Monitoring Soil Moisture Drought over Northern High Latitudes from Space

Blyverket, Jostein; Hamer, Paul; Schneider, Philipp; Albergel, Clément; Lahoz, W. A.

doi:10.20944/preprints201904.0009.v1

Cited by 6 publications

(3 citation statements)

References 26 publications

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“…In Europe, temperature for the whole summer 2018 was above climatology. The summer 2018 heatwave in Europe was already reported in the scientific literature (e.g Magnusson et al, 2018, Blyverket et al, 2019…”

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confidence: 73%

“…They also cost society billions of dollars every year (WMO 2017). It is therefore of paramount importance to implement tools that can monitor and warn about drought conditions (Svoboda, 2002;Luo and Wood, 2007;Blyverket et al, 2019) as well as their impact on land surface variables (LSVs) and society (Di Napoli et al, 2019). A major scientific challenge in relation to the adaptation to climate change is to observe and simulate how land biophysical variables respond to those extreme events (IPCC, 2012).…”

Section: Introductionmentioning

confidence: 99%

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Data assimilation for continuous global assessment of severe conditions over terrestrial surfaces

Albergel¹,

Zheng²,

Bonan³

et al. 2019

Preprint

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Abstract. This study demonstrates that LDAS-Monde, a global and offline Land Data Assimilation System (LDAS), that integrates satellite Earth observations into the ISBA (Interaction between Soil Biosphere and Atmosphere) Land Surface Model (LSM), is able to detect, monitor and forecast the impact of extreme weather on land surface states. LDAS-Monde jointly assimilates satellite derived Earth observations of surface soil moisture (SSM) and Leaf Area Index (LAI). It is run at global scale forced by ERA5 (LDAS_ERA5), the latest atmospheric reanalysis from the European Centre for Medium Range Weather Forecast (ECMWF) over 2010–2018 leading to a 9-yr, ~ 0.25° × 0.25° spatial resolution reanalysis of Land Surface Variables (LSVs). This reanalysis is then used to compute anomalies of land surface states, in order to (i) detect regions exposed to extreme weather such as droughts and heatwave events and (ii) address specific monitoring and forecasting requirements of LSVs for those regions. In this study, LDAS_ERA5 analysis is first successfully evaluated worldwide using several satellite-based datasets (SSM, LAI, Evapotranspiration, Gross Primary Production and Sun Induced Fluorescence), as well as in situ measurements (SSM, evapotranspiration and river discharge). The added value of assimilating the soil moisture and LAI is demonstrated with respect to a model simulation (openloop, with no assimilation). Since the global LDAS_ERA5 has relatively coarse resolution, two higher spatial resolution experiments over two areas particularly affected by heatwaves and/or droughts in 2018 were run: North Western Europe and the Murray-Darling basin in South Eastern Australia. These experiments were forced with ECMWF Integrated Forecasting System (IFS) high resolution operational analysis (LDAS_HRES, ~ 0.10° × 0.10° spatial resolution) over 2017–2018, and both openloop and analysis experiments compared once again. Since the IFS is a forecast system, it also allows LDAS-Monde to be used in forecast mode, and we demonstrate the added value of initializing 4- and 8-day LDAS-HRES forecasts of the LSVs, from the LDAS-HRES assimilation run, compared to the openloop experiments. This is particularly true for LAI that evolves on longer time space than SSM and is more sensitive to initial conditions than to atmospheric forcing, even at an 8-day lead time. This confirms that slowly evolving land initial conditions are paramount for forecasting LSVs and that LDAS-systems should jointly analyse both soil moisture and vegetation states. Finally evaluation of the modelled snowpack is presented and the perspectives for snow data assimilation in LDAS-Monde are discussed.

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confidence: 73%

Section: Introductionmentioning

confidence: 99%

Data assimilation for continuous global assessment of severe conditions over terrestrial surfaces

Albergel¹,

Zheng²,

Bonan³

et al. 2019

Preprint

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“…The simplest approaches are based on SM anomalies, percentiles or indices, such as the Standardized Brightness Temperature Index (SBTI), Standardized Soil Moisture Index (SSMI or SSI), Soil Moisture Deficit Index (SMDI), Soil Moisture Condition Index (SMCI) and Soil Moisture Anomaly Percentage Index (SMAPI) (Champagne et al, 2011;Scaini et al, 2015;Carrão et al, 2016;Velpuri et al, 2016;Cammalleri et al, 2017;Nicolai-Shaw et al, 2017;Pablos et al, 2017;Padhee et al, 2017;Eswar et al, 2018;Sadri et al, 2018;Xu et al, 2018;Blyverket et al, 2019;Ford and Quiring, 2019;Liu et al, 2019;Zhu et al, 2019;Modanesi et al, 2020;Fang et al, 2021;van Hateren et al, 2021;Vroege et al, 2021). There are many ways to prepare raw SM data to calculate robust anomalies from the long-term mean.…”

Section: Anomaly Approachesmentioning

confidence: 99%

Microwave remote sensing for agricultural drought monitoring: Recent developments and challenges

et al. 2022

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Agricultural droughts are extreme events which are often a result of interplays between multiple hydro-meteorological processes. Therefore, assessing drought occurrence, extent, duration and intensity is complex and requires the combined use of multiple variables, such as temperature, rainfall, soil moisture (SM) and vegetation state. The benefit of using information on SM and vegetation state is that they integrate information on precipitation, temperature and evapotranspiration, making them direct indicators of plant available water and vegetation productivity. Microwave remote sensing enables the retrieval of both SM and vegetation information, and satellite-based SM and vegetation products are available operationally and free of charge on a regional or global scale and daily basis. As a result, microwave remote sensing products play an increasingly important role in drought monitoring applications. Here, we provide an overview of recent developments in using microwave remote sensing for large-scale agricultural drought monitoring. We focus on the intricacy of monitoring the complex process of drought development using multiple variables. First, we give a brief introduction on fundamental concepts of microwave remote sensing together with an overview of recent research, development and applications of drought indicators derived from microwave-based satellite SM and vegetation observations. This is followed by a more detailed overview of the current research gaps and challenges in combining microwave-based SM and vegetation measurements with hydro-meteorological data sets. The potential of using microwave remote sensing for drought monitoring is demonstrated through a case study over Senegal using multiple satellite- and model-based data sets on rainfall, SM, vegetation and combinations thereof. The case study demonstrates the added-value of microwave-based SM and vegetation observations for drought monitoring applications. Finally, we provide an outlook on potential developments and opportunities.

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Can multiscalar meteorological drought indices detect soil moisture droughts? A study of Indian regions

Das

Mohinuddin

Midya

et al. 2021

Hydrological Sciences Journal

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Monitoring Soil Moisture Drought over Northern High Latitudes from Space

Cited by 6 publications

References 26 publications

Data assimilation for continuous global assessment of severe conditions over terrestrial surfaces

Data assimilation for continuous global assessment of severe conditions over terrestrial surfaces

Microwave remote sensing for agricultural drought monitoring: Recent developments and challenges

Can multiscalar meteorological drought indices detect soil moisture droughts? A study of Indian regions

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