Assessment of spatial transferability of process‐based hydrological model parameters in two neighbouring catchments in the Himalayan Region

Estimating the hydrological regime of ungauged catchments in the Himalayan region is challenging due to a lack of sufficient monitoring stations. In this paper, the spatial transferability of the model parameters of the process‐oriented J2000 hydrological model was investigated in 2 glaciated subcatchments of the Koshi river basin in eastern Nepal. The catchments have a high degree of similarity with respect to their static landscape features. The model was first calibrated (1986–1991) and validated (1992–1997) in the Dudh Koshi subcatchment. The calibrated and validated model parameters were then transferred to the nearby Tamor catchment (2001–2009). Sensitivity and uncertainty analyses were carried out for both subcatchments to discover the sensitivity range of the parameters in the two catchments. The model represented the overall hydrograph well in both subcatchments, including baseflow, rising and falling limbs; however, the peak flows were underestimated. The efficiency results according to both Nash–Sutcliffe (ENS) and the coefficient of determination (r²) were above 0.84 in both catchments (1986–1997 in Dudh Koshi and 2001–2009 in Tamor). The ranking of the parameters in respect to their sensitivity matched well for both catchments while taking ENS and log Nash–Sutcliffe (LNS) efficiencies into account. However, there were some differences in sensitivity to ENS and LNS for moderately and less‐sensitive parameters, although the majority (13 out of 16 for ENS and 16 out of 16 for LNS) had a sensitivity response in a similar range. The generalized uncertainty likelihood estimation results suggest that the parameter uncertainty are most of the time within the range and the ensemble mean matches very good (ENS: 0.84) with observed discharge. The results indicate that transfer of the J2000 parameters to a neighbouring catchment in the Himalayan region with similar physiographic landscape characteristics is viable. This indicates the possibility of applying a calibrated process‐based J2000 model to other ungauged catchments in the Himalayan region, which could provide important insights into the hydrological system dynamics and provide much needed information to support water resources planning and management.     

Can climate variability information constrain a hydrological model for an ungauged Costa Rican catchment?

Long‐term hydrological data are key to understanding catchment behaviour and for decision making within water management and planning. Given the lack of observed data in many regions worldwide, such as Central America, hydrological models are an alternative for reproducing historical streamflow series. Additional types of information—to locally observed discharge—can be used to constrain model parameter uncertainty for ungauged catchments. Given the strong influence that climatic large‐scale processes exert on streamflow variability in the Central American region, we explored the use of climate variability knowledge as process constraints to constrain the simulated discharge uncertainty for a Costa Rican catchment, assumed to be ungauged. To reduce model uncertainty, we first rejected parameter relationships that disagreed with our understanding of the system. Then, based on this reduced parameter space, we applied the climate‐based process constraints at long‐term, inter‐annual, and intra‐annual timescales. In the first step, we reduced the initial number of parameters by 52%, and then, we further reduced the number of parameters by 3% with the climate constraints. Finally, we compared the climate‐based constraints with a constraint based on global maps of low‐flow statistics. This latter constraint proved to be more restrictive than those based on climate variability (further reducing the number of parameters by 66% compared with 3%). Even so, the climate‐based constraints rejected inconsistent model simulations that were not rejected by the low‐flow statistics constraint. When taken all together, the constraints produced constrained simulation uncertainty bands, and the median simulated discharge followed the observed time series to a similar level as an optimized model. All the constraints were found useful in constraining model uncertainty for an—assumed to be—ungauged basin. This shows that our method is promising for modelling long‐term flow data for ungauged catchments on the Pacific side of Central America and that similar methods can be developed for ungauged basins in other regions where climate variability exerts a strong control on streamflow variability.     

Predicting dry‐season flows with a monthly rainfall–runoff model: Performance for gauged and ungauged catchments

Hydrologic models are useful to understand the effects of climate and land‐use changes on dry‐season flows. In practice, there is often a trade‐off between simplicity and accuracy, especially when resources for catchment management are scarce. Here, we evaluated the performance of a monthly rainfall–runoff model (dynamic water balance model, DWBM) for dry‐season flow prediction under climate and land‐use change. Using different methods with decreasing amounts of catchment information to set the four model parameters, we predicted dry‐season flow for 89 Australian catchments and verified model performance with an independent dataset of 641 catchments in the United States. For the Australian catchments, model performance without catchment information (other than climate forcing) was fair; it increased significantly as the information to infer the four model parameters increased. Regressions to infer model parameters from catchment characteristics did not hold for catchments in the United States, meaning that a new calibration effort was needed to increase model performance there. Recognizing the interest in relative change for practical applications, we also examined how DWBM could be used to simulate a change in dry‐season flow following land‐use change. We compared results with and without calibration data and showed that predictions of changes in dry‐season flow were robust with respect to uncertainty in model parameters. Our analyses confirm that climate is a strong driver of dry‐season flow and that parsimonious models such as DWBM have useful management applications: predicting seasonal flow under various climate forcings when calibration data are available and providing estimates of the relative effect of land use on seasonal flow for ungauged catchments.     

Rainfall–runoff modelling of ephemeral streams in the Valencia region (eastern Spain)

This paper presents preliminary results from the application of a transfer‐function rainfall–runoff model to ephemeral streams in Mediterranean Spain. Flow simulations have been conducted for two small catchments (Carraixet and Poyo basins), located in close proximity to one another yet with significantly different geological characteristics. Analysis of flow simulations for a number of high‐flow events has revealed the dominant influence of the rainfall on the catchment response, particularly for high‐rainfall events. Particular success has been attained modelling the highest magnitude events in both catchments and for all events in the faster responding (Poyo) catchment. In order to investigate the viability of the model for forecasting floods in ungauged catchments, additional investigations have been conducted by calibrating the model for one catchment (donor catchment) and then applying it to another (receptor catchment). The results indicate that this can be successful when either the donor catchment is a fast response catchment or when the model is calibrated using a high‐magnitude event in the donor catchment, providing that the modelled receptor catchment event is of a lower magnitude. Copyright © 2002 John Wiley & Sons, Ltd.     

Derivation and verification of empirical catchment response time equations for medium to large catchments in South Africa

Despite uncertainties and errors in measurement, observed peak discharges are the best estimate of the true peak discharge from a catchment. However, in ungauged catchments, the catchment response time is a fundamental input to all methods of estimating peak discharges; hence, errors in estimated catchment response time directly impact on estimated peak discharges. In South Africa, this is particularly the case in ungauged medium to large catchments where practitioners are limited to use empirical methods that were calibrated on small catchments not located in South Africa. The time to peak (T_P), time of concentration (T_C) and lag time (T_L) are internationally the most frequently used catchment response time parameters and are normally estimated using either hydraulic or empirical methods. Almost 95% of all the time parameter estimation methods developed internationally are empirically based. This paper presents the derivation and verification of empirical T_P equations in a pilot scale study using 74 catchments located in four climatologically different regions of South Africa, with catchment areas ranging from 20 km² to 35 000 km². The objective is to develop unique relationships between observed T_P values and key climatological and geomorphological catchment predictor variables in order to estimate catchment T_P values at ungauged catchments. The results show that the derived empirical T_P equation(s) meet the requirement of consistency and ease of application. Independent verification tests confirmed the consistency, while the statistically significant independent predictor variables included in the regressions provide a good estimation of catchment response times and are also easy to determine by practitioners when required for future applications in ungauged catchments. It is recommended that the methodology used in this study should be expanded to other catchments to enable the development of a regional approach to improve estimation of time parameters on a national‐scale. However, such a national‐scale application would not only increase the confidence in using the suggested methodology and equation(s) in South Africa, but also highlights that a similar approach could be adopted internationally. Copyright © 2016 John Wiley & Sons, Ltd.     

Process‐based hydrological modelling: The potential of a bottom‐up approach for runoff predictions in ungauged catchments

Conceptual rainfall–runoff models are a valuable tool for predictions in ungauged catchments. However, most of them rely on calibration to determine parameter values. Improving the representation of runoff processes in models is an attractive alternative to calibration. Such an approach requires a straightforward, a priori parameter allocation procedure applicable on a wide range of spatial scales. However, such a procedure has not been developed yet. In this paper, we introduce a process‐based runoff generation module (RGM‐PRO) as a spin‐off of the traditional runoff generation module of the PREVAH hydrological modelling system. RGM‐PRO is able to exploit information from maps of runoff types, which are developed on the basis of field investigations and expert knowledge. It is grid based, and within each grid cell, the process heterogeneity is considered to avoid information loss due to grid resolution. The new module is event based, and initial conditions are assimilated and downscaled from continuous simulations of PREVAH, which are also available for real‐time applications. Four parameter allocation strategies were developed, on the basis of the results of sprinkling experiments on 60‐m² hillslope plots at several grassland locations in Switzerland, and were tested on five catchments on the Swiss Plateau and Prealps. For the same catchments, simulation results obtained with the best parameter allocation strategy were compared with those obtained with different configurations of the traditional runoff generation module of PREVAH, which was also applied as an event‐based module here. These configurations include a version that avoids calibration, one that transfers calibrated parameters, and one that uses regionalised parameter values. RGM‐PRO simulated heavy events in a more realistic way than the uncalibrated traditional runoff generation module of PREVAH, and, in some instances, it even exceeded the performance of the calibrated traditional one. The use of information on the spatial distribution of runoff types additionally proved to be valuable as a regionalisation technique and showed advantages over the other regionalisation approaches, also in terms of robustness and transferability.     

Modelling daily streamflow at ungauged catchments: what information is necessary?

Rainfall–runoff modelling at ungauged catchments often involves the transfer of calibrated model parameters from ‘donor’ gauged catchments. However, in any rainfall–runoff model, some parameters tend to be more sensitive to the objective function, whereas others are insensitive over their entire feasible range. In this paper, we analyse the effect of selectively transferring sensitive versus insensitive parameters on streamflow predictability at ungauged catchments. We develop a simple daily time‐step rainfall–runoff model [exponential bucket hydrologic model (EXP‐HYDRO)] and calibrate it at 756 catchments within the continental USA. Nash–Sutcliffe efficiency of (NS) is used as the objective function. The model simulates satisfactorily at 323 catchments (NS > 0.6), most of which are located in the eastern part of the USA, along the Rocky Mountain Range, and near the western Pacific coast. Of the six calibration parameters, only three parameters are found to be sensitive to NS. Two of these parameters control the hydrograph recession behaviour of a catchment, and the third parameter controls the snowmelt rate. We find that when only sensitive parameters are transferred, model performance at ungauged catchments is almost at par with that of transferring all six parameters. Conversely, the transfer of only insensitive parameters results in a significant deterioration in model performance. Results suggest that streamflow predictability at ungauged catchments using rainfall–runoff models is largely dependent on the transfer of a small subset of parameters. We recommend that, in any modelling framework, such parameters should be identified and further characterized to better understand the information controlling streamflow predictability at ungauged catchments. Copyright © 2012 John Wiley & Sons, Ltd.     

Estimating daily inflows of large lakes using a water‐balance‐based runoff coefficient scaling approach

Daily river inflow time series are highly valuable for water resources and water environment management of large lakes. However, the availability of continuous inflow data for large lakes is still relatively limited, especially for large lakes situated within humid plain regions with tens or even hundreds of tributaries. In this study, we choose the fifth largest freshwater Lake Chaohu in China as our study area to introduce a new approach to reconstruct historical daily inflows at ungauged subcatchments of large lakes. This approach makes use of water level, lake surface rainfall, evaporation from the lake, and catchment rainfall observations. Rainfall–runoff relationship at a reference catchment was analysed to select rainfall input and estimate run‐off coefficient firstly, and the run‐off coefficient was then transferred to ungauged subcatchments to initially estimate daily inflows. Run‐off coefficient was scaled to adjust daily inflows at ungauged subcatchments according to water balance of the lake. This approach was evaluated using sparsely measured inflows at eight subcatchments of Lake Chaohu and compared with the commonly used drainage area ratio method. Results suggest that the inflow time series reconstructed from this approach consistent well to corresponding observations, with mean R² and Nash–Sutcliffe efficiency values of 0.69 and 0.6, respectively. This approach outperforms drainage area ratio method in terms of mean R² and Nash–Sutcliffe efficiency values. Accuracy of this approach holds well when the number of water‐level station being used decreased from four to one.     

A comparison of three approaches to spatial generalization of rainfall–runoff models

The generalization of the parameters of rainfall–runoff models, to enable application at ungauged sites, is an important and ongoing area of research. This paper compares the performance of three alternative methods of generalization, for two parameter‐sparse conceptual models (PDM and TATE), specifically for use in flood frequency estimation using continuous simulation. Two of the methods are based on fitting regression relationships between catchment properties and calibrated parameter values, using weighted or sequential regression (with weights based on estimates of calibration uncertainty), and the third is based on the use of pooling groups, defined through measures of site‐similarity based on catchment properties. The study uses a relatively large sample of catchments in Britain. For the PDM, the site‐similarity method performs best, but not greatly better than either regression method, so there may be cases where the use of regression would be preferable. For the TATE model, weighted regression performs best (with a very similar level of performance to that of the PDM with site‐similarity), whereas site‐similarity performs worst (due to poor performance for catchments with higher baseflow), indicating that the choice of model and generalization method should not be separated. The use of sequential regression, which was developed to try to allow for parameter interdependence, shows no clear advantage for either model. Other than the poor performance of the TATE model with site‐similarity for catchments with a higher baseflow index, there are no clear relationships between performance of any model/method and catchment type. Copyright © 2006 John Wiley & Sons, Ltd.     

Coupling the WRF model with a temperature index model based on remote sensing for snowmelt simulations in a river basin in the Altay Mountains,north‐west China

Snowmelt water is an important freshwater resource in the Altay Mountains in north‐west China; however, warming climate and rapid spring snowmelt can cause floods that endanger both public and personal property and safety. This study simulates snowmelt in the Kayiertesi River catchment using a temperature index model based on remote sensing coupled with high‐resolution meteorological data obtained from National Centers for Environmental Prediction (NCEP) reanalysis fields that were downscaled using the Weather Research Forecasting model and then bias corrected using a statistical downscaled model. Validation of the forcing data revealed that the high‐resolution meteorological fields derived from the downscaled NCEP reanalysis were reliable for driving the snowmelt model. Parameters of the temperature index model based on remote sensing were calibrated for spring 2014, and model performance was validated using Moderate Resolution Imaging Spectroradiometer snow cover and snow observations from spring 2012. The results show that the temperature index model based on remote sensing performed well, with a simulation mean relative error of 6.7% and a Nash–Sutcliffe efficiency of 0.98 in spring 2012 in the river of Altay Mountains. Based on the reliable distributed snow water equivalent simulation, daily snowmelt run‐off was calculated for spring 2012 in the basin. In the study catchment, spring snowmelt run‐off accounts for 72% of spring run‐off and 21% of annual run‐off. Snowmelt is the main source of run‐off for the catchment and should be managed and utilized effectively. The results provide a basis for snowmelt run‐off predictions, so as to prevent snowmelt‐induced floods, and also provide a generalizable approach that can be applied to other remote locations where high‐density, long‐term observational data are lacking. Copyright © 2016 John Wiley & Sons, Ltd.     

Transferability of hydrological model parameter spaces in the estimation of runoff in ungauged catchments

Abstract

In this study, transferability options of the Hydrologiska Byråns Vattenbalansavdelning (HBV) hydrological model parameter (MP) spaces were investigated to estimate ungauged catchment runoff. Three approaches were applied in the study: MP space transfer from single, neighbouring and all potential donor catchments. The model performance was evaluated by a jackknife procedure, where one catchment at a time was treated as if ungauged, and behavioural MP sets from candidate donor catchments were used to estimate the “ungauged” runoff. The results showed that ungauged catchment runoff estimation could not be guaranteed by transferring MP sets from a single physiographically nearest donor catchment. Integrating MP sets typically from one to six donor catchments supplemented the lack of effective MP sets and improved the model performance at the ungauged catchments. In addition, the analysis results revealed that the model performance converged to an average performance when the MP sets of all potential donor catchments were integrated.     

Storm‐event rainfall–runoff modelling approach for ungauged sites in Taiwan

This work develops a top‐down modelling approach for storm‐event rainfall–runoff model calibration at unmeasured sites in Taiwan. Twenty‐six storm events occurring in seven sub‐catchments in the Kao‐Ping River provided the analytical data set. Regional formulas for three important features of a streamflow hydrograph, i.e. time to peak, peak flow, and total runoff volume, were developed via the characteristics of storm event and catchment using multivariate regression analysis. Validation of the regional formulas demonstrates that they reasonably predict the three features of a streamflow hydrograph at ungauged sites. All of the sub‐catchments in the study area were then adopted as ungauged areas, and the three streamflow hydrograph features were calculated by the regional formulas and substituted into the fuzzy multi‐objective function for rainfall–runoff model calibration. Calibration results show that the proposed approach can effectively simulate the streamflow hydrographs at the ungauged sites. The simulated hydrographs more closely resemble observed hydrographs than hydrographs synthesized using the Soil Conservation Service (SCS) dimensionless unit hydrograph method, a conventional method for hydrograph estimation at ungauged sites in Taiwan. Copyright © 2008 John Wiley & Sons, Ltd.     

Streamflow prediction under extreme data scarcity: a step toward hydrologic process understanding within severely data-limited regions

Streamflow prediction in ungauged basins is necessary to support water resources management decisions. Herein we refine and evaluate the Streamflow Prediction under Extreme Data-scarcity (SPED) model, a framework designed for streamflow prediction within regions of sparse hydrometeorological observation. With the SPED framework, inclusion of soft data directs optimization to balance runoff efficiency with the selection of hydrologically representative parameters. Here SPED is tested in catchments around the world, including four well-gauged catchments, by mimicking data-scarcity and comparing against data-intensive approaches. By differentiating equifinal models, SPED succeeds where traditional approaches are likely to fail: partially dissimilar reference/target catchments. For instance, in a pair of reference/target catchments with different base flow regimes, SPED outperforms a model calibrated only to maximize efficiency (NSE of 0.54 versus 0.08). SPED performs consistently (NSE range: 0.54–0.74) across the diverse climatological and physiographic settings tested and proves comparable to state-of-the-science methods that use robust data networks.     

Hydrological footprints of urban developments in the Lake Simcoe watershed,Canada: a combined paired‐catchment and change detection modelling approach

Urban sprawl and regional climate variability are major stresses on surface water resources in many places. The Lake Simcoe watershed (LSW) Ontario, Canada, is no exception. The LSW is predominantly agricultural but is experiencing rapid population growth because of its proximity to the Greater Toronto area. This has led to extensive land use changes that have impacted its water resources and altered run‐off patterns in some rivers draining to the lake. Here, we use a paired‐catchment approach, hydrological change detection modelling and remote sensing analysis of satellite images to evaluate the impacts of land use change on the hydrology of the LSW (1994 to 2008). Results show that urbanization increased up to 16% in Lovers Creek, the most urban‐impacted catchment. Annual run‐off from Lovers Creek increased from 239 to 442 mm/year in contrast to the reference catchment (Black River at Washago) where run‐off was relatively stable with an annual mean of 474 mm/year. Increased annual run‐off from Lovers Creek was not accompanied by an increase in annual precipitation. Discriminant function analysis suggests that early (1992–1997; pre‐major development) and late (2004–2009; fully urbanized) periods for Lovers Creek separated mainly based on model parameter sets related to run‐off flashiness and evapotranspiration. As a result, parameterization in either period cannot be used interchangeably to produce credible run‐off simulations in Lovers Creek because of greater scatter between the parameters in canonical space. Separation of early and late‐period parameter sets for the reference catchment was based on climate and snowmelt‐related processes. This suggests that regional climatic variability could be influencing hydrologic change in the reference catchment, whereas urbanization amplified the regional natural hydrologic changes in urbanizing catchments of the LSW. Copyright © 2014 John Wiley & Sons, Ltd.     

Nonparametric catchment clustering using the data depth function

ABSTRACT

The clustering of catchments is important for prediction in ungauged basins, model parameterization and watershed development and management. The aim of this study is to explore a new measure of similarity among catchments, using a data depth function and comparing it with catchment clustering indices based on flow and physical characteristics. A cluster analysis was performed for each similarity measure using the affinity propagation clustering algorithm. We evaluated the similarity measure based on depth–depth plots (DD-plots) as a basis for transferring parameter sets of a hydrological model between catchments. A case study was developed with 21 catchments in a diverse New Zealand region. Results show that clustering based on the depth–depth measure is dissimilar to clustering on catchment characteristics, flow, or flow indices. A hydrological model was calibrated for the 21 catchments and the transferability of model parameters among similar catchments was tested within and between clusters defined by each clustering method. The mean model performance for parameters transferred within a group always outperformed those from outside the group. The DD-plot based method was found to produce the best in-group performance and second-highest difference between in-group and out-group performance.

EDITOR D. Koutsoyiannis; ASSOCIATE EDITOR A. Viglione