scholarly journals Effect of Using Multi-Year LULC and Monthly LAI Inputs on the Calibration of a Distributed Hydrologic Model

2021 ◽  
Author(s):  
Ibrahim Olayode Busari ◽  
Mehmet Cüneyd Demirel ◽  
Alice Newton
Keyword(s):  
Land Cover ◽  
Potential Evapotranspiration ◽  
Hydrologic Model ◽  
Parameter Uncertainties ◽  
Area Index ◽  
Distributed Hydrologic Model ◽  
Land Cover Data ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

This study explores the use of satellite-based LULC (Land Use / Land Cover) data while simultaneously correcting potential evapotranspiration (PET) input with Leaf Area Index (LAI) to increase the performance of a physically distributed hydrologic model. The mesoscale hydrologic model (mHM) was selected for this purpose due to its unique features. Since LAI input informs the model about vegetation dynamics, we incorporated the LAI based PET correction option together with multi-year LULC data. The Globcover land cover data was selected for the single land cover cases, and hybrid of CORINE (coordination of information on the environment) and MODIS (Moderate Resolution Imaging Spectroradiometer) land cover datasets were chosen for the cases with multiple land cover datasets. These two datasets complement each other since MODIS has no separate forest class but more frequent (yearly) observations than CORINE. Calibration period spans from 1990 to 2006 and corresponding NSE (Nash-Sutcliffe Efficiency) values varies between 0.23 and 0.42, while the validation period spans from 2007 to 2010 and corresponding NSE values are between 0.13 and 0.39. The results revealed that the best performance is obtained when multiple land cover datasets are provided to the model and LAI data is used to correct PET, instead of default aspect-based PET correction in mHM. This study suggests that to minimize errors due to parameter uncertainties in physically distributed hydrologic models, adequate information can be supplied to the model with care taken to avoid over-parameterizing the model.

scholarly journals Effect of Using Multi-Year Land Use Land Cover and Monthly LAI Inputs on the Calibration of a Distributed Hydrologic Model

Water ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1538
Author(s):  
Ibrahim Busari ◽  
Mehmet Demirel ◽  
Alice Newton
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Potential Evapotranspiration ◽  
Hydrologic Model ◽  
Land Use Land Cover ◽  
Parameter Uncertainties ◽  
Area Index ◽  
Distributed Hydrologic Model ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

Effective management of water resources entails the understanding of spatiotemporal changes in hydrologic fluxes with variation in land use, especially with a growing trend of urbanization, agricultural lands and non-stationarity of climate. This study explores the use of satellite-based Land Use Land Cover (LULC) data while simultaneously correcting potential evapotranspiration (PET) input with Leaf Area Index (LAI) to increase the performance of a physically distributed hydrologic model. The mesoscale hydrologic model (mHM) was selected for this purpose due to its unique features. Since LAI input informs the model about vegetation dynamics, we incorporated the LAI based PET correction option together with multi-year LULC data. The Globcover land cover data was selected for the single land cover cases, and hybrid of CORINE (coordination of information on the environment) and MODIS (Moderate Resolution Imaging Spectroradiometer) land cover datasets were chosen for the cases with multiple land cover datasets. These two datasets complement each other since MODIS has no separate forest class but more frequent (yearly) observations than CORINE. Calibration period spans from 1990 to 2006 and corresponding NSE (Nash-Sutcliffe Efficiency) values varies between 0.23 and 0.42, while the validation period spans from 2007 to 2010 and corresponding NSE values are between 0.13 and 0.39. The results revealed that the best performance is obtained when multiple land cover datasets are provided to the model and LAI data is used to correct PET, instead of default aspect-based PET correction in mHM. This study suggests that to minimize errors due to parameter uncertainties in physically distributed hydrologic models, adequate information can be supplied to the model with care taken to avoid over-parameterizing the model.

Distributed Hydrologic Modeling Using Satellite-Derived Potential Evapotranspiration

2015 ◽  
Vol 16 (1) ◽  
pp. 129-146 ◽  
Author(s):  
Ryan R. Spies ◽  
Kristie J. Franz ◽  
Terri S. Hogue ◽  
Angela L. Bowman
Keyword(s):  
Potential Evapotranspiration ◽  
Hydrologic Model ◽  
Laboratory Research ◽  
Model Parameters ◽  
Taylor Formula ◽  
Verification Period ◽  
Moderate Resolution ◽  
Discharge Simulation ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract Satellite-derived potential evapotranspiration (PET) estimates computed from Moderate Resolution Imaging Spectroradiometer (MODIS) observations and the Priestley–Taylor formula (M-PET) are evaluated as input to the Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM). The HL-RDHM is run at a 4-km spatial and 6-h temporal resolution for 13 watersheds in the upper Mississippi and Red River basins for 2003–10. Simulated discharge using inputs of daily M-PET is evaluated for all watersheds, and simulated evapotranspiration (ET) is evaluated at two watersheds using nearby latent heat flux observations. M-PET–derived model simulations are compared to output using the long-term average PET values (default-PET) provided as part of the HL-RDHM application. In addition, uncalibrated and calibrated simulations are evaluated for both PET data sources. Calibrating select model parameters is found to substantially improve simulated discharge for both datasets. Overall average percent bias (PBias) and Nash–Sutcliffe efficiency (NSE) values for simulated discharge are better from the default-PET than the M-PET for the calibrated models during the verification period, indicating that the time-varying M-PET input did not improve the discharge simulation in the HL-RDHM. M-PET tends to produce higher NSE values than the default-PET for the Wisconsin and Minnesota basins, but lower NSE values for the Iowa basins. M-PET–simulated ET matches the range and variability of observed ET better than the default-PET at two sites studied and may provide potential model improvements in that regard.

scholarly journals An Effective Method for Generating Spatiotemporally Continuous 30 m Vegetation Products

2021 ◽  
Vol 13 (4) ◽  
pp. 719
Author(s):  
Xiuxia Li ◽  
Shunlin Liang ◽  
Huaan Jin
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Continuous Data ◽  
Landsat Data ◽  
Area Index ◽  
Free Data ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Resolution Data

Leaf area index (LAI) and normalized difference vegetation index (NDVI) are key parameters for various applications. However, due to sensor tradeoff and cloud contaminations, these data are often temporally intermittent and spatially discontinuous. To address the discontinuities, this study proposed a method based on spectral matching of 30 m discontinuous values from Landsat data and 500 m temporally continuous values from Moderate-resolution Imaging Spectroradiometer (MODIS) data. Experiments have proven that the proposed method can effectively yield spatiotemporally continuous vegetation products at 30 m spatial resolution. The results for three different study areas with NDVI and LAI showed that the method performs well in restoring the time series, fills in the missing data, and reasonably predicts the images. Remarkably, the proposed method could address the issue when no cloud-free data pairs are available close to the prediction date, because of the temporal information “borrowed” from coarser resolution data. Hence, the proposed method can make better use of partially obscured images. The reconstructed spatiotemporally continuous data have great potential for monitoring vegetation, agriculture, and environmental dynamics.

scholarly journals MODIS 3 km aerosol product: applications over land in an urban/suburban region

2013 ◽  
Vol 6 (1) ◽  
pp. 1683-1716 ◽  
Author(s):  
L. A. Munchak ◽  
R. C. Levy ◽  
S. Mattoo ◽  
L. A. Remer ◽  
B. N. Holben ◽  
...  
Keyword(s):  
Urban Areas ◽  
High Spectral Resolution ◽  
New Information ◽  
Land Cover Data ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Langley Research Center ◽  
High Spectral Resolution Lidar ◽  
Error Envelope

Abstract. MODerate resolution Imaging Spectroradiometer (MODIS) instruments aboard the Terra and Aqua satellites have provided a rich dataset of aerosol information at a 10 km spatial scale. Although originally intended for climate applications, the air quality community quickly became interested in using the MODIS aerosol data. However, 10 km resolution is not sufficient to resolve local scale aerosol features. With this in mind, MODIS Collection 6 is including a global aerosol product with a 3 km resolution. Here, we evaluate the 3 km product over the Baltimore/Washington D.C., USA, corridor during the summer of 2011, by comparing with spatially dense data collected as part of the DISCOVER-AQ campaign; these data were measured by the NASA Langley Research Center airborne High Spectral Resolution Lidar (HSRL) and a network of 44 sun photometers (SP) spaced approximately 10 km apart. The HSRL instrument shows that AOD can vary by up to 0.2 within a single 10 km MODIS pixel, meaning that higher resolution satellite retrievals may help to characterize aerosol spatial distributions in this region. Different techniques for validating a high-resolution aerosol product against SP measurements are considered. Although the 10 km product is more statistically reliable than the 3 km product, the 3 km product still performs acceptably, with more than two-thirds of MODIS/SP collocations falling within the expected error envelope with high correlation (R > 0.90). The 3 km product can better resolve aerosol gradients and retrieve closer to clouds and shorelines than the 10 km product, but tends to show more significant noise especially in urban areas. This urban degradation is quantified using ancillary land cover data. Overall, we show that the MODIS 3 km product adds new information to the existing set of satellite derived aerosol products and validates well over the region, but due to noise and problems in urban areas, should be treated with some degree of caution.

scholarly journals Global White-Sky and Black-Sky FAPAR Retrieval Using the Energy Balance Residual Method: Algorithm and Validation

2019 ◽  
Vol 11 (9) ◽  
pp. 1004 ◽  
Author(s):  
Liu ◽  
Zhang ◽  
Xie ◽  
Liu ◽  
Song ◽  
...  
Keyword(s):  
Energy Balance ◽  
Area Index ◽  
Global Soil ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Comparison Of The Results ◽  
Spectral Mixture ◽  
Sky Conditions ◽  
Absorbed Photosynthetically Active Radiation

The fraction of absorbed photosynthetically active radiation by vegetation (FAPAR) is a key variable in describing the light absorption ability of the vegetation canopy. Most global FAPAR products, such as MCD15A2H and GEOV1, correspond to FAPAR under black-sky conditions at the satellite overpass time only. In this paper, we aim to produce both the global white-sky and black-sky FAPAR products based on the moderate resolution imaging spectroradiometer (MODIS) visible (VIS) albedo, leaf area index (LAI), and clumping index (CI) products. Firstly, a non-linear spectral mixture model (NSM) was designed to retrieve the soil visible (VIS) albedo. The global soil VIS albedo and its dynamics were successfully mapped at a resolution of 500 m using the MCD43A3 VIS albedo product and the MCD15A2H LAI product. Secondly, a method based on the energy balance residual (EBR) principle was presented to retrieve the white-sky and black-sky FAPAR using the MODIS broadband VIS albedo (white-sky and black-sky) product (MCD43A3), the LAI product (MCD15A2H) and CI products. Finally, the two EBR FAPAR products were compared with the MCD15A2H and Geoland2/BioPar version 1 (GEOV1) black-sky FAPAR products. A comparison of the results indicates that these FAPAR products show similar spatial and seasonal patterns. Direct validation using FAPAR observations from the Validation of Land European Remote sensing Instrument (VALERI) project demonstrates that the EBR black-sky FAPAR product was more accurate and had a lower bias (R2 = 0.917, RMSE = 0.088, and bias = −2.8 %) than MCD15A2H (R2 = 0.901, RMSE = 0.096, and bias = 7.6 % ) and GEOV1 (R2 = 0.868, RMSE = 0.105, and bias = 6.1%).

scholarly journals Implementation of a soil albedo scheme in the CABLEv1.4b land surface model and evaluation against MODIS estimates over Australia

2014 ◽  
Vol 7 (2) ◽  
pp. 1671-1707
Author(s):  
J. Kala ◽  
J. P. Evans ◽  
A. J. Pitman ◽  
C. B. Schaaf ◽  
M. Decker ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Surface Albedo ◽  
Land Surface Model ◽  
Surface Model ◽  
Parameter Uncertainties ◽  
Australian Continent ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. Land surface albedo, the fraction of incoming solar radiation reflected by the land surface, is a key component of the earth system. This study evaluates snow-free surface albedo simulations by the Community Atmosphere Biosphere Land Exchange (CABLEv1.4b) model with the Moderate Resolution Imaging Spectroradiometer (MODIS) albedo. We compare results from two offline simulations over the Australian continent, one with prescribed background snow-free and vegetation-free soil albedo derived from MODIS (the control), and the other with a simple parameterisation based on soil moisture and colour. The control simulation shows that CABLE simulates albedo over Australia reasonably well, with differences with MODIS within an acceptable range. Inclusion of the parameterisation for soil albedo however introduced large errors for the near infra red albedo, especially for desert regions of central Australia. These large errors were not fully explained by errors in soil moisture or parameter uncertainties, but are similar to errors in albedo in other land surface models which use the same soil albedo scheme. Although this new parameterisation has introduced larger errors as compared to prescribing soil albedo, dynamic soil moisture-albedo feedbacks are now enabled in CABLE. Future directions for albedo parameterisations development in CABLE are discussed.

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