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Abstract
This study uses National Centers for Environmental Prediction (NCEP) Stage IV (Stage IV) precipitation data over the state of Alaska to assess and cross compare precipitation estimates from the most recent versions of multiple precipitation products, including satellite-based passive microwave (PMW) [Special Sensor Microwave Imager/Sounder (SSMIS)–F17, Microwave Humidity Sounder (MHS)–MetOp-B, MHS–NOAA-19, Advanced Microwave Scanning Radiometer 2 (AMSR2), Advanced Technology Microwave Sounder (ATMS), and Global Precipitation Measurement Microwave Imager (GMI) in V05 and V07], active microwave [AMW or radar; Global Precipitation Measurement (GPM) dual-frequency precipitation radar (DPR) in V06 and V07], combined active and passive microwave (DPRGMI in V06 and V07), infrared [Atmospheric Infrared Sounder (AIRS)], reanalysis [fifth major global reanalysis produced by ECMWF (ERA5) and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), and satellite–gauge [Global Precipitation Climatology Project (GPCP) V1.3 and GPCP V3.2] products. PMW estimates are generally improved in V07 compared to V05 in terms of overall bias, pattern, and capturing precipitation extremes. DPR and DPRGMI show low skill in capturing different precipitation features. ERA5 and MERRA-2 show the highest agreement with Stage IV for all precipitation rate metrics. AIRS and GPCP capture the overall precipitation pattern and magnitude fairly well, performing better than the radar and comparable to the PMW V07 products, although the geographical maps suggest that they provide a relatively smoothed spatial distribution of mean precipitation rates. The outcomes of this study shed light on the performance of various precipitation products over Alaska (partly representing high-latitude regions) and can be useful to guide the development of multisensor products.
Abstract
This study uses National Centers for Environmental Prediction (NCEP) Stage IV (Stage IV) precipitation data over the state of Alaska to assess and cross compare precipitation estimates from the most recent versions of multiple precipitation products, including satellite-based passive microwave (PMW) [Special Sensor Microwave Imager/Sounder (SSMIS)–F17, Microwave Humidity Sounder (MHS)–MetOp-B, MHS–NOAA-19, Advanced Microwave Scanning Radiometer 2 (AMSR2), Advanced Technology Microwave Sounder (ATMS), and Global Precipitation Measurement Microwave Imager (GMI) in V05 and V07], active microwave [AMW or radar; Global Precipitation Measurement (GPM) dual-frequency precipitation radar (DPR) in V06 and V07], combined active and passive microwave (DPRGMI in V06 and V07), infrared [Atmospheric Infrared Sounder (AIRS)], reanalysis [fifth major global reanalysis produced by ECMWF (ERA5) and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), and satellite–gauge [Global Precipitation Climatology Project (GPCP) V1.3 and GPCP V3.2] products. PMW estimates are generally improved in V07 compared to V05 in terms of overall bias, pattern, and capturing precipitation extremes. DPR and DPRGMI show low skill in capturing different precipitation features. ERA5 and MERRA-2 show the highest agreement with Stage IV for all precipitation rate metrics. AIRS and GPCP capture the overall precipitation pattern and magnitude fairly well, performing better than the radar and comparable to the PMW V07 products, although the geographical maps suggest that they provide a relatively smoothed spatial distribution of mean precipitation rates. The outcomes of this study shed light on the performance of various precipitation products over Alaska (partly representing high-latitude regions) and can be useful to guide the development of multisensor products.
Abstract
The spatial description of high-resolution extreme daily rainfall fields is challenging because of the high spatial and temporal variability of rainfall, particularly in tropical regions due to the stochastic nature of convective rainfall. Geostatistical simulations offer a solution to this problem. In this study, a stochastic geostatistical simulation technique based on the spectral turning bands method is presented for modeling daily rainfall extremes in the data-scarce tropical Ouémé River basin (Benin). This technique uses meta-Gaussian frameworks built on Gaussian random fields, which are transformed into realistic rainfall fields using statistical transfer functions. The simulation framework can be conditioned on point observations and is computationally efficient in generating multiple ensembles of extreme rainfall fields. The results of tests and evaluations for multiple extremes demonstrate the effectiveness of the simulation framework in modeling more realistic rainfall fields and capturing their variability. It successfully reproduces the empirical cumulative distribution function of the observation samples and outperforms classical interpolation techniques like ordinary kriging in terms of spatial continuity and rainfall variability. The study also addresses the challenge of dealing with uncertainty in data-poor areas and proposes a novel approach for determining the spatial correlation structure even with low station density, resulting in a performance boost of 9.5% compared to traditional techniques. Additionally, we present a low-skill reference simulation method to facilitate a comprehensive comparison of the geostatistical simulation approaches. The simulations generated have the potential to provide valuable inputs for hydrological modeling.
Abstract
The spatial description of high-resolution extreme daily rainfall fields is challenging because of the high spatial and temporal variability of rainfall, particularly in tropical regions due to the stochastic nature of convective rainfall. Geostatistical simulations offer a solution to this problem. In this study, a stochastic geostatistical simulation technique based on the spectral turning bands method is presented for modeling daily rainfall extremes in the data-scarce tropical Ouémé River basin (Benin). This technique uses meta-Gaussian frameworks built on Gaussian random fields, which are transformed into realistic rainfall fields using statistical transfer functions. The simulation framework can be conditioned on point observations and is computationally efficient in generating multiple ensembles of extreme rainfall fields. The results of tests and evaluations for multiple extremes demonstrate the effectiveness of the simulation framework in modeling more realistic rainfall fields and capturing their variability. It successfully reproduces the empirical cumulative distribution function of the observation samples and outperforms classical interpolation techniques like ordinary kriging in terms of spatial continuity and rainfall variability. The study also addresses the challenge of dealing with uncertainty in data-poor areas and proposes a novel approach for determining the spatial correlation structure even with low station density, resulting in a performance boost of 9.5% compared to traditional techniques. Additionally, we present a low-skill reference simulation method to facilitate a comprehensive comparison of the geostatistical simulation approaches. The simulations generated have the potential to provide valuable inputs for hydrological modeling.
Abstract
Under the joint effects of climate change and human activities, the assumption of the stationarity of hydrological series has been overturned, which is of great significance in the field of hydrology. However, the current research on drought propagation is generally based on the assumption of sequence stationarity, in which related results may be biased. To this end, the Generalized Additive Models for Location, Scale, and Shape (GAMLSS) model was used to construct a time-varying drought index. The seasonal propagation characteristics from meteorological to hydrological drought were examined based on conditional probability. The dynamic change of propagation characteristics was explored by utilizing the moving window, and the driving factors were revealed by using the variable importance in projection. The Luanhe River basin, which has a fragile ecological environment, was selected as a case study. The results indicated that 1) using a time-varying drought index was more reasonable than using a stationary assumption, and the latter relatively easily underestimated the propagation time, especially in summer; 2) drought propagation in summer and autumn was faster than that in spring and winter, and the propagation time showed a significant downward trend; 3) the trigger threshold (absolute value) of meteorological drought in spring was significantly higher than that in other seasons, and hydrological drought was more likely to be triggered by meteorological drought in autumn and winter; and 4) the precipitation P, decreasing runoff R, and increasing evaporation E were the main factors affecting the seasonal propagation characteristics. The findings of this study are of great significance for water resource management and further understanding of drought propagation mechanisms.
Abstract
Under the joint effects of climate change and human activities, the assumption of the stationarity of hydrological series has been overturned, which is of great significance in the field of hydrology. However, the current research on drought propagation is generally based on the assumption of sequence stationarity, in which related results may be biased. To this end, the Generalized Additive Models for Location, Scale, and Shape (GAMLSS) model was used to construct a time-varying drought index. The seasonal propagation characteristics from meteorological to hydrological drought were examined based on conditional probability. The dynamic change of propagation characteristics was explored by utilizing the moving window, and the driving factors were revealed by using the variable importance in projection. The Luanhe River basin, which has a fragile ecological environment, was selected as a case study. The results indicated that 1) using a time-varying drought index was more reasonable than using a stationary assumption, and the latter relatively easily underestimated the propagation time, especially in summer; 2) drought propagation in summer and autumn was faster than that in spring and winter, and the propagation time showed a significant downward trend; 3) the trigger threshold (absolute value) of meteorological drought in spring was significantly higher than that in other seasons, and hydrological drought was more likely to be triggered by meteorological drought in autumn and winter; and 4) the precipitation P, decreasing runoff R, and increasing evaporation E were the main factors affecting the seasonal propagation characteristics. The findings of this study are of great significance for water resource management and further understanding of drought propagation mechanisms.
Abstract
Quantifying spatiotemporal variability in snow water resources is a challenge especially relevant for regions that rely on snowmelt for water supply. Model accuracy is often limited by uncertainties in meteorological forcings and/or suboptimal physics representation. In this study, we evaluate the performance and sensitivity of Noah land surface model with multiparameterization options (Noah-MP) snow simulations from ten model configurations across 199 sites in the western United States. Nine experiments are constrained by observed meteorology to test snow-related physics options, and the 10th experiment tests an alternative source of meteorological forcings. We find that the base case, which aligns with the National Water Model configuration and uses observation-based forcings, overestimates observed accumulated snow water equivalent (SWE) at 90% of stations by a median of 9.6%. The model performs better in the accumulation season at colder, drier sites and in the melt season at wetter, warmer sites. Accumulation metrics are sensitive to model configuration in two experiments, and melt metrics, in six experiments. Alterations to model physics cause changes to median accumulation metrics from −13% to 2.3% with the greatest change due to precipitation partitioning and to melt metrics from −10% to 3% with the greatest change due to surface resistance configuration. The experiment with alternative forcings causes even greater and wider-ranging changes (medians ranging from −29% to 6%). Not all stations share the same best-performing model configuration. At most stations, the base case is outperformed by four alternative physics options which also significantly impact snow simulation. This research provides insights into the performance and sensitivity of snow predictions across site conditions and model configurations.
Significance Statement
The purpose of this work is to evaluate the performance and sensitivity of a land surface model’s simulation of snow across site conditions and in response to different model configurations. This is important because estimating snow distribution is a challenge especially relevant for regions that rely on snowmelt for water supply. While land surface models can provide useful large-scale estimates, they are often limited by uncertainties in forcings and/or suboptimal physics representation. The results, which show varying model behavior across geography, climate, vegetation types, and model configurations, highlight inadequacies in model physics representation, emphasize the need for accurate meteorological forcings, and suggest that customizing model configurations to the unique characteristics of the domain could yield more accurate and useful results.
Abstract
Quantifying spatiotemporal variability in snow water resources is a challenge especially relevant for regions that rely on snowmelt for water supply. Model accuracy is often limited by uncertainties in meteorological forcings and/or suboptimal physics representation. In this study, we evaluate the performance and sensitivity of Noah land surface model with multiparameterization options (Noah-MP) snow simulations from ten model configurations across 199 sites in the western United States. Nine experiments are constrained by observed meteorology to test snow-related physics options, and the 10th experiment tests an alternative source of meteorological forcings. We find that the base case, which aligns with the National Water Model configuration and uses observation-based forcings, overestimates observed accumulated snow water equivalent (SWE) at 90% of stations by a median of 9.6%. The model performs better in the accumulation season at colder, drier sites and in the melt season at wetter, warmer sites. Accumulation metrics are sensitive to model configuration in two experiments, and melt metrics, in six experiments. Alterations to model physics cause changes to median accumulation metrics from −13% to 2.3% with the greatest change due to precipitation partitioning and to melt metrics from −10% to 3% with the greatest change due to surface resistance configuration. The experiment with alternative forcings causes even greater and wider-ranging changes (medians ranging from −29% to 6%). Not all stations share the same best-performing model configuration. At most stations, the base case is outperformed by four alternative physics options which also significantly impact snow simulation. This research provides insights into the performance and sensitivity of snow predictions across site conditions and model configurations.
Significance Statement
The purpose of this work is to evaluate the performance and sensitivity of a land surface model’s simulation of snow across site conditions and in response to different model configurations. This is important because estimating snow distribution is a challenge especially relevant for regions that rely on snowmelt for water supply. While land surface models can provide useful large-scale estimates, they are often limited by uncertainties in forcings and/or suboptimal physics representation. The results, which show varying model behavior across geography, climate, vegetation types, and model configurations, highlight inadequacies in model physics representation, emphasize the need for accurate meteorological forcings, and suggest that customizing model configurations to the unique characteristics of the domain could yield more accurate and useful results.
Abstract
The flood that would result from the greatest depth of precipitation “meteorologically possible” or probable maximum precipitation (PMP) is used in the design of dam spillways and other high-risk structures. Historically, PMP has been estimated by scaling precipitation totals obtained from severe historical storms, assuming more moisture could have been available. Over the last decade, numerical weather prediction models have been used to instead predict precipitation resulting from the addition of moisture in the simulations [called relative humidity maximization (RHM)]. Despite the major improvement they represent, two important barriers limit the applicability of model-based methods: first, the existence of different moisture amplification approaches that produce different estimates, and second, the need for a regional implementation of those techniques that were developed for individual basins. Taking Oregon’s mountainous coastal watersheds affected by atmospheric river storms as a case study, we develop a moisture amplification approach, which we call relative humidity perturbation (RHP) ratio that is physically constrained by historical maximum moisture. We find that both the magnitude and location of moisture increase matter and that RHP ratio produces lower amplified precipitation totals but storms that are more consistent with observed events than other methods such as RHM. We additionally find that it is possible to position a storm near-optimally over several basins in a homogeneous area, enabling the production of regional PMP estimates. The understanding we develop of the control moisture exerts on PMP-magnitude precipitation totals allows us to develop a more physically based methodology for the development of reliable storm amplification guidance.
Abstract
The flood that would result from the greatest depth of precipitation “meteorologically possible” or probable maximum precipitation (PMP) is used in the design of dam spillways and other high-risk structures. Historically, PMP has been estimated by scaling precipitation totals obtained from severe historical storms, assuming more moisture could have been available. Over the last decade, numerical weather prediction models have been used to instead predict precipitation resulting from the addition of moisture in the simulations [called relative humidity maximization (RHM)]. Despite the major improvement they represent, two important barriers limit the applicability of model-based methods: first, the existence of different moisture amplification approaches that produce different estimates, and second, the need for a regional implementation of those techniques that were developed for individual basins. Taking Oregon’s mountainous coastal watersheds affected by atmospheric river storms as a case study, we develop a moisture amplification approach, which we call relative humidity perturbation (RHP) ratio that is physically constrained by historical maximum moisture. We find that both the magnitude and location of moisture increase matter and that RHP ratio produces lower amplified precipitation totals but storms that are more consistent with observed events than other methods such as RHM. We additionally find that it is possible to position a storm near-optimally over several basins in a homogeneous area, enabling the production of regional PMP estimates. The understanding we develop of the control moisture exerts on PMP-magnitude precipitation totals allows us to develop a more physically based methodology for the development of reliable storm amplification guidance.
Abstract
Accurate subseasonal forecasts for snow cover have significant socioeconomic value. This paper evaluates subseasonal forecasts for winter snow cover in the Northern Hemisphere as predicted by three numerical models: the Model for Prediction Across Scales–Atmosphere (MPAS-A), the China Meteorological Administration (CMA) model, and the European Centre for Medium-Range Weather Forecasts (ECMWF) model. While these models can generally simulate the spatial distribution of winter snow cover climatology and subseasonal variability, they tend to underestimate both the climatology and the intensity of subseasonal variability. Compared to persistence forecasts, these models demonstrate skill in subseasonal snow cover forecasting. Notably, the ECMWF model outperforms the MPAS and CMA models. The sensitivity of the surface air temperature subseasonal forecast skill to the predicted snow cover was also investigated using the MPAS. The results show that for forecasts with lead times of 1–2 weeks, the predicted snow cover contributes to the temperature forecasting skill. However, for forecasts with lead times of 3–4 weeks, the predicted snow cover does not enhance the temperature forecasting skill. Furthermore, part of the errors in temperature forecasts can be attributed to inaccuracies in snow cover forecasts with lead times of 2 weeks or more. These findings suggest that refining snow cover parameterization schemes and effectively exploiting predictability from snow cover can enhance the skill of subseasonal atmospheric forecasts.
Significance Statement
Snow cover is a crucial variable in hydrometeorology. Subseasonal forecasting, which involves predicting snow cover anomalies 1–4 weeks in advance, has socioeconomic value. We conducted an evaluation of the subseasonal forecasts for Northern Hemisphere winter snow cover produced by three numerical models. This evaluation provides insights into the accuracy and reliability of these models, which could contribute to their enhancement. Furthermore, we examined the impact of the predicted snow cover on the skill of surface air temperature subseasonal forecasts. The results suggest that improvements in snow cover modeling and forecasting can lead to more accurate subseasonal atmospheric forecasts. Therefore, future efforts to refine snow cover parameterization schemes suitable for subseasonal forecasting are promising and worthwhile.
Abstract
Accurate subseasonal forecasts for snow cover have significant socioeconomic value. This paper evaluates subseasonal forecasts for winter snow cover in the Northern Hemisphere as predicted by three numerical models: the Model for Prediction Across Scales–Atmosphere (MPAS-A), the China Meteorological Administration (CMA) model, and the European Centre for Medium-Range Weather Forecasts (ECMWF) model. While these models can generally simulate the spatial distribution of winter snow cover climatology and subseasonal variability, they tend to underestimate both the climatology and the intensity of subseasonal variability. Compared to persistence forecasts, these models demonstrate skill in subseasonal snow cover forecasting. Notably, the ECMWF model outperforms the MPAS and CMA models. The sensitivity of the surface air temperature subseasonal forecast skill to the predicted snow cover was also investigated using the MPAS. The results show that for forecasts with lead times of 1–2 weeks, the predicted snow cover contributes to the temperature forecasting skill. However, for forecasts with lead times of 3–4 weeks, the predicted snow cover does not enhance the temperature forecasting skill. Furthermore, part of the errors in temperature forecasts can be attributed to inaccuracies in snow cover forecasts with lead times of 2 weeks or more. These findings suggest that refining snow cover parameterization schemes and effectively exploiting predictability from snow cover can enhance the skill of subseasonal atmospheric forecasts.
Significance Statement
Snow cover is a crucial variable in hydrometeorology. Subseasonal forecasting, which involves predicting snow cover anomalies 1–4 weeks in advance, has socioeconomic value. We conducted an evaluation of the subseasonal forecasts for Northern Hemisphere winter snow cover produced by three numerical models. This evaluation provides insights into the accuracy and reliability of these models, which could contribute to their enhancement. Furthermore, we examined the impact of the predicted snow cover on the skill of surface air temperature subseasonal forecasts. The results suggest that improvements in snow cover modeling and forecasting can lead to more accurate subseasonal atmospheric forecasts. Therefore, future efforts to refine snow cover parameterization schemes suitable for subseasonal forecasting are promising and worthwhile.
Abstract
Flash droughts (FDs) have attracted widespread attention in recent years due to their sudden onset and rapid intensification with significant impacts on ecosystems, water resources, and agriculture. These features of FDs pose unique challenges for their forecast, monitoring, and mitigation. The impact of FDs on society can vary depending on several factors, such as the frequency of their occurrence, rate of intensification, and mean severity, which are not well understood and remain unclear specifically over India. This study developed a novel approach to quantitatively define FD based on the aridity index. This new approach was used to examine spatiotemporal characteristics (including trends) and triggers of FDs over 25 river basins across India from 1981 to 2021. The hydrometeorological conditions, including soil moisture percentiles, anomalies of precipitation, temperature, and vapor pressure deficit were investigated at different stages of FD. Results suggest that FDs with high intensification rates are more common in humid areas compared to subhumid and semiarid areas. Both precipitation and temperature are primary triggers of FDs over a major part of the study area. The individual effects of soil moisture and precipitation also act as a trigger across some regions (like northeast India and the Western Ghats). Additionally, atmospheric aridity can create conditions conducive to FDs, and when combined with depleted soil moisture, it can accelerate their rapid onset. Besides the scientific novelty, the findings of this study will facilitate policymakers to formulate effective strategies to mitigate the consequences of FDs on water resources and agriculture in India.
Significance Statement
Flash droughts have attracted widespread attention due to their sudden onset and rapid intensification with significant impacts on multiple vectors. The impact of flash drought on society depends on their frequency, rate of intensification, and mean severity, which are not well understood and remain unclear specifically over India. This study develops a novel approach to quantitatively define flash drought based on the aridity index. This new approach is used to examine spatiotemporal characteristics and triggers of flash drought over 25 river basins across India from 1981 to 2021. Besides the scientific novelty, the findings of this study will facilitate policymakers to formulate effective strategies to mitigate the consequences of FDs on water resources and agriculture in India.
Abstract
Flash droughts (FDs) have attracted widespread attention in recent years due to their sudden onset and rapid intensification with significant impacts on ecosystems, water resources, and agriculture. These features of FDs pose unique challenges for their forecast, monitoring, and mitigation. The impact of FDs on society can vary depending on several factors, such as the frequency of their occurrence, rate of intensification, and mean severity, which are not well understood and remain unclear specifically over India. This study developed a novel approach to quantitatively define FD based on the aridity index. This new approach was used to examine spatiotemporal characteristics (including trends) and triggers of FDs over 25 river basins across India from 1981 to 2021. The hydrometeorological conditions, including soil moisture percentiles, anomalies of precipitation, temperature, and vapor pressure deficit were investigated at different stages of FD. Results suggest that FDs with high intensification rates are more common in humid areas compared to subhumid and semiarid areas. Both precipitation and temperature are primary triggers of FDs over a major part of the study area. The individual effects of soil moisture and precipitation also act as a trigger across some regions (like northeast India and the Western Ghats). Additionally, atmospheric aridity can create conditions conducive to FDs, and when combined with depleted soil moisture, it can accelerate their rapid onset. Besides the scientific novelty, the findings of this study will facilitate policymakers to formulate effective strategies to mitigate the consequences of FDs on water resources and agriculture in India.
Significance Statement
Flash droughts have attracted widespread attention due to their sudden onset and rapid intensification with significant impacts on multiple vectors. The impact of flash drought on society depends on their frequency, rate of intensification, and mean severity, which are not well understood and remain unclear specifically over India. This study develops a novel approach to quantitatively define flash drought based on the aridity index. This new approach is used to examine spatiotemporal characteristics and triggers of flash drought over 25 river basins across India from 1981 to 2021. Besides the scientific novelty, the findings of this study will facilitate policymakers to formulate effective strategies to mitigate the consequences of FDs on water resources and agriculture in India.
Abstract
The northeasterly facing, windward side of the Island of Kaua‘i (part of the State of Hawai‘i, United States) is prone to heavy rainfall events due to its topographical features and geographical location. Persistent northeasterly trade winds, coupled with steep changes in elevation, create an ideal environment for orographic precipitation. In addition, due to Kaua‘i’s 22°N latitude, the island often experiences midlatitude weather features such as kona lows, upper-level lows, and cold fronts that frequently result in high rainfall and river discharge conditions. This work uses data from river gauges in Halele‘a to understand the seasonality and impacts of the main atmospheric disturbances on two rivers in the region. The seasonality study showed that the majority of extreme flooding events occurred during the cool season and were predominantly caused by cold fronts and upper-level troughs. The historical analysis used atmospheric disturbance cases to determine that kona lows were likely to cause high streamflow in both studied Halele‘a rivers, and upper-level lows had an approximately equal probability of causing high streamflow or not. The findings that come from this project can provide context to atmospheric disturbances in Halele‘a and help community members identify and anticipate the types of events that may contribute to flooding.
Significance Statement
The north shore of the Island of Kaua‘i is prone to extreme rainfall and flooding due to interactions between the typical wind patterns and the nearby mountain range. A majority of flooding events occur during the cool season (October–April) because most of the weather events that produce extreme rainfall occur during these months. Here, we examined the top 50 flooding events in two of Kaua‘i’s north shore rivers and found that cold fronts and upper-level low pressure systems are often responsible for flooding. Additionally, any kona low is likely to cause high streamflow. This study increases the understanding of flood causes and likelihood in northern Kaua‘i.
Abstract
The northeasterly facing, windward side of the Island of Kaua‘i (part of the State of Hawai‘i, United States) is prone to heavy rainfall events due to its topographical features and geographical location. Persistent northeasterly trade winds, coupled with steep changes in elevation, create an ideal environment for orographic precipitation. In addition, due to Kaua‘i’s 22°N latitude, the island often experiences midlatitude weather features such as kona lows, upper-level lows, and cold fronts that frequently result in high rainfall and river discharge conditions. This work uses data from river gauges in Halele‘a to understand the seasonality and impacts of the main atmospheric disturbances on two rivers in the region. The seasonality study showed that the majority of extreme flooding events occurred during the cool season and were predominantly caused by cold fronts and upper-level troughs. The historical analysis used atmospheric disturbance cases to determine that kona lows were likely to cause high streamflow in both studied Halele‘a rivers, and upper-level lows had an approximately equal probability of causing high streamflow or not. The findings that come from this project can provide context to atmospheric disturbances in Halele‘a and help community members identify and anticipate the types of events that may contribute to flooding.
Significance Statement
The north shore of the Island of Kaua‘i is prone to extreme rainfall and flooding due to interactions between the typical wind patterns and the nearby mountain range. A majority of flooding events occur during the cool season (October–April) because most of the weather events that produce extreme rainfall occur during these months. Here, we examined the top 50 flooding events in two of Kaua‘i’s north shore rivers and found that cold fronts and upper-level low pressure systems are often responsible for flooding. Additionally, any kona low is likely to cause high streamflow. This study increases the understanding of flood causes and likelihood in northern Kaua‘i.
Abstract
Flash flooding remains a challenging prediction problem, which is exacerbated by the lack of a universally accepted definition of the phenomenon. In this article, we extend prior analysis to examine the correspondence of various combinations of quantitative precipitation estimates (QPEs) and precipitation thresholds to observed occurrences of flash floods, additionally considering short-term quantitative precipitation forecasts from a convection-allowing model. Consistent with previous studies, there is large variability between QPE datasets in the frequency of “heavy” precipitation events. There is also large regional variability in the best thresholds for correspondence with reported flash floods. In general, flash flood guidance (FFG) exceedances provide the best correspondence with observed flash floods, although the best correspondence is often found for exceedances of ratios of FFG above or below unity. In the interior western United States, NOAA Atlas 14 derived recurrence interval thresholds (for the southwestern United States) and static thresholds (for the northern and central Rockies) provide better correspondence. The 6-h QPE provides better correspondence with observed flash floods than 1-h QPE in all regions except the West Coast and southwestern United States. Exceedances of precipitation thresholds in forecasts from the operational High-Resolution Rapid Refresh (HRRR) generally do not correspond with observed flash flood events as well as QPE datasets, but they outperform QPE datasets in some regions of complex terrain and sparse observational coverage such as the southwestern United States. These results can provide context for forecasters seeking to identify potential flash flood events based on QPE or forecast-based exceedances of precipitation thresholds.
Significance Statement
Flash floods result from heavy rainfall, but it is difficult to know exactly how much rain will cause a flash flood in a particular location. Furthermore, different precipitation datasets can show very different amounts of precipitation, even from the same storm. This study examines how well different precipitation datasets and model forecasts, used by forecasters to warn the public of flash flooding, represent heavy rainfall leading to flash flooding around the United States. We found that different datasets have dramatically different numbers of heavy rainfall events and that high-resolution model forecasts of heavy rain correspond with observed flash flood events about as well as precipitation datasets based on rain gauge and radar in some regions of the country with few observations.
Abstract
Flash flooding remains a challenging prediction problem, which is exacerbated by the lack of a universally accepted definition of the phenomenon. In this article, we extend prior analysis to examine the correspondence of various combinations of quantitative precipitation estimates (QPEs) and precipitation thresholds to observed occurrences of flash floods, additionally considering short-term quantitative precipitation forecasts from a convection-allowing model. Consistent with previous studies, there is large variability between QPE datasets in the frequency of “heavy” precipitation events. There is also large regional variability in the best thresholds for correspondence with reported flash floods. In general, flash flood guidance (FFG) exceedances provide the best correspondence with observed flash floods, although the best correspondence is often found for exceedances of ratios of FFG above or below unity. In the interior western United States, NOAA Atlas 14 derived recurrence interval thresholds (for the southwestern United States) and static thresholds (for the northern and central Rockies) provide better correspondence. The 6-h QPE provides better correspondence with observed flash floods than 1-h QPE in all regions except the West Coast and southwestern United States. Exceedances of precipitation thresholds in forecasts from the operational High-Resolution Rapid Refresh (HRRR) generally do not correspond with observed flash flood events as well as QPE datasets, but they outperform QPE datasets in some regions of complex terrain and sparse observational coverage such as the southwestern United States. These results can provide context for forecasters seeking to identify potential flash flood events based on QPE or forecast-based exceedances of precipitation thresholds.
Significance Statement
Flash floods result from heavy rainfall, but it is difficult to know exactly how much rain will cause a flash flood in a particular location. Furthermore, different precipitation datasets can show very different amounts of precipitation, even from the same storm. This study examines how well different precipitation datasets and model forecasts, used by forecasters to warn the public of flash flooding, represent heavy rainfall leading to flash flooding around the United States. We found that different datasets have dramatically different numbers of heavy rainfall events and that high-resolution model forecasts of heavy rain correspond with observed flash flood events about as well as precipitation datasets based on rain gauge and radar in some regions of the country with few observations.
Abstract
A multitude of Earth observation (EO) products are available for monitoring the terrestrial water cycle. These EO datasets have resulted in a multiplicity of datasets for the same geophysical variable. Furthermore, inconsistencies between the water components prevent the water budget closure. A maximum a posteriori (MAP) estimator has been used in the past to optimally combine EO datasets. This framework has many advantages, but it can only be utilized when all four water components are available (precipitation P, evapotranspiration E, total water storage change dS, and river discharge R) and solely at the basin scale. By combining physical expertise with the statistical inference of neural networks (NNs), we designed a custom deep learning scheme to optimize EO data. This hybrid approach benefits from the optimization capabilities of NNs to estimate the parameters of interconnected physical modules. The NN is trained using basin-scale data (from MAP results) over 38 basins to obtain optimized EOs globally. The NN integration offers several enhancements compared to MAP: Independent calibration/mixing models are obtained with imbalance reduction and optimization at the pixel level, and environmental variables can be used to extrapolate results to unmonitored regions. The NN integration enables combining EO estimates of individual water components (P, E, dS, and R) in a hydrologically coherent manner, resulting in a significant decrease in the water budget imbalance at the global scale. Mean imbalance errors can be significant on raw EOs, but they become negligible when EOs are integrated. The standard deviation (STD) of the imbalance is around 26 mm month−1 for raw EOs, and they decrease to 21 when combined and 19 when mixed.
Abstract
A multitude of Earth observation (EO) products are available for monitoring the terrestrial water cycle. These EO datasets have resulted in a multiplicity of datasets for the same geophysical variable. Furthermore, inconsistencies between the water components prevent the water budget closure. A maximum a posteriori (MAP) estimator has been used in the past to optimally combine EO datasets. This framework has many advantages, but it can only be utilized when all four water components are available (precipitation P, evapotranspiration E, total water storage change dS, and river discharge R) and solely at the basin scale. By combining physical expertise with the statistical inference of neural networks (NNs), we designed a custom deep learning scheme to optimize EO data. This hybrid approach benefits from the optimization capabilities of NNs to estimate the parameters of interconnected physical modules. The NN is trained using basin-scale data (from MAP results) over 38 basins to obtain optimized EOs globally. The NN integration offers several enhancements compared to MAP: Independent calibration/mixing models are obtained with imbalance reduction and optimization at the pixel level, and environmental variables can be used to extrapolate results to unmonitored regions. The NN integration enables combining EO estimates of individual water components (P, E, dS, and R) in a hydrologically coherent manner, resulting in a significant decrease in the water budget imbalance at the global scale. Mean imbalance errors can be significant on raw EOs, but they become negligible when EOs are integrated. The standard deviation (STD) of the imbalance is around 26 mm month−1 for raw EOs, and they decrease to 21 when combined and 19 when mixed.
