A soil water based index as a suitable agricultural drought indicator

Martínez-Fernández, José; González-Zamora, Ángel; Sánchez, Nilda; Gumuzzio, A.

doi:10.1016/j.jhydrol.2014.12.051

Cited by 176 publications

(155 citation statements)

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“…The temporal variations of the upper soil moisture are mainly driven by the variations of the atmospheric water deficit near to topsoil [22,37]. In order to verify the impact of droughts on the upper soil moisture of the NEB, daily values of the Atmospheric Water Deficit (AWD) were used.…”

Section: In Situ Databasementioning

confidence: 99%

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Evaluation of the SMOS-Derived Soil Water Deficit Index as Agricultural Drought Index in Northeast of Brazil

Paredes‐Trejo

Barbosa

2017

Water

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Northeast Brazil (NEB) has recently experienced one of its worst droughts in the last decades, with large losses on rainfed agriculture. Soil moisture is the main variable to monitor agricultural drought. The remote sensing approach for drought monitoring has been enriched with the launch of the Soil Moisture and Ocean Salinity (SMOS) in November 2009 by European Space Agency (ESA). In this work, the Soil Water Deficit Index (SWDI) was calculated using the SMOS L2 soil moisture in the NEB. The SMOS-derived SWDI data (SWDIS) were evaluated against the atmospheric water deficit (AWD) calculated from in situ observations. Comparisons were made at seven-day and 0.25 • scales, over the time-span of June 2010 to December 2013. It was found that the SWDIS has a reasonably good overall performance in terms of the drought-weeks detection (skill = 0.986) and capture of the upper soil moisture temporal dynamic (r = 0.652), implying that the SWDIS could be used to track agricultural droughts. Furthermore, SWDIS shows poor performance at sites located in mountains regions affected by severe droughts (−0.10 ≤ r ≤ 0.10). It is also noted that the vegetal cover/use, climate regime, and soil texture have little influence on the AWD-SWDIS coupling.

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Section: In Situ Databasementioning

confidence: 99%

“…The Soil Water Deficit Index (SWDI) was proposed by Martínez-Fernández et al [22] in order to characterize the agricultural drought based on in situ soil moisture series and basic soil water parameters. The SWDI was formulated as follows:…”

Section: Smos-derived Soil Water Deficit Index (Swdis)mentioning

confidence: 99%

Evaluation of the SMOS-Derived Soil Water Deficit Index as Agricultural Drought Index in Northeast of Brazil

Paredes‐Trejo

Barbosa

2017

Water

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“…The rationale behind the construction of blended indictors is that a single indicator is not sufficient to adequately capture different types of drought, and the corresponding multiplicity of drought impacts that differ markedly in response time (Hao and Singh, 2015). There have been several studies assessing the link between indicators of different types of droughts, e.g., between meteorological drought and streamflow, soil moisture, or remotely sensed vegetation stress indicators (Haslinger et al, 2014;Ji and Peters, 2003;Martínez-Fernández et al, 2015;Vicente-Serrano and López-Moreno, 2005;Vicente-Serrano et al, 2012). These are useful when there is an assumption that the lag between, e.g., meteorological and hydrological drought represents the response time for impact occurrence in, e.g., riverine ecosystems.…”

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confidence: 99%

A quantitative analysis to objectively appraise drought indicators and model drought impacts

Bachmair

Svensson

Hannaford

et al. 2016

Hydrol. Earth Syst. Sci.

120

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Abstract. Drought monitoring and early warning is an important measure to enhance resilience towards drought. While there are numerous operational systems using different drought indicators, there is no consensus on which indicator best represents drought impact occurrence for any given sector. Furthermore, thresholds are widely applied in these indicators but, to date, little empirical evidence exists as to which indicator thresholds trigger impacts on society, the economy, and ecosystems. The main obstacle for evaluating commonly used drought indicators is a lack of information on drought impacts. Our aim was therefore to exploit text-based data from the European Drought Impact report Inventory (EDII) to identify indicators that are meaningful for region-, sector-, and season-specific impact occurrence, and to empirically determine indicator thresholds. In addition, we tested the predictability of impact occurrence based on the best-performing indicators. To achieve these aims we applied a correlation analysis and an ensemble regression tree approach, using Germany and the UK (the most data-rich countries in the EDII) as test beds. As candidate indicators we chose two meteorological indicators (Standardized Precipitation Index, SPI, and Standardized Precipitation Evaporation Index, SPEI) and two hydrological indicators (streamflow and groundwater level percentiles). The analysis revealed that accumulation periods of SPI and SPEI best linked to impact occurrence are longer for the UK compared with Germany, but there is variability within each country, among impact categories and, to some degree, seasons. The median of regression tree splitting values, which we regard as estimates of thresholds of impact occurrence, was around −1 for SPI and SPEI in the UK; distinct differences between northern/northeastern vs. southern/central regions were found for Germany. Predictions with the ensemble regression tree approach yielded reasonable results for regions with good impact data coverage. The predictions also provided insights into the EDII, in particular highlighting drought events where missing impact reports may reflect a lack of recording rather than true absence of impacts. Overall, the presented quantitative framework proved to be a useful tool for evaluating drought indicators, and to model impact occurrence. In summary, this study demonstrates the information gain for drought monitoring and early warning through impact data collection and analysis. It highlights the important role that quantitative analysis with impact data can have in providing "ground truth" for drought indicators, alongside more traditional stakeholder-led approaches.

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“…These missions are a challenging opportunity to incorporate the remotely sensed-soil moisture into composite approaches based on coupled climate, soil and water data indicators. However, the novel availability of soil moisture at global scale led to a limited research using satellite soil moisture observations for drought analysis (Chakrabarti et al, 2014;Martínez-Fernández et al, 2016;Martínez-Fernández et al, 2015;Scaini et al, 2015). In this line, this work aimed to merge LST and NDVI from the Moderate Resolution Imaging Spectroradiometer (MODIS) and SMOS-SSM to develop a new agricultural drought indicator, the Soil Moisture Agricultural Drought Index (SMADI), including actual soil and temperature conditions together with the lagged response of the vegetation based on vegetation indices.…”

Section: Introductionmentioning

confidence: 99%

“…Alternatively, normalized water indices based on the short-wave infrared MODIS bands were also tested as a vegetation water status indicator. In order to assess the results, two comparisons were made with other agricultural indices calculated at the Soil Moisture Measurement Stations Network of the University of Salamanca, REMEDHUS, where several drought indices have been previously tested Martínez-Fernández et al, 2015;Sánchez et al, 2016;Scaini et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

A Combined Approach With Smos and Modis to Monitor Agricultural Drought

Sánchez¹,

Martínez-Fernández²,

González-Zamora³

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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A synergistic fusion of the Soil Moisture and Ocean Salinity (SMOS) L2 soil moisture with the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived land surface temperature (LST) and several water/vegetation indices for agricultural drought monitoring was tested. The rationale of the calculation is based on the inverse relationship between LST and vegetation condition, related in turn with the soil moisture content. All the products were time-integrated, including the lagged response of vegetation. The product aims to detect and characterize soil moisture drought conditions and, particularly, to identify potential short-term agricultural droughts among them. The new index, so-called the Soil Moisture Agricultural Drought Index (SMADI), was retrieved at 500 m spatial resolution at the Soil Moisture Measurement Stations Network of the University of Salamanca (REMEDHUS) area from 2010 to 2014 at 8-days temporal scale. SMADI was compared with other agricultural indices in REMEDHUS through statistical correlation, affording a good agreement with them, and depicting a suitable description of the drought conditions in this area during the study period.

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A soil water based index as a suitable agricultural drought indicator

Cited by 176 publications

References 48 publications

Evaluation of the SMOS-Derived Soil Water Deficit Index as Agricultural Drought Index in Northeast of Brazil

Evaluation of the SMOS-Derived Soil Water Deficit Index as Agricultural Drought Index in Northeast of Brazil

A quantitative analysis to objectively appraise drought indicators and model drought impacts

A Combined Approach With Smos and Modis to Monitor Agricultural Drought

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