Predicting U.S. Drought Monitor States Using Precipitation, Soil Moisture, and Evapotranspiration Anomalies. Part I: Development of a Nondiscrete USDM Index

Abstract The U.S. Drought Monitor (USDM) classifies drought into five discrete dryness/drought categories based on expert synthesis of numerous data sources. In this study, an empirical methodology is presented for creating a nondiscrete USDM index that simultaneously 1) represents the dryness/wetness value on a continuum and 2) is most consistent with the time scales and processes of the actual USDM. A continuous USDM representation will facilitate USDM forecasting methods, which will benefit from knowledge of where, within a discrete drought class, the current drought state most probably lies. The continuous USDM is developed such that the actual discrete USDM can be reconstructed by discretizing the continuous USDM based on the 30th, 20th, 10th, 5th, and 2nd percentiles—corresponding with USDM definitions for the D4–D0 drought classes. Anomalies in precipitation, soil moisture, and evapotranspiration over a range of different time scales are used as predictors to estimate the continuous USDM. The methodology is fundamentally probabilistic, meaning that the probability density function (PDF) of the continuous USDM is estimated and therefore the degree of uncertainty in the fit is properly characterized. Goodness-of-fit metrics and direct comparisons between the actual and predicted USDM analyses during different seasons and years indicate that this objective drought classification method is well correlated with the current USDM analyses. In Part II, this continuous USDM index will be used to improve intraseasonal USDM intensification forecasts because it is capable of distinguishing between USDM states that are either far from or near to the next-higher drought category.

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Characterizing and Monitoring Drought over Upper Blue Nile of Ethiopia with the Aid of Copula Analysis

International Journal of Environment and Climate Change ◽

10.9734/ijecc/2018/v8i427181 ◽

2018 ◽

pp. 283-294 ◽

Cited By ~ 1

Author(s):

Abebe Kebede ◽

U. Jaya Prakash Raju ◽

Diriba Koricha ◽

Melessew Nigussie

Keyword(s):

Soil Moisture ◽

Time Scales ◽

Joint Distribution ◽

Goodness Of Fit ◽

Severe Drought ◽

Precipitation Data ◽

Moisture Index ◽

Drought Duration ◽

Distribution Method ◽

Blue Nile

The main aim of this study is to characterize and monitor drought distribution and expansion over Upper Blue Nile of Ethiopia by using univariate standard precipitation index (SPI) and standardized soil moisture index (SMI) whose joint distribution leads to multi standardized drought index (MSDI). The soil moisture and CHIRPS precipitation data from first January 1980 to 2016 are modeled. The indices of SPI, SMI and the joint MSDI value over the Upper Blue Nile are analyzed. The SPI for different time scales is implemented. The correlation between severity, duration and intensity including wetness and drought strengths is computed and analyzed. It is found that the correlation between duration and severity is 0.96 and normal conditions for SPI 3, 6, 12 month time scales are frequently observed rather than moderate, severe and extreme severe drought or wetness. Building on soil moisture and precipitation data of the summer season, the Clayton copula model is selected based on goodness of fit parameters. After setting the best copula family for the Upper Blue Nile then we applied the joint distribution method is applied for characterizing and monitoring drought. It is found that the MSDI more clearly showed that the severity of drought across the time series of each time scales, than SPI and SMI. As the time scale increases there is decline of fluctuation or frequency of drought and the rising of drought duration is shown by SPI, SMI and MSDI. By using SPI6, SMI6 and MSDI6 the spatial distribution of drought is determined from June to August in the years 1984 and 2015 indicate the drought expansions in the eastern and western parts of Upper Blue Nile during the respective years.

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Soil moisture-based drought monitoring at different time scales: a case study for the U.S. Great Plains

JAWRA Journal of the American Water Resources Association ◽

10.1111/1752-1688.12367 ◽

2015 ◽

Vol 52 (1) ◽

pp. 77-88 ◽

Cited By ~ 5

Author(s):

T.A. Engda ◽

T.J. Kelleners

Keyword(s):

Soil Moisture ◽

Time Scales ◽

Great Plains ◽

Drought Monitoring ◽

The U.S ◽

Different Time Scales

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Quantifying the Land–Atmosphere Coupling Behavior in Modern Reanalysis Products over the U.S. Southern Great Plains

Journal of Climate ◽

10.1175/jcli-d-14-00680.1 ◽

2015 ◽

Vol 28 (14) ◽

pp. 5813-5829 ◽

Cited By ~ 30

Author(s):

Joseph A. Santanello ◽

Joshua Roundy ◽

Paul A. Dirmeyer

Keyword(s):

Soil Moisture ◽

Great Plains ◽

Southern Great Plains ◽

Diurnal Cycles ◽

The North ◽

Energy Cycle ◽

Coupling Behavior ◽

Regional Reanalysis ◽

Modern Era ◽

The U.S

Abstract The coupling of the land with the planetary boundary layer (PBL) on diurnal time scales is critical to regulating the strength of the connection between soil moisture and precipitation. To improve understanding of land–atmosphere (L–A) interactions, recent studies have focused on the development of diagnostics to quantify the strength and accuracy of the land–PBL coupling at the process level. In this paper, the authors apply a suite of local land–atmosphere coupling (LoCo) metrics to modern reanalysis (RA) products and observations during a 17-yr period over the U.S. southern Great Plains. Specifically, a range of diagnostics exploring the links between soil moisture, evaporation, PBL height, temperature, humidity, and precipitation is applied to the summertime monthly mean diurnal cycles of the North American Regional Reanalysis (NARR), Modern-Era Retrospective Analysis for Research and Applications (MERRA), and Climate Forecast System Reanalysis (CFSR). Results show that CFSR is the driest and MERRA the wettest of the three RAs in terms of overall surface–PBL coupling. When compared against observations, CFSR has a significant dry bias that impacts all components of the land–PBL system. CFSR and NARR are more similar in terms of PBL dynamics and response to dry and wet extremes, while MERRA is more constrained in terms of evaporation and PBL variability. Each RA has a unique land–PBL coupling that has implications for downstream impacts on the diurnal cycle of PBL evolution, clouds, convection, and precipitation as well as representation of extremes and drought. As a result, caution should be used when treating RAs as truth in terms of their water and energy cycle processes.

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

Journal of Hydrometeorology ◽

10.1175/jhm-d-15-0122.1 ◽

2016 ◽

Vol 17 (6) ◽

pp. 1763-1779 ◽

Cited By ~ 48

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.

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Trend Dynamics of GRACE Terrestrial Water Storage in the Nile River Basin

10.20944/preprints201909.0042.v1 ◽

2019 ◽

Cited By ~ 1

Author(s):

Emad Hasan ◽

Aondover Tarhule

Keyword(s):

Soil Moisture ◽

Population Density ◽

River Basin ◽

Goodness Of Fit ◽

Water Storage ◽

Least Square ◽

Nile River ◽

Terrestrial Water Storage ◽

Terrestrial Water ◽

Nile River Basin

GRACE-derived Terrestrial Water Storage Anomalies (TWSA) continue to be used in an expanding array of studies to analyze numerous processes and phenomena related to terrestrial water storage dynamics, including groundwater depletions, lake storage variations, snow, and glacial mass changes, as well as floods, droughts, among others. So far, however, few studies have investigated how the factors that affect total water storage (e.g., precipitation, runoff, soil moisture, evapotranspiration) interact and combine over space and time to produce the mass variations that GRACE detects. This paper is an attempt to fill that gap and stimulate needed research in this area. Using the Nile River Basin as case study, it explicitly analyzes nine hydroclimatic and anthropogenic processes, as well as their relationship to TWS in different climatic zones in the Nile River Basin. The analytic method employed the trends in both the dependent and independent variables applying two geographically multiple regression (GMR) approaches: (i) an unweighted or ordinary least square regression (OLS) model in which the contributions of all variables to TWS variability are deemed equal at all locations; and (ii) a geographically weighted regression (GWR) which assigns a weight to each variable at different locations based on the occurrence of trend clusters, determined by Moran’s cluster index. In both cases, model efficacy was investigated using standard goodness of fit diagnostics. The OLS showed that trends in five variables (i.e., precipitation, runoff, surface water soil moisture, and population density) significantly (p<0.0001) explain the trends in TWSA for the basin at large. However, the models R2 value is only 0.14. In contrast, the GWR produced R2 values ranging between 0.40 and 0.89, with an average of 0.86 and normally distributed standard residuals. The models retained in the GWR differ by climatic zone. The results showed that all nine variables contribute significantly to the trend in TWS in the Tropical region; population density is an important contributor to TWSA variability in all zones; ET and Population density are the only significant variables in the semiarid zone. This type of information is critical for developing robust statistical models for reconstructing time series of proxy GRACE anomalies that predate the launch of the GRACE mission and for gap-filling between GRACE and GRACE-FO.

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Large Scale Operational Soil Moisture Mapping from Passive MW Radiometry: SMOS product evaluation in Europe & USA

10.31219/osf.io/rzfbu ◽

2019 ◽

Author(s):

Khidir Abdalla Kwal Deng ◽

Salim Lamine ◽

Andrew Pavlides ◽

Yansong Bao ◽

George Petropoulos ◽

...

Keyword(s):

Soil Moisture ◽

Large Scale ◽

Water Security ◽

Product Evaluation ◽

Practical Applications ◽

Study Results ◽

Different Seasons ◽

Lower Correlation ◽

Land Types

Earth Observation (EO) allows deriving from a range of sensors, often globally, operational estimates of surface soil moisture (SSM) at range of spatiotemporal resolutions. Yet, an evaluation of the accuracy of those products in a variety of environmental conditions has been often limited. In this study the accuracy of the SMOS SSM global operational product across 2 continents (USA, and Europe) is investigated. SMOS predictions were compared against near concurrent in-situ SSM measurements from the FLUXNET observational network. In total, 7 experimental sites were used to assess the accuracy of SMOS derived soil moisture for 2 complete years of observations (2010 to 2011). The accuracy of the SMOS SSM product is investigated in different seasons for the seasonal cycle as well as different continents and land types. Results showed a generally reasonable agreement between the SMOS product and the in-situ soil moisture measurements in the 0-5 cm soil moisture layer. Root Mean Square Error (RMSE) in most cases was close to 0.1 m3 m-3 (minimum 0.067 m3 m-3). With a few exceptions, Pearson’s correlation coefficient was found up to approx. 55%. Grassland, shrublands and woody savanna land cover types attained a satisfactory agreement between satellite derived and in-situ measurements but needleleaf forests had lower correlation. Better agreement was found for the grassland sites in both continents. Seasonally, summer and autumn underperformed spring and winter. Our study results provide supportive evidence of the potential value of this operational product for meso-scale studies in a range of practical applications, helping to address key challenges present nowadays linked to food and water security.

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Impacts of Local Soil Moisture Anomalies on the Atmospheric Circulation and on Remote Surface Meteorological Fields during Boreal Summer: A Comprehensive Analysis over North America

Journal of Climate ◽

10.1175/jcli-d-16-0192.1 ◽

2016 ◽

Vol 29 (20) ◽

pp. 7345-7364 ◽

Cited By ~ 37

Author(s):

Randal D. Koster ◽

Yehui Chang ◽

Hailan Wang ◽

Siegfried D. Schubert

Keyword(s):

Soil Moisture ◽

North America ◽

Atmospheric Circulation ◽

General Circulation ◽

Boreal Summer ◽

Near Surface ◽

Local Soil ◽

Lower Mississippi River ◽

Continental Interior ◽

The U.S

Abstract A series of stationary wave model (SWM) experiments are performed in which the boreal summer atmosphere is forced, over a number of locations in the continental United States, with an idealized diabatic heating anomaly that mimics the atmospheric heating associated with a dry land surface. For localized heating within a large portion of the continental interior, regardless of the specific location of this heating, the spatial pattern of the forced atmospheric circulation anomaly (in terms of 250-hPa eddy streamfunction) is largely the same: a high anomaly forms over west-central North America and a low anomaly forms to the east. In supplemental atmospheric general circulation model (AGCM) experiments, similar results are found; imposing soil moisture dryness in the AGCM in different locations within the U.S. interior tends to produce the aforementioned pattern, along with an associated near-surface warming and precipitation deficit in the center of the continent. The SWM-based and AGCM-based patterns generally agree with composites generated using reanalysis and precipitation gauge data. The AGCM experiments also suggest that dry anomalies imposed in the lower Mississippi River valley have remote surface impacts of particularly large spatial extent, and a region along the eastern half of the U.S.–Canadian border is particularly sensitive to dry anomalies in a number of remote areas. Overall, the SWM and AGCM experiments support the idea of a positive feedback loop operating over the continent: dry surface conditions in many interior locations lead to changes in atmospheric circulation that act to enhance further the overall dryness of the continental interior.

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Establishing an Empirical Model for Surface Soil Moisture Retrieval at the U.S. Climate Reference Network Using Sentinel-1 Backscatter and Ancillary Data

Remote Sensing ◽

10.3390/rs12081242 ◽

2020 ◽

Vol 12 (8) ◽

pp. 1242 ◽

Cited By ~ 2

Author(s):

Sumanta Chatterjee ◽

Jingyi Huang ◽

Alfred E. Hartemink

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Soil Properties ◽

Soil Water ◽

Surface Soil ◽

Reference Network ◽

Surface Soil Moisture ◽

Ancillary Data ◽

Terrain Parameters ◽

The U.S

Progress in sensor technologies has allowed real-time monitoring of soil water. It is a challenge to model soil water content based on remote sensing data. Here, we retrieved and modeled surface soil moisture (SSM) at the U.S. Climate Reference Network (USCRN) stations using Sentinel-1 backscatter data from 2016 to 2018 and ancillary data. Empirical machine learning models were established between soil water content measured at the USCRN stations with Sentinel-1 data from 2016 to 2017, the National Land Cover Dataset, terrain parameters, and Polaris soil data, and were evaluated in 2018 at the same USCRN stations. The Cubist model performed better than the multiple linear regression (MLR) and Random Forest (RF) model (R2 = 0.68 and RMSE = 0.06 m3 m-3 for validation). The Cubist model performed best in Shrub/Scrub, followed by Herbaceous and Cultivated Crops but poorly in Hay/Pasture. The success of SSM retrieval was mostly attributed to soil properties, followed by Sentinel-1 backscatter data, terrain parameters, and land cover. The approach shows the potential for retrieving SSM using Sentinel-1 data in a combination of high-resolution ancillary data across the conterminous United States (CONUS). Future work is required to improve the model performance by including more SSM network measurements, assimilating Sentinel-1 data with other microwave, optical and thermal remote sensing products. There is also a need to improve the spatial resolution and accuracy of land surface parameter products (e.g., soil properties and terrain parameters) at the regional and global scales.

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