Comparison of soil moisture at different depths for drought monitoring based on improved soil moisture anomaly percentage index

Abstract. The lagged rank cross-correlation between model-derived root-zone soil moisture estimates and remotely sensed vegetation indices (VI) is examined between January 2000 and December 2010 to quantify the skill of various soil moisture models for agricultural drought monitoring. Examined modeling strategies range from a simple antecedent precipitation index to the application of modern land surface models (LSMs) based on complex water and energy balance formulations. A quasi-global evaluation of lagged VI/soil moisture cross-correlation suggests, when globally averaged across the entire annual cycle, soil moisture estimates obtained from complex LSMs provide little added skill (< 5% in relative terms) in anticipating variations in vegetation condition relative to a simplified water accounting procedure based solely on observed precipitation. However, larger amounts of added skill (5–15% in relative terms) can be identified when focusing exclusively on the extra-tropical growing season and/or utilizing soil moisture values acquired by averaging across a multi-model ensemble.

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Assessing the soil moisture drought index for agricultural drought monitoring based on green vegetation fraction retrieval methods

Natural Hazards ◽

10.1007/s11069-021-04693-x ◽

2021 ◽

Author(s):

Rongjun Wu ◽

Qi Li

Keyword(s):

Soil Moisture ◽

Drought Index ◽

Agricultural Drought ◽

Drought Monitoring ◽

Vegetation Fraction ◽

Green Vegetation

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Exploring the potential of soil moisture reanalysis data for improving the identification of regional landslide triggering thresholds

10.5194/egusphere-egu21-2243 ◽

2021 ◽

Author(s):

Nunziarita Palazzolo ◽

David J. Peres ◽

Enrico Creaco ◽

Antonino Cancelliere

Keyword(s):

Soil Moisture ◽

Rainfall Event ◽

Rainfall Thresholds ◽

Rainfall Duration ◽

Soil Moisture Data ◽

Initial Soil Moisture ◽

Initial Soil ◽

Different Depths ◽

Moisture Conditions ◽

Landslide Triggering

Landslide triggering thresholds provide the rainfall conditions that are likely to trigger landslides, therefore their derivation is key for prediction purposes. Different variables can be considered for the identification of thresholds, which commonly are in the form of a power-law relationship linking rainfall event duration and intensity or cumulated event rainfall. The assessment of such rainfall thresholds generally neglects initial soil moisture conditions at each rainfall event, which are indeed a predisposing factor that can be crucial for the proper definition of the triggering scenario. Thus, more studies are needed to understand whether and the extent to which the integration of the initial soil moisture conditions with rainfall thresholds could improve the conventional precipitation-based approach. Although soil moisture data availability has hindered such type of studies, yet now this information is increasingly becoming available at the large scale, for instance as an output of meteorological reanalysis initiatives. In particular, in this study, we focus on the use of the ERA5-Land reanalysis soil moisture dataset. Climate reanalysis combines past observations with models in order to generate consistent time series and the ERA5-Land data actually provides the volume of water in soil layer at different depths and at global scale. Era5-Land project is, indeed, a global dataset at 9 km horizontal resolution in which atmospheric data are at an hourly scale from 1981 to present. Volumetric soil water data are available at four depths ranging from the surface level to 289 cm, namely 0-7 cm, 7-28 cm, 28-100 cm, and 100-289 cm. After collecting the rainfall and soil moisture data at the desired spatio-temporal resolution, together with the target data discriminating landslide and no-landslide events, we develop automatic triggering/non-triggering classifiers and test their performances via confusion matrix statistics. In particular, we compare the performances associated with the following set of precursors: a) event rainfall duration and depth (traditional approach), b) initial soil moisture at several soil depths, and c) event rainfall duration and depth and initial soil moisture at different depths. The approach is applied to the Oltrep&#242; Pavese region (northern Italy), for which the historical observed landslides have been provided by the IFFI project (Italian landslides inventory). Results show that soil moisture may allow an improvement in the performances of the classifier, but that the quality of the landslide inventory is crucial.

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Drought Propagation in Semi-Arid River Basins in Latin America: Lessons from Mexico to the Southern Cone

Water ◽

10.3390/w10111564 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1564 ◽

Cited By ~ 7

Author(s):

Melanie Oertel ◽

Francisco Meza ◽

Jorge Gironás ◽

Christopher A. Scott ◽

Facundo Rojas ◽

...

Keyword(s):

Soil Moisture ◽

Drought Monitoring ◽

Moisture Index ◽

Data Set ◽

Basin Scale ◽

Soil Moisture Index ◽

Streamflow Data ◽

Governmental Institutions ◽

Negative Impacts ◽

Combined Data

Detecting droughts as early as possible is important in avoiding negative impacts on economy, society, and environment. To improve drought monitoring, we studied drought propagation (i.e., the temporal manifestation of a precipitation deficit on soil moisture and streamflow). We used the Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Streamflow Index (SSI), and Standardized Soil Moisture Index (SSMI) in three drought-prone regions: Sonora (Mexico), Maipo (Chile), and Mendoza-Tunuyán (Argentina) to study their temporal interdependence. For this evaluation we use precipitation, temperature, and streamflow data from gauges that are managed by governmental institutions, and satellite-based soil moisture from the ESA CCI SM v03.3 combined data set. Results confirm that effective drought monitoring should be carried out (1) at river-basin scale, (2) including several variables, and (3) considering hydro-meteorological processes from outside its boundaries.

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Field-scale soil moisture bridges the spatial-scale gap between drought monitoring and agricultural yields

10.5194/hess-2020-364 ◽

2020 ◽

Author(s):

Noemi Vergopolan ◽

Sitian Xiong ◽

Lyndon Estes ◽

Niko Wanders ◽

Nathaniel W. Chaney ◽

...

Keyword(s):

Soil Moisture ◽

Spatial Scale ◽

Root Zone ◽

Maize Yield ◽

Remotely Sensed ◽

Drought Monitoring ◽

Surface Model ◽

Field Scale ◽

Yield Data ◽

Scale Modeling

Abstract. Soil moisture is highly variable in space, and its deficits (i.e. droughts) plays an important role in modulating crop yields and its variability across landscapes. Limited hydroclimate and yield data, however, hampers drought impact monitoring and assessment at the farmer field-scale. This study demonstrates the potential of field-scale soil moisture simulations to advance high-resolution agricultural yield prediction and drought monitoring at the smallholder farm field-scale. We present a multi-scale modeling approach that combines HydroBlocks, a physically-based hyper-resolution Land Surface Model (LSM), and machine learning. We applied HydroBlocks to simulate root zone soil moisture and soil temperature in Zambia at 3-hourly 30-m resolution. These simulations along with remotely sensed vegetation indices, meteorological conditions, and data describing the physical properties of the landscape (topography, land cover, soil properties) were combined with district-level maize data to train a random forest model (RF) to predict maize yields at the district- and field-scale (250-m) levels. Our model predicted yields with a coefficient of variation (R2) of 0.61, Mean Absolute Error (MAE) of 349 kg ha−1, and mean normalized error of 22 %. We captured maize losses due to the 2015/2016 El Niño drought at similar levels to losses reported by the Food and Agriculture Organization (FAO). Our results revealed that soil moisture is the strongest and most reliable predictor of maize yield, driving its spatial and temporal variability. Consequently, soil moisture was also the most effective indicator of drought impacts in crops when compared with precipitation, soil and air temperatures, and remotely-sensed NDVI-based drought indices. By combining field-scale root zone soil moisture estimates with observed maize yield data, this research demonstrates how field-scale modeling can help bridge the spatial scale discontinuity gap between drought monitoring and agricultural impacts.

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Desarrollo de un sistema inalámbrico escalable de medición de humedad del suelo en un cultivo de vid

Revista de Ingeniería Tecnológica ◽

10.35429/jten.2020.14.4.20.30 ◽

2020 ◽

pp. 20-30

Author(s):

Ortega-Corral César ◽

B. Ricardo Eaton-González ◽

Florencio López Cruz ◽

Laura Rocío, Díaz-Santana Rocha

Keyword(s):

Soil Moisture ◽

Wireless Sensor Network ◽

Sensor Network ◽

Precision Agriculture ◽

Drip Irrigation ◽

Base Station ◽

Sensor Nodes ◽

Wireless Sensor ◽

Wireless System ◽

Different Depths

We present a wireless system applied to precision agriculture, made up of sensor nodes that measure soil moisture at different depths, applied to vine crops where drip irrigation is applied. The intention is to prepare a system for scaling, and to create a Wireless Sensor Network (WSN) that communicates by radio frequency with a base station (ET), so that the gathered data is stored locally and can be sent out an Internet gateway.

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Earth Observation Data for Agricultural Drought Monitoring in the Pannonian Basin

10.5194/egusphere-egu2020-16916 ◽

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

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.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&#176;) 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.This study is funded by the DryPan project of the European Space Agency (https://www.eodc.eu/esa-drypan/).

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