Hydrological Model Parameters Research Articles

Multi-dimensional hydrological–hydraulic model with variational data assimilation for river networks and floodplains

Abstract. This contribution presents a novel multi-dimensional (multi-D) hydraulic–hydrological numerical model with variational data assimilation capabilities. It allows multi-scale modeling over large domains, combining in situ observations with high-resolution hydrometeorology and satellite data. The multi-D hydraulic model relies on the 2D shallow-water equations solved with a 1D–2D adapted single finite-volume solver. One-dimensional-like reaches are built through meshing methods that cause the 2D solver to degenerate into 1D. They are connected to 2D portions that act as local zooms, for modeling complex flow zones such as floodplains and confluences, via 1D-like–2D interfaces. An existing parsimonious hydrological model, GR4H, is implemented and coupled to the hydraulic model. The forward-inverse multi-D computational model is successfully validated on virtual and real cases of increasing complexity, including using the second-order scheme version. Assimilating multiple observations of flow signatures leads to accurate inferences of multi-variate and spatially distributed parameters among bathymetry friction, upstream and lateral hydrographs and hydrological model parameters. This notably demonstrates the possibility for information feedback towards upstream hydrological catchments, that is, backward hydrology. A 1D-like model of part of the Garonne River is built and accurately reproduces flow lines and propagations of a 2D reference model. A multi-D model of the complex Adour basin network, with inflow from the semi-distributed hydrological model, is built. High-resolution flow simulations are obtained on a large domain, including fine zooms on floodplains, with a relatively low computational cost since the network contains mostly 1D-like reaches. The current work constitutes an upgrade of the DassFlow computational platform. The adjoint of the whole tool chain is obtained by automatic code differentiation.

Analysing the capability of a catchment's spectral signature to regionalize hydrological parameters

AbstractWater resource management in ungauged catchments is complex due to uncertainties around the hydrological parameters that characterize streamflow behaviour. These parameters are usually defined by regionalization approaches, in which the hydrological response patterns of ungauged basins are inferred from those of gauged basins. Regression‐based methods using physical properties derived from cartographic data sources are widely used. The current remote sensing techniques offer new opportunities for the regionalization of hydrological parameters since the hydrological response depends on the physical attributes related to the spectral responses of a given land surface. Moreover, machine learning approaches have not been specifically applied to the regionalization of hydrological parameters in forested areas. This work studies the capability of a catchment's spectral signature based on Sentinel‐1 and Sentinel‐2 data to address a regression‐based regionalization of hydrological model parameters using a machine learning approach. Hydrological modelling was conducted by the HBV‐light model. We tested the random forest algorithm in several regionalization scenarios: the new approach using the catchments' spectral signature, the traditional method using physical properties and a fusion of these methods. The calibration results were excellent (median KGE = 0.83), and the regionalized parameters achieved good performance, in which the three scenarios showed almost the same goodness of fit (median KGE = 0.45–0.50). We found that the effectiveness depends on the climatic environment and that predictions in humid catchments exhibited better performance than those in the driest catchments. The physical approach (median KGE = 0.71) exhibited better performance than the spectral approach (median KGE = 0.64) in humid catchments, whereas spectral regionalization (median KGE = 0.33) enhanced the physical scenario in the driest catchments (median KGE = 0.25). Our results confirm that regionalization is still challenging in drier climates, such as in the Mediterranean environment. The new spectral approach showed promising results and it was effective in the analysis of the relationship between the spectral response of the territory and its hydrological characteristics, specially, where no cartographic data is available.

Formulation of Wavelet Based Multi‐Scale Multi‐Objective Performance Evaluation (WMMPE) Metric for Improved Calibration of Hydrological Models

AbstractEstimation of the hydrological model parameters, known as model calibration, is an important step in application of these models for various water resources problems. In the auto‐calibration approach, the match between the simulated and observed variables is assessed using the statistical performance measures, which are used as objective functions. Earlier studies show that the use of traditional single‐objective functions alone cannot capture all the features of the hydrographs, motivating the utilization of multi‐objective calibration. Further, wavelet‐based approaches can capture features that occur at different time scales. In this study, Wavelet Transforms are employed to formulate a Wavelet based Multi‐Scale Multi‐Objective Performance Evaluation (WMMPE) metric that combines multiple standard metrics at multiple time scales. The performance of WMMPE along with other objective functions such as Nash Sutcliffe Efficiency (NSE), Kling Gupta Efficiency (KGE)), Euclidean distance based Multi Objective Calibration Function and wavelet based Multi‐Scale NSE in calibrating two conceptual hydrologic models were tested on 53 watersheds. The performance of the five calibrated models were evaluated for their efficacy in simulating different characteristics of hydrograph such as, low and high flows, water balance components, frequency domain characteristics and average error magnitude. The results show that WMMPE calibrated models are performing well in matching all the flow segments of hydrograph in >22 basins for the two conceptual models. Further, results of the analysis suggested that the presence of outliers have minimum impact on the simulation of WMMPE calibrated models in the validation period.

Quantifying the contribution of SWAT modeling and CMIP6 inputting to streamflow prediction uncertainty under climate change

Assessing the impacts of climate change on hydrologic systems is essential for establishing adaptive strategies for water resources management, flood risk control, mitigation, and ecological protection. Such assessments are typically performed by obtaining future levels of climate factors such as precipitation and temperature from climate models and coupling them with hydrological models to simulate future hydrological processes. Different algorithm choices for the hydrological cycle module lead to non-negligible uncertainties in the simulation results. Based on a case study of a small watershed located in the upper Huaihe River, China, we develop a SWAT model-based analyzing workflow that integrates two evapotranspiration calculation methods (Haegeraves and Penman-Monteith), two river routing models (Variable Storage and Muskingum), and four calibrated parameters sets. This workflow can identify the uncertainties from algorithms of the internal modules of the hydrological model, model parameterization, and climate model (i.e., General Circulation Models, GCMs, and climate scenarios in Shared Socioeconomic Pathways, SSPs) in the future runoff projections, and quantify their relative contributions. The results show that (1) the monthly runoff will increase in June and August and have little change in other months in 2021–2060, and increase in all months in 2061–2100; (2) annual mean runoff (Qm) and annual maximum runoff (Qp) will increase by 1.8% and 2.6% in the 2040s, and 14.7% and 18.6% in the 2080s; (3) the results of Bayesian model averaging (BMA) analysis indicates the probability of increase in Qm and Qp in all emission scenarios save the SSP 1–2.6 in the 2080s, and especially the probability of increase in Qp will be up to 97%; (4) for Qm, the evapotranspiration calculation method is the main source of uncertainty, accounting for more than half of the total uncertainty; and for Qp, GCM, SSP, and their interaction are the main sources of uncertainty with the contribution of about 70%. In contrast, the uncertainty arising from the parameterization of the hydrological model is small and can be ignored.

The value of satellite soil moisture and snow cover data for the transfer of hydrological model parameters to ungauged sites

Abstract. The recent advances in remote sensing provide opportunities for estimating the parameters of conceptual hydrologic models more reliably. However, the question of whether and to what extent the use of satellite data in model calibration may assist in transferring model parameters to ungauged catchments has not been fully resolved. The aim of this study is to evaluate the efficiency of different methods for transferring model parameters obtained by multiple-objective calibrations to ungauged sites and to assess the model performance in terms of runoff, soil moisture, and snow cover predictions relative to existing regionalization approaches. The model parameters are calibrated to daily runoff, satellite soil moisture (Advanced Scatterometer – ASCAT), and snow cover (Moderate Resolution Imaging Spectroradiometer – MODIS) data. The assessment is based on 213 catchments situated in different physiographic and climate zones of Austria. For the transfer of model parameters, eight methods (global and local variants of arithmetic mean, regression, spatial proximity, and similarity) are examined in two periods, i.e., the period in which the model is calibrated (2000–2010) and an independent validation period (2010–2014). The predictive accuracy is evaluated by the leave-one-out cross-validation. The results show that the method by which the model is calibrated in the gauged catchment has a larger impact on runoff prediction accuracy in the ungauged catchments than the choice of the parameter transfer method. The best transfer methods are global and local similarity and the kriging approach. The performance of the transfer methods differs between lowland and alpine catchments. While the soil moisture and snow cover prediction efficiencies are higher in lowland catchments, the runoff prediction efficiency is higher in alpine catchments. A comparison of the model transfer methods, based on parameters calibrated to runoff, snow cover, and soil moisture with those based on parameters calibrated to runoff, only indicates that the former outperforms the latter in terms of simulating soil moisture and snow cover. The performance of simulating runoff is similar, and the accuracy depends mainly on the weight given to the runoff objective in the multiple-objective calibrations.

Information content of soil hydrology in a west Amazon watershed as informed by GRACE

Abstract. The seasonal-to-decadal terrestrial water balance on river basin scales depends on several well-characterized but uncertain soil physical processes, including soil moisture, plant available water, rooting depth, and recharge to lower soil layers. Reducing uncertainties in these quantities using observations is a key step toward improving the data fidelity and skill of land surface models. In this study, we quantitatively characterize the capability of Gravity Recovery and Climate Experiment (NASA-GRACE) measurements – a key constraint on total water storage (TWS) – to inform and constrain these processes. We use a reduced-complexity physically based model capable of simulating the hydrologic cycle, and we apply Bayesian inference on the model parameters using a Markov chain Monte Carlo algorithm, to minimize mismatches between model-simulated and GRACE-observed TWS anomalies. Based on the prior and posterior model parameter distributions, we further quantify information gain with regard to terrestrial water states, associated fluxes, and time-invariant process parameters. We show that the data-constrained terrestrial water storage model can capture basic physics of the hydrologic cycle for a watershed in the western Amazon during the period January 2003 through December 2012, with an r2 of 0.98 and root mean square error of 30.99 mm between observed and simulated TWS. Furthermore, we show a reduction of uncertainty in many of the parameters and state variables, ranging from a 2 % reduction in uncertainty for the porosity parameter to an 85 % reduction for the rooting depth parameter. The annual and interannual variability of the system are also simulated accurately, with the model simulations capturing the impacts of the 2005–2006 and 2010–2011 South American droughts. The results shown here suggest the potential of using gravimetric observations of TWS to identify and constrain key parameters in soil hydrologic models.

Development of a stochastic hydrological modeling system for improving ensemble streamflow prediction

Streamflow prediction plays a crucial role in water resources systems planning and the mitigation of hydrological extremes such as floods and droughts. Since a variety of uncertainties exist in streamflow prediction, it is necessary to enhance our efforts to robustly address uncertainties and their interactions for improving the reliability of streamflow prediction. This paper presents a stochastic hydrological modeling system (SHMS) for improving daily streamflow prediction by explicitly addressing uncertainties in error and model parameters as well as in forcing data and model outputs. Specifically, the SHMS merges the strengths of the ensemble Kalman filter and the particle filter algorithms for improving the effectiveness and robustness of daily streamflow assimilation. Factorial analysis of variance and variance-based global sensitivity analysis are performed to reveal parameter interactions affecting predictive performance and temporal dynamics of parameter sensitivities, maximizing the accuracy of streamflow prediction. The SHMS has been applied to the Guadalupe River basin located in Texas of the United States to demonstrate feasibility and applicability. Our findings indicate that the SHMS improves upon the well-known ensemble Kalman filter for sequential estimation of hydrological model parameters through a more rapid and accurate convergence of model parameters in streamflow simulation. The SHMS also demonstrates a higher level of skill in streamflow prediction compared to the conditional vine copula model. The proposed SHMS can be applied straightforwardly to other river basins for probabilistic hydrological prediction.

Quantifying interaction uncertainty between subwatersheds and base-flow partitions on hydrological processes.

Base flow, as an important component of runoff, is the main recharge source of runoff during the dry period, especially in the Yellow River Basin located in a semiarid area. However, the process of obtaining base flow has great uncertainty when considering hydrological simulations. Thus, in this study, a three-step framework is proposed, i.e., the particle swarm optimization (PSO) algorithm is used to calibrate model parameters under different subbasin partitioning schemes; then, the hydrograph separation (HYSEP), Improved United Kingdom Institute of Hydrology (IUKIH) and Lyne and Hollick filter (Lyne-Hollick) methods are used to separate the baseflow from the total runoff process, thereby exploring the uncertainty impacts of baseflow segmentation methods on the hydrological simulation process. The subsample-variance-decomposition method is used to quantify the independent and interactive uncertainty in the hydrological simulation process. The results show that the Topmodel model can be better applied to the source area of the Yellow River (the KGE values in the Sub5, Sub13, Sub21, Sub29, Sub37 and Sub13 scenarios were 0.91 and 0.65, 0.94 and 0.86, 0.94 and 0.88, 0.92 and 0.82, 0.95 and 0.89, and 0.92 and 0.83, respectively). The subbasin division uncertainty had less impact on simulated streamflow during the dry season and had a significant impact in the wet season, such as, the subbasin division uncertainty caused the difference between the median of the simulated streamflow to be as high as 213.09 m3/s in August but only 107.19 m3/s in January; Meanwhile, the baseflow segmentation method uncertainty has a significant impact on the annual mean streamflow values under different subbasin segmentation schemes. In addition, the baseflow values estimated by the Lyne-Hollick and HYSEP methods were obviously higher than those estimated by the IUKIH method during the wet season. The uncertainty influence of subbasin partitioning schemes and baseflow segmentation methods had significant differences on hydrological processes in different periods. The uncertainty influence of subbasin partitioning schemes was dominant in the dry season, accounting for 86%, and the baseflow segmentation methods took second place, accounting for approximately 12%. In the wet season, the uncertainty influence of the baseflow segmentation methods was gradually weakened, which may have been due to the uncertainty influence of the hydrological model. These results provide a reference for the calibration and validation of hydrological model parameters using baseflow components.

The importance of vegetation in understanding terrestrial water storage variations

Abstract. So far, various studies have aimed at decomposing the integrated terrestrial water storage variations observed by satellite gravimetry (GRACE, GRACE-FO) with the help of large-scale hydrological models. While the results of the storage decomposition depend on model structure, little attention has been given to the impact of the way that vegetation is represented in these models. Although vegetation structure and activity represent the crucial link between water, carbon, and energy cycles, their representation in large-scale hydrological models remains a major source of uncertainty. At the same time, the increasing availability and quality of Earth-observation-based vegetation data provide valuable information with good prospects for improving model simulations and gaining better insights into the role of vegetation within the global water cycle. In this study, we use observation-based vegetation information such as vegetation indices and rooting depths for spatializing the parameters of a simple global hydrological model to define infiltration, root water uptake, and transpiration processes. The parameters are further constrained by considering observations of terrestrial water storage anomalies (TWS), soil moisture, evapotranspiration (ET) and gridded runoff (Q) estimates in a multi-criteria calibration approach. We assess the implications of including varying vegetation characteristics on the simulation results, with a particular focus on the partitioning between water storage components. To isolate the effect of vegetation, we compare a model experiment in which vegetation parameters vary in space and time to a baseline experiment in which all parameters are calibrated as static, globally uniform values. Both experiments show good overall performance, but explicitly including varying vegetation data leads to even better performance and more physically plausible parameter values. The largest improvements regarding TWS and ET are seen in supply-limited (semi-arid) regions and in the tropics, whereas Q simulations improve mainly in northern latitudes. While the total fluxes and storages are similar, accounting for vegetation substantially changes the contributions of different soil water storage components to the TWS variations. This suggests an important role of the representation of vegetation in hydrological models for interpreting TWS variations. Our simulations further indicate a major effect of deeper moisture storages and groundwater–soil moisture–vegetation interactions as a key to understanding TWS variations. We highlight the need for further observations to identify the adequate model structure rather than only model parameters for a reasonable representation and interpretation of vegetation–water interactions.

Temporal changes in rainfall intensity–duration thresholds for post-wildfire flash floods in southern California

Abstract. Rainfall intensity–duration (ID) thresholds are commonly used to assess flash flood potential downstream of burned watersheds. High-intensity and/or long-duration rainfall is required to generate flash floods as landscapes recover from fire, but there is little guidance on how thresholds change as a function of time since fire. Here, we force a hydrological model with radar-derived precipitation to estimate ID thresholds for post-fire flash floods in a 41.5 km2 watershed in southern California, USA. Prior work in this study area constrains temporal changes in hydrological model parameters, allowing us to estimate temporal changes in ID thresholds. The results indicate that ID thresholds increase by more than a factor of 2 from post-fire year 1 to post-fire year 5. Thresholds based on averaging rainfall intensity over durations of 15–60 min perform better than those that average rainfall intensity over shorter time intervals. Moreover, thresholds based on the 75th percentile of radar-derived rainfall intensity over the watershed perform better than thresholds based on the 25th or 50th percentile of rainfall intensity. Results demonstrate how hydrological models can be used to estimate changes in ID thresholds following disturbance and provide guidance on the rainfall metrics that are best suited for predicting post-fire flash floods.

Inference of Parameters for a Global Hydrological Model: Identifiability and Predictive Uncertainties of Climate‐Based Parameters

AbstractCalibration of global hydrological models (GHMs) has been attempted for over two decades; however, an effective and generic calibration method has not been explored. We present a novel framework for calibrating GHMs assuming that parameters can be regionalized by climate similarities. We calibrated four sensitive parameters of the H08 global hydrological model by aggregating the results of 5,000 simulations with randomly generated parameters into 11 Köppen climate classes and using an objective function Nash–Sutcliffe Efficiency (NSE) with random sampling from the proposed parameter distribution. From a 100‐fold split‐sampling test, we found that both the representativeness and robustness of the transferred parameter sets were guaranteed when the upper 5% of the samples were accepted and assign the median of each accepted parameter distribution for the climate class. The simulation with the climate‐based parameters yielded satisfactory (NSE > 0.0) and good (NSE > 0.5) performances at 480 and 234 stations (61.7% and 30.1% of 777 stations), respectively. The storage capacity (SD) and the conductive coefficient (CD) were sensitive to the climate classes and exhibited well‐constrained distributions of the accepted samples, whereas the recession parameters for the subsurface storage (γ and τ) showed little or no explanatory power to climate. The identified parameters for climate classes exhibited consistency with the physical interpretation of soil formation and efficiencies in vapor transfer. The consistency of the identified parameter values with physical underpinnings indicates that the appropriate parameters were determined, which ensured the robustness of parameters, especially when they are transferred to ungauged watersheds.

Regionalization of hydrological model parameters using gradient boosting machine

Abstract. The regionalization of hydrological model parameters is key to hydrological predictions in ungauged basins. The commonly used multiple linear regression (MLR) method may not be applicable in complex and nonlinear relationships between model parameters and watershed properties. Moreover, most regionalization methods assume lumped parameters for each catchment without considering within-catchment heterogeneity. Here we incorporated the Penman–Monteith–Leuning (PML) equation into the Distributed Time Variant Gain Model (DTVGM) to improve the mechanistic representation of the evapotranspiration (ET) process. We calibrated six key model parameters, grid by grid across China, using a multivariable calibration strategy which incorporates spatiotemporal runoff and ET datasets (0.25∘; monthly) as reference. In addition, we used the gradient boosting machine (GBM), a machine learning technique, to portray the dependence of model parameters on soil and terrain attributes in four distinct climatic zones across China. We show that the modified DTVGM could reasonably estimate the runoff and ET over China using the calibrated parameters but performed better in humid rather than arid regions for the validation period. The regionalized parameters by the GBM method exhibited better spatial coherence relative to the calibrated grid-by-grid parameters. In addition, GBM outperformed the stepwise MLR method in both parameter regionalization and gridded runoff simulations at a national scale, though the improvement pertaining to watershed streamflow validation is not significant due to most of the watersheds being located in humid regions. We also revealed that the slope, saturated soil moisture content, and elevation are the most important explanatory variables to inform model parameters based on the GBM approach. The machine-learning-based regionalization approach provides an effective alternative to deriving hydrological model parameters from watershed properties, particularly in ungauged regions.

Evaluating the Potential of Different Evapotranspiration Datasets for Distributed Hydrological Model Calibration

Evapotranspiration (ET), a key component of the hydrological cycle, has a direct impact on runoff and water balance. Various global satellite-based and numerical datasets provide continuous and high spatiotemporal resolution data, which makes it possible to calibrate hydrological parameters against ET. However, the accuracy of ET datasets varies with region and algorithm, introducing uncertainties in hydrological parameter calibration. This study focused on evaluating the potential of different ET datasets in the calibration of distributed hydrological model parameters. Five different ET datasets (PML, SEBAL, EB-ET, GLASS, REA-ET) were evaluated using the water balance method to explore the effect of intrinsic dataset accuracy on applications. The benchmark calibration scheme calibrated parameters by using observed streamflow data from the outlet. Two calibration schemes were proposed to take advantage of the temporal dynamics and spatial patterns of the raw ET datasets. The results show that the model parameters calibrated by all selected ET datasets produced satisfactory results in streamflow simulations. These results were dependent on the calibration schemes and accuracy of ET datasets. Overall, the scheme calibrated by using temporal dynamics of ET at the grid scale provided better streamflow simulations at the basin outlet than the scheme calibrated by using spatial patterns of ET at the basin scale. Three metrics (bias, root mean square error [RMSE], and correlation coefficient [R]) showed that there is a high potential for selected ET datasets to improve soil moisture simulations, as compared to the benchmark scheme. Parameters calibrated by EB-ET and PML datasets provided the best performance in the simulation of streamflow at the outlet and the sub-basin scale. The calibration case with the SEBAL dataset showed the highest potential to improve soil moisture simulation. The annual average ET estimates of these three datasets were closest to the water balance-based ET values.

Climate change impact on extreme precipitation and peak flood magnitude and frequency: observations from CMIP6 and hydrological models

Changes in climate intensity and frequency, including extreme events, heavy and intense rainfall, have the greatest impact on water resource management and flood risk management. Significant changes in air temperature, precipitation, and humidity are expected in future due to climate change. The influence of climate change on flood hazards is subject to considerable uncertainty that comes from the climate model discrepancies, climate bias correction methods, flood frequency distribution, and hydrological model parameters. These factors play a crucial role in flood risk planning and extreme event management. With the advent of the Coupled Model Inter-comparison Project Phase 6, flood managers and water resource planners are interested to know how changes in catchment flood risk are expected to alter relative to previous assessments. We examine catchment-based projected changes in flood quantiles and extreme high flow events for Awash catchments. Conceptual hydrological models (HBV, SMART, NAM and HYMOD), three downscaling techniques (EQM, DQM, and SQF), and an ensemble of hydrological parameter sets were used to examine changes in peak flood magnitude and frequency under climate change in the mid and end of the century. The result shows that projected annual extreme precipitation and flood quantiles could increase substantially in the next several decades in the selected catchments. The associated uncertainty in future flood hazards was quantified using aggregated variance decomposition and confirms that climate change is the dominant factor in Akaki (C2) and Awash Hombole (C5) catchments, whereas in Awash Bello (C4) and Kela (C3) catchments bias correction types is dominate, and Awash Kuntura (C1) both climate models and bias correction methods are essential factors. For the peak flow quantiles, climate models and hydrologic models are two main sources of uncertainty (31% and 18%, respectively). In contrast, the role of hydrological parameters to the aggregated uncertainty of changes in peak flow hazard variable is relatively small (5%), whereas the flood frequency contribution is much higher than the hydrologic model parameters. These results provide useful knowledge for policy-relevant flood indices, water resources and flood risk control and for studies related to uncertainty associated with peak flood magnitude and frequency.

Early termination strategies with asynchronous parallel optimization in application to automatic calibration of groundwater PDE models

Automatic calibration is widely used to estimate parameters in hydrological models. The main idea is to use optimization algorithms to minimize the discrepancy between field data and simulation prediction. This process involves iterative exchanges of a parameter set chosen by the optimization and simulation. However, for computationally expensive models, such as groundwater flow and transport models, the calibration process may be extremely computationally demanding. In this study, we introduce and demonstrate a new asynchronous parallel surrogate-assisted optimization algorithm with an early truncation feature and different knowledge extraction strategies (SO-AET-k). Results show that our asynchronous algorithm performs significantly better than the synchronous analogue (SO-SP), requiring only 40%–70% of the computation time to achieve the same averaged results. In addition, various knowledge extraction strategies of the asynchronous SO-AET-k are tested. In comparisons with two other algorithms, SCE-UA and APPSPACK, asynchronous SO-AET-k shows significantly better performance in terms of both efficiency and robustness.

Understanding and modelling the effects of wetland on the hydrology and water resources of large African river basins

Wetlands are important components of many large river systems. Some basin scale hydrological models do include explicit sub-models to deal with wetland impacts, but one of the key challenges is to estimate appropriate parameter values to represent the channel-wetland exchange processes. A combined modelling approach is applied in this study and involves the use of the detailed, daily time-step, LISFLOOD-FP hydraulic model to improve the understanding of channel-wetland exchange dynamics and to quantify the wetland parameters of a basin scale hydrological model. While there remain, many uncertainties associated with a lack of sufficient validation data, the LISFLOOD-FP results largely reflect the physical characteristics of the two floodplains (the Luangwa and Barotse in the Zambezi River basin). The LISFLOOD-FP results were also used to estimate the wetland parameters of the basin scale hydrological model with greater confidence and improved the downstream simulations results (albeit marginally). The results suggest that the influence of the floodplain on the monthly Luangwa flow regime is minimal, whereas the Barotse floodplain significantly attenuates the high flows and regulates the low flows of the Upper Zambezi River. The approach implemented in this study presents an important step towards the improvements of water resource assessments modelling for research and practical purposes in data-scarce river basins, however, further work is required to refine the model setup using additional field information related to exchange dynamics as well as high quality remote sensing data.

Hydrologic Model Parameter Estimation in Ungauged Basins Using Simulated SWOT Discharge Observations

AbstractIn situ gauge networks are often used in hydrologic model calibration, but these networks are limited or nonexistent in many regions. The upcoming Surface Water Ocean Topography (SWOT) mission promises to fill this observation gap by providing discharge estimates for rivers wider than 100 m. SWOT observation utility for model parameter selection in regions devoid of in situ gauges is assessed using proxy SWOT discharge estimates derived from an observing system simulation experiment and Monte Carlo methods. The sensitivity of the parameter selection to measurement error and observation frequency is also evaluated. Single‐ and multi‐point parameter selection are performed for ten sub‐basins within the Susitna and upper Tanana river basins in Alaska. SWOT is expected to observe Alaskan river points 4–7 times per 21‐day repeat cycle with 120‐km swath coverage. For an expected SWOT measurement error of 35%, parameter estimation is successful for 50% (90%) of sub‐basins using single‐ (multi‐) point parameter selection. Decreasing observation frequency to simulate lower latitudes resulted in success for only 10% of midlatitude and tropical sub‐basins for single‐point selection, whereas multi‐point selection was successful in 80% (60%) of midlatitude (tropical) sub‐basins. Single‐point parameter selection is more sensitive to measurement error than multi‐point parameter selection. The results strongly support the use of multi‐point over single‐point parameter selection, yielding robust results nearly independent of observation frequency. Most importantly, this study suggests SWOT can be used to successfully select hydrologic model parameters in basins without an in situ gauge network.

Uncertainties and their interaction in flood hazard assessment with climate change

Abstract. The assessment of future impacts of climate change is associated with a cascade of uncertainty linked to the modelling chain employed in assessing local-scale changes. Understanding and quantifying this cascade is essential for developing effective adaptation actions. We evaluate and quantify uncertainties in future flood quantiles associated with climate change for four catchments, incorporating within our modelling chain uncertainties associated with 12 global climate models contained in the Coupled Model Intercomparison Project Phase 6, five different bias correction approaches, hydrological model parameter uncertainty and the use of three different extreme value distributions for flood frequency analysis. Results indicate increased flood hazard in all catchments for different Shared Socioeconomic Pathways (SSPs), with changes in flooding consistent with changes in annual maximum precipitation. We use additive chains and analysis of variance (ANOVA) to quantify and decompose uncertainties and their interactions in estimating selected flood quantiles for each catchment. We find that not only do the contributions of different sources of uncertainty vary by catchment, but that the dominant sources of uncertainty can be very different on a catchment-by-catchment basis. While uncertainties in future projections are widely assumed to be dominated by the ensemble of climate models used, we find that in one of our catchments uncertainties associated with bias correction methods dominate, while in another the uncertainty associated with the use of different extreme value distributions outweighs the uncertainty associated with the ensemble of climate models. These findings highlight the inability to generalise a priori about the importance of different components of the cascade of uncertainty in future flood hazard at the catchment scale. Moreover, we find that the interaction of components of the modelling chain employed are substantial (> 20 % of overall uncertainty in two catchments). While our sample is small, there is evidence that the dominant components of the cascade of uncertainty may be linked to catchment characteristics and rainfall–runoff processes. Future work that seeks to further explore the characteristics of the uncertainty cascade as they relate to catchment characteristics may provide insight into a priori identifying the key components of modelling chains to be targeted in climate change impact assessments.

Multimodel assessment of water budget in Indian sub-continental river basins

Estimation of the water budget is essential for water resources and environmental management. However, a reliable assessment of the water budget and water yield for the Indian sub-continental river basins has been lacking. We used 0.25° gridded meteorological observations and five hydrological models to construct water budget estimates for the Indian sub-continental river basins for the 1951–2016 period. The hydrological models (i.e. VIC, CLM, Noah-MP, H08, and CWatM) were calibrated against observed streamflow and evaluated for evapotranspiration (ET) and soil moisture. Considerable uncertainty in the water budget components (ET and total runoff) was found due to the process representation and parameterization of hydrological models. Multimodel ensemble mean of the five hydrological models performed better than individual models for most basins for water budget components. West coast, Brahmaputra, and Brahmani are the three wettest river basins. At the same time, Sabarmati, Indus, and Pennar are the three driest basins in terms of mean annual precipitation in the Indian sub-continent. The three transboundary river basins (Brahmaputra, Ganga, and Indus) have the highest water yield of 731.9 (±50.2), 582.4 (±89.4), and 231.1(±71) km3, respectively. The Budyko framework showed that only two basins (West Coast and the Brahmaputra) over the Indian sub-continent fall under the energy-limited (relatively lower atmospheric water demands) conditions. In comparison, the other sixteen river basins are in water-limited (higher atmospheric water demands) condition. Our findings have implications for water resources planning and management in one of the world's most populated regions.

Hydrological Similarity-Based Parameter Regionalization under Different Climate and Underlying Surfaces in Ungauged Basins

Hydrological similarity-based parameter regionalization is the dominant method used for runoff prediction in ungauged basin. However, the application of this approach depends on assessing hydrological similarity between basins. This study used data for runoff, climate, and the underlying surface of the Hulan River Basin and Poyang Lake Basin to construct a novel physical hydrological similarity index (HSI). The index was used to compare the efficiency of transfer of the parameters of commonly used regionalization methods and to finally apply parameters to ungauged basins. The results showed that: (1) Precipitation is the main climatic factor regulating magnitude of runoff in the Poyang Lake Basin. Spring runoff in Hulan River Basin was regulated by precipitation and temperature. (2) The GR4J and CemaNeigeGR4J models achieved reasonable simulations of runoff of Poyang Lake Basin and Hulan River Basin. Although CemaNeigeGR4J considers snowmelt, the model simulations of spring runoff in the Hulan River Basin were not accurate. (3) There was a significant correlation between climate, the underlying surface, and hydrological model parameters. There were fewer significant correlations between environmental factors and between environmental factors and hydrological model parameters in the Hulan River Basin compared to those in the Poyang Lake Basin, possibly due to less sub-basins in the Hulan River Basin. (4) The HSI based on a combination of principal component analysis and the entropy method efficiently identified the most similar gauged basin for an ungauged basin. A significant positive correlation existed between the HSI and parameter transfer efficiency. The relationship between the HSI and transfer efficiency could be represented by logistic regression and linear regression in the Poyang Lake Basin and Hulan River Basin, respectively. The HSI was better able to quantify the hydrological similarity between basins in terms of climate and underlying surface and can provide a scientific reference for the transfer of hydrological model parameters in an ungauged basin.