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Abstract
Drought is a complex phenomenon that is difficult to accurately describe because its definition is both spatially variant and context dependent. Decision makers in local, state, and federal agencies commonly use operational drought definitions that are based on specific drought index thresholds to trigger water conservation measures and determine levels of drought assistance. Unfortunately, many state drought plans utilize operational drought definitions that are derived subjectively and therefore may not be appropriate for triggering drought responses. This paper presents an objective methodology for establishing operational drought definitions. The advantages of this methodology are demonstrated by calculating meteorological drought thresholds for the Palmer drought severity index, the standardized precipitation index, and percent of normal precipitation using both station and climate division data from Texas. Results indicate that using subjectively derived operational drought definitions may lead to over- or underestimating true drought severity. Therefore, it is more appropriate to use an objective location-specific method for defining operational drought thresholds.
Abstract
Drought is a complex phenomenon that is difficult to accurately describe because its definition is both spatially variant and context dependent. Decision makers in local, state, and federal agencies commonly use operational drought definitions that are based on specific drought index thresholds to trigger water conservation measures and determine levels of drought assistance. Unfortunately, many state drought plans utilize operational drought definitions that are derived subjectively and therefore may not be appropriate for triggering drought responses. This paper presents an objective methodology for establishing operational drought definitions. The advantages of this methodology are demonstrated by calculating meteorological drought thresholds for the Palmer drought severity index, the standardized precipitation index, and percent of normal precipitation using both station and climate division data from Texas. Results indicate that using subjectively derived operational drought definitions may lead to over- or underestimating true drought severity. Therefore, it is more appropriate to use an objective location-specific method for defining operational drought thresholds.
Abstract
Because of the lack of field measurements, models are often used to monitor soil moisture conditions. Therefore, it is important to find a model that can accurately simulate soil moisture under a variety of land surface conditions. In this paper, three models of varying complexities [the Variable Infiltration Capacity (VIC), Decision Support System for Agrotechnology Transfer (DSSAT), and Climatic Water Budget (CWB) models] that are commonly used for simulating soil moisture were evaluated and compared using soil moisture data (1997–2005) from three Soil Climate Analysis Network (SCAN) sites (Bushland, Texas; Prairie View, Texas; Powder Mill, Maryland). Results demonstrated that DSSAT and VIC simulated soil moisture more accurately than CWB at the three SCAN sites. DSSAT and VIC both accurately simulated the annual cycle of soil moisture and the wetting and drying in response to weather conditions, as evidenced by the relatively strong correlations, but could not accurately simulate the actual soil water content in the upper soil layers (the mean coefficients of efficiency E for all DSSAT and VIC simulations were −0.8 and −2.6, respectively). CWB could not accurately simulate soil moisture at any of the SCAN sites. Model performance varied significantly not only from model to model but also from year to year and from location to location. Model sensitivity analysis using the factorial approach suggests that DSSAT is more sensitive than VIC and that model sensitivity varies by locations, indicating that parameter sensitivity is more strongly controlled by climatic gradients than by changes in soil properties.
Abstract
Because of the lack of field measurements, models are often used to monitor soil moisture conditions. Therefore, it is important to find a model that can accurately simulate soil moisture under a variety of land surface conditions. In this paper, three models of varying complexities [the Variable Infiltration Capacity (VIC), Decision Support System for Agrotechnology Transfer (DSSAT), and Climatic Water Budget (CWB) models] that are commonly used for simulating soil moisture were evaluated and compared using soil moisture data (1997–2005) from three Soil Climate Analysis Network (SCAN) sites (Bushland, Texas; Prairie View, Texas; Powder Mill, Maryland). Results demonstrated that DSSAT and VIC simulated soil moisture more accurately than CWB at the three SCAN sites. DSSAT and VIC both accurately simulated the annual cycle of soil moisture and the wetting and drying in response to weather conditions, as evidenced by the relatively strong correlations, but could not accurately simulate the actual soil water content in the upper soil layers (the mean coefficients of efficiency E for all DSSAT and VIC simulations were −0.8 and −2.6, respectively). CWB could not accurately simulate soil moisture at any of the SCAN sites. Model performance varied significantly not only from model to model but also from year to year and from location to location. Model sensitivity analysis using the factorial approach suggests that DSSAT is more sensitive than VIC and that model sensitivity varies by locations, indicating that parameter sensitivity is more strongly controlled by climatic gradients than by changes in soil properties.
Abstract
The evidence shows that soil moisture has an important influence on North American monsoon (NAM) precipitation. This study evaluates the local and nonlocal feedbacks of soil moisture on summer (June–September) precipitation in the NAM region using observational data. We applied a multivariate statistical method known as the Stepwise Generalized Equilibrium Feedback Assessment (SGEFA) to control for internal atmospheric variability and sea surface temperature (SST) forcings so that we could isolate the impact of soil moisture feedbacks on NAM precipitation. Our results identify feedback pathways between soil moisture and precipitation in the NAM region and in the southern Rocky Mountains (SRM) region. Wet soils in the SRM result in lower-than-normal local surface temperature, weaker water vapor transport from the eastern Pacific and the Gulf of California (GOC), and less monsoon precipitation. Precipitation over the U.S. Great Plains also significantly increases when there are wet soils in the SRM. This occurs due to an enhanced water vapor influx into this region. On the other hand, anomalously wet soils in the NAM region increase NAM precipitation by enhancing local moist static energy and increasing the strength of the monsoonal circulation. Our observational results using SGEFA agree well with previous numerical modeling studies. This study highlights the critical role of land–atmosphere interactions for understanding NAM variability.
Abstract
The evidence shows that soil moisture has an important influence on North American monsoon (NAM) precipitation. This study evaluates the local and nonlocal feedbacks of soil moisture on summer (June–September) precipitation in the NAM region using observational data. We applied a multivariate statistical method known as the Stepwise Generalized Equilibrium Feedback Assessment (SGEFA) to control for internal atmospheric variability and sea surface temperature (SST) forcings so that we could isolate the impact of soil moisture feedbacks on NAM precipitation. Our results identify feedback pathways between soil moisture and precipitation in the NAM region and in the southern Rocky Mountains (SRM) region. Wet soils in the SRM result in lower-than-normal local surface temperature, weaker water vapor transport from the eastern Pacific and the Gulf of California (GOC), and less monsoon precipitation. Precipitation over the U.S. Great Plains also significantly increases when there are wet soils in the SRM. This occurs due to an enhanced water vapor influx into this region. On the other hand, anomalously wet soils in the NAM region increase NAM precipitation by enhancing local moist static energy and increasing the strength of the monsoonal circulation. Our observational results using SGEFA agree well with previous numerical modeling studies. This study highlights the critical role of land–atmosphere interactions for understanding NAM variability.
Abstract
Fire affects virtually all terrestrial ecosystems but occurs more commonly in some than in others. This paper investigates how climate, specifically the moisture regime, influences the flammability of different landscapes in the eastern United States. A previous study of spatial differences in fire regimes across the central Appalachian Mountains suggested that intra-annual precipitation variability influences fire occurrence more strongly than does total annual precipitation. The results presented here support that conclusion. The relationship of fire occurrence to moisture regime is also considered for the entire eastern United States. To do so, mean annual wildfire density and mean annual area burned were calculated for 34 national forests and parks representing the major vegetation and climatic conditions throughout the eastern forests. The relationship between fire activity and two climate variables was analyzed: mean annual moisture balance [precipitation P − potential evapotranspiration (PET)] and daily precipitation variability (coefficient of variability for daily precipitation). Fire activity is related to both climate variables but displays a stronger relationship with precipitation variability. The southeastern United States is particularly noteworthy for its high wildfire activity, which is associated with a warm, humid climate and a variable precipitation regime, which promote heavy fuel production and rapid drying of fuels.
Abstract
Fire affects virtually all terrestrial ecosystems but occurs more commonly in some than in others. This paper investigates how climate, specifically the moisture regime, influences the flammability of different landscapes in the eastern United States. A previous study of spatial differences in fire regimes across the central Appalachian Mountains suggested that intra-annual precipitation variability influences fire occurrence more strongly than does total annual precipitation. The results presented here support that conclusion. The relationship of fire occurrence to moisture regime is also considered for the entire eastern United States. To do so, mean annual wildfire density and mean annual area burned were calculated for 34 national forests and parks representing the major vegetation and climatic conditions throughout the eastern forests. The relationship between fire activity and two climate variables was analyzed: mean annual moisture balance [precipitation P − potential evapotranspiration (PET)] and daily precipitation variability (coefficient of variability for daily precipitation). Fire activity is related to both climate variables but displays a stronger relationship with precipitation variability. The southeastern United States is particularly noteworthy for its high wildfire activity, which is associated with a warm, humid climate and a variable precipitation regime, which promote heavy fuel production and rapid drying of fuels.
Abstract
Soil moisture–vegetation interactions are an important component of land–atmosphere coupling, especially in semiarid regions such as the North American Great Plains. However, many land surface models parameterize vegetation using an interannually invariant leaf area index (LAI). This study quantifies how utilizing a dynamic vegetation parameter in the variability infiltration capacity (VIC) hydrologic model influences model-simulated soil moisture. Accuracy is assessed using in situ soil moisture observations from 20 stations from the Oklahoma Mesonet. Results show that VIC simulations generated with an interannually variant LAI parameter are not consistently more accurate than those generated with the invariant (static) LAI parameter. However, the static LAI parameter tends to overestimate LAI during anomalously dry periods. This has the greatest influence on the accuracy of the soil moisture simulations in the deeper soil layers. Soil moisture drought, as simulated with the static LAI parameter, tends to be more severe and persist for considerably longer than drought simulated using the interannually variant LAI parameter. Dynamic vegetation parameters can represent interannual variations in vegetation health and growing season length. Therefore, simulations with a dynamic LAI parameter better capture the intensity and duration of drought conditions and are recommended for use in drought monitoring.
Abstract
Soil moisture–vegetation interactions are an important component of land–atmosphere coupling, especially in semiarid regions such as the North American Great Plains. However, many land surface models parameterize vegetation using an interannually invariant leaf area index (LAI). This study quantifies how utilizing a dynamic vegetation parameter in the variability infiltration capacity (VIC) hydrologic model influences model-simulated soil moisture. Accuracy is assessed using in situ soil moisture observations from 20 stations from the Oklahoma Mesonet. Results show that VIC simulations generated with an interannually variant LAI parameter are not consistently more accurate than those generated with the invariant (static) LAI parameter. However, the static LAI parameter tends to overestimate LAI during anomalously dry periods. This has the greatest influence on the accuracy of the soil moisture simulations in the deeper soil layers. Soil moisture drought, as simulated with the static LAI parameter, tends to be more severe and persist for considerably longer than drought simulated using the interannually variant LAI parameter. Dynamic vegetation parameters can represent interannual variations in vegetation health and growing season length. Therefore, simulations with a dynamic LAI parameter better capture the intensity and duration of drought conditions and are recommended for use in drought monitoring.
Abstract
On the basis of snowfall observations from 1929 to 1999, positive (negative) snowfall anomalies are associated with wetter (drier) than normal conditions during the summer [July–August (JJA)] in the northern Great Plains. The five driest summers are associated with negative snowfall anomalies during the preceding winter (−66.7 mm) and spring (−62.4 mm) that cover most of the study region (∼85%). Snowfall anomalies during the late spring (April–May) are more important for determining summer moisture conditions than snowfall anomalies in fall [September–November (SON)] or winter [December–February (DJF)]. The link between snowfall anomalies and summer moisture conditions appears to be, at least partly, through soil moisture since positive (negative) snowfall anomalies are associated with wetter (drier) soils, a later (earlier) date of snowmelt, cooler (warmer) air temperatures, and more (less) evaporation during spring and summer. However, the relationship between spring snowfall and summer moisture conditions is only statistically significant when the moisture anomaly index (Z), which accounts for both temperature and precipitation, is used to characterize summer moisture conditions and the signal is weak when just considering precipitation (e.g., standardized precipitation index). Results also indicate that the strength of the relationship between winter/spring snowfall and summer moisture varies significantly over space and time, which limits its utility for seasonal forecasting.
Abstract
On the basis of snowfall observations from 1929 to 1999, positive (negative) snowfall anomalies are associated with wetter (drier) than normal conditions during the summer [July–August (JJA)] in the northern Great Plains. The five driest summers are associated with negative snowfall anomalies during the preceding winter (−66.7 mm) and spring (−62.4 mm) that cover most of the study region (∼85%). Snowfall anomalies during the late spring (April–May) are more important for determining summer moisture conditions than snowfall anomalies in fall [September–November (SON)] or winter [December–February (DJF)]. The link between snowfall anomalies and summer moisture conditions appears to be, at least partly, through soil moisture since positive (negative) snowfall anomalies are associated with wetter (drier) soils, a later (earlier) date of snowmelt, cooler (warmer) air temperatures, and more (less) evaporation during spring and summer. However, the relationship between spring snowfall and summer moisture conditions is only statistically significant when the moisture anomaly index (Z), which accounts for both temperature and precipitation, is used to characterize summer moisture conditions and the signal is weak when just considering precipitation (e.g., standardized precipitation index). Results also indicate that the strength of the relationship between winter/spring snowfall and summer moisture varies significantly over space and time, which limits its utility for seasonal forecasting.
Abstract
Tropical cyclone precipitation (TCP) can cause significant flooding in coastal areas around the world. This study compares multiple options of a new technique for developing a gridded daily TCP product at a spatial resolution of 0.25°. These options were evaluated using NASA’s Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42 product to determine the optimal approach. Results indicate that the technique is very sensitive to changes in wind corrections, interpolation method, and gauge density. The optimal method accounts for wind-induced gauge undercatch and uses a customized interpolation approach. It significantly reduces precipitation biases associated with gauge undercatch during windy conditions. The new TCP extraction approach can be used to examine variability and long-term trends in TCP, even in regions with relatively few gauges.
Abstract
Tropical cyclone precipitation (TCP) can cause significant flooding in coastal areas around the world. This study compares multiple options of a new technique for developing a gridded daily TCP product at a spatial resolution of 0.25°. These options were evaluated using NASA’s Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42 product to determine the optimal approach. Results indicate that the technique is very sensitive to changes in wind corrections, interpolation method, and gauge density. The optimal method accounts for wind-induced gauge undercatch and uses a customized interpolation approach. It significantly reduces precipitation biases associated with gauge undercatch during windy conditions. The new TCP extraction approach can be used to examine variability and long-term trends in TCP, even in regions with relatively few gauges.
Abstract
As the private sector becomes increasingly aware of the risks associated with climate change, climatologists have been engaging with companies to assess climate change risks and opportunities. Here, we outline how we have collaborated with a Fortune 500 company to assess the physical risks of climate change to their facilities. We provide a template for a climate change report card that we generated for >100 facilities globally. This report card is designed to communicate risk to company leadership and local facility managers. We believe that by sharing our experiences, climate scientists will be able to quantify and communicate climate risk more effectively to the private sector.
Abstract
As the private sector becomes increasingly aware of the risks associated with climate change, climatologists have been engaging with companies to assess climate change risks and opportunities. Here, we outline how we have collaborated with a Fortune 500 company to assess the physical risks of climate change to their facilities. We provide a template for a climate change report card that we generated for >100 facilities globally. This report card is designed to communicate risk to company leadership and local facility managers. We believe that by sharing our experiences, climate scientists will be able to quantify and communicate climate risk more effectively to the private sector.
Abstract
Drought monitoring is critical for managing agriculture and water resources and for triggering state emergency response plans and hazard mitigation activities. Fixed thresholds serve as guidelines for the U.S. Drought Monitor (USDM). However, fixed drought thresholds (i.e., using the same threshold in all seasons and climate regions) may not properly reflect local conditions and impacts. Therefore, this study develops impacts-based drought thresholds that are appropriate for drought monitoring in Ohio. We examined four drought indices that are currently used by the state of Ohio: standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI), Palmer’s Z index, and Palmer hydrological drought index (PHDI). Streamflow and corn yield are used as indicators of hydrological and agricultural drought impacts, respectively. Our results show that fixed thresholds tend to indicate milder drought conditions in Ohio, while the proposed impacts-based drought thresholds are more sensitive to exceptional drought (D4) conditions. The area percentage of D4 based on impacts-based drought thresholds is more strongly correlated with corn yield and streamflow. This study provides a methodology for developing local impacts-based drought thresholds that can be applied to other regions where long-term drought impact records exist to provide regionally representative depictions of conditions and improve drought monitoring.
Abstract
Drought monitoring is critical for managing agriculture and water resources and for triggering state emergency response plans and hazard mitigation activities. Fixed thresholds serve as guidelines for the U.S. Drought Monitor (USDM). However, fixed drought thresholds (i.e., using the same threshold in all seasons and climate regions) may not properly reflect local conditions and impacts. Therefore, this study develops impacts-based drought thresholds that are appropriate for drought monitoring in Ohio. We examined four drought indices that are currently used by the state of Ohio: standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI), Palmer’s Z index, and Palmer hydrological drought index (PHDI). Streamflow and corn yield are used as indicators of hydrological and agricultural drought impacts, respectively. Our results show that fixed thresholds tend to indicate milder drought conditions in Ohio, while the proposed impacts-based drought thresholds are more sensitive to exceptional drought (D4) conditions. The area percentage of D4 based on impacts-based drought thresholds is more strongly correlated with corn yield and streamflow. This study provides a methodology for developing local impacts-based drought thresholds that can be applied to other regions where long-term drought impact records exist to provide regionally representative depictions of conditions and improve drought monitoring.
Abstract
Tropical cyclone precipitation (TCP) contributes a significant amount of precipitation each year in the contiguous United States and Mexico, and it can cause damaging floods. In this study, we evaluate the ability of two precipitation estimates from the latest Integrated Multisatellite Retrievals for GPM (IMERG Final Run V06, hereafter referred to as IMERG-F) and its predecessor, the TRMM Multisatellite Precipitation Analysis (TMPA research product 3B42V7, hereafter referred to as TMPA), to capture TCP at daily, event, and annual scales by comparing the satellite observations with gauge measurements based on data from 2014 to 2018. The results show that both TMPA and IMERG-F are able to accurately capture the general TCP patterns. IMERG-F provides a noticeable improvement in accuracy over TMPA, especially for times and locations with light and heavy TCP. However, both IMERG-F and TMPA still systematically underestimate TCP during extreme events. At the annual scale, both TMPA and IMERG-F slightly underestimate annual TCP, but IMERG-F to a lesser degree. For individual TC events, IMERG-F has lower bias and a higher Nash–Sutcliffe efficiency than TMPA in the majority of the events. The differences between IMERG-F and TMPA are especially pronounced for extreme TCP events, such as Hurricane Harvey in 2017. At the daily scale, both IMERG-F and TMPA underestimate TCP when daily TCP exceeds ~150 mm. However, IMERG-F shows closer agreements with gauge-based measurements than TMPA. This study demonstrates that IMERG-F can more accurately measure TCP than TMPA. However, there are still systematic biases in IMERG-F when it comes to heavy TCP at all of the time scales.
Abstract
Tropical cyclone precipitation (TCP) contributes a significant amount of precipitation each year in the contiguous United States and Mexico, and it can cause damaging floods. In this study, we evaluate the ability of two precipitation estimates from the latest Integrated Multisatellite Retrievals for GPM (IMERG Final Run V06, hereafter referred to as IMERG-F) and its predecessor, the TRMM Multisatellite Precipitation Analysis (TMPA research product 3B42V7, hereafter referred to as TMPA), to capture TCP at daily, event, and annual scales by comparing the satellite observations with gauge measurements based on data from 2014 to 2018. The results show that both TMPA and IMERG-F are able to accurately capture the general TCP patterns. IMERG-F provides a noticeable improvement in accuracy over TMPA, especially for times and locations with light and heavy TCP. However, both IMERG-F and TMPA still systematically underestimate TCP during extreme events. At the annual scale, both TMPA and IMERG-F slightly underestimate annual TCP, but IMERG-F to a lesser degree. For individual TC events, IMERG-F has lower bias and a higher Nash–Sutcliffe efficiency than TMPA in the majority of the events. The differences between IMERG-F and TMPA are especially pronounced for extreme TCP events, such as Hurricane Harvey in 2017. At the daily scale, both IMERG-F and TMPA underestimate TCP when daily TCP exceeds ~150 mm. However, IMERG-F shows closer agreements with gauge-based measurements than TMPA. This study demonstrates that IMERG-F can more accurately measure TCP than TMPA. However, there are still systematic biases in IMERG-F when it comes to heavy TCP at all of the time scales.
