Impact of Vegetation Feedback on the Response of Precipitation to Antecedent Soil Moisture Anomalies over North America

2007 ◽  
Vol 8 (3) ◽  
pp. 534-550 ◽  
Author(s):  
Yeonjoo Kim ◽  
Guiling Wang
Keyword(s):  
Soil Moisture ◽  
North America ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Area Index ◽  
Vegetation Feedback ◽  
Community Land Model ◽  
Initial Soil Moisture ◽  
Initial Soil ◽  
The Impact

Abstract Previous studies support a positive soil moisture–precipitation feedback over a major fraction of North America; that is, initial soil moisture anomalies lead to precipitation anomalies of the same sign. To investigate how vegetation feedback modifies the sensitivity of precipitation to initial soil moisture conditions over North America, a series of ensemble simulations are carried out using a modified version of the coupled Community Atmosphere Model–Community Land Model (CAM–CLM). The modified CLM includes a predictive vegetation phenology scheme so that the coupled model can represent interactions between soil moisture, vegetation, and precipitation at the seasonal time scale. The focus of this study is on how the impact of vegetation feedback varies with the timing and direction of initial soil moisture anomalies. During summer, wet soil moisture anomalies lead to increase in leaf area index and, consequently, increase in evapotranspiration and surface heating. Such increases tend to favor precipitation. Therefore, under wet summer soil moisture anomalies, the soil moisture–induced precipitation increase is reinforced when predictive phenology is included. That is, the vegetation feedback to precipitation is positive. The response of vegetation to dry soil moisture anomalies in the summer months, however, is not significant due probably to a dry bias in the model, so the resulting vegetation feedback on precipitation is minimal. To soil moisture anomalies in spring, the leaf area index (LAI) response is delayed since LAI is still limited by cold temperature at that time of the year. During the summer following wet spring soil moisture anomalies, vegetation feedback is negative; that is, it tends to suppress the response of precipitation through the depletion of soil moisture by vegetation.

Impact of Initial Soil Moisture Anomalies on Subsequent Precipitation over North America in the Coupled Land–Atmosphere Model CAM3–CLM3

2007 ◽  
Vol 8 (3) ◽  
pp. 513-533 ◽  
Author(s):  
Yeonjoo Kim ◽  
Guiling Wang
Keyword(s):  
Soil Moisture ◽  
North America ◽  
Continental Scale ◽  
Model Version ◽  
Initial Soil Moisture ◽  
Spatial Coverage ◽  
Initial Soil ◽  
Atmosphere Model ◽  
Shallow Soils ◽  
The Impact

Abstract To investigate the impact of anomalous soil moisture conditions on subsequent precipitation over North America, a series of numerical experiments is performed using a modified version of the Community Atmosphere Model version 3 and the Community Land Model version 3 (CAM3–CLM3). First, the mechanisms underlying the impact of spring and summer soil moisture on subsequent precipitation are examined based on simulations starting on 1 April and 1 June, respectively. How the response of precipitation to initial soil moisture anomalies depends on the characteristics of such anomalies, including the timing, magnitude, spatial coverage, and vertical depth, is then investigated. There are five main findings. First, the impact of spring soil moisture anomalies is not evident until early summer although their impact on the large-scale circulation results in slight changes in precipitation during spring. Second, precipitation increases with initial soil moisture almost linearly within a certain range of soil moisture. Beyond this range, precipitation is less responsive. Third, during the first month following the onset of summer soil moisture anomalies, the precipitation response to wet anomalies is larger in magnitude than that to dry anomalies. However, the resulting wet anomalies in precipitation quickly dissipate within a month or so, while the resulting dry anomalies in precipitation remain at a considerable magnitude for a longer period. Consistently, wet spring anomalies are likely to be ameliorated before summer, and thus have a smaller impact (in magnitude) on summer precipitation than dry spring anomalies. Fourth, soil moisture anomalies of smaller spatial coverage lead to precipitation anomalies that are smaller and less persistent, compared to anomalies at the continental scale. Finally, anomalies in shallow soil can persist long enough to influence the subsequent precipitation at the seasonal time scale. Dry anomalies in deep soils last much longer than those in shallow soils.

scholarly journals Integrating ASCAT surface soil moisture and GEOV1 leaf area index into the SURFEX modelling platform: a land data assimilation application over France

2014 ◽  
Vol 18 (1) ◽  
pp. 173-192 ◽  
Author(s):  
A. L. Barbu ◽  
J.-C. Calvet ◽  
J.-F. Mahfouf ◽  
S. Lafont
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Surface Soil ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
Area Index ◽  
Land Data Assimilation ◽  
The Impact

Abstract. The land monitoring service of the European Copernicus programme has developed a set of satellite-based biogeophysical products, including surface soil moisture (SSM) and leaf area index (LAI). This study investigates the impact of joint assimilation of remotely sensed SSM derived from Advanced Scatterometer (ASCAT) backscatter data and the Copernicus Global Land GEOV1 satellite-based LAI product into the the vegetation growth version of the Interactions between Soil Biosphere Atmosphere (ISBA-A-gs) land surface model within the the externalised surface model (SURFEX) modelling platform of Météo-France. The ASCAT data were bias corrected with respect to the model climatology by using a seasonal-based CDF (Cumulative Distribution Function) matching technique. A multivariate multi-scale land data assimilation system (LDAS) based on the extended Kalman Filter (EKF) is used for monitoring the soil moisture, terrestrial vegetation, surface carbon and energy fluxes across the domain of France at a spatial resolution of 8 km. Each model grid box is divided into a number of land covers, each having its own set of prognostic variables. The filter algorithm is designed to provide a distinct analysis for each land cover while using one observation per grid box. The updated values are aggregated by computing a weighted average. In this study, it is demonstrated that the assimilation scheme works effectively within the ISBA-A-gs model over a four-year period (2008–2011). The EKF is able to extract useful information from the data signal at the grid scale and distribute the root-zone soil moisture and LAI increments throughout the mosaic structure of the model. The impact of the assimilation on the vegetation phenology and on the water and carbon fluxes varies from one season to another. The spring drought of 2011 is an interesting case study of the potential of the assimilation to improve drought monitoring. A comparison between simulated and in situ soil moisture gathered at the twelve SMOSMANIA (Soil Moisture Observing System–Meteorological Automatic Network Integrated Application) stations shows improved anomaly correlations for eight stations.

scholarly journals Integrating ASCAT surface soil moisture and GEOV1 leaf area index into the SURFEX modelling platform: a land data assimilation application over France

2013 ◽  
Vol 10 (7) ◽  
pp. 9057-9103 ◽  
Author(s):  
A. L. Barbu ◽  
J.-C. Calvet ◽  
J.-F. Mahfouf ◽  
S. Lafont
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Surface Soil ◽  
Cumulative Distribution ◽  
Surface Soil Moisture ◽  
Area Index ◽  
Land Data Assimilation ◽  
The Impact

Abstract. The land monitoring service of the European Copernicus programme has developed a set of satellite-based biogeophysical products, including surface soil moisture (SSM) and leaf area index (LAI). This study investigates the impact of joint assimilation of remotely sensed SSM derived from ASCAT backscatter data and the GEOV1 satellite-based LAI into the ISBA-A-gs land surface model within the SURFEX modelling platform of Meteo-France. The ASCAT data were bias corrected with respect to the model climatology by using a seasonal-based CDF (Cumulative Distribution Function) matching technique. A multivariate multi-scale land data assimilation system (LDAS) based on the Extended Kalman Filter (EKF) is used for monitoring the soil moisture, terrestrial vegetation, surface carbon and energy fluxes across the France domain at a spatial resolution of 8 km. Each model grid box is divided in a number of land covers, each having its own set of prognostic variables. The filter algorithm is designed to provide a distinct analysis for each land cover while using one observation per grid box. The updated values are aggregated by computing a weighted average. In this study, it is demonstrated that the assimilation scheme works effectively within the ISBA-A-gs model over a four-year period (2008–2011). The EKF is able to extract useful information from the data signal at the grid scale and to distribute the root-zone soil moisture and LAI increments among the mosaic structure of the model. The impact of the assimilation on the vegetation phenology and on the water and carbon fluxes varies from one season to another. The spring drought of 2011 is an interesting case study showing the potential of the assimilation to improve drought monitoring. A comparison between simulated and in situ soil moisture gathered at the twelve SMOSMANIA stations shows improved anomaly correlations for eight stations.

scholarly journals Remote Sensing to Study Mangrove Fragmentation and Its Impacts on Leaf Area Index and Gross Primary Productivity in the South of Peninsular Malaysia

2021 ◽  
Vol 13 (8) ◽  
pp. 1427
Author(s):  
Kasturi Devi Kanniah ◽  
Chuen Siang Kang ◽  
Sahadev Sharma ◽  
A. Aldrie Amir
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Primary Productivity ◽  
Peninsular Malaysia ◽  
Area Index ◽  
Mangrove Area ◽  
The Mean ◽  
Shape Complexity ◽  
The Impact

Mangrove is classified as an important ecosystem along the shorelines of tropical and subtropical landmasses, which are being degraded at an alarming rate despite numerous international treaties having been agreed. Iskandar Malaysia (IM) is a fast-growing economic region in southern Peninsular Malaysia, where three Ramsar Sites are located. Since the beginning of the 21st century (2000–2019), a total loss of 2907.29 ha of mangrove area has been estimated based on medium-high resolution remote sensing data. This corresponds to an annual loss rate of 1.12%, which is higher than the world mangrove depletion rate. The causes of mangrove loss were identified as land conversion to urban, plantations, and aquaculture activities, where large mangrove areas were shattered into many smaller patches. Fragmentation analysis over the mangrove area shows a reduction in the mean patch size (from 105 ha to 27 ha) and an increase in the number of mangrove patches (130 to 402), edge, and shape complexity, where smaller and isolated mangrove patches were found to be related to the rapid development of IM region. The Moderate Resolution Imaging Spectro-radiometer (MODIS) Leaf Area Index (LAI) and Gross Primary Productivity (GPP) products were used to inspect the impact of fragmentation on the mangrove ecosystem process. The mean LAI and GPP of mangrove areas that had not undergone any land cover changes over the years showed an increase from 3.03 to 3.55 (LAI) and 5.81 g C m−2 to 6.73 g C m−2 (GPP), highlighting the ability of the mangrove forest to assimilate CO2 when it is not disturbed. Similarly, GPP also increased over the gained areas (from 1.88 g C m−2 to 2.78 g C m−2). Meanwhile, areas that lost mangroves, but replaced them with oil palm, had decreased mean LAI from 2.99 to 2.62. In fragmented mangrove patches an increase in GPP was recorded, and this could be due to the smaller patches (<9 ha) and their edge effects where abundance of solar radiation along the edges of the patches may increase productivity. The impact on GPP due to fragmentation is found to rely on the type of land transformation and patch characteristics (size, edge, and shape complexity). The preservation of mangrove forests in a rapidly developing region such as IM is vital to ensure ecosystem, ecology, environment, and biodiversity conservation, in addition to providing economical revenue and supporting human activities.

scholarly journals Representing winter wheat in the Community Land Model (version 4.5)

2017 ◽  
Vol 10 (5) ◽  
pp. 1873-1888 ◽  
Author(s):  
Yaqiong Lu ◽  
Ian N. Williams ◽  
Justin E. Bagley ◽  
Margaret S. Torn ◽  
Lara M. Kueppers
Keyword(s):  
Heat Flux ◽  
Winter Wheat ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Latent Heat Flux ◽  
Carbon Allocation ◽  
Net Ecosystem Exchange ◽  
Area Index ◽  
Community Land Model ◽  
The Us

Abstract. Winter wheat is a staple crop for global food security, and is the dominant vegetation cover for a significant fraction of Earth's croplands. As such, it plays an important role in carbon cycling and land–atmosphere interactions in these key regions. Accurate simulation of winter wheat growth is not only crucial for future yield prediction under a changing climate, but also for accurately predicting the energy and water cycles for winter wheat dominated regions. We modified the winter wheat model in the Community Land Model (CLM) to better simulate winter wheat leaf area index, latent heat flux, net ecosystem exchange of CO2, and grain yield. These included schemes to represent vernalization as well as frost tolerance and damage. We calibrated three key parameters (minimum planting temperature, maximum crop growth days, and initial value of leaf carbon allocation coefficient) and modified the grain carbon allocation algorithm for simulations at the US Southern Great Plains ARM site (US-ARM), and validated the model performance at eight additional sites across North America. We found that the new winter wheat model improved the prediction of monthly variation in leaf area index, reduced latent heat flux, and net ecosystem exchange root mean square error (RMSE) by 41 and 35 % during the spring growing season. The model accurately simulated the interannual variation in yield at the US-ARM site, but underestimated yield at sites and in regions (northwestern and southeastern US) with historically greater yields by 35 %.

The Impact of Noah-MP Physical Parameterizations on Modeling Water Availability during Droughts in the Texas-Gulf Region

2021 ◽  
Author(s):  
Wen-Ying Wu ◽  
Zong-Liang Yang ◽  
Michael Barlage
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Water Availability ◽  
Water Storage ◽  
Rooting Depth ◽  
Surface Model ◽  
Terrestrial Water Storage ◽  
Area Index ◽  
Terrestrial Water ◽  
The Impact

AbstractTexas is subject to severe droughts, including the record-breaking one in 2011. To investigate the critical hydrometeorological processes during drought, we use a land surface model, Noah-MP, to simulate water availability and investigate the causes of the record drought. We conduct a series of experiments with runoff schemes, vegetation phenology, and plant rooting depth. Observation-based terrestrial water storage, evapotranspiration, runoff, and leaf area index are used to compare with results from the model. Overall, the results suggest that using different parameterizations can influence the modeled water availability, especially during drought. The drought-induced vegetation responses not only interact with water availability but also affect the ground temperature. Our evaluation shows that Noah-MP with a groundwater scheme produces a better temporal relationship in terrestrial water storage compared with observations. Leaf area index from dynamic vegetation is better simulated in wet years than dry years. Reduction of positive biases in runoff and reduction of negative biases in evapotranspiration are found in simulations with groundwater, dynamic vegetation, and deeper rooting zone depth. Multi-parameterization experiments show the uncertainties of drought monitoring and provide a mechanistic understanding of disparities in dry anomalies.

scholarly journals Watershed Modeling with Remotely Sensed Big Data: MODIS Leaf Area Index Improves Hydrology and Water Quality Predictions

2020 ◽  
Vol 12 (13) ◽  
pp. 2148 ◽  
Author(s):  
Adnan Rajib ◽  
I Luk Kim ◽  
Heather E. Golden ◽  
Charles R. Lane ◽  
Sujay V. Kumar ◽  
...  
Keyword(s):  
Water Quality ◽  
Soil Moisture ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Dynamics ◽  
Watershed Modeling ◽  
Remotely Sensed ◽  
Area Index ◽  
Daily Streamflow ◽  
Modis Lai

Traditional watershed modeling often overlooks the role of vegetation dynamics. There is also little quantitative evidence to suggest that increased physical realism of vegetation dynamics in process-based models improves hydrology and water quality predictions simultaneously. In this study, we applied a modified Soil and Water Assessment Tool (SWAT) to quantify the extent of improvements that the assimilation of remotely sensed Leaf Area Index (LAI) would convey to streamflow, soil moisture, and nitrate load simulations across a 16,860 km2 agricultural watershed in the midwestern United States. We modified the SWAT source code to automatically override the model’s built-in semiempirical LAI with spatially distributed and temporally continuous estimates from Moderate Resolution Imaging Spectroradiometer (MODIS). Compared to a “basic” traditional model with limited spatial information, our LAI assimilation model (i) significantly improved daily streamflow simulations during medium-to-low flow conditions, (ii) provided realistic spatial distributions of growing season soil moisture, and (iii) substantially reproduced the long-term observed variability of daily nitrate loads. Further analysis revealed that the overestimation or underestimation of LAI imparted a proportional cascading effect on how the model partitions hydrologic fluxes and nutrient pools. As such, assimilation of MODIS LAI data corrected the model’s LAI overestimation tendency, which led to a proportionally increased rootzone soil moisture and decreased plant nitrogen uptake. With these new findings, our study fills the existing knowledge gap regarding vegetation dynamics in watershed modeling and confirms that assimilation of MODIS LAI data in watershed models can effectively improve both hydrology and water quality predictions.

scholarly journals Development and Evaluation of a Leaf Disease Damage Extension in Cropsim-CERES Wheat

Agronomy ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 120 ◽  
Author(s):  
Georg Röll ◽  
William Batchelor ◽  
Ana Castro ◽  
María Simón ◽  
Simone Graeff-Hönninger
Keyword(s):  
Sensitivity Analysis ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Mean Squared Error ◽  
Septoria Tritici Blotch ◽  
Septoria Tritici ◽  
Extended Model ◽  
Simple Method ◽  
Area Index ◽  
The Impact

Developing disease models to simulate and analyse yield losses for various pathogens is a challenge for the crop modelling community. In this study, we developed and tested a simple method to simulate septoria tritici blotch (STB) in the Cropsim-CERES Wheat model studying the impacts of damage on wheat (Triticum aestivum L.) yield. A model extension was developed by adding a pest damage module to the existing wheat model. The module simulates the impact of daily damage on photosynthesis and leaf area index. The approach was tested on a two-year dataset from Argentina with different wheat cultivars. The accuracy of the simulated yield and leaf area index (LAI) was improved to a great extent. The Root mean squared error (RMSE) values for yield (1144 kg ha−1) and LAI (1.19 m2 m−2) were reduced by half (499 kg ha−1) for yield and LAI (0.69 m2 m−2). In addition, a sensitivity analysis of different disease progress curves on leaf area index and yield was performed using a dataset from Germany. The sensitivity analysis demonstrated the ability of the model to reduce yield accurately in an exponential relationship with increasing infection levels (0–70%). The extended model is suitable for site specific simulations, coupled with for example, available remote sensing data on STB infection.

Sign in / Sign up

Export Citation Format

Share Document