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Abstract
Knowledge gain in the characteristics and mechanisms of drought propagation is indispensable for timely drought early warning and risk reduction over the grassland eco-region. This study focused on the Xilin River basin, which is a typical inland river basin located in the Inner Mongolia temperate steppe, China. The characteristics of meteorological and hydrological drought were assessed by applying the standardized precipitation index and standardized streamflow index. The propagation relationship between meteorological and hydrological droughts was then investigated from both static and dynamic perspectives, and the possible reasons for its temporal dynamics were discussed by considering environmental factors. Our results showed that the Xilin River basin has suffered from more serious meteorological drought than hydrological drought during the past 60 years, with a stationary evolution of meteorological drought but an overall drying trend in hydrological drought. The propagation from meteorological to hydrological droughts exhibited obvious seasonality, characterized by stronger intensity and shorter response time in the wet season. Nonstationary behaviors were identified for the temporal patterns of drought propagation time, especially showing a significant trend in April, May, and August. The dynamic changes in propagation time affected by regional forces were principally ruled by the precipitation variation positively and strongly, and they were moderately controlled by temperature, vegetation cover, and deep-layer soil moisture, with season-dependent effects. The effects of low-frequency atmospheric anomalies on drought propagation will be further investigated in future studies, which are expected to provide a better understanding of the physical mechanism of the large-scale climate forcing on local drought condition.
Significance Statement
A new research approach was proposed to assess the propagation relationship between meteorological and hydrological drought from both static and dynamic perspectives, and the possible reasons for the temporal dynamics were discussed by considering environmental factors. Focusing on an inland river basin over the Inner Mongolia typical steppe, the propagation from meteorological to hydrological droughts showed obvious seasonality. Nonstationary behaviors were identified for the temporal patterns of drought propagation time, which could be explained by the regional hydrometeorological conditions. The advanced understanding of drought propagation provides a scientific base for water resources planning and drought management within a grassland region.
Abstract
Knowledge gain in the characteristics and mechanisms of drought propagation is indispensable for timely drought early warning and risk reduction over the grassland eco-region. This study focused on the Xilin River basin, which is a typical inland river basin located in the Inner Mongolia temperate steppe, China. The characteristics of meteorological and hydrological drought were assessed by applying the standardized precipitation index and standardized streamflow index. The propagation relationship between meteorological and hydrological droughts was then investigated from both static and dynamic perspectives, and the possible reasons for its temporal dynamics were discussed by considering environmental factors. Our results showed that the Xilin River basin has suffered from more serious meteorological drought than hydrological drought during the past 60 years, with a stationary evolution of meteorological drought but an overall drying trend in hydrological drought. The propagation from meteorological to hydrological droughts exhibited obvious seasonality, characterized by stronger intensity and shorter response time in the wet season. Nonstationary behaviors were identified for the temporal patterns of drought propagation time, especially showing a significant trend in April, May, and August. The dynamic changes in propagation time affected by regional forces were principally ruled by the precipitation variation positively and strongly, and they were moderately controlled by temperature, vegetation cover, and deep-layer soil moisture, with season-dependent effects. The effects of low-frequency atmospheric anomalies on drought propagation will be further investigated in future studies, which are expected to provide a better understanding of the physical mechanism of the large-scale climate forcing on local drought condition.
Significance Statement
A new research approach was proposed to assess the propagation relationship between meteorological and hydrological drought from both static and dynamic perspectives, and the possible reasons for the temporal dynamics were discussed by considering environmental factors. Focusing on an inland river basin over the Inner Mongolia typical steppe, the propagation from meteorological to hydrological droughts showed obvious seasonality. Nonstationary behaviors were identified for the temporal patterns of drought propagation time, which could be explained by the regional hydrometeorological conditions. The advanced understanding of drought propagation provides a scientific base for water resources planning and drought management within a grassland region.
Abstract
One of the primary sources of predictability for seasonal hydroclimate forecasts are sea surface temperatures (SSTs) in the tropical Pacific, including El Niño–Southern Oscillation. Multiyear La Niña events in particular may be both predictable at long lead times and favor drought in the bimodal rainfall regions of East Africa. However, SST patterns in the tropical Pacific and adjacent ocean basins often differ substantially between first- and second-year La Niñas, which can change how these events affect regional climate. Here, we demonstrate that multiyear La Niña events favor drought in the Horn of Africa in three consecutive seasons [October–December (OND), March–May (MAM), OND]. But they do not tend to increase the probability of a fourth season of drought owing to the sea surface temperatures and associated atmospheric teleconnections in the MAM long rains season following second-year La Niña events. First-year La Niñas tend to have both greater subsidence over the Horn of Africa, associated with warmer waters in the west Pacific that enhance the Walker circulation, and greater cross-continental moisture transport, associated with a warm tropical Atlantic, as compared to second-year La Niñas. Both the increased subsidence and enhanced cross-continental moisture transport favors drought in the Horn of Africa. Our results provide a physical understanding of the sources and limitations of predictability for using multiyear La Niña forecasts to predict drought in the Horn of Africa.
Abstract
One of the primary sources of predictability for seasonal hydroclimate forecasts are sea surface temperatures (SSTs) in the tropical Pacific, including El Niño–Southern Oscillation. Multiyear La Niña events in particular may be both predictable at long lead times and favor drought in the bimodal rainfall regions of East Africa. However, SST patterns in the tropical Pacific and adjacent ocean basins often differ substantially between first- and second-year La Niñas, which can change how these events affect regional climate. Here, we demonstrate that multiyear La Niña events favor drought in the Horn of Africa in three consecutive seasons [October–December (OND), March–May (MAM), OND]. But they do not tend to increase the probability of a fourth season of drought owing to the sea surface temperatures and associated atmospheric teleconnections in the MAM long rains season following second-year La Niña events. First-year La Niñas tend to have both greater subsidence over the Horn of Africa, associated with warmer waters in the west Pacific that enhance the Walker circulation, and greater cross-continental moisture transport, associated with a warm tropical Atlantic, as compared to second-year La Niñas. Both the increased subsidence and enhanced cross-continental moisture transport favors drought in the Horn of Africa. Our results provide a physical understanding of the sources and limitations of predictability for using multiyear La Niña forecasts to predict drought in the Horn of Africa.
Abstract
The Madden–Julian oscillation (MJO) is a unique type of organized tropical convection varying primarily on subseasonal time scales and is recognized as an important source of subseasonal predictability for midlatitude weather phenomena. This study provides observational evidence of MJO impacts on precipitation extreme intensity, frequency, and duration over the western United States. The results suggest a robust increase in precipitation extremes, especially in frequency, relative to climatological conditions over most of the western United States when the MJO is in its western Pacific phases during the extended boreal winter (October–March). Opposite changes are observed when the MJO is located over the Indian Ocean and Maritime Continent. The above MJO influence is characterized by strong seasonality, with the increase in extreme frequency mainly found in late autumn/early winter (OND) over California and a weaker or opposite response found in late winter (JFM). Also, MJO impacts have stronger regional consistency and persist for a longer time in OND compared to JFM. The seasonality of MJO impacts largely originates from the different amplitudes and patterns of both the MJO and basic states that are weaker and located/retreated more northwestward in OND than in JFM. This leads to different responses in MJO teleconnections including moisture transport and AR activity that contribute to the different precipitation extreme changes. The strong seasonality of the relationship between the MJO and western U.S. extreme precipitation shown in this study has implications to the source of subseasonal-to-seasonal predictions, which has a potential value to stakeholders including water resource managers.
Abstract
The Madden–Julian oscillation (MJO) is a unique type of organized tropical convection varying primarily on subseasonal time scales and is recognized as an important source of subseasonal predictability for midlatitude weather phenomena. This study provides observational evidence of MJO impacts on precipitation extreme intensity, frequency, and duration over the western United States. The results suggest a robust increase in precipitation extremes, especially in frequency, relative to climatological conditions over most of the western United States when the MJO is in its western Pacific phases during the extended boreal winter (October–March). Opposite changes are observed when the MJO is located over the Indian Ocean and Maritime Continent. The above MJO influence is characterized by strong seasonality, with the increase in extreme frequency mainly found in late autumn/early winter (OND) over California and a weaker or opposite response found in late winter (JFM). Also, MJO impacts have stronger regional consistency and persist for a longer time in OND compared to JFM. The seasonality of MJO impacts largely originates from the different amplitudes and patterns of both the MJO and basic states that are weaker and located/retreated more northwestward in OND than in JFM. This leads to different responses in MJO teleconnections including moisture transport and AR activity that contribute to the different precipitation extreme changes. The strong seasonality of the relationship between the MJO and western U.S. extreme precipitation shown in this study has implications to the source of subseasonal-to-seasonal predictions, which has a potential value to stakeholders including water resource managers.
Abstract
Parameterization schemes such as soil thermal conductivity (STC) have an important impact on precipitation simulation. The precipitation in the rainy season (April–September) is the main factor affecting aridification in northern China. However, it is unclear how STC affects precipitation simulation during the rainy season. In this study, comparative experiments were conducted using the regional climate model RegCM4.6 coupled with the third-generation land surface model NCAR CLM4.5 to assess the effect of the Johansen and Lu–Ren STC schemes on precipitation. The results show that the STC had a significant effect on the simulation of rainy season precipitation and its variation in northern China. The precipitation variation characteristics simulated by the Lu–Ren scheme were closer than that of the Johansen scheme to the observation. The difference in land surface temperatures (LSTs) simulated by the two STC schemes could be a major cause of the sensitivity in the simulated precipitation. When the local LST increases by 1 K, precipitation decreases by 5–30 mm in most areas of northern China. The numerical experiments revealed that the rise of LST increases the longwave radiation, reduces the surface net radiation, and causes the redistribution of sensible and latent heat flux, forming local water vapor and thermal conditions that are not conducive to precipitation. Moreover, the difference of LST significantly changes the 500-hPa large-scale circulation field, the 700-hPa vapor transportation, and its divergence. The combined action of local heat, water vapor, and large-scale circulation factors reduces the precipitation in the rainy season. On the other side, the variation of the East Asia summer monsoon (EASM) affects the soil water content. In addition, a new STC scheme was added to NCAR CLM4.5, promoting the development of this land surface model.
Abstract
Parameterization schemes such as soil thermal conductivity (STC) have an important impact on precipitation simulation. The precipitation in the rainy season (April–September) is the main factor affecting aridification in northern China. However, it is unclear how STC affects precipitation simulation during the rainy season. In this study, comparative experiments were conducted using the regional climate model RegCM4.6 coupled with the third-generation land surface model NCAR CLM4.5 to assess the effect of the Johansen and Lu–Ren STC schemes on precipitation. The results show that the STC had a significant effect on the simulation of rainy season precipitation and its variation in northern China. The precipitation variation characteristics simulated by the Lu–Ren scheme were closer than that of the Johansen scheme to the observation. The difference in land surface temperatures (LSTs) simulated by the two STC schemes could be a major cause of the sensitivity in the simulated precipitation. When the local LST increases by 1 K, precipitation decreases by 5–30 mm in most areas of northern China. The numerical experiments revealed that the rise of LST increases the longwave radiation, reduces the surface net radiation, and causes the redistribution of sensible and latent heat flux, forming local water vapor and thermal conditions that are not conducive to precipitation. Moreover, the difference of LST significantly changes the 500-hPa large-scale circulation field, the 700-hPa vapor transportation, and its divergence. The combined action of local heat, water vapor, and large-scale circulation factors reduces the precipitation in the rainy season. On the other side, the variation of the East Asia summer monsoon (EASM) affects the soil water content. In addition, a new STC scheme was added to NCAR CLM4.5, promoting the development of this land surface model.
Abstract
Soil organic matter (SOM) is enriched on the eastern Tibetan Plateau, but its effects on the hydrothermal state of the coupled land–atmosphere system remain unclear. This study comprehensively investigates these effects during summer from multiple perspectives based on regional climate modeling, land surface modeling, and observations. Using a regional climate model, we show that accounting for SOM effects lowers cold and wet biases in simulations of this region. SOM increases 2-m air temperature, decreases 2-m specific/relative humidity, and reduces precipitation in coupled simulations. Inclusion of SOM also warms the shallow soil while cooling the deep soil, which may help to preserve frozen soil in this region. This cooling effect is captured by both observations and offline land surface simulations, but it is overestimated in the offline simulations due to no feedback from the atmosphere compared to the coupled ones. Including SOM in coupled climate models could therefore not only imrove their representations of atmospheric energy and water cycles, but also help to simulate the past, present, and future evolution of frozen soil with increased confidence and reliability. Note that these findings are from one regional climate model and do not apply to wetlands.
Significance Statement
The eastern Tibetan Plateau is rich in soil organic matter (SOM), which increases the amount of water the soil can hold while decreasing the rate at which heat moves through it. Although SOM is expected to preserve frozen soil by insulating it from atmospheric warming, researchers have not yet tested the effects of coupled land–atmosphere interactions on this relationship. Using a regional climate model, we show that SOM typically warms and dries the near-surface air, warms the shallow soil, and cools the deep soil by modifying both soil properties and energy exchanges at the land–atmosphere interface. The results suggest that the cooling effect of SOM on deep soil is overestimated when atmospheric feedbacks are excluded.
Abstract
Soil organic matter (SOM) is enriched on the eastern Tibetan Plateau, but its effects on the hydrothermal state of the coupled land–atmosphere system remain unclear. This study comprehensively investigates these effects during summer from multiple perspectives based on regional climate modeling, land surface modeling, and observations. Using a regional climate model, we show that accounting for SOM effects lowers cold and wet biases in simulations of this region. SOM increases 2-m air temperature, decreases 2-m specific/relative humidity, and reduces precipitation in coupled simulations. Inclusion of SOM also warms the shallow soil while cooling the deep soil, which may help to preserve frozen soil in this region. This cooling effect is captured by both observations and offline land surface simulations, but it is overestimated in the offline simulations due to no feedback from the atmosphere compared to the coupled ones. Including SOM in coupled climate models could therefore not only imrove their representations of atmospheric energy and water cycles, but also help to simulate the past, present, and future evolution of frozen soil with increased confidence and reliability. Note that these findings are from one regional climate model and do not apply to wetlands.
Significance Statement
The eastern Tibetan Plateau is rich in soil organic matter (SOM), which increases the amount of water the soil can hold while decreasing the rate at which heat moves through it. Although SOM is expected to preserve frozen soil by insulating it from atmospheric warming, researchers have not yet tested the effects of coupled land–atmosphere interactions on this relationship. Using a regional climate model, we show that SOM typically warms and dries the near-surface air, warms the shallow soil, and cools the deep soil by modifying both soil properties and energy exchanges at the land–atmosphere interface. The results suggest that the cooling effect of SOM on deep soil is overestimated when atmospheric feedbacks are excluded.
Abstract
The Las Vegas metropolitan area in Nevada has experienced extensive urban growth since 1950 coincident with regional and local climate change. This study explores the nonstationary flood history of the Las Vegas Wash (LVW) watershed by deconstructing it into its constituent physical drivers. Observations and reanalysis products are used to examine the hydroclimatology, hydrometeorology, and hydrology of flash flooding in the watershed. Annual peak flows have increased nonlinearly over the past seven decades, with an abrupt changepoint detected in the mid-1990s, which is attributed to the implementation of flood conveyance systems rather than changes in land use. The LVW watershed exhibits two pronounced flood seasons, associated with distinct synoptic atmospheric circulations: winter floods linked to inland-penetrating atmospheric rivers and summer floods linked to the North American monsoon. El Niño–Southern Oscillation also plays a role in modulating extreme rainfall and the resultant floods because annual maximum daily rainfall totals positively correlate with El Niño, with Spearman’s correlation coefficient of 0.36 (p value < 0.05). Winter maximum daily rainfall totals have increased since 1950, whereas summer daily rainfall maxima have decreased. The trends in hydrometeorological drivers interact with urbanization to shift flood seasonality toward more frequent winter floods in the LVW watershed. A process-based understanding of the flood hydrology of the watershed also provides insights into flood frequency analysis and flood forecasting.
Abstract
The Las Vegas metropolitan area in Nevada has experienced extensive urban growth since 1950 coincident with regional and local climate change. This study explores the nonstationary flood history of the Las Vegas Wash (LVW) watershed by deconstructing it into its constituent physical drivers. Observations and reanalysis products are used to examine the hydroclimatology, hydrometeorology, and hydrology of flash flooding in the watershed. Annual peak flows have increased nonlinearly over the past seven decades, with an abrupt changepoint detected in the mid-1990s, which is attributed to the implementation of flood conveyance systems rather than changes in land use. The LVW watershed exhibits two pronounced flood seasons, associated with distinct synoptic atmospheric circulations: winter floods linked to inland-penetrating atmospheric rivers and summer floods linked to the North American monsoon. El Niño–Southern Oscillation also plays a role in modulating extreme rainfall and the resultant floods because annual maximum daily rainfall totals positively correlate with El Niño, with Spearman’s correlation coefficient of 0.36 (p value < 0.05). Winter maximum daily rainfall totals have increased since 1950, whereas summer daily rainfall maxima have decreased. The trends in hydrometeorological drivers interact with urbanization to shift flood seasonality toward more frequent winter floods in the LVW watershed. A process-based understanding of the flood hydrology of the watershed also provides insights into flood frequency analysis and flood forecasting.
Abstract
High-resolution numerical weather prediction (NWP) systems present a strong potential to provide meteorological information in alpine terrain for diverse applications. However, they still suffer from biases highly detrimental for practical purposes. In this study, we investigate the origin of a significant wintertime screen-level temperature bias in forecasts of the AROME-France NWP system in high-altitude, snow-covered alpine terrain. For this purpose, a thorough set of meteorological and snow observations from two high-altitude instrumental sites is used. Targeted numerical simulations are carried out to disentangle the contributions to this bias coming from atmospheric fields, from the snow scheme, and from the coupling between the snowpack and the atmosphere. At both sites, the wind speed and incoming longwave radiation appear significantly negatively biased in AROME in the winter season. Using targeted offline simulations, we show that the simulation errors in these screen-level fields contribute to an average of 67% of the screen-level temperature bias of AROME, while the contribution of errors in the incoming shortwave radiation is negligible. Additionally, the screen-level temperature of AROME is not majorly impacted by changes in the complexity and especially the vertical layering of the snow model. However, it appears particularly sensitive to the parameterization of turbulent fluxes in stable conditions. Evidence suggest that these findings could at least partially be generalized to the whole AROME-France alpine domain. Hence, reducing the high-altitude, winter screen-level temperature bias in AROME may in great part proceed from improving the simulation of atmospheric fields and eliminating some bias compensations in the model.
Abstract
High-resolution numerical weather prediction (NWP) systems present a strong potential to provide meteorological information in alpine terrain for diverse applications. However, they still suffer from biases highly detrimental for practical purposes. In this study, we investigate the origin of a significant wintertime screen-level temperature bias in forecasts of the AROME-France NWP system in high-altitude, snow-covered alpine terrain. For this purpose, a thorough set of meteorological and snow observations from two high-altitude instrumental sites is used. Targeted numerical simulations are carried out to disentangle the contributions to this bias coming from atmospheric fields, from the snow scheme, and from the coupling between the snowpack and the atmosphere. At both sites, the wind speed and incoming longwave radiation appear significantly negatively biased in AROME in the winter season. Using targeted offline simulations, we show that the simulation errors in these screen-level fields contribute to an average of 67% of the screen-level temperature bias of AROME, while the contribution of errors in the incoming shortwave radiation is negligible. Additionally, the screen-level temperature of AROME is not majorly impacted by changes in the complexity and especially the vertical layering of the snow model. However, it appears particularly sensitive to the parameterization of turbulent fluxes in stable conditions. Evidence suggest that these findings could at least partially be generalized to the whole AROME-France alpine domain. Hence, reducing the high-altitude, winter screen-level temperature bias in AROME may in great part proceed from improving the simulation of atmospheric fields and eliminating some bias compensations in the model.
Abstract
An ideal spatial interpolation approach is indispensable for obtaining high-quality gridded climatic data in mountainous regions with scarce observations, particularly for the Hengduan Mountains Region (HMR) with dense longitudinal ranges and gorges. However, there is much controversy about the applicability of thin plate smooth spline (TPSS), cokriging, and inverse distance weighting (IDW) in mountainous regions. Here, we use the daily observations of temperature and precipitation at 125 stations in HMR and its surroundings from 1961 to 2018 and adopt three interpolation methods to map the annual average temperature and precipitation at a resolution of 500 m in HMR. Then, we assess the applicability of three interpolation methods in HMR from the perspectives of interpolation accuracy and effects. The evaluation implies a satisfactory interpolation accuracy of TPSS with the highest correlation and lowest error, whether for temperature (R2 = 0.92, RMSE = 1.2°C) or precipitation (R2 = 0.54, RMSE = 165.9 mm). In addition, the TPSS could better display the temperature (precipitation) gradient along elevation and depict dry valleys’ high-temperature and low-precipitation characteristics. Moreover, the satisfactory interpolation performance of TPSS mainly benefits from the screening of optimal TPSS model that varied primarily with the regional topography feature and meteorological observation density. The uncertainty of gridded climate datasets has become an urgent problem to solve in the complex terrain. This research illustrates the satisfactory applicability of TPSS for climatic spatial interpolation in HMR, providing theoretical support for high-precision interpolation in complex terrain, hopefully improving the regional weather forecasts and disaster warnings.
Abstract
An ideal spatial interpolation approach is indispensable for obtaining high-quality gridded climatic data in mountainous regions with scarce observations, particularly for the Hengduan Mountains Region (HMR) with dense longitudinal ranges and gorges. However, there is much controversy about the applicability of thin plate smooth spline (TPSS), cokriging, and inverse distance weighting (IDW) in mountainous regions. Here, we use the daily observations of temperature and precipitation at 125 stations in HMR and its surroundings from 1961 to 2018 and adopt three interpolation methods to map the annual average temperature and precipitation at a resolution of 500 m in HMR. Then, we assess the applicability of three interpolation methods in HMR from the perspectives of interpolation accuracy and effects. The evaluation implies a satisfactory interpolation accuracy of TPSS with the highest correlation and lowest error, whether for temperature (R2 = 0.92, RMSE = 1.2°C) or precipitation (R2 = 0.54, RMSE = 165.9 mm). In addition, the TPSS could better display the temperature (precipitation) gradient along elevation and depict dry valleys’ high-temperature and low-precipitation characteristics. Moreover, the satisfactory interpolation performance of TPSS mainly benefits from the screening of optimal TPSS model that varied primarily with the regional topography feature and meteorological observation density. The uncertainty of gridded climate datasets has become an urgent problem to solve in the complex terrain. This research illustrates the satisfactory applicability of TPSS for climatic spatial interpolation in HMR, providing theoretical support for high-precision interpolation in complex terrain, hopefully improving the regional weather forecasts and disaster warnings.
Abstract
Wind-driven snow transport has important implications for spatial–temporal heterogeneity of snow distribution and snowpack evolution in mountainous areas, such as the French Alps. Due to the paucity of near-surface observations, our knowledge on the spatiotemporal variability of blowing snow occurrences is rather limited. Based on multiyear in situ observations, the spatial–temporal variability in the occurrence of blowing snow events in the French Alps was presented to investigate potential links with ambient meteorological conditions. Statistical analysis of the observations demonstrates that blowing snow events are frequently observed with substantial spatiotemporal variability. Most stations experienced snow transport one out of every five days throughout winter, and the corresponding cumulative hours with blowing snow occurrence accounted for 8% of the month in winter. Blowing snow events generally last 4–8 h in winter and early spring. The likelihood of blowing snow occurrences increases with wind speed but with divergent patterns across snow types. The frequency of blowing snow occurrences with concurrent snowfall is substantially higher than that without concurrent snowfall, although high spatiotemporal variability was found. The considerable variation in snow transport frequency can be explained by contrasting meteorological conditions, local climate, snowpack properties, and topography (elevation and aspect). The temperature-based empirical scheme failed to recognize individual occurrence of blowing snow events because of the significantly overestimated threshold wind speeds, highlighting the importance of validation using in situ observations. Our results contribute to the understanding of spatiotemporal occurrence of blowing snow events and facilitate the development of blowing snow models.
Abstract
Wind-driven snow transport has important implications for spatial–temporal heterogeneity of snow distribution and snowpack evolution in mountainous areas, such as the French Alps. Due to the paucity of near-surface observations, our knowledge on the spatiotemporal variability of blowing snow occurrences is rather limited. Based on multiyear in situ observations, the spatial–temporal variability in the occurrence of blowing snow events in the French Alps was presented to investigate potential links with ambient meteorological conditions. Statistical analysis of the observations demonstrates that blowing snow events are frequently observed with substantial spatiotemporal variability. Most stations experienced snow transport one out of every five days throughout winter, and the corresponding cumulative hours with blowing snow occurrence accounted for 8% of the month in winter. Blowing snow events generally last 4–8 h in winter and early spring. The likelihood of blowing snow occurrences increases with wind speed but with divergent patterns across snow types. The frequency of blowing snow occurrences with concurrent snowfall is substantially higher than that without concurrent snowfall, although high spatiotemporal variability was found. The considerable variation in snow transport frequency can be explained by contrasting meteorological conditions, local climate, snowpack properties, and topography (elevation and aspect). The temperature-based empirical scheme failed to recognize individual occurrence of blowing snow events because of the significantly overestimated threshold wind speeds, highlighting the importance of validation using in situ observations. Our results contribute to the understanding of spatiotemporal occurrence of blowing snow events and facilitate the development of blowing snow models.
Abstract
Errors associated with the location of precipitation in QPFs present challenges when used for hydrologic prediction, particularly in small watersheds. This work builds on a past study that systematically shifted QPFs prior to inputting them into a hydrologic model to generate streamflow ensembles. In the original study, which used static, predetermined shifting distances, flood detection improved, but false alarms increased due to large ensemble spread. The present research tests a more informed approach by randomly selecting shift directions and distances based on the distribution of displacement errors from a sample of QPFs. Precipitation forecasts were taken from the High-Resolution Rapid Refresh Ensemble (HRRRE), and streamflow predictions were generated using the Weather Research and Forecasting hydrological modeling system, version 5.1.1, in a National Water Model 2.0 configuration. A 63-member streamflow ensemble was generated using the 9 original HRRRE and 54 shifted HRRRE members. Two ensemble updating schemes were tested in which ensemble member weights were adjusted using precipitation location and QPF displacement present at convective initiation. The ensembles using QPF shifted based on climatological spatial errors showed higher probabilistic forecasting skill, while having comparable dichotomous forecasting skill to the original HRRRE ensemble. Other methods of selecting nine ensemble members from the full 63-member suite did not show significant improvement. Flood peak timing showed frequent errors, with average timing errors around five hours early. Larger watersheds tended to have better skill metric scores than smaller basins, with increased skill added by the shifting of QPF.
Abstract
Errors associated with the location of precipitation in QPFs present challenges when used for hydrologic prediction, particularly in small watersheds. This work builds on a past study that systematically shifted QPFs prior to inputting them into a hydrologic model to generate streamflow ensembles. In the original study, which used static, predetermined shifting distances, flood detection improved, but false alarms increased due to large ensemble spread. The present research tests a more informed approach by randomly selecting shift directions and distances based on the distribution of displacement errors from a sample of QPFs. Precipitation forecasts were taken from the High-Resolution Rapid Refresh Ensemble (HRRRE), and streamflow predictions were generated using the Weather Research and Forecasting hydrological modeling system, version 5.1.1, in a National Water Model 2.0 configuration. A 63-member streamflow ensemble was generated using the 9 original HRRRE and 54 shifted HRRRE members. Two ensemble updating schemes were tested in which ensemble member weights were adjusted using precipitation location and QPF displacement present at convective initiation. The ensembles using QPF shifted based on climatological spatial errors showed higher probabilistic forecasting skill, while having comparable dichotomous forecasting skill to the original HRRRE ensemble. Other methods of selecting nine ensemble members from the full 63-member suite did not show significant improvement. Flood peak timing showed frequent errors, with average timing errors around five hours early. Larger watersheds tended to have better skill metric scores than smaller basins, with increased skill added by the shifting of QPF.
