This page has only limited features, please log in for full access.

Unclaimed
Julian Koch
Department of hydrology, Geological Survey of Denmark and Greenland, 1350 Copenhagen, Denmark

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 19 June 2021 in Remote Sensing
Reads 0
Downloads 0

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
Reads 0
Downloads 0

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.

Preprint content
Published: 04 March 2021
Reads 0
Downloads 0

One active journal. Fourteen legacy titles. More than 3000 articles published since 1893 – some digitised, some not. One full-time member of staff. A small team of dedicated geoscientists. Limited budget. PlanS. Open-source journal software. If these are the ingredients, what is the recipe? 

Like many surveys, the Geological Survey of Denmark and Greenland (GEUS) has a long history of publishing. Our full catalogue of titles extends back to 1893 and our current title, GEUS Bulletin (www.geusbulletin.org; formerly Geological Survey of Denmark and Greenland Bulletin), has been active since 2003. Our journals have always been grassroots initiatives – run by scientists, for scientists. But two years ago, amid the fast-changing demands of digital publishing, the Survey faced a quandary: should we continue publishing our own journal? At a time of rapid proliferation of journals for any discipline imaginable, what niche did a geographically-focused journal fill? What should we modernise? Could we relaunch as an online, diamond open-access journal on our existing budget? Could we implement more of the services our authors wanted and attract more authors beyond our traditional audience? 

Two years later, we have successfully re-launched our collection of journals, without increasing our overall budget. Using open-source solutions, we have transformed our print-focused publication workflow to a new online, open-access platform and data repository. We are currently migrating our entire back catalogue of legacy titles to the same platform. Although we only have visitor data for our new platform since November 2020, we can see early signs of increased article views (c. +82% in Nov–Dec 2020, compared with the same months in 2018 and 2019) and a jump in traffic from external websites like Google Scholar (from 5% before re-launch to 35% after re-launch). In this presentation, we present a recipe that we hope other geological surveys, societies and institutions can follow when launching (or relaunching) their own journals using open-source solutions. We review the options available to small survey or society publishers on a limited budget, from journal hosting to typesetting. We highlight the advantages of non-profit open-access publishing and open source, community-driven solutions that currently exist. We close by highlighting the barriers that remain for small non-profit publishers when balancing discoverability, journal impact and compliance with the latest open-access initiatives such as Plan S, and web accessibility regulations.  

It is still early days for GEUS Bulletin, but we see the adoption of open-source platforms as the key ingredient to our potential for success in the coming years. Such platforms allow us to offer diamond open-access publishing and a data repository, while maintaining our non-profit, publishing model with neither author nor reader fees. 

ACS Style

Catherine Jex; Wiliiam Colgan; Michael Bryld Wessel Fyhn; Adam A Garde; Jon R Ineson; Adam Hambly; Kim Hyojin; Julian Koch; Thomas Find Kokfelt; Signe Hillerup Larsen; Sofie Lindström; Stefanie Lode; Rasmus Bødker Madsen; Mette Olivarius; Kerstin Saalmann; Sara Salehi; Marit-Solveig Seidenkrantz; Lars Stemmerik; Kristian Svennevig. A recipe for launching a diamond open-access journal with a century of geological knowledge in the pantry: Lessons learned from GEUS Bulletin. 2021, 1 .

AMA Style

Catherine Jex, Wiliiam Colgan, Michael Bryld Wessel Fyhn, Adam A Garde, Jon R Ineson, Adam Hambly, Kim Hyojin, Julian Koch, Thomas Find Kokfelt, Signe Hillerup Larsen, Sofie Lindström, Stefanie Lode, Rasmus Bødker Madsen, Mette Olivarius, Kerstin Saalmann, Sara Salehi, Marit-Solveig Seidenkrantz, Lars Stemmerik, Kristian Svennevig. A recipe for launching a diamond open-access journal with a century of geological knowledge in the pantry: Lessons learned from GEUS Bulletin. . 2021; ():1.

Chicago/Turabian Style

Catherine Jex; Wiliiam Colgan; Michael Bryld Wessel Fyhn; Adam A Garde; Jon R Ineson; Adam Hambly; Kim Hyojin; Julian Koch; Thomas Find Kokfelt; Signe Hillerup Larsen; Sofie Lindström; Stefanie Lode; Rasmus Bødker Madsen; Mette Olivarius; Kerstin Saalmann; Sara Salehi; Marit-Solveig Seidenkrantz; Lars Stemmerik; Kristian Svennevig. 2021. "A recipe for launching a diamond open-access journal with a century of geological knowledge in the pantry: Lessons learned from GEUS Bulletin." , no. : 1.

Journal article
Published: 25 February 2021 in Remote Sensing
Reads 0
Downloads 0

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.

Journal article
Published: 23 December 2020 in Water Resources Research
Reads 0
Downloads 0

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
Reads 0
Downloads 0

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
Reads 0
Downloads 0

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.

Article
Published: 11 March 2020
Reads 0
Downloads 0

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.

Research article
Published: 15 November 2019 in Hydrology and Earth System Sciences
Reads 0
Downloads 0

Machine learning provides great potential for modelling hydrological variables at a spatial resolution beyond the capabilities of physically based modelling. This study features an application of random forests (RF) to model the depth to the shallow water table, for a wintertime minimum event, at a 50 m resolution over a 15 000 km2 domain in Denmark. In Denmark, the shallow groundwater poses severe risks with respect to groundwater-induced flood events, affecting both urban and agricultural areas. The risk is especially critical in wintertime, when the shallow groundwater is close to terrain. In order to advance modelling capabilities of the shallow groundwater system and to provide estimates at the scales required for decision-making, this study introduces a simple method to unify RF and physically based modelling. Results from the national water resources model in Denmark (DK-model) at a 500 m resolution are employed as covariates in the RF model. Thus, RF ensures physical consistency at a coarse scale and fully exhausts high-resolution information from readily available environmental variables. The vertical distance to the nearest water body was rated as the most important covariate in the trained RF model followed by the DK-model. The evaluation test of the trained RF model was very satisfying with a mean absolute error of 76 cm and a coefficient of determination of 0.56. The resulting map underlines the severity of groundwater flooding risk in Denmark, as the average depth to the shallow groundwater is 1.9 m and approximately 29 % of the area is characterized as having a depth of less than 1 m during a typical wintertime minimum event. This study brings forward a novel method for assessing the spatial patterns of covariate importance of the RF predictions that contributes to an increased interpretability of the RF model. Quantifying the uncertainty of RF models is still rare for hydrological applications. Two approaches, namely random forests regression kriging (RFRK) and quantile regression forests (QRF), were tested to estimate uncertainties related to the predicted groundwater levels.

ACS Style

Julian Koch; Helen Berger; Hans Jørgen Henriksen; Torben Obel Sonnenborg. Modelling of the shallow water table at high spatial resolution using random forests. Hydrology and Earth System Sciences 2019, 23, 4603 -4619.

AMA Style

Julian Koch, Helen Berger, Hans Jørgen Henriksen, Torben Obel Sonnenborg. Modelling of the shallow water table at high spatial resolution using random forests. Hydrology and Earth System Sciences. 2019; 23 (11):4603-4619.

Chicago/Turabian Style

Julian Koch; Helen Berger; Hans Jørgen Henriksen; Torben Obel Sonnenborg. 2019. "Modelling of the shallow water table at high spatial resolution using random forests." Hydrology and Earth System Sciences 23, no. 11: 4603-4619.

Preprint content
Published: 22 July 2019
Reads 0
Downloads 0
ACS Style

Julian Koch. Author response to Katherine Ransom. 2019, 1 .

AMA Style

Julian Koch. Author response to Katherine Ransom. . 2019; ():1.

Chicago/Turabian Style

Julian Koch. 2019. "Author response to Katherine Ransom." , no. : 1.

Preprint content
Published: 22 July 2019
Reads 0
Downloads 0
ACS Style

Julian Koch. Author response to Anders Bjørn Møller. 2019, 1 .

AMA Style

Julian Koch. Author response to Anders Bjørn Møller. . 2019; ():1.

Chicago/Turabian Style

Julian Koch. 2019. "Author response to Anders Bjørn Møller." , no. : 1.

Preprint content
Published: 10 May 2019
Reads 0
Downloads 0

Machine learning provides a great potential to model hydrological variables at a spatial resolution beyond the capabilities of traditional physically-based modelling. This study features an application of Random Forests (RF) to model the depth to the shallow water table, for a wintertime minimum event, at 50 m resolution over a 15,000 km2 large domain in Denmark. In Denmark, the shallow groundwater poses severe risks of groundwater induced flood events affecting both, urban and agricultural areas. The risk is especially critical in wintertime, when the shallow groundwater is close to terrain. In order to advance modelling capabilities of the shallow groundwater system and to provide estimates at scales required for decision making, this study introduces a simple method to unify RF and physically-based modelling. Results from the national water resources model in Denmark (DK-model) at 500 m resolution are employed as covariate in the RF model. Thereby, RF ensures physical consistency at coarse scale and fully exhausts high-resolution information from readily available environmental variables. The vertical distance to the nearest waterbody was rated the most important covariate in the trained RF model followed by the DK-model. The validation test of the trained RF model was very satisfying with a mean absolute error of 79 cm and a coefficient of determination of 0.55. The resulting map underlines the severity of groundwater flooding risk in Denmark, as the average depth to the shallow groundwater is 1.9 m and approximately 29 % of the area is characterised with a depth less than 1 m during a typical wintertime minimum event. This study brings forward a novel method to assess the spatial patterns of covariate importance of the RF predictions which contributes to an increased interpretability of the RF model. Quantifying uncertainty of RF models is still rare for hydrological applications. Two approaches, namely Random Forests Regression Kriging (RFRK) and Quantile Regression Forests (QRF) were tested to estimate uncertainties related to the predicted groundwater levels. This study argues that the uncertainty sources captured by RFRK and QRF can be considered independent and hence, they can be combined to a total variance through simple uncertainty propagation.

ACS Style

Julian Koch; Helen Berger; Hans Henriksen; Torben Obel Sonnenborg. Modelling of the shallow water table at high spatial resolution using Random Forests. 2019, 1 -26.

AMA Style

Julian Koch, Helen Berger, Hans Henriksen, Torben Obel Sonnenborg. Modelling of the shallow water table at high spatial resolution using Random Forests. . 2019; ():1-26.

Chicago/Turabian Style

Julian Koch; Helen Berger; Hans Henriksen; Torben Obel Sonnenborg. 2019. "Modelling of the shallow water table at high spatial resolution using Random Forests." , no. : 1-26.

Journal article
Published: 08 May 2019 in Advances in Water Resources
Reads 0
Downloads 0

Regardless of the complexity of the hydrological model employed, uncertainty assessment (UA) is predominantly performed for the aggregated catchment response discharge. For coupled integrated models that simulate various hydrological states and fluxes on a grid cell basis, this represents a severe shortcoming. We test a simple data-driven technique (k-NN resampling) to evaluate its ability to provide reliable residual uncertainty estimates for the multi-variable (discharge, hydraulic head, soil moisture and actual evapotranspiration), deterministic output of two coupled groundwater-surface water models with different complexities. Being a nonparametric method, no explicit prior assumptions about the error distribution of different hydrological variables are required. When conditioning the algorithm, we propose to limit the number of error lags to be included based on inspection of the partial autocorrelation function (PACF). Our results confirm previous findings regarding reliability and robustness of the k-NN technique for discharge simulations and conclude that k-NN resampling also provides reliable and robust results for other variables like hydraulic head, soil moisture and actual evapotranspiration, even for underlying hydrological models with varying levels of performance. The 90 % prediction intervals (PI) capture the observations in the testing period satisfactorily for all hydrological variables (92.6–97.3 %), while Alpha indices (0.84–0.95) indicate very reliable PIs for all error quantiles. Differences in error structure between hydrological variables are successfully inferred from historical data and reflected in the results. We conclude that k-NN resampling represents a potent, cost-efficient UA technique for applications in operational hydrology, facilitating a near-simultaneous, easy uncertainty assessment for various outputs of computationally heavy hydrological models.

ACS Style

L.B. Ehlers; Omar Wani; J. Koch; T.O. Sonnenborg; J.C. Refsgaard. Using a simple post-processor to predict residual uncertainty for multiple hydrological model outputs. Advances in Water Resources 2019, 129, 16 -30.

AMA Style

L.B. Ehlers, Omar Wani, J. Koch, T.O. Sonnenborg, J.C. Refsgaard. Using a simple post-processor to predict residual uncertainty for multiple hydrological model outputs. Advances in Water Resources. 2019; 129 ():16-30.

Chicago/Turabian Style

L.B. Ehlers; Omar Wani; J. Koch; T.O. Sonnenborg; J.C. Refsgaard. 2019. "Using a simple post-processor to predict residual uncertainty for multiple hydrological model outputs." Advances in Water Resources 129, no. : 16-30.

Journal article
Published: 09 February 2019 in Water Resources Research
Reads 0
Downloads 0

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
Reads 0
Downloads 0

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
Reads 0
Downloads 0

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.

Journal article
Published: 01 August 2018 in Journal of Hydrometeorology
Reads 0
Downloads 0

With the advance of the weather radar technology, dual-polarization (dual-pol) radar data are now available for hydrological studies, which go beyond the traditional rainfall products relying purely on rain gauge data. Previous studies have focused on the evaluation of rainfall products and their hydrological responses using point-based observational data; however, spatial patterns of simulated hydrological variables are equally important to be considered in order to fully address the distributed effect of the precipitation estimates. In the present study, we compare three rainfall estimations based on rain gauge, single-polarization, and dual-pol radar data. Special attention is given to the use of the two radar products and their corresponding hydrological simulations of both surface water and groundwater. Performance of the hydrological simulations is evaluated based first on traditional point-based observations of stream discharge and groundwater head, and second on remotely sensed land surface temperature data. For the latter, the empirical orthogonal function analysis, which quantifies spatial pattern similarities, is employed. The Skjern River catchment in western Denmark is selected as the study site, and the results show that all three models perform equally well in terms of the traditional aggregated evaluation criteria, such as Nash–Sutcliffe efficiency (NSE) and RMSE on time series data. It is found that the differences of simulated hydrological spatial patterns are sensitive to rainfall signal intensity, as well as the simulation scale in space (<100 km2) and time (subdaily). Our study suggests that the currently available observational data have limited capabilities to clearly differentiate the performance of the three applied models due to the low resolution.

ACS Style

Xin He; Julian Koch; Chunmiao Zheng; Thomas Bøvith; Karsten H. Jensen. Comparison of Simulated Spatial Patterns Using Rain Gauge and Polarimetric-Radar-Based Precipitation Data in Catchment Hydrological Modeling. Journal of Hydrometeorology 2018, 19, 1273 -1288.

AMA Style

Xin He, Julian Koch, Chunmiao Zheng, Thomas Bøvith, Karsten H. Jensen. Comparison of Simulated Spatial Patterns Using Rain Gauge and Polarimetric-Radar-Based Precipitation Data in Catchment Hydrological Modeling. Journal of Hydrometeorology. 2018; 19 (8):1273-1288.

Chicago/Turabian Style

Xin He; Julian Koch; Chunmiao Zheng; Thomas Bøvith; Karsten H. Jensen. 2018. "Comparison of Simulated Spatial Patterns Using Rain Gauge and Polarimetric-Radar-Based Precipitation Data in Catchment Hydrological Modeling." Journal of Hydrometeorology 19, no. 8: 1273-1288.

Research article
Published: 19 July 2018 in Hydrological Processes
Reads 0
Downloads 0

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
Reads 0
Downloads 0

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
Reads 0
Downloads 0

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.