An Chang, K. Bogner, C. Grams, S. Monhart, D. Domeisen, M. Zappa
Across the globe, there has been an increasing interest in improving the predictability of sub-seasonal hydro-meteorological forecasts as they play a valuable role in medium- to long-term planning in many sectors such as agriculture, navigation, hydropower, and emergency management. However, these forecasts still have very limited skill at the monthly time scale; hence this study explores the possibilities for improving forecasts through different pre- and post-processing techniques at the interface with a hydrological model (PREVAH). Specifically, this research aims to assess the benefit from European Weather Regime (WR) data into a hybrid forecasting setup, a combination of a traditional hydrological model and a machine learning (ML) algorithm, to improve the performance of sub-seasonal hydro-meteorological forecasts in Switzerland. The WR data contains information about the large-scale atmospheric circulation in the North-Atlantic European region, and thus allows the hydrological model to exploit potential flow-dependent predictability. Four hydrological variables are investigated: total runoff, baseflow, soil moisture, and snowmelt. The improvements in the forecasts achieved with the pre- and post-processing techniques vary with catchments, lead times, and variables. Adding WR data has clear benefits, but these benefits are not consistent across the study area or among the variables. The usefulness of WR data is generally observed for longer lead times, e.g., beyond the third week. Furthermore, a multi-model approach is applied to determine the “best practice” for each catchment and improve forecast skill over the entire study area. This study highlights the potential and limitations of using WR information to improve sub-seasonal hydro-meteorological forecasts in a hybrid forecasting system in an operational mode.
{"title":"Exploring the use of European weather regimes for improving user-relevant hydrological forecasts at the sub-seasonal scale in Switzerland","authors":"An Chang, K. Bogner, C. Grams, S. Monhart, D. Domeisen, M. Zappa","doi":"10.1175/jhm-d-21-0245.1","DOIUrl":"https://doi.org/10.1175/jhm-d-21-0245.1","url":null,"abstract":"\u0000Across the globe, there has been an increasing interest in improving the predictability of sub-seasonal hydro-meteorological forecasts as they play a valuable role in medium- to long-term planning in many sectors such as agriculture, navigation, hydropower, and emergency management. However, these forecasts still have very limited skill at the monthly time scale; hence this study explores the possibilities for improving forecasts through different pre- and post-processing techniques at the interface with a hydrological model (PREVAH). Specifically, this research aims to assess the benefit from European Weather Regime (WR) data into a hybrid forecasting setup, a combination of a traditional hydrological model and a machine learning (ML) algorithm, to improve the performance of sub-seasonal hydro-meteorological forecasts in Switzerland. The WR data contains information about the large-scale atmospheric circulation in the North-Atlantic European region, and thus allows the hydrological model to exploit potential flow-dependent predictability. Four hydrological variables are investigated: total runoff, baseflow, soil moisture, and snowmelt. The improvements in the forecasts achieved with the pre- and post-processing techniques vary with catchments, lead times, and variables. Adding WR data has clear benefits, but these benefits are not consistent across the study area or among the variables. The usefulness of WR data is generally observed for longer lead times, e.g., beyond the third week. Furthermore, a multi-model approach is applied to determine the “best practice” for each catchment and improve forecast skill over the entire study area. This study highlights the potential and limitations of using WR information to improve sub-seasonal hydro-meteorological forecasts in a hybrid forecasting system in an operational mode.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"24 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76869278","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}
Dizhou Wang, Xinping Zhang, Zidong Luo, Xiong Xiao, Zhong-fang Liu, Xinguang He, Z. Rao, H. Guan
The transport of atmospheric water vapor plays a crucial role in the production of precipitation and the variation of precipitation isotopic composition (δ18Op). This study investigates three precipitation events with extremely depleted precipitation isotopes in the summer rainfall of the Adelaide, Australia. Using fundamental water vapor diagnostic and moisture calculation methods, this research analyzes the impact of rainout levels along moisture transport paths, atmospheric circulation patterns, water vapor sources, and moisture transport on the extreme depletion of precipitation isotopes in the study area. The purpose of this study is to reveal the direct cause of generating extremely depleted δ18Op at hourly time scale, and to understand the influence of water vapor transport on δ18Op. The results show the diversity and complexity of δ18Op variation in summer precipitation events in Adelaide. The rainout caused by local and upstream large precipitation may be the main reason for the steep drop to an extremely low value of δ18Op. The phenomenon of sub-cloud secondary evaporation, which is driven by the interaction between relatively low humidity and high temperature at near-surface levels, plays a pivotal role in the entire precipitation process. This mechanism is particularly pronounced during the onset or cessation of precipitation events, thereby resulting in the observed enrichment of δ18Op values. The oxygen stable isotopic composition of water vapor (δ18Oa) would usually become higher, when the air mass mixes with new moisture with relatively high δ18Oa suppressing the influence of the previous rainout. The evapotranspiration(ET) from the underlying surface along water vapor transport pathways modulates the isotopic composition of atmospheric water vapor . When the δ18O in ET exceeds that in precipitation, δ18Oa gradually becomes enriched.
{"title":"Meteorological analysis on extremely depleted 18O rainfall events during the summer in Adelaide, Australia","authors":"Dizhou Wang, Xinping Zhang, Zidong Luo, Xiong Xiao, Zhong-fang Liu, Xinguang He, Z. Rao, H. Guan","doi":"10.1175/jhm-d-22-0228.1","DOIUrl":"https://doi.org/10.1175/jhm-d-22-0228.1","url":null,"abstract":"\u0000The transport of atmospheric water vapor plays a crucial role in the production of precipitation and the variation of precipitation isotopic composition (δ18Op). This study investigates three precipitation events with extremely depleted precipitation isotopes in the summer rainfall of the Adelaide, Australia. Using fundamental water vapor diagnostic and moisture calculation methods, this research analyzes the impact of rainout levels along moisture transport paths, atmospheric circulation patterns, water vapor sources, and moisture transport on the extreme depletion of precipitation isotopes in the study area. The purpose of this study is to reveal the direct cause of generating extremely depleted δ18Op at hourly time scale, and to understand the influence of water vapor transport on δ18Op. The results show the diversity and complexity of δ18Op variation in summer precipitation events in Adelaide. The rainout caused by local and upstream large precipitation may be the main reason for the steep drop to an extremely low value of δ18Op. The phenomenon of sub-cloud secondary evaporation, which is driven by the interaction between relatively low humidity and high temperature at near-surface levels, plays a pivotal role in the entire precipitation process. This mechanism is particularly pronounced during the onset or cessation of precipitation events, thereby resulting in the observed enrichment of δ18Op values. The oxygen stable isotopic composition of water vapor (δ18Oa) would usually become higher, when the air mass mixes with new moisture with relatively high δ18Oa suppressing the influence of the previous rainout. The evapotranspiration(ET) from the underlying surface along water vapor transport pathways modulates the isotopic composition of atmospheric water vapor . When the δ18O in ET exceeds that in precipitation, δ18Oa gradually becomes enriched.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"11 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90989175","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}
M. Parrens, Ahmad Al Bitar, Ayan Santos Fleischmann
Extreme droughts and floods in the Amazon have great implications for ecosystems and societies. Over the last decade, the region has undergone major extreme events with no equivalent in the previous 100 years. Wetlands have been greatly impacted by these events. This study aims at presenting new indicators for wetlands based on Water Surface Extent (WSE): duration of the flooded and non-flooded season, number of days of extreme events, delay of the start of the flooded season, and severity for each season. These indicators are more adapted for monitoring of wetlands than those based on precipitation, discharge or groundwater information. They are computed for seven major Amazon sub-basins for flooded and non-flooded seasons. These indicators improve our knowledge of the temporal behavior of water surface during different extreme events, such as the 2015/2016 drought and the 2014 flood occurred in the Madeira basin. For the Negro basin and from the point of view of wetlands, the 2015 non-flooded season was 55% more severe than the average of the non-flooded season during the 2011-2018 period. For the Paru, Trombetas, Negro and Solimões basins, we found that a delay in the arrival of the flooded season led to a weak flood season in terms of severity. No correlation between the onset of the flooded season and its severity was found for the Madeira, Xingu and Tapajós basins. Future hydrometeorological monitoring systems would benefit from including, in addition to variables such as river discharge and water elevation, precipitation and vegetation dynamics, a severity index based on water surfaces as proposed in this study.
{"title":"Monitoring extreme floods and droughts in the Amazon basin with surface water based indices","authors":"M. Parrens, Ahmad Al Bitar, Ayan Santos Fleischmann","doi":"10.1175/jhm-d-22-0170.1","DOIUrl":"https://doi.org/10.1175/jhm-d-22-0170.1","url":null,"abstract":"\u0000Extreme droughts and floods in the Amazon have great implications for ecosystems and societies. Over the last decade, the region has undergone major extreme events with no equivalent in the previous 100 years. Wetlands have been greatly impacted by these events. This study aims at presenting new indicators for wetlands based on Water Surface Extent (WSE): duration of the flooded and non-flooded season, number of days of extreme events, delay of the start of the flooded season, and severity for each season. These indicators are more adapted for monitoring of wetlands than those based on precipitation, discharge or groundwater information. They are computed for seven major Amazon sub-basins for flooded and non-flooded seasons. These indicators improve our knowledge of the temporal behavior of water surface during different extreme events, such as the 2015/2016 drought and the 2014 flood occurred in the Madeira basin. For the Negro basin and from the point of view of wetlands, the 2015 non-flooded season was 55% more severe than the average of the non-flooded season during the 2011-2018 period. For the Paru, Trombetas, Negro and Solimões basins, we found that a delay in the arrival of the flooded season led to a weak flood season in terms of severity. No correlation between the onset of the flooded season and its severity was found for the Madeira, Xingu and Tapajós basins. Future hydrometeorological monitoring systems would benefit from including, in addition to variables such as river discharge and water elevation, precipitation and vegetation dynamics, a severity index based on water surfaces as proposed in this study.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"116 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77454664","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}
Increasing evaporative demand from storage reservoirs is aggravating water scarcity issues across the American West. In the Rio Grande Basin, open water evaporation estimates represent approximately one-fifth of all water losses from the Basin. However, most estimates of reservoir evaporation rely on outdated methods, point measurements, or simplistic models. Warming temperatures and increasing atmospheric evaporative demand are stressing over-allocated resources, increasing the need for improved evaporation estimates. In response to this need, we develop open water evaporation estimates at Elephant Butte Reservoir (EBR), New Mexico, using three evaporation models and field measurements. Few studies quantify spatial heterogeneity in evaporation rates across large reservoirs; we therefore focus our efforts on using the Weather, Research, and Forecasting model coupled to an energy budget lake model, WRF-Lake, to simulate evaporation across EBR over the course of two years. We compare results from WRF-Lake, which simulates lake heat storage, to results from the Complementary Relationship Lake Evaporation (CRLE) model and the Global Lake Evaporation Volume dataset (GLEV). Results indicate that monthly and annual evaporation totals from WRF-Lake and GLEV are similar, while CRLE overestimates annual evaporation totals, with monthly peak evaporation offset compared to WRF-Lake and GLEV. While WRF-Lake and GLEV appear to capture monthly and annual evaporation totals, only WRF-Lake simulates differences in evaporation totals across the reservoir surface. Average annual evaporation at EBR was approximately 1487 mm, yet annual totals differed by up to 545 mm depending on location. This study improves understanding of open water evaporation and elucidates limitations of extrapolating point in-situ or bulk evaporation estimates across large reservoirs.
{"title":"Characterizing Spatial Heterogeneity in Reservoir Evaporation within the Rio Grande Basin using a Coupled Version of the Weather, Research, and Forecasting Model","authors":"K. D. Holman, K. Mikkelson, D. Llewellyn","doi":"10.1175/jhm-d-22-0210.1","DOIUrl":"https://doi.org/10.1175/jhm-d-22-0210.1","url":null,"abstract":"\u0000Increasing evaporative demand from storage reservoirs is aggravating water scarcity issues across the American West. In the Rio Grande Basin, open water evaporation estimates represent approximately one-fifth of all water losses from the Basin. However, most estimates of reservoir evaporation rely on outdated methods, point measurements, or simplistic models. Warming temperatures and increasing atmospheric evaporative demand are stressing over-allocated resources, increasing the need for improved evaporation estimates. In response to this need, we develop open water evaporation estimates at Elephant Butte Reservoir (EBR), New Mexico, using three evaporation models and field measurements. Few studies quantify spatial heterogeneity in evaporation rates across large reservoirs; we therefore focus our efforts on using the Weather, Research, and Forecasting model coupled to an energy budget lake model, WRF-Lake, to simulate evaporation across EBR over the course of two years. We compare results from WRF-Lake, which simulates lake heat storage, to results from the Complementary Relationship Lake Evaporation (CRLE) model and the Global Lake Evaporation Volume dataset (GLEV). Results indicate that monthly and annual evaporation totals from WRF-Lake and GLEV are similar, while CRLE overestimates annual evaporation totals, with monthly peak evaporation offset compared to WRF-Lake and GLEV. While WRF-Lake and GLEV appear to capture monthly and annual evaporation totals, only WRF-Lake simulates differences in evaporation totals across the reservoir surface. Average annual evaporation at EBR was approximately 1487 mm, yet annual totals differed by up to 545 mm depending on location. This study improves understanding of open water evaporation and elucidates limitations of extrapolating point in-situ or bulk evaporation estimates across large reservoirs.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"39 5 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78925664","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}
As global mean temperature rises, extreme drought events are expected to increasingly affect regions of the US that are crucial for agriculture, forestry, and natural ecology. A pressing need is to better understand and anticipate the conditions under which extreme drought causes catastrophic failure to vegetation in these areas. In order to better predict drought impacts on ecosystems, we first must understand how specific drivers, namely, atmospheric aridity and soil water stress, affect land-surface processes during the evolution of flash drought events. In this study, we evaluated when vapor pressure deficit (VPD) and soil moisture thresholds corresponding to photosynthetic shutdown were crossed during flash drought events across different climate zones and land surface characteristics in the US. First, the Dynamic Canopy Biophysical Properties (DCBP) model was used to estimate the thresholds that define reduced photosynthesis by assimilating vegetation phenology data from MODIS to a predictive phenology model. Next, we characterized and quantified flash drought onset, intensity, and duration using the Standardized Evaporative Stress Ratio (SESR) and NLDAS-2 reanalysis. Once periods of flash drought were identified, we investigated how VPD and soil moisture co-evolved across regions and plant functional types. Results demonstrate that croplands and grasslands tend to be more sensitive to soil water limitations than trees across different regions of the US. We found that whether VPD or soil moisture was the primary driver of plant water stress during drought was largely region-specific. The results of this work will help to inform land managers of early warning signals relevant for specific ecosystems under threat of flash drought events.
{"title":"How land surface characteristics influence the development of flash drought through the drivers of soil moisture and vapor pressure deficit","authors":"L. Lowman, J. Christian, E. Hunt","doi":"10.1175/jhm-d-22-0158.1","DOIUrl":"https://doi.org/10.1175/jhm-d-22-0158.1","url":null,"abstract":"\u0000As global mean temperature rises, extreme drought events are expected to increasingly affect regions of the US that are crucial for agriculture, forestry, and natural ecology. A pressing need is to better understand and anticipate the conditions under which extreme drought causes catastrophic failure to vegetation in these areas. In order to better predict drought impacts on ecosystems, we first must understand how specific drivers, namely, atmospheric aridity and soil water stress, affect land-surface processes during the evolution of flash drought events. In this study, we evaluated when vapor pressure deficit (VPD) and soil moisture thresholds corresponding to photosynthetic shutdown were crossed during flash drought events across different climate zones and land surface characteristics in the US. First, the Dynamic Canopy Biophysical Properties (DCBP) model was used to estimate the thresholds that define reduced photosynthesis by assimilating vegetation phenology data from MODIS to a predictive phenology model. Next, we characterized and quantified flash drought onset, intensity, and duration using the Standardized Evaporative Stress Ratio (SESR) and NLDAS-2 reanalysis. Once periods of flash drought were identified, we investigated how VPD and soil moisture co-evolved across regions and plant functional types. Results demonstrate that croplands and grasslands tend to be more sensitive to soil water limitations than trees across different regions of the US. We found that whether VPD or soil moisture was the primary driver of plant water stress during drought was largely region-specific. The results of this work will help to inform land managers of early warning signals relevant for specific ecosystems under threat of flash drought events.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"47 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89664061","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}
S. Yatheendradas, D. Mocko, C. Peters-Lidard, Kamalesh Kumar
Using information theory, our study quantifies the importance of selected indicators for the U.S. Drought Monitor (USDM) maps. We use the technique of mutual information (MI) to measure the importance of any indicator to the USDM, and because MI is derived solely from the data, our findings are independent of any model structure (conceptual, physically-based, or empirical). We also compare these MIs against the drought representation effectiveness ratings in the North America Drought Indices and Indicators Assessment (NADIIA) survey for Koeppen climate zones. This reveals: [1] agreement between some ratings and our MI values (high for example indicators like Standardized Precipitation-Evapotranspiration Index or SPEI); [2] some divergences (for example, soil moisture has high ratings but near-zero MIs for ESA-CCI soil moisture in the Western U.S., indicating the need of another remotely sensed soil moisture source); and [3] new insights into the importance of variables such as Snow Water Equivalent (SWE) that are not included in sources like NADIIA. Further analysis of the MI results yields findings related to: [1] hydrological mechanisms (summertime SWE domination during individual drought events through snowmelt into the water-scarce soil); [2] hydroclimatic types (the top pair of inputs in the Western and non-Western regions are SPEIs and soil moistures respectively); and [3] predictability (high for the California 2012-2017 event, with longer-timescale indicators dominating). Finally, the high MIs between multiple indicators jointly and the USDM indicate potentially high drought forecasting accuracies achievable using only model-based inputs, and the potential for global drought monitoring using only remotely sensed inputs, especially for locations having insufficient in situ observations.
{"title":"Quantifying the Importance of Selected Drought Indicators for the United States Drought Monitor","authors":"S. Yatheendradas, D. Mocko, C. Peters-Lidard, Kamalesh Kumar","doi":"10.1175/jhm-d-22-0180.1","DOIUrl":"https://doi.org/10.1175/jhm-d-22-0180.1","url":null,"abstract":"\u0000Using information theory, our study quantifies the importance of selected indicators for the U.S. Drought Monitor (USDM) maps. We use the technique of mutual information (MI) to measure the importance of any indicator to the USDM, and because MI is derived solely from the data, our findings are independent of any model structure (conceptual, physically-based, or empirical). We also compare these MIs against the drought representation effectiveness ratings in the North America Drought Indices and Indicators Assessment (NADIIA) survey for Koeppen climate zones. This reveals: [1] agreement between some ratings and our MI values (high for example indicators like Standardized Precipitation-Evapotranspiration Index or SPEI); [2] some divergences (for example, soil moisture has high ratings but near-zero MIs for ESA-CCI soil moisture in the Western U.S., indicating the need of another remotely sensed soil moisture source); and [3] new insights into the importance of variables such as Snow Water Equivalent (SWE) that are not included in sources like NADIIA. Further analysis of the MI results yields findings related to: [1] hydrological mechanisms (summertime SWE domination during individual drought events through snowmelt into the water-scarce soil); [2] hydroclimatic types (the top pair of inputs in the Western and non-Western regions are SPEIs and soil moistures respectively); and [3] predictability (high for the California 2012-2017 event, with longer-timescale indicators dominating). Finally, the high MIs between multiple indicators jointly and the USDM indicate potentially high drought forecasting accuracies achievable using only model-based inputs, and the potential for global drought monitoring using only remotely sensed inputs, especially for locations having insufficient in situ observations.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"140 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77687507","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}
Tropical cyclones (TCs) are high-impact events responsible for devastating rainfall and freshwater flooding. Quantitative precipitation estimates (QPEs) are thus essential to better understand and assess TC impacts. QPEs based on different observing platforms (e.g., satellites, ground-based radars, and rain-gauges), however, may vary substantially and must be systematically compared. The objectives of this study are to 1) compute the TC rainfall climatology, 2) investigate TC rainfall extremes and flooding potential, and 3) compare these fundamental quantities over the continental US across a set of widely-used QPE products. We examine five datasets over an 18-year span (2002-2019). The products include three satellite-based products, CPC MORPHing technique (CMORPH), Integrated Multi-satellitE Retrievals for GPM (IMERG), Tropical Rainfall Measuring Mission - Multisatellite Precipitation Analysis (TRMM-TMPA), the ground-radar and rain-gauge-based NCEP Stage IV, and a state-of-the-art, high-resolution reanalysis (ERA5). TC rainfall is highest along the coastal region, especially in North Carolina, northeast Florida, and in the New Orleans and Houston metropolitan areas. Along the East Coast, TC can contribute up to 20% of the warm-season rainfall and to more than 40% of all daily and 6-hourly extreme rain events. Our analysis shows that the Stage IV detects far higher precipitation rates in landfalling TCs, relative to IMERG, CMORPH, TRMM and ERA5. As a result, satellite- and reanalysis-based QPEs underestimate both the TC rainfall climatology and extreme events, particularly in the coastal region. This uncertainty is further reflected in the TC flooding potential measured by the Extreme Rain Multiplier (ERM) values, whose single-cell maxima are substantially underestimated and misplaced compared to the NCEP Stage IV.
{"title":"Tropical Cyclone Rainfall Climatology, Extremes and Flooding Potential over the Continental U.S.","authors":"E. Mazza, Shuyi S. Chen","doi":"10.1175/jhm-d-22-0199.1","DOIUrl":"https://doi.org/10.1175/jhm-d-22-0199.1","url":null,"abstract":"Tropical cyclones (TCs) are high-impact events responsible for devastating rainfall and freshwater flooding. Quantitative precipitation estimates (QPEs) are thus essential to better understand and assess TC impacts. QPEs based on different observing platforms (e.g., satellites, ground-based radars, and rain-gauges), however, may vary substantially and must be systematically compared. The objectives of this study are to 1) compute the TC rainfall climatology, 2) investigate TC rainfall extremes and flooding potential, and 3) compare these fundamental quantities over the continental US across a set of widely-used QPE products. We examine five datasets over an 18-year span (2002-2019). The products include three satellite-based products, CPC MORPHing technique (CMORPH), Integrated Multi-satellitE Retrievals for GPM (IMERG), Tropical Rainfall Measuring Mission - Multisatellite Precipitation Analysis (TRMM-TMPA), the ground-radar and rain-gauge-based NCEP Stage IV, and a state-of-the-art, high-resolution reanalysis (ERA5). TC rainfall is highest along the coastal region, especially in North Carolina, northeast Florida, and in the New Orleans and Houston metropolitan areas. Along the East Coast, TC can contribute up to 20% of the warm-season rainfall and to more than 40% of all daily and 6-hourly extreme rain events. Our analysis shows that the Stage IV detects far higher precipitation rates in landfalling TCs, relative to IMERG, CMORPH, TRMM and ERA5. As a result, satellite- and reanalysis-based QPEs underestimate both the TC rainfall climatology and extreme events, particularly in the coastal region. This uncertainty is further reflected in the TC flooding potential measured by the Extreme Rain Multiplier (ERM) values, whose single-cell maxima are substantially underestimated and misplaced compared to the NCEP Stage IV.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"24 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72795689","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}
As an important reference of reservoir regulation, more and more attention has been paid to the numeric precipitation forecast. Due to the uncertainty of meteorological prediction, reservoir regulation based on precipitation forecasts may lead to flood control risks. Therefore, the reliability of precipitation forecasts is crucial to the formulation of reservoir regulation strategy based on it. In this paper, a reliability assessment model for a continuous precipitation forecast is proposed based on the first-passage problem and fuzzy mathematics. The uncertainty of precipitation forecast is described by the generalized Bayesian model, and the fuzzy reliability of a continuous precipitation forecast can be obtained by the first-passage fuzzy probability model (FFPM). Due to the importance of a no-rain period in flood resource utilization, the no-rain forecasts from four different forecast centers in the Meishan basin are used as an example. The results show that the fuzzy mathematics is helpful in describing the uncertainty of the boundary for the no-rain set, and the fuzzy reliability of the no-rain forecast is affected by the selection of the range for the no-rain forecast, while the influence of the membership function is limited. Furthermore, due to the downward trend of fuzzy reliability as the lead time increases, there is a contradiction between excess water storage of the reservoir and the fuzzy reliability of the no-rain forecast. A longer continuous no-rain period means more excess water storage, but it also faces lower reliability. In actual reservoir regulation, the results of FFPM can be combined with more information to formulate better strategies for reservoir regulation.
{"title":"Evaluating and Modeling the Reliability of Continuous No-Rain Forecast from TIGGE Based on the First-Passage Problem and Fuzzy Mathematics","authors":"Chenkai Cai, Jianqun Wang, Zhijia Li, Xinyi Shen, Jinhua Wen, Helong Wang, Xinyan Zhou","doi":"10.1175/jhm-d-22-0126.1","DOIUrl":"https://doi.org/10.1175/jhm-d-22-0126.1","url":null,"abstract":"\u0000As an important reference of reservoir regulation, more and more attention has been paid to the numeric precipitation forecast. Due to the uncertainty of meteorological prediction, reservoir regulation based on precipitation forecasts may lead to flood control risks. Therefore, the reliability of precipitation forecasts is crucial to the formulation of reservoir regulation strategy based on it. In this paper, a reliability assessment model for a continuous precipitation forecast is proposed based on the first-passage problem and fuzzy mathematics. The uncertainty of precipitation forecast is described by the generalized Bayesian model, and the fuzzy reliability of a continuous precipitation forecast can be obtained by the first-passage fuzzy probability model (FFPM). Due to the importance of a no-rain period in flood resource utilization, the no-rain forecasts from four different forecast centers in the Meishan basin are used as an example. The results show that the fuzzy mathematics is helpful in describing the uncertainty of the boundary for the no-rain set, and the fuzzy reliability of the no-rain forecast is affected by the selection of the range for the no-rain forecast, while the influence of the membership function is limited. Furthermore, due to the downward trend of fuzzy reliability as the lead time increases, there is a contradiction between excess water storage of the reservoir and the fuzzy reliability of the no-rain forecast. A longer continuous no-rain period means more excess water storage, but it also faces lower reliability. In actual reservoir regulation, the results of FFPM can be combined with more information to formulate better strategies for reservoir regulation.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"5 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81168055","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}
Satellite-based rainfall estimates are a great resource for data-scarce regions, including urban regions, because of its finer resolution. Integrated Multi-satellitE Retrievals for GPM (IMERG) is a widely used product and is evaluated at a city scale for the Hyderabad region using two different ground truths, i.e., India Meteorological Department (IMD) gridded rainfall and Telangana State Development Planning Society (TSDPS) automatic weather station (AWS) measured rainfall. The IMERG rainfall estimates are evaluated on multiple spatial and temporal scales as well as on a rainfall event scale. Both continuous and categorical verification metrics suggest good performance of IMERG on the daily scale; however, relatively decreased performance was observed on the hourly scale. Underestimated and overestimated IMERG estimates with respect to IMD gridded rainfall and AWS measured rainfall, respectively, suggest the performance depends on type of ground truth. Unlike categorical metrics, RMSE and PBIAS have a pattern implying a systematic error with respect to rainfall amount. Further, sample size, diurnal variations, and season are found to have a role in IMERG estimates’ performance. Temporal aggregation of hourly to daily time scales showed the improved IMERG performance; however, no spatial-scale dependence was observed among zonewise and Hyderabad region–wise rainfall estimates. Comparison of raw and bias-corrected IMERG rainfall-based intensity–duration–frequency (IDF) curves with corresponding hourly rain gauge IDF curves showcases the value addition via simple bias correction techniques. Overall, the study suggests the IMERG estimates can be used as an alternative data source, and it can be further improved by modifying the retrieval algorithm. Many urban regions are typically data sparse, which limits scientific understanding and reliable engineering designs of various urban hydrometeorology-relevant tasks, including climatological and extreme rainfall characterization, flood hazard assessment, and stormwater management systems. Satellite rainfall estimates come as a great resource and Integrated Multi-satellitE Retrievals for GPM (IMERG) acts as a best alternative. The Hyderabad region, the sixth-largest metropolitan area in India, is selected to analyze the widely used satellite estimates, i.e., retrievals for GPM. The study observed inaccuracies in the IMERG estimates that varied with rainfall magnitudes and space and time scales; nonetheless, the estimates can be used as an alternative data source for decision-making such as whether rain exceeds a certain threshold or not.
{"title":"Insights on Satellite-Based IMERG Precipitation Estimates at Multiple Space and Time Scales for a Developing Urban Region in India","authors":"Padmini Ponukumati, Azharuddin Mohammed, Satish Regonda","doi":"10.1175/jhm-d-22-0160.1","DOIUrl":"https://doi.org/10.1175/jhm-d-22-0160.1","url":null,"abstract":"\u0000Satellite-based rainfall estimates are a great resource for data-scarce regions, including urban regions, because of its finer resolution. Integrated Multi-satellitE Retrievals for GPM (IMERG) is a widely used product and is evaluated at a city scale for the Hyderabad region using two different ground truths, i.e., India Meteorological Department (IMD) gridded rainfall and Telangana State Development Planning Society (TSDPS) automatic weather station (AWS) measured rainfall. The IMERG rainfall estimates are evaluated on multiple spatial and temporal scales as well as on a rainfall event scale. Both continuous and categorical verification metrics suggest good performance of IMERG on the daily scale; however, relatively decreased performance was observed on the hourly scale. Underestimated and overestimated IMERG estimates with respect to IMD gridded rainfall and AWS measured rainfall, respectively, suggest the performance depends on type of ground truth. Unlike categorical metrics, RMSE and PBIAS have a pattern implying a systematic error with respect to rainfall amount. Further, sample size, diurnal variations, and season are found to have a role in IMERG estimates’ performance. Temporal aggregation of hourly to daily time scales showed the improved IMERG performance; however, no spatial-scale dependence was observed among zonewise and Hyderabad region–wise rainfall estimates. Comparison of raw and bias-corrected IMERG rainfall-based intensity–duration–frequency (IDF) curves with corresponding hourly rain gauge IDF curves showcases the value addition via simple bias correction techniques. Overall, the study suggests the IMERG estimates can be used as an alternative data source, and it can be further improved by modifying the retrieval algorithm.\u0000\u0000\u0000Many urban regions are typically data sparse, which limits scientific understanding and reliable engineering designs of various urban hydrometeorology-relevant tasks, including climatological and extreme rainfall characterization, flood hazard assessment, and stormwater management systems. Satellite rainfall estimates come as a great resource and Integrated Multi-satellitE Retrievals for GPM (IMERG) acts as a best alternative. The Hyderabad region, the sixth-largest metropolitan area in India, is selected to analyze the widely used satellite estimates, i.e., retrievals for GPM. The study observed inaccuracies in the IMERG estimates that varied with rainfall magnitudes and space and time scales; nonetheless, the estimates can be used as an alternative data source for decision-making such as whether rain exceeds a certain threshold or not.\u0000","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"107 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80797450","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}
This work explores the relationship between catchment size, rainfall duration and future streamflow increases on 133 North American catchments with sizes ranging from 66.5 to 9886 km2. It uses the outputs from a high spatial (0.11°) and temporal (1-hour) resolution Single Model Initial condition Large Ensemble (SMILE) and a hydrological model to compute extreme rainfall and streamflow for durations ranging from 1 to 72 hours and for return periods of between 2 and 300 years. Increases in extreme precipitation are observed across all durations and return periods. The projected increases are strongly related to duration, frequency and catchment size, with the shortest durations, longest return periods and smaller catchments witnessing the largest relative rainfall increases. These increases can be quite significant, with the 100-year rainfall becoming up to 20 times more frequent over the smaller catchments. A similar duration-frequency-size pattern of increases is also observed for future extreme streamflow, but with even larger relative increases. These results imply that future increases in extreme rainfall will disproportionately impact smaller catchments, and particularly so for impervious urban catchments which are typically small, and whose stormwater drainage infrastructures are designed for long-return period flows, both being conditions for which the amplification of future flow will be maximized.
{"title":"Temporal and spatial amplification of extreme rainfall and extreme floods in a warmer climate","authors":"M. Faghih, F. Brissette","doi":"10.1175/jhm-d-22-0224.1","DOIUrl":"https://doi.org/10.1175/jhm-d-22-0224.1","url":null,"abstract":"\u0000This work explores the relationship between catchment size, rainfall duration and future streamflow increases on 133 North American catchments with sizes ranging from 66.5 to 9886 km2. It uses the outputs from a high spatial (0.11°) and temporal (1-hour) resolution Single Model Initial condition Large Ensemble (SMILE) and a hydrological model to compute extreme rainfall and streamflow for durations ranging from 1 to 72 hours and for return periods of between 2 and 300 years. Increases in extreme precipitation are observed across all durations and return periods. The projected increases are strongly related to duration, frequency and catchment size, with the shortest durations, longest return periods and smaller catchments witnessing the largest relative rainfall increases. These increases can be quite significant, with the 100-year rainfall becoming up to 20 times more frequent over the smaller catchments. A similar duration-frequency-size pattern of increases is also observed for future extreme streamflow, but with even larger relative increases. These results imply that future increases in extreme rainfall will disproportionately impact smaller catchments, and particularly so for impervious urban catchments which are typically small, and whose stormwater drainage infrastructures are designed for long-return period flows, both being conditions for which the amplification of future flow will be maximized.","PeriodicalId":15962,"journal":{"name":"Journal of Hydrometeorology","volume":"41 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90345320","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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