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Prof. Simon Stisen
Department of Hydrology, Geological Survey of Denmark and Greenland (GEUS), Øster Voldgade 10, 1350 Copenhagen, Denmark

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Research Keywords & Expertise

0 Climate Change
0 Groundwater
0 Remote Sensing
0 hydrological modeling
0 Spatial patterns in hydrology

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Groundwater
Remote Sensing
Spatial patterns in hydrology
Climate Change
hydrological modeling

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Journal article
Published: 19 June 2021 in Remote Sensing
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Spatial patterns in long-term average evapotranspiration (ET) represent a unique source of information for evaluating the spatial pattern performance of distributed hydrological models on a river basin to continental scale. This kind of model evaluation is getting increased attention, acknowledging the shortcomings of traditional aggregated or timeseries-based evaluations. A variety of satellite remote sensing (RS)-based ET estimates exist, covering a range of methods and resolutions. There is, therefore, a need to evaluate these estimates, not only in terms of temporal performance and similarity, but also in terms of long-term spatial patterns. The current study evaluates four RS-ET estimates at moderate resolution with respect to spatial patterns in comparison to two alternative continental-scale gridded ET estimates (water-balance ET and Budyko). To increase comparability, an empirical correction factor between clear sky and all-weather ET, based on eddy covariance data, is derived, which could be suitable for simple corrections of clear sky estimates. Three RS-ET estimates (MODIS16, TSEB and PT-JPL) and the Budyko method generally display similar spatial patterns both across the European domain (mean SPAEF = 0.41, range 0.25–0.61) and within river basins (mean SPAEF range 0.19–0.38), although the pattern similarity within river basins varies significantly across basins. In contrast, the WB-ET and PML_V2 produced very different spatial patterns. The similarity between different methods ranging over different combinations of water, energy, vegetation and land surface temperature constraints suggests that robust spatial patterns of ET can be achieved by combining several methods.

ACS Style

Simon Stisen; Mohsen Soltani; Gorka Mendiguren; Henrik Langkilde; Monica Garcia; Julian Koch. Spatial Patterns in Actual Evapotranspiration Climatologies for Europe. Remote Sensing 2021, 13, 2410 .

AMA Style

Simon Stisen, Mohsen Soltani, Gorka Mendiguren, Henrik Langkilde, Monica Garcia, Julian Koch. Spatial Patterns in Actual Evapotranspiration Climatologies for Europe. Remote Sensing. 2021; 13 (12):2410.

Chicago/Turabian Style

Simon Stisen; Mohsen Soltani; Gorka Mendiguren; Henrik Langkilde; Monica Garcia; Julian Koch. 2021. "Spatial Patterns in Actual Evapotranspiration Climatologies for Europe." Remote Sensing 13, no. 12: 2410.

Preprint content
Published: 04 March 2021
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The DK-model (https://vandmodel.dk/in-english) is a national water resource model, covering all of Denmark. Its core is a distributed, integrated surface-subsurface hydrological model in 500m horizontal resolution. With recent efforts, a version at a higher resolution of 100m was created. The higher resolution was, amongst others, desired by end-users and to better represent surface and surface-near phenomena such as the location of the uppermost groundwater table. Being presently located close to the surface across substantial parts of the country and partly expected to rise, the groundwater table and its future development due to climate change is of great interest. A rising groundwater table is associated with potential risks for infrastructure, agriculture and ecosystems. However, the 25-fold jump in resolution of the hydrological model also increases the computational effort. Hence, it was deemed unfeasible to run the 100m resolution hydrological model nation-wide with an ensemble of climate models to evaluate climate change impact. The full ensemble run could only be performed with the 500m version of the model. To still produce the desired outputs at 100m resolution, a downscaling method was applied as described in the following.

Five selected subcatchment models covering around 9% of Denmark were run with five selected climate models at 100m resolution (using less than 3% of the computational time for hydrological models compared to a national, full ensemble run at 100m). Using the simulated changes at 100m resolution from those models as training data, combined with a set of covariates including the simulated changes in 500m resolution, Random Forest (RF) algorithms were trained to downscale simulated changes from 500m to 100m.

Generalizing the trained RF algorithms, Denmark-wide maps of expected climate change induced changes to the shallow groundwater table at 100m resolution were modelled. To verify the downscaling results, amongst others, the RF algorithms were successfully validated against results from a sixth hydrological subcatchment model at 100m resolution not used in training the algorithms.

The experience gained also opens for various other applications of similar algorithms where computational limitations inhibit running distributed hydrological models at fine resolutions: The results suggest the potential to downscale other model outputs that are desired at fine resolutions.

ACS Style

Raphael Schneider; Hans Jørgen Henriksen; Julian Koch; Lars Troldborg; Simon Stisen. Using machine learning to downscale simulations of climate change induced changes to the shallow groundwater table. 2021, 1 .

AMA Style

Raphael Schneider, Hans Jørgen Henriksen, Julian Koch, Lars Troldborg, Simon Stisen. Using machine learning to downscale simulations of climate change induced changes to the shallow groundwater table. . 2021; ():1.

Chicago/Turabian Style

Raphael Schneider; Hans Jørgen Henriksen; Julian Koch; Lars Troldborg; Simon Stisen. 2021. "Using machine learning to downscale simulations of climate change induced changes to the shallow groundwater table." , no. : 1.

Journal article
Published: 25 February 2021 in Remote Sensing
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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.

ACS Style

Mohsen Soltani; Julian Koch; Simon Stisen. Using a Groundwater Adjusted Water Balance Approach and Copulas to Evaluate Spatial Patterns and Dependence Structures in Remote Sensing Derived Evapotranspiration Products. Remote Sensing 2021, 13, 853 .

AMA Style

Mohsen Soltani, Julian Koch, Simon Stisen. Using a Groundwater Adjusted Water Balance Approach and Copulas to Evaluate Spatial Patterns and Dependence Structures in Remote Sensing Derived Evapotranspiration Products. Remote Sensing. 2021; 13 (5):853.

Chicago/Turabian Style

Mohsen Soltani; Julian Koch; Simon Stisen. 2021. "Using a Groundwater Adjusted Water Balance Approach and Copulas to Evaluate Spatial Patterns and Dependence Structures in Remote Sensing Derived Evapotranspiration Products." Remote Sensing 13, no. 5: 853.

Research paper
Published: 03 February 2021 in Groundwater
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Due to increasing water demands globally, freshwater ecosystems are under constant pressure. Groundwater resources, as the main source of accessible freshwater, are crucially important for irrigation worldwide. Over‐abstraction of groundwater leads to declines in groundwater levels; consequently, the groundwater inflow to streams decreases. The reduction in base flow and alteration of the stream flow regime can potentially have an adverse impact on groundwater‐dependent ecosystems. A spatially distributed, coupled groundwater‐surface water model can simulate the impacts of groundwater abstraction on aquatic ecosystems. A constrained optimization algorithm and a simulation model in combination can provide an objective tool for the water practitioner to evaluate the interplay between economic benefits of groundwater abstractions and requirements to environmental flow. In this study, a holistic catchment‐scale groundwater abstraction optimization framework has been developed that allows for a spatially explicit optimization of groundwater abstraction, while fulfilling a pre‐defined maximum allowed reduction of stream flow (base flow (Q95) or median flow (Q50)) as constraint criteria for 1484 stream locations across the catchment. A balanced K‐Means clustering method was implemented to reduce the computational burden of the optimization. The model parameters and observation uncertainties calculated based on Bayesian linear theory allow for a risk assessment on the optimized groundwater abstraction values. The results from different optimization scenarios indicated that using the linear programming optimization algorithm in conjunction with integrated models provides valuable information for guiding the water practitioners in designing an effective groundwater abstraction plan with the consideration of environmental flow criteria important for the ecological status of the entire system.

ACS Style

Mehrdis Danapour; Michael N. Fienen; Anker Lajer Højberg; Karsten Høgh Jensen; Simon Stisen. Multi‐constrained catchment scale optimization of groundwater abstraction using linear programming. Groundwater 2021, 1 .

AMA Style

Mehrdis Danapour, Michael N. Fienen, Anker Lajer Højberg, Karsten Høgh Jensen, Simon Stisen. Multi‐constrained catchment scale optimization of groundwater abstraction using linear programming. Groundwater. 2021; ():1.

Chicago/Turabian Style

Mehrdis Danapour; Michael N. Fienen; Anker Lajer Højberg; Karsten Høgh Jensen; Simon Stisen. 2021. "Multi‐constrained catchment scale optimization of groundwater abstraction using linear programming." Groundwater , no. : 1.

Journal article
Published: 23 December 2020 in Water Resources Research
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Irrigation is the greatest human interference with the terrestrial water cycle. Detailed knowledge on irrigation is required to better manage water resources and to increase water use efficiency (WUE). This study applies a framework to quantify net irrigation at monthly timescale at a spatial resolution of 1 km2 providing high spatial and temporal detail for regional water resources management. The study is conducted in the Haihe River Basin (HRB) in China encompassing the North China Plain (NCP), a global hotspot of groundwater depletion. Net irrigation is estimated based on the systematic evapotranspiration (ET) residuals between a remote sensing based model and a hydrologic model that does not include an irrigation scheme. The results suggest an average annual net irrigation of 126 mm yr‐1 (15.2 km3 yr‐1) for NCP and 108 mm yr‐1 (18.6 km3 yr‐1) for HRB. It is found that net irrigation can be estimated with higher fidelity for winter crops than for summer crops. The simulated water balance of the HRB is evaluated with GRACE data and the net irrigation estimates can close the water balance gap. Annual winter wheat classifications reveal an increasing crop area with a trend of 2200 km2 yr‐1. This trend is not accompanied by a likewise increasing trend in irrigation water use, which suggests an increased WUE in the NCP, which is further supported by net primary productivity data. The proposed framework has potential to be transferred to other regions and support decision makers to support sustainable water management.

ACS Style

Julian Koch; Wenmin Zhang; Grith Martinsen; Xin He; Simon Stisen. Estimating Net Irrigation Across the North China Plain Through Dual Modeling of Evapotranspiration. Water Resources Research 2020, 56, 1 .

AMA Style

Julian Koch, Wenmin Zhang, Grith Martinsen, Xin He, Simon Stisen. Estimating Net Irrigation Across the North China Plain Through Dual Modeling of Evapotranspiration. Water Resources Research. 2020; 56 (12):1.

Chicago/Turabian Style

Julian Koch; Wenmin Zhang; Grith Martinsen; Xin He; Simon Stisen. 2020. "Estimating Net Irrigation Across the North China Plain Through Dual Modeling of Evapotranspiration." Water Resources Research 56, no. 12: 1.

Preprint content
Published: 23 March 2020
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Knowledge of irrigation water use is crucial for ensuring food and water security in water scarce regions. Even though irrigation is one of the most important direct human interferences with the terrestrial water cycle, there exists limited knowledge on the extent of irrigated areas and in particular the amount of water applied for irrigation. In this study, we develop a novel approach that estimates net water loss due to irrigation and apply it over the North China Plain domain, which is a global hotspot for severe groundwater depletion caused by extensive irrigation practices. Our goal is to retrieve spatio-temporal patterns of net irrigation amounts, constituted as evaporative loss of irrigated water, at monthly timescale at 1km2 spatial resolution. The analysis is based on a direct comparison of two alternative evapotranspiration (ET) models: (1) A remote sensing based model (PT-JPL-thermal) using various MODIS products as input and (2) a one-dimensional, free drainage hydrological model (mHM). The hydrological model is purely driven by rainfall and will therefore naturally show a strong disagreement with the remote sensing based ET during periods of extensive irrigation. We use this systematic residual term that reflects a non-precipitation-based water source, as quantification of net irrigation. The hydrological model is calibrated against the remote sensing based ET at grids that are not affected by irrigation and discharge records representing natural flow. Total water storage anomalies retrieved by GRACE are utilized to evaluate the derived net irrigation amounts over the North China Plain. We find, that irrigation peaks in May, which corresponds to the peak of the growing season of winter wheat. Moreover total irrigation amounts to 116 mm per year (14km3), which is in good agreement with previous studies. The net irrigation estimates are at an unprecedented spatial and temporal resolution and are extremely valuable input for water resources management as well as for subsequent groundwater modelling where net irrigation can be utilized as pumping boundary condition.

ACS Style

Julian Koch; Simon Stisen; Xin He; Grith Martinsen. Quantifying net irrigation across the North China Plain through dual modelling of evapotranspiration. 2020, 1 .

AMA Style

Julian Koch, Simon Stisen, Xin He, Grith Martinsen. Quantifying net irrigation across the North China Plain through dual modelling of evapotranspiration. . 2020; ():1.

Chicago/Turabian Style

Julian Koch; Simon Stisen; Xin He; Grith Martinsen. 2020. "Quantifying net irrigation across the North China Plain through dual modelling of evapotranspiration." , no. : 1.

Preprint content
Published: 23 March 2020
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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 km2). 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 –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 – 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 – 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, ∆ET values are regionally small for most watersheds (~60% of all). Also, catchment-based analysis highlights that RS/WB ∆ET decreases from <100km2 to >200km2 watersheds, and about 56% (67%) of all catchments have ∆ET ±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.

ACS Style

Mohsen Soltani; Simon Stisen; Julian Koch. 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, 1 .

AMA Style

Mohsen Soltani, Simon Stisen, Julian Koch. 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; ():1.

Chicago/Turabian Style

Mohsen Soltani; Simon Stisen; Julian Koch. 2020. "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." , no. : 1.

Preprint content
Published: 23 March 2020
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The Continuous Ranked Probability Score (CRPS) is a popular evaluation tool for probabilistic forecasts. We suggest using it, outside its original scope, as an objective function in the calibration of large-scale groundwater models, due to its robustness to large residuals in the calibration data.

Groundwater models commonly require their parameters to be estimated in an optimization where some objective function measuring the model’s performance is to be minimized. Many performance metrics are squared error-based, which are known to be sensitive to large values or outliers. Consequently, an optimization algorithm using squared error-based metrics will focus on reducing the very largest residuals of the model. In many cases, for example when working with large-scale groundwater models in combination with calibration data from large datasets of groundwater heads with varying and unknown quality, there are two issues with that focus on the largest residuals: Such outliers are often i) related to observational uncertainty or ii) model structural uncertainty and model scale. Hence, fitting groundwater models to such deficiencies can be undesired, and calibration often results in parameter compensation for such deficiencies.

Therefore, we suggest the use of a CRPS-based objective function that is less sensitive to (the few) large residuals, and instead is more sensitive to fitting the majority of observations with least bias. We apply the novel CRPS-based objective function to the calibration of large-scale coupled surface-groundwater models and compare to conventional squared error-based objective functions. These calibration tests show that the CRPS-based objective function successfully limits the influence of the largest residuals and reduces overall bias. Moreover, it allows for better identification of areas where the model fails to simulate groundwater heads appropriately (e.g. due to model structural errors), that is, where model structure should be investigated.

Many real-world large-scale hydrological models face similar optimizations problems related to uncertain model structures and large, uncertain calibration datasets where observation uncertainty is hard to quantify. The CRPS-based objective function is an attempt to practically address the shortcomings of squared error minimization in model optimization, and is expected to also be of relevance outside our context of groundwater models.

ACS Style

Raphael Schneider; Hans Henriksen; Simon Stisen. The CRPS – used as a robust objective function for groundwater model calibration in light of observation and model structural uncertainty. 2020, 1 .

AMA Style

Raphael Schneider, Hans Henriksen, Simon Stisen. The CRPS – used as a robust objective function for groundwater model calibration in light of observation and model structural uncertainty. . 2020; ():1.

Chicago/Turabian Style

Raphael Schneider; Hans Henriksen; Simon Stisen. 2020. "The CRPS – used as a robust objective function for groundwater model calibration in light of observation and model structural uncertainty." , no. : 1.

Article
Published: 11 March 2020
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Irrigation is the greatest human interference with the terrestrial water cycle. Detailed knowledge on irrigation is required to better manage water resources and to increase water use efficiency (WUE). This study brings forward a novel framework to quantify net irrigation at monthly timescale at a spatial resolution of 1 kmproviding unprecedented spatial and temporal detail. Net irrigation refers to the evaporative loss of irrigation water. The study is conducted in the Haihe River Basin (HRB) in China encompassing the North China Plain (NCP), a global hotspot of groundwater depletion. Net irrigation is estimated based on the systematic evapotranspiration (ET) residuals between a remote sensing based model and a hydrologic model that does not include an irrigation scheme. The results suggest an average annual net irrigation of 126 mm (15.2 km) for NCP and 108 mm (18.6 km) for HRB. It is found that net irrigation can be estimated with higher fidelity for winter crops than for summer crops. The simulated water balance of the HRB was evaluated with GRACE data and it was found that the net irrigation estimates could close the water balance gap. Annual winter wheat classifications reveal an increasing crop area with a trend of 2200 km yr. This trend is not accompanied by a likewise increasing trend in irrigation, which suggests an increased WUE in the NCP. The proposed framework can easily be scaled up or transferred to other regions and support decision makers to tackle irrigation induced water crises and support sustainable water management.

ACS Style

Julian KochiD; Wenmin ZHANGiD; Grith Martinsen; Xin Heid; Simon Stisen. Estimating net irrigation across the North China Plain through dual modelling of evapotranspiration. 2020, 1 .

AMA Style

Julian KochiD, Wenmin ZHANGiD, Grith Martinsen, Xin Heid, Simon Stisen. Estimating net irrigation across the North China Plain through dual modelling of evapotranspiration. . 2020; ():1.

Chicago/Turabian Style

Julian KochiD; Wenmin ZHANGiD; Grith Martinsen; Xin Heid; Simon Stisen. 2020. "Estimating net irrigation across the North China Plain through dual modelling of evapotranspiration." , no. : 1.

Preprint content
Published: 09 March 2020
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In the era of big data, missing data imputation remains a delicate topic for both the analysis of natural processes and to provide input data for physical models. We propose here a comparative study for missing data imputation on daily rainfall, a variable that can exhibit a complex structure composed of a dry/wet pattern and anisotropic sharp variations.

The seven algorithms considered can be grouped in two families: geostatistical interpolation techniques based on inverse-distance weighting and Kriging, widely used in gap-filling [1], and data-driven techniques based on the analysis of historical data patterns. This latter family of algorithms has been already applied to rainfall generation [2, 3], but it is not originally suitable to historical datasets presenting many data gaps. This happens because they usually operate in a rigid framework where, when a rainfall value is estimated for a station, the others are considered as predictor variables and require to be informed. To overcome this limitation, we propose here i) an adaptation of k-nearest neighbor (KNN) and ii) a new algorithm called Vector Sampling (VS), that combines concepts of multiple-point statistics and resampling. These data-driven algorithms can draw estimations from largely and variably incomplete data patterns, allowing the target dataset to be at the same time the training dataset.

Tested on different case studies from Denmark, Australia, and Switzerland, the algorithms show a different performance that seems to be related to the terrain type: on flat terrains with spatially uniform rain events, geostatistical interpolation tends to minimize the error, while, in mountainous regions with non-stationary rainfall statistics, data mining can recover better the complex rainfall patterns. The VS algorithm, being faster than KNN and requiring minimal parametrization, turns out to be a convenient option for routine application if a representative historical dataset is available. VS is open-source and freely available at https://bitbucket.org/orianif/vs/src/master/.

 

REFERENCES:

[1] Di Piazza, A., F. Lo Conti, L. V. Noto, F. Viola, and G. La Loggia, 2011: Comparative analysis of different techniques for spatial interpolation of rainfall data to create a serially complete monthly time series of precipitation for Sicily, Italy. International Journal of Applied Earth Observation and Geoinformation, 13 (3), 396–408, https://doi.org/10.1016/j.jag.2011.01.005

[2] Oriani, F., J. Straubhaar, P. Renard, and G. Mariethoz, 2014: Simulation of rainfall time series from different climatic regions using the direct sampling technique. Hydrology and Earth System Sciences, 18 (8), 3015–3031, https://doi.org/10.5194/hess-18-3015-2014

[3] Apipattanavis, S., G. Podesta, B. Rajagopalan, and R. W. Katz, 2007: A semiparametric multivariate and multisite weather generator. Water Resources Research, 43 (11), W11 401, https://doi.org/10.1029/2006WR005714

ACS Style

Fabio Oriani; Simon Stisen; Mehmet C. Demirel; Gregoire Mariethoz. Missing data imputation for multisite rainfall networks: a comparison between geostatistical interpolation and data-mining estimation on different terrain types. 2020, 1 .

AMA Style

Fabio Oriani, Simon Stisen, Mehmet C. Demirel, Gregoire Mariethoz. Missing data imputation for multisite rainfall networks: a comparison between geostatistical interpolation and data-mining estimation on different terrain types. . 2020; ():1.

Chicago/Turabian Style

Fabio Oriani; Simon Stisen; Mehmet C. Demirel; Gregoire Mariethoz. 2020. "Missing data imputation for multisite rainfall networks: a comparison between geostatistical interpolation and data-mining estimation on different terrain types." , no. : 1.

Preprint content
Published: 09 March 2020
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About half of the Danish agricultural land is artificially drained to make land arable and increase crop yield. Those artificial drains, mostly in the form on tile drains, have a significant effect on the groundwater flow patterns and the whole water cycle. Consequently, the drainage system must also be represented in hydrological models that are used to understand and simulate, for example, recharge patterns, groundwater flow paths, or the transport and retention of nutrients. However, representation of drain in regional- and large-scale hydrological models is challenging due to i) issues with scale, ii) a lack of data on the distribution of the drain network, and iii) a lack of direct observations of drain flow. This calls for more indirect methods to inform such models.

We assume that drain flow leaves a signal in certain hydrograph signatures, as it impacts the generation of streamflow. Based on a dataset of observed discharge covering all of Denmark, and simulation results from regional-scale hydrological models, we use machine learning regressors to shed light on possible correlations between hydrograph signatures and artificial drainage. Building up on this step, we run a series of calibration exercises on a hydrological model of the agriculturally dominated Norsminde catchment, Denmark (~100 km2). The model is set up in the DHI MIKE SHE software, as distributed coupled groundwater-surface water models with a grid size of 100 m. The different calibration exercises differed in the objective functions used: either we only use conventional stream flow metrics (KGE), or also include hydrograph signatures that showed sensitive towards drain flow in our regression analysis. We then evaluate the results from the different calibration exercises, in terms of how well the model reproduces directly observed drain flow, and spatial drainage patterns.

Despite including hydrologic signatures in the calibration process, the representation of drain flow in large-scale models remains challenging. Eventually, the insight gained from this and similar studies will be incorporated in the National Water Resources Model for Denmark, to help improving national targeted regulation of nitrate application through fertilizers.

ACS Style

Simon Stisen; Raphael Schneider; Anker Lajer Højberg. Including hydrologic signatures in the calibration of a groundwater-surface water model to improve representation of artificial drain. 2020, 1 .

AMA Style

Simon Stisen, Raphael Schneider, Anker Lajer Højberg. Including hydrologic signatures in the calibration of a groundwater-surface water model to improve representation of artificial drain. . 2020; ():1.

Chicago/Turabian Style

Simon Stisen; Raphael Schneider; Anker Lajer Højberg. 2020. "Including hydrologic signatures in the calibration of a groundwater-surface water model to improve representation of artificial drain." , no. : 1.

Preprint content
Published: 16 January 2020
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Groundwater models require parameter optimization based on the minimization of objective functions describing, for example, the residual between observed and simulated groundwater head. At larger scales, constraining these models requires large datasets of groundwater head observations, due to the size of the inverse problem. These observations are typically only available from databases comprised of varying quality data from a variety of sources and will be associated with unknown observational uncertainty. At the same time the model structure, especially the hydrogeological description, will inevitably be a simplification of the complex natural system. As a result, calibration of groundwater models often results in parameter compensation for model structural deficiency. This problem can be amplified by the application of common squared error-based performance criteria, which are most sensitive to the largest errors. We assume that the residuals that remain large during the optimization process likely do so because of either model structural error or observation error. Based on this assumption it is desirable to design an objective function that is less sensitive to these large residuals of low probability, and instead favours the majority of observations that can fit the given model structure. We suggest a Continuous Ranked Probability Score (CRPS) based objective function that limits the influence of large residuals in the optimization process as the metric puts more emphasis on the position of the residual along the cumulative distribution function than on the magnitude of the residual. The CRPS-based objective function was applied in two regional scale coupled surface-groundwater models and compared to calibrations using conventional sum of absolute and squared errors. The optimization tests illustrated that the novel CRPS-based objective function successfully limited the dominance of large residuals in the optimization process and consistently reduced overall bias. Furthermore, it highlighted areas in the model where the structural model should be revisited.

ACS Style

Raphael Schneider; Hans Henriksen; Simon Stisen. A robust objective function for calibration of groundwater models in light of deficiencies of model structure and observations. 2020, 2020, 1 -26.

AMA Style

Raphael Schneider, Hans Henriksen, Simon Stisen. A robust objective function for calibration of groundwater models in light of deficiencies of model structure and observations. . 2020; 2020 ():1-26.

Chicago/Turabian Style

Raphael Schneider; Hans Henriksen; Simon Stisen. 2020. "A robust objective function for calibration of groundwater models in light of deficiencies of model structure and observations." 2020, no. : 1-26.

Article
Published: 06 October 2019 in Water
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Although the complexity of physically-based models continues to increase, they still need to be calibrated. In recent years, there has been an increasing interest in using new satellite technologies and products with high resolution in model evaluations and decision-making. The aim of this study is to investigate the value of different remote sensing products and groundwater level measurements in the temporal calibration of a well-known hydrologic model i.e., Hydrologiska Bryåns Vattenbalansavdelning (HBV). This has rarely been done for conceptual models, as satellite data are often used in the spatial calibration of the distributed models. Three different soil moisture products from the European Space Agency Climate Change Initiative Soil Measure (ESA CCI SM v04.4), The Advanced Microwave Scanning Radiometer on the Earth Observing System (EOS) Aqua satellite (AMSR-E), soil moisture active passive (SMAP), and total water storage anomalies from Gravity Recovery and Climate Experiment (GRACE) are collected and spatially averaged over the Moselle River Basin in Germany and France. Different combinations of objective functions and search algorithms, all targeting a good fit between observed and simulated streamflow, groundwater and soil moisture, are used to analyze the contribution of each individual source of information.

ACS Style

Mehmet Cüneyd Demirel; Alparslan Özen; Selen Orta; Emir Toker; Hatice Kübra Demir; Ömer Ekmekcioğlu; Hüsamettin Tayşi; Sinan Eruçar; Ahmet Bilal Sağ; Ömer Sarı; Ecem Tuncer; Hayrettin Hancı; Türkan Irem Özcan; Hilal Erdem; Mehmet Melih Koşucu; Eyyup Ensar Başakın; Kamal Ahmed; Awat Anwar; Muhammet Bahattin Avcuoğlu; Ömer Vanlı; Simon Stisen; Martijn J. Booij. Additional Value of Using Satellite-Based Soil Moisture and Two Sources of Groundwater Data for Hydrological Model Calibration. Water 2019, 11, 2083 .

AMA Style

Mehmet Cüneyd Demirel, Alparslan Özen, Selen Orta, Emir Toker, Hatice Kübra Demir, Ömer Ekmekcioğlu, Hüsamettin Tayşi, Sinan Eruçar, Ahmet Bilal Sağ, Ömer Sarı, Ecem Tuncer, Hayrettin Hancı, Türkan Irem Özcan, Hilal Erdem, Mehmet Melih Koşucu, Eyyup Ensar Başakın, Kamal Ahmed, Awat Anwar, Muhammet Bahattin Avcuoğlu, Ömer Vanlı, Simon Stisen, Martijn J. Booij. Additional Value of Using Satellite-Based Soil Moisture and Two Sources of Groundwater Data for Hydrological Model Calibration. Water. 2019; 11 (10):2083.

Chicago/Turabian Style

Mehmet Cüneyd Demirel; Alparslan Özen; Selen Orta; Emir Toker; Hatice Kübra Demir; Ömer Ekmekcioğlu; Hüsamettin Tayşi; Sinan Eruçar; Ahmet Bilal Sağ; Ömer Sarı; Ecem Tuncer; Hayrettin Hancı; Türkan Irem Özcan; Hilal Erdem; Mehmet Melih Koşucu; Eyyup Ensar Başakın; Kamal Ahmed; Awat Anwar; Muhammet Bahattin Avcuoğlu; Ömer Vanlı; Simon Stisen; Martijn J. Booij. 2019. "Additional Value of Using Satellite-Based Soil Moisture and Two Sources of Groundwater Data for Hydrological Model Calibration." Water 11, no. 10: 2083.

Paper
Published: 04 June 2019 in Hydrogeology Journal
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The need for regional-scale integrated hydrological models for the purpose of water resource management is increasing. Distributed physically based coupled surface-subsurface models are usually complex and contain a large amount of spatio-temporal information that leads to a relatively long forward runtime. One of the main challenges with regard to regional-scale inverse modeling relates to parameterization and how to adequately exploit the information embedded in the existing observational data while avoiding parameter identifiability issues. This study examined and compared the calibration of a “highly parameterized” model with a “classical” unit-based parameterization scheme in which the dominant geological features were assumed to be known. The physically based coupled surface-subsurface model MIKE SHE was used for conducting the study of five river basins (4,900 km2) in central Jutland in Denmark, characterized by heterogeneous geology and a considerable amount of groundwater flux across topographical catchment boundaries. The results indicated that introducing more flexibility in the parameter estimation process through a regularized approach significantly improved the model performance, in particular head and water balance errors. The highly parameterized calibration results additionally provided very useful insights into the model deficiencies in terms of conceptual model structure and incorrectly imposed boundary conditions. Furthermore, the results from data-worth analysis indicated that the highly parameterized model has more effectively utilized the information in the dataset compared to a traditional unit-based calibration approach. Le besoin de modèles hydrologiques intégrés à l’échelle régionale pour la gestion des ressources en eau est. en augmentation. Les modèles distribués à base physique, couplant surface-souterrain, sont souvent complexes et contiennent un volume d’informations spatio-temporelles important qui entraine des calculs directs relativement longs. Un des principaux défis avec les modèles régionaux inverses concerne la paramétrisation et comment exploiter de manière adéquate l’information comprise dans les données observées existantes tout en évitant les questions d’identification des paramètres. Cette étude a examiné et comparé la calibration d’un modèle « hautement paramétré » avec un schéma de paramétrisation « classique » basé sur des unités dans lesquels les caractéristiques géologiques dominantes ont été supposées connues. Le modèle à base physique couplant les écoulements de surface et souterrains, MIKE-SHE, a été utilisé pour mener l’étude de cinq bassins versants de cours d’eau (4,900 km2) dans la région centrale du Jutlan au Danemark, caractérisée par une géologie hétérogène et un écoulement d’eau souterraine considérable dans les limites topographiques des bassins versants. Les résultats ont indiqué qu’introduire plus...

ACS Style

Mehrdis Danapour; Anker Lajer Højberg; Karsten H. Jensen; Simon Stisen. Assessment of regional inter-basin groundwater flow using both simple and highly parameterized optimization schemes. Hydrogeology Journal 2019, 27, 1929 -1947.

AMA Style

Mehrdis Danapour, Anker Lajer Højberg, Karsten H. Jensen, Simon Stisen. Assessment of regional inter-basin groundwater flow using both simple and highly parameterized optimization schemes. Hydrogeology Journal. 2019; 27 (6):1929-1947.

Chicago/Turabian Style

Mehrdis Danapour; Anker Lajer Højberg; Karsten H. Jensen; Simon Stisen. 2019. "Assessment of regional inter-basin groundwater flow using both simple and highly parameterized optimization schemes." Hydrogeology Journal 27, no. 6: 1929-1947.

Journal article
Published: 09 February 2019 in Water Resources Research
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The management of water resources needs robust methods to efficiently reduce nitrate loads. Knowledge on where natural denitrification takes place in the subsurface is thereby essential. Nitrate is naturally reduced in anoxic environments and high resolution information of the redox interface, i.e. the depth of the uppermost reduced zone is crucial to understand the variability of the denitrification potential. In this study we explore the opportunity to use Random Forest (RF) regression to model redox depth across Denmark at 100m resolution based on ~13,000 boreholes as training data. We highlight the importance of expert knowledge to guide the RF model in areas where our conceptual understanding is not represented correctly in the training dataset by addition of artificial observations. We apply random forest regression kriging (RFRK) in which sequential Gaussian simulation (sGs) models the RF residuals. The RF model reaches a R2 score of 0.48 for an independent validation test. Including sGs honors observations through local conditioning and the spread of 800 realizations can be utilized to map uncertainty. Emphasis is put on adequate handling of non‐stationarities in variance and spatial correlation of the RF residuals. The RF residuals show no spatial correlation for large parts of the modelling domain and a local variance scaling method is applied to account for the non‐stationary variance. Moreover, we present and exemplify a framework where newly acquired field data can easily be integrated into RFRK to quickly update local models.

ACS Style

Julian Koch; Simon Stisen; Jens C. Refsgaard; Vibeke Ernstsen; Peter R. Jakobsen; Anker L. Højberg. Modeling Depth of the Redox Interface at High Resolution at National Scale Using Random Forest and Residual Gaussian Simulation. Water Resources Research 2019, 55, 1451 -1469.

AMA Style

Julian Koch, Simon Stisen, Jens C. Refsgaard, Vibeke Ernstsen, Peter R. Jakobsen, Anker L. Højberg. Modeling Depth of the Redox Interface at High Resolution at National Scale Using Random Forest and Residual Gaussian Simulation. Water Resources Research. 2019; 55 (2):1451-1469.

Chicago/Turabian Style

Julian Koch; Simon Stisen; Jens C. Refsgaard; Vibeke Ernstsen; Peter R. Jakobsen; Anker L. Højberg. 2019. "Modeling Depth of the Redox Interface at High Resolution at National Scale Using Random Forest and Residual Gaussian Simulation." Water Resources Research 55, no. 2: 1451-1469.

Journal article
Published: 04 September 2018 in Water
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Hydrologic models are conventionally constrained and evaluated using point measurements of streamflow, which represent an aggregated catchment measure. As a consequence of this single objective focus, model parametrization and model parameter sensitivity typically do not reflect other aspects of catchment behavior. Specifically for distributed models, the spatial pattern aspect is often overlooked. Our paper examines the utility of multiple performance measures in a spatial sensitivity analysis framework to determine the key parameters governing the spatial variability of predicted actual evapotranspiration (AET). The Latin hypercube one-at-a-time (LHS-OAT) sampling strategy with multiple initial parameter sets was applied using the mesoscale hydrologic model (mHM) and a total of 17 model parameters were identified as sensitive. The results indicate different parameter sensitivities for different performance measures focusing on temporal hydrograph dynamics and spatial variability of actual evapotranspiration. While spatial patterns were found to be sensitive to vegetation parameters, streamflow dynamics were sensitive to pedo-transfer function (PTF) parameters. Above all, our results show that behavioral model definitions based only on streamflow metrics in the generalized likelihood uncertainty estimation (GLUE) type methods require reformulation by incorporating spatial patterns into the definition of threshold values to reveal robust hydrologic behavior in the analysis.

ACS Style

Mehmet Cüneyd Demirel; Julian Koch; Gorka Mendiguren; Simon Stisen. Spatial Pattern Oriented Multicriteria Sensitivity Analysis of a Distributed Hydrologic Model. Water 2018, 10, 1188 .

AMA Style

Mehmet Cüneyd Demirel, Julian Koch, Gorka Mendiguren, Simon Stisen. Spatial Pattern Oriented Multicriteria Sensitivity Analysis of a Distributed Hydrologic Model. Water. 2018; 10 (9):1188.

Chicago/Turabian Style

Mehmet Cüneyd Demirel; Julian Koch; Gorka Mendiguren; Simon Stisen. 2018. "Spatial Pattern Oriented Multicriteria Sensitivity Analysis of a Distributed Hydrologic Model." Water 10, no. 9: 1188.

Preprint
Published: 11 August 2018
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Hydrologic models are conventionally constrained and evaluated using point measurements of streamflow, which represents an aggregated catchment measure. As a consequence of this single objective focus, model parametrization and model parameter sensitivity are typically not reflecting other aspects of catchment behavior. Specifically for distributed models, the spatial pattern aspect is often overlooked. Our paper examines the utility of multiple performance measures in a spatial sensitivity analysis framework to determine the key parameters governing the spatial variability of predicted actual evapotranspiration (AET). Latin hypercube one-at-a-time (LHS-OAT) sampling strategy with multiple initial parameter sets was applied using the mesoscale hydrologic model (mHM) and a total of 17 model parameters were identified as sensitive. The results indicate different parameter sensitivities for different performance measures focusing on temporal hydrograph dynamics and spatial variability of actual evapotranspiration. While spatial patterns were found to be sensitive to vegetation parameters, streamflow dynamics were sensitive to pedo-transfer function (PTF) parameters. Above all, our results show that behavioral model definition based only on streamflow metrics in the generalized likelihood uncertainty estimation (GLUE) type methods require reformulation by incorporating spatial patterns into the definition of threshold values to reveal robust hydrologic behavior in the analysis.

ACS Style

Mehmet Cüneyd Demirel; Julian Koch; Gorka Mendiguren; Simon Stisen. Spatial Pattern Oriented Multi-Criteria Sensitivity Analysis of a Distributed Hydrologic Model. 2018, 1 .

AMA Style

Mehmet Cüneyd Demirel, Julian Koch, Gorka Mendiguren, Simon Stisen. Spatial Pattern Oriented Multi-Criteria Sensitivity Analysis of a Distributed Hydrologic Model. . 2018; ():1.

Chicago/Turabian Style

Mehmet Cüneyd Demirel; Julian Koch; Gorka Mendiguren; Simon Stisen. 2018. "Spatial Pattern Oriented Multi-Criteria Sensitivity Analysis of a Distributed Hydrologic Model." , no. : 1.

Research article
Published: 19 July 2018 in Hydrological Processes
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Spatially distributed hydrological models are traditionally calibrated and evaluated against few spatially aggregated observations such as river discharge. This model evaluation approach does not enable an assessment of the model predictive capabilities of other hydrological states and fluxes nor does it give any insight into the model ability to mimic the spatial patterns within a catchment. The current study explores a multi‐variable optimization of a complex coupled surface‐subsurface‐atmosphere model at the catchment scale in an attempt to move beyond simple runoff calibration. The model is evaluated against five independent observational datasets of discharge (Q), hydraulic head (h), actual evapotranspiration (ET), soil moisture (SM) and remotely sensed land surface temperature (LST). It is shown that a balanced optimization can be achieved where errors on objective functions (OF) for all five observation data sets can be reduced simultaneously. Additionally, the multi‐variable calibration proved more robust, compared to calibration against Q and h only, during the validation period, even for Q and h. The current parameterization and calibration framework was mainly suitable for reducing model biases and allowed only limited improvements in the spatio‐temporal patterns of the model simulations. This points towards development of better parametrization schemes that will allow simulated spatial patterns to adjust during calibration. Additionally, analysis showed that systematic spatial patterns in the errors of the LST maps could be a very valuable diagnostic tool for assessing deficiencies in the model structure, spatial parameterization or process description.

ACS Style

Simon Stisen; Julian Koch; Torben O. Sonnenborg; Jens Christian Refsgaard; Simone Bircher; Rasmus Ringgaard; Karsten H. Jensen. Moving beyond run-off calibration-Multivariable optimization of a surface-subsurface-atmosphere model. Hydrological Processes 2018, 32, 2654 -2668.

AMA Style

Simon Stisen, Julian Koch, Torben O. Sonnenborg, Jens Christian Refsgaard, Simone Bircher, Rasmus Ringgaard, Karsten H. Jensen. Moving beyond run-off calibration-Multivariable optimization of a surface-subsurface-atmosphere model. Hydrological Processes. 2018; 32 (17):2654-2668.

Chicago/Turabian Style

Simon Stisen; Julian Koch; Torben O. Sonnenborg; Jens Christian Refsgaard; Simone Bircher; Rasmus Ringgaard; Karsten H. Jensen. 2018. "Moving beyond run-off calibration-Multivariable optimization of a surface-subsurface-atmosphere model." Hydrological Processes 32, no. 17: 2654-2668.

Journal article
Published: 15 May 2018 in Geoscientific Model Development
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The process of model evaluation is not only an integral part of model development and calibration but also of paramount importance when communicating modelling results to the scientific community and stakeholders. The modelling community has a large and well-tested toolbox of metrics to evaluate temporal model performance. In contrast, spatial performance evaluation does not correspond to the grand availability of spatial observations readily available and to the sophisticate model codes simulating the spatial variability of complex hydrological processes. This study makes a contribution towards advancing spatial-pattern-oriented model calibration by rigorously testing a multiple-component performance metric. The promoted SPAtial EFficiency (SPAEF) metric reflects three equally weighted components: correlation, coefficient of variation and histogram overlap. This multiple-component approach is found to be advantageous in order to achieve the complex task of comparing spatial patterns. SPAEF, its three components individually and two alternative spatial performance metrics, i.e. connectivity analysis and fractions skill score, are applied in a spatial-pattern-oriented model calibration of a catchment model in Denmark. Results suggest the importance of multiple-component metrics because stand-alone metrics tend to fail to provide holistic pattern information. The three SPAEF components are found to be independent, which allows them to complement each other in a meaningful way. In order to optimally exploit spatial observations made available by remote sensing platforms, this study suggests applying bias insensitive metrics which further allow for a comparison of variables which are related but may differ in unit. This study applies SPAEF in the hydrological context using the mesoscale Hydrologic Model (mHM; version 5.8), but we see great potential across disciplines related to spatially distributed earth system modelling.

ACS Style

Julian Koch; Mehmet Cüneyd Demirel; Simon Stisen. The SPAtial EFficiency metric (SPAEF): multiple-component evaluation of spatial patterns for optimization of hydrological models. Geoscientific Model Development 2018, 11, 1873 -1886.

AMA Style

Julian Koch, Mehmet Cüneyd Demirel, Simon Stisen. The SPAtial EFficiency metric (SPAEF): multiple-component evaluation of spatial patterns for optimization of hydrological models. Geoscientific Model Development. 2018; 11 (5):1873-1886.

Chicago/Turabian Style

Julian Koch; Mehmet Cüneyd Demirel; Simon Stisen. 2018. "The SPAtial EFficiency metric (SPAEF): multiple-component evaluation of spatial patterns for optimization of hydrological models." Geoscientific Model Development 11, no. 5: 1873-1886.

Journal article
Published: 20 February 2018 in Hydrology and Earth System Sciences
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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.

ACS Style

Mehmet C. Demirel; Juliane Mai; Gorka Mendiguren; Julian Koch; Luis Samaniego; Simon Stisen. Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model. Hydrology and Earth System Sciences 2018, 22, 1299 -1315.

AMA Style

Mehmet C. Demirel, Juliane Mai, Gorka Mendiguren, Julian Koch, Luis Samaniego, Simon Stisen. Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model. Hydrology and Earth System Sciences. 2018; 22 (2):1299-1315.

Chicago/Turabian Style

Mehmet C. Demirel; Juliane Mai; Gorka Mendiguren; Julian Koch; Luis Samaniego; Simon Stisen. 2018. "Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model." Hydrology and Earth System Sciences 22, no. 2: 1299-1315.