scholarly journals Using a Groundwater Adjusted Water Balance Approach and Copulas to Evaluate Spatial Patterns and Dependence Structures in Remote Sensing Derived Evapotranspiration Products

2021 ◽  
Vol 13 (5) ◽  
pp. 853
Author(s):  
Mohsen Soltani ◽  
Julian Koch ◽  
Simon Stisen
Keyword(s):  
Remote Sensing ◽  
Water Balance ◽  
Spatial Pattern ◽  
Spatial Patterns ◽  
Principal Component ◽  
Small Scale ◽  
Dependence Structure ◽  
Test Case ◽  
Catchment Scale ◽  
Balance Approach

This study aims to improve the standard water balance evapotranspiration (WB ET) estimate, which is typically used as benchmark data for catchment-scale ET estimation, by accounting for net intercatchment groundwater flow in the ET calculation. Using the modified WB ET approach, we examine errors and shortcomings associated with the long-term annual mean (2002–2014) spatial patterns of three remote-sensing (RS) MODIS-based ET products from MODIS16, PML_V2, and TSEB algorithms at 1 km spatial resolution over Denmark, as a test case for small-scale, energy-limited regions. Our results indicate that the novel approach of adding groundwater net in water balance ET calculation results in a more trustworthy ET spatial pattern. This is especially relevant for smaller catchments where groundwater net can be a significant component of the catchment water balance. Nevertheless, large discrepancies are observed both amongst RS ET datasets and compared to modified water balance ET spatial pattern at the national scale; however, catchment-scale analysis highlights that difference in RS ET and WB ET decreases with increasing catchment size and that 90%, 87%, and 93% of all catchments have ∆ET < ±150 mm/year for MODIS16, PML_V2, and TSEB, respectively. In addition, Copula approach captures a nonlinear structure of the joint relationship with multiple densities amongst the RS/WB ET products, showing a complex dependence structure (correlation); however, among the three RS ET datasets, MODIS16 ET shows a closer spatial pattern to the modified WB ET, as identified by a principal component analysis also. This study will help improve the water balance approach by the addition of groundwater net in the ET estimation and contribute to better understand the true correlations amongst RS/WB ET products especially over energy-limited environments.

Spatial pattern evaluation of remote-sensing evapotranspiration products using surface water-balance approach: application of geostatistical functions for quantifying drivers and dependence structures of ET data

2020 ◽  
Author(s):  
Mohsen Soltani ◽  
Simon Stisen ◽  
Julian Koch
Keyword(s):  
Remote Sensing ◽  
Surface Water ◽  
Water Balance ◽  
Spatial Pattern ◽  
Land Surface ◽  
Spatial Scales ◽  
Principal Component ◽  
Balance Model ◽  
Dependence Structure ◽  
Dependence Structures

&lt;p&gt;Remote sensing-based RS observations can provide evapotranspiration ET estimations across temporal and spatial scales. In this study, two MODIS-based global ET, namely MODIS16 and two-source energy balance model TSEB are compared and evaluated using the surface water-balance WB ET method at monthly time-scale with 1 km spatial resolution for the entire land phase of Denmark (42,087 km&lt;sup&gt;2&lt;/sup&gt;). Then, the drivers and underlying dependence structures of ET datasets against land-atmosphere parameters are appropriately quantified using a linear-based multivariate principal component analysis PCA &amp;#8211;and nonlinear-based bivariate empirical Copula analysis. For calculation of the surface WB ET method, in addition to the standard WB ET procedure (ET = precipitation P &amp;#8211; discharge Q), we introduce a novel modification of standard WB method, which considers a groundwater exchange term. Here, modelled net intercatchment groundwater flow (GW_net) is also included in the ET calculation (ET = P &amp;#8211; Q + GW_net); where the simulations are done by the national water resources model of Denmark (the DK-model) executed in the physically-based distributed MIKE-SHE hydrologic modelling code. The differences between the two WB methods are presented and discussed in detail to highlight the importance of considering GW data when investigating water-budget of small catchments. Our analysis will also be extended to compare ET datasets at different spatial scales (catchment size), aiming at further exploring the performance and ET uncertainties of remote sensing-based models. Our results indicate that the novel approach of adding GW-data in WB ET calculation results in a more trustworthy WB ET spatial pattern. This is especially relevant for smaller catchments where GW-exchange can be significant. Large discrepancy is observed in TSEB/MODIS16 ET compared to WB ET spatial pattern at the national scale; however, &amp;#8710;ET values are regionally small for most watersheds (~60% of all). Also, catchment-based analysis highlights that RS/WB &amp;#8710;ET decreases from &lt;100km&lt;sup&gt;2&lt;/sup&gt; to &gt;200km&lt;sup&gt;2&lt;/sup&gt; watersheds, and about 56% (67%) of all catchments have &amp;#8710;ET &amp;#177;50 mm/year for TSEB (MODIS16). PCA-based analysis revealed that each ET dataset is largely driven by different parameters. However, land surface temperature LST and solar radiation Rs are found as most relevant driving variables. In addition, Copula-based analysis captures a nonlinear structure of the joint relationship with multiple densities amongst ET products and the parameters, showing a complex underlying dependence structure. Overall, both PCA and Copula analyses indicate that WB and MODIS16 ET products represent a closer spatial pattern compared to TSEB. This study will help improve standard WB ET estimate method and contribute to deeper understanding the inter-correlations and real complex relationships between ET datasets and the nature of land-atmosphere parameters.&lt;/p&gt;

scholarly journals Spatial pattern evaluation of a calibrated national hydrological model – a remote sensing based diagnostic approach

2017 ◽  
Author(s):  
Gorka Mendiguren ◽  
Julian Koch ◽  
Simon Stisen
Keyword(s):  
Remote Sensing ◽  
Spatial Pattern ◽  
Spatial Patterns ◽  
Hydrological Model ◽  
Remote Sensing Data ◽  
Driving Mechanism ◽  
Distributed Hydrological Model ◽  
Eof Analysis ◽  
Sensing Data ◽  
Spatio Temporal

Abstract. Distributed hydrological models are traditionally evaluated against discharge stations, emphasizing the temporal and neglecting the spatial component of a model. The present study widens the traditional paradigm by highlighting spatial patterns of evapotranspiration (ET), a key variable at the land-atmosphere interface, obtained from two different approaches at the national scale of Denmark. The first approach is based on a national water resources model (DK-model), using the MIKE-SHE model code, and the second approach utilizes a two source energy balance model (TSEB) driven mainly by satellite remote sensing data. The main hypothesis of the study is that while both approaches are essentially estimates, the spatial patterns of the remote sensing based approach are explicitly driven by observed land surface temperature and therefore represent the most direct spatial pattern information of ET; enabling its use for distributed hydrological model evaluation. Ideally the hydrological model simulation and remote sensing based approach should present similar spatial patterns and driving mechanism of ET. However, the spatial comparison showed that the differences are significant and indicating insufficient spatial pattern performance of the hydrological model. The differences in spatial patterns can partly be explained by the fact that the hydrological model is configured to run in 6 domains that are calibrated independently from each other, as it is often the case for large scale multi-basin calibrations. Furthermore, the model incorporates predefined temporal dynamics of Leaf Area Index (LAI), root depth (RD) and Crop coefficient (Kc) for each land cover type. This zonal approach of model parametrization ignores the spatio-temporal complexity of the natural system. To overcome this limitation, the study features a modified version of the DK-Model in which LAI, RD, and KC are empirically derived using remote sensing data and detailed soil property maps in order to generate a higher degree of spatio-temporal variability and spatial consistency between the 6 domains. The effects of these changes are analyzed by using the empirical orthogonal functions (EOF) analysis to evaluate spatial patterns. The EOF-analysis shows that including remote sensing derived LAI, RD and KC in the distributed hydrological model adds spatial features found in the spatial pattern of remote sensing based ET.

scholarly journals Satellite-based Sun-Induced Chlorophyll Fluorescence in the Greater Alpine Space: Spatial Patterns and Relationship to Gross Primary Productivity

2020 ◽  
Author(s):  
Ulisse Gomarasca ◽  
Gregory Duveiller ◽  
Alessandro Cescatti ◽  
Georg Wohlfahrt
Keyword(s):  
Remote Sensing ◽  
Chlorophyll Fluorescence ◽  
Land Cover ◽  
Spatial Patterns ◽  
Primary Productivity ◽  
Climate Models ◽  
Gross Primary Productivity ◽  
Accurate Estimation ◽  
Small Scale ◽  
Alpine Space

&lt;p&gt;Accurate estimation of terrestrial gross primary productivity is essential for the development of credible future carbon cycle and climate simulations. Current remote sensing techniques allow retrieval of sun-induced chlorophyll fluorescence (SIF) as a valid proxy for GPP, but low resolution, sparse coverage, or resolution mismatches between the different satellite sensors hinder our ability to effectively link SIF to many environmental variables at fine scales. In order to better characterize heterogeneous landscapes, several attempts to downscale SIF products to higher resolutions have been made. We investigate the ability of the downscaled GOME-2 product developed by Duveiller et al. (2019), to capture the differences in spatiotemporal dynamics over the Greater Alpine Space. We analyse SIF in connection to land cover and elevation, and calculate land phenology metrics based on seasonal SIF time series. Ground-based GPP validation suggests biome-specific SIF-GPP relationships, but the comparison was hindered by the resolution mismatch of the data. Moreover, missing data are present at high elevations, diminishing the suitability of current SIF products to analyse cloud-prone mountainous areas. Important insights into spatial patterns and seasonal trends could be inferred at forest and other large-area land cover types, typical of mid elevations in the Alps, but many anthropogenic habitats at low elevations, as well as high elevation grasslands and other small-scale heterogeneous environments could not be thoroughly investigated and are likely to be underrepresented or prone to biases. Similar downscaling procedures might be applied at finer scales to e.g. TROPOMI products, or alternative advanced remote sensing SIF techniques and instruments might be needed in order to enable detailed and systematic evaluations of the Alpine region or similar highly heterogenous landscapes, before a process-oriented monitoring and unbiased implementation into climate models may be performed.&lt;/p&gt;

scholarly journals Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model

2018 ◽  
Vol 22 (2) ◽  
pp. 1299-1315 ◽  
Author(s):  
Mehmet C. Demirel ◽  
Juliane Mai ◽  
Gorka Mendiguren ◽  
Julian Koch ◽  
Luis Samaniego ◽  
...  
Keyword(s):  
Water Balance ◽  
Spatial Pattern ◽  
Spatial Patterns ◽  
Spatial Model ◽  
Hydrologic Model ◽  
Actual Evapotranspiration ◽  
Parameter Distribution ◽  
Objective Functions ◽  
Performance Metric ◽  
Spatial Performance

Abstract. Satellite-based earth observations offer great opportunities to improve spatial model predictions by means of spatial-pattern-oriented model evaluations. In this study, observed spatial patterns of actual evapotranspiration (AET) are utilised for spatial model calibration tailored to target the pattern performance of the model. The proposed calibration framework combines temporally aggregated observed spatial patterns with a new spatial performance metric and a flexible spatial parameterisation scheme. The mesoscale hydrologic model (mHM) is used to simulate streamflow and AET and has been selected due to its soil parameter distribution approach based on pedo-transfer functions and the build in multi-scale parameter regionalisation. In addition two new spatial parameter distribution options have been incorporated in the model in order to increase the flexibility of root fraction coefficient and potential evapotranspiration correction parameterisations, based on soil type and vegetation density. These parameterisations are utilised as they are most relevant for simulated AET patterns from the hydrologic model. Due to the fundamental challenges encountered when evaluating spatial pattern performance using standard metrics, we developed a simple but highly discriminative spatial metric, i.e. one comprised of three easily interpretable components measuring co-location, variation and distribution of the spatial data. The study shows that with flexible spatial model parameterisation used in combination with the appropriate objective functions, the simulated spatial patterns of actual evapotranspiration become substantially more similar to the satellite-based estimates. Overall 26 parameters are identified for calibration through a sequential screening approach based on a combination of streamflow and spatial pattern metrics. The robustness of the calibrations is tested using an ensemble of nine calibrations based on different seed numbers using the shuffled complex evolution optimiser. The calibration results reveal a limited trade-off between streamflow dynamics and spatial patterns illustrating the benefit of combining separate observation types and objective functions. At the same time, the simulated spatial patterns of AET significantly improved when an objective function based on observed AET patterns and a novel spatial performance metric compared to traditional streamflow-only calibration were included. Since the overall water balance is usually a crucial goal in hydrologic modelling, spatial-pattern-oriented optimisation should always be accompanied by traditional discharge measurements. In such a multi-objective framework, the current study promotes the use of a novel bias-insensitive spatial pattern metric, which exploits the key information contained in the observed patterns while allowing the water balance to be informed by discharge observations.

scholarly journals Empirical attribution of a drying Himalayan river through remote sensing and secondary data

2021 ◽  
Author(s):  
Gopal Penny ◽  
Zubair A. Dar ◽  
Marc F. Müller
Keyword(s):  
Remote Sensing ◽  
Water Balance ◽  
Water Policy ◽  
Secondary Data ◽  
Hydrological Regime ◽  
Hydrological Processes ◽  
Indus Basin ◽  
Hydrological Models ◽  
Catchment Scale ◽  
Holistic Understanding

Abstract. Streamflow regimes are rapidly changing in many regions of the world. The ability to attribute these changes to specific hydrological processes and their underlying climatic and anthropogenic drivers is essential to formulate effective water policy. Traditional approaches to hydrologic attribution rely on the ability to infer hydrological processes through the development of catchment-scale hydrological models. However, such approaches are challenging to implement in practice. In particular, models have difficulty capturing hydrological regime shifts, where changes in the dominant hydrological processes alters the relationship among hydrological fluxes. Additionally, observational uncertainties might preclude closure of the catchment-scale water balance, which is a pre-requisite for most catchment-scale hydrological models. Here we present an alternative approach to hydrological attribution that leverages the method of multiple hypotheses. We generate and empirically evaluate a series of alternative and complementary hypotheses that pertain to hydrological change. These hypotheses concern distinct components of the water balance and are evaluated independently. This process allows a holistic understanding of watershed-scale processes to be developed, even if the catchment-scale water balance remains open. We apply the approach to understand changes in the Upper Jhelum river, an important tributary headwaters of the Indus basin, where streamflow has declined dramatically since 2000 and has yet to be adequately attributed to its corresponding drivers. Using remote sensing and secondary data collected from the watershed, we explore changes in climate, surface water, and groundwater. The evidence reveals that climate, rather than land use, had a considerably stronger influence on reductions in streamflow, both through reduced precipitation and increased evapotranspiration.

scholarly journals Detecting spatial patterns with the cumulant function – Part 1: The theory

2008 ◽  
Vol 15 (1) ◽  
pp. 159-167 ◽  
Author(s):  
A. Bernacchia ◽  
P. Naveau
Keyword(s):  
Spatial Patterns ◽  
Principal Component ◽  
Second Order ◽  
Empirical Orthogonal Functions ◽  
Dependence Structure ◽  
Orthogonal Functions ◽  
Sample Mean ◽  
Cumulant Function ◽  
Non Gaussian ◽  
Fast Procedure

Abstract. In climate studies, detecting spatial patterns that largely deviate from the sample mean still remains a statistical challenge. Although a Principal Component Analysis (PCA), or equivalently a Empirical Orthogonal Functions (EOF) decomposition, is often applied for this purpose, it provides meaningful results only if the underlying multivariate distribution is Gaussian. Indeed, PCA is based on optimizing second order moments, and the covariance matrix captures the full dependence structure of multivariate Gaussian vectors. Whenever the application at hand can not satisfy this normality hypothesis (e.g. precipitation data), alternatives and/or improvements to PCA have to be developed and studied. To go beyond this second order statistics constraint, that limits the applicability of the PCA, we take advantage of the cumulant function that can produce higher order moments information. The cumulant function, well-known in the statistical literature, allows us to propose a new, simple and fast procedure to identify spatial patterns for non-Gaussian data. Our algorithm consists in maximizing the cumulant function. Three families of multivariate random vectors, for which explicit computations are obtained, are implemented to illustrate our approach. In addition, we show that our algorithm corresponds to selecting the directions along which projected data display the largest spread over the marginal probability density tails.

scholarly journals Spatial pattern evaluation of a calibrated national hydrological model – a remote-sensing-based diagnostic approach

2017 ◽  
Vol 21 (12) ◽  
pp. 5987-6005 ◽  
Author(s):  
Gorka Mendiguren ◽  
Julian Koch ◽  
Simon Stisen
Keyword(s):  
Remote Sensing ◽  
Spatial Pattern ◽  
Spatial Patterns ◽  
Hydrological Model ◽  
Temporal Dynamics ◽  
Remote Sensing Data ◽  
Spatiotemporal Variability ◽  
Eof Analysis ◽  
Area Index ◽  
Sensing Data

Abstract. Distributed hydrological models are traditionally evaluated against discharge stations, emphasizing the temporal and neglecting the spatial component of a model. The present study widens the traditional paradigm by highlighting spatial patterns of evapotranspiration (ET), a key variable at the land–atmosphere interface, obtained from two different approaches at the national scale of Denmark. The first approach is based on a national water resources model (DK-model), using the MIKE-SHE model code, and the second approach utilizes a two-source energy balance model (TSEB) driven mainly by satellite remote sensing data. Ideally, the hydrological model simulation and remote-sensing-based approach should present similar spatial patterns and driving mechanisms of ET. However, the spatial comparison showed that the differences are significant and indicate insufficient spatial pattern performance of the hydrological model.The differences in spatial patterns can partly be explained by the fact that the hydrological model is configured to run in six domains that are calibrated independently from each other, as it is often the case for large-scale multi-basin calibrations. Furthermore, the model incorporates predefined temporal dynamics of leaf area index (LAI), root depth (RD) and crop coefficient (Kc) for each land cover type. This zonal approach of model parameterization ignores the spatiotemporal complexity of the natural system. To overcome this limitation, this study features a modified version of the DK-model in which LAI, RD and Kc are empirically derived using remote sensing data and detailed soil property maps in order to generate a higher degree of spatiotemporal variability and spatial consistency between the six domains. The effects of these changes are analyzed by using empirical orthogonal function (EOF) analysis to evaluate spatial patterns. The EOF analysis shows that including remote-sensing-derived LAI, RD and Kc in the distributed hydrological model adds spatial features found in the spatial pattern of remote-sensing-based ET.

scholarly journals Mapping of Intrusive Complex on a Small Scale Using Multi-Source Remote Sensing Images

2020 ◽  
Vol 9 (9) ◽  
pp. 543
Author(s):  
Yuzhou Zhang ◽  
Dengrong Zhang ◽  
Jinwei Duan ◽  
Tangao Hu
Keyword(s):  
Remote Sensing ◽  
Thermal Emission ◽  
Remote Sensing Data ◽  
Principal Component ◽  
Northwest China ◽  
Fusion Process ◽  
Small Scale ◽  
Intrusive Complex ◽  
Statistical Regression ◽  
Band Ratio

Multi-stage intrusive complex mapping plays an important role in regional mineralization research. The similarity of lithology characteristics between different stages of intrusions necessitates the use of richer spectral bands, while higher spatial resolution is also essential in small-scale research. In this paper, a multi-source remote sensing data application method was proposed. This method includes a spectral synergy process based on statistical regression and a fusion process using Gram–Schmidt (GS) spectral sharpening. We applied the method with Gaofen-2 (GF2), Sentinel-2, and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data to the mapping of the Mountain Sanfeng intrusive complex in northwest China in which Carboniferous intrusions have been proven to be directly related to the formation of Au deposits in the area. The band ratio (BR) and relative absorption band depth (RBD) were employed to enhance the spectral differences between two stage intrusions, and the Red-Green-Blue (RGB) false colour of the BR and RBD enhancement images performed well in the west and centre. Excellent enhancement results were obtained by making full use of all bands of the synergistic image and using the Band Ratio Matrix (BRM)-Principal Component Analysis (PCA) method in the northeast part of the study area. A crucial improvement in enhancement performance by the GS fusion process and spectral synergy process was thus shown. An accurate mapping result was obtained at the Mountain Sanfeng intrusive complex. This method could support small-scale regional geological survey and mineralization research in this region.

scholarly journals Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model

2017 ◽  
Author(s):  
Mehmet C. Demirel ◽  
Juliane Mai ◽  
Gorka Mendiguren ◽  
Julian Koch ◽  
Luis Samaniego ◽  
...  
Keyword(s):  
Water Balance ◽  
Spatial Pattern ◽  
Spatial Patterns ◽  
Spatial Model ◽  
Hydrologic Model ◽  
Actual Evapotranspiration ◽  
Parameter Distribution ◽  
Objective Functions ◽  
Performance Metric ◽  
Spatial Performance

Abstract. Satellite based earth observations offer great opportunities to improve spatial model predictions by means of spatial pattern oriented model evaluations. In this study, observed spatial patterns of actual evapotranspiration (AET) are utilized for spatial model calibration tailored to target the pattern performance of the model. The proposed calibration framework combines temporally aggregated observed spatial patterns with a new spatial performance metric and a flexible spatial parameterisation scheme. The mesoscale Hydrologic Model (mHM) is used to simulate streamflow and AET and has been selected due to its soil parameter distribution approach based on pedotransfer functions and the build in multiscale parameter regionalization. In addition two new domain specific spatial parameter distribution options have been incorporated in the model in order to increase the flexibility of root fraction coefficient and potential evapotranspiration correction parameterisations, based on soil type and vegetation density. These parametrisations are utilized as they are most relevant for simulated AET patterns from the hydrologic model. Due to the fundamental challenges encountered when evaluating spatial pattern performance using standard metrics, we developed a simple but highly discriminative spatial metric i.e. comprised of three easily interpretable components measuring co-location, variation and distribution of the spatial data. The study shows that with flexible spatial model parameterisation used in combination with the appropriate objective functions, the simulated spatial patterns of actual evapotranspiration become substantially more similar to the satellite based estimates. Overall 26 parameters are identified for calibration through a sequential screening approach based on a combination of streamflow and spatial pattern metrics. The robustness of the calibrations is tested using an ensemble of nine calibrations based on different seed numbers using the shuffled complex evolution optimizer. The calibration results reveal a limited trade-offs between streamflow dynamics and spatial patterns illustrating the benefit of combining separate observation types and objective functions. At the same time, the simulated spatial patterns of AET significantly improved when including an objective function based on observed AET patterns and a novel spatial performance metric compared to traditional streamflow only calibration. Since the overall water balance is usually a crucial goal in the hydrologic modelling, spatial pattern oriented optimization should always be accompanied by traditional discharge measurements. In such a multi-objective framework, the current study promotes the use of a novel bias-insensitive spatial pattern metric, which exploits the key information contained in the observed patterns while allowing the water balance to be informed by discharge observations.

scholarly journals Suitability of Satellite-Based Precipitation Products for Water Balance Simulations Using Multiple Observations in a Humid Catchment

2019 ◽  
Vol 11 (2) ◽  
pp. 151 ◽  
Author(s):  
Dan Zhang ◽  
Xiaomang Liu ◽  
Peng Bai ◽  
Xiang-Hu Li
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Water Balance ◽  
Spatial Patterns ◽  
Hydrological Modeling ◽  
Hydrological Model ◽  
Distributed Hydrological Model ◽  
Multiple Observations ◽  
Humid Region ◽  
Regional Water

This study assesses the suitability of five popular satellite-based precipitation products in modeling water balance in a humid region of China during the period 1998–2012. The satellite-based precipitation products show similar spatial patterns with varying degrees of overestimation or underestimation, compared with the gauged precipitation. A distributed hydrological model is used to evaluate the suitability of satellite-based precipitation products in simulating streamflow, evapotranspiration and soil moisture. The simulations of streamflow and evapotranspiration forced by the MSWEP precipitation perform best among the five satellite-based precipitation products, where the Kling-Gupta efficiency (KGE) between the simulated and observed streamflow ranges from 0.75 to 0.91, and the KGE between the simulated and observed evapotranspiration ranges from 0.46 to 0.61. However, the KGE between the simulated and observed soil moisture is negative, indicating that the performance of soil moisture simulation forced by satellite-based precipitation is poor. In addition, this study finds the spatial pattern of simulated streamflow is dominated by the distribution of precipitation, whereas the distribution of evapotranspiration and soil moisture is controlled by the parameters of the hydrological model. This study is useful for the improvement of hydrological modeling based on remote sensing and the monitoring of regional water resources.

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