Calibration and multiple data set-based validation of a land surface model in a mountainous Mediterranean study area

2008 ◽  
Vol 356 (1-2) ◽  
pp. 223-233 ◽  
Author(s):  
Juan Carlos Loaiza Usuga ◽  
Valentijn R.N. Pauwels
Keyword(s):  
Land Surface ◽  
Land Surface Model ◽  
Surface Model ◽  
Data Set ◽  
Multiple Data

scholarly journals Sensitivity of the NCEP/Noah land surface model to the MODIS green vegetation fraction data set

2006 ◽  
Vol 33 (13) ◽  
Author(s):  
Jesse Miller ◽  
Michael Barlage ◽  
Xubin Zeng ◽  
Helin Wei ◽  
Kenneth Mitchell ◽  
...  
Keyword(s):  
Land Surface ◽  
Land Surface Model ◽  
Surface Model ◽  
Data Set ◽  
Vegetation Fraction ◽  
Green Vegetation ◽  
Noah Land Surface Model

scholarly journals A leaf area index data set acquired in Sahelian rangelands of Gourma in Mali over the 2005–2017 period

2019 ◽  
Vol 11 (2) ◽  
pp. 675-686
Author(s):  
Eric Mougin ◽  
Mamadou Oumar Diawara ◽  
Nogmana Soumaguel ◽  
Ali Amadou Maïga ◽  
Valérie Demarez ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface ◽  
Model Development ◽  
Land Surface Model ◽  
Product Evaluation ◽  
Tree Canopy ◽  
Surface Model ◽  
Data Set ◽  
Area Index

Abstract. The leaf area index of Sahelian rangelands and related variables such as the vegetation cover fraction, the fraction of absorbed photosynthetically active radiation and the clumping index were measured between 2005 and 2017 in the Gourma region of northern Mali. These variables, known as climate essential variables, were derived from the acquisition and the processing of hemispherical photographs taken along 1 km linear sampling transects for five contrasted canopies and one millet field. The same sampling protocol was applied in a seasonally inundated Acacia open forest, along a 0.5 km transect, by taking photographs of the understorey and the tree canopy. These observations collected over more than a decade, in a remote and not very accessible region, provide a relevant and unique data set that can be used for a better understanding of the Sahelian vegetation response to the current rainfall changes. The collected data can also be used for satellite product evaluation and land surface model development and validation. This paper aims to present the field work that was carried out during 13 successive rainy seasons, the measured vegetation variables, and the associated open database. Finally, a few examples of data use are shown. DOI of the referenced data set: https://doi.org/10.17178/AMMA-CATCH.CE.Veg_Gh.

scholarly journals Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals

2011 ◽  
Vol 15 (2) ◽  
pp. 425-436 ◽  
Author(s):  
Y. Y. Liu ◽  
R. M. Parinussa ◽  
W. A. Dorigo ◽  
R. A. M. De Jeu ◽  
W. Wagner ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Land Surface Model ◽  
Cumulative Distribution ◽  
Degree Of Saturation ◽  
Combination Method ◽  
Surface Model ◽  
Volumetric Soil Water Content ◽  
Data Set ◽  
Spatial Coverage

Abstract. Combining information derived from satellite-based passive and active microwave sensors has the potential to offer improved estimates of surface soil moisture at global scale. We develop and evaluate a methodology that takes advantage of the retrieval characteristics of passive (AMSR-E) and active (ASCAT) microwave satellite estimates to produce an improved soil moisture product. First, volumetric soil water content (m3 m−3) from AMSR-E and degree of saturation (%) from ASCAT are rescaled against a reference land surface model data set using a cumulative distribution function matching approach. While this imposes any bias of the reference on the rescaled satellite products, it adjusts them to the same range and preserves the dynamics of original satellite-based products. Comparison with in situ measurements demonstrates that where the correlation coefficient between rescaled AMSR-E and ASCAT is greater than 0.65 ("transitional regions"), merging the different satellite products increases the number of observations while minimally changing the accuracy of soil moisture retrievals. These transitional regions also delineate the boundary between sparsely and moderately vegetated regions where rescaled AMSR-E and ASCAT, respectively, are used for the merged product. Therefore the merged product carries the advantages of better spatial coverage overall and increased number of observations, particularly for the transitional regions. The combination method developed has the potential to be applied to existing microwave satellites as well as to new missions. Accordingly, a long-term global soil moisture dataset can be developed and extended, enhancing basic understanding of the role of soil moisture in the water, energy and carbon cycles.

Coupling an individual-based boreal forest model with a permafrost land-surface model to forecast biomass development in boreal larch forests at the Siberian treeline

2020 ◽  
Author(s):  
Simone Stünzi ◽  
Stefan Kruse ◽  
Julia Boike ◽  
Ulrike Herzschuh ◽  
Moritz Langer
Keyword(s):  
Land Surface ◽  
Boreal Forests ◽  
Environmental Changes ◽  
Carbon Sink ◽  
Land Surface Model ◽  
Radiation Budget ◽  
Surface Model ◽  
Data Set ◽  
Forest Model ◽  
Larch Forests

<p>The fate of boreal forests under global warming and forced rapid environmental changes is still highly uncertain, in terms of remaining a carbon sink or becoming a future carbon source. Forest dynamics and resulting ecosystem services are strongly interlinked in the vast permafrost-covered regions of the Siberian treeline ecotone. Consequently, understanding the role of current and future active layer dynamics is crucial for the prediction of aboveground biomass and thus carbon stock developments.</p><p>We present a coupled model version combining CryoGrid, a sophisticated one-dimensional permafrost land surface model adapted for the use in forest ecosystems, with LAVESI, a detailed, individual-based and spatially explicit larch forest model. Subsequently, parameterizing against an extensive field data set of >100 forest inventories conducted along the treeline of larch-dominated boreal forests in Siberia (97-169° E), we run simulations covering the upcoming decades under contrasting climatic change scenarios.</p><p>The model setup can reproduce the energy transfer and thermal regime in permafrost ground as well as the radiation budget, nitrogen and photosynthetic profiles, canopy turbulence and leaf fluxes and predict the expected establishment, die-off and treeline movements of larch forests. Our results will show vegetation and permafrost dynamics, quantify the magnitudes of different feedback processes between permafrost, vegetation, and climate and reveal their impact on carbon stocks in Northern Siberia.</p>

The detection of irrigation through remote sensing soil moisture and a land surface model: a case study in Spain

2020 ◽  
Author(s):  
Jacopo Dari ◽  
Pere Quintana-Seguí ◽  
María José Escorihuela ◽  
Luca Brocca ◽  
Renato Morbidelli ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Land Surface ◽  
Hydrological Cycle ◽  
Land Surface Model ◽  
Surface Model ◽  
Data Sets ◽  
Data Set ◽  
Agricultural Areas ◽  
Irrigation Practices

<p>Irrigation practices introduce imbalances in the natural hydrological cycle at different spatial scales and put pressure on water resources, especially under climate changing and population increasing scenarios. Despite the implications of irrigation on food production and on the rational management of the available freshwater, detailed information about the areas where irrigation actually occurs is still lacking. For this reason, the comprehensive knowledge of the dynamics of the hydrological cycle over agricultural areas is often tricky.</p><p>The first aim of this study is to evaluate the capability of five remote sensing soil moisture data sets to detect the irrigation signal over an intensely irrigated area located within the Ebro river basin, in the North of Spain, during the biennium 2016-2017. As a second objective, a methodology to map the irrigated areas through the K-means clustering algorithm is proposed. The remotely sensed soil moisture products used in this study are: SMOS (Soil Moisture and Ocean Salinity) at 1 km, SMAP (Soil Moisture Active Passive) at 1 km and 9 km, Sentinel-1 at 1 km and ASCAT (Advanced SCATterometer) at 12.5 km. The 1 km versions of SMOS and SMAP are DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) downscaled versions of the corresponding coarser resolution products. An additional data set of soil moisture simulated by the SURFEX-ISBA (<em>Surface Externalisée - Interaction Sol Biosphère Atmosphère</em>) land surface model is used as a support for the performed analyses.</p><p>The capability of soil moisture products to detect irrigation has been investigated by exploiting indices representing the spatial and temporal dynamics of soil moisture. The L-band passive microwave downscaled products, especially SMAP at 1 km, result the best performing ones in detecting the irrigation signal over the pilot area; on the basis of these data sets, the K-means algorithm has been employed to classify three kinds of surfaces within the study area: the dryland, the forest or natural areas, and the actually irrigated areas. The resulting maps have been validated by exploiting maps of crops in Catalonia as ground truth data set. The percentage of irrigated areas well classified by the proposed method reaches the value of 78%; this result is obtained for the period May - September 2017. In addition, the method performs well in distinguishing the irrigated areas from rainfed agricultural areas, which are dry during summer, thus representing a useful tool to obtain explicit spatial information about where irrigation practices actually occur over agricultural areas equipped for this purpose.</p>

scholarly journals Impact of Using Near Real-Time Green Vegetation Fraction in Noah Land Surface Model of NOAA NCEP on Numerical Weather Predictions

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Li Fang ◽  
Xiwu Zhan ◽  
Christopher R. Hain ◽  
Jicheng Liu
Keyword(s):  
Real Time ◽  
Land Surface ◽  
Land Surface Model ◽  
Surface Model ◽  
Data Set ◽  
Numerical Weather ◽  
Vegetation Fraction ◽  
Green Vegetation ◽  
Noah Land Surface Model ◽  
The Impact

Green vegetation fraction (GVF) is one of the input parameters of the Noah land surface model (LSM) that is the land component of a number of operational numerical weather prediction (NWP) models at the National Centers for Environmental Prediction (NCEP) of NOAA. The Noah LSM in current NCEP operational NWP models has been using static multiyear averages of monthly GVF derived from satellite observations of NOAA Advanced Very High Resolution Radiometer (AVHRR) normalized difference vegetation index. The multiyear averages of GVF are evidently not the representative of actual conditions of the land surface vegetation cover. This study used a near-real-time (NRT) GVF data set generated from the 8-day composite of the leaf area index product from the Moderate Resolution Imaging Spectroradiometer (MODIS) to assess the impact of NRT GVF on off-line Noah LSM simulations and NWP forecast model. Simulations of the off-line Noah LSM in the Land Information System (LIS) and weather forecasts of the NASA-Unified Weather and Research Forecasting (NUWRF) were obtained using either the static multiyear average AVHRR GVF data set or the NRT MODIS GVF while meteorological forcing data and other settings were kept the same. The off-line simulations and WRF forecasts were then compared against in situ measurements or reanalysis products to assess the impact of using NRT GVF. Improvements of both soil moisture simulations as well as forecasts of 2-meter air temperature and humidity and precipitation from NUWRF were observed using the NRT GVF data products. The RMSE in SM estimates from the off-line Noah model is reduced by around 1.0% (1.41%) during the green-up phase and by 1.48% (2.24%) over the senescence phase for the surface (root zone) SM simulations. Around 82.3% validation sites (out of 1178 sites) showed positive impact on coupled WRF model with the insertion of NRT GVF.

scholarly journals Development of Integrated Land Simulator

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Tomoko Nitta ◽  
Takashi Arakawa ◽  
Misako Hatono ◽  
Akira Takeshima ◽  
Kei Yoshimura
Keyword(s):  
Land Surface ◽  
Land Surface Model ◽  
General Purpose ◽  
Climate Simulation ◽  
Surface Model ◽  
Drought Prediction ◽  
Multiple Data ◽  
Computational Speed ◽  
Macro Scale ◽  
Change Impact

Abstract Accurate simulations of land processes are crucial for many purposes, such as climate simulation, weather, flood, and drought prediction, and climate change impact assessment studies. In this paper, we present a new land simulator called the Integrated Land Simulator (ILS). The ILS consists of multiple models that represent processes related to land (hereafter, referred to as “land models”). They are coupled by a general-purpose coupler, Jcup, and executed using the Multiple Program Multiple Data approach. Currently, ILS includes a physical land surface model, the Minimal Advanced Treatments of Surface Interaction and Runoff model, and a hydrodynamic model, the Catchment-based Macro-scale Floodplain model, and the inclusion of additional land models is planned. We conducted several test simulations to evaluate the computational speed and scalability and the basic physical performance of the ILS. The results will become a benchmark for further development.

Calibration of a land surface model using multiple data sets

2005 ◽  
Vol 302 (1-4) ◽  
pp. 209-222 ◽  
Author(s):  
M.F. McCabe ◽  
S.W. Franks ◽  
J.D. Kalma
Keyword(s):  
Land Surface ◽  
Land Surface Model ◽  
Surface Model ◽  
Data Sets ◽  
Multiple Data ◽  
Multiple Data Sets

scholarly journals A leaf area index, LAI, data set acquired in Sahelian rangelands (Gourma, Mali) over the 2005–2017 period

2018 ◽  
Author(s):  
Eric Mougin ◽  
Mamadou Oumar Diawara ◽  
Nogmana Soumaguel ◽  
Ali Amadou Maiga ◽  
Valerie Demarez ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface ◽  
Model Development ◽  
Land Surface Model ◽  
Product Evaluation ◽  
Tree Canopy ◽  
Surface Model ◽  
Data Set ◽  
Area Index

Abstract. The leaf area index, LAI, of Sahelian rangelands and related variables such as the vegetation cover fraction, fCover, the fraction of absorbed photosynthetically active radiation, fAPAR, and the clumping index, λo, were measured in the Gourma region (Mali) during 13 successive rainy seasons, between 2005 and 2017. These variables, known as climate essential variables, were derived from the acquisition and the processing of hemispherical photographs taken along 1 km linear sampling transects, for 5 contrasted canopies and one millet field. The same sampling protocol was applied in a seasonally inundated Acacia open forest, along a 0.5 km transect, by taking photographs of the understorey and the tree canopy. These observations collected over more than a decade, in a remote and not very accessible region, provide a relevant and unique data set that can be used for a better understanding of the Sahelian vegetation response to the current rainfall changes. The collected data can also be used for satellite product evaluation and land surface model development and validation.

Sign in / Sign up

Export Citation Format

Share Document