scholarly journals A framework to regionalize conceptual model parameters for global hydrological modeling

2020 ◽  
Author(s):  
Wenyan Qi ◽  
Jie Chen ◽  
Lu Li ◽  
Chong-yu Xu ◽  
Jingjing Li ◽  
...  
Keyword(s):  
Water Resources ◽  
Conceptual Model ◽  
Hydrological Modeling ◽  
Global Scale ◽  
Spatial Proximity ◽  
Model Parameters ◽  
Hydrological Models ◽  
Physical Similarity ◽  
Similarity Method ◽  
Global Water

Abstract. To provide an accurate estimate of global water resources and help to formulate water allocation policies, global hydrological models (GHMs) have been developed. However, it is difficult to obtain parameter values for GHMs, which results in large uncertainty in estimation of the global water balance components. In this study, a framework is developed for building GHMs based on parameter regionalization of catchment scale conceptual hydrological models. That is, using appropriate global scale regionalization scheme (GSRS) and conceptual hydrological models to simulate runoff at the grid scale globally and the Network Response Routing (NRF) method to converge the grid runoff to catchment streamflow. To achieve this, five regionalization methods (i.e. the global mean method, the spatial proximity method, the physical similarity method, the physical similarity method considering distance, and the regression method) are first tested for four conceptual hydrological models over thousands medium-sized catchments (2500–50000 km2) around the world to find the appropriate global scale regionalization scheme. The selected GSRS is then used to regionalize conceptual model parameters for global land grids with 0.5°×0.5° resolution on latitude and longitude. The results show that: (1) Spatial proximity method with the Inverse Distance Weighting (IDW) method and the output average option (SPI-OUT) offers the best regionalization solution, and the greatest gains of the SPI-OUT method were achieved with mean distance between the donor catchments and the target catchment is no more than 1500 km. (2) It was found the Kling-Gupta efficiency (KGE) value of 0.5 is a good threshold value to select donor catchments. And (3) Four different GHMs established based on framework were able to produce reliable streamflow simulations. Overall, the proposal framework can be used with any conceptual hydrological model for estimating global water resources, even though uncertainty exists in terms of using difference conceptual models.

Comparison between dynamic and static sensitivity analysis approaches for impact assessment of different potential evapotranspiration methods on hydrological models performance

2021 ◽  
Author(s):  
Rodric Mérimé Nonki ◽  
André Lenouo ◽  
Christopher J. Lennard ◽  
Raphael M. Tshimanga ◽  
Clément Tchawoua
Keyword(s):  
Sensitivity Analysis ◽  
Impact Assessment ◽  
Hydrological Modeling ◽  
Potential Evapotranspiration ◽  
Model Performance ◽  
Estimation Methods ◽  
Model Parameters ◽  
Hydrological Models ◽  
Sensitivity Approach ◽  
Static Sensitivity

AbstractPotential Evapotranspiration (PET) plays a crucial role in water management, including irrigation systems design and management. It is an essential input to hydrological models. Direct measurement of PET is difficult, time-consuming and costly, therefore a number of different methods are used to compute this variable. This study compares the two sensitivity analysis approaches generally used for PET impact assessment on hydrological model performance. We conducted the study in the Upper Benue River Basin (UBRB) located in northern Cameroon using two lumped-conceptual rainfall-runoff models and nineteen PET estimation methods. A Monte-Carlo procedure was implemented to calibrate the hydrological models for each PET input while considering similar objective functions. Although there were notable differences between PET estimation methods, the hydrological models performance was satisfactory for each PET input in the calibration and validation periods. The optimized model parameters were significantly affected by the PET-inputs, especially the parameter responsible to transform PET into actual ET. The hydrological models performance was insensitive to the PET input using a dynamic sensitivity approach, while he was significantly affected using a static sensitivity approach. This means that the over-or under-estimation of PET is compensated by the model parameters during the model recalibration. The model performance was insensitive to the rescaling PET input for both dynamic and static sensitivities approaches. These results demonstrate that the effect of PET input to model performance is necessarily dependent on the sensitivity analysis approach used and suggest that the dynamic approach is more effective for hydrological modeling perspectives.

scholarly journals Flow routing with Semi-distributed hydrological model HEC- HMS in case of Narayani River Basin

2014 ◽  
Vol 10 (1) ◽  
pp. 45-58
Author(s):  
Narayan Prasad Gautam
Keyword(s):  
Water Resources ◽  
River Basin ◽  
Hydrological Modeling ◽  
Numerical Solutions ◽  
Peak Flow ◽  
Channel Cross Section ◽  
Hydrological Models ◽  
Distributed Hydrological Model ◽  
Element Discretization ◽  
Verification Period

 Routing is the modeling process to determine the outflow at an outlet from given inflow at upstream of the channel. A hydrological simulation model use mathematical equations that establish relationships between inputs and outputs of water system and simulates the catchment response to the rainfall input. Several hydrological models have been developed to assist in understanding of hydrologic system and water resources management. A model, once calibrated and verified on catchments, provides a multi-purpose tool for further analysis. Semi-Distributed models in hydrology are usually physically based in that they are defined in terms of theoretically acceptable continuum equations. They do, however, involve some degree of lumping since analytical solutions to the equations cannot be found, and so approximate numerical solutions, based on a finite difference or finite element discretization of the space and time dimensions, are implemented. Many rivers in Nepal are either ungauged or poorly gauged due to extreme complex terrains, monsoon climate and lack of technical and financial supports. In this context the role of hydrological models are extremely useful. In practical applications, hydrological routing methods are relatively simple to implement reasonably accurate. In this study, Gandaki river basin was taken for the study area. Kinematic wave method was used for overland routing and Muskingum cunge method was applied for channel routing to describe the discharge on Narayani river and peak flow attenuation and dispersion observed in the direct runoff hydrograph. Channel cross section parameters are extracted using HEC- GeoRAS extension tool of GIS. From this study result, Annual runoff, Peak flow and time of peak at the outlet are similar to the observed flow in calibration and verification period using trapezoidal channel. Hence Hydrological modeling is a powerful technique in the planning and development of integrated approach for management of water resources. DOI: http://dx.doi.org/10.3126/jie.v10i1.10877Journal of the Institute of Engineering, Vol. 10, No. 1, 2014 pp. 45-58

scholarly journals Bayesian joint inference of hydrological and generalized error models with the enforcement of Total Laws

2017 ◽  
Author(s):  
Mario R. Hernández-López ◽  
Félix Francés
Keyword(s):  
Hydrological Modeling ◽  
Error Model ◽  
Joint Probability Distribution ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Hydrological Models ◽  
Inference Problem ◽  
Inference Models ◽  
Error Models ◽  
Joint Inference

Abstract. Over the years, the Standard Least Squares (SLS) has been the most commonly adopted criterion for the calibration of hydrological models, despite the fact that they generally do not fulfill the assumptions made by the SLS method: very often errors are autocorrelated, heteroscedastic, biased and/or non-Gaussian. Similarly to recent papers, which suggest more appropriate models for the errors in hydrological modeling, this paper addresses the challenging problem of jointly estimate hydrological and error model parameters (joint inference) in a Bayesian framework, trying to solve some of the problems found in previous related researches. This paper performs a Bayesian joint inference through the application of different inference models, as the known SLS or WLS and the new GL++ and GL++Bias error models. These inferences were carried out on two lumped hydrological models which were forced with daily hydrometeorological data from a basin of the MOPEX project. The main finding of this paper is that a joint inference, to be statistically correct, must take into account the joint probability distribution of the state variable to be predicted and its deviation from the observations (the errors). Consequently, the relationship between the marginal and conditional distributions of this joint distribution must be taken into account in the inference process. This relation is defined by two general statistical expressions called the Total Laws (TLs): the Total Expectation and the Total Variance Laws. Only simple error models, as SLS, do not explicitly need the TLs implementation. An important consequence of the TLs enforcement is the reduction of the degrees of freedom in the inference problem namely, the reduction of the parameter space dimension. This research demonstrates that non-fulfillment of TLs produces incorrect error and hydrological parameter estimates and unreliable predictive distributions. The target of a (joint) inference must be fulfilling the error model hypotheses rather than to achieve the better fitting to the observations. Consequently, for a given hydrological model, the resulting performance of the prediction, the reliability of its predictive uncertainty, as well as the robustness of the parameter estimates, will be exclusively conditioned by the degree in which errors fulfill the error model hypotheses.

scholarly journals Value of river discharge data for global-scale hydrological modeling

2008 ◽  
Vol 12 (3) ◽  
pp. 841-861 ◽  
Author(s):  
M. Hunger ◽  
P. Döll
Keyword(s):  
Water Resources ◽  
Correction Factor ◽  
River Discharge ◽  
Hydrological Modeling ◽  
Flow Characteristics ◽  
Global Scale ◽  
Station Correction ◽  
Discharge Data ◽  
Study Results

Abstract. This paper investigates the value of observed river discharge data for global-scale hydrological modeling of a number of flow characteristics that are e.g. required for assessing water resources, flood risk and habitat alteration of aquatic ecosystems. An improved version of the WaterGAP Global Hydrology Model (WGHM) was tuned against measured discharge using either the 724-station dataset (V1) against which former model versions were tuned or an extended dataset (V2) of 1235 stations. WGHM is tuned by adjusting one model parameter (γ) that affects runoff generation from land areas in order to fit simulated and observed long-term average discharge at tuning stations. In basins where γ does not suffice to tune the model, two correction factors are applied successively: the areal correction factor corrects local runoff in a basin and the station correction factor adjusts discharge directly the gauge. Using station correction is unfavorable, as it makes discharge discontinuous at the gauge and inconsistent with runoff in the upstream basin. The study results are as follows. (1) Comparing V2 to V1, the global land area covered by tuning basins increases by 5% and the area where the model can be tuned by only adjusting γ increases by 8%. However, the area where a station correction factor (and not only an areal correction factor) has to be applied more than doubles. (2) The value of additional discharge information for representing the spatial distribution of long-term average discharge (and thus renewable water resources) with WGHM is high, particularly for river basins outside of the V1 tuning area and in regions where the refined dataset provides a significant subdivision of formerly extended tuning basins (average V2 basin size less than half the V1 basin size). If the additional discharge information were not used for tuning, simulated long-term average discharge would differ from the observed one by a factor of, on average, 1.8 in the formerly untuned basins and 1.3 in the subdivided basins. The benefits tend to be higher in semi-arid and snow-dominated regions where the model is less reliable than in humid areas and refined tuning compensates for uncertainties with regard to climate input data and for specific processes of the water cycle that cannot be represented yet by WGHM. Regarding other flow characteristics like low flow, inter-annual variability and seasonality, the deviation between simulated and observed values also decreases significantly, which, however, is mainly due to the better representation of average discharge but not of variability. (3) The choice of the optimal sub-basin size for tuning depends on the modeling purpose. While basins over 60 000 km2 are performing best, improvements in V2 model performance are strongest in small basins between 9000 and 20 000 km2, which is primarily related to a low level of V1 performance. Increasing the density of tuning stations provides a better spatial representation of discharge, but it also decreases model consistency, as almost half of the basins below 20 000 km2 require station correction.

scholarly journals Selection of a Hydrological Model and Objective Function for Water Resources Management in Predominantly Rural Watershed using Criteria-Based Evaluation

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Pratik Singh Thakuri ◽  
NT Sohan Wijesekera
Keyword(s):  
Water Resources ◽  
Objective Function ◽  
Water Resources Management ◽  
Hydrological Modeling ◽  
Hydrological Model ◽  
Suitable Model ◽  
Hydrological Models ◽  
Objective Functions ◽  
Resources Management ◽  
Selection Of

Selection of a fitting up-to-date hydrological model using an evaluation of the functionality, modeler’s requirements, and modeling experiences are very important for water resources management in rural watersheds. Similarly, the selection of appropriate objective function is equally crucial in hydrological modeling processes. Accordingly, A review study was carried to select an appropriate model and objective function for water resources modeling in the predominantly rural watershed. Hydrological models namely HEC-HMS, MIKE SHE, SWAT, TOPMODEL, and SWMM, and objective functions namely NSE, RMSE, MRAE, and RAEM were reviewed. Hydrological models were reviewed under several criteria viz. temporal scale, spatial scale, hydrological processes, documentation, resources requirement, user interface and, model acquisition cost. Whereas, criteria for the review of objective functions were mathematical implication, flow regime, and modeling purpose. Each of the review criteria was comprised of several factors. The criteria-based evaluation was done to quantify the review outcome of the hydrological model and objective function. SWMM was found to be the most suitable model for simulating rural watersheds for water resources management purposes whereas, MRAE was found to be the most appropriate objective function to evaluate the performance of the model selected for rural watershed modeling.

scholarly journals Towards hybrid modeling of the global hydrological cycle

2021 ◽  
Author(s):  
Basil Kraft ◽  
Martin Jung ◽  
Marco Körner ◽  
Sujan Koirala ◽  
Markus Reichstein
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Soil Moisture ◽  
Data Streams ◽  
Hydrological Modeling ◽  
Water Cycle ◽  
Hybrid Modeling ◽  
Hydrological Models ◽  
Global Water Cycle ◽  
Global Water

Abstract. Progress in machine learning in conjunction with the increasing availability of relevant Earth observation data streams may help to overcome uncertainties of global hydrological models due to the complexity of the processes, diversity, and heterogeneity of the land surface and subsurface, as well as scale-dependency of processes and parameters. In this study, we exemplify a hybrid approach to global hydrological modeling that exploits the data-adaptiveness of machine learning for representing uncertain processes within a model structure based on physical principles like mass conservation. Our H2M model simulates the dynamics of snow, soil moisture, and groundwater pools globally at 1º spatial resolution and daily time step where simulated water fluxes depend on an embedded recurrent neural network. We trained the model simultaneously against observational products of terrestrial water storage variations (TWS), runoff, evapotranspiration, and snow water equivalent with a multi-task learning approach. We find that H2M is capable of reproducing the key patterns of global water cycle components with model performances being at least on par with four state-of-the-art global hydrological models. The neural network learned hydrological responses of evapotranspiration and runoff generation to antecedent soil moisture state that are qualitatively consistent with our understanding and theory. Simulated contributions of groundwater, soil moisture, and snowpack variability to TWS variations are plausible and within the large range of traditional GHMs. H2M indicates a somewhat stronger role of soil moisture for TWS variations in transitional and tropical regions compared to GHMs. Overall, we present a proof of concept for global hybrid hydrological modeling in providing a new, complementary, and data-driven perspective on global water cycle variations. With further increasing Earth observations, hybrid modeling has a large potential to advance our capability to monitor and understand the Earth system by facilitating a data-adaptive yet physically consistent, joint interpretation of heterogeneous data streams.

scholarly journals Global-scale evaluation of 23 precipitation datasets using gauge observations and hydrological modeling

2017 ◽  
Author(s):  
Hylke E. Beck ◽  
Noemi Vergopolan ◽  
Ming Pan ◽  
Vincenzo Levizzani ◽  
Albert I. J. M. van Dijk ◽  
...  
Keyword(s):  
Conceptual Model ◽  
Hydrological Modeling ◽  
Daily Precipitation ◽  
Comprehensive Evaluation ◽  
Global Scale ◽  
Scale Evaluation

Abstract. We undertook a comprehensive evaluation of 23 gridded (quasi-)global (sub-)daily precipitation (P) datasets for the period 2000–2016. Thirteen non-gauge-corrected P datasets were evaluated using daily P gauge observations from 76 086 gauges worldwide. Another ten gauge-corrected datasets were evaluated using hydrological modeling, by calibrating the conceptual model HBV against streamflow records for each of 9053 small to medium-sized (

scholarly journals Impacts of Data Quantity and Quality on Model Calibration: Implications for Model Parameterization in Data-Scarce Catchments

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2352
Author(s):  
Yingchun Huang ◽  
Andras Bardossy
Keyword(s):  
Model Calibration ◽  
Spatial Proximity ◽  
External Information ◽  
Model Parameters ◽  
Hydrological Models ◽  
Limited Data ◽  
Model Parameterization ◽  
Robust Model ◽  
Using Data ◽  
Data Length

The application of hydrological models in data-scarce catchments is usually limited by the amount of available data. It is of great significance to investigate the impacts of data quantity and quality on model calibration—as well as to further improve the understanding of the effective estimation of robust model parameters. How to make adequate utilization of external information to identify model parameters of data-scarce catchments is also worthy of further exploration. HBV (Hydrologiska Byråns Vattenbalansavdelning) models was used to simulate streamflow at 15 catchments using input data of different lengths. The transferability of all calibrated model parameters was evaluated for two validation periods. A simultaneous calibration approach was proposed for data-scarce catchment by using data from the catchment with minimal spatial proximity. The results indicate that the transferability of model parameters increases with the increase of data used for calibration. The sensitivity of data length in calibration varies between the study catchments, while flood events show the key impacts on surface runoff parameters. In general, ten-year data are relatively sufficient to obtain robust parameters. For data-scarce catchments, simultaneous calibration with neighboring catchment may yield more reliable parameters than only using the limited data.

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