scholarly journals Large-sample assessment of spatial scaling effects of the distributed wflow_sbm hydrological model shows that finer spatial resolution does not necessarily lead to better streamflow estimates

2021 ◽  
Author(s):  
Jerom P.M. Aerts ◽  
Rolf W. Hut ◽  
Nick C. van de Giesen ◽  
Niels Drost ◽  
Willem J. van Verseveld ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Hydrological Model ◽  
Hydrological Modelling ◽  
Statistical Testing ◽  
Scaling Effects ◽  
Spatial Scaling ◽  
Data Set ◽  
Large Sample ◽  
Hydrological System ◽  
Result Interpretation

Abstract. Distributed hydrological modelling moves into the realm of hyper-resolution modelling. This results in a plethora of scaling related challenges that remain unsolved. In light of model result interpretation, finer resolution output might implicate to the user an increase in understanding of the complex interplay of heterogeneity within the hydrological system. Here we investigate spatial scaling in the realm of hyper-resolution by evaluating the streamflow estimates of the distributed wflow_sbm hydrological model based on 454 basins from the large-sample CAMELS data set. Model instances were derived at 3 spatial resolutions, namely 3 km, 1 km, and 200 m. The results show that a finer spatial resolution does not necessarily lead to better streamflow estimates at the basin outlet. Statistical testing of the objective function distributions (KGE score) of the 3 model instances show only a statistical difference between the 3 km and 200 m streamflow estimates. However, results indicate strong locality in scaling behaviour between model instances expressed by differences in KGE scores of on average 0.22. This demonstrates the presence of scaling behavior throughout the domain and indicates where locality in results is strong. The results of this study open up research paths that can investigate the changes in flux and state partitioning due to spatial scaling. This will help further understand the challenges that need to be resolved for hyper resolution hydrological modelling.

scholarly journals Improved large-scale hydrological modelling through the assimilation of streamflow and downscaled satellite soil moisture observations

2016 ◽  
Vol 20 (7) ◽  
pp. 3059-3076 ◽  
Author(s):  
Patricia López López ◽  
Niko Wanders ◽  
Jaap Schellekens ◽  
Luigi J. Renzullo ◽  
Edwin H. Sutanudjaja ◽  
...  
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Data Assimilation ◽  
Spatial Resolution ◽  
Hydrological Model ◽  
Meteorological Data ◽  
Observation System ◽  
Data Set ◽  
Coarse Resolution ◽  
The Impact

Abstract. The coarse spatial resolution of global hydrological models (typically  >  0.25°) limits their ability to resolve key water balance processes for many river basins and thus compromises their suitability for water resources management, especially when compared to locally tuned river models. A possible solution to the problem may be to drive the coarse-resolution models with locally available high-spatial-resolution meteorological data as well as to assimilate ground-based and remotely sensed observations of key water cycle variables. While this would improve the resolution of the global model, the impact of prediction accuracy remains largely an open question. In this study, we investigate the impact of assimilating streamflow and satellite soil moisture observations on the accuracy of global hydrological model estimations, when driven by either coarse- or high-resolution meteorological observations in the Murrumbidgee River basin in Australia. To this end, a 0.08° resolution version of the PCR-GLOBWB global hydrological model is forced with downscaled global meteorological data (downscaled from 0.5° to 0.08° resolution) obtained from the WATCH Forcing Data methodology applied to ERA-Interim (WFDEI) and a local high-resolution, gauging-station-based gridded data set (0.05°). Downscaled satellite-derived soil moisture (downscaled from  ∼  0.5° to 0.08° resolution) from the remote observation system AMSR-E and streamflow observations collected from 23 gauging stations are assimilated using an ensemble Kalman filter. Several scenarios are analysed to explore the added value of data assimilation considering both local and global meteorological data. Results show that the assimilation of soil moisture observations results in the largest improvement of the model estimates of streamflow. The joint assimilation of both streamflow and downscaled soil moisture observations leads to further improvement in streamflow simulations (20 % reduction in RMSE). Furthermore, results show that the added contribution of data assimilation, for both soil moisture and streamflow, is more pronounced when the global meteorological data are used to force the models. This is caused by the higher uncertainty and coarser resolution of the global forcing. We conclude that it is possible to improve PCR-GLOBWB simulations forced by coarse-resolution meteorological data with assimilation of downscaled spaceborne soil moisture and streamflow observations. These improved model results are close to the ones from a local model forced with local meteorological data. These findings are important in light of the efforts that are currently made to move to global hyper-resolution modelling and can help to advance this research.

scholarly journals On the influence of cell size in physically-based distributed hydrological modelling to assess extreme values in water resource planning

2012 ◽  
Vol 12 (5) ◽  
pp. 1573-1582 ◽  
Author(s):  
M. Egüen ◽  
C. Aguilar ◽  
J. Herrero ◽  
A. Millares ◽  
M. J. Polo
Keyword(s):  
Cell Size ◽  
Water Resource ◽  
Spatial Resolution ◽  
Hydrological Model ◽  
Resource Planning ◽  
Hydrological Modelling ◽  
Water Resource Planning ◽  
Distributed Hydrological Modelling ◽  
Hydrological Variables ◽  
Calibration Parameters

Abstract. This paper studies the influence of changing spatial resolution on the implementation of distributed hydrological modelling for water resource planning in Mediterranean areas. Different cell sizes were used to investigate variations in the basin hydrologic response given by the model WiMMed, developed in Andalusia (Spain), in a selected watershed. The model was calibrated on a monthly basis from the available daily flow data at the reservoir that closes the watershed, for three different cell sizes, 30, 100, and 500 m, and the effects of this change on the hydrological response of the basin were analysed by means of the comparison of the hydrological variables at different time scales for a 3-yr-period, and the effective values for the calibration parameters obtained for each spatial resolution. The variation in the distribution of the input parameters due to using different spatial resolutions resulted in a change in the obtained hydrological networks and significant differences in other hydrological variables, both in mean basin-scale and values distributed in the cell level. Differences in the magnitude of annual and global runoff, together with other hydrological components of the water balance, became apparent. This study demonstrated the importance of choosing the appropriate spatial scale in the implementation of a distributed hydrological model to reach a balance between the quality of results and the computational cost; thus, 30 and 100-m could be chosen for water resource management, without significant decrease in the accuracy of the simulation, but the 500-m cell size resulted in significant overestimation of runoff and consequently, could involve uncertain decisions based on the expected availability of rainfall excess for storage in the reservoirs. Particular values of the effective calibration parameters are also provided for this hydrological model and the study area.

Parameter transferability between multiple gridded input datasets challenges hydrological model performance under changing climate

2021 ◽  
Author(s):  
Moctar Dembélé ◽  
Bettina Schaefli ◽  
Grégoire Mariéthoz
Keyword(s):  
West Africa ◽  
Large Scale ◽  
Hydrological Model ◽  
Meteorological Data ◽  
Model Performance ◽  
Hydrologic Model ◽  
Hydrological Modelling ◽  
Climate Change Scenarios ◽  
Depth Analysis ◽  
Input Dataset

<p>The diversity of remotely sensed or reanalysis-based rainfall data steadily increases, which on one hand opens new perspectives for large scale hydrological modelling in data scarce regions, but on the other hand poses challenging question regarding parameter identification and transferability under multiple input datasets. This study analyzes the variability of hydrological model performance when (1) a set of parameters is transferred from the calibration input dataset to a different meteorological datasets and reversely, when (2) an input dataset is used with a parameter set, originally calibrated for a different input dataset.</p><p>The research objective is to highlight the uncertainties related to input data and the limitations of hydrological model parameter transferability across input datasets. An ensemble of 17 rainfall datasets and 6 temperature datasets from satellite and reanalysis sources (Dembélé et al., 2020), corresponding to 102 combinations of meteorological data, is used to force the fully distributed mesoscale Hydrologic Model (mHM). The mHM model is calibrated for each combination of meteorological datasets, thereby resulting in 102 calibrated parameter sets, which almost all give similar model performance. Each of the 102 parameter sets is used to run the mHM model with each of the 102 input datasets, yielding 10404 scenarios to that serve for the transferability tests. The experiment is carried out for a decade from 2003 to 2012 in the large and data-scarce Volta River basin (415600 km2) in West Africa.</p><p>The results show that there is a high variability in model performance for streamflow (mean CV=105%) when the parameters are transferred from the original input dataset to other input datasets (test 1 above). Moreover, the model performance is in general lower and can drop considerably when parameters obtained under all other input datasets are transferred to a selected input dataset (test 2 above). This underlines the need for model performance evaluation when different input datasets and parameter sets than those used during calibration are used to run a model. Our results represent a first step to tackle the question of parameter transferability to climate change scenarios. An in-depth analysis of the results at a later stage will shed light on which model parameterizations might be the main source of performance variability.</p><p>Dembélé, M., Schaefli, B., van de Giesen, N., & Mariéthoz, G. (2020). Suitability of 17 rainfall and temperature gridded datasets for large-scale hydrological modelling in West Africa. Hydrology and Earth System Sciences (HESS). https://doi.org/10.5194/hess-24-5379-2020</p>

scholarly journals Improving the potential accuracy and usability of EURO-CORDEX estimates of future rainfall climate using frequentist model averaging

2021 ◽  
Vol 28 (3) ◽  
pp. 329-346
Author(s):  
Stephen Jewson ◽  
Giuliana Barbato ◽  
Paola Mercogliano ◽  
Jaroslav Mysiak ◽  
Maximiliano Sassi
Keyword(s):  
Climate Model ◽  
Model Averaging ◽  
Large Data ◽  
Statistical Testing ◽  
Complex Method ◽  
Distribution Fitting ◽  
Data Set ◽  
Potential Accuracy ◽  
Predictive Skill ◽  
Ensemble Mean

Abstract. Probabilities of future climate states can be estimated by fitting distributions to the members of an ensemble of climate model projections. The change in the ensemble mean can be used as an estimate of the change in the mean of the real climate. However, the level of sampling uncertainty around the change in the ensemble mean varies from case to case and in some cases is large. We compare two model-averaging methods that take the uncertainty in the change in the ensemble mean into account in the distribution fitting process. They both involve fitting distributions to the ensemble using an uncertainty-adjusted value for the ensemble mean in an attempt to increase predictive skill relative to using the unadjusted ensemble mean. We use the two methods to make projections of future rainfall based on a large data set of high-resolution EURO-CORDEX simulations for different seasons, rainfall variables, representative concentration pathways (RCPs), and points in time. Cross-validation within the ensemble using both point and probabilistic validation methods shows that in most cases predictions based on the adjusted ensemble means show higher potential accuracy than those based on the unadjusted ensemble mean. They also perform better than predictions based on conventional Akaike model averaging and statistical testing. The adjustments to the ensemble mean vary continuously between situations that are statistically significant and those that are not. Of the two methods we test, one is very simple, and the other is more complex and involves averaging using a Bayesian posterior. The simpler method performs nearly as well as the more complex method.

scholarly journals The suitability of remotely sensed soil moisture for improving operational flood forecasting

2014 ◽  
Vol 18 (6) ◽  
pp. 2343-2357 ◽  
Author(s):  
N. Wanders ◽  
D. Karssenberg ◽  
A. de Roo ◽  
S. M. de Jong ◽  
M. F. P. Bierkens
Keyword(s):  
Soil Moisture ◽  
Satellite Data ◽  
Hydrological Model ◽  
Flood Forecasting ◽  
Remotely Sensed ◽  
Added Value ◽  
Lead Times ◽  
Validation Data ◽  
Data Set ◽  
Discharge Data

Abstract. We evaluate the added value of assimilated remotely sensed soil moisture for the European Flood Awareness System (EFAS) and its potential to improve the prediction of the timing and height of the flood peak and low flows. EFAS is an operational flood forecasting system for Europe and uses a distributed hydrological model (LISFLOOD) for flood predictions with lead times of up to 10 days. For this study, satellite-derived soil moisture from ASCAT (Advanced SCATterometer), AMSR-E (Advanced Microwave Scanning Radiometer - Earth Observing System) and SMOS (Soil Moisture and Ocean Salinity) is assimilated into the LISFLOOD model for the Upper Danube Basin and results are compared to assimilation of discharge observations only. To assimilate soil moisture and discharge data into the hydrological model, an ensemble Kalman filter (EnKF) is used. Information on the spatial (cross-) correlation of the errors in the satellite products, is included to ensure increased performance of the EnKF. For the validation, additional discharge observations not used in the EnKF are used as an independent validation data set. Our results show that the accuracy of flood forecasts is increased when more discharge observations are assimilated; the mean absolute error (MAE) of the ensemble mean is reduced by 35%. The additional inclusion of satellite data results in a further increase of the performance: forecasts of baseflows are better and the uncertainty in the overall discharge is reduced, shown by a 10% reduction in the MAE. In addition, floods are predicted with a higher accuracy and the continuous ranked probability score (CRPS) shows a performance increase of 5–10% on average, compared to assimilation of discharge only. When soil moisture data is used, the timing errors in the flood predictions are decreased especially for shorter lead times and imminent floods can be forecasted with more skill. The number of false flood alerts is reduced when more observational data is assimilated into the system. The added values of the satellite data is largest when these observations are assimilated in combination with distributed discharge observations. These results show the potential of remotely sensed soil moisture observations to improve near-real time flood forecasting in large catchments.

RAT (Robustness Assessment Test): a straightforward evaluation of hydrological model robustness to a changing climate

2021 ◽  
Author(s):  
Vazken Andréassian ◽  
Léonard Santos ◽  
Torben Sonnenborg ◽  
Alban de Lavenne ◽  
Göran Lindström ◽  
...  
Keyword(s):  
Climate Change ◽  
Hydrological Model ◽  
Hydrological Models ◽  
Climate Variables ◽  
Data Set ◽  
Hydrological Characteristics ◽  
Changing Climate ◽  
Split Sample ◽  
Sample Test ◽  
Model Robustness

<p>Hydrological models are increasingly used under evolving climatic conditions. They should thus be evaluated regarding their temporal transferability (application in different time periods) and extrapolation capacity (application beyond the range of known past conditions). In theory, parameters of hydrological models are independent of climate. In practice, however, many published studies based on the Split-Sample Test (Klemeš, 1986), have shown that model performances decrease systematically when it is used out of its calibration period. The RAT test proposed here aims at evaluating model robustness to a changing climate by assessing potential undesirable dependencies of hydrological model performances to climate variables. The test compares, over a long data period, the annual value of several climate variables (temperature, precipitation and aridity index) and the bias of the model over each year. If a significant relation exists between the climatic variable and the bias, the model is not considered to be robust to climate change on the catchment. The test has been compared to the Generalized Split-Sample Test (Coron et al., 2012) and showed similar results.</p><p>Here, we report on a large scale application of the test for three hydrological models with different level of complexity (GR6J, HYPE, MIKE-SHE) on a data set of 352 catchments in Denmark, France and Sweden. The results show that the test behaves differently given the evaluated variable (be temperature, precipitation or aridity) and the hydrological characteristics of each catchment. They also show that, although of different level of complexity, the robustness of the three models is similar on the overall data set. However, they are not robust on the same catchments and, then, are not sensitive to the same hydrological characteristics. This example highlights the applicability of the RAT test regardless of the model set-up and calibration procedure and its ability to provide a first evaluation of the model robustness to climate change.</p><p> </p><p><strong>References</strong></p><p>Coron, L., V. Andréassian, C. Perrin, J. Lerat, J. Vaze, M. Bourqui, and F. Hendrickx, 2012. Crash testing hydrological models in contrasted climate conditions: An experiment on 216 Australian catchments, Water Resour. Res., 48, W05552, doi:10.1029/2011WR011721</p><p>Klemeš, V., 1986. Operational testing of hydrological simulation models, Hydrol. Sci. J., 31, 13–24, doi:10.1080/02626668609491024</p><p> </p>

A 10-year regional reanalysis of desert dust aerosol at high spatial resolution

2021 ◽  
Author(s):  
Enza Di Tomaso ◽  
Jerónimo Escribano ◽  
Paul Ginoux ◽  
Sara Basart ◽  
Francesca Macchia ◽  
...  
Keyword(s):  
Middle East ◽  
Data Assimilation ◽  
Spatial Resolution ◽  
Weather Prediction ◽  
Reanalysis Data ◽  
Northern Africa ◽  
Dust Aerosol ◽  
Data Set ◽  
Desert Dust ◽  
Deep Blue

<p>Desert dust is the most abundant aerosol by mass residing in the atmosphere. It plays a key role in the Earth’s system by influencing the radiation balance, by affecting cloud formation and cloud chemistry, and by acting as a fertilizer for the growth of phytoplankton and for soil through its deposition over the ocean and land.</p><p>Due to the nature of its emission and transport, atmospheric dust concentrations are highly variable in space and time and, therefore, require a continuous monitoring by measurements. Dust observations are best exploited by being combined with model simulations for the production of analyses and reanalyses, i.e., complete and consistent four dimensional reconstructions of the atmosphere. Existing aerosol (and dust) reanalyses for the global domain have been produced by total aerosol constraint and at relatively coarse spatial resolution, while regional reanalyses exclude some of the regions containing the major sources of desert dust in Northern Africa and the Middle East.</p><p>We present here a 10-year reanalysis data set of desert dust at a horizontal resolution of 0.1°, and which covers the domain of Northern Africa, the Middle East and Europe. The reanalysis has been produced by assimilating in the MONARCH chemical weather prediction system (Di Tomaso et al., 2017) satellite retrievals over dust source regions with specific dust observational constraint (Ginoux et al., 2012; Pu and Ginoux, 2016).</p><p>Furthermore, we describe its evaluation in terms of data assimilation diagnostics and comparison against independent observations. Statistics of analysis departures from assimilated observations prove the consistency of the data assimilation system showing that the analysis is closer to the observations than the first-guess. Temporal mean of analysis increments show that the assimilation led to an overall reduction of dust with pattern of systematic corrections that vary with the seasons, and can be linked primarily to misrepresentation of source strength.</p><p>Independent evaluation of the analysis with AERONET observations indicates that the reanalysis data set is highly accurate, and provides therefore a reliable historical record of atmospheric desert dust concentrations in a recent decade.</p><p><strong>References</strong></p><p>Di Tomaso, E., Schutgens, N. A. J., Jorba, O., and Pérez García-Pando, C. (2017): Assimilation of MODIS Dark Target and Deep Blue observations in the dust aerosol component of NMMB-MONARCH version 1.0, Geosci. Model Dev., 10, 1107-1129.</p><p>Ginoux, P., Prospero, J. M., Gill, T. E., Hsu, N. C. and Zhao, M. (2012): Global-Scale Attribution of Anthropogenic and Natural Dust Sources and Their Emission Rates Based on Modis Deep Blue Aerosol Products. Rev Geophys 50.</p><p>Pu, B., and Ginoux, P. (2016). The impact of the Pacific Decadal Oscillation on springtime dust activity in Syria. Atmospheric Chemistry and Physics, 16(21), 13431-13448.</p><p><strong>Acknowledgements </strong></p><p>The authors acknowledge the DustClim project which is part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union (435690462); PRACE (eDUST/eFRAGMENT1/eFRAGMENT2), RES (AECT-2020-3-0013/AECT-2019-3-0001/AECT-2020-1-0007) for awarding access to MareNostrum at BSC and for technical support.</p>

scholarly journals Harmonizing Multi-Source Remote Sensing Images for Summer Corn Growth Monitoring

2019 ◽  
Vol 11 (11) ◽  
pp. 1266 ◽  
Author(s):  
Mingzheng Zhang ◽  
Dehai Zhu ◽  
Wei Su ◽  
Jianxi Huang ◽  
Xiaodong Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Kalman Filter ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Meteorological Data ◽  
High Temporal Resolution ◽  
Growth Monitoring ◽  
Landsat 8 ◽  
Data Set

Continuous monitoring of crop growth status using time-series remote sensing image is essential for crop management and yield prediction. The growing season of summer corn in the North China Plain with the period of rain and hot, which makes the acquisition of cloud-free satellite imagery very difficult. Therefore, we focused on developing image datasets with both a high temporal resolution and medium spatial resolution by harmonizing the time-series of MOD09GA Normalized Difference Vegetation Index (NDVI) images and 30-m-resolution GF-1 WFV images using the improved Kalman filter model. The harmonized images, GF-1 images, and Landsat 8 images were then combined and used to monitor the summer corn growth from 5th June to 6th October, 2014, in three counties of Hebei Province, China, in conjunction with meteorological data and MODIS Evapotranspiration Data Set. The prediction residuals ( Δ P R K ) in NDVI between the GF-1 observations and the harmonized images was in the range of −0.2 to 0.2 with Gauss distribution. Moreover, the obtained phenological curves manifested distinctive growth features for summer corn at field scales. Changes in NDVI over time were more effectively evaluated and represented corn growth trends, when considered in conjunction with meteorological data and MODIS Evapotranspiration Data Set. We observed that the NDVI of summer corn showed a process of first decreasing and then rising in the early growing stage and discuss how the temperature and moisture of the environment changed with the growth stage. The study demonstrated that the synthesized dataset constructed using this methodology was highly accurate, with high temporal resolution and medium spatial resolution and it was possible to harmonize multi-source remote sensing imagery by the improved Kalman filter for long-term field monitoring.

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