Toward a Robust Canopy Hydrology Scheme with Precipitation Subgrid Variability

2007 ◽  
Vol 8 (3) ◽  
pp. 439-446 ◽  
Author(s):  
Dagang Wang ◽  
Guiling Wang
Keyword(s):  
Land Surface ◽  
Surface Characteristics ◽  
Hydrological Processes ◽  
Canopy Interception ◽  
Time Step ◽  
Covered Area ◽  
Surface Models ◽  
Physically Based ◽  
Interception Loss ◽  
The Impact

Abstract Representation of the canopy hydrological processes has been challenging in land surface modeling due to the subgrid heterogeneity in both precipitation and surface characteristics. The Shuttleworth dynamic–statistical method is widely used to represent the impact of the precipitation subgrid variability on canopy hydrological processes but shows unwanted sensitivity to temporal resolution when implemented into land surface models. This paper presents a canopy hydrology scheme that is robust at different temporal resolutions. This scheme is devised by applying two physically based treatments to the Shuttleworth scheme: 1) the canopy hydrological processes within the rain-covered area are treated separately from those within the nonrain area, and the scheme tracks the relative rain location between adjacent time steps; and 2) within the rain-covered area, the canopy interception is so determined as to sustain the potential evaporation from the wetted canopy or is equal to precipitation, whichever is less, to maintain somewhat wet canopy during any rainy time step. When applied to the Amazon region, the new scheme establishes interception loss ratios of 0.3 at a 10-min time step and 0.23 at a 2-h time step. Compared to interception loss ratios of 0.45 and 0.09 at the corresponding time steps established by the original Shuttleworth scheme, the new scheme is much more stable under different temporal resolutions.

scholarly journals SPATIOLTEMPORAL MODELING THE IMPACT OF SURFACE CHARACTERISTICS VARIATIONS ON LAND SURFACE TEMPERATURE VARIATIONS: A CASE STUDY OF SAMALGHAN VALLY

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 401-405
Author(s):  
M. K. Firozjaei ◽  
M. Makki ◽  
J. Lentschke ◽  
M. Kiavarz ◽  
S. K. Alavipanah
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Single Channel ◽  
Incident Angle ◽  
Surface Characteristics ◽  
Slope Aspect ◽  
Biophysical Parameters ◽  
The Impact

Abstract. Spatiotemporal mapping and modeling of Land Surface Temperature (LST) variations and characterization of parameters affecting these variations are of great importance in various environmental studies. The aim of this study is a spatiotemporal modeling the impact of surface characteristics variations on LST variations for the studied area in Samalghan Valley. For this purpose, a set of satellite imagery and meteorological data measured at the synoptic station during 1988–2018, were used. First, single-channel algorithm, Tasseled Cap Transformation (TCT) and Biophysical Composition Index (BCI) were employed to estimate LST and surface biophysical parameters including brightness, greenness and wetness and BCI. Also, spatial modeling was used to modeling of terrain parameters including slope, aspect and local incident angle based on DEM. Finally, the principal component analysis (PCA) and the Partial Least Squares Regression (PLSR) were used to modeling and investigate the impact of surface characteristics variations on LST variations. The results indicated that surface characteristics vary significantly for case study in spatial and temporal dimensions. The correlation coefficient between the PC1 of LST and PC1s of brightness, greenness, wetness, BCI, DEM, and solar local incident angle were 0.65, −0.67, −0.56, 0.72, −0.43 and 0.53, respectively. Furthermore, the coefficient coefficient and RMSE between the observed LST variation and modelled LST variation based on PC1s of brightness, greenness, wetness, BCI, DEM, and local incident angle were 0.83 and 0.14, respectively. The results of study indicated the LST variation is a function of s terrain and surface biophysical parameters variations.

Upscaling runoff and evapotranspiration fluxes in the Little Washita watershed using physically-based hillslope models

2020 ◽  
Author(s):  
Fanny Picourlat ◽  
Emmanuel Mouche ◽  
Claude Mugler
Keyword(s):  
Land Surface ◽  
Reference Model ◽  
Drainage Density ◽  
Equivalent Model ◽  
Distributed Model ◽  
Climatic Data ◽  
Good Prediction ◽  
Surface Models ◽  
Physically Based ◽  
Physically Based Models

<p>Several authors in the literature, such as Khan (2014) and Loritz (2017), have previously suggested that 3D catchment hydrology can be predicted from 2D hillslope simulations. Following this idea, we propose an upscaling methodology for runoff and evapotranspiration fluxes. The first step consists of a geomorphic analysis of the studied watershed. The average mean slope and hillslope length are then used to build a 2D equivalent-hillslope model. The validity of the methodology is tested by comparing the resulting water balance with a 3D physically-based distributed model. 2D fluxes of the equivalent hillslope are converted into 3D by using the drainage density. This upscaling methodology is applied to the Little Washita (LW) watershed (Oklahoma, USA). Both the 3D reference model and the 2D equivalent model are built with the physically-based distributed code HydroGeoSphere, which is forced by LW reanalysis climatic data. Two decades are simulated. Regarding the evapotranspiration, the upscaling methodology with only one equivalent hillslope gives a good prediction of 3D fluxes. However, a combination of several hillslopes is needed for simulating the 3D flow rate at the basin’s outlet. This work on the decrease of model dimensionality is a first step in the upscaling process from 3D physically-based models to 1D column models used in global Land Surface Models.</p>

scholarly journals Improving calibration and validation of cosmic-ray neutron sensors in the light of spatial sensitivity

2017 ◽  
Vol 21 (10) ◽  
pp. 5009-5030 ◽  
Author(s):  
Martin Schrön ◽  
Markus Köhli ◽  
Lena Scheiffele ◽  
Joost Iwema ◽  
Heye R. Bogena ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Cosmic Ray ◽  
Water Dynamics ◽  
Hydrological Processes ◽  
Spatial Sensitivity ◽  
Calibration And Validation ◽  
Physically Based ◽  
Point Data ◽  
Neutron Sensors

Abstract. In the last few years the method of cosmic-ray neutron sensing (CRNS) has gained popularity among hydrologists, physicists, and land-surface modelers. The sensor provides continuous soil moisture data, averaged over several hectares and tens of decimeters in depth. However, the signal still may contain unidentified features of hydrological processes, and many calibration datasets are often required in order to find reliable relations between neutron intensity and water dynamics. Recent insights into environmental neutrons accurately described the spatial sensitivity of the sensor and thus allowed one to quantify the contribution of individual sample locations to the CRNS signal. Consequently, data points of calibration and validation datasets are suggested to be averaged using a more physically based weighting approach. In this work, a revised sensitivity function is used to calculate weighted averages of point data. The function is different from the simple exponential convention by the extraordinary sensitivity to the first few meters around the probe, and by dependencies on air pressure, air humidity, soil moisture, and vegetation. The approach is extensively tested at six distinct monitoring sites: two sites with multiple calibration datasets and four sites with continuous time series datasets. In all cases, the revised averaging method improved the performance of the CRNS products. The revised approach further helped to reveal hidden hydrological processes which otherwise remained unexplained in the data or were lost in the process of overcalibration. The presented weighting approach increases the overall accuracy of CRNS products and will have an impact on all their applications in agriculture, hydrology, and modeling.

Assimilation of snow covered area information into hydrologic and land-surface models

2006 ◽  
Vol 29 (8) ◽  
pp. 1209-1221 ◽  
Author(s):  
Martyn P. Clark ◽  
Andrew G. Slater ◽  
Andrew P. Barrett ◽  
Lauren E. Hay ◽  
Gregory J. McCabe ◽  
...  
Keyword(s):  
Land Surface ◽  
Land Surface Models ◽  
Snow Covered Area ◽  
Covered Area ◽  
Surface Models

scholarly journals How to reconstruct aerosol-induced diffuse radiation scenario for simulating GPP in land surface models? An evaluation of reconstruction methods with ORCHIDEE_DFv1.0_DFforc

2021 ◽  
Vol 14 (4) ◽  
pp. 2029-2039
Author(s):  
Yuan Zhang ◽  
Olivier Boucher ◽  
Philippe Ciais ◽  
Laurent Li ◽  
Nicolas Bellouin
Keyword(s):  
Land Surface ◽  
Volcanic Eruptions ◽  
Diffuse Radiation ◽  
Surface Model ◽  
Diffuse Light ◽  
Land Surface Models ◽  
Aerosol Emission ◽  
Reconstruction Methods ◽  
Surface Models ◽  
The Impact

Abstract. The impact of diffuse radiation on photosynthesis has been widely documented in field measurements. This impact may have evolved over time during the last century due to changes in cloudiness, increased anthropogenic aerosol loads over polluted regions, and to sporadic volcanic eruptions curtaining the stratosphere with sulfate aerosols. The effects of those changes in diffuse light on large-scale photosynthesis (GPP) are difficult to quantify, and land surface models have been designed to simulate them. Investigating how anthropogenic aerosols have impacted GPP through diffuse light in those models requires carefully designed factorial simulations and a reconstruction of background diffuse light levels during the preindustrial period. Currently, it remains poorly understood how diffuse radiation reconstruction methods can affect GPP estimation and what fraction of GPP changes can be attributed to aerosols. In this study, we investigate different methods to reconstruct spatiotemporal distribution of the fraction of diffuse radiation (Fdf) under preindustrial aerosol emission conditions using a land surface model with a two-stream canopy light transmission scheme that resolves diffuse light effects on photosynthesis in a multi-layered canopy, ORCHIDEE_DF. We show that using a climatologically averaged monthly Fdf, as has been done by earlier studies, can bias the global GPP by up to 13 PgC yr−1 because this reconstruction method dampens the variability of Fdf and produces Fdf that is inconsistent with shortwave incoming surface radiation. In order to correctly simulate preindustrial GPP modulated by diffuse light, we thus recommend that the Fdf forcing field should be calculated consistently with synoptic, monthly, and inter-annual aerosol and cloud variability for preindustrial years. In the absence of aerosol and cloud data, alternative reconstructions need to retain the full variability in Fdf. Our results highlight the importance of keeping consistent Fdf and radiation for land surface models in future experimental designs that seek to investigate the impacts of diffuse radiation on GPP and other carbon fluxes.

Increased vulnerability of European ecosystems to two consecutive extreme summers

2021 ◽  
Author(s):  
Ana Bastos ◽  
René Orth ◽  
Markus Reichstein ◽  
Philippe Ciais ◽  
Nicolas Viovy ◽  
...  
Keyword(s):  
Central Europe ◽  
Land Surface ◽  
Vegetation Index ◽  
Legacy Effects ◽  
The Past ◽  
Surface Models ◽  
Summer Temperatures ◽  
The Impact ◽  
Observational Record

<p>Extreme summer temperatures in western and central Europe have become more frequent and heatwaves more prolonged over the past decades. The summer of 2018 was one of the driest and hottest in the observational record and led to losses in vegetation productivity in central Europe by up to 50%. Legacy effects from such extreme summers can affect ecosystem functioning over several years, as vegetation slowly recovers. In 2019 an extremely dry and hot summer was registered again in the region, imposing stress conditions at a time when ecosystems were still recovering from summer 2018.</p><p>Using Enhanced Vegetation Index (EVI) fields from MODIS, we evaluate how ecosystems in central Europe responded to the occurrence of two consecutive extreme summers. We find that only ca. 21% of the area negatively impacted by drought in summer 2018 fully recovered in 2019.</p><p>We find that the strongest EVI anomalies in 2018/19 diverge from the long-term relationships between EVI and climate, indicating an increase in ecosystem vulnerability to heat and drought events. Furthermore, 18% of the area showed a worsening of plant status during summer 2019 in spite of drought alleviation, which could be explained by interannual legacy effects from 2018, such as impaired growth and increased biotic disturbances.</p><p>Land-surface models do not simulate interannual legacy effects from summer 2018 and thereby underestimate the impact of drought in 2019 on ecosystems. The poor representation of drought-induced damage and mortality and lack of biotic disturbances in these models may result in an overestimation of the resilience and stability of temperate ecosystems in the future.</p>

Comparison of the ecohydrological models AnnAGNPS and ZIN-AgriTra for a small agricultural catchment 

2021 ◽  
Author(s):  
Johanna Schwenkel ◽  
Stephanie Zeunert ◽  
Huyen Le ◽  
Hannes Müller-Thomy ◽  
Matthias Schöniger ◽  
...  
Keyword(s):  
Mass Balance ◽  
Plant Protection ◽  
Richards Equation ◽  
Computational Time ◽  
Lower Saxony ◽  
Time Step ◽  
Small Stream ◽  
Physically Based ◽  
The Impact ◽  
Precipitation Events

<p>The ecohydrological models AnnAGNPS and ZIN-AgriTra are compared regarding their performance in a small watershed. Both models are presently applied for the transport simulation of plant protection products (PPP) from an agricultural area to a small stream to quantify the impact of reduction measures as part of a comprehensive study.</p><p>The spatial discretization of AnnAGNPS is based on hydrologic response units with homogeneous characteristics (land use, slope and soil type). For the continuous simulations daily time steps are used, only soil moisture is simulated using hourly time steps. The underlying equations are physically based, mostly simple calculation methods are used.<br>ZIN-AgriTra operates on grid cells, which allows a more accurate representation of the flow paths. The model is physically based, e. g. for the unsaturated soil zone the Richards equation is used. This requires detailed soil properties for its parameterization and leads to small computational time steps (minutes to hours) to fulfil the mass balance requirements. The detailed spatial and temporal scales, as well as the complex equations, result in a long computation time in comparison to AnnAGNPS.   <br>AnnAGNPS and ZIN-AgriTra are compared regarding their accuracy in the water balance and the mass balance simulation. For the mass balance different constituents as e. g. sediment, phosphorus and selected pesticides are simulated.</p><p>The study area is located in southern Lower Saxony, Germany. The catchment area has a size of 5 km<sup>2</sup>. The investigated stream (Lahbach) flows along agriculturally cultivated land. The relatively high slopes and the fine soil texture lead to a high fraction of generated discharge (as surface runoff, erosion and rapid interflow) from precipitation events. In the ongoing study the catchment was intensively monitored regarding meteorological and hydrological data. In addition, an event-based monitoring campaign was performed to quantify the reaction of the Lahbach during precipitation events, particularly the change in constituent concentrations. Due to the close cooperation with a local farmer, management measures are known very precisely.</p><p>The different temporal resolution of the input data and the time step of output parameters lead to differences in the agreement between measured and simulated time series among the two models. Overall, ZIN-AgriTra led to a more accurate reproduction of the rainfall-runoff events.</p>

scholarly journals The Plumbing of Land Surface Models: Benchmarking Model Performance

2015 ◽  
Vol 16 (3) ◽  
pp. 1425-1442 ◽  
Author(s):  
M. J. Best ◽  
G. Abramowitz ◽  
H. R. Johnson ◽  
A. J. Pitman ◽  
G. Balsamo ◽  
...  
Keyword(s):  
Heat Flux ◽  
Land Surface ◽  
Model Performance ◽  
Land Surface Model ◽  
Shortwave Radiation ◽  
Surface Model ◽  
Land Surface Models ◽  
Downward Shortwave Radiation ◽  
Surface Models ◽  
Physically Based

Abstract The Protocol for the Analysis of Land Surface Models (PALS) Land Surface Model Benchmarking Evaluation Project (PLUMBER) was designed to be a land surface model (LSM) benchmarking intercomparison. Unlike the traditional methods of LSM evaluation or comparison, benchmarking uses a fundamentally different approach in that it sets expectations of performance in a range of metrics a priori—before model simulations are performed. This can lead to very different conclusions about LSM performance. For this study, both simple physically based models and empirical relationships were used as the benchmarks. Simulations were performed with 13 LSMs using atmospheric forcing for 20 sites, and then model performance relative to these benchmarks was examined. Results show that even for commonly used statistical metrics, the LSMs’ performance varies considerably when compared to the different benchmarks. All models outperform the simple physically based benchmarks, but for sensible heat flux the LSMs are themselves outperformed by an out-of-sample linear regression against downward shortwave radiation. While moisture information is clearly central to latent heat flux prediction, the LSMs are still outperformed by a three-variable nonlinear regression that uses instantaneous atmospheric humidity and temperature in addition to downward shortwave radiation. These results highlight the limitations of the prevailing paradigm of LSM evaluation that simply compares an LSM to observations and to other LSMs without a mechanism to objectively quantify the expectations of performance. The authors conclude that their results challenge the conceptual view of energy partitioning at the land surface.

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