Air temperature can be estimated from remote sensing by combining information in thermal infrared... more Air temperature can be estimated from remote sensing by combining information in thermal infrared and optical wavelengths. The empirical TVX algorithm is based on an estimated linear relationship between observed Land Surface Temperature (LST) and a Spectral Vegetation Index (NDVI). Air temperature is assumed to be equal to the LST corresponding to the effective full vegetation cover, and is found by extrapolating the line to a maximum value of NDVI max . The algorithm has been tested and reported in the literature previously. However, the effect of vegetation types and climates and the potential variation in NDVI of the effective full cover has not been subject for investigation. The present study proposes a novel methodology to estimate NDVI max that uses observed air temperature to calibrate the NDVI max for each vegetation type. To assess the validity of this methodology, we have compared the accuracy of estimates using the new NDVI max and the previous NDVI max that have been proposed in literature with MSG-SEVIRI images in Spain during the year 2005. In addition, a spatio-temporal assessment of residuals has been performed to evaluate the accuracy of retrievals in terms of daily and seasonal variation, land cover, landscape heterogeneity and topography. Results showed that the new calibrated NDVI max perform well, with a Mean Absolute Error ranging between 2.8°C and 4°C. In addition, vegetation-specific NDVI max improve the accuracy compared with a unique NDVI max .
Obtaining adequate input data for distributed hydrological models is a major callenge to the use ... more Obtaining adequate input data for distributed hydrological models is a major callenge to the use of such models. This is especially true in areas where conventionel data sources like synoptic stations are few and far apart. This is true for the Okavango Delta in Botswana, where no operational synoptic stations are present within the delta, and where access to the delta is difficult due to annual floods. Furthermore the complex hydrology of the delta requires spatially distributed, high quality data to adequately model the distribution of water and the evapotranspiration from the surface. Conventional outflow validation is not viable due to the low percentage of water leaving the delta in streams (approximately 3%). In this study methods for estimating some of the key driving variables for the model based on data from the SEVIRI sensor onboard the Meteosat Second Generation satellites have been applied. The SEVIRI sensor provides imagery every 15 minutes in 12 spectral channels at a ...
Hydrology and Earth System Sciences Discussions, 2012
An existing rain gauge catch correction method addressing solid and liquid precipitation was appl... more An existing rain gauge catch correction method addressing solid and liquid precipitation was applied both as monthly mean correction factors based on a 30 yr climatology (standard correction) and as daily correction factors based on daily observations of wind speed and temperature (dynamic correction). The two methods resulted in differ-5 ent winter precipitation rates for the period 1990-2010. The resulting precipitation data sets were evaluated through the comprehensive Danish National Water Resources model (DK-Model) revealing major differences in both model performance and optimized model parameter sets. Simulated stream discharge is improved significantly when introducing a dynamic precipitation correction, whereas the simulated hydraulic 10 heads and multi-annual water balances performed similarly due to recalibration adjusting model parameters to compensate for input biases. The resulting optimized model parameters are much more physically plausible for the model based on dynamic correction of precipitation. A proxy-basin test where calibrated DK-Model parameters were transferred to another region without site specific calibration showed better per-15 formance for parameter values based on the dynamic correction. Similarly, the performances of the dynamic correction method were superior when considering two single years with a much dryer and a much wetter winter, respectively, as compared to the winters in the calibration period (differential split-sample tests). We conclude that dynamic precipitation correction should be carried out for studies requiring a sound 20 dynamic description of hydrological processes and it is of particular importance when using hydrological models to make predictions for future climates when the snow/rain composition will differ from the past climate. This conclusion is expected to be applicable for mid to high latitudes especially in coastal climates where winter precipitation type (solid/liquid) fluctuate significantly causing climatological mean correction factors 25 to be inadequate.
SVAT (Soil Vegetation Atmosphere Transfer) modeling is essential in understanding land surface pr... more SVAT (Soil Vegetation Atmosphere Transfer) modeling is essential in understanding land surface processes both at the plot scale and at the regional scale. However, data availability at regional scale usually becomes limited in the details required, both spatially and temporally. Remotely sensed data solve some of the inherent problems, spatially by using medium resolution products from e.g. MODIS (1 km
West Africa has undergone some of the most dramatic recorded changes in climate during the last d... more West Africa has undergone some of the most dramatic recorded changes in climate during the last decades, having affected people directly due to large impact on food production and water resources. The livelihood of future generations in this very vulnerable region of the World is dependent on access to water, influenced directly by changes in climate and land cover. The goal of this study is thus to investigate the relative effects of future changes in climate and land cover in one of the large river basins in the region, the transnational Senegal Basin, from a water resource perspective. Based on predictions from an ensemble of up to nine regional climate models over West Africa and associated land cover change scenarios, the effect on components of the water balance in the Senegal Basin is analysed. We apply a distributed hydrological model based on the MIKE SHE code to simulate changes in stream flow and in the partitioning of evapotranspiration for the years 2030-2050. The model...
Abstract Land surface data from MODIS and AVHRR have been extensively used for vegetation monitor... more Abstract Land surface data from MODIS and AVHRR have been extensively used for vegetation monitoring. In cloud-prone areas like West Africa the use of Normalized Difference Vegetation Index (NDVI) data for vegetation monitoring is hampered by ...
Global 8 km resolution AVHRR (advanced very high resolution radiometer) NDVI (normalized differen... more Global 8 km resolution AVHRR (advanced very high resolution radiometer) NDVI (normalized difference vegetation index) 10-day composite data sets have been used for numerous local to global scale vegetation time series studies during recent years. AVHRR Pathfinder (PAL) NDVI was available from 1981 until 2001, and the new AVHRR GIMMS NDVI was available from 1981 to the present time. A number of aspects potentially introduce noise in the NDVI data set due to the AVHRR sensor design and data processing. NDVI from SPOT-4 VGT data is considered an improvement over AVHRR, and for this reason it is important to examine how and if the differences in sensor design and processing influence continental scale NDVI composite products. In this study, the quality of these AVHRR NDVI time series are evaluated by the continental scale 1 km resolution SPOT-4 vegetation (VGT) 10-day composite (S10) NDVI data. Three years of AVHRR PAL (1998)(1999) and seven years of GIMMS (1998GIMMS ( -2004 have been compared to 8 km resampled SPOT-4 VGT (1998 data. The dynamic range of SPOT-4 VGT NDVI tends to be higher than the AVHRR PAL NDVI, whereas there is an exact match between AVHRR GIMMS NDVI and SPOT-4 VGT NDVI. Ortho-regression analysis on annually integrated values of AVHRR PAL/GIMMS and SPOT-4 VGT on a continental scale reveals high correlations amongst the AVHRR and the SPOT data set, with lowest RMSE (root mean square error) on the GIMMS/SPOT-4 VGT compared to the PAL/ SPOT-4 VGT.
Precipitation gauge catch correction is often given very little attention in hydrological modelli... more Precipitation gauge catch correction is often given very little attention in hydrological modelling compared to model parameter calibration. This is critical because significant precipitation biases often make the calibration exercise pointless, especially when supposedly physically-based models are in play. This study addresses the general importance of appropriate precipitation catch correction through a detailed modelling exercise. An existing precipitation gauge catch correction method addressing solid and liquid precipitation is applied, both as national mean monthly correction factors based on a historic 30 yr record and as gridded daily correction factors based on local daily observations of wind speed and temperature. The two methods, named the historic mean monthly (HMM) and the time-space variable (TSV) correction, resulted in different winter precipitation rates for the period 1990-2010. The resulting precipitation datasets were evaluated through the comprehensive Danish National Water Resources model (DK-Model), revealing major differences in both model performance and optimised model parameter sets. Simulated stream discharge is improved significantly when introducing the TSV correction, whereas the simulated hydraulic heads and multi-annual water balances performed similarly due to recalibration adjusting model parameters to compensate for input biases. The resulting optimised model parameters are much more physically plausible for the model based on the TSV correction of precipitation. A proxy-basin test where calibrated DK-Model parameters were transferred to another region without site specific calibration showed better performance for parameter values based on the TSV correction. Similarly, the performances of the TSV correction method were superior when considering two single years with a much dryer and a much wetter winter, respectively, as compared to the winters in the calibration period (differential split-sample tests). We conclude that TSV precipitation correction should be carried out for studies requiring a sound dynamic description of hydrological processes, and it is of particular importance when using hydrological models to make predictions for future climates when the snow/rain composition will differ from the past climate. This conclusion is expected to be applicable for mid to high latitudes, especially in coastal climates where winter precipitation types (solid/liquid) fluctuate significantly, causing climatological mean correction factors to be inadequate.
The emergence of regional and global satellite-based rainfall products with high spatial and temp... more The emergence of regional and global satellite-based rainfall products with high spatial and temporal resolution has opened up new large-scale hydrological applications in data-sparse or ungauged catchments. Particularly, distributed hydrological models can benefit from the good spatial coverage and distributed nature of satellite-based rainfall estimates (SRFE). In this study, five SRFEs with temporal resolution of 24 h and spatial resolution between 8 and 27 km have been evaluated through their predictive capability in a distributed hydrological model of the Senegal River basin in West Africa. The main advantage of this evaluation methodology is the integration of the rainfall model input in time and space when evaluated at the sub-catchment scale. An initial data analysis revealed significant biases in the SRFE products and large variations in rainfall amounts between SRFEs, although the spatial patterns were similar. The results showed that the Climate Prediction Center/Famine Early Warning System (CPC-FEWS) and cold cloud duration (CCD) products, which are partly based on rain gauge data and produced specifically for the African continent, performed better in the modelling context than the global SRFEs, Climate Prediction Center MORPHing technique (CMORPH), Tropical Rainfall Measuring Mission (TRMM) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). The best performing SRFE, CPC-FEWS, produced good results with values of R 2 NS between 0Ð84 and 0Ð87 after bias correction and model recalibration. This was comparable to model simulations based on traditional rain gauge data. The study highlights the need for input specific calibration of hydrological models, since major differences were observed in model performances even when all SRFEs were scaled to the same mean rainfall amounts. This is mainly attributed to differences in temporal dynamics between products.
In recent years, a number of simulation platforms with varying complexity which couple groundwate... more In recent years, a number of simulation platforms with varying complexity which couple groundwater, land surface, and atmospheric models have emerged. These platforms are designed to include processes affecting energy fluxes and soil moisture variations at the land surface such as shallow groundwater, overland flow, and subsurface lateral flow. Previous studies demonstrate the sensitivity of atmospheric boundary layer dynamics and precipitation to land surface energy fluxes and groundwater dynamics, as well as the importance of capturing these interactions through coupled models. This study compares two distributed, physically-based, state-of-the-art hydrological modelling platforms: The ParFlow-CLM-COSMO platform TerrSysMP (Terrestrial System Modelling Platform), developed within the Transregional Collaborative Research Centre 32 (TR32), and the HIRHAM-MIKE SHE platform developed within the HOBE Centre for Hydrology and the HYdrological Modelling for Assessing Climate Change Impact...
Air temperature can be estimated from remote sensing by combining information in thermal infrared... more Air temperature can be estimated from remote sensing by combining information in thermal infrared and optical wavelengths. The empirical TVX algorithm is based on an estimated linear relationship between observed Land Surface Temperature (LST) and a Spectral Vegetation Index (NDVI). Air temperature is assumed to be equal to the LST corresponding to the effective full vegetation cover, and is found by extrapolating the line to a maximum value of NDVI max . The algorithm has been tested and reported in the literature previously. However, the effect of vegetation types and climates and the potential variation in NDVI of the effective full cover has not been subject for investigation. The present study proposes a novel methodology to estimate NDVI max that uses observed air temperature to calibrate the NDVI max for each vegetation type. To assess the validity of this methodology, we have compared the accuracy of estimates using the new NDVI max and the previous NDVI max that have been proposed in literature with MSG-SEVIRI images in Spain during the year 2005. In addition, a spatio-temporal assessment of residuals has been performed to evaluate the accuracy of retrievals in terms of daily and seasonal variation, land cover, landscape heterogeneity and topography. Results showed that the new calibrated NDVI max perform well, with a Mean Absolute Error ranging between 2.8°C and 4°C. In addition, vegetation-specific NDVI max improve the accuracy compared with a unique NDVI max .
Obtaining adequate input data for distributed hydrological models is a major callenge to the use ... more Obtaining adequate input data for distributed hydrological models is a major callenge to the use of such models. This is especially true in areas where conventionel data sources like synoptic stations are few and far apart. This is true for the Okavango Delta in Botswana, where no operational synoptic stations are present within the delta, and where access to the delta is difficult due to annual floods. Furthermore the complex hydrology of the delta requires spatially distributed, high quality data to adequately model the distribution of water and the evapotranspiration from the surface. Conventional outflow validation is not viable due to the low percentage of water leaving the delta in streams (approximately 3%). In this study methods for estimating some of the key driving variables for the model based on data from the SEVIRI sensor onboard the Meteosat Second Generation satellites have been applied. The SEVIRI sensor provides imagery every 15 minutes in 12 spectral channels at a ...
Hydrology and Earth System Sciences Discussions, 2012
An existing rain gauge catch correction method addressing solid and liquid precipitation was appl... more An existing rain gauge catch correction method addressing solid and liquid precipitation was applied both as monthly mean correction factors based on a 30 yr climatology (standard correction) and as daily correction factors based on daily observations of wind speed and temperature (dynamic correction). The two methods resulted in differ-5 ent winter precipitation rates for the period 1990-2010. The resulting precipitation data sets were evaluated through the comprehensive Danish National Water Resources model (DK-Model) revealing major differences in both model performance and optimized model parameter sets. Simulated stream discharge is improved significantly when introducing a dynamic precipitation correction, whereas the simulated hydraulic 10 heads and multi-annual water balances performed similarly due to recalibration adjusting model parameters to compensate for input biases. The resulting optimized model parameters are much more physically plausible for the model based on dynamic correction of precipitation. A proxy-basin test where calibrated DK-Model parameters were transferred to another region without site specific calibration showed better per-15 formance for parameter values based on the dynamic correction. Similarly, the performances of the dynamic correction method were superior when considering two single years with a much dryer and a much wetter winter, respectively, as compared to the winters in the calibration period (differential split-sample tests). We conclude that dynamic precipitation correction should be carried out for studies requiring a sound 20 dynamic description of hydrological processes and it is of particular importance when using hydrological models to make predictions for future climates when the snow/rain composition will differ from the past climate. This conclusion is expected to be applicable for mid to high latitudes especially in coastal climates where winter precipitation type (solid/liquid) fluctuate significantly causing climatological mean correction factors 25 to be inadequate.
SVAT (Soil Vegetation Atmosphere Transfer) modeling is essential in understanding land surface pr... more SVAT (Soil Vegetation Atmosphere Transfer) modeling is essential in understanding land surface processes both at the plot scale and at the regional scale. However, data availability at regional scale usually becomes limited in the details required, both spatially and temporally. Remotely sensed data solve some of the inherent problems, spatially by using medium resolution products from e.g. MODIS (1 km
West Africa has undergone some of the most dramatic recorded changes in climate during the last d... more West Africa has undergone some of the most dramatic recorded changes in climate during the last decades, having affected people directly due to large impact on food production and water resources. The livelihood of future generations in this very vulnerable region of the World is dependent on access to water, influenced directly by changes in climate and land cover. The goal of this study is thus to investigate the relative effects of future changes in climate and land cover in one of the large river basins in the region, the transnational Senegal Basin, from a water resource perspective. Based on predictions from an ensemble of up to nine regional climate models over West Africa and associated land cover change scenarios, the effect on components of the water balance in the Senegal Basin is analysed. We apply a distributed hydrological model based on the MIKE SHE code to simulate changes in stream flow and in the partitioning of evapotranspiration for the years 2030-2050. The model...
Abstract Land surface data from MODIS and AVHRR have been extensively used for vegetation monitor... more Abstract Land surface data from MODIS and AVHRR have been extensively used for vegetation monitoring. In cloud-prone areas like West Africa the use of Normalized Difference Vegetation Index (NDVI) data for vegetation monitoring is hampered by ...
Global 8 km resolution AVHRR (advanced very high resolution radiometer) NDVI (normalized differen... more Global 8 km resolution AVHRR (advanced very high resolution radiometer) NDVI (normalized difference vegetation index) 10-day composite data sets have been used for numerous local to global scale vegetation time series studies during recent years. AVHRR Pathfinder (PAL) NDVI was available from 1981 until 2001, and the new AVHRR GIMMS NDVI was available from 1981 to the present time. A number of aspects potentially introduce noise in the NDVI data set due to the AVHRR sensor design and data processing. NDVI from SPOT-4 VGT data is considered an improvement over AVHRR, and for this reason it is important to examine how and if the differences in sensor design and processing influence continental scale NDVI composite products. In this study, the quality of these AVHRR NDVI time series are evaluated by the continental scale 1 km resolution SPOT-4 vegetation (VGT) 10-day composite (S10) NDVI data. Three years of AVHRR PAL (1998)(1999) and seven years of GIMMS (1998GIMMS ( -2004 have been compared to 8 km resampled SPOT-4 VGT (1998 data. The dynamic range of SPOT-4 VGT NDVI tends to be higher than the AVHRR PAL NDVI, whereas there is an exact match between AVHRR GIMMS NDVI and SPOT-4 VGT NDVI. Ortho-regression analysis on annually integrated values of AVHRR PAL/GIMMS and SPOT-4 VGT on a continental scale reveals high correlations amongst the AVHRR and the SPOT data set, with lowest RMSE (root mean square error) on the GIMMS/SPOT-4 VGT compared to the PAL/ SPOT-4 VGT.
Precipitation gauge catch correction is often given very little attention in hydrological modelli... more Precipitation gauge catch correction is often given very little attention in hydrological modelling compared to model parameter calibration. This is critical because significant precipitation biases often make the calibration exercise pointless, especially when supposedly physically-based models are in play. This study addresses the general importance of appropriate precipitation catch correction through a detailed modelling exercise. An existing precipitation gauge catch correction method addressing solid and liquid precipitation is applied, both as national mean monthly correction factors based on a historic 30 yr record and as gridded daily correction factors based on local daily observations of wind speed and temperature. The two methods, named the historic mean monthly (HMM) and the time-space variable (TSV) correction, resulted in different winter precipitation rates for the period 1990-2010. The resulting precipitation datasets were evaluated through the comprehensive Danish National Water Resources model (DK-Model), revealing major differences in both model performance and optimised model parameter sets. Simulated stream discharge is improved significantly when introducing the TSV correction, whereas the simulated hydraulic heads and multi-annual water balances performed similarly due to recalibration adjusting model parameters to compensate for input biases. The resulting optimised model parameters are much more physically plausible for the model based on the TSV correction of precipitation. A proxy-basin test where calibrated DK-Model parameters were transferred to another region without site specific calibration showed better performance for parameter values based on the TSV correction. Similarly, the performances of the TSV correction method were superior when considering two single years with a much dryer and a much wetter winter, respectively, as compared to the winters in the calibration period (differential split-sample tests). We conclude that TSV precipitation correction should be carried out for studies requiring a sound dynamic description of hydrological processes, and it is of particular importance when using hydrological models to make predictions for future climates when the snow/rain composition will differ from the past climate. This conclusion is expected to be applicable for mid to high latitudes, especially in coastal climates where winter precipitation types (solid/liquid) fluctuate significantly, causing climatological mean correction factors to be inadequate.
The emergence of regional and global satellite-based rainfall products with high spatial and temp... more The emergence of regional and global satellite-based rainfall products with high spatial and temporal resolution has opened up new large-scale hydrological applications in data-sparse or ungauged catchments. Particularly, distributed hydrological models can benefit from the good spatial coverage and distributed nature of satellite-based rainfall estimates (SRFE). In this study, five SRFEs with temporal resolution of 24 h and spatial resolution between 8 and 27 km have been evaluated through their predictive capability in a distributed hydrological model of the Senegal River basin in West Africa. The main advantage of this evaluation methodology is the integration of the rainfall model input in time and space when evaluated at the sub-catchment scale. An initial data analysis revealed significant biases in the SRFE products and large variations in rainfall amounts between SRFEs, although the spatial patterns were similar. The results showed that the Climate Prediction Center/Famine Early Warning System (CPC-FEWS) and cold cloud duration (CCD) products, which are partly based on rain gauge data and produced specifically for the African continent, performed better in the modelling context than the global SRFEs, Climate Prediction Center MORPHing technique (CMORPH), Tropical Rainfall Measuring Mission (TRMM) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). The best performing SRFE, CPC-FEWS, produced good results with values of R 2 NS between 0Ð84 and 0Ð87 after bias correction and model recalibration. This was comparable to model simulations based on traditional rain gauge data. The study highlights the need for input specific calibration of hydrological models, since major differences were observed in model performances even when all SRFEs were scaled to the same mean rainfall amounts. This is mainly attributed to differences in temporal dynamics between products.
In recent years, a number of simulation platforms with varying complexity which couple groundwate... more In recent years, a number of simulation platforms with varying complexity which couple groundwater, land surface, and atmospheric models have emerged. These platforms are designed to include processes affecting energy fluxes and soil moisture variations at the land surface such as shallow groundwater, overland flow, and subsurface lateral flow. Previous studies demonstrate the sensitivity of atmospheric boundary layer dynamics and precipitation to land surface energy fluxes and groundwater dynamics, as well as the importance of capturing these interactions through coupled models. This study compares two distributed, physically-based, state-of-the-art hydrological modelling platforms: The ParFlow-CLM-COSMO platform TerrSysMP (Terrestrial System Modelling Platform), developed within the Transregional Collaborative Research Centre 32 (TR32), and the HIRHAM-MIKE SHE platform developed within the HOBE Centre for Hydrology and the HYdrological Modelling for Assessing Climate Change Impact...
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