Spatiotemporal Variability of Precipitation, Modeled Soil Moisture, and Vegetation Greenness in North America within the Recent Observational Record

2009 ◽  
Vol 10 (6) ◽  
pp. 1355-1378 ◽  
Author(s):  
Christopher L. Castro ◽  
Adriana B. Beltrán-Przekurat ◽  
Roger A. Pielke
Keyword(s):  
Soil Moisture ◽  
North America ◽  
Time Scales ◽  
Land Surface ◽  
Standardized Precipitation Index ◽  
Spatiotemporal Patterns ◽  
Spatiotemporal Variability ◽  
Vegetation Greenness ◽  
The Standardized Precipitation Index ◽  
Observational Record

Abstract Dominant spatiotemporal patterns of precipitation, modeled soil moisture, and vegetation are determined in North America within the recent observational record (late twentieth century onward). These data are from a gridded U.S.–Mexico precipitation product, retrospective long-term integrations of two land surface models, and satellite-derived vegetation greenness. The analysis procedure uses three statistical techniques. First, all the variables are normalized according to the standardized precipitation index procedure. Second, dominant patterns of spatiotemporal variability are determined using multitaper method–singular value decomposition for interannual and longer time scales. The dominant spatiotemporal patterns of precipitation generally conform to known and distinct Pacific SST forcing in the cool and warm seasons. Two specific time scales in precipitation at 9 and 6–7 yr correspond to significant variability in soil moisture and vegetation, respectively. The 9-yr signal is related to precipitation in late fall to early winter, whereas the 6–7-yr signal is related to earlysummer precipitation. Canonical correlation analysis is finally used to confirm that strong covariability between land surface variables and precipitation exists at these specific times of the year. Both signals are strongest in the central and western United States and are consistent with prior global modeling and paleoclimate studies that have investigated drought in North America.

scholarly journals Drought Monitoring for Washington State: Indicators and Applications

2011 ◽  
Vol 12 (1) ◽  
pp. 66-83 ◽  
Author(s):  
Shraddhanand Shukla ◽  
Anne C. Steinemann ◽  
Dennis P. Lettenmaier
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Standardized Precipitation Index ◽  
Land Surface Model ◽  
Washington State ◽  
Drought Monitoring ◽  
Surface Model ◽  
Drought Conditions ◽  
The Standardized Precipitation Index

Abstract A drought monitoring system (DMS) can help to detect and characterize drought conditions and reduce adverse drought impacts. The authors evaluate how a DMS for Washington State, based on a land surface model (LSM), would perform. The LSM represents current soil moisture (SM), snow water equivalent (SWE), and runoff over the state. The DMS incorporates the standardized precipitation index (SPI), standardized runoff index (SRI), and soil moisture percentile (SMP) taken from the LSM. Four historical drought events (1976–77, 1987–89, 2000–01, and 2004–05) are constructed using DMS indicators of SPI/SRI-3, SPI/SRI-6, SPI/SRI-12, SPI/SRI-24, SPI/SRI-36, and SMP, with monthly updates, in each of the state’s 62 Water Resource Inventory Areas (WRIAs). The authors also compare drought triggers based on DMS indicators with the evolution of drought conditions and management decisions during the four droughts. The results show that the DMS would have detected the onset and recovery of drought conditions, in many cases, up to four months before state declarations.

scholarly journals Hydrological response to different time scales of climatological drought: an evaluation of the Standardized Precipitation Index in a mountainous Mediterranean basin

2005 ◽  
Vol 9 (5) ◽  
pp. 523-533 ◽  
Author(s):  
S. M. Vicente-Serrano ◽  
J. I. López-Moreno
Keyword(s):  
Time Scales ◽  
Hydrological Cycle ◽  
Standardized Precipitation Index ◽  
Precipitation Index ◽  
Drought Indices ◽  
Long Time ◽  
The Standardized Precipitation Index ◽  
Hydrological Droughts ◽  
Different Time Scales

Abstract. At present, the Standardized Precipitation Index (SPI) is the most widely used drought index to provide good estimations about the intensity, magnitude and spatial extent of droughts. The main advantage of the SPI in comparison with other indices is the fact that the SPI enables both determination of drought conditions at different time scales and monitoring of different drought types. It is widely accepted that SPI time scales affect different sub-systems in the hydrological cycle due to the fact that the response of the different water usable sources to precipitation shortages can be very different. The long time scales of SPI are related to hydrological droughts (river flows and reservoir storages). Nevertheless, few analyses empirically verify these statements or the usefulness of the SPI time scales to monitor drought. In this paper, the SPI at different time scales is compared with surface hydrological variables in a big closed basin located in the central Spanish Pyrenees. We provide evidence about the way in which the longer (>12 months) SPI time scales may not be useful for drought quantification in this area. In general, the surface flows respond to short SPI time scales whereas the reservoir storages respond to longer time scales (7–10 months). Nevertheless, important seasonal differences can be identified in the SPI-usable water sources relationships. This suggests that it is necessary to test the drought indices and time scales in relation to their usefulness for monitoring different drought types under different environmental conditions and water demand situations.

scholarly journals The effect of climate type on timescales of drought propagation in an ensemble of global hydrological models

2018 ◽  
Vol 22 (9) ◽  
pp. 4649-4665 ◽  
Author(s):  
Anouk I. Gevaert ◽  
Ted I. E. Veldkamp ◽  
Philip J. Ward
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Spatial Scales ◽  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
Hydrological Drought ◽  
Propagation Time ◽  
Hydrological Models ◽  
Land Surface Models ◽  
Surface Models

Abstract. Drought is a natural hazard that occurs at many temporal and spatial scales and has severe environmental and socioeconomic impacts across the globe. The impacts of drought change as drought evolves from precipitation deficits to deficits in soil moisture or streamflow. Here, we quantified the time taken for drought to propagate from meteorological drought to soil moisture drought and from meteorological drought to hydrological drought. We did this by cross-correlating the Standardized Precipitation Index (SPI) against standardized indices (SIs) of soil moisture, runoff, and streamflow from an ensemble of global hydrological models (GHMs) forced by a consistent meteorological dataset. Drought propagation is strongly related to climate types, occurring at sub-seasonal timescales in tropical climates and at up to multi-annual timescales in continental and arid climates. Winter droughts are usually related to longer SPI accumulation periods than summer droughts, especially in continental and tropical savanna climates. The difference between the seasons is likely due to winter snow cover in the former and distinct wet and dry seasons in the latter. Model structure appears to play an important role in model variability, as drought propagation to soil moisture drought is slower in land surface models (LSMs) than in global hydrological models, but propagation to hydrological drought is faster in land surface models than in global hydrological models. The propagation time from SPI to hydrological drought in the models was evaluated against observed data at 127 in situ streamflow stations. On average, errors between observed and modeled drought propagation timescales are small and the model ensemble mean is preferred over the use of a single model. Nevertheless, there is ample opportunity for improvement as substantial differences in drought propagation are found at 10 % of the study sites. A better understanding and representation of drought propagation in models may help improve seasonal drought forecasting as well as constrain drought variability under future climate scenarios.

scholarly journals The Evaporative Demand Drought Index. Part II: CONUS-Wide Assessment against Common Drought Indicators

2016 ◽  
Vol 17 (6) ◽  
pp. 1763-1779 ◽  
Author(s):  
Daniel J. McEvoy ◽  
Justin L. Huntington ◽  
Michael T. Hobbins ◽  
Andrew Wood ◽  
Charles Morton ◽  
...  
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Time Scales ◽  
Drought Index ◽  
Standardized Precipitation Index ◽  
Stress Index ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Evaporative Demand ◽  
Hydrologic Drought

Abstract Precipitation, soil moisture, and air temperature are the most commonly used climate variables to monitor drought; however, other climatic factors such as solar radiation, wind speed, and humidity can be important drivers in the depletion of soil moisture and evolution and persistence of drought. This work assesses the Evaporative Demand Drought Index (EDDI) at multiple time scales for several hydroclimates as the second part of a two-part study. EDDI and individual evaporative demand components were examined as they relate to the dynamic evolution of flash drought over the central United States, characterization of hydrologic drought over the western United States, and comparison to commonly used drought metrics of the U.S. Drought Monitor (USDM), Standardized Precipitation Index (SPI), Standardized Soil Moisture Index (SSI), and the evaporative stress index (ESI). Two main advantages of EDDI over other drought indices are that it is independent of precipitation (similar to ESI) and it can be decomposed to identify the role individual evaporative drivers have on drought onset and persistence. At short time scales, spatial distributions and time series results illustrate that EDDI often indicates drought onset well in advance of the USDM, SPI, and SSI. Results illustrate the benefits of physically based evaporative demand estimates and demonstrate EDDI’s utility and effectiveness in an easy-to-implement agricultural early warning and long-term hydrologic drought–monitoring tool with potential applications in seasonal forecasting and fire-weather monitoring.

scholarly journals Drought Relief and Reversal over North America from 1500 to 2016

2021 ◽  
Vol 25 (1) ◽  
pp. 94-107
Author(s):  
M. C. A. Torbenson ◽  
D. W. Stahle ◽  
I. M. Howard ◽  
D. J. Burnette ◽  
D. Griffin ◽  
...  
Keyword(s):  
Soil Moisture ◽  
North America ◽  
Land Surface ◽  
Large Scale ◽  
Temporal Stability ◽  
Warm Season ◽  
Cool Season ◽  
Continental Scale ◽  
The North ◽  
Drought Relief

Abstract Season-to-season persistence of soil moisture drought varies across North America. Such interseasonal autocorrelation can have modest skill in forecasting future conditions several months in advance. Because robust instrumental observations of precipitation span less than 100 years, the temporal stability of the relationship between seasonal moisture anomalies is uncertain. The North American Seasonal Precipitation Atlas (NASPA) is a gridded network of separately reconstructed cool-season (December–April) and warm-season (May–July) precipitation series and offers new insights on the intra-annual changes in drought for up to 2000 years. Here, the NASPA precipitation reconstructions are rescaled to represent the long-term soil moisture balance during the cool season and 3-month-long atmospheric moisture during the warm season. These rescaled seasonal reconstructions are then used to quantify the frequency, magnitude, and spatial extent of cool-season drought that was relieved or reversed during the following summer months. The adjusted seasonal reconstructions reproduce the general patterns of large-scale drought amelioration and termination in the instrumental record during the twentieth century and are used to estimate relief and reversals for the most skillfully reconstructed past 500 years. Subcontinental-to-continental-scale reversals of cool-season drought in the following warm season have been rare, but the reconstructions display periods prior to the instrumental data of increased reversal probabilities for the mid-Atlantic region and the U.S. Southwest. Drought relief at the continental scale may arise in part from macroscale ocean–atmosphere processes, whereas the smaller-scale regional reversals may reflect land surface feedbacks and stochastic variability.

scholarly journals Appraisal of SMAP Operational Soil Moisture Product from a Global Perspective

2020 ◽  
Vol 12 (12) ◽  
pp. 1977 ◽  
Author(s):  
Swati Suman ◽  
Prashant K. Srivastava ◽  
George P. Petropoulos ◽  
Dharmendra K. Pandey ◽  
Peggy E. O’Neill
Keyword(s):  
Soil Moisture ◽  
North America ◽  
Soil Layer ◽  
Product Evaluation ◽  
Global Scale ◽  
Global Perspective ◽  
Spatiotemporal Variability ◽  
Local Networks ◽  
Independent Verification ◽  
Wide Range

Space-borne soil moisture (SM) satellite products such as those available from Soil Moisture Active Passive (SMAP) offer unique opportunities for global and frequent monitoring of SM and also to understand its spatiotemporal variability. The present study investigates the performance of the SMAP L4 SM product at selected experimental sites across four continents, namely North America, Europe, Asia and Australia. This product provides global scale SM estimates at 9 km × 9 km spatial resolution at daily intervals. For the product evaluation, co-orbital in situ SM measurements were used, acquired at 14 test sites in North America, Europe, and Australia belonging to the International Soil Moisture Network (ISMN) and local networks in India. The satellite SM estimates of up to 0–5 cm soil layer were compared against collocated ground measurements using a series of statistical scores. Overall, the best performance of the SMAP product was found in North America (RMSE = 0.05 m3/m3) followed by Australia (RMSE = 0.08 m3/m3), Asia (RMSE = 0.09 m3/m3) and Europe (RMSE = 0.14 m3/m3). Our findings provide important insights into the spatiotemporal variability of the specific operational SM product in different ecosystems and environments. This study also furnishes an independent verification of this global product, which is of international interest given its suitability for a wide range of practical and research applications.

scholarly journals A Multilayer Soil Moisture Dataset Based on the Gravimetric Method in China and Its Characteristics

2019 ◽  
Vol 20 (8) ◽  
pp. 1721-1736 ◽  
Author(s):  
Aihui Wang ◽  
Xueli Shi
Keyword(s):  
Soil Moisture ◽  
Soil Depth ◽  
Linear Trend ◽  
Standardized Precipitation Index ◽  
Gravimetric Method ◽  
Northwest China ◽  
Soil Layers ◽  
Significance Level ◽  
The Mean ◽  
The Standardized Precipitation Index

Abstract Based on the gravimetric-technique-measured soil relative wetness and the observed soil characteristic parameters from 1992 to 2013 in China, this study derives a user-convenient monthly volumetric soil moisture (SM) dataset from 732 stations for five soil layers (10, 20, 50, 70, and 100 cm). The temporal–spatial variations in SM and its relationship with precipitation (Pr) in different subregions are then explored. The magnitude of SM is relatively large in south China and is low in northwest China, and it generally increases with soil depth in each region. The maximum SM appears in spring and/or autumn and the minimum in summer, and the SM seasonality does not vary as distinctly as that of Pr. For the top three soil layers (10-, 20-, and 50-cm levels), the linear trend analysis indicates an overall increasing SM tendency, and the mean trends (averaged across stations with trends passing a 95% significance level test) are 9.35 × 10−7, 7.37 × 10−3, and 2.45 × 10−3 cm3 cm−3 yr−1, respectively. SM memory depends on the soil depth and regions, and it has longer retention time in the deeper layers. Furthermore, the correlation between SM and antecedent Pr varies with soil depth and lag time. The antecedent Pr anomaly (1 or 2 months in advance) can be used to some extent as a surrogate SM anomaly in most regions except for in arid regions. This result is further demonstrated by the relationships between the SM anomaly and the standardized precipitation index. The current SM dataset can be used in various applications, such as validating satellite-retrieved products and model outputs.

scholarly journals Mechanisms of Seasonal Soil Moisture Drought Onset and Termination in the Southern Great Plains

2019 ◽  
Vol 20 (4) ◽  
pp. 751-771 ◽  
Author(s):  
Richard Seager ◽  
Jennifer Nakamura ◽  
Mingfang Ting
Keyword(s):  
Soil Moisture ◽  
North America ◽  
Great Plains ◽  
Land Surface ◽  
Tropical Pacific ◽  
Moisture Change ◽  
Southern Plains ◽  
Southern Great Plains ◽  
Continental Scale ◽  
Over Time

AbstractMechanisms of drought onset and termination are examined across North America with a focus on the southern Plains using data from land surface models and regional and global reanalyses for 1979–2017. Continental-scale analysis of covarying patterns reveals a tight coupling between soil moisture change over time and intervening precipitation anomalies. The southern Great Plains are a geographic center of patterns of hydrologic change. Drying is induced by atmospheric wave trains that span the Pacific and North America and place northerly flow anomalies above the southern Plains. In the southern Plains winter is least likely, and fall most likely, for drought onset and spring is least likely, and fall or summer most likely, for drought termination. Southern Plains soil moisture itself, which integrates precipitation over time, has a clear relationship to tropical Pacific sea surface temperature (SST) anomalies with cold conditions favoring dry soils. Soil moisture change, however, though clearly driven by precipitation, has a weaker relation to SSTs and a strong relation to internal atmospheric variability. Little evidence is found of connection of drought onset and termination to driving by temperature anomalies. An analysis of particular drought onsets and terminations on the seasonal time scale reveals commonalities in terms of circulation and moisture transport anomalies over the southern Plains but a variety of ways in which these are connected into the large-scale atmosphere and ocean state. Some onsets are likely to be quite predictable due to forcing by cold tropical Pacific SSTs (e.g., fall 2010). Other onsets and all terminations are likely not predictable in terms of ocean conditions.

scholarly journals Commonly Used Drought Indices as Indicators of Soil Moisture in China

2015 ◽  
Vol 16 (3) ◽  
pp. 1397-1408 ◽  
Author(s):  
Hongshuo Wang ◽  
Jeffrey C. Rogers ◽  
Darla K. Munroe
Keyword(s):  
Soil Moisture ◽  
Time Scales ◽  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
Severity Index ◽  
Agricultural Drought ◽  
Weather Data ◽  
Drought Indices ◽  
Drought Severity ◽  
Better Than

Abstract Soil moisture shortages adversely affecting agriculture are significantly associated with meteorological drought. Because of limited soil moisture observations with which to monitor agricultural drought, characterizing soil moisture using drought indices is of great significance. The relationship between commonly used drought indices and soil moisture is examined here using Chinese surface weather data and calculated station-based drought indices. Outside of northeastern China, surface soil moisture is more affected by drought indices having shorter time scales while deep-layer soil moisture is more related on longer index time scales. Multiscalar drought indices work better than drought indices from two-layer bucket models. The standardized precipitation evapotranspiration index (SPEI) works similarly or better than the standardized precipitation index (SPI) in characterizing soil moisture at different soil layers. In most stations in China, the Z index has a higher correlation with soil moisture at 0–5 cm than the Palmer drought severity index (PDSI), which in turn has a higher correlation with soil moisture at 90–100-cm depth than the Z index. Soil bulk density and soil organic carbon density are the two main soil properties affecting the spatial variations of the soil moisture–drought indices relationship. The study may facilitate agriculture drought monitoring with commonly used drought indices calculated from weather station data.

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