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Abstract
Across the globe, there has been an increasing interest in improving the predictability of subseasonal hydrometeorological 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 postprocessing techniques at the interface with a Precipitationn–Runoff–Evapotranspiration Hydrological Response Unit Model (PREVAH). Specifically, this research aims to assess the benefit of European weather regime (WR) data within a hybrid forecasting setup, a combination of a traditional hydrological model and a machine learning (ML) algorithm, to improve the performance of subseasonal hydrometeorological forecasts in Switzerland. The WR data contain information about the large-scale atmospheric circulation in the North Atlantic–European region, and thus allow 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 postprocessing 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 multimodel 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 subseasonal hydrometeorological forecasts in a hybrid forecasting system in an operational mode.
Abstract
Across the globe, there has been an increasing interest in improving the predictability of subseasonal hydrometeorological 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 postprocessing techniques at the interface with a Precipitationn–Runoff–Evapotranspiration Hydrological Response Unit Model (PREVAH). Specifically, this research aims to assess the benefit of European weather regime (WR) data within a hybrid forecasting setup, a combination of a traditional hydrological model and a machine learning (ML) algorithm, to improve the performance of subseasonal hydrometeorological forecasts in Switzerland. The WR data contain information about the large-scale atmospheric circulation in the North Atlantic–European region, and thus allow 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 postprocessing 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 multimodel 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 subseasonal hydrometeorological forecasts in a hybrid forecasting system in an operational mode.
Abstract
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 overallocated 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.
Significance Statement
Changes in climate are amplifying the loss of stored water in reservoirs due to increases in evaporation. Water managers need to account for this water loss, but many current methods do not accurately reflect the temporal and spatial variability in evaporation across large, heterogeneous reservoirs. To address this gap, we use a numerical weather prediction model coupled to a lake model to simulate spatial heterogeneity in reservoir evaporation on a subdaily time step. Our results suggest that bulk evaporation models may be sufficient for estimating evaporation at smaller, more homogeneous reservoirs, but more complex formulations may be more appropriate for estimating evaporation rates at large, complex reservoirs and for better understanding the heat storage affects that influence temporal variability of evaporation.
Abstract
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 overallocated 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.
Significance Statement
Changes in climate are amplifying the loss of stored water in reservoirs due to increases in evaporation. Water managers need to account for this water loss, but many current methods do not accurately reflect the temporal and spatial variability in evaporation across large, heterogeneous reservoirs. To address this gap, we use a numerical weather prediction model coupled to a lake model to simulate spatial heterogeneity in reservoir evaporation on a subdaily time step. Our results suggest that bulk evaporation models may be sufficient for estimating evaporation at smaller, more homogeneous reservoirs, but more complex formulations may be more appropriate for estimating evaporation rates at large, complex reservoirs and for better understanding the heat storage affects that influence temporal variability of evaporation.
Abstract
As global mean temperature rises, extreme drought events are expected to increasingly affect regions of the United States that are crucial for agriculture, forestry, and natural ecology. A pressing need is to understand and anticipate the conditions under which extreme drought causes catastrophic failure to vegetation in these areas. 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 United States. First, the Dynamic Canopy Biophysical Properties (DCBP) model was used to estimate the thresholds that define reduced photosynthesis by assimilating vegetation phenology data from the Moderate Resolution Imaging Spectroradiometer (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 coevolved 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 United States. 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.
Abstract
As global mean temperature rises, extreme drought events are expected to increasingly affect regions of the United States that are crucial for agriculture, forestry, and natural ecology. A pressing need is to understand and anticipate the conditions under which extreme drought causes catastrophic failure to vegetation in these areas. 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 United States. First, the Dynamic Canopy Biophysical Properties (DCBP) model was used to estimate the thresholds that define reduced photosynthesis by assimilating vegetation phenology data from the Moderate Resolution Imaging Spectroradiometer (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 coevolved 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 United States. 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.
Abstract
Changes in surface water and energy balance can influence weather through interactions between the land and lower atmosphere. In convecting atmospheres, increases in convective available potential energy (CAPE) at the base of the column are driven by surface turbulent fluxes and can lead to precipitation. Using two global satellite datasets, we analyze the impact of surface energy balance partitioning on convective development by tracking CAPE over soil moisture drydowns (interstorms) during the summer, when land–atmosphere coupling is strongest. Our results show that the sign and magnitude of CAPE development during summertime drydowns depends on regional hydroclimate and initial soil moisture content. On average, CAPE increases between precipitation events over humid regions (e.g., the eastern United States) and decreases slightly over arid regions (e.g., the western United States). The soil moisture content at the start of a drydown was found to only impact CAPE evolution over arid regions, leading to greater decreases in CAPE when initial soil moisture content was high. The effect of these factors on CAPE can be explained by their influence principally on surface evaporation, demonstrating the importance of evaporative controls on CAPE and providing a basis for understanding the soil moisture–precipitation relationship, as well as land–atmosphere interaction as a whole.
Significance Statement
Land–atmosphere coupling is a long-standing topic with growing interest within the climate and modeling communities. Understanding and characterizing the feedbacks between the land surface and lower atmosphere has important implications for weather and climate prediction. One component of land–atmosphere coupling not yet fully understood is the soil moisture–precipitation relationship. Our work quantifies the land influence on one pathway for precipitation, convection, by tracking the evolution of atmospheric convective energy as soils dry between storms. Using global satellite observations, we find clear spatial and temporal trends that link summertime convective development to soil moisture content and evaporation. Our observational results provide a benchmark for evaluating how well weather and climate models capture the complex coupling between land and atmosphere.
Abstract
Changes in surface water and energy balance can influence weather through interactions between the land and lower atmosphere. In convecting atmospheres, increases in convective available potential energy (CAPE) at the base of the column are driven by surface turbulent fluxes and can lead to precipitation. Using two global satellite datasets, we analyze the impact of surface energy balance partitioning on convective development by tracking CAPE over soil moisture drydowns (interstorms) during the summer, when land–atmosphere coupling is strongest. Our results show that the sign and magnitude of CAPE development during summertime drydowns depends on regional hydroclimate and initial soil moisture content. On average, CAPE increases between precipitation events over humid regions (e.g., the eastern United States) and decreases slightly over arid regions (e.g., the western United States). The soil moisture content at the start of a drydown was found to only impact CAPE evolution over arid regions, leading to greater decreases in CAPE when initial soil moisture content was high. The effect of these factors on CAPE can be explained by their influence principally on surface evaporation, demonstrating the importance of evaporative controls on CAPE and providing a basis for understanding the soil moisture–precipitation relationship, as well as land–atmosphere interaction as a whole.
Significance Statement
Land–atmosphere coupling is a long-standing topic with growing interest within the climate and modeling communities. Understanding and characterizing the feedbacks between the land surface and lower atmosphere has important implications for weather and climate prediction. One component of land–atmosphere coupling not yet fully understood is the soil moisture–precipitation relationship. Our work quantifies the land influence on one pathway for precipitation, convection, by tracking the evolution of atmospheric convective energy as soils dry between storms. Using global satellite observations, we find clear spatial and temporal trends that link summertime convective development to soil moisture content and evaporation. Our observational results provide a benchmark for evaluating how well weather and climate models capture the complex coupling between land and atmosphere.
Abstract
Eurasian spring snowmelt plays an important role in the subsequent climate and hydrological cycle, however, the understanding of snowmelt itself and its causes remains insufficient. This study explored the basic characteristics of spring snowmelt in the eastern Europe–western Siberia (EEWS) region by classifying snowmelt anomalies into two categories based on the different factors that dominate spring snowmelt, and then investigated the associated atmospheric circulation anomalies and local physical processes. The first category of anomalous snowmelt (category 1) is controlled by both the initial snow mass and the later snowmelt process, while the second category of anomalous snowmelt (category 2) is mainly linked to the later snowmelt process. Specifically, category 1 is characterized by an anomalous trough in EEWS in winter, where water vapor transported and converged, accompanied by anomalous upward motion, which promotes snowfall and snow accumulation, providing initial conditions conducive to snowmelt. In April, this region is controlled by an anomalous ridge, with significant warm advection anomalies and subsidence promoting surface warming, thereby accelerating snow melting. In contrast, the winter circulation anomalies are insignificant in category 2, while the anomalous ridge in April is stronger than in category 1, accompanied by more intense snowmelt processes. In addition, from the surface energy balance perspective, atmospheric downward sensible heat transport is an important factor influencing the anomalous snowmelt in category 1, while shortwave radiation plays a secondary role. Conversely, the snowmelt in category 2 is dominated by shortwave radiation forcing, but the sensible heat effect is slightly weaker.
Significance Statement
Eurasian spring snowmelt significantly impacts the subsequent climate and hydrological cycle, but the understanding of snowmelt itself and its causes is still inadequate. The purpose of this study is to explore the monthly evolution of atmospheric circulation associated with anomalous snowmelt and its local physical processes associated by categorizing them based on snowmelt characteristics. Category 1 is jointly affected by winter snow accumulation and later warming, while category 2 is dominated by strong snowmelt process in late spring. These two categories are accompanied by different winter and spring circulation configurations. Our results provide a basis for further investigation of snowmelt precursor signals.
Abstract
Eurasian spring snowmelt plays an important role in the subsequent climate and hydrological cycle, however, the understanding of snowmelt itself and its causes remains insufficient. This study explored the basic characteristics of spring snowmelt in the eastern Europe–western Siberia (EEWS) region by classifying snowmelt anomalies into two categories based on the different factors that dominate spring snowmelt, and then investigated the associated atmospheric circulation anomalies and local physical processes. The first category of anomalous snowmelt (category 1) is controlled by both the initial snow mass and the later snowmelt process, while the second category of anomalous snowmelt (category 2) is mainly linked to the later snowmelt process. Specifically, category 1 is characterized by an anomalous trough in EEWS in winter, where water vapor transported and converged, accompanied by anomalous upward motion, which promotes snowfall and snow accumulation, providing initial conditions conducive to snowmelt. In April, this region is controlled by an anomalous ridge, with significant warm advection anomalies and subsidence promoting surface warming, thereby accelerating snow melting. In contrast, the winter circulation anomalies are insignificant in category 2, while the anomalous ridge in April is stronger than in category 1, accompanied by more intense snowmelt processes. In addition, from the surface energy balance perspective, atmospheric downward sensible heat transport is an important factor influencing the anomalous snowmelt in category 1, while shortwave radiation plays a secondary role. Conversely, the snowmelt in category 2 is dominated by shortwave radiation forcing, but the sensible heat effect is slightly weaker.
Significance Statement
Eurasian spring snowmelt significantly impacts the subsequent climate and hydrological cycle, but the understanding of snowmelt itself and its causes is still inadequate. The purpose of this study is to explore the monthly evolution of atmospheric circulation associated with anomalous snowmelt and its local physical processes associated by categorizing them based on snowmelt characteristics. Category 1 is jointly affected by winter snow accumulation and later warming, while category 2 is dominated by strong snowmelt process in late spring. These two categories are accompanied by different winter and spring circulation configurations. Our results provide a basis for further investigation of snowmelt precursor signals.
Abstract
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-h) 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 h 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-yr 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.
Abstract
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-h) 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 h 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-yr 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.
