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Remote Sensing for Distributed Hydrologic Models: New Satellite Data, Model Parametrization and Spatial Metrics to Calibrate and Evaluate Models

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 11567

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Special Issue Editors

Dr. Mehmet Cüneyd Demirel

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1. Department of Civil Engineering, Istanbul Technical University, 34469 Maslak, Istanbul, Turkey
2. Geological Survey of Denmark and Greenland, Øster Voldgade 10, 1350 Copenhagen, Denmark
Interests: hydrologic modeling; climate change; low flows; uncertainty analysis; spatial calibration
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Dr. Julian Koch

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Department of Hydrology, Geological Survey of Denmark and Greenland, 1350 Copenhagen, Denmark
Interests: hydrological modelling; remote sensing; machine learning; evapotranspiration
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Dr. Hongxiang Yan

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Pacific Northwest National Laboratory, Richland, WA, USA
Interests: hydrologic modeling and forecasting; land data assimilation; extreme events; climate change impact
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Dr. Fabio Oriani

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Institute of Earth Surface Dynamics (IDYST), University of Lausanne, Lausanne, Switzerland
Interests: stochastic meteorology; hydrology; remote sensing; climate change
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Prof. Dr. Gregoire Mariethoz

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Institute of Earth Surface Dynamics, University of Lausanne, Lausanne, Switzerland
Interests: The main research interests of the research group reside in the development of stochastic methods that characterize the spatial and temporal variability inherent to natural systems, in particular related to the water cycle. We use numerical techniques using high-order, nonparametric statistics. These allow us to analyze complex datasets such as remote sensing data or the outputs of complex models (climate models or flow/transport models). The work pursued is at the frontier between Earth modeling and computer science, with a strong emphasis on stochastic models, training images and example-based modeling.
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Dr. Martijn J. Booij

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Department of Water Engineering and Management, Faculty of Engineering Technology, University of Twente, 7500 AE Enschede, The Netherlands
Interests: catchment hydrology; hydrological modelling; environmental change impacts; uncertainty analysis
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Special Issue Information

Dear Colleagues,

In this special issue of Remote Sensing we aim to collect contributions integrating satellite based remote sensing data into distributed hydrologic models using suitable or new spatial performance metrics (such as SSIM and SPAEF etc.) to evaluate spatial pattern agreement of satellite based estimations and hydrological predictions. Reviews and case studies reporting recent advancements in spatial metrics and remote sensing for precipitation, land surface temperature, actual evapotranspiration estimation, soil moisture, snow coverage, terrestrial water storage (MODIS, AMSR-E, Sentinel, SMOS, SMAP, GRACE etc.) are welcome. We are particularly interested in how satellite based data can improve spatial-temporal behavior of distributed hydrologic models and how they can be used in calibration and validation of hydrologic models. Also we aim to attract novel approaches for parameter regionalization and model parametrizations adding flexibility to the model structures to fit their outputs to the remote sensing data.

You may choose our Joint Special Issue in Hydrology.

Assoc. Prof. Mehmet Cüneyd Demirel
Dr. Julian Koch
Dr. Hongxiang Yan
Dr. Fabio Oriani
Prof. Dr. Gregoire Mariethoz
Assoc. Prof. Dr. ir Martijn J. Booij
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

precipitation
spatial hydrology
soil moisture
spatial calibration
groundwater hydrology
bias insensitive metrics
evapotranspiration
hydrologic model calibration
spatial metrics
SMAP
GRACE
MODIS
Histogram match
Spatial efficiency metric (SPAEF)
Structural similarity index (SSIM)

Published Papers (4 papers)

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Research

19 pages, 3065 KiB

Open AccessArticle

Climate Normalized Spatial Patterns of Evapotranspiration Enhance the Calibration of a Hydrological Model

by Julian Koch, Mehmet Cüneyd Demirel and Simon Stisen

Remote Sens. 2022, 14(2), 315; https://doi.org/10.3390/rs14020315 - 11 Jan 2022

Cited by 6 | Viewed by 2080

Abstract

Spatial pattern-oriented evaluations of distributed hydrological models have contributed towards an improved realism of hydrological simulations. This advancement has been supported by the broad range of readily available satellite-based datasets of key hydrological variables, such as evapotranspiration (ET). At larger scale, spatial patterns of ET are often driven by underlying climate gradients, and with this study, we argue that gradient dominated patterns may hamper the potential of spatial pattern-oriented evaluation frameworks. We hypothesize that the climate control of spatial patterns of ET overshadows the effect model parameters have on the simulated patterns. To address this, we propose a climate normalization strategy. This is demonstrated for the Senegal River basin as a modeling case study, where the dominant north-south precipitation gradient is the main driver of the observed hydrological variability. We apply the mesoscale Hydrological Model (mHM) to model the hydrological cycle of the Senegal River basin. Two multi-objective calibration experiments investigate the effect of climate normalization. Both calibrations utilize observed discharge (Q) in combination with remote sensing ET data, where one is based on the original ET pattern and the other utilizes the normalized ET pattern. As objective functions we applied the Kling-Gupta-Efficiency (KGE) for Q and the Spatial Efficiency (SPAEF) for ET. We identify parameter sets that balance the tradeoffs between the two independent observations and find that the calibration using the normalized ET pattern does not compromise the spatial pattern performance of the original pattern. However, vice versa, this is not necessarily the case, since the calibration using the original ET pattern showed a poorer performance for the normalized pattern, i.e., a 30% decrease in SPAEF. Both calibrations reached comparable performance of Q, i.e., KGE around 0.7. With this study, we identified a general shortcoming of spatial pattern-oriented model evaluations using ET in basins dominated by a climate gradient, but we argue that this also applies to other variables such as, soil moisture or land surface temperature. Full article

(This article belongs to the Special Issue Remote Sensing for Distributed Hydrologic Models: New Satellite Data, Model Parametrization and Spatial Metrics to Calibrate and Evaluate Models)

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23 pages, 6023 KiB

Open AccessArticle

Spatial Patterns in Actual Evapotranspiration Climatologies for Europe

by Simon Stisen, Mohsen Soltani, Gorka Mendiguren, Henrik Langkilde, Monica Garcia and Julian Koch

Remote Sens. 2021, 13(12), 2410; https://doi.org/10.3390/rs13122410 - 19 Jun 2021

Cited by 16 | Viewed by 3664

Abstract

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. Full article

(This article belongs to the Special Issue Remote Sensing for Distributed Hydrologic Models: New Satellite Data, Model Parametrization and Spatial Metrics to Calibrate and Evaluate Models)

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24 pages, 7756 KiB

Open AccessArticle

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

by Mohsen Soltani, Julian Koch and Simon Stisen

Remote Sens. 2021, 13(5), 853; https://doi.org/10.3390/rs13050853 - 25 Feb 2021

Cited by 6 | Viewed by 2339

Abstract

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. Full article

(This article belongs to the Special Issue Remote Sensing for Distributed Hydrologic Models: New Satellite Data, Model Parametrization and Spatial Metrics to Calibrate and Evaluate Models)

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20 pages, 5698 KiB

Open AccessArticle

Using Satellite Gravity and Hydrological Data to Estimate Changes in Evapotranspiration Induced by Water Storage Fluctuations in the Three Gorges Reservoir of China

by Yuhao Zheng, Linsong Wang, Chao Chen, Zhengyan Fu and Zhenran Peng

Remote Sens. 2020, 12(13), 2143; https://doi.org/10.3390/rs12132143 - 03 Jul 2020

Cited by 6 | Viewed by 2462

Abstract

The change in water storage driven by the Three Gorges Project directly affects the terrestrial water migration and redistribution in the Yangtze River Basin (YRB). As a result, a new water balance is established and regional evapotranspiration (ET) fluctuates in the process. In this paper, data from multiple-sources including from the Gravity Recovery and Climate Experiment (GRACE) satellite, land surface models (LSMs), remote sensing, and in-situ observations were used to monitor the temporal and spatial evolution of terrestrial water and estimate changes in ET in the Three Gorges Reservoir (TGR) from 2002 to 2016. Our results showed that GRACE data scaled using the scale factor method significantly improved the signal amplitude and highlighted its spatial differences in the TGR area. Combining GRACE with surface hydrological observations, ET in the TGR area was estimated to have overall change characteristics highly consistent with results from the MOD16 Moderate Resolution Imaging Spectroradiometer (MODIS), and the uncertainties of monthly ET are mainly from TWS changes derived by GRACE uncertainties such as measurement errors and leakage errors. During our study period, the cyclical ET was mainly driven by climate precipitation but short-term (monthly) ET in the TGR area was also directly affected by human-driven water storage. For example, rising water levels in the three water storage stages (2003, 2006, and 2008) caused an abnormal increase in regional ET (up to 22.4 cm/month, 19.2 cm/month and 29.5 cm/month, respectively). Usually, high precipitation will cause increase in ET but the high precipitation during the water release periods (spring and summer) did not have a significant impact on the increased ET due to the water level in the TGR having decreased 30 m in this stage. Our results also indicate that the short-term fluctuations in flooded area and storage capacity of the TGR, i.e., the man-made mass changes in the main branch and tributaries of the Yangtze River, were the main factors that influenced the ET. This further illustrated that a quantitative estimation of changes in the ET in the TGR allows for a deeper understanding of the water balance in the regional land water cycle process as driven by both climate and human factors. Full article

(This article belongs to the Special Issue Remote Sensing for Distributed Hydrologic Models: New Satellite Data, Model Parametrization and Spatial Metrics to Calibrate and Evaluate Models)

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