yao, Dabang Jiang","doi":"10.1175/jhm-d-22-0201.1","DOIUrl":"https://doi.org/10.1175/jhm-d-22-0201.1","url":null,"abstract":"In this study, the FLEXible PARTicle Dispersion Model (FLEXPART) is applied to analyze the moisture sources of Northeast China precipitation from March 1979 to February 2018. The results show that there is mainly one particle aggregation channel in winter, namely the Eastern Europe–Siberia–Lake Baikal–Northeast Asia channel (the western channel). Compared with winter, there are two extra channels in summer, namely the Indochina Peninsula–South China Sea–East China channel (the southern channel) and the Philippine Sea–Ryukyu Islands channel (the southeastern channel). From the long-term mean, Siberia–Mongolia–Xinjiang (SMX) is the most dominant moisture source of Northeast China precipitation in all seasons. As for the moisture contribution rate of each source region to Northeast China precipitation, there is a seesaw interannual relationship between SMX and other source regions. The moisture from the Central–East China is critical to the interdecadal shift of Northeast China summer precipitation. This interdecadal shift is related to the moisture transport from low latitudes to Northeast China, which is modulated by the positive phase of the Pacific Decadal Oscillation and the negative phase of the Atlantic Multidecadal Oscillation.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"95 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139338534","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}
Abstract This article explores the application of thermodynamic perturbations to a historical mid-latitude, winter-time, rain-on-snow flood event to evaluate how similar events may evolve under different climate forcings. In particular, we generate a hindcast of the 1996 Mid-Atlantic flood using an ensemble of 14km variable-resolution simulations completed with the Department of Energy’s global Energy Exascale Earth System Model (E3SM). We show the event is skillfully reproduced over the Susquehanna River Basin (SRB) by E3SM when benchmarked against in situ observational data and high-resolution reanalyses. In addition, we perform five counterfactual experiments to simulate the flood under pre-industrial conditions and four different levels of warming as projected by the Community Earth System Model Large Ensemble. We find a nonlinear response in simulated surface runoff and streamflow as a function of atmospheric warming. This is attributed to changing contributions of liquid water input from a shallower initial snowpack (decreased snowmelt), increased surface temperatures and rainfall rates, and increased soil water storage. Flooding associated with this event peaks around +1 to +2K of global average surface warming and decreases with additional warming beyond this. There are noticeable timing shifts in peak runoff and streamflow associated with changes in the flashiness of the event. This work highlights the utility of using storyline approaches for communicating climate risk and demonstrates the potential non-linearities associated with hydrologic extremes in areas that experience ephemeral snowpack, such as the SRB.
{"title":"The 1996 Mid-AtlanticWinter Flood: Exploring climate risk through a storyline approach","authors":"Abigail Pettett, Colin M. Zarzycki","doi":"10.1175/jhm-d-22-0146.1","DOIUrl":"https://doi.org/10.1175/jhm-d-22-0146.1","url":null,"abstract":"Abstract This article explores the application of thermodynamic perturbations to a historical mid-latitude, winter-time, rain-on-snow flood event to evaluate how similar events may evolve under different climate forcings. In particular, we generate a hindcast of the 1996 Mid-Atlantic flood using an ensemble of 14km variable-resolution simulations completed with the Department of Energy’s global Energy Exascale Earth System Model (E3SM). We show the event is skillfully reproduced over the Susquehanna River Basin (SRB) by E3SM when benchmarked against in situ observational data and high-resolution reanalyses. In addition, we perform five counterfactual experiments to simulate the flood under pre-industrial conditions and four different levels of warming as projected by the Community Earth System Model Large Ensemble. We find a nonlinear response in simulated surface runoff and streamflow as a function of atmospheric warming. This is attributed to changing contributions of liquid water input from a shallower initial snowpack (decreased snowmelt), increased surface temperatures and rainfall rates, and increased soil water storage. Flooding associated with this event peaks around +1 to +2K of global average surface warming and decreases with additional warming beyond this. There are noticeable timing shifts in peak runoff and streamflow associated with changes in the flashiness of the event. This work highlights the utility of using storyline approaches for communicating climate risk and demonstrates the potential non-linearities associated with hydrologic extremes in areas that experience ephemeral snowpack, such as the SRB.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135152927","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}
Janice L. Bytheway, Elizabeth J. Thompson, Jie Yang, Haonan Chen
Abstract High-resolution oceanic precipitation estimates are needed to increase our understanding of and ability to monitor ocean-atmosphere coupled processes. Satellite multisensor precipitation products such as IMERG provide global precipitation estimates at relatively high-resolution (0.1°, 30 min), but the resolution at which IMERG precipitation estimates are considered reliable is coarser than the nominal resolution of the product itself. In this study, we examine the ability of the Rainfall Autoregressive Model (RainFARM) statistical downscaling technique to produce ensembles of precipitation fields at relatively high spatial and temporal resolution when applied to spatially and temporally coarsened precipitation fields from IMERG. The downscaled precipitation ensembles are evaluated against in-situ oceanic rain rate observations collected by Passive Aquatic Listeners (PAL) in eleven different ocean domains. We also evaluate IMERG coarsened to the same resolution as the downscaled fields to determine whether the process of coarsening then downscaling improves precipitation estimates more than averaging IMERG to coarser resolution only. Evaluations were performed on individual months, seasons, by ENSO phase, and based on precipitation characteristics. Results were inconsistent, with downscaling improving precipitation estimates in some domains and time periods, and producing worse performance in others. While the results imply that the performance of the downscaled precipitation estimates is related to precipitation characteristics, it is still unclear what characteristic or combinations thereof leads to the most improvement or consistent improvement when applying RainFARM to IMERG.
{"title":"Evaluation of the RainFARM Statistical Downscaling Technique Applied to IMERG over Global Oceans using Passive Aquatic Listener in situ rain measurements","authors":"Janice L. Bytheway, Elizabeth J. Thompson, Jie Yang, Haonan Chen","doi":"10.1175/jhm-d-23-0090.1","DOIUrl":"https://doi.org/10.1175/jhm-d-23-0090.1","url":null,"abstract":"Abstract High-resolution oceanic precipitation estimates are needed to increase our understanding of and ability to monitor ocean-atmosphere coupled processes. Satellite multisensor precipitation products such as IMERG provide global precipitation estimates at relatively high-resolution (0.1°, 30 min), but the resolution at which IMERG precipitation estimates are considered reliable is coarser than the nominal resolution of the product itself. In this study, we examine the ability of the Rainfall Autoregressive Model (RainFARM) statistical downscaling technique to produce ensembles of precipitation fields at relatively high spatial and temporal resolution when applied to spatially and temporally coarsened precipitation fields from IMERG. The downscaled precipitation ensembles are evaluated against in-situ oceanic rain rate observations collected by Passive Aquatic Listeners (PAL) in eleven different ocean domains. We also evaluate IMERG coarsened to the same resolution as the downscaled fields to determine whether the process of coarsening then downscaling improves precipitation estimates more than averaging IMERG to coarser resolution only. Evaluations were performed on individual months, seasons, by ENSO phase, and based on precipitation characteristics. Results were inconsistent, with downscaling improving precipitation estimates in some domains and time periods, and producing worse performance in others. While the results imply that the performance of the downscaled precipitation estimates is related to precipitation characteristics, it is still unclear what characteristic or combinations thereof leads to the most improvement or consistent improvement when applying RainFARM to IMERG.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135395349","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}
Despite the intensifying interest in flash drought both within the U.S. and globally, moist tropical landscapes have largely escaped the attention of the flash drought community. Because these ecozones are acclimatized to receiving regular, near-daily precipitation, they are especially vulnerable to rapid-drying events. This is particularly true within the Caribbean basin where numerous small islands lack the surface and groundwater resources to cope with swiftly developing drought conditions. This study fills the tropical flash drought gap by examining the pervasiveness of flash drought across the pan-Caribbean region using a recently proposed criterion based on the Evaporative Demand Drought Index (EDDI). The EDDI identifies 46 instances of widespread flash drought “outbreaks” in which significant fractions of the pan-Caribbean encounter rapid drying over 15 days and then maintain this condition for another 15 days. Moreover, a self-organizing maps (SOM) classification reveals a tendency for flash drought to assume recurring typologies concentrated in either the Central American, South American, or Greater Antilles coastlines, though a simultaneous, Caribbean-wide drought is never observed within the 40-year (1981-2020) period examined. Further, three of the six flash drought typologies identified by the SOM initiate most often during Phase 2 of the Madden-Julian Oscillation. Collectively, these findings motivate the need to more critically examine the transferability of flash drought definitions into the global tropics, particularly for small water-vulnerable islands where even island-wide flash droughts may only occupy a few pixels in most reanalysis datasets.
{"title":"Atmospheric Flash Drought in the Caribbean","authors":"Craig A. Ramseyer, Paul W. Miller","doi":"10.1175/jhm-d-22-0226.1","DOIUrl":"https://doi.org/10.1175/jhm-d-22-0226.1","url":null,"abstract":"Despite the intensifying interest in flash drought both within the U.S. and globally, moist tropical landscapes have largely escaped the attention of the flash drought community. Because these ecozones are acclimatized to receiving regular, near-daily precipitation, they are especially vulnerable to rapid-drying events. This is particularly true within the Caribbean basin where numerous small islands lack the surface and groundwater resources to cope with swiftly developing drought conditions. This study fills the tropical flash drought gap by examining the pervasiveness of flash drought across the pan-Caribbean region using a recently proposed criterion based on the Evaporative Demand Drought Index (EDDI). The EDDI identifies 46 instances of widespread flash drought “outbreaks” in which significant fractions of the pan-Caribbean encounter rapid drying over 15 days and then maintain this condition for another 15 days. Moreover, a self-organizing maps (SOM) classification reveals a tendency for flash drought to assume recurring typologies concentrated in either the Central American, South American, or Greater Antilles coastlines, though a simultaneous, Caribbean-wide drought is never observed within the 40-year (1981-2020) period examined. Further, three of the six flash drought typologies identified by the SOM initiate most often during Phase 2 of the Madden-Julian Oscillation. Collectively, these findings motivate the need to more critically examine the transferability of flash drought definitions into the global tropics, particularly for small water-vulnerable islands where even island-wide flash droughts may only occupy a few pixels in most reanalysis datasets.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"302 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139340451","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}
Abstract The expansion of the boreal forest poleward is a potentially important driver of feedbacks between the land surface and Arctic climate. A growing body of work has highlighted the importance of differences in evaporative resistance between different possible future Arctic land covers, which in turn alters humidity and cloudiness in the boundary layer, for these feedbacks. While thus far this problem has been studied primarily with complex Earth system models, we turn to a locally focused, idealized model capable of diagnosing and testing the sensitivity of first order processes connecting vegetation, the atmospheric boundary layer, and low clouds in this critical region. This allows us to benchmark the mechanisms and results at the center of predictions from larger-scale simulations. A surface dominated by broadleaf trees, characterized by higher albedo and lower surface evaporative resistance, drives cooling and moistening of the boundary layer relative to a surface of needleleaf trees, characterized by lower albedo and higher surface evaporative resistance. Differences in evaporative resistance between these hypothetical Arctic vegetation covers are of equal importance to changes in albedo for the initial response of the boundary layer to boreal expansion, even with our idealized approach. However, compensation between the elevation of the lifting condensation level (LCL) and more rapid growth of the mixed layer over higher evaporative resistance surfaces can minimize changes in the favorability of shallow clouds over different land cover types under some conditions. We then perform two tests on the sensitivity of this compensating effect, to changes in water availability, represented first by a reduction in boundary layer humidity and then by both a reduction in humidity and soil moisture available to our vegetation surface. Finally, given the importance of this potential LCL-mixed layer height compensation in our idealized modelling results, we look to determine its relevance in observational data from a field campaign in boreal Finland. These observations do confirm that such a coupling plays an important role in cumulus-topped boundary layers over a needleleaf forest surface. While our results confirm some underlying mechanisms at the center of prior work with Earth system models, they also provide motivation for future work to constrain the impact of boreal forest expansion. This will include both large eddy simulations to examine the impact of processes and feedbacks not resolved by a mixed layer model, as well as a more systematic evaluation and comparison of relevant observations at the site in Finland and sites from prior boreal field campaigns.
{"title":"Grounding our understanding of the impacts of boreal forest expansion on shallow cumulus clouds with a simple modelling framework","authors":"Sam Pennypacker, Robert Wood","doi":"10.1175/jhm-d-22-0165.1","DOIUrl":"https://doi.org/10.1175/jhm-d-22-0165.1","url":null,"abstract":"Abstract The expansion of the boreal forest poleward is a potentially important driver of feedbacks between the land surface and Arctic climate. A growing body of work has highlighted the importance of differences in evaporative resistance between different possible future Arctic land covers, which in turn alters humidity and cloudiness in the boundary layer, for these feedbacks. While thus far this problem has been studied primarily with complex Earth system models, we turn to a locally focused, idealized model capable of diagnosing and testing the sensitivity of first order processes connecting vegetation, the atmospheric boundary layer, and low clouds in this critical region. This allows us to benchmark the mechanisms and results at the center of predictions from larger-scale simulations. A surface dominated by broadleaf trees, characterized by higher albedo and lower surface evaporative resistance, drives cooling and moistening of the boundary layer relative to a surface of needleleaf trees, characterized by lower albedo and higher surface evaporative resistance. Differences in evaporative resistance between these hypothetical Arctic vegetation covers are of equal importance to changes in albedo for the initial response of the boundary layer to boreal expansion, even with our idealized approach. However, compensation between the elevation of the lifting condensation level (LCL) and more rapid growth of the mixed layer over higher evaporative resistance surfaces can minimize changes in the favorability of shallow clouds over different land cover types under some conditions. We then perform two tests on the sensitivity of this compensating effect, to changes in water availability, represented first by a reduction in boundary layer humidity and then by both a reduction in humidity and soil moisture available to our vegetation surface. Finally, given the importance of this potential LCL-mixed layer height compensation in our idealized modelling results, we look to determine its relevance in observational data from a field campaign in boreal Finland. These observations do confirm that such a coupling plays an important role in cumulus-topped boundary layers over a needleleaf forest surface. While our results confirm some underlying mechanisms at the center of prior work with Earth system models, they also provide motivation for future work to constrain the impact of boreal forest expansion. This will include both large eddy simulations to examine the impact of processes and feedbacks not resolved by a mixed layer model, as well as a more systematic evaluation and comparison of relevant observations at the site in Finland and sites from prior boreal field campaigns.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135741965","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}
Annual spring and summer runoff from western Colorado is relied upon by 40 million people, six states, and two countries. Cool season precipitation and snowpack have historically been robust predictors of seasonal runoff in western Colorado. Forecasts made with this information allow water managers to plan for the season ahead. Antecedent hydrological conditions, such as root zone soil moisture and groundwater storage, and weather conditions following peak snowpack, also impact seasonal runoff. The role of such factors were scrutinized in 2020 and 2021: seasonal runoff was much lower than expectations based on snowpack values alone. We investigate the relative importance of meteorological and hydrological conditions occurring before and after the snowpack season in predicting seasonal runoff in western Colorado. This question is critical because the most effective investment strategy for improving forecasts depends on if errors arise before or after the snowpack season. This study is conducted using observations from the Snow Telemetry Network, root zone soil moisture and groundwater data from the Western Land Data Assimilation Systems, and a Random Forest-based statistical forecasting framework. We find that on average antecedent root zone soil moisture and groundwater storage values do not add significant skill to seasonal water supply forecasts in western Colorado. In contrast, precipitation and temperature data after the time of peak snowpack improve water supply forecasts significantly. 2020 and 2021 runoffs were hampered by dry conditions both before and after the snowpack season. Both antecedent soil moisture and spring/summer precipitation data improved water supply forecast accuracy in these years.
{"title":"On the Sources of Water Supply Forecast Error in Western Colorado","authors":"Peter E. Goble, Russ S. Schumacher","doi":"10.1175/jhm-d-23-0004.1","DOIUrl":"https://doi.org/10.1175/jhm-d-23-0004.1","url":null,"abstract":"\u0000Annual spring and summer runoff from western Colorado is relied upon by 40 million people, six states, and two countries. Cool season precipitation and snowpack have historically been robust predictors of seasonal runoff in western Colorado. Forecasts made with this information allow water managers to plan for the season ahead. Antecedent hydrological conditions, such as root zone soil moisture and groundwater storage, and weather conditions following peak snowpack, also impact seasonal runoff. The role of such factors were scrutinized in 2020 and 2021: seasonal runoff was much lower than expectations based on snowpack values alone. We investigate the relative importance of meteorological and hydrological conditions occurring before and after the snowpack season in predicting seasonal runoff in western Colorado. This question is critical because the most effective investment strategy for improving forecasts depends on if errors arise before or after the snowpack season. This study is conducted using observations from the Snow Telemetry Network, root zone soil moisture and groundwater data from the Western Land Data Assimilation Systems, and a Random Forest-based statistical forecasting framework. We find that on average antecedent root zone soil moisture and groundwater storage values do not add significant skill to seasonal water supply forecasts in western Colorado. In contrast, precipitation and temperature data after the time of peak snowpack improve water supply forecasts significantly. 2020 and 2021 runoffs were hampered by dry conditions both before and after the snowpack season. Both antecedent soil moisture and spring/summer precipitation data improved water supply forecast accuracy in these years.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"20 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84929698","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}
Zhijun Huang, Hua Wu, Guojun Gu, Xiaomeng Li, Nergui Nanding, Robert F. Adler, K. Yilmaz, Lorenzo Alfieri, Sirong Chen
Precipitation data is known to be the key driver of hydrological simulations. Hence, reliable quantitative precipitation estimates and forecasts are vital for accurate hydrological forecasting. Satellite-based precipitation estimates from Integrated Multi-satellite Retrievals for GPM Early Run (IMERG-E) and forecasted precipitation from NASA’s Goddard Earth Observing System (GEOS-FP) have shown values in global flood nowcasting and forecasting. However, few studies have comprehensively evaluated their hydrological performance, let alone exploring the potential value of combining them. Therefore, this study undertakes a quasi-global evaluation of their utility in real-time hydrological monitoring and 1-5-day forecasting with the DRIVE model. The gauge-corrected IMERG Final Run precipitation estimates and corresponding hydrological simulation are used as the references. Results showed that the hit bias is the dominant error source of IMERG-E, while the false precipitation is more noticeable in GEOS-FP. In terms of hydrological performance, GEOS-FP driven model (DRIVE-FP) performance is close to IMERG-E driven model (DRIVE-E) performance on Day 1, indicating that GEOS-FP could nicely fill the gap of nowcasting caused by the IMERG-E time latency. For longer lead time forecasts, the bias tends to diminish in most regions likely because the under-/over-estimation in IMERG-E is generally offset by the distinct types of misestimation in GEOS-FP. The skillful initial hydrological conditions present outperformed forecasts in most region, except for tropical areas where the accuracy of GEOS-FP prevails. Overall, this study provides a valuable view of the combined use of IMERG-E and GEOS-FP precipitation in the context of hydrological nowcasts and forecasts.
众所周知，降水数据是水文模拟的关键驱动因素。因此，可靠的定量降水估计和预报对于准确的水文预报至关重要。基于卫星的GPM早期综合多卫星检索(imerge)降水估算和来自美国宇航局戈达德地球观测系统(GEOS-FP)的预报降水在全球洪水临近预报和预报中显示出价值。然而，很少有研究全面评估它们的水文性能，更不用说探索它们结合的潜在价值。因此，本研究对它们在实时水文监测和使用DRIVE模型进行1-5天预报中的效用进行了准全球评估。以经量规校正的IMERG Final Run降水估算值和相应的水文模拟为参考。结果表明，命中偏差是imerge的主要误差来源，而假降水在GEOS-FP中更为明显。在水文性能方面，GEOS-FP驱动模型(DRIVE-FP)在第1天的性能接近imerge驱动模型(DRIVE-E)的性能，表明GEOS-FP可以很好地填补imerge时间延迟造成的临近预报的空白。对于较长的提前期预测，偏差在大多数地区趋于减少，这可能是因为imerge中的过低/过高估计通常被GEOS-FP中不同类型的错误估计所抵消。在大多数地区，熟练的初始水文条件都优于预报，但在热带地区，GEOS-FP的准确性普遍较高。总的来说，本研究为imerge和GEOS-FP降水在水文预报和预报背景下的联合使用提供了有价值的观点。
{"title":"Paired satellite and NWP precipitation for global flood forecasting","authors":"Zhijun Huang, Hua Wu, Guojun Gu, Xiaomeng Li, Nergui Nanding, Robert F. Adler, K. Yilmaz, Lorenzo Alfieri, Sirong Chen","doi":"10.1175/jhm-d-23-0044.1","DOIUrl":"https://doi.org/10.1175/jhm-d-23-0044.1","url":null,"abstract":"\u0000Precipitation data is known to be the key driver of hydrological simulations. Hence, reliable quantitative precipitation estimates and forecasts are vital for accurate hydrological forecasting. Satellite-based precipitation estimates from Integrated Multi-satellite Retrievals for GPM Early Run (IMERG-E) and forecasted precipitation from NASA’s Goddard Earth Observing System (GEOS-FP) have shown values in global flood nowcasting and forecasting. However, few studies have comprehensively evaluated their hydrological performance, let alone exploring the potential value of combining them. Therefore, this study undertakes a quasi-global evaluation of their utility in real-time hydrological monitoring and 1-5-day forecasting with the DRIVE model. The gauge-corrected IMERG Final Run precipitation estimates and corresponding hydrological simulation are used as the references. Results showed that the hit bias is the dominant error source of IMERG-E, while the false precipitation is more noticeable in GEOS-FP. In terms of hydrological performance, GEOS-FP driven model (DRIVE-FP) performance is close to IMERG-E driven model (DRIVE-E) performance on Day 1, indicating that GEOS-FP could nicely fill the gap of nowcasting caused by the IMERG-E time latency. For longer lead time forecasts, the bias tends to diminish in most regions likely because the under-/over-estimation in IMERG-E is generally offset by the distinct types of misestimation in GEOS-FP. The skillful initial hydrological conditions present outperformed forecasts in most region, except for tropical areas where the accuracy of GEOS-FP prevails. Overall, this study provides a valuable view of the combined use of IMERG-E and GEOS-FP precipitation in the context of hydrological nowcasts and forecasts.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"54 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88852419","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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