scholarly journals The Evaporative Demand Drought Index. Part I: Linking Drought Evolution to Variations in Evaporative Demand

2016 ◽  
Vol 17 (6) ◽  
pp. 1745-1761 ◽  
Author(s):  
Michael T. Hobbins ◽  
Andrew Wood ◽  
Daniel J. McEvoy ◽  
Justin L. Huntington ◽  
Charles Morton ◽  
...  
Keyword(s):  
Land Surface ◽  
Drought Index ◽  
Drought Indices ◽  
Evaporative Demand ◽  
The North ◽  
Physically Based ◽  
Surface Drying ◽  
American Society ◽  
Land Data Assimilation System ◽  
Reference Et

Abstract Many operational drought indices focus primarily on precipitation and temperature when depicting hydroclimatic anomalies, and this perspective can be augmented by analyses and products that reflect the evaporative dynamics of drought. The linkage between atmospheric evaporative demand E0 and actual evapotranspiration (ET) is leveraged in a new drought index based solely on E0—the Evaporative Demand Drought Index (EDDI). EDDI measures the signal of drought through the response of E0 to surface drying anomalies that result from two distinct land surface–atmosphere interactions: 1) a complementary relationship between E0 and ET that develops under moisture limitations at the land surface, leading to ET declining and increasing E0, as in sustained droughts, and 2) parallel ET and E0 increases arising from increased energy availability that lead to surface moisture limitations, as in flash droughts. To calculate EDDI from E0, a long-term, daily reanalysis of reference ET estimated from the American Society of Civil Engineers (ASCE) standardized reference ET equation using radiation and meteorological variables from the North American Land Data Assimilation System phase 2 (NLDAS-2) is used. EDDI is obtained by deriving empirical probabilities of aggregated E0 depths relative to their climatologic means across a user-specific time period and normalizing these probabilities. Positive EDDI values then indicate drier-than-normal conditions and the potential for drought. EDDI is a physically based, multiscalar drought index that that can serve as an indicator of both flash and sustained droughts, in some hydroclimates offering early warning relative to current operational drought indices. The performance of EDDI is assessed against other commonly used drought metrics across CONUS in Part II.

scholarly journals Assessing the Roles of Terrestrial Stilling and Solar Dimming in Land Surface Drying/Wetting across China

Water ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1996
Author(s):  
Kai Duan ◽  
Jiali Guo ◽  
Tiesong Hu ◽  
Xianxun Wang ◽  
Yadong Mei
Keyword(s):  
Wind Speed ◽  
Land Surface ◽  
River Basins ◽  
Evaporative Demand ◽  
The Past ◽  
The North ◽  
Attribution Analysis ◽  
Huai River ◽  
Surface Drying ◽  
Spatially Varying

Decreases in wind speed (i.e., terrestrial stilling) and radiation (i.e., solar dimming) have been identified as important causes of aridity change both globally and regionally. To understand how their roles have varied across different natural and socioeconomic circumstances in China, this study presents a nationwide attribution analysis of land surface drying/wetting across the ten first-level river basins. The results suggest that consistent warming and reductions in relative humidity have significantly enhanced atmospheric evaporative demand and driven the land surface to become drier over the past six decades. However, the widespread terrestrial stilling and solar dimming have largely offset such trends by suppressing evaporation. While spatially varying changes in precipitation were the most influential driver of aridity change over half of the 713 used climate sites, decreasing wind speed and radiation were identified as the dominant cause of wetting at 15% and 13% of the sites, respectively. The impacts of terrestrial stilling and solar dimming were generally more prominent in the north (e.g., the Liao River, Songhuajiang, Hai River, and Huai River basins) and south (e.g., the Southeast, Pearl River, and Yangtze River basins) respectively, which could be associated with the weakening monsoon and intensified anthropogenic disturbances such as ecological restoration, urbanization, and air pollution. We conclude that more attention needs to be paid to the independent and combined climatological impacts of global- and regional-level human activities to develop proactive adaptation strategies of water and land management.

scholarly journals A Statistical Method for Categorical Drought Prediction Based on NLDAS-2

2016 ◽  
Vol 55 (4) ◽  
pp. 1049-1061 ◽  
Author(s):  
Zengchao Hao ◽  
Fanghua Hao ◽  
Youlong Xia ◽  
Vijay P. Singh ◽  
Yang Hong ◽  
...  
Keyword(s):  
Statistical Method ◽  
Early Warning ◽  
Land Surface ◽  
Statistical Prediction ◽  
Natural Phenomenon ◽  
Drought Indices ◽  
The North ◽  
Drought Prediction ◽  
Drought Preparedness ◽  
Land Data Assimilation System

AbstractDrought is a slowly varying natural phenomenon and may have wide impacts on a range of sectors. Tremendous efforts have therefore been devoted to drought monitoring and prediction to reduce potential impacts of drought. Reliable drought prediction is critically important to provide information ahead of time for early warning to facilitate drought-preparedness plans. The U.S. Drought Monitor (USDM) is a composite drought product that depicts drought conditions in categorical forms, and it has been widely used to track drought and its impacts for operational and research purposes. The USDM is an assessment of drought condition but does not provide drought prediction information. Given the wide application of USDM, drought prediction in a categorical form similar to that of USDM would be of considerable importance, but it has not been explored thus far. This study proposes a statistical method for categorical drought prediction by integrating the USDM drought category as an initial condition with drought information from other sources such as drought indices from land surface simulation or statistical prediction. Incorporating USDM drought categories and drought indices from phase 2 of the North American Land Data Assimilation System (NLDAS-2), the proposed method is tested in Texas for 2001–14. Results show satisfactory performance of the proposed method for categorical drought prediction, which provides useful information to aid early warning for drought-preparedness plans.

scholarly journals Evaluation of Drought Indices Based on Thermal Remote Sensing of Evapotranspiration over the Continental United States

2011 ◽  
Vol 24 (8) ◽  
pp. 2025-2044 ◽  
Author(s):  
Martha C. Anderson ◽  
Christopher Hain ◽  
Brian Wardlow ◽  
Agustin Pimstein ◽  
John R. Mecikalski ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Drought Index ◽  
Temporal Correlation ◽  
Meteorological Drought ◽  
Rainfall Data ◽  
Added Value ◽  
Stress Index ◽  
Drought Indices ◽  
Shallow Water Table

Abstract The reliability of standard meteorological drought indices based on measurements of precipitation is limited by the spatial distribution and quality of currently available rainfall data. Furthermore, they reflect only one component of the surface hydrologic cycle, and they cannot readily capture nonprecipitation-based moisture inputs to the land surface system (e.g., irrigation) that may temper drought impacts or variable rates of water consumption across a landscape. This study assesses the value of a new drought index based on remote sensing of evapotranspiration (ET). The evaporative stress index (ESI) quantifies anomalies in the ratio of actual to potential ET (PET), mapped using thermal band imagery from geostationary satellites. The study investigates the behavior and response time scales of the ESI through a retrospective comparison with the standardized precipitation indices and Palmer drought index suite, and with drought classifications recorded in the U.S. Drought Monitor for the 2000–09 growing seasons. Spatial and temporal correlation analyses suggest that the ESI performs similarly to short-term (up to 6 months) precipitation-based indices but can be produced at higher spatial resolution and without requiring any precipitation data. Unique behavior is observed in the ESI in regions where the evaporative flux is enhanced by moisture sources decoupled from local rainfall: for example, in areas of intense irrigation or shallow water table. Normalization by PET serves to isolate the ET signal component responding to soil moisture variability from variations due to the radiation load. This study suggests that the ESI is a useful complement to the current suite of drought indicators, with particular added value in parts of the world where rainfall data are sparse or unreliable.

scholarly journals FORECASTING AND MONITORING AGRICULTURAL DROUGHT IN THE PHILIPPINES

2016 ◽  
Vol XLI-B8 ◽  
pp. 1263-1269
Author(s):  
G. J. Perez ◽  
M. Macapagal ◽  
R. Olivares ◽  
E. M. Macapagal ◽  
J. C. Comiso
Keyword(s):  
Land Surface ◽  
Drought Index ◽  
Vegetation Index ◽  
The Philippines ◽  
Agricultural Drought ◽  
Stress Index ◽  
Drought Indices ◽  
Drought Event ◽  
Ancillary Data ◽  
Vulnerability Maps

A monitoring and forecasting sytem is developed to assess the extent and severity of agricultural droughts in the Philippines at various spacial scales and across different time periods. Using Earth observation satellite data, drought index, hazard and vulnerability maps are created. The drought index, called Standardized Vegetation-Temperature Ratio (SVTR), is derived using the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST). SVTR is evaluated by correlating its values with existing agricultural drought index, particulary Evaporative Stress Index (ESI). Moreover, the performance of SVTR in detecting drought occurrences was assessed for the 2015-2016 drought event. This period is a strong El Niño year and a large portion of the country was affected by drought at varying degrees, making it a good case study for evaluating drought indices. Satellitederived SVTR was validated through several field visits and surveys across different major agricultural areas in the country, and was found to be 73% accurate. The drought hazard and vulnerability maps are produced by utilizing the evapotranspration product of MODIS, rainfall climatology from the Tropical Rainfall Microwave Mission (TRMM) and ancillary data, including irrigation, water holding capacity and land use. Finally, we used statistical techniques to determine trends in NDVI and LST and generate a sixmonth forecast of drought index. Outputs of this study are being assessed by the Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA) and the Department of Agriculture Bureau of Soils and Water Management (DABSWM) for future integration in their operations.

scholarly journals Drought Monitoring over Yellow River Basin from 2003–2019 Using Reconstructed MODIS Land Surface Temperature in Google Earth Engine

2021 ◽  
Vol 13 (18) ◽  
pp. 3748
Author(s):  
Xiaoyang Zhao ◽  
Haoming Xia ◽  
Li Pan ◽  
Hongquan Song ◽  
Wenhui Niu ◽  
...  
Keyword(s):  
River Basin ◽  
Land Surface ◽  
Drought Index ◽  
Yellow River ◽  
Yellow River Basin ◽  
Condition Index ◽  
Drought Indices ◽  
The Yellow River ◽  
Drought Conditions ◽  
The Yellow River Basin

Drought is one of the most complex and least-understood environmental disasters that can trigger environmental, societal, and economic problems. To accurately assess the drought conditions in the Yellow River Basin, this study reconstructed the Land Surface Temperature (LST) using the Annual Temperature Cycle (ATC) model and the Normalized Difference Vegetation Index (NDVI). The Temperature Condition Index (TCI), Vegetation Condition Index (VCI), Vegetation Health Index (VHI), and Temperature-Vegetation Drought Index (TVDI), which are four typical remote sensing drought indices, were calculated. Then, the air temperature, precipitation, and soil moisture data were used to evaluate the applicability of each drought index to different land types. Finally, this study characterized the spatial and temporal patterns of drought in the Yellow River Basin from 2003 to 2019. The results show that: (1) Using the LST reconstructed by the ATC model to calculate the drought index can effectively improve the accuracy of drought monitoring. In most areas, the reconstructed TCI, VHI, and TVDI are more reliable for monitoring drought conditions than the unreconstructed VCI. (2) The four drought indices (TCI, VCI, VH, TVDI) represent the same temporal and spatial patterns throughout the study area. However, in some small areas, the temporal and spatial patterns represented by different drought indices are different. (3) In the Yellow River Basin, the drought level is highest in the northwest and lowest in the southwest and southeast. The dry conditions in the Yellow River Basin were stable from 2003 to 2019. The results in this paper provide a basis for better understanding and evaluating the drought conditions in the Yellow River Basin and can guide water resources management, agricultural production, and ecological protection of this area.

Flash Drought in CMIP5 Models

2021 ◽  
Author(s):  
David Hoffmann ◽  
Ailie J. E. Gallant ◽  
Mike Hobbins
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Climate Models ◽  
Standardized Precipitation Index ◽  
Rapid Onset ◽  
Stress Index ◽  
Cmip5 Models ◽  
Drought Indices ◽  
Similar Proportion ◽  
Evaporative Demand

Abstract‘Flash drought’ (FD) describes the rapid onset of drought on sub-seasonal times scales. It is of particular interest for agriculture as it can deplete soil moisture for crop growth in just a few weeks. To better understand the processes causing FD, we evaluate the importance of evaporative demand and precipitation by comparing three different drought indices that estimate this hazard using meteorological and hydrological parameters from the CMIP5 suite of models. We apply the Standardized Precipitation Index (SPI); the Evaporative Demand Drought Index (EDDI), derived from evaporative demand (E0); and the Evaporative Stress Index (ESI), which connects atmospheric and soil moisture conditions by measuring the ratio of actual and potential evaporation. The results show moderate-to-strong relationships (r2 > 0.5) between drought indices and upper level soil moisture on daily time scales, especially in drought-prone regions. We find that all indices are able to identify FD in the top 10-cm layer of soil moisture in a similar proportion to that in the models’ climatologies. However, there is significant inter-model spread in the characteristics of the FDs identified. This spread is mainly caused by an overestimation of E0, indicating stark differences in the land surface models and coupling in individual CMIP5 models. Of all indices, the SPI provides the highest skill in predicting FD prior to or at the time of onset in soil moisture, while both EDDI and ESI show significantly lower skill. The results highlight that the lack of precipitation is the main contributor to FDs in climate models, with E0 playing a secondary role.

scholarly journals Assessing Spatiotemporal Drought Dynamics and Its Related Environmental Issues in the Mekong River Delta

2019 ◽  
Vol 11 (23) ◽  
pp. 2742 ◽  
Author(s):  
Tran ◽  
Tran ◽  
Myint ◽  
Latorre-Carmona ◽  
Ho ◽  
...  
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Drought Index ◽  
Negative Impact ◽  
Statistical Significance ◽  
Standardized Precipitation Index ◽  
Agricultural Drought ◽  
Series Data ◽  
Drought Indices ◽  
Drought Severity

Drought is a major natural disaster that creates a negative impact on socio-economic development and environment. Drought indices are typically applied to characterize drought events in a meaningful way. This study aims at examining variations in agricultural drought severity based on the relationship between standardized ratio of actual and potential evapotranspiration (ET and PET), enhanced vegetation index (EVI), and land surface temperature (LST) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) platform. A new drought index, called the enhanced drought severity index (EDSI), was developed by applying spatiotemporal regression methods and time-series biophysical data derived from remote sensing. In addition, time-series trend analysis in the 2001–2018 period, along with the Mann–Kendal (MK) significance test and the Theil Sen (TS) slope, were used to examine the spatiotemporal dynamics of environmental parameters (i.e., LST, EVI, ET, and PET), and geographically weighted regression (GWR) was subsequently applied in order to analyze the local correlations among them. Results showed that a significant correlation was discovered among LST, EVI, ET, and PET, as well as their standardized ratios (|r| > 0.8, p < 0.01). Additionally, a high performance of the new developed drought index, showing a strong correlation between EDSI and meteorological drought indices (i.e., standardized precipitation index (SPI) or the reconnaissance drought index (RDI)), measured at meteorological stations, giving r > 0.7 and a statistical significance p < 0.01. Besides, it was found that the temporal tendency of this phenomenon was the increase in intensity of drought, and that coastal areas in the study area were more vulnerable to this phenomenon. This study demonstrates the effectiveness of EDSI and the potential application of integrating spatial regression and time-series data for assessing regional drought conditions.

Evaluation of Model-Based Soil Moisture Drought Monitoring over Three Key Regions in China

2018 ◽  
Vol 57 (9) ◽  
pp. 1989-2004 ◽  
Author(s):  
Yiping Li ◽  
Yaohui Li ◽  
Xing Yuan ◽  
Liang Zhang ◽  
Sha Sha
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Northern China ◽  
Drought Monitoring ◽  
Drought Indices ◽  
China Meteorological Administration ◽  
Objective Monitoring ◽  
Land Data Assimilation ◽  
Land Data Assimilation System ◽  
Land Exchange

AbstractLand surface models (LSMs) have been widely used to provide objective monitoring of soil moisture during drought, but large uncertainties exist because of the different parameterizations in LSMs. This study aims to evaluate the ability to monitor soil moisture drought over three key regions in China by using the Noah LSM from the Global Land Data Assimilation System, version 2 (GLDASv2), and the Community Atmosphere Biosphere Land Exchange (CABLE) model that is currently used at the China Meteorological Administration. The modeled soil moisture drought indices were verified against the standardized precipitation evapotranspiration index (SPEI), which served as a reference drought indicator over northern China (NC), northwestern China (NWC), and southwestern China (SWC) from 1961 to 2010. The results show that the precipitation forcing data that drive both LSMs have high accuracy when compared with local observational data. GLDASv2/Noah outperforms CABLE in capturing soil moisture anomalies and variability, especially in SWC, but both show good correlations with the 3-month SPEI (SPEI3) in NC, NWC, and SWC. The autumn drought of 2002 and spring drought of 2010 were selected for the comparison of the modeled drought categories with the SPEI3 drought category, where GLDASv2/Noah performed slightly better than CABLE. This work demonstrates that the choice of LSM is crucial for monitoring soil moisture drought and that the GLDASv2/Noah LSM can be a good candidate for the development of a new operational drought-monitoring system in China.

scholarly journals Benchmarking NLDAS-2 Soil Moisture and Evapotranspiration to Separate Uncertainty Contributions

2016 ◽  
Vol 17 (3) ◽  
pp. 745-759 ◽  
Author(s):  
Grey S. Nearing ◽  
David M. Mocko ◽  
Christa D. Peters-Lidard ◽  
Sujay V. Kumar ◽  
Youlong Xia
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Model Parameters ◽  
Land Surface Models ◽  
Surface Mass ◽  
The North ◽  
Surface Models ◽  
Moisture State ◽  
Using Data ◽  
Land Data Assimilation System

Abstract Model benchmarking allows us to separate uncertainty in model predictions caused by model inputs from uncertainty due to model structural error. This method is extended with a “large sample” approach (using data from multiple field sites) to measure prediction uncertainty caused by errors in 1) forcing data, 2) model parameters, and 3) model structure, and use it to compare the efficiency of soil moisture state and evapotranspiration flux predictions made by the four land surface models in phase 2 of the North American Land Data Assimilation System (NLDAS-2). Parameters dominated uncertainty in soil moisture estimates and forcing data dominated uncertainty in evapotranspiration estimates; however, the models themselves used only a fraction of the information available to them. This means that there is significant potential to improve all three components of NLDAS-2. In particular, continued work toward refining the parameter maps and lookup tables, the forcing data measurement and processing, and also the land surface models themselves, has potential to result in improved estimates of surface mass and energy balances.

Contribution of Meteorological Downscaling to Skill and Precision of Seasonal Drought Forecasts

2021 ◽  
Author(s):  
Ryan A. Zamora ◽  
Benjamin F. Zaitchik ◽  
Matthew Rodell ◽  
Augusto Getirana ◽  
Sujay Kumar ◽  
...  
Keyword(s):  
United States ◽  
Land Surface ◽  
Land Surface Model ◽  
Surface Model ◽  
Drought Forecasting ◽  
Surface Hydrology ◽  
Seasonal Drought ◽  
Earth Observing System ◽  
The North ◽  
Land Data Assimilation System

AbstractResearch in meteorological prediction on sub-seasonal to seasonal (S2S) timescales has seen growth in recent years. Concurrent with this, demand for seasonal drought forecasting has risen. While there is obvious synergy between these fields, S2S meteorological forecasting has typically focused on low resolution global models, while the development of drought can be sensitive to the local expression of weather anomalies and their interaction with local surface properties and processes. This suggests that downscaling might play an important role in the application of meteorological S2S forecasts to skillful forecasting of drought. Here, we apply the Generalized Analog Regression Downscaling (GARD) algorithm to downscale meteorological hindcasts from the NASA Goddard Earth Observing System (GEOS) global S2S forecast system. Downscaled meteorological fields are then applied to drive offline simulations with the Catchment Land Surface Model (CLSM) to forecast United States Drought Monitor (USDM) style drought indicators derived from simulated surface hydrology variables. We compare the representation of drought in these downscaled hindcasts to hindcasts that are not downscaled, using the North American Land Data Assimilation System Phase 2 (NLDAS-2) dataset as an observational reference. We find that downscaling using GARD improves hindcasts of temperature and temperature anomalies, but the results for precipitation are mixed and generally small. Overall, GARD downscaling led to improved hindcast skill for total drought across the Contiguous United States (CONUS), and improvements were greatest for extreme (D3) and exceptional (D4) drought categories.

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