Utilizing the MODIS-derived leaf area index to investigate the impact of vegetation processes on hydrological simulation of macroscale catchment

2017 ◽  
Vol 77 (1) ◽  
Author(s):  
H. G. Zhao ◽  
S. T. Yang ◽  
Y. C. Huang
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Hydrological Simulation ◽  
Area Index ◽  
The Impact

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.

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 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.

scholarly journals The effect of different crops and slopes on runoff and soil erosion

2020 ◽  
Vol 15 (3) ◽  
pp. 773-780
Author(s):  
Jinhua Gao ◽  
Yu Bai ◽  
Haifeng Cui ◽  
Yu Zhang
Keyword(s):  
Soil Erosion ◽  
Field Experiment ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Environmental Issues ◽  
Area Index ◽  
The Impact

Abstract Runoff and soil erosion are serious environmental issues in farmland management. In a field experiment in Xingmu, China, data from nine plots with different slopes and crops were collected, and the crops' leaf area index (LAI) used to represent the impact of vegetation on runoff and soil erosion. The results show that slope and crop both have significant effects on runoff and soil erosion, and that the LAI can indicate the effects of different crops.

scholarly journals Contribution of Dynamic Vegetation Phenology to Decadal Climate Predictability

2014 ◽  
Vol 27 (22) ◽  
pp. 8563-8577 ◽  
Author(s):  
Martina Weiss ◽  
Paul A. Miller ◽  
Bart J. J. M. van den Hurk ◽  
Twan van Noije ◽  
Simona Ştefănescu ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Degrees Of Freedom ◽  
Reference Model ◽  
Coupled Model ◽  
Coupled System ◽  
Cold Bias ◽  
Area Index ◽  
Skill Scores ◽  
The Impact

Abstract In this study, the impact of coupling and initializing the leaf area index from the dynamic vegetation model Lund–Potsdam–Jena General Ecosystem Simulator (LPJ-GUESS) is analyzed on skill of decadal predictions in the fully coupled atmosphere–land–ocean–sea ice model, the European Consortium Earth System Model (EC-Earth). Similar to the impact of initializing the model with the observed oceanic state, initializing the leaf area index (LAI) fields obtained from an offline LPJ-GUESS simulation forced by the observed atmospheric state leads to a systematic drift. A different treatment of the water and soil moisture budget in LPJ-GUESS is a likely cause of this drift. The coupled system reduces the cold bias of the reference model over land by reducing LAI (and the associated evaporative cooling), particularly outside the growing season. The coupling with the interactive vegetation module implies more degrees of freedom in the coupled model, which generates more noise that can mask a portion of the extra signal that is generated. The forecast reliability improves marginally, particularly early in the forecast. Ranked probability skill scores are also improved slightly in most areas analyzed, but the signal is not fully coherent over the forecast interval because of the relatively low number of ensemble members. Methods to remove the LAI drift and allow coupling of other variables probably need to be implemented before significant forecast skill can be expected.

Impact Assessment of Satellite-Derived Leaf Area Index Datasets Using a General Circulation Model

2007 ◽  
Vol 20 (6) ◽  
pp. 993-1015 ◽  
Author(s):  
Hyun-Suk Kang ◽  
Yongkang Xue ◽  
G. James Collatz
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
General Circulation ◽  
Circulation Model ◽  
West African ◽  
Radiative Energy ◽  
Area Index ◽  
Near Surface ◽  
Surface Climate ◽  
The Impact

Abstract This study assesses the impact of two different remote sensing–derived leaf area index (RSLAI) datasets retrieved from the same source (i.e., Advanced Very High Resolution Radiometer measurements) on a general circulation model’s (GCM) seasonal climate simulations as well as the mechanisms that lead to the improvement in simulations over several regions. Based on the analysis of these two RSLAI datasets for 17 yr from 1982 to 1998, their spatial distribution patterns and characteristics are discussed. Despite some disagreements in the RSLAI magnitudes and the temporal variability between these two datasets over some areas, their effects on the simulation of near-surface climate and the regions with significant impact are generally similar to each other. Major disagreements in the simulated climate appear in a few limited regions. The GCM experiment using the RSLAI and other satellite-derived land surface products showed substantial improvements in the near-surface climate in the East Asian and West African summer monsoon areas and boreal forests of North America compared to the control experiment that used LAI extrapolated from limited ground surveys. For the East Asia and northwest U.S. regions, the major role of RSLAI changes is in partitioning the net radiative energy into latent and sensible heat fluxes, which results in discernable warming and decrease of precipitation due to the smaller RSLAI values compared to the control. Meanwhile, for the West African semiarid regions, where the LAI difference between RSLAI and control experiments is negligible, the decrease in surface albedo caused by the high vegetation cover fraction in the satellite-derived dataset plays an important role in altering local circulation that produces a positive feedback in land/atmosphere interaction.

scholarly journals Seasonal Variation in Canopy Aerodynamics and the Sensitivity of Transpiration Estimates to Wind Velocity in Broadleaved Deciduous Species

2016 ◽  
Vol 17 (12) ◽  
pp. 3029-3043 ◽  
Author(s):  
D. M. Barnard ◽  
W. L. Bauerle
Keyword(s):  
Seasonal Variation ◽  
Wind Speed ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Canopy Structure ◽  
Biophysical Model ◽  
Structure Model ◽  
Area Index ◽  
Wind Speeds ◽  
The Impact

Abstract Characterization of seasonal dynamics in wind speed attenuation within a plant canopy α is necessary for modeling leaf boundary layer conductance , canopy–atmosphere coupling Ω, and transpiration at multiple scales. The goals of this study were to characterize seasonal variation in α in four tree species with canopy wind profiles and a canopy-structure model, to quantify the impact of α on estimates of and Ω, and to determine the influence of variable wind speed on transpiration estimates from a biophysical model [Multi-Array Evaporation Stand Tree Radiation Assemblage (MAESTRA)]. Among species, α varied significantly with above-canopy wind speed and seasonal canopy development. At the mean above-canopy wind speed (1.5 m s−1), α could be predicted using a linear model with leaf area index as the input variable (coefficient of determination R2 = 0.78). However, the canopy-structure model yielded improved predictions (R2 = 0.92) by including canopy height and leaf width. By midseason, increasing canopy leaf area and α resulted in lower within-canopy wind speeds, a decrease in by 20%–50%, and a peak in Ω. Testing a discrete increase in wind speed (0.6–2.4 m s−1; seasonal mean plus/minus one standard deviation) had variable influence on transpiration estimates (from −30% to +20%), which correlated strongly with vapor pressure deficit (R2 = 0.83). Given the importance of α in accurate representation of , Ω, and transpiration, it is concluded that α needs to be given special attention in plant canopies that undergo substantial seasonal changes, especially densely foliated canopies (i.e., leaf area index &gt;1) and in areas with lower native wind speeds (i.e., &lt;2 m s−1).

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