scholarly journals Land surface modeling over the Dry Chaco: the impact of model structures, and soil, vegetation and land cover parameters

2021 ◽  
Vol 25 (7) ◽  
pp. 4099-4125 ◽  
Author(s):  
Michiel Maertens ◽  
Gabriëlle J. M. De Lannoy ◽  
Sebastian Apers ◽  
Sujay V. Kumar ◽  
Sarith P. P. Mahanama
Keyword(s):  
Soil Moisture ◽  
Land Cover ◽  
Land Surface ◽  
Water Budget ◽  
Large Scale ◽  
Vegetation Indices ◽  
Soil Parameters ◽  
Area Index ◽  
Vegetation Fraction ◽  
The Impact

Abstract. In this study, we tested the impact of a revised set of soil, vegetation and land cover parameters on the performance of three different state-of-the-art land surface models (LSMs) within the NASA Land Information System (LIS). The impact of this revision was tested over the South American Dry Chaco, an ecoregion characterized by deforestation and forest degradation since the 1980s. Most large-scale LSMs may lack the ability to correctly represent the ongoing deforestation processes in this region, because most LSMs use climatological vegetation indices and static land cover information. The default LIS parameters were revised with (i) improved soil parameters, (ii) satellite-based interannually varying vegetation indices (leaf area index and green vegetation fraction) instead of climatological vegetation indices, and (iii) yearly land cover information instead of static land cover. A relative comparison in terms of water budget components and “efficiency space” for various baseline and revised experiments showed that large regional and long-term differences in the simulated water budget partitioning relate to different LSM structures, whereas smaller local differences resulted from updated soil, vegetation and land cover parameters. Furthermore, the different LSM structures redistributed water differently in response to these parameter updates. A time-series comparison of the simulations to independent satellite-based estimates of evapotranspiration and brightness temperature (Tb) showed that no LSM setup significantly outperformed another for the entire region and that not all LSM simulations improved with updated parameter values. However, the revised soil parameters generally reduced the bias between simulated surface soil moisture and pixel-scale in situ observations and the bias between simulated Tb and regional Soil Moisture Ocean Salinity (SMOS) observations. Our results suggest that the different hydrological responses of various LSMs to vegetation changes may need further attention to gain benefits from vegetation data assimilation.

scholarly journals Simulating the Dry Chaco Hydrology under Deforestation Pressure

2020 ◽  
Author(s):  
Michiel Maertens ◽  
Gabriëlle J. M. De Lannoy ◽  
Sebastian Apers ◽  
Sujay V. Kumar ◽  
Sarith P. P. Mahanama
Keyword(s):  
Soil Moisture ◽  
Land Cover ◽  
Land Surface ◽  
Land Use Changes ◽  
Forest Degradation ◽  
Soil Parameters ◽  
Simulated Surface ◽  
Vegetation Parameters ◽  
Land Information System ◽  
The Impact

Abstract. Various regions in the world experience land cover and land use changes. One such a region is the Dry Chaco ecoregion in South America, characterized by deforestation and forest degradation since the 1980s. In this study, we simulated the water balance over the Dry Chaco and assessed the impact of land cover changes thereon, using three different state-of-the-art land surface models (LSMs) within the NASA Land Information System (LIS) with updated parameters. The default LIS parameters were revised with (i) improved soil parameters, (ii) satellite-based dynamic vegetation parameters instead of default climatological vegetation parameters, and (iii) yearly land cover information instead of static land cover. A relative comparison in terms of water budget components and ‘efficiency space’ for various baseline and revised experiments showed that large regional and long-term differences relate to different LSM structures, whereas smaller local differences resulted from updated soil, vegetation and land cover parameters. Furthermore, different LSM structures redistributed water differently in response to these parameter updates. A time series comparison of the simulations to independent satellite-based estimates of evapotranspiration and brightness temperature showed that no LSM setup significantly outperformed another for the entire region, and that not all LSM simulations improved with updated parameter values. However, the revised soil parameters generally reduced the simulated surface soil moisture bias relative to pixel-scale in situ observations, and the simulated Tb bias relative to regional Soil Moisture Ocean Salinity (SMOS) observations.

Revealing the impact of deforestation on hydrology using remote sensing and land surface modeling

2020 ◽  
Author(s):  
Michiel Maertens ◽  
Gabrielle De Lannoy ◽  
Sebastian Apers ◽  
Sujay Kumar
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Land Cover ◽  
Land Surface ◽  
Water Budget ◽  
Land Surface Modeling ◽  
Operational Level ◽  
Vegetation Parameters ◽  
The Impact ◽  
Level 2

<p>This study aims at better understanding the impact of deforestation on the local hydrology over the Argentinian Chaco, using land surface modeling and remote sensing data. The Chaco is an ecoregion characterized by unprecedented deforestation since the 1980s, mainly for cattle ranging and soybean production. More specifically, default climatological vegetation parameters (LAI, GVF) and static land cover in state-of-the-art land surface models (LSM), grouped within the NASA Land Information System (LIS), are updated using satellite-based dynamic vegetation parameters and yearly land use maps to feed the models with deforestation.</p><p>The presentation will show a spatio-temporal analysis of long-term water budget simulations using a range of LSMs (Noah, CLM, CLSM) in which dynamically updated vegetation and land cover parameters are included. Our simulations indicate that different LSMs result in a different partitioning of the total water budget, but all indicate an increase in soil moisture and percolation over the deforested areas.  Model output is evaluated using in situ soil moisture data, and various soil moisture retrieval products from SMOS (operational Level 2 and SMOS-IC) and SMAP (operational Level 2) and evapotranspiration data from GLEAM.</p>

scholarly journals Optimizing a backscatter forward operator using Sentinel-1 data over irrigated land

2021 ◽  
Vol 25 (12) ◽  
pp. 6283-6307
Author(s):  
Sara Modanesi ◽  
Christian Massari ◽  
Alexander Gruber ◽  
Hans Lievens ◽  
Angelica Tarpanelli ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Large Scale ◽  
Bayesian Optimization ◽  
Irrigation Scheme ◽  
Area Index ◽  
Observation Operator ◽  
Future Data ◽  
The Impact

Abstract. Worldwide, the amount of water used for agricultural purposes is rising, and the quantification of irrigation is becoming a crucial topic. Because of the limited availability of in situ observations, an increasing number of studies is focusing on the synergistic use of models and satellite data to detect and quantify irrigation. The parameterization of irrigation in large-scale land surface models (LSMs) is improving, but it is still hampered by the lack of information about dynamic crop rotations, or the extent of irrigated areas, and the mostly unknown timing and amount of irrigation. On the other hand, remote sensing observations offer an opportunity to fill this gap as they are directly affected by, and hence potentially able to detect, irrigation. Therefore, combining LSMs and satellite information through data assimilation can offer the optimal way to quantify the water used for irrigation. This work represents the first and necessary step towards building a reliable LSM data assimilation system which, in future analysis, will investigate the potential of high-resolution radar backscatter observations from Sentinel-1 to improve irrigation quantification. Specifically, the aim of this study is to couple the Noah-MP LSM running within the NASA Land Information System (LIS), with a backscatter observation operator for simulating unbiased backscatter predictions over irrigated lands. In this context, we first tested how well modelled surface soil moisture (SSM) and vegetation estimates, with or without irrigation simulation, are able to capture the signal of aggregated 1 km Sentinel-1 backscatter observations over the Po Valley, an important agricultural area in northern Italy. Next, Sentinel-1 backscatter observations, together with simulated SSM and leaf area index (LAI), were used to optimize a Water Cloud Model (WCM), which will represent the observation operator in future data assimilation experiments. The WCM was calibrated with and without an irrigation scheme in Noah-MP and considering two different cost functions. Results demonstrate that using an irrigation scheme provides a better calibration of the WCM, even if the simulated irrigation estimates are inaccurate. The Bayesian optimization is shown to result in the best unbiased calibrated system, with minimal chances of having error cross-correlations between the model and observations. Our time series analysis further confirms that Sentinel-1 is able to track the impact of human activities on the water cycle, highlighting its potential to improve irrigation, soil moisture, and vegetation estimates via future data assimilation.

scholarly journals Optimizing a backscatter forward operator using Sentinel-1 data over irrigated land

2021 ◽  
Author(s):  
Sara Modanesi ◽  
Christian Massari ◽  
Alexander Gruber ◽  
Hans Lievens ◽  
Angelica Tarpanelli ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Large Scale ◽  
Cloud Model ◽  
Bayesian Optimization ◽  
Irrigation Scheme ◽  
Area Index ◽  
Observation Operator ◽  
The Impact

Abstract. Worldwide, the amount of water used for agricultural purposes is rising and the quantification of irrigation is becoming a crucial topic. Because of the the limited availability of in situ observations, an increasing number of studies is focusing on the synergistic use of models and satellite data to detect and quantify irrigation. The parameterization of irrigation in large scale Land Surface Models (LSM) is improving, but it is still hampered by the lack of information about dynamic crop rotations or the extent of irrigated areas, and the mostly unknown timing and amount of irrigation. On the other hand, remote sensing observations offer an opportunity to fill this gap as they are directly affected by, and hence potentially able to detect, irrigation. Therefore, combining LSMs and satellite information through data assimilation can offer the optimal way to quantify the water used for irrigation. The aim of this study is to optimize a land modeling system, consisting of the Noah-MP LSM, coupled with a backscatter observation operator, over irrigated land in order to simulate backscatter predictions. This is a first step towards building a reliable data assimilation system to ingest level-1 Sentinel-1 observations. In this context, we tested how well modeled soil moisture and vegetation estimates from the Noah-MP LSM running within the NASA Land Information System (LIS), with or without irrigation simulation, are able to capture the signal of high-resolution Sentinel-1 backscatter observations over the Po river Valley, an important agricultural area in Northern Italy. Next, aggregated 1-km Sentinel-1 backscatter observations were used to calibrate a Water Cloud Model (WCM) as observation operator using simulated soil moisture and Leaf Area Index estimates. The WCM was calibrated with and without activating an irrigation scheme in Noah-MP and considering two different cost functions. Results demonstrate that activating an irrigation scheme provides the optimal calibration of the WCM, even if the irrigation estimates are inaccurate. The Bayesian optimization is shown to result in the best unbiased calibrated system, with minimal chance of having error cross correlations between the model and observations. Our time series analysis further confirms that Sentinel-1 is able to track the impact of human activities on the water cycle, highlighting its potential to improve irrigation, soil moisture and vegetation estimates via future data assimilation.

Physical factors of influence in the interactions between rainfall, evapotranspiration and soil moisture driving the spatio-temporal evolution of drought patterns in the Ebro Basin (NE Spain).

2021 ◽  
Author(s):  
Jaime Gaona ◽  
Pere Quintana-Seguí ◽  
Maria José Escorihuela
Keyword(s):  
Soil Moisture ◽  
Land Cover ◽  
Iberian Peninsula ◽  
Land Surface ◽  
Temporal Analysis ◽  
Drought Indices ◽  
Surface Model ◽  
Physical Factors ◽  
Spatio Temporal ◽  
The Impact

<p>Droughts in the Iberian Peninsula are a natural hazard of great relevance due to their recurrence, severity and impact on multiple environmental and socioeconomic aspects. The Ebro Basin, located in the NE of the Iberian Peninsula, is particularly vulnerable to drought with consequences on agriculture, urban water supply and hydropower. This study, performed within the Project HUMID (CGL2017-85687-R), aims at evaluating the influence of the climatic, land cover and soil characteristics on the interactions between rainfall, evapotranspiration and soil moisture anomalies which define the spatio-temporal drought patterns in the basin.</p><p>The onset, propagation and mitigation of droughts in the Iberian Peninsula is driven by anomalies of rainfall, evapotranspiration and soil moisture, which are related by feedback processes. To test the relative importance of such anomalies, we evaluate the contribution of climatic, land-cover and geologic heterogeneity on the definition of the spatio-temporal patterns of drought. We use the Köppen-Geiger climatic classification to assess how the contrasting climatic types within the basin determine differences on drought behavior. Land-cover types that govern the partition between evaporation and transpiration are also of great interest to discern the influence of vegetation and crop types on the anomalies of evapotranspiration across the distinct regions of the basin (e.g. forested mountains vs. crop-dominated areas). The third physical characteristic whose effect on drought we investigate is the impact of soil properties on soil moisture anomalies.</p><p>The maps and time series used for the spatio-temporal analysis are based on drought indices calculated with high-resolution datasets from remote sensing (MOD16A2ET and SMOS1km) and the land-surface model SURFEX-ISBA. The Standardized Precipitation Index (SPI), the EvapoTranspiration Deficit Index (ETDI) and the Soil Moisture Deficit Index (SMDI) are the three indices chosen to characterize the anomalies of the corresponding rainfall (atmospheric), evapotranspiration (atmosphere-land interface) and soil moisture (land) anomalies (components of the water balance). The comparison of the correlations of the indices (with different time lags) between contrasting regions offers insights about the impact of climate, land-cover and soil properties in the dominance, the timing of the response and memory aspects of the interactions. The high spatial and temporal resolution of remote sensing and land-surface model data allows adopting time and spatial scales suitable to investigate the influence of these physical factors with detail beyond comparison with ground-based datasets.</p><p>The spatial and temporal analysis prove useful to investigate the physical factors of influence on the anomalies between rainfall, evapotranspiration and soil moisture. This approach facilitates the physical interpretation of the anomalies of drought indices aiming to improve the characterization of drought in heterogeneous semi-arid areas like the Ebro River Basin.</p>

The influence of the land surface on hydrometeorology and ecology: new advances from modeling and satellite remote sensing

2011 ◽  
Vol 42 (2-3) ◽  
pp. 95-112 ◽  
Author(s):  
Venkat Lakshmi ◽  
Seungbum Hong ◽  
Eric E. Small ◽  
Fei Chen
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Wrf Model ◽  
Land Surface Model ◽  
Surface Processes ◽  
Surface Model ◽  
Land Surface Processes ◽  
Area Index ◽  
Vegetation Fraction ◽  
Moisture Variation

The importance of land surface processes has long been recognized in hydrometeorology and ecology for they play a key role in climate and weather modeling. However, their quantification has been challenging due to the complex nature of the land surface amongst other reasons. One of the difficult parts in the quantification is the effect of vegetation that are related to land surface processes such as soil moisture variation and to atmospheric conditions such as radiation. This study addresses various relational investigations among vegetation properties such as Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), surface temperature (TSK), and vegetation water content (VegWC) derived from satellite sensors such as Moderate Resolution Imaging Spectroradiometer (MODIS) and EOS Advanced Microwave Scanning Radiometer (AMSR-E). The study provides general information about a physiological behavior of vegetation for various environmental conditions. Second, using a coupled mesoscale/land surface model, we examine the effects of vegetation and its relationship with soil moisture on the simulated land–atmospheric interactions through the model sensitivity tests. The Weather Research and Forecasting (WRF) model was selected for this study, and the Noah land surface model (Noah LSM) implemented in the WRF model was used for the model coupled system. This coupled model was tested through two parameterization methods for vegetation fraction using MODIS data and through model initialization of soil moisture from High Resolution Land Data Assimilation System (HRLDAS). Finally, this study evaluates the model improvements for each simulation method.

Land–Atmosphere Interaction in Tropical Africa

2020 ◽  
Author(s):  
Cathy Hohenegger
Keyword(s):  
Heat Flux ◽  
Soil Moisture ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Surface State ◽  
Land Surface ◽  
Large Scale ◽  
Moisture Distribution ◽  
Sahel Region ◽  
The Impact

Even though many features of the vegetation and of the soil moisture distribution over Africa reflect its climatic zones, the land surface has the potential to feed back on the atmosphere and on the climate of Africa. The land surface and the atmosphere communicate via the surface energy budget. A particularly important control of the land surface, besides its control on albedo, is on the partitioning between sensible and latent heat flux. In a soil moisture-limited regime, for instance, an increase in soil moisture leads to an increase in latent heat flux at the expanse of the sensible heat flux. The result is a cooling and a moistening of the planetary boundary layer. On the one hand, this thermodynamically affects the atmosphere by altering the stability and the moisture content of the vertical column. Depending on the initial atmospheric profile, convection may be enhanced or suppressed. On the other hand, a confined perturbation of the surface state also has a dynamical imprint on the atmospheric flow by generating horizontal gradients in temperature and pressure. Such gradients spin up shallow circulations that affect the development of convection. Whereas the importance of such circulations for the triggering of convection over the Sahel region is well accepted and well understood, the effect of such circulations on precipitation amounts as well as on mature convective systems remains unclear. Likewise, the magnitude of the impact of large-scale perturbations of the land surface state on the large-scale circulation of the atmosphere, such as the West African monsoon, has long been debated. One key issue is that such interactions have been mainly investigated in general circulation models where the key involved processes have to rely on uncertain parameterizations, making a definite assessment difficult.

scholarly journals V2Karst V1.0: A parsimonious large-scale integrated vegetation-recharge model to simulate the impact of climate and land cover change in karst regions

2018 ◽  
Author(s):  
Fanny Sarrazin ◽  
Andreas Hartmann ◽  
Francesca Pianosi ◽  
Thorsten Wagener
Keyword(s):  
Land Cover ◽  
Groundwater Recharge ◽  
Land Cover Change ◽  
Large Scale ◽  
Synthetic Data ◽  
Temporal Distribution ◽  
Soil Parameters ◽  
Large Scale Assessment ◽  
Karst Areas ◽  
The Impact

Abstract. Karst aquifers are an important source of drinking water in many regions of the world. Karst areas are highly permeable and produce large amounts of groundwater recharge, while surface runoff is typically negligible. As a result, recharge in these systems may have a different sensitivity to climate and land cover changes compared to other less permeable systems. However, little effort has been directed toward assessing the impact of climate and land cover change in karst areas at large-scales. In this study, we address this gap by (1) introducing the first large-scale hydrological model including an explicit representation of both karst and land cover properties, and by (2) analysing the model's recharge production behaviour. To achieve these points, we first improve the evapotranspiration estimation of a previous large-scale karst recharge model (VarKarst). The new model (V2Karst V1.0) includes a parsimonious representation of relevant ET processes for climate and land cover change impact studies. We demonstrate the plausibility of V2Karst simulations at carbonate rock FLUXNET sites using soft rules and global sensitivity analysis. Then, we use virtual experiments with synthetic data to assess the sensitivity of simulated recharge to precipitation characteristics and land cover. Results reveal how both vegetation and soil parameters control the model behaviour, and they suggest that simulated recharge is sensitive to both precipitation (overall amount and temporal distribution) and land cover. Large-scale assessment of future karst groundwater recharge should therefore consider the combined impact of changes in land cover and precipitation properties, if it is to produce realistic projections of future change impacts.

scholarly journals Improving arable land heterogeneity information in available land cover products for land surface modelling using MERIS NDVI data

2010 ◽  
Vol 14 (10) ◽  
pp. 2073-2084 ◽  
Author(s):  
F. Zabel ◽  
T. B. Hank ◽  
W. Mauser
Keyword(s):  
Land Cover ◽  
Central Europe ◽  
Land Surface ◽  
Vegetation Index ◽  
Arable Land ◽  
Area Index ◽  
Temporal Behaviour ◽  
Danube Catchment ◽  
Crop Types ◽  
The Impact

Abstract. Regionalization of physical land surface models requires the supply of detailed land cover information. Numerous global and regional land cover maps already exist but generally, they do not resolve arable land into different crop types. However, arable land comprises a huge variety of different crops with characteristic phenological behaviour, demonstrated in this paper with Leaf Area Index (LAI) measurements exemplarily for maize and winter wheat. This affects the mass and energy fluxes on the land surface and thus its hydrology. The objective of this study is the generation of a land cover map for central Europe based on CORINE Land Cover (CLC) 2000, merged with CORINE Switzerland, but distinguishing different crop types. Accordingly, an approach was developed, subdividing the land cover class arable land into the regionally most relevant subclasses for central Europe using multiseasonal MERIS Normalized Difference Vegetation Index (NDVI) data. The satellite data were used for the separation of spring and summer crops due to their different phenological behaviour. Subsequently, the generated phenological classes were subdivided following statistical data from EUROSTAT. This database was analysed concerning the acreage of different crop types. The impact of the improved land use/cover map on evapotranspiration was modelled exemplarily for the Upper Danube catchment with the hydrological model PROMET. Simulations based on the newly developed land cover approach showed a more detailed evapotranspiration pattern compared to model results using the traditional CLC map, which is ignorant of most arable subdivisions. Due to the improved temporal behaviour and spatial allocation of evapotranspiration processes in the new land cover approach, the simulated water balance more closely matches the measured gauge.

scholarly journals Effects of multi-temporal high-resolution remote sensing products on simulated hydrometeorological variables in a cultivated area (southwestern France)

2017 ◽  
Author(s):  
Jordi Etchanchu ◽  
Vincent Rivalland ◽  
Simon Gascoin ◽  
Jérôme Cros ◽  
Aurore Brut ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Land Cover ◽  
Land Surface ◽  
Agricultural Landscapes ◽  
Annual Precipitation ◽  
Surface Model ◽  
Area Index ◽  
The Impact ◽  
Sentinel 2

Abstract. Agricultural landscapes often include a patchwork of crop fields whose seasonal evolution is dependent on specific crop rotation patterns and phenologies. This temporal and spatial heterogeneity affects surface hydrometeorological processes as simulated by land surface and distributed hydrological models. Sentinel-2 mission satellite remote sensing products allow for the monitoring of land cover and vegetation dynamics at unprecedented spatial resolutions and revisit frequencies (20 m and 5 days, respectively) that are fully compatible with such heterogeneous agricultural landscapes. Here, we evaluate the impact of Sentinel-2-like remote sensing data on the simulation of surface water and energy flux via the ISBA-SURFEX land surface model. The study area is a 24 km by 24 km agricultural zone in southwestern France. An initial reference simulation was conducted from 2006–2010 using the ECOCLIMAP-II database. This global numerical land ecosystem database was created at a 1 km resolution and includes an ecosystem classification with a consistent set of land surface parameters required for the model, such as the Leaf Area Index (LAI) and albedo measures. The LAI of ECOCLIMAP is climatologic and derived from a 2000–2005 analysis of MODIS satellite products. This low resolution induces that several vegetation covers can be mixed in a model cell. The climatic construction of LAI dynamics also suggests that there is no interannual variability in the vegetation cycle. A second simulation was performed by forcing the same model with annual land cover maps and monthly LAI values derived from a series of 105 8 m-resolution Formosat-2 images for the same period. Both simulations were conducted at the parcel scale, i.e., a computation unit covers an area of connected pixels of the same vegetation type (a crop field, forest patch, etc.). To evaluate our simulations, we used in situ measurements of evapotranspiration and latent and sensible heat flux from two eddy covariance stations in the study area. Our results show that the use of Formosat-2 high-resolution products significantly improves simulated evapotranspiration results with respect to ECOCLIMAP-II, especially when a surface is covered with summer crops (the correlation coefficient with monthly measurements is increased by roughly 0.3 and the root mean square error is decreased by roughly 31 %). This finding is attributable to a better description of LAI evolution processes reflected by Formosat-2 data, which further modify soil water content and drainage levels of deep soil reservoirs. Effects on annual drainage patterns remain small but significant, i.e., an increase roughly equivalent to 4 % of annual precipitation levels from Formosat-2 data in comparison to reference values. In smaller proportions, runoff is also increased by roughly 1 % of annual precipitation when using Formosat-2 data. This study illustrates the potential for the Sentinel-2 mission to better represent effects of crop management on water budgeting for large, anthropized river basins.

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