scholarly journals Model-Based Drought Indices over the United States

2008 ◽  
Vol 9 (6) ◽  
pp. 1212-1230 ◽  
Author(s):  
Kingtse C. Mo

Abstract Drought indices derived from the North American Land Data Assimilation System (NLDAS) Variable Infiltration Capacity (VIC) and Noah models from 1950 to 2000 are intercompared and evaluated for their ability to classify drought across the United States. For meteorological drought, the standardized precipitation index (SPI) is used to measure precipitation deficits. The standardized runoff index (SRI), which is similar to the SPI, is used to classify hydrological drought. Agricultural drought is measured by monthly-mean soil moisture (SM) anomaly percentiles based on probability distributions (PDs). The PDs for total SM are regionally dependent and influenced by the seasonal cycle, but the PDs for SM monthly-mean anomalies are unimodal and Gaussian. Across the eastern United States (east of 95°W), the indices derived from VIC and Noah are similar, and they are able to detect the same drought events. Indices are also well correlated. For river forecast centers (RFCs) across the eastern United States, different drought indices are likely to detect the same drought events. The monthly-mean soil moisture (SM) percentiles and runoff indices between VIC and Noah have large differences across the western interior of the United States. For small areas with a horizontal resolution of 0.5° on the time scales of one to three months, the differences of SM percentiles and SRI between VIC and Noah are larger than the thresholds used to classify drought. For the western RFCs, drought events selected according to SM percentiles or SRI derived from different NLDAS systems do not always overlap.

2015 ◽  
Vol 16 (3) ◽  
pp. 1397-1408 ◽  
Author(s):  
Hongshuo Wang ◽  
Jeffrey C. Rogers ◽  
Darla K. Munroe

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.


2021 ◽  
Author(s):  
Yafei Huang ◽  
Jonas Weis ◽  
Harry Vereecken ◽  
Harrie-Jan Hendricks Franssen

Abstract. Droughts can have important impacts on environment and economy like in the year 2018 in parts of Europe. Droughts can be analyzed in terms of meteorological drought, agricultural drought, hydrological drought and social-economic drought. In this paper, we focus on meteorological and agricultural drought and analyzed drought trends for the period 1965–2019 and assessed how extreme the drought year 2018 was in Germany and the Netherlands. The analysis was made on the basis of the following drought indices: standardized precipitation index (SPI), standardized soil moisture index (SSI), potential precipitation deficit (PPD) and ET deficit. SPI and SSI were computed at two time scales, the period April-September and a 12-months period. In order to analyze drought trends and the ranking of the year 2018, HYDRUS 1-D simulations were carried out for 31 sites with long-term meteorological observations and soil moisture, potential evapotranspiration (ET) and actual ET were determined for five soil types (clay, silt, loam, sandy loam and loamy sand). The results show that the year 2018 was severely dry, which was especially related to the highest potential ET in the time series 1965–2019, for most of the sites. For around half of the 31 sites the year 2018 had the lowest SSI, and largest PPD and ET-deficit in the 1965–2019 time series, followed by 1976 and 2003. The trend analysis reveals that meteorological drought (SPI) hardly shows significant trends over 1965–2019 over the studied domain, but agricultural droughts (SSI) are increasing, at several sites significantly, and at even more sites PPD and ET deficit show significant trends. The increasing droughts over Germany and Netherlands are mainly driven by increasing potential ET and increasing vegetation water demand.


2020 ◽  
Vol 21 (12) ◽  
pp. 2793-2811 ◽  
Author(s):  
Chul-Su Shin ◽  
Bohua Huang ◽  
Paul A. Dirmeyer ◽  
Subhadeep Halder ◽  
Arun Kumar

AbstractIn addition to remote SST forcing, realistic representation of land forcing (i.e., soil moisture) over the United States is critical for a prediction of U.S. severe drought events approximately one season in advance. Using “identical twin” experiments with different land initial conditions (ICs) in the 32-yr (1979–2010) CFSv2 reforecasts (NASA GLDAS-2 reanalysis versus NCEP CFSR), sensitivity and skill of U.S. drought predictions to land ICs are evaluated. Although there is no outstanding performer between the two sets of forecasts with different land ICs, each set shows greater skill in some regions, but their locations vary with forecast lead time and season. The 1999 case study demonstrates that although a pattern of below-normal SSTs in the Pacific in the fall and winter is realistically reproduced in both reforecasts, GLDAS-2 land initial states display a stronger east–west gradient of soil moisture, particularly drier in the eastern United States and more consistent with observations, leading to warmer surface temperature anomalies over the United States. Anomalies lasting for one season are accompanied by more persistent barotropic (warm core) anomalous high pressure over CONUS, which results in better prediction skill of this drought case up to 4 months in advance in the reforecasts with GLDAS-2 land ICs. Therefore, it is essential to minimize the uncertainty of land initial states among the current land analyses for improving U.S. drought prediction on seasonal time scales.


2020 ◽  
Author(s):  
Maria Jose Escorihuela ◽  
Pere Quintana Quintana-Seguí ◽  
Vivien Stefan ◽  
Jaime Gaona

<p>Drought is a major climatic risk resulting from complex interactions between the atmosphere, the continental surface and water resources management. Droughts have large socioeconomic impacts and recent studies show that drought is increasing in frequency and severity due to the changing climate.</p><p>Drought is a complex phenomenon and there is not a common understanding about drought definition. In fact, there is a range of definitions for drought. In increasing order of severity, we can talk about: meteorological drought is associated to a lack of precipitation, agricultural drought, hydrological drought and socio-economic drought is when some supply of some goods and services such as energy, food and drinking water are reduced or threatened by changes in meteorological and hydrological conditions. 
</p><p>A number of different indices have been developed to quantify drought, each with its own strengths and weaknesses. The most commonly used are based on precipitation such as the precipitation standardized precipitation index (SPI; McKee et al., 1993, 1995), on precipitation and temperature like the Palmer drought severity index (PDSI; Palmer 1965), others rely on vegetation status like the crop moisture index (CMI; Palmer, 1968) or the vegetation condition index (VCI; Liu and Kogan, 1996). Drought indices can also be derived from climate prediction models outputs. Drought indices base on remote sensing based have traditionally been limited to vegetation indices, notably due to the difficulty in accurately quantifying precipitation from remote sensing data. The main drawback in assessing drought through vegetation indices is that the drought is monitored when effects are already causing vegetation damage. In order to address drought in their early stages, we need to monitor it from the moment the lack of precipitation occurs.</p><p>Thanks to recent technological advances, L-band (21 cm, 1.4 GHz) radiometers are providing soil moisture fields among other key variables such as sea surface salinity or thin sea ice thickness. Three missions have been launched: the ESA’s SMOS was the first in 2009 followed by Aquarius in 2011 and SMAP in 2015.</p><p>A wealth of applications and science topics have emerged from those missions, many being of operational value (Kerr et al. 2016, Muñoz-Sabater et al. 2016, Mecklenburg et al. 2016). Those applications have been shown to be key to monitor the water and carbon cycles. Over land, soil moisture measurements have enabled to get access to root zone soil moisture, yield forecasts, fire and flood risks, drought monitoring, improvement of rainfall estimates, etc.</p><p>The advent of soil moisture dedicated missions (SMOS, SMAP) paves the way for drought monitoring based on soil moisture data. Initial assessment of a drought index based on SMOS soil moisture data has shown to be able to precede drought indices based on vegetation by 1 month (Albitar et al. 2013).</p><p>In this presentation we will be analysing different drought episodes in the Ebro basin using both soil moisture and vegetation based indices to compare their different performances and test the hypothesis that soil moisture based indices are earlier indicators of drought than vegetation ones.</p>


Author(s):  
Parwati ◽  
Miao Jungang ◽  
Orbita Roswintiarti

In this research, several meteorological and agricultural drought indices based on remote sensing data are built for drought monitoring over paddy area in Indramayu District, West Java, Indonesia. The meteorological drought index of Standardized Precipitation Index (SPI) is developed from monthly Outgoing Long Wave Radiation (OLR) data from 1980 to 2005. The SPI represents the deficient of precipitation. Meanwhile, the agricultural drought of Vegetation Health Index (VHI) was developed from daily Moderate-resolution ImagingSpectroradiometer (MODIS) data during dry season (May-August) 2003-2006. The VHI was designed to monitoring vegetation health, soil moisture, and thermal conditions. The result shows that the agricultural drought occurate in Indramayu District, especially in the northern and southern part during the dry season in 2003 and 2004. It is found that there is a strong correlation between VHI and soil moisture measured in the field (r=0.84). Key words:Agricultural drought, Meteorological drought, Standardized Precipitation Index, Temperature Condition Index, Vegetation Condition Index.


Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1375 ◽  
Author(s):  
Ali Ajaz ◽  
Saleh Taghvaeian ◽  
Kul Khand ◽  
Prasanna H. Gowda ◽  
Jerry E. Moorhead

A new agricultural drought index was developed for monitoring drought impacts on agriculture in Oklahoma. This new index, called the Soil Moisture Evapotranspiration Index (SMEI), estimates the departure of aggregated root zone moisture from reference evapotranspiration. The SMEI was estimated at five locations across Oklahoma representing different climates. The results showed good agreement with existing soil moisture-based (SM) and meteorological drought indices. In addition, the SMEI had improved performance compared to other indices in capturing the effects of temporal and spatial variations in drought. The relationship with crop production is a key characteristic of any agricultural drought index. The correlations between winter wheat production and studied drought indices estimated during the growing period were investigated. The correlation coefficients were largest for SMEI (r > 0.9) during the critical crop growth stages when compared to other drought indices, and r decreased by moving from semi-arid to more humid regions across Oklahoma. Overall, the results suggest that the SMEI can be used effectively for monitoring the effects of drought on agriculture in Oklahoma.


Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2592 ◽  
Author(s):  
María del Pilar Jiménez-Donaire ◽  
Juan Vicente Giráldez ◽  
Tom Vanwalleghem

The early and accurate detection of drought episodes is crucial for managing agricultural yield losses and planning adequate policy responses. This study aimed to evaluate the potential of two novel indices, static and dynamic plant water stress, for drought detection and yield prediction. The study was conducted in SW Spain (Córdoba province), covering a 13-year period (2001–2014). The calculation of static and dynamic drought indices was derived from previous ecohydrological work but using a probabilistic simulation of soil moisture content, based on a bucket-type soil water balance, and measured climate data. The results show that both indices satisfactorily detected drought periods occurring in 2005, 2006 and 2012. Both their frequency and length correlated well with annual precipitation, declining exponentially and increasing linearly, respectively. Static and dynamic drought stresses were shown to be highly sensitive to soil depth and annual precipitation, with a complex response, as stress can either increase or decrease as a function of soil depth, depending on the annual precipitation. Finally, the results show that both static and dynamic drought stresses outperform traditional indicators such as the Standardized Precipitation Index (SPI)-3 as predictors of crop yield, and the R2 values are around 0.70, compared to 0.40 for the latter. The results from this study highlight the potential of these new indicators for agricultural drought monitoring and management (e.g., as early warning systems, insurance schemes or water management tools).


2021 ◽  
Vol 4 ◽  
Author(s):  
Colin Brust ◽  
John S. Kimball ◽  
Marco P. Maneta ◽  
Kelsey Jencso ◽  
Rolf H. Reichle

Drought is one of the most ecologically and economically devastating natural phenomena affecting the United States, causing the U.S. economy billions of dollars in damage, and driving widespread degradation of ecosystem health. Many drought indices are implemented to monitor the current extent and status of drought so stakeholders such as farmers and local governments can appropriately respond. Methods to forecast drought conditions weeks to months in advance are less common but would provide a more effective early warning system to enhance drought response, mitigation, and adaptation planning. To resolve this issue, we introduce DroughtCast, a machine learning framework for forecasting the United States Drought Monitor (USDM). DroughtCast operates on the knowledge that recent anomalies in hydrology and meteorology drive future changes in drought conditions. We use simulated meteorology and satellite observed soil moisture as inputs into a recurrent neural network to accurately forecast the USDM between 1 and 12 weeks into the future. Our analysis shows that precipitation, soil moisture, and temperature are the most important input variables when forecasting future drought conditions. Additionally, a case study of the 2017 Northern Plains Flash Drought shows that DroughtCast was able to forecast a very extreme drought event up to 12 weeks before its onset. Given the favorable forecasting skill of the model, DroughtCast may provide a promising tool for land managers and local governments in preparing for and mitigating the effects of drought.


2012 ◽  
Vol 13 (3) ◽  
pp. 1010-1022 ◽  
Author(s):  
Rui Mei ◽  
Guiling Wang

Abstract This study examines the land–atmosphere coupling strength during summer over subregions of the United States based on observations [Climate Prediction Center (CPC)–Variable Infiltration Capacity (VIC)], reanalysis data [North American Regional Reanalysis (NARR) and NCEP Climate Forecast System Reanalysis (CFSR)], and models [Community Atmosphere Model, version 3 (CAM3)–Community Land Model, version 3 (CLM3) and CAM4–CLM4]. The probability density function of conditioned correlation between soil moisture and subsequent precipitation or surface temperature during the years of large precipitation anomalies is used as a measure for the coupling strength. There are three major findings: 1) among the eight subregions (classified by land cover types), the transition zone Great Plains (and, to a lesser extent, the Midwest and Southeast) are identified as hot spots for strong land–atmosphere coupling; 2) soil moisture–precipitation coupling is weaker than soil moisture–surface temperature coupling; and 3) the coupling strength is stronger in observational and reanalysis products than in the models examined, especially in CAM4–CLM4. The conditioned correlation analysis also indicates that the coupling strength in CAM4–CLM4 is weaker than in CAM3–CLM3, which is further supported by Global Land–Atmosphere Coupling Experiments1 (GLACE1)-type experiments and attributed to changes in CAM rather than modifications in CLM. Contrary to suggestions in previous studies, CAM–CLM models do not seem to overestimate the land–atmosphere coupling strength.


2019 ◽  
Vol 11 (15) ◽  
pp. 1773 ◽  
Author(s):  
Jae-Hyun Ryu ◽  
Kyung-Soo Han ◽  
Yang-Won Lee ◽  
No-Wook Park ◽  
Sungwook Hong ◽  
...  

Satellite-based remote sensing techniques have been widely used to monitor droughts spanning large areas. Various agricultural drought indices have been developed to assess the intensity of agricultural drought and to detect damaged crop areas. However, to better understand the responses of agricultural drought to meteorological drought, agricultural management practices should be taken into consideration. This study aims to evaluate the responses to drought under different forms of agricultural management for the extreme drought that occurred on the Korean Peninsula in 2014 and 2015. The 3-month standardized precipitation index (SPI3) and the 3-month vegetation health index (VHI3) were selected as a meteorological drought index and an agricultural drought index, respectively. VHI3, which comprises the 3-month temperature condition index (TCI3) and the 3-month vegetation condition index (VCI3), differed significantly in the study area during the extreme drought. VCI3 had a different response to the lack of precipitation in South and North Korea because it was affected by irrigation. However, the time series of TCI3 were similar in South and North Korea. These results meant that each drought index has different characteristics and should be utilized with caution. Our results are expected to help comprehend the responses of the agricultural drought index on meteorological drought depending on agricultural management.


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