scholarly journals Behind the scenes of streamflow model performance

2020 ◽  
Author(s):  
Laurène J. E. Bouaziz ◽  
Guillaume Thirel ◽  
Tanja de Boer-Euser ◽  
Lieke A. Melsen ◽  
Joost Buitink ◽  
...  

Abstract. Streamflow is often the only variable used to constrain hydrological models. In a previous international comparison study, eight research groups followed an identical protocol to calibrate a total of twelve hydrological models using observed streamflow of catchments within the Meuse basin. In the current study, we hypothesize that these twelve process-based models with similar streamflow performance have similar representations of internal states and fluxes. We test our hypothesis by comparing internal states and fluxes between models and we assess their plausibility using remotely-sensed products of evaporation, snow cover, soil moisture and total storage anomalies. Our results indicate that models with similar streamflow performance represent internal states and fluxes differently. Substantial dissimilarities between models are found for annual and seasonal evaporation and interception rates, the number of days per year with water stored as snow, the mean annual maximum snow storage and the size of the root-zone storage capacity. Relatively small root-zone storage capacities for several models lead to drying-out of the root-zone storage and significant reduction of evaporative fluxes each summer, which is not suggested by remotely-sensed estimates of evaporation and root-zone soil moisture. These differences in internal process representation imply that these models cannot all simultaneously be close to reality. Using remotely-sensed products, we could evaluate the plausibility of model representations only to some extent, as many of these internal variables remain unknown, highlighting the need for experimental research. We also encourage modelers to rely on multi-model and multi-parameter studies to reveal to decision-makers the uncertainties inherent to the heterogeneity of catchments and the lack of evaluation data.

2021 ◽  
Vol 25 (2) ◽  
pp. 1069-1095
Author(s):  
Laurène J. E. Bouaziz ◽  
Fabrizio Fenicia ◽  
Guillaume Thirel ◽  
Tanja de Boer-Euser ◽  
Joost Buitink ◽  
...  

Abstract. Streamflow is often the only variable used to evaluate hydrological models. In a previous international comparison study, eight research groups followed an identical protocol to calibrate 12 hydrological models using observed streamflow of catchments within the Meuse basin. In the current study, we quantify the differences in five states and fluxes of these 12 process-based models with similar streamflow performance, in a systematic and comprehensive way. Next, we assess model behavior plausibility by ranking the models for a set of criteria using streamflow and remote-sensing data of evaporation, snow cover, soil moisture and total storage anomalies. We found substantial dissimilarities between models for annual interception and seasonal evaporation rates, the annual number of days with water stored as snow, the mean annual maximum snow storage and the size of the root-zone storage capacity. These differences in internal process representation imply that these models cannot all simultaneously be close to reality. Modeled annual evaporation rates are consistent with Global Land Evaporation Amsterdam Model (GLEAM) estimates. However, there is a large uncertainty in modeled and remote-sensing annual interception. Substantial differences are also found between Moderate Resolution Imaging Spectroradiometer (MODIS) and modeled number of days with snow storage. Models with relatively small root-zone storage capacities and without root water uptake reduction under dry conditions tend to have an empty root-zone storage for several days each summer, while this is not suggested by remote-sensing data of evaporation, soil moisture and vegetation indices. On the other hand, models with relatively large root-zone storage capacities tend to overestimate very dry total storage anomalies of the Gravity Recovery and Climate Experiment (GRACE). None of the models is systematically consistent with the information available from all different (remote-sensing) data sources. Yet we did not reject models given the uncertainties in these data sources and their changing relevance for the system under investigation.


2020 ◽  
Author(s):  
Tanja de Boer-Euser ◽  
Laurène Bouaziz ◽  
Guillaume Thirel ◽  
Lieke Melsen ◽  
Joost Buitink ◽  
...  

<p>Hydrological models are valuable tools for short-term forecasting of river flows, long-term predictions for water resources management and to increase our understanding of the complex interactions of water storage and release processes at the catchment scale. Hydrological models provide relatively robust estimates of streamflow dynamics, as shown by the countless applications in many regions across the world. However, various model structures can lead to similar aggregated outputs, i.e. model equifinality. To provide reliable estimates, it is of critical importance that not only the aggregated response but also the internal behaviors are consistent with their real-world equivalents. In a previous international comparison study (de Boer-Euser et al., 2017), eight research groups followed the same protocol to calibrate their twelve models on streamflow for several catchments within the Meuse basin. In the current study, we hypothesize that these twelve process-based models with similar runoff performance have similar representations of internal states and fluxes. We test our hypothesis by comparing internal states and fluxes between models and we assess their plausibility using remotely-sensed products of actual evaporation, snow cover, soil moisture and total storage anomalies. Our results indicate that models with similar runoff performance represent internal states and fluxes differently. The dissimilarities in internal process representation imply that these models cannot all simultaneously be close to reality. Using remotely-sensed products, the plausibility of process representation could only be evaluated to some extent as many variables remain unknown, highlighting the need for more experimental research. The study further emphasizes the value of multi-model, multi-parameter studies to reveal to decision-makers the uncertainty inherent to the lack of evaluation data and the heterogeneous hydrological landscape.</p><p>References:<br>de Boer-Euser, T., Bouaziz, L., De Niel, J., Brauer, C., Dewals, B., Drogue, G., Fenicia, F., Grelier, B., Nossent, J., Pereira, F., Savenije, H., Thirel, G., and Willems, P.: Looking beyond general metrics for model comparison – lessons from an international model intercomparison study, Hydrol. Earth Syst. Sci., 21, 423–440, https://doi.org/10.5194/hess-21-423-2017, 2017.</p>


2020 ◽  
Author(s):  
Noemi Vergopolan ◽  
Sitian Xiong ◽  
Lyndon Estes ◽  
Niko Wanders ◽  
Nathaniel W. Chaney ◽  
...  

Abstract. Soil moisture is highly variable in space, and its deficits (i.e. droughts) plays an important role in modulating crop yields and its variability across landscapes. Limited hydroclimate and yield data, however, hampers drought impact monitoring and assessment at the farmer field-scale. This study demonstrates the potential of field-scale soil moisture simulations to advance high-resolution agricultural yield prediction and drought monitoring at the smallholder farm field-scale. We present a multi-scale modeling approach that combines HydroBlocks, a physically-based hyper-resolution Land Surface Model (LSM), and machine learning. We applied HydroBlocks to simulate root zone soil moisture and soil temperature in Zambia at 3-hourly 30-m resolution. These simulations along with remotely sensed vegetation indices, meteorological conditions, and data describing the physical properties of the landscape (topography, land cover, soil properties) were combined with district-level maize data to train a random forest model (RF) to predict maize yields at the district- and field-scale (250-m) levels. Our model predicted yields with a coefficient of variation (R2) of 0.61, Mean Absolute Error (MAE) of 349 kg ha−1, and mean normalized error of 22 %. We captured maize losses due to the 2015/2016 El Niño drought at similar levels to losses reported by the Food and Agriculture Organization (FAO). Our results revealed that soil moisture is the strongest and most reliable predictor of maize yield, driving its spatial and temporal variability. Consequently, soil moisture was also the most effective indicator of drought impacts in crops when compared with precipitation, soil and air temperatures, and remotely-sensed NDVI-based drought indices. By combining field-scale root zone soil moisture estimates with observed maize yield data, this research demonstrates how field-scale modeling can help bridge the spatial scale discontinuity gap between drought monitoring and agricultural impacts.


2008 ◽  
Vol 12 (3) ◽  
pp. 751-767 ◽  
Author(s):  
T. Vischel ◽  
G. G. S. Pegram ◽  
S. Sinclair ◽  
W. Wagner ◽  
A. Bartsch

Abstract. The paper compares two independent approaches to estimate soil moisture at the regional scale over a 4625 km2 catchment (Liebenbergsvlei, South Africa). The first estimate is derived from a physically-based hydrological model (TOPKAPI). The second estimate is derived from the scatterometer on board the European Remote Sensing satellite (ERS). Results show a good correspondence between the modelled and remotely sensed soil moisture, particularly with respect to the soil moisture dynamic, illustrated over two selected seasons of 8 months, yielding regression R2 coefficients lying between 0.68 and 0.92. Such a close similarity between these two different, independent approaches is very promising for (i) remote sensing in general (ii) the use of hydrological models to back-calculate and disaggregate the satellite soil moisture estimate and (iii) for hydrological models to assimilate the remotely sensed soil moisture.


1997 ◽  
Vol 77 (3) ◽  
pp. 431-442 ◽  
Author(s):  
O. O. Akinremi ◽  
S. M. McGinn ◽  
A. E. Howard

The recurrence of agricultural drought on the prairies has increased the demand for soil moisture information by farmers, regional planners and supporting sectors of agriculture. In response, estimation of regional soil moisture by soil survey is conducted despite its being resource intensive and having limited resolution in time and space. Models that estimate soil moisture on a regional scale would contribute to the evaluation of regional water deficits and overcome problems related to conducting field surveys. This study uses a modified version of the versatile soil moisture budget to estimate available soil moisture within the root zone on a regional scale. The spatial pattern of modelled soil moisture in the fall was similar to that mapped by soil survey. Of the 145 grid points compared, agreement between modelled and field survey was 60% or higher in 5 out of 8 yr. However, too few years of data were available for a reliable assessment of model performance in the spring. The simulated soil moisture was sensitive, and directly related to the value used for available water capacity (AWC). Accurate values of AWC are necessary for accurate simulation of regional soil moisture. Key words: Soil moisture, modelling, water capacity, regional estimates, Canadian prairies


2018 ◽  
Vol 19 (8) ◽  
pp. 1305-1320 ◽  
Author(s):  
Ashley J. Wright ◽  
Jeffrey P. Walker ◽  
Valentijn R. N. Pauwels

Abstract An increased understanding of the uncertainties present in rainfall time series can lead to improved confidence in both short- and long-term streamflow forecasts. This study presents an analysis that considers errors arising from model input data, model structure, model parameters, and model states with the objective of finding a self-consistent set that includes hydrological models, model parameters, streamflow, remotely sensed (RS) soil moisture (SM), and rainfall. This methodology can be used by hydrologists to aid model and satellite selection. Taking advantage of model input data reduction and model inversion techniques, this study uses a previously developed methodology to estimate areal rainfall time series for the study catchment of Warwick, Australia, for multiple rainfall–runoff models. RS SM observations from the Soil Moisture Ocean Salinity (SMOS) and Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) satellites were assimilated into three different rainfall–runoff models using an ensemble Kalman filter (EnKF). Innovations resulting from the observed and predicted SM were analyzed for Gaussianity. The findings demonstrate that consistency between hydrological models, model parameters, streamflow, RS SM, and rainfall can be found. Joint estimation of rainfall time series and model parameters consistently improved streamflow simulations. For all models rainfall estimates are less than the observed rainfall, and rainfall estimates obtained using the Sacramento Soil Moisture Accounting (SAC-SMA) model are the most consistent with gauge-based observations. The SAC-SMA model simulates streamflow that is most consistent with observations. EnKF innovations obtained when SMOS RS SM observations were assimilated into the SAC-SMA model demonstrate consistency between SM products.


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