On the utility of land surface models for agricultural drought monitoring

Crow, Wade T.; Kumar, Sujay V.; Bolten, J. D.

doi:10.5194/hess-16-3451-2012

Cited by 72 publications

(52 citation statements)

References 26 publications

(26 reference statements)

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“…Here, we evaluate the utility contributed by existing remotely‐sensed surface soil moisture products for quasi‐global agricultural drought monitoring. Following Peled et al [2010] and Crow et al [2012], our approach is based on sampling the lagged correlation between root‐zone soil moisture anomalies obtained from a water balance model and subsequent anomalies in vegetation conditions (as captured by satellite‐based visible/near‐infrared vegetation indices). Since this approach measures the ability of current soil moisture estimates to anticipate future variations in vegetation health, it provides a direct valuation of soil moisture products in an agricultural drought context.…”

Section: Introductionmentioning

confidence: 99%

Improved prediction of quasi‐global vegetation conditions using remotely‐sensed surface soil moisture

Bolten¹,

Crow²

2012

Geophysical Research Letters

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The added value of satellite‐based surface soil moisture retrievals for agricultural drought monitoring is assessed by calculating the lagged rank correlation between remotely‐sensed vegetation indices (VI) and soil moisture estimates obtained both before and after the assimilation of surface soil moisture retrievals derived from the Advanced Microwave Scanning Radiometer‐EOS (AMSR‐E) into a soil water balance model. Higher soil moisture/VI lag correlations imply an enhanced ability to predict future vegetation conditions using estimates of current soil moisture. Results demonstrate that the assimilation of AMSR‐E surface soil moisture retrievals substantially improve the performance of a global drought monitoring system ‐ particularly in sparsely‐instrumented areas of the world where high‐quality rainfall observations are unavailable.

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Section: Introductionmentioning

confidence: 99%

Improved prediction of quasi‐global vegetation conditions using remotely‐sensed surface soil moisture

Bolten¹,

Crow²

2012

Geophysical Research Letters

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“…Recently, land surface models (LSMs) have been developed in order to simulate different crop types and their responses to climatic variability, resulting in improved early detection of agricultural drought with further mitigation responses (Kucharik et al 2000;Kucharik and Brye 2003;Kucharik and Twine 2007;Lokupitiya et al 2009;Cuadra et al 2012;Mo et al 2011;Ingwersen et al 2011;Crow et al 2012;Song et al 2013;Twine et al 2013). These models take into account crop phenological and physiological processes and their influence on surface water, energy, and carbon exchanges.…”

Section: Introductionmentioning

confidence: 99%

Influence of Soil Properties in Different Management Systems: Estimating Soybean Water Changes in the Agro-IBIS Model

et al. 2018

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The water balance in agricultural cropping systems is dependent on the physical and hydraulic characteristics of the soil and the type of farming, both of which are sensitive to the soil management. Most models that describe the interaction between the surface and the atmosphere do not efficiently represent the physical differences across different soil management areas. In this study, the authors analyzed the dynamics of the water exchange in f Corresponding author: Debora Regina Roberti, debora@ufsm.br the agricultural version of the Integrated Biosphere Simulator (IBIS) model (Agro-IBIS) in the presence of different physical soil properties because of the different long-term soil management systems. The experimental soil properties were obtained from two management systems, no tillage (NT) and conventional tillage (CT) in a long-term experiment in southern Brazil in the soybean growing season of 2009/10. To simulate NT management, this study modified the top soil layer in the model to represent the residual layer. Moreover, a mathematical adjustment to the computation of leaf area index (LAI) is suggested to obtain a better representation of the grain fill to the physiological maturity period. The water exchange dynamics simulated using Agro-IBIS were compared against experimental data collected from both tillage systems. The results show that the model well represented the water dynamics in the soil and the evapotranspiration (ET) in both management systems, in particular during the wet periods. Better results were found for the conventional tillage management system for the water balance. However, with the incorporation of a residual layer and soil properties in NT, the model improved the estimation of evapotranspiration by 6%. The ability of the Agro-IBIS model to estimate ET indicates its potential application in future climate scenarios.

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“…In the context of climate change and of natural climate variability, there is a need to monitor the impacts of droughts on crops and water resources at continental and global scales (Quiroga et al, 2011;Van der Velde et al, 2012;Crow et al, 2012;Bastos et al, 2014). Modelling of continental surfaces into atmospheric and hydrological models has evolved in recent decades towards land surface models (LSMs) able to simulate the coupling of the water, energy and carbon cycles (Calvet et al, 1998;Krinner et al, 2005;Gibelin et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

“…The use of these satellite-derived products to verify LSM simulations or to optimise key LSM parameters has been assessed by several authors (e.g. Becker-Reshef et al, 2010;Crow et al, 2012;Ferrant et al, 2014;Ford et al, 2014;Ghilain et al, 2012;Ichii et al, 2009;Kowalik et al, 2014;Szczypta et al, 2012Szczypta et al, , 2014. Data assimilation is a field of active research.…”

Section: Introductionmentioning

confidence: 99%

Parameter optimisation for a better representation of drought by LSMs: inverse modelling vs. sequential data assimilation

Dewaele¹,

Munier²,

Albergel³

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. Soil maximum available water content (Max-AWC) is a key parameter in land surface models (LSMs). However, being difficult to measure, this parameter is usually uncertain. This study assesses the feasibility of using a 15-year (1999-2013) time series of satellite-derived lowresolution observations of leaf area index (LAI) to estimate MaxAWC for rainfed croplands over France. LAI interannual variability is simulated using the CO 2 -responsive version of the Interactions between Soil, Biosphere and Atmosphere (ISBA) LSM for various values of MaxAWC. Optimal value is then selected by using (1) a simple inverse modelling technique, comparing simulated and observed LAI and (2) a more complex method consisting in integrating observed LAI in ISBA through a land data assimilation system (LDAS) and minimising LAI analysis increments. The evaluation of the MaxAWC estimates from both methods is done using simulated annual maximum above-ground biomass (B ag ) and straw cereal grain yield (GY) values from the Agreste French agricultural statistics portal, for 45 administrative units presenting a high proportion of straw cereals. Significant correlations (p value < 0.01) between B ag and GY are found for up to 36 and 53 % of the administrative units for the inverse modelling and LDAS tuning methods, respectively. It is found that the LDAS tuning experiment gives more realistic values of MaxAWC and maximum B ag than the inverse modelling experiment. Using undisaggregated LAI observations leads to an underestimation of MaxAWC and maximum B ag in both experiments. Median annual maximum values of disaggregated LAI observations are found to correlate very well with MaxAWC.

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On the utility of land surface models for agricultural drought monitoring

Cited by 72 publications

References 26 publications

Improved prediction of quasi‐global vegetation conditions using remotely‐sensed surface soil moisture

Improved prediction of quasi‐global vegetation conditions using remotely‐sensed surface soil moisture

Influence of Soil Properties in Different Management Systems: Estimating Soybean Water Changes in the Agro-IBIS Model

Parameter optimisation for a better representation of drought by LSMs: inverse modelling vs. sequential data assimilation

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