scholarly journals A statistical evaluation of Earth-observation-based composite drought indices for a localized assessment of agricultural drought in Pakistan

2022 ◽  
Vol 106 ◽  
pp. 102646
Author(s):  
Caily Schwartz ◽  
W. Lee Ellenburg ◽  
Vikalp Mishra ◽  
Timothy Mayer ◽  
Robert Griffin ◽  
...  
Keyword(s):  
Statistical Evaluation ◽  
Earth Observation ◽  
Agricultural Drought ◽  
Drought Indices

scholarly journals Multi-Dimensional Drought Assessment in Abbay/Upper Blue Nile Basin: The Importance of Shared Management and Regional Coordination Efforts for Mitigation

2021 ◽  
Vol 13 (9) ◽  
pp. 1835
Author(s):  
Yared Bayissa ◽  
Semu Moges ◽  
Assefa Melesse ◽  
Tsegaye Tadesse ◽  
Anteneh Z. Abiy ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Regional Scale ◽  
Earth Observation ◽  
Drought Indices ◽  
Severe Drought ◽  
Nile Basin ◽  
Blue Nile ◽  
Blue Nile Basin ◽  
Small Streams ◽  
Upper Blue Nile Basin

Drought is one of the least understood and complex natural hazards often characterized by a significant decrease in water availability for a prolonged period. It can be manifested in one or more forms as meteorological, agricultural, hydrological, and/or socio-economic drought. The overarching objective of this study is to demonstrate and characterize the different forms of droughts and to assess the multidimensional nature of drought in the Abbay/ Upper Blue Nile River (UBN) basin and its national and regional scale implications. In this study, multiple drought indices derived from in situ and earth observation-based hydro-climatic variables were used. The meteorological drought was characterized using the Standardized Precipitation Index (SPI) computed from the earth observation-based gridded CHIRPS (Climate Hazards Group InfraRed Precipitation with Station) rainfall data. Agricultural and hydrological droughts were characterized by using the Soil Moisture Deficit Index (SMDI) and Standardized Runoff-discharge Index (SRI), respectively. The monthly time series of SMDI was derived from model-based gridded soil moisture and SRI from observed streamflow data from 1982 to 2019. The preliminary result illustrates the good performance of the drought indices in capturing the historic severe drought events (e.g., 1984 and 2002) and the spatial extents across the basin. The results further indicated that all forms of droughts (i.e., meteorological, agricultural, and hydrological) occurred concurrently in Abbay/Upper Blue Nile basin with a Pearson correlation coefficient ranges from 0.5 to 0.85 both Kiremt and annual aggregate periods. The concurrent nature of drought is leading to a multi-dimensional socio-economic crisis as indicated by rainfall, and soil moisture deficits, and drying of small streams. Multi-dimensional drought mitigation necessitates regional cooperation and watershed management to protect both the common water sources of the Abbay/Upper Blue Nile basin and the socio-economic activities of the society in the basin. This study also underlines the need for multi-scale drought monitoring and management practices in the basin.

Evaluation of time scale of meteorological, hydrological and agricultural drought indices

2021 ◽  
Author(s):  
V. K. Prajapati ◽  
M. Khanna ◽  
M. Singh ◽  
R. Kaur ◽  
R. N. Sahoo ◽  
...  
Keyword(s):  
Time Scale ◽  
Agricultural Drought ◽  
Drought Indices

scholarly journals Earth Observation Data for Agricultural Drought Monitoring in the Pannonian Basin

2020 ◽  
Author(s):  
Laura Crocetti ◽  
Milan Fischer ◽  
Matthias Forkel ◽  
Aleš Grlj ◽  
Wai-Tim Ng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Pannonian Basin ◽  
Spatial Scales ◽  
European Space Agency ◽  
Earth Observation ◽  
Machine Learning Algorithms ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Observation Data

<p>The Pannonian Basin is a region in the southeastern part of Central Europe that is heavily used for agricultural purposes. It is geomorphological defined as the plain area that is surrounded by the Alps in the west, the Dinaric Alps in the Southwest, and the Carpathian mountains in the North, East and Southeast. In recent decades, the Pannonian Basin has experienced several drought episodes, leading to severe impacts on the environment, society, and economy. Ongoing human-induced climate change, characterised by increasing temperature and potential evapotranspiration as well as changes in precipitation distribution will further exacerbate the frequency and intensity of extreme events. Therefore, it is important to monitor, model, and forecast droughts and their impact on the environment for a better adaption to the changing weather and climate extremes. The increasing availability of long-term Earth observation (EO) data with high-resolution, combined with the progress in machine learning algorithms and artificial intelligence, are expected to improve the drought monitoring and impact prediction capacities.</p><p>Here, we assess novel EO-based products with respect to drought processes in the Pannonian Basin. To identify meteorological and agricultural drought, the Standardized Precipitation-Evapotranspiration Index was computed from the ERA5 meteorological reanalysis and compared with drought indicators based on EO time series of soil moisture and vegetation like the Soil Water Index or the Normalized Difference Vegetation Index. We suggest that at resolution representing the ERA5 reanalysis (~0.25°) or coarser, both meteorological as well as EO data can identify drought events similarly well. However, at finer spatial scales (e.g. 1 km) the variability of biophysical properties between fields cannot be represented by meteorological data but can be captured by EO data. Furthermore, we analyse historical drought events and how they occur in different EO datasets. It is planned to enhance the forecasting of agricultural drought and estimating drought impacts on agriculture through exploiting the potential of EO soil moisture and vegetation data in a data-driven machine learning framework.</p><p>This study is funded by the DryPan project of the European Space Agency (https://www.eodc.eu/esa-drypan/).</p>

Development of a Next Generation Drought Index: combining multiple global and local data sources to enable detection of sector-specific drought impacts

2021 ◽  
Author(s):  
Dimmie Hendriks ◽  
Pieter Hazenberg ◽  
Jonas Gotte ◽  
Patricia Trambauer ◽  
Arjen Haag ◽  
...  
Keyword(s):  
Drought Index ◽  
Agricultural Drought ◽  
Data Sources ◽  
Drought Indices ◽  
Next Generation ◽  
Hazard Indices ◽  
Local Data ◽  
Drought Impacts ◽  
Drought Hazard ◽  
Impact Data

<p>An increasing number of regions and countries are confronted with droughts as well as an increase in water demand. Inevitably, this leads to an increasing pressure on the available water resources and associated risks and economic impact for the water dependent sectors. In order to prevent big drought impacts, such as agricultural damage and food insecurity, timely and focused drought mitigation measures need to be carried out. To enable this, the detection of drought and its sector-specific risks at early stages needs to be improved. One of the main challenges is to develop compound and impact-oriented drought indices, that make optimal use of innovative techniques, satellite products, local data and other big data sets.</p><p>Here, we present the development of a Next Generation Drought Index (NGDI) that combines multiple freely available global data sources (eg. ERA5, MODIS, PCR-GLOBWB) to calculate a range of relevant drought hazard indices related to meteorological, hydrological, soil moisture and agricultural drought (eg. SPI, SPEI, SRI, SGI, VCI). The drought hazard indices are aggregated at district level, while considering the percentage area exposure of the drought impacted sector (exposure). In addition, the indices are enriched with local and national scale drought impact information (eg. online news items, social media data, EM-DAT database, GDO Drought news, national drought reports). Results are presented at sub-national scales in interactive spatial and temporal views, showing the combined drought indices and impact data.</p><p>The NGDI approach is being tested for the agricultural sector in Mali, a country with a vulnerable population and economy that faces frequent dry spells which heavily impact the functioning of the important agricultural activities that sustain a large part of the population. The computed drought indices are compared with local drought data and an analysis is made of the cross-correlations between the indices within the NGDI and collected impact data.</p><p>We aim at providing the NGDI information to a broad audience as well as co-creation of further NGDI developments. Hence, we would like to reach out to interested parties and identify collaboration opportunities.</p>

scholarly journals Earth Observation for agricultural drought monitoring in the Pannonian Basin (southeastern Europe): current state and future directions

2020 ◽  
Vol 20 (4) ◽  
Author(s):  
Laura Crocetti ◽  
Matthias Forkel ◽  
Milan Fischer ◽  
František Jurečka ◽  
Aleš Grlj ◽  
...  
Keyword(s):  
Potential Evapotranspiration ◽  
Pannonian Basin ◽  
Earth Observation ◽  
Agricultural Drought ◽  
Drought Monitoring ◽  
Southeastern Europe ◽  
Seasonal Forecasts ◽  
Impact Prediction ◽  
Drought Impacts ◽  
Current State

AbstractThe Pannonian Basin in southeastern Europe is heavily used for rain-fed agriculture. The region experienced several droughts in the last years, causing major yield losses. Ongoing climate change, characterised by increasing temperatures and potential evapotranspiration, and by changes in precipitation distribution will likely increase the frequency and intensity of drought episodes in the future. Hence, ongoing monitoring of droughts and estimation of their impact on agriculture is necessary to adapt agricultural practices to changing weather and climate extremes. Several regional initiatives, projects and online tools have been established to facilitate drought monitoring and management in the Pannonian Basin. However, reliable systems to forecast potential drought impacts on plant productivity and agricultural yields at monthly to seasonal scales are only in their infancy, as plant response to climatic extremes is still poorly understood. With the increasing availability of high-resolution and long-term Earth Observation (EO) data and recent progress in machine learning and artificial intelligence, further improvements in drought monitoring and impact prediction capacities are expected. Here we review the current state of drought monitoring in the Pannonian Basin, identify EO-based variables to potentially improve regional drought impact monitoring and outline future perspectives for seasonal forecasts of drought impacts on agriculture.

An evaluation of agricultural drought indices for the Canadian prairies

2003 ◽  
Vol 118 (1-2) ◽  
pp. 49-62 ◽  
Author(s):  
Steven M. Quiring ◽  
Timothy N. Papakryiakou
Keyword(s):  
Agricultural Drought ◽  
Drought Indices ◽  
Canadian Prairies

Drought

Geography ◽  
2020 ◽  
Author(s):  
Woonsup Choi
Keyword(s):  
Soil Moisture ◽  
Atmospheric Circulation ◽  
Agricultural Drought ◽  
Subsurface Water ◽  
Drought Indices ◽  
Human Society ◽  
Atmospheric Conditions ◽  
Spatial And Temporal Patterns ◽  
Circulation Anomalies ◽  
Atmospheric Circulation Anomalies

Drought is a natural disaster that has plagued human society throughout history. However, the meaning of drought varies by perspective and academic discipline, and the cause of drought is difficult to pinpoint. Despite the variation in its meaning, drought generally refers to the condition of an abnormally low amount of water for a given climate. Here the water can be precipitation, streamflow, soil moisture, groundwater, reservoir storage, and the like, but the lack of precipitation is a precursor for other types of drought. The lack of precipitation is often associated with anomalous atmospheric conditions such as atmospheric-circulation anomalies, higher-than-normal temperatures, and lower-than-normal relative humidity. Sea surface temperature anomalies may lead to sustained atmospheric-circulation anomalies. Drought defined as a lack of precipitation is often called meteorological or climatological drought. Other drought types can be classified within the context of the affected sectors, such as agricultural, hydrological, and socioeconomic drought. Agricultural drought generally refers to a lack of soil moisture, and hydrological drought refers to a lack of surface and subsurface water (e.g., streamflow and groundwater). Socioeconomic drought hampers human activities such as industry or water supply. As meteorological drought persists, other types of drought can follow. Such definitions of drought are regarded as conceptual definitions, but operational definitions are also necessary for quantitative understanding and management of drought events. Operational definitions use quantitative indices to identify the occurrence and characteristics of drought events such as onset, duration, termination, and deficit volume of drought. Much of existing drought research concerns developing, revising, and applying drought indices to investigate spatial and temporal patterns of drought at various geographical scales. Drought research has progressed along several directions, such as causes of drought, characteristics of drought events, impacts, and mitigation. Each of these directions is represented by the works cited in this article.

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.

scholarly journals Temporal and Spatial Comparison of Agricultural Drought Indices from Moderate Resolution Satellite Soil Moisture Data over Northwest Spain

2017 ◽  
Vol 9 (11) ◽  
pp. 1168 ◽  
Author(s):  
Miriam Pablos ◽  
José Martínez-Fernández ◽  
Nilda Sánchez ◽  
Ángel González-Zamora
Keyword(s):  
Soil Moisture ◽  
Agricultural Drought ◽  
Drought Indices ◽  
Soil Moisture Data ◽  
Moderate Resolution ◽  
Temporal And Spatial
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