Understanding factors influencing the wetland parameters of a monthly rainfall-runoff model in the Upper Congo River basin

2020 ◽  
Author(s):  
Pierre Kabuya ◽  
Denis Hughes ◽  
Raphael Tshimanga ◽  
Mark Trigg
Keyword(s):  
Hydrological Model ◽  
Return Flow ◽  
Model Parameters ◽  
Morphometric Characteristics ◽  
Practical Advantage ◽  
Flow Parameters ◽  
Basin Scale ◽  
Storage Volume ◽  
Wetland Processes ◽  
Clockwise Hysteresis

<p>Wetland processes considerably influence the flow regime of the downstream river channel, and are important to consider for a better representation of runoff generation within a basin scale hydrological model. The need to understand these processes lead to the development of a wetland sub-model for the monthly time step Pitman hydrological model. However, previous studies highlighted the need to provide guidance to explicitly estimate the wetland parameters rather than using a trial and error calibration approach. In this study, a 2D hydrodynamic river-wetland model (LISFLOOD-FP) is used to explicitly represent the inundation process exchanges between river channels and wetland systems and thereby inform the choice of Pitman wetland model parameters. The hysteretic patterns of these river-wetland processes are quantified through the use of hysteresis indices. Additionally, the hysteretic patterns are connected with the spill and return flow parameters of the wetland sub-model and eventually with the wetland morphometric characteristics. The results show that there is a consistent connection between the degree of hysteresis found in the channel-wetland exchange processes and the Pitman wetland parameters which are also explicitly linked to the wetland morphometric characteristics. The channel capacity to spill (Qcap) is consistently correlated with the hysteresis found between the channel inflow and the wetland storage volume. This anti-clockwise hysteresis represents the time delay between the inundation and drainage processes. The channel spill factor (QSF), in addition to the inundation processes, is also connected with the drainage processes represented by the wetland storage volume and channel outflow anti-clockwise hysteresis. On the other hand, the parameters of the return flow equation have shown a strong consistent relationship with the channel inflow-wetland storage hysteresis. It has also been observed that the wetland average surface slope and the proportion of the wetland storage below the channel banks are the morphometric characteristics that influence the spill and the return flow parameters of the Pitman wetland sub-model. This understanding has a practical advantage for the estimation of the Pitman wetland parameters in the many areas where it is not possible to run complex hydrodynamic models.</p>

scholarly journals Streamflow prediction in ungauged basins: benchmarking the efficiency of deep learning

2020 ◽  
Vol 163 ◽  
pp. 01001
Author(s):  
Georgy Ayzel ◽  
Liubov Kurochkina ◽  
Eduard Kazakov ◽  
Sergei Zhuravlev
Keyword(s):  
Deep Learning ◽  
Hydrological Model ◽  
State Of The Art ◽  
Flash Flood ◽  
Early Warning Systems ◽  
Model Parameters ◽  
Ungauged Basins ◽  
Streamflow Prediction ◽  
Basin Scale ◽  
The Impact

Streamflow prediction is a vital public service that helps to establish flash-flood early warning systems or assess the impact of projected climate change on water management. However, the availability of streamflow observations limits the utilization of the state-of-the-art streamflow prediction techniques to the basins where hydrometric gauging stations exist. Since the most river basins in the world are ungauged, the development of the specialized techniques for the reliable streamflow prediction in ungauged basins (PUB) is of crucial importance. In recent years, the emerging field of deep learning provides a myriad of new models that can breathe new life into the stagnating PUB methods. In the presented study, we benchmark the streamflow prediction efficiency of Long Short-Term Memory (LSTM) networks against the standard technique of GR4J hydrological model parameters regionalization (HMREG) at 200 basins in Northwest Russia. Results show that the LSTM-based regional hydrological model significantly outperforms the HMREG scheme in terms of median Nash-Sutcliffe efficiency (NSE), which is 0.73 and 0.61 for LSTM and HMREG, respectively. Moreover, LSTM demonstrates the comparable median NSE with that for basin-scale calibration of GR4J (0.75). Therefore, this study underlines the high utilization potential of deep learning for the PUB by demonstrating the new state-of-the-art performance in this field.

scholarly journals Uncertainty analysis in model parameters regionalization: a case study involving the SWAT model in Mediterranean catchments (Southern France)

2014 ◽  
Vol 18 (6) ◽  
pp. 2393-2413 ◽  
Author(s):  
H. Sellami ◽  
I. La Jeunesse ◽  
S. Benabdallah ◽  
N. Baghdadi ◽  
M. Vanclooster
Keyword(s):  
Model Prediction ◽  
Hydrological Model ◽  
Swat Model ◽  
Behavioral Model ◽  
Model Parameters ◽  
Ungauged Catchments ◽  
Southern France ◽  
Model Parameter ◽  
Ungauged Catchment ◽  
Prediction Uncertainty

Abstract. In this study a method for propagating the hydrological model uncertainty in discharge predictions of ungauged Mediterranean catchments using a model parameter regionalization approach is presented. The method is developed and tested for the Thau catchment located in Southern France using the SWAT hydrological model. Regionalization of model parameters, based on physical similarity measured between gauged and ungauged catchment attributes, is a popular methodology for discharge prediction in ungauged basins, but it is often confronted with an arbitrary criterion for selecting the "behavioral" model parameter sets (Mps) at the gauged catchment. A more objective method is provided in this paper where the transferrable Mps are selected based on the similarity between the donor and the receptor catchments. In addition, the method allows propagating the modeling uncertainty while transferring the Mps to the ungauged catchments. Results indicate that physically similar catchments located within the same geographic and climatic region may exhibit similar hydrological behavior and can also be affected by similar model prediction uncertainty. Furthermore, the results suggest that model prediction uncertainty at the ungauged catchment increases as the dissimilarity between the donor and the receptor catchments increases. The methodology presented in this paper can be replicated and used in regionalization of any hydrological model parameters for estimating streamflow at ungauged catchment.

A TOPSIS-Based Multicriteria Approach to the Calibration of a Basin-Scale SWAT Hydrological Model

2018 ◽  
Vol 33 (1) ◽  
pp. 439-452 ◽  
Author(s):  
Bentolhoda Asl-Rousta ◽  
S. Jamshid Mousavi
Keyword(s):  
Hydrological Model ◽  
Basin Scale

scholarly journals Diagnostic calibration of a hydrological model in an alpine area

2014 ◽  
Vol 11 (1) ◽  
pp. 1253-1300 ◽  
Author(s):  
Z. He ◽  
F. Tian ◽  
H. C. Hu ◽  
H. V. Gupta ◽  
H. P. Hu
Keyword(s):  
Hydrological Modeling ◽  
Hydrological Model ◽  
Generation Mechanism ◽  
Spatiotemporal Variability ◽  
Model Parameters ◽  
Distributed Hydrological Model ◽  
Runoff Generation ◽  
Alpine Area ◽  
Long Time ◽  
Tailan River Basin

Abstract. Hydrological modeling depends on single- or multiple-objective strategies for parameter calibration using long time sequences of observed streamflow. Here, we demonstrate a diagnostic approach to the calibration of a hydrological model of an alpine area in which we partition the hydrograph based on the dominant runoff generation mechanism (groundwater baseflow, glacier melt, snowmelt, and direct runoff). The partitioning reflects the spatiotemporal variability in snowpack, glaciers, and temperature. Model parameters are grouped by runoff generation mechanism, and each group is calibrated separately via a stepwise approach. This strategy helps to reduce the problem of equifinality and, hence, model uncertainty. We demonstrate the method for the Tailan River basin (1324 km2) in the Tianshan Mountains of China with the help of a semi-distributed hydrological model (THREW).

scholarly journals Estimation of the HEC-HMS model parameters in data-scarce regions. Application to the Ouergha watershed (Sebou, Morocco)

2021 ◽  
Vol 314 ◽  
pp. 05002
Author(s):  
Hasna Moumni ◽  
Karima Sebari ◽  
Laila Stour ◽  
Abdellatif Ahbari
Keyword(s):  
Remote Sensing ◽  
Hydrological Model ◽  
Model Parameters ◽  
Flow Data ◽  
Random Parameters ◽  
Quality Of Data ◽  
Initial Values ◽  
Digital Elevation ◽  
Elevation Model

The availability, accessibility and quality of data are significant obstacles to hydrological modelling. Estimating the initial values of the hydrological model´’ ’s parameters is a laborious and determining task requiring much attention. Geographic information systems (GIS) and spatial remote sensing are prometting tools for processing and collecting data. In this work, we use an innovative approach to estimate the HEC-HMS hydrological model parameters from the soil map of Africa (250m), the land use map GLC30, the depth to bedrock map, the digital elevation model and observed flow data. The estimation approach is applied to the Ouergha basin (Sebou, Morocco). The proposed approach’s interest is to feed the HEC-HMS hydrological model with initial values of parameters close to the study area reality instead of using random parameters.

scholarly journals Robust estimation of hydrological model parameters

2008 ◽  
Vol 5 (3) ◽  
pp. 1641-1675 ◽  
Author(s):  
A. Bárdossy ◽  
S. K. Singh
Keyword(s):  
Measurement Errors ◽  
Input Data ◽  
Hydrological Model ◽  
Optimal Parameter ◽  
Model Performance ◽  
Model Parameters ◽  
Geometrical Approach ◽  
State Variables ◽  
Parameter Vector ◽  
Robust Parameter

Abstract. The estimation of hydrological model parameters is a challenging task. With increasing capacity of computational power several complex optimization algorithms have emerged, but none of the algorithms gives an unique and very best parameter vector. The parameters of hydrological models depend upon the input data. The quality of input data cannot be assured as there may be measurement errors for both input and state variables. In this study a methodology has been developed to find a set of robust parameter vectors for a hydrological model. To see the effect of observational error on parameters, stochastically generated synthetic measurement errors were applied to observed discharge and temperature data. With this modified data, the model was calibrated and the effect of measurement errors on parameters was analysed. It was found that the measurement errors have a significant effect on the best performing parameter vector. The erroneous data led to very different optimal parameter vectors. To overcome this problem and to find a set of robust parameter vectors, a geometrical approach based on the half space depth was used. The depth of the set of N randomly generated parameters was calculated with respect to the set with the best model performance (Nash-Sutclife efficiency was used for this study) for each parameter vector. Based on the depth of parameter vectors, one can find a set of robust parameter vectors. The results show that the parameters chosen according to the above criteria have low sensitivity and perform well when transfered to a different time period. The method is demonstrated on the upper Neckar catchment in Germany. The conceptual HBV model was used for this study.

scholarly journals Prediction of snowmelt derived streamflow in a wetland dominated prairie basin

2010 ◽  
Vol 7 (1) ◽  
pp. 1103-1141 ◽  
Author(s):  
X. Fang ◽  
J. W. Pomeroy ◽  
C. J. Westbrook ◽  
X. Guo ◽  
A. G. Minke ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Hydrological Model ◽  
Snow Accumulation ◽  
Model Parameters ◽  
Field Observations ◽  
Runoff Generation ◽  
Stream Channels ◽  
Physically Based ◽  
Set Up ◽  
Spot 5

Abstract. The eastern Canadian Prairies are dominated by cropland, pasture, woodland and wetland areas. The region is characterized by many poor and internal drainage systems and large amounts of surface water storage. Consequently, basins here have proven challenging to hydrological model predictions which assume good drainage to stream channels. The Cold Regions Hydrological Modelling platform (CRHM) is an assembly system that can be used to set up physically based, flexible, object oriented models. CRHM was used to create a prairie hydrological model for the externally drained Smith Creek Research Basin (~400 km2), east-central Saskatchewan. Physically based modules were sequentially linked in CRHM to simulate snow processes, frozen soils, variable contributing area and wetland storage and runoff generation. Five "representative basins" (RBs) were used and each was divided into seven hydrological response units (HRUs): fallow, stubble, grassland, river channel, open water, woodland, and wetland as derived from a supervised classification of SPOT 5 imagery. Two types of modelling approaches calibrated and uncalibrated, were set up for 2007/08 and 2008/09 simulation periods. For the calibrated modelling, only the surface depression capacity of upland area was calibrated in the 2007/08 simulation period by comparing simulated and observed hydrographs; while other model parameters and all parameters in the uncalibrated modelling were estimated from field observations of soils and vegetation cover, SPOT 5 imagery, and analysis of drainage network and wetland GIS datasets as well as topographic map based and LiDAR DEMs. All the parameters except for the initial soil properties and antecedent wetland storage were kept the same in the 2008/09 simulation period. The model performance in predicting snowpack, soil moisture and streamflow was evaluated and comparisons were made between the calibrated and uncalibrated modelling for both simulation periods. Calibrated and uncalibrated predictions of snow accumulation were very similar and compared fairly well with the distributed field observations for the 2007/08 period with slightly poorer results for the 2008/09 period. Soil moisture content at a point during the early spring was adequately simulated and very comparable between calibrated and uncalibrated results for both simulation periods. The calibrated modelling had somewhat better performance in simulating spring streamflow in both simulation periods, whereas the uncalibrated modelling was still able to capture the streamflow hydrographs with good accuracy. This suggests that prediction of prairie basins without calibration is possible if sufficient data on meteorology, basin landcover and physiography are available.

scholarly journals On the selection of precipitation products for the regionalisation of hydrological model parameters

2021 ◽  
Vol 25 (11) ◽  
pp. 5805-5837
Author(s):  
Oscar M. Baez-Villanueva ◽  
Mauricio Zambrano-Bigiarini ◽  
Pablo A. Mendoza ◽  
Ian McNamara ◽  
Hylke E. Beck ◽  
...  
Keyword(s):  
Hydrological Model ◽  
Model Performance ◽  
Temporal Distribution ◽  
Hydrological Regime ◽  
Hydrologic Model ◽  
Spatial Proximity ◽  
Model Parameters ◽  
Ungauged Catchments ◽  
Water Balance Components ◽  
Feature Similarity

Abstract. Over the past decades, novel parameter regionalisation techniques have been developed to predict streamflow in data-scarce regions. In this paper, we examined how the choice of gridded daily precipitation (P) products affects the relative performance of three well-known parameter regionalisation techniques (spatial proximity, feature similarity, and parameter regression) over 100 near-natural catchments with diverse hydrological regimes across Chile. We set up and calibrated a conceptual semi-distributed HBV-like hydrological model (TUWmodel) for each catchment, using four P products (CR2MET, RF-MEP, ERA5, and MSWEPv2.8). We assessed the ability of these regionalisation techniques to transfer the parameters of a rainfall-runoff model, implementing a leave-one-out cross-validation procedure for each P product. Despite differences in the spatio-temporal distribution of P, all products provided good performance during calibration (median Kling–Gupta efficiencies (KGE′s) > 0.77), two independent verification periods (median KGE′s >0.70 and 0.61, for near-normal and dry conditions, respectively), and regionalisation (median KGE′s for the best method ranging from 0.56 to 0.63). We show how model calibration is able to compensate, to some extent, differences between P forcings by adjusting model parameters and thus the water balance components. Overall, feature similarity provided the best results, followed by spatial proximity, while parameter regression resulted in the worst performance, reinforcing the importance of transferring complete model parameter sets to ungauged catchments. Our results suggest that (i) merging P products and ground-based measurements does not necessarily translate into an improved hydrologic model performance; (ii) the spatial resolution of P products does not substantially affect the regionalisation performance; (iii) a P product that provides the best individual model performance during calibration and verification does not necessarily yield the best performance in terms of parameter regionalisation; and (iv) the model parameters and the performance of regionalisation methods are affected by the hydrological regime, with the best results for spatial proximity and feature similarity obtained for rain-dominated catchments with a minor snowmelt component.

scholarly journals Improving the snow physics of WEB-DHM and its point evaluation at two SnowMIP alpine sites

2010 ◽  
Vol 7 (3) ◽  
pp. 3481-3519 ◽  
Author(s):  
M. Shrestha ◽  
L. Wang ◽  
T. Koike ◽  
Y. Xue ◽  
Y. Hirabayashi
Keyword(s):  
Surface Temperature ◽  
Liquid Water ◽  
Snow Depth ◽  
Hydrological Model ◽  
Snow Water Equivalent ◽  
Bias Error ◽  
Snow Albedo ◽  
Basin Scale ◽  
Snow Water ◽  
Snow Physics

Abstract. The snow physics of a distributed biosphere hydrological model, referred to as the Water and Energy Budget based Distributed Hydrological Model (WEB-DHM) is improved by incorporating the three-layer physically based energy balance snowmelt model of Simplified Simple Biosphere 3 (SSiB3) and the Biosphere-Atmosphere Transfer Scheme (BATS) albedo scheme. WEB-DHM with improved snow physics (WEB-DHM-S) can simulate the variability of snow density, snow depth and snow water equivalent, liquid water and ice content in each layer, prognostic snow albedo, diurnal variation in snow surface temperature, thermal heat due to conduction and liquid water retention. The performance of WEB-DHM-S is evaluated at two alpine sites of the Snow Model Intercomparison Project with different climate characteristics: Col de Porte in France and Weissfluhjoch in Switzerland. The simulation results of the snow depth, snow water equivalent, surface temperature, snow albedo and snowmelt runoff reveal that WEB-DHM-S is capable of simulating the internal snow process better than the original WEB-DHM, with the root mean square error and bias error being remarkably reduced. Although WEB-DHM-S is only evaluated at a point scale for the simulation of snow processes, this study provides a benchmark for the application of WEB-DHM-S in cold regions in the assessment of the basin-scale snow water equivalent and seasonal discharge simulation for water resources management.

scholarly journals Photosynthesis-irradiance parameters of marine phytoplankton: synthesis of a global data set

2017 ◽  
Author(s):  
Heather A. Bouman ◽  
Trevor Platt ◽  
Martina Doblin ◽  
Francisco G. Figueiras ◽  
Kristinn Gudmudsson ◽  
...  
Keyword(s):  
Primary Production ◽  
Broad Band ◽  
Marine Phytoplankton ◽  
Production Model ◽  
Initial Slope ◽  
Model Parameters ◽  
Data Set ◽  
Spatial Coverage ◽  
Basin Scale ◽  
Marine Primary Production

Abstract. The photosynthetic performance of marine phytoplankton varies in response to a variety of factors, environmental and taxonomic. One of the aims of the MArine primary Production: model Parameters from Space (MAPPS) project of the European Space Agency is to assemble a global database of photosynthesis-irradiance (P-E) parameters from a range of oceanographic regimes as an aid to examining the basin-scale variability in the photophysiological response of marine phytoplankton and to use this information to improve the assignment of P-E parameters in the estimation of global marine primary production using satellite data. The MAPPS P-E Database, which consists of over 5000 P-E experiments, provides information on the spatio-temporal variability in the two P-E parameters (the assimilation number, PmB, and the initial slope, αB, where the superscripts B indicate normalisation to concentration of chlorophyll) that are fundamental inputs for models (satellite-based and otherwise) of marine primary production that use chlorophyll as the state variable. Quality-control measures consisted of removing samples with abnormally-high parameter values and flags were added to denote whether the spectral quality of the incubator lamp was used to calculate a broad-band value of αB. The MAPPS database provides a photophysiological dataset that is unprecedented in number of observations and in spatial coverage. The database would be useful to a variety of research communities, including marine ecologists, biogeochemical modellers, remote-sensing scientists and algal physiologists. The compiled data are available at https://doi.org/10.1594/PANGAEA.874087 (Bouman et al., 2017).

Sign in / Sign up

Export Citation Format

Share Document