Hydrological Model Uncertainty Research Articles

A Risk-Based strategy for the Scour Analysis around bridge piers

Local scour around bridge piers, especially during high floods, has been a critical issue for both the safety and serviceability of bridge foundations for decades. The objective of this study is to implement a risk-based strategy to account for local scour near the bridge piers. The methodology comprises of four fundamental steps wherein the first step involves evaluating hydrological hazards of an extreme nature. To precisely estimate the behavior of the river, the second phase uses several flow properties and depth of scour. In the third phase, the risk of scour connected with the bridge is evaluated by comparing the scour depth (DT) with foundation depth (DF) with a prioritizing factor (PF) which aids in determining the qualitative assessment of the scour risk. Lastly, an assessment of the risk of scour rating is assigned to find out the severity of the scour risk for the bridge piers. The present research discusses the incorporation of uncertainty in hydrological modeling through a predictive approach in the definition of the return period. To simulate flow parameters and assess bridge scour, the HEC-RAS program is utilized which incorporates a scour module. The study concludes with a qualitative evaluation of the impact of scouring on bridge susceptibility and safety. A case study on the Netaji Subhas Bridge across the Gomati (Gumti) river, Tripura served to validate the risk-based technique and it has been suggested that it can be effectively integrated into routine schedules for bridge examinations as a reliable risk management tool to avoid disastrous incidents.

Effectiveness and resilience of BMPs to watershed climate adaptation considering the uncertainty of hydrological model and GCMs

Climate change significantly impacts catchment hydrologic and water quality processes. Best management practices (BMPs) can serve as adaptation strategies to negate the impact of climate change on sediment and total nitrogen (TN) loads. One major controversial issue in climate change adaptation studies is the highly uncertain nature of such changes. Previous studies have rarely focused on the combined impact of the uncertainty in climate change and watershed model parameters, which could be the main sources of uncertainty in climate change adaptation research. In this study, the effectiveness and resilience (defined as continued effectiveness under a changing climate) of BMPs in reducing sediment and TN loads were explored under future climate change in the Shanmei Reservoir watershed (SMW) of Southeast China. Climate change projections provided by 10 general circulation models (GCMs) under the SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios from 2031 to 2060 were applied in the Annualized Agricultural Nonpoint Source (AnnAGNPS) model to evaluate the effectiveness and resilience of 4 BMPs (riparian buffers (RB), no-tillage (NT), fertilization reduction (FR), and parallel terraces (PT)). The ensemble average of 10 GCMs and 10 behavior parameter groups were adopted to reduce the uncertainty resulting from the hydrological model parameters and GCMs. The results indicated that the average annual temperature and precipitation in the SMW will increase in the future. On a seasonal scale, the average temperature during all seasons will increase, and precipitation will decrease in summer and autumn but increase in spring and winter. The annual sediment and TN loads will decrease, but the loads in spring and winter will increase. BMPs could be effective as climate adaptation strategies for reducing sediment and TN loads under future climate conditions, with PT as the most effective option. Structural BMPs were more effective in reducing sediment and TN loads in spring and winter, whereas nonstructural BMPs were more effective in reducing loads in summer and autumn. BMPs were more resilient when future watershed runoff changes were slight or climate sensitivity was reduced, with higher BMP resilience in spring and winter than in summer and autumn. This study aimed to provide systematic references for watersheds through the implementation of BMPs for mitigating the effects of climate change and extending the boundaries of the AnnAGNPS model application.

Uncertainty quantization of meteorological input and model parameters for hydrological modelling using a Bayesian‐based integrated approach

AbstractThe traditional treatment of uncertainty in hydrological modelling primarily attributes it to model parameters, but rarely systematically considers meteorological input errors, especially in quantifying the impact of meteorological input errors on parameter uncertainty. This study developed a Bayesian‐based integrated approach to quantitatively investigate uncertainties in meteorological inputs (precipitation and temperature) and model parameters as well as the variation in parameter uncertainty due to meteorological input errors. Additionally, we analysed the propagation from these uncertainties to runoff response in snowmelt and non‐snowmelt periods. The applicability and advantages of this approach were presented by applying of the Soil and Water Assessment Tool to the Shitoukoumen Reservoir Catchment. Differential Evolution Adaptive Metropolis‐Markov Chain Monte Carlo was applied for the straightforward Bayesian inference the uncertainties of meteorological inputs and model parameters. On this basis, multilevel factorial analysis technology was used to quantitatively investigate the specific impact of the model parameters' individual and interactive effects due to meteorological input errors. Finally, the impact of meteorological input errors and model parameter uncertainty on the model performance were analysed and quantified systematically. The results showed that the meteorological input errors could affect the random characteristics of multiple model parameters. Moreover, meteorological input errors could further affect the model parameters' effects on annual average runoff. Overall, the above results have significant implications in enhancing hydrological model to simulate/predict runoff and understanding hydrological processes during different periods.

Assessment of the structural uncertainty of hydrological models and its impact on flood inundation mapping

ABSTRACTFloods are frequent natural disasters, and the management of floods involves predicting flood hydrographs and inundation maps. However, the uncertainty associated with these predictions is a significant challenge in flood management. This study focuses on the uncertainty associated with the selection of mathematical models to estimate flood hydrographs and their impact on inundation map predictions. The study involved setting up 27 hydrological models using different mathematical models for the loss, transform, and routing methods within the Hydrologic Engineering center – Hydrologic Modelling system (HEC-HMS) model. The results showed that the peak flow estimation depends on the models adopted for predicting the flood hydrograph. This uncertainty was translated to the flood inundation maps, where the inundated area varied across different flood events. The study also found that the hydrological conditions prior to the start of the event impact the hydrological model predictive uncertainty, and ensemble predictions by Bayesian model averaging can reduce the uncertainty.

Comparing the ability of different remotely sensed evapotranspiration products in enhancing hydrological model performance and reducing prediction uncertainty

The mitigation of uncertainties in the identification of natural systems is a fundamental aspect in the development of hydrological models, and represents a major challenge for the improvement of modelling techniques. In particular, the calibration of hydrological models based on streamflow measurements at the outlet of a catchment is exposed to significant sources of uncertainty, such as the impact of landscape features on runoff generation. Remote sensing-based actual evapotranspiration (AET) data can be incorporated with streamflow to improve model accuracy and reduce the uncertainty in hydrological modelling, resulting in a significant enhancement of the model performance. The selection of the right AET dataset for hydrological modelling is a crucial task, in front of the availability of multi-source datasets that differ in methods, parameters, and spatiotemporal resolution. Despite the existence of a few studies proposing the usage of remote sensing-based AET data, there is a lack of systematic comparisons between different products, in terms of performance for hydrological modelling. This paper aims to compare the efficacy of different remote sensing-based AET products in improving the simulation of hydrological responses, both in single and in multi-variable scenarios. In this investigation, the Soil and Water Assessment Tool (SWAT) hydrological model was calibrated with observed streamflow data by experimenting with eight different AET datasets. The findings of our study suggest that the incorporation of remote sensing-based AET data in the calibration process of a hydrological model can significantly enhance the accuracy and reliability of model predictions. Thus, the proposed approach can contribute to improving the effectiveness of hydrological modelling as a quantitative tool for the management of water resources. Another finding of this study is that the calibration of the model based solely on AET yields reasonable simulation results of the streamflow, which is an advantageous and promising feature for ungauged basins.

Partitioning model uncertainty in multi-model ensemble river flow projections

Floods are the largest natural disaster currently facing the UK, whilst the incidents of droughts have increased in recent years. Floods and droughts can have devastating consequences on society, resulting in significant financial damage to the economy. Climate models suggest that precipitation and temperature changes will exacerbate future hydrological extremes (i.e., floods and droughts). Such events are likely to become more frequent and intense in the future; thus to develop adaptation plans climate model projections feed hydrological models to provide future water resource projections. ‘eFLaG’ is one set of future river flow projections produced for the UK driven by UKCP18 climate projections from the UK Met Office. The UKCP18-derived eFLaG dataset provides state-of-the-art projections for a single GCM driven by RCP 8.5 across the entire UK. A QE-ANOVA approach has been used to partition contributing sources of uncertainty for two flow quantiles (Q5 high flows and Q95 low flows), at near and far future time scales, for each of the 186 GB catchments in the eFLaG dataset. Results suggest a larger hydrological model uncertainty associated with low flows and greater regional climate model uncertainty for high flows which remains stationary between flow indicators. Total uncertainty increases from near to far future and highly uncertain catchments have been identified with a high concentration in South-East England.

Effect of soil spatial aggregation caused by the calculation unit division on runoff and sediment load simulation in the SWAT model

Distributed hydrological model divides watersheds into distinct calculation units to simulate hydrological processes. Within the model, different calculation unit division schemes induce input elements, such as soil and land use, to certain degrees of generalization, thus affecting the simulation result. Analyzing the effect of the calculation unit division on the simulation result is an important methodology to reduce uncertainty and improve the simulation accuracy of the model. Previous researchers have focused on the comprehensive effect of underlying surface generalization (such as soil, land use, and terrain generalization) caused by the calculation unit division on the simulation results, while it did not separate the effect of the generalization of the individual input factor on the simulation result. In this paper, the Dongjiang headwater region in southern China was selected as the study area. The single type of land use and slope were input into the Soil and Water Assessment Tool (SWAT) model to fix the effects of land use and terrain caused by calculation unit division on the simulation results. Eleven calculation unit division schemes were used to analyze the impact of soil spatial aggregation on runoff and sediment load simulations. The calculation unit division schemes included 11 sub-watershed division schemes and their hydrological response unit (HRU) thresholds were set at 8 %. The drainage area thresholds of the 11 sub-watershed division schemes were 30 ha, 60 ha, 120 ha, 240 ha, 480 ha, 1000 ha, 1600 ha, 2200 ha, 2800 ha, 3400 ha and 4000 ha. The results demonstrated that the soil spatial distribution showed an obvious aggregation with an increase in the drainage area threshold in the SWAT model. Of these, the Krasnozem soil type which covers the largest area in the watershed, increased significantly with an increase in the drainage area threshold. However, the areas of all the other soil types decreased significantly. The Lioth soil type covers the smallest area in the watershed decreased and even disappeared when the threshold in the SWAT model was greater than 1000 ha. Furthermore, soil spatial aggregation caused a significant increase in the annual runoff and sediment load (P < 0.05), with the sediment load (Re = 29.78 %) increasing at a greater rate compared to runoff (Re = 1.74 %). Increased soil aggregation significantly increased the sediment loads in the maximum 1-day, the continuous maximum 5-day, the continuous maximum 7-day, and the sediment load in flood processes (P < 0.001). Refining the calculation unit division scheme will cause a significant increase in the number of calculation units. Due to the computer processing power limitations, we cannot further refine the calculation unit division scheme. Based on our current operational calculation unit division scheme, we did not identify the optimal division scheme in the study region. These results can provide reference information for future research into hydrological model uncertainty and can improve the SWAT model simulation accuracy.

Enhancing the predictability of flood forecasts by combining Numerical Weather Prediction ensembles with multiple hydrological models

Ensemble Prediction Systems (EPS) from a single Numerical Weather Prediction (NWP) model often miss out on a few extreme events. Similarly, a single hydrological model cannot capture all types of flows, such as flood peaks, occurring within a watershed due to the inaccurate representation of the real world processes in their mathematical structure. To focus on these aspects, this study proposes five ‘NWP+HM’ combinations (HM refers to Hydrologic Model) to investigate which combination performs better in providing quality flood forecasts. The EPS obtained from THORPEX Interactive Grand Global Ensemble (TIGGE) archive are forced into calibrated hydrologic models to generate ensemble flood forecasts (EFFs). The methodology also includes a machine-learning post-processing technique, Quantile Regression Forests (QRF), for temporal correction of bias and under-dispersion in the raw EPS data. The study finds that both the EPS used show similar performance in generating flood forecasts. However, the study demonstrates that the hydrologic model uncertainty has a more significant impact on capturing flood peaks compared to input model uncertainty. Finally, the simulated flood forecasts are verified for their quality using both deterministic and probabilistic metrics. For the above five combinations, an increase in leadtime up to 10 days is observed when the raw data are post-processed using QRF technique. The study identifies a computationally efficient ‘NWP+HM’ combination for use in the hydrological forecasting system, which improves the predictability of flood forecasts several days ahead.

Characterizing Errors Using Satellite Metadata for Eco‐Hydrological Model Calibration

AbstractUnderstanding the origins of errors between model predictions and catchment observations is a critical element in hydrologic model calibration and uncertainty estimation. Difficulties arise because there are a variety of error sources but only one measure of the total residual error between model predictions and catchment observations. One promising approach is to collect extra information a priori to characterize the data error before calibration. We implement here a new model calibration strategy for an ecohydrological model, using the satellite metadata information as a means to inform the model priors, to decompose data error from total residual error. This approach, referred to as Bayesian ecohydrological error model (BEEM), is first examined in a synthetic setting to establish its validity, and then applied to three real catchments across Australia. Results show that (a) BEEM is valid in a synthetic setting, as it can perfectly ascertain the true underlying error; (b) in real catchments the model error is reduced when utilizing the observation error variance as added error contributing to total error variance, while the magnitude of total residual error is more robust when utilizing metadata about the data quality proportionality as the basis for assigning total error variance; (c) BEEM improves model calibration by estimating the model error appropriately and estimating the uncertainty interval more precisely. Overall, our work demonstrates a new approach to collect prior error information in satellite metadata and reveals the potential for fully utilizing metadata about error sources in uncertainty estimation.

SEDIMENT TRANSPORT ESTIMATION FROM HYDROLOGICAL AND AUTOREGRESSIVE MODELS

Hydrologic modeling allows the simulation of runoff and sediment processes, which are applied in integrated watershed management, soil and water nutrients, among others. However, these models require considerable amounts of input data. Sediment data is often lacking in quantity and quality, which leads to uncertainty in hydrological models. The objective of the present study was to propose a methodological alternative based on sediment time series in the Santa Cruz de Aquismón sub-basin, San Luis Potosí, Mexico, by means of autoregressive integrated moving average models (ARIMA) and the Soil and Water Assessment Tool (SWAT) model. The SWAT model was calibrated and validated with measured flows from the National Surface Water Data Bank (BANDAS) of station 26 241 (Ballesmi). Model calibration and validation performance was assessed with Nash-Sutcliffe Coefficient (NSE), percent bias (PBIAS), and the root mean squared error (RMSE). The SWAT model fit was rated as very good. The hydrologic model results were compared to the daily sediment estimates from three months in 1985 (June, September, and November) obtained from ARIMA models. The mean absolute percent error (MAPE) was 0.571, 0.168, and 0.029, respectively. The results indicated that the use of the ARIMA model for sediment estimation is useful when there are short time series with limited information, since it allows the completion of missing data series or short-term estimates.

Impacts of Uncontrolled Operator Splitting Methods on Parameter Identification, Prediction Uncertainty, and Subsurface Flux Representation in Conceptual Hydrological Models

AbstractThe proper numerical representation of physical processes in mechanistic hydrological models is essential to produce robust predictions. A common problem with numerical schemes in hydrological models is that multiple concurrent fluxes are calculated sequentially. Although the importance of errors introduced by inappropriate numerical schemes is well recognized in the literature, many hydrological models calculate concurrent fluxes sequentially. Here, two versions of the HYPE model are used to investigate the limitations of sequential calculations. A fourth order Gear‐Nordsieck solution of the continuous state‐space formulation of HYPE (I‐HYPE) is developed to provide a robust solution, and a fixed‐step implicit Euler scheme (IE‐HYPE) is implemented to provide a computationally efficient and robust approximation of the I‐HYPE simulations. In contrast to I‐HYPE, results show that the original HYPE and the sequential calculation implemented in the continuous state‐space formulation of HYPE (SQ‐HYPE) typically simulate no interflow when soil moisture levels exceed the field capacity. The discrepancy between SQ‐HYPE and I‐HYPE grows with the size of the computation time step, and this implies a compromised representation of flow paths by sequential schemes. IE‐HYPE provides responses comparable with I‐HYPE for both daily and hourly time steps. IE‐HYPE and SQ‐HYPE are compared in terms of their groundwater representation, parameter identifiability, and predictive skills for two catchments. The sequential models have larger groundwater contributions to flow than IE‐HYPE because the splitting errors in SQ‐HYPE limit the interflow flux. IE‐HYPE estimates of the groundwater flux are more consistent with literature values of groundwater contributions to flow for the basins studied.

The enhanced future Flows and Groundwater dataset: development and evaluation of nationally consistent hydrological projections based on UKCP18

Abstract. This paper details the development and evaluation of the enhanced future FLows and Groundwater (eFLaG) dataset of nationally consistent hydrological projections for the UK, based on the latest UK Climate Projections (UKCP18). The projections are derived from a range of hydrological models. For river flows, multiple models (Grid-to-Grid, PDM (Probability Distributed Model) and GR (Génie Rural; both four- and six-parameter versions, GR4J and GR6J)) are used to provide an indication of hydrological model uncertainty. For groundwater, two models are used, a groundwater level model (AquiMod) and a groundwater recharge model (ZOODRM: zooming object-oriented distributed-recharge model). A 12-member ensemble of transient projections of present and future (up to 2080) daily river flows, groundwater levels and groundwater recharge was produced using bias-corrected data from the UKCP18 regional (12 km) climate ensemble. Projections are provided for 200 river catchments, 54 groundwater level boreholes and 558 groundwater bodies, all sampling across the diverse hydrological and geological conditions of the UK. An evaluation was carried out to appraise the quality of hydrological model simulations against observations and also to appraise the reliability of hydrological models driven by the regional climate model (RCM) ensemble in terms of their capacity to reproduce hydrological regimes in the current period. The dataset was originally conceived as a prototype climate service for drought planning for the UK water sector and so has been developed with drought, low river flow and low groundwater level applications as the primary objectives. The evaluation metrics show that river flows and groundwater levels are, for the majority of catchments and boreholes, well simulated across the flow and level regime, meaning that the eFLaG dataset could be applied to a wider range of water resources research and management contexts, pending a full evaluation for the designated purpose. Only a single climate model and one emissions scenario are used, so any applications should ideally contextualise the outcomes with other climate model–scenario combinations. The dataset can be accessed in Hannaford et al. (2022): https://doi.org/10.5285/1bb90673-ad37-4679-90b9-0126109639a9.

The Impact of Meteorological Forcing Uncertainty on Hydrological Modeling: A Global Analysis of Cryosphere Basins

AbstractMeteorological forcing is a major source of uncertainty in hydrological modeling. The recent development of probabilistic large‐domain meteorological data sets enables convenient uncertainty characterization, which however is rarely explored in large‐domain research. This study analyzes how uncertainties in meteorological forcing data affect hydrological modeling in 289 representative cryosphere basins by forcing the Structure for Unifying Multiple Modeling Alternatives (SUMMA) and mizuRoute models with precipitation and air temperature ensembles from the Ensemble Meteorological Data set for Planet Earth (EM‐Earth). EM‐Earth probabilistic estimates are used in ensemble simulation for uncertainty analysis. The results reveal the magnitude, spatial distribution, and scale effect of uncertainties in meteorological, snow, runoff, soil water, and energy variables. There are three main findings. (a) The uncertainties in precipitation and temperature lead to substantial uncertainties in hydrological model outputs, some of which exceed 100% of the magnitude of the output variables themselves. (b) The uncertainties of different variables show distinct scale effects caused by spatial averaging or temporal averaging. (c) Precipitation uncertainties have the dominant impact for most basins and variables, while air temperature uncertainties are also nonnegligible, sometimes contributing more to modeling uncertainties than precipitation uncertainties. We find that three snow‐related variables (snow water equivalent, snowfall amount, and snowfall fraction) can be used to estimate the impact of air temperature uncertainties for different model output variables. In summary, this study provides insight into the impact of probabilistic data sets on hydrological modeling and quantifies the uncertainties in cryosphere basin modeling that stem from the meteorological forcing data.

A Physics‐Aware Machine Learning‐Based Framework for Minimizing Prediction Uncertainty of Hydrological Models

AbstractModeling hydrological processes for managing the available water resources effectively is often complex due to the existence of high nonlinearity, and the associated prediction uncertainty mainly arising from model inputs, parameters, and structure. Despite several attempts to quantify the model prediction uncertainty, reducing the same for improving the reliability of models is indispensable for their wider acceptance. This paper presents a novel modeling framework for minimizing the prediction uncertainty in the streamflow simulation of the conceptual hydrological model (HBV) by integrating with the Bayesian‐based Particle Filter technique (PF) and machine learning algorithm (Random Forest algorithm, RF). Initially, the streamflow prediction interval (PI) is derived from the stochastically estimated parameters of the HBV model through the PF technique (HBV‐PF model). As the HBV‐PF model quantifies only parametric uncertainty, the RF algorithm was employed (HBV‐PF‐RF model) for further minimizing the prediction uncertainty by inherently taking care of different sources of uncertainty. The RF algorithm inherently combines the physics of the hydrological system (i.e., process‐based variables) with machine learning‐based approach to minimize the overall prediction uncertainty. The proposed framework was analyzed on Nepal and India's Sunkoshi and Beas River basins, through several statistical performance indices for assessing the accuracy and uncertainty of the model prediction. The framework was observed to be consistently improving the model performance minimizing the uncertainty in both watersheds. Therefore, the proposed framework can be considered to be more reliable in improving the prediction capability of hydrological models.

Reduce uncertainty in soil hydrological modeling: A comparison of soil hydraulic parameters generated by random sampling and pedotransfer function

Numerical simulation of unsaturated soil hydrology relies on calibrated soil hydraulic parameters, which are subject to uncertainty due to imperfect information during the inverse modelling. This study investigates the effectiveness of reducing parameter uncertainty using the recently developed Rosetta 3 pedotransfer function. The GLUE method was employed for numerical modeling using the Darcy-Richards equation under two strategies for sampling Mualem-van Genuchten (MvG) parameters: the first uses conventional random generation of MvG parameters (GLUE-random), while the second adopts Rosetta 3 to transfer soil particle composition to MvG parameter (GLUE-Rosetta). Both approaches were used for inverse modeling of 9 typical soils, each with a recommended parameter set defined as true values and associated soil moisture dynamics as observations. The posterior parameters selected with both GLUE-random and GLUE-Rosetta show an equifinality phenomenon. GLUE-random fails to provide well-constrained posterior parameters to recover the pre-defined true values, and its posterior results of soil water characteristic curve (SWCC) and soil hydraulic conductivity function (HCF) are poorly constrained. In contrast, GLUE-Rosetta significantly improves the accuracy of the inversely-estimated soil hydraulic parameters, and the ensemble of posterior SWCC and HCF also encompasses the predefined true curves. The results demonstrate the effectiveness of using Rosetta 3 to reduce the dimensionality of the optimization problem, which results in reliable estimation of soil hydraulic parameters and soil particle compositions. Moreover, GLUE-Rosetta outperforms GLUE-random in predicting soil moisture dynamics under different rainfall intensities. Overall, it is recommended to integrate Rosetta 3 with existing optimization tools to reduce the uncertainty of soil parameters and support more reliable modeling of unsaturated soil hydrology.

Evaluation of the Impact of Multi-Source Uncertainties on Meteorological and Hydrological Ensemble Forecasting

Evaluating the impact of multi-source uncertainties in complex forecasting systems is essential to understanding and improving the systems Previous studies have paid little attention to the influence of multi-source uncertainties in complex meteorological and hydrological forecasting systems. In this study, we developed a general ensemble framework based on Bayesian model averaging (BMA) for evaluating the impact of multi-source uncertainties in complex forecast systems. Based on this framework, we used eight numerical weather prediction products from the International Grand Global Ensemble (TIGGE) dataset, four hydrological models with different structures, and 1000 sets of parameters to comprehensively account for the input, structure, and parameter uncertainties. The framework’s application to the Chitan Basin in China revealed that the numerical weather prediction input uncertainty in the forecasting system was more significant than the hydrological model uncertainty. The hydrological model structure uncertainty was more prominent than the parameter uncertainty. The accuracy of the numerical weather prediction dominates the accuracy of the forecast of high flows. In addition, the structures and parameters of the hydrological model and their interactions contributed to the main uncertainty of the low flow forecasts. The streamflow was more realistically represented when the three uncertainty sources were considered jointly. By accounting for the significant uncertainty sources in complex forecast systems, the BMA ensemble forecasting produces more realistic and reliable predictions and reduces the influences of other incomplete considerations. The developed multi-source uncertainty assessment framework improves our understanding of the complex meteorological and hydrological forecasting system. Therefore, the framework is promising for improving the accuracy and reliability of complex forecasting systems.

Hydrological modelling of largest braided river of India using MIKE Hydro River software with rainfall runoff, hydrodynamic and snowmelt modules

Abstract The Brahmaputra River is one of the world's largest river systems, India's largest braided river, and its springtime runoff and downstream streamflow are mostly due to snowmelt processes. This study analysed and used Tropical Rainfall Measuring Mission (TRMM)/Global Forecast System (GFS) rainfall, PET, and snowmelt data as inputs to a Rainfall–Runoff (RR) model, which is based on the MIKE Hydro River NAM software package. The mathematical model was calibrated against the available observed discharge data for the sub-catchments. The model performed reasonably well and simulated discharge in good agreement with observed discharge in terms of timing, rate, volume, and shape of the hydrograph. During the calibration procedure, the optimum values of the nine RR-NAM parameters are obtained. The performance of each model has been checked against measured discharge using a coefficient of determination (R2). It is observed that the value of R2 varies from 0.6 to 0.86. This is deemed acceptable for the purposes of this study. In addition to R2, the overall Water Balance error is also checked. The WBL error is less than 6%. Despite the inherent uncertainties in hydrological modelling, it is determined that the calibrated RR-NAM model can be utilized for the Brahmaputra basin's Flood Forecasting and Early Warning System design, as well as water resource management and planning.

Modeling variability of infiltration tests in ephemeral stream beds as a random function for uncertainty quantification

Infiltration processes are highly variable in space and time, and therefore, building reliable hydrological models without considering the variability is questionable. In this research, we propose a methodology that can systematically handle the variability in the infiltration process. The methodology is based on the theory of random functions in a dimensionless formalism that allows the derivation of a generalized model from the observed infiltration test data. The Monte Carlo technique is utilized to generate hypothetical infiltration tests that carputer the characteristics of the real tests. The methodology is applied to a case study in ephemeral stream beds located in Al Madinah Al Munawarah Province in Saudi Arabia. The measurements are made by the double-ring infiltrometer. Beta distribution fits the dimensionless cumulative infiltration relatively well at a 1% significant level at all times, and therefore, it can be used to model the uncertainty in hydrological modeling. High variability is observed in infiltration tests at the early time (a platykurtic distribution with high dispersion); however, it decreases at the late time (Leptokurtic distribution with low dispersion) since the infiltration reaches a steady infiltration. Some extreme tests show different behavior from the fourteen tests that cannot be captured by the model and therefore need special treatment.

Uncertainty in hydrological modelling: A review

Availability of hydrological data and various soft wares for developing models make easy way to answer frequently asked questions to hydrologists. A great deal of concentration has given to the development of models in the last decades. But the thorough study regarding uncertainty of simulations has not carried out in comparison with the development of models. Uncertainty in models emanates from input data, calibrated data, parameters and from the structure of models. The sources of uncertainty, cause of generation and how these can be dealt with are reviewed here. This also comprises a review about five different methods viz. Monte Carlo sampling, Bayesian approach, Generalized Likelihood Uncertainty Estimation, Bootstrap Approach and Machine learning methods which were applied in the estimation of the model and parameter uncertainty. This will indicate the comparison between the methods which were applied to measure the uncertainty of hydrological models and highlight the strengths and weaknesses of the methods in identifying the usefulness of the models. By the comparison of the methods the improvement of the model reliability, slackening of the prediction error of the hydrological models can be suggested. By a proper quantification of uncertainty of data applied for the building up and evaluation of models, model performance can be improved, cost can be reduced and unambiguous results can lead the proper water resources management.

Comparison of Hydrological Platforms in Assessing Rainfall-Runoff Behavior in a Mediterranean Watershed of Northern Morocco

This research evaluates the applicability of different types of hydrological models to simulate discharge behavior scenarios in a northern Moroccan watershed, Oued Laou Watershed (OLW). In this context, an improved understanding of the runoff mechanisms through hydrological modeling of the OLW can assist in the hazard risk management and facilitate the effective planning of water resources. For that end, a multitude of hydrological models were used to perform a very efficient modelling, and a comparative approach was adopted. Comparison of the models allowed the determination of potential sources of uncertainty in hydrological modelling of a subhumid watershed. Three models (ATelier Hydrologique Spatialisé (ATHYS), Hydrologic Modeling System (HEC-HMS), and Soil and Water Assessment Tool (SWAT)) with different characteristics were employed for a continuous modelling approach. The models were calibrated and validated using observed daily rainfall and streamflow data for 4 years (2004–2008) and 3 years (2009–2011), respectively. The multi-criteria model comparison (R², NSE, RSR, and PBIAS) showed that all three models are capable of reproducing the observed flows. The SWAT model performed well over both periods (NSE = 0.76 for calibration), with an improvement in validation (NSE = 0.84). A good agreement was also observed in the HEC-HMS model outputs, with an approximately stable NSE of 0.77 and 0.78 for calibration and validation phases, respectively. The ATHYS model showed a NSE value of 0.67 during the calibration, with a decrease of 0.06 towards the validation period. The other performance criteria confirmed these findings. Additionally, results suggest that semi-distributed and conceptual hydrological models are particularly suitable for the OLW given their physical heterogeneity. Generally, the integration of these models may be suitable for water resources assessment in OLW.