scholarly journals The role and value of distributed precipitation data in hydrological models

2021 ◽  
Vol 25 (1) ◽  
pp. 147-167
Author(s):  
Ralf Loritz ◽  
Markus Hrachowitz ◽  
Malte Neuper ◽  
Erwin Zehe
Keyword(s):  
Hydrological Model ◽  
Predictive Performance ◽  
Distributed Model ◽  
Precipitation Data ◽  
Hydrological Models ◽  
Adaptive Model ◽  
Runoff Generation ◽  
Spatially Adaptive ◽  
Model Execution ◽  
Model Setup

Abstract. This study investigates the role and value of distributed rainfall for the runoff generation of a mesoscale catchment (20 km2). We compare four hydrological model setups and show that a distributed model setup driven by distributed rainfall only improves the model performances during certain periods. These periods are dominated by convective summer storms that are typically characterized by higher spatiotemporal variabilities compared to stratiform precipitation events that dominate rainfall generation in winter. Motivated by these findings, we develop a spatially adaptive model that is capable of dynamically adjusting its spatial structure during model execution. This spatially adaptive model allows the varying relevance of distributed rainfall to be represented within a hydrological model without losing predictive performance compared to a fully distributed model. Our results highlight that spatially adaptive modeling has the potential to reduce computational times as well as improve our understanding of the varying role and value of distributed precipitation data for hydrological models.

scholarly journals The role and value of distributed precipitation data for hydrological models

2020 ◽  
Author(s):  
Ralf Loritz ◽  
Markus Hrachowitz ◽  
Malte Neuper ◽  
Erwin Zehe
Keyword(s):  
Predictive Performance ◽  
Distributed Model ◽  
Hydrological Models ◽  
Runoff Generation ◽  
Convective Storms ◽  
Model Driven ◽  
Spatially Adaptive ◽  
Spatially Distributed ◽  
Mean Similarity ◽  
Precipitation Events

Abstract. This study investigates the role and value of distributed rainfall for the runoff generation of a mesoscale catchment (20 km2). We compare the performance of three hydrological models at different periods and show that a distributed model driven by distributed rainfall yields only to improved performances during certain periods. These periods are dominated by convective storms that are typically characterized by higher spatial and temporal variabilities compared to stratiform precipitation events that dominate the rainfall generation in winter. Motivated by these findings we develop a spatially adaptive model that is capable to dynamically adjust its spatial structure during runtime to represent the varying importance of distributed rainfall within a hydrological model without losing predictive performance compared to a spatially distributed model. Our results highlight that adaptive modeling might be a promising way to better understand the varying relevance of distributed rainfall in hydrological models as well as reiterate that it might be one way to reduce computational times. They furthermore show that hydrological similarity concerning the runoff generation does not necessarily mean similarity for other dynamic variables such as the distribution of soil moisture.

scholarly journals Ensemble flood forecasting considering dominant runoff processes – Part 1: Set-up and application to nested basins (Emme, Switzerland)

2019 ◽  
Vol 19 (1) ◽  
pp. 19-40 ◽  
Author(s):  
Manuel Antonetti ◽  
Christoph Horat ◽  
Ioannis V. Sideris ◽  
Massimiliano Zappa
Keyword(s):  
Predictive Power ◽  
Hydrological Model ◽  
Expert Knowledge ◽  
Flash Flood ◽  
Weather Prediction ◽  
Flood Forecasting ◽  
Hydrological Models ◽  
Runoff Generation ◽  
Short Period ◽  
Set Up

Abstract. Flash floods evolve rapidly during and after heavy precipitation events and represent a potential risk for society. To predict the timing and magnitude of a peak runoff, it is common to couple meteorological and hydrological models in a forecasting chain. However, hydrological models rely on strong simplifying assumptions and hence need to be calibrated. This makes their application difficult in catchments where no direct observation of runoff is available. To address this gap, a flash-flood forecasting chain is presented based on (i) a nowcasting product which combines radar and rain gauge rainfall data (CombiPrecip); (ii) meteorological data from state-of-the-art numerical weather prediction models (COSMO-1, COSMO-E); (iii) operationally available soil moisture estimations from the PREVAH hydrological model; and (iv) a process-based runoff generation module with no need for calibration (RGM-PRO). This last component uses information on the spatial distribution of dominant runoff processes from the so-called maps of runoff types, which can be derived with different mapping approaches with increasing involvement of expert knowledge. RGM-PRO is event-based and parametrised a priori based on the results of sprinkling experiments. This prediction chain has been evaluated using data from April to September 2016 in the Emme catchment, a medium-sized flash-flood-prone basin in the Swiss Prealps. Two novel forecasting chains were set up with two different maps of runoff types, which allowed sensitivity of the forecast performance to the mapping approaches to be analysed. Furthermore, special emphasis was placed on the predictive power of the new forecasting chains in nested subcatchments when compared with a prediction chain including an original version of the runoff generation module of PREVAH calibrated for one event. Results showed a low sensitivity of the predictive power to the amount of expert knowledge included for the mapping approach. The forecasting chain including a map of runoff types with high involvement of expert knowledge did not guarantee more skill. In the larger basins of the Emme region, process-based forecasting chains revealed comparable skill to a prediction system including a conventional hydrological model. In the small nested subcatchments, although the process-based forecasting chains outperformed the original runoff generation module, no forecasting chain showed satisfying skill in the sense that it could be useful for decision makers. Despite the short period available for evaluation, preliminary outcomes of this study show that operational flash-flood predictions in ungauged basins can benefit from the use of information on runoff processes, as no long-term runoff measurements are needed for calibration.

scholarly journals When does higher spatial resolution rainfall information improve streamflow simulation? An evaluation on 3620 flood events

2013 ◽  
Vol 10 (10) ◽  
pp. 12485-12536 ◽  
Author(s):  
F. Lobligeois ◽  
V. Andréassian ◽  
C. Perrin ◽  
P. Tabary ◽  
C. Loumagne
Keyword(s):  
Spatial Variability ◽  
Hydrological Model ◽  
Distributed Model ◽  
Large Set ◽  
Runoff Generation ◽  
Lumped Model ◽  
Climate Conditions ◽  
Key Factor ◽  
The Impact ◽  
Streamflow Simulation

Abstract. Precipitation is the key factor controlling the high-frequency hydrological response in catchments, and streamflow simulation is thus dependent on the way rainfall is represented in the hydrological model. A characteristic that distinguishes distributed from lumped models is the ability to explicitly represent the spatial variability of precipitation. Although the literature on this topic is abundant, the results are contrasted and sometimes contradictory. This paper investigates the impact of spatial rainfall on runoff generation to better understand the conditions where higher-resolution rainfall information improves streamflow simulations. In this study, we used the rainfall reanalysis developed by Météo-France over the whole French territory at 1 km and 1 h resolution over a 10 yr period. A hydrological model was applied in the lumped mode (a single spatial unit) and in the semi-distributed mode using three unit sizes of sub-catchments. The model was evaluated against observed streamflow data using split-sample tests on a large set of 181 French catchments representing a variety of size and climate conditions. The results were analyzed by catchment classes and types of rainfall events based on the spatial variability of precipitation. The evaluation clearly showed different behaviors. The lumped model performed as well as the semi-distributed model in western France where catchments are under oceanic climate conditions with quite spatially uniform precipitation fields. In contrast, higher resolution in precipitation inputs significantly improved the simulated streamflow dynamics and accuracy in southern France (Cévennes and Mediterranean regions) for catchments in which precipitation fields were identified to be highly variable in space. In all regions, natural variability allows for contradictory examples to be found, showing that analyzing a large number of events over varied catchments is warranted.

scholarly journals Annual Baseflow Estimation by Combining Hydrological Models and Graphic Hydrograph Methods

2020 ◽  
Vol 9 (7) ◽  
pp. 426-434
Keyword(s):  
Water Resource Management ◽  
River Flow ◽  
Hydrological Model ◽  
Hydrological Cycle ◽  
Comparative Method ◽  
Precipitation Data ◽  
Hydrological Models ◽  
Hydrograph Separation ◽  
Discharge Data ◽  
The Relationship

Streamflow consists of runoff, interflow and baseflow which is reflected / illustrated in the river flow hydrograph. Predicting the interflow and baseflow in a catchment is important for the water resource management, specifically to find out the relationship between groundwater with surface water and ground water potential. Although, baseflow analysis according to some researchers produces uncertainty, due to difficulties in measuring baseflow. In the hydrological cycle, the relationship between precipitation, evapotranspiration, runoff, recharge, interflow, percolation, groundwater storage changes, baseflow and streamflow is described in hydrological model. In this study, a simulation of the hydrological model above, evapotranspiration and runoff was analysed by the tank method, the recharge was analysed by using equations from previous researches, using precipitation data in 2011-2015. In addition, the analysis of interflow and baseflow was performed by using graphic hydrograph comparative method from the years 2011-2015 discharge data on catchment in Katulampa, Bogor, Indonesia. The results showed that streamflow is the same as baseflow at the end of the dry season in August or September or October. The annual flow of the model is smaller than the baseflow with the UKIH , HYSEP and PART hydrograph separation methods. The interflow value is 7 % and the baseflow is 53% from the precipitation in a year.

scholarly journals A physically-based distributed karst hydrological model (QMG model-V1.0) for flood simulations

2021 ◽  
Author(s):  
Ji Li ◽  
Daoxian Yuan ◽  
Fuxi Zhang ◽  
Yongjun Jiang ◽  
Jiao Liu ◽  
...  
Keyword(s):  
Hydrological Model ◽  
Complex Model ◽  
Hydrological Models ◽  
Runoff Generation ◽  
Average Value ◽  
Distributed Models ◽  
Physically Based ◽  
Flood Simulations ◽  
Distributed Hydrological Models ◽  
Hydrogeological Data

Abstract. Karst trough valleys are prone to flooding, primarily because of the unique hydrogeological features of karst landform, which are conducive to the spread of rapid runoff. Hydrological models that represent the complicated hydrological processes in karst regions are effective for predicting karst flooding, but their application has been hampered by their complex model structures and associated parameter set, especially so for distributed hydrological models, which require large amounts of hydrogeological data. Distributed hydrological models for predicting the Karst flooding is highly dependent on distributed structrues modeling, complicated boundary parameters setting, and tremendous hydrogeological data processing that is both time and computational power consuming. Proposed here is a distributed physically-based karst hydrological model, known as the QMG (Qingmuguan) model. The structural design of this model is relatively simple, and it is generally divided into surface and underground double-layered structures. The parameters that represent the structural functions of each layer have clear physical meanings, and the parameters are less than those of the current distributed models. This allows modeling in karst areas with only a small amount of necessary hydrogeological data. 18 flood processes across the karst underground river in the Qingmuguan karst trough valley are simulated by the QMG model, and the simulated values agree well with observations, for which the average value of Nash–Sutcliffe coefficient was 0.92. A sensitivity analysis shows that the infiltration coefficient, permeability coefficient, and rock porosity are the parameters that require the most attention in model calibration and optimization. The improved predictability of karst flooding by the proposed QMG model promotes a better mechanistic depicting of runoff generation and confluence in karst trough valleys.

Can an ensemble of downscaled global hydrological model outputs improve the performance and spatially disaggregate the output of a catchment scale model in data scarce contexts?

2020 ◽  
Author(s):  
Marko Kallio ◽  
Joseph H.A. Guillaume ◽  
Alexander J. Horton ◽  
Timo A. Räsänen
Keyword(s):  
Hydrological Model ◽  
Interpolation Method ◽  
Global Climate ◽  
Model Averaging ◽  
Scale Model ◽  
Distributed Model ◽  
Hydrological Models ◽  
Catchment Scale ◽  
Stream Network ◽  
Set Up

<p>Global climate and hydrological modelling have shown that human influence on the hydrosphere has been growing and is projected to continue increasing. Global models can inform us of the regional trends and events occurring in the stream network, however, operational water management and research often require tailored and detailed modelling to support decision making. Decisions on which kind of hydrological model (lumped, distributed) and at what scale can, however, impact on the usability of the model outputs for use cases which were not anticipated during the model set-up.</p><p>Here we conduct two experiments with an objective to determine whether an ensemble of a downscaled Global Hydrological Models (GHM) can be used 1) to improve the performance, and 2) to spatially disaggregate the output of a catchment scale model to its sub-basins. We use two existing distributed models set up for research purposes in the Sekong, Sesan, and Srepok Rivers (a major tributary of the Mekong), and in the Grijalva-Usumacinta catchments in Mexico. In the first experiment, we downscale off-the-shelf runoff products from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) using a recently developed areal interpolation method, route the downscaled runoff, and apply model averaging on an ensemble consisting of the downscaled GHM timeseries and the output of the distributed model at the observation stations. In the second experiment, we downscale and route runoff from the GHMs down the river network, as in the first experiment. During the routing step we record the sub-basin of origin and the timestep of runoff as it reaches an observation station. This record is then used to reconstruct a distributed estimate of discharge (back-traced from the existing model output) in all river reaches. We validate the reconstructed distributed estimates by comparing their spatial distribution to the outputs of the original distributed hydrological models, and against streamflow records.</p><p>Our initial experiments show that the downscaled estimates from GHMs have potential to increase the performance of the model outputs. We also show that the reconstruction of hydrographs in sub-basins of the modelled area is possible, however, the uncertainties related to the method are large and the estimates are sensitive to the routing solution used in the back-tracing, and to the performance of the ensemble of GHMs.</p><p>The methodology has potential for improving the usability of GHMs in local contexts. Owing to the promptly available GHM outputs, the method allows for swift exploration of hydrological questions before a proper modelling experiment is set up. Using GHMs as supplementary ensemble members can also aid in locations where calibration of the models is difficult due to scarce or ill-fitting data, or when the original choice of model fails to capture some aspects of the hydrograph.</p>

scholarly journals Ensemble flood forecasting considering dominant runoff processes: I. Setup and application to nested basins (Emme, Switzerland)

2018 ◽  
Author(s):  
Manuel Antonetti ◽  
Christoph Horat ◽  
Ioannis V. Sideris ◽  
Massimiliano Zappa
Keyword(s):  
Predictive Power ◽  
Hydrological Model ◽  
Prediction Models ◽  
Expert Knowledge ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Medium Size ◽  
Hydrological Models ◽  
Runoff Generation ◽  
Forecast Performance

Abstract. Flash floods (FFs) evolve rapidly during and after heavy precipitation events and represent a risk for society. To predict the timing and magnitude of a peak runoff, it is common to couple meteorological and hydrological models in a forecasting chain. However, hydrological models rely on strong simplifying assumptions and hence need to be calibrated. This makes their application difficult in catchments where no direct observation of runoff is available. To address this gap, a FF forecasting chain is presented based on: (i) a nowcasting product which combines radar and rain gauge rainfall data (CombiPrecip), (ii) meteorological data from state-of-the-art numerical weather prediction models (COSMO-1, COSMO-E), (iii) operationally available soil moisture estimations from the PREVAH hydrological model, and (iv) a process-based runoff generation module with no need for calibration (RGM-PRO). This last component uses information on the spatial distribution of dominant runoff processes from the so-called maps of runoff types (RTs), which can be derived with different mapping approaches with increasing involvement of expert knowledge. RGM-PRO is then parametrised a priori based on the results of sprinkling experiments. This prediction chain has been evaluated using data from April to September 2016 in the Emme catchment, a medium-size FF prone basin in the Swiss Prealps. Two novel forecasting chains were set up with two different maps of RTs, which allowed sensitivity of the forecast performance on the mapping approaches to be analysed. Furthermore, special emphasis was placed on the predictive power of the new forecasting chains in nested subcatchments when compared with a prediction chain including a conventional hydrological model relying on calibration. Results showed a low sensitivity of the predictive power on the amount of expert knowledge included for the mapping approach. The forecasting chain including a map of RTs with high involvement of expert knowledge did not guarantee more skill. In the larger basins of the Emme region, process-based forecasting chains revealed comparable skill as a prediction system including a conventional hydrological model. In the small nested subcatchments, the process-based forecasting chains outperformed the conventional system, however, no forecasting chain showed satisfying skill. The outcomes of this study show that operational FF predictions in ungauged basins can benefit from the use of information on runoff processes, as no long-term runoff measurements are needed for calibration.

scholarly journals Quantile-Based Hydrological Modelling

Water ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 3420
Author(s):  
Hristos Tyralis ◽  
Georgia Papacharalampous
Keyword(s):  
Hydrological Model ◽  
Methodological Approach ◽  
Predictive Performance ◽  
Predictive Distribution ◽  
Hydrological Modelling ◽  
Hydrological Models ◽  
Proof Of Concept ◽  
Post Processing ◽  
Predictive Uncertainty ◽  
Quantile Loss

Predictive uncertainty in hydrological modelling is quantified by using post-processing or Bayesian-based methods. The former methods are not straightforward and the latter ones are not distribution-free (i.e., assumptions on the probability distribution of the hydrological model’s output are necessary). To alleviate possible limitations related to these specific attributes, in this work we propose the calibration of the hydrological model by using the quantile loss function. By following this methodological approach, one can directly simulate pre-specified quantiles of the predictive distribution of streamflow. As a proof of concept, we apply our method in the frameworks of three hydrological models to 511 river basins in the contiguous US. We illustrate the predictive quantiles and show how an honest assessment of the predictive performance of the hydrological models can be made by using proper scoring rules. We believe that our method can help towards advancing the field of hydrological uncertainty.

scholarly journals Utilizing Satellite Surface Soil Moisture Data in Calibrating a Distributed Hydrological Model Applied in Humid Regions Through a Multi-Objective Bayesian Hierarchical Framework

2019 ◽  
Vol 11 (11) ◽  
pp. 1335 ◽  
Author(s):  
Han Yang ◽  
Lihua Xiong ◽  
Qiumei Ma ◽  
Jun Xia ◽  
Jie Chen ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Satellite Data ◽  
Hydrological Model ◽  
Distributed Model ◽  
Hydrological Models ◽  
Distributed Hydrological Model ◽  
Bayesian Hierarchical ◽  
Hierarchical Framework ◽  
Multi Objective ◽  
Soil Moisture Data

The traditional calibration objective of hydrological models is to optimize streamflow simulations. To identify the value of satellite soil moisture data in calibrating hydrological models, a new objective of optimizing soil moisture simulations has been added to bring in satellite data. However, it leads to problems: (i) how to consider the trade-off between various objectives; (ii) how to consider the uncertainty these satellite data bring in. Among existing methods, the multi-objective Bayesian calibration framework has the potential to solve both problems but is more suitable for lumped models since it can only deal with constant variances (in time and space) of model residuals. In this study, to investigate the utilization of a soil moisture product from the Soil Moisture Active Passive (SMAP) satellite in calibrating a distributed hydrological model, the DEM (Digital Elevation Model) -based Distributed Rainfall-Runoff Model (DDRM), a multi-objective Bayesian hierarchical framework is employed in two humid catchments of southwestern China. This hierarchical framework is superior to the non-hierarchical framework when applied to distributed models since it considers the spatial and temporal residual heteroscedasticity of distributed model simulations. Taking the streamflow-based single objective calibration as the benchmark, results of adding satellite soil moisture data in calibration show that (i) there is less uncertainty in streamflow simulations and better performance of soil moisture simulations either in time and space; (ii) streamflow simulations are largely affected, while soil moisture simulations are slightly affected by weights of objectives. Overall, the introduction of satellite soil moisture data in addition to observed streamflow in calibration and putting more weights on the streamflow calibration objective lead to better hydrological performance. The multi-objective Bayesian hierarchical framework implemented here successfully provides insights into the value of satellite soil moisture data in distributed model calibration.

scholarly journals Comparison Of Two Hydrological Models (Lumped And Distributed) Over A Pilot Area Of The Issen Watershed In The Souss Basin, Morocco

2016 ◽  
Vol 12 (18) ◽  
pp. 347 ◽  
Author(s):  
Hassan Brirhet ◽  
Lahcen Benaabidate
Keyword(s):  
Hydrological Model ◽  
Arid Environment ◽  
Flood Forecasting ◽  
Global Model ◽  
Distributed Model ◽  
Hydrological Models ◽  
Validation Phase ◽  
Study Case

The present study aims to develop a hydrological model of flood forecasting to arid environment in the Issen basin (sub-chatchement of Aguenza basin) through a comparison between two conceptual hydrological models (HEC HMS) and ATHYS which is a conceptual distributed model rarely used in the Moroccan context. The aim is to measure the degree of adaptability of these models to the study area in order to generalize the selected model to the entire watershed. The obtained results from the validation phase of the two models were satisfactory, the two models were able to reproduce the hydrological behavior of the Aguenza watershed during flooding periods. Besides, this study has shown that a good distributed model can provide improvements over a global model for flood forecasting and particularly in terms of volume as in the present study case.

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