Improving streamflow estimation in ungauged basins using a multi-modelling approach

ABSTRACT

In this study, a multi-modelling approach is proposed for improved continuous daily streamflow estimation in ungauged basins using regionalization—the process of transferring hydrological data from gauged to ungauged watersheds. Four regionalization models, two data-driven and two hydrological, were used for continuous daily streamflow estimation. Comparison of the individual models reveals that each of the four models performed well on a limited number of ungauged basins while none of them performed well for the entire 90 selected watersheds. The results obtained from the four models are evaluated and reported in a deterministic way by a model combination approach along with its uncertainty range consisting of 16 ensemble members. It is shown that a combined model of the four individual models performed well on all 90 watersheds and the ensemble range can account for the uncertainty of models. The combined model was more efficient and appeared more robust compared to the individual models. Furthermore, continuous ranked probability scores (CRPS) calculated for the ensemble model outputs indicate better performance compared to individual models and competitive with the combined model.

EDITOR A. Castellarin ASSOCIATE EDITOR G. Di Baldassarre     

Multi-model averaging for continuous streamflow prediction in ungauged basins

ABSTRACT

This paper assesses the possibility of using multi-model averaging techniques for continuous streamflow prediction in ungauged basins. Three hydrological models were calibrated on the Nash-Sutcliffe Efficiency metric and were used as members of four multi-model averaging schemes. Model weights were estimated through optimization on the donor catchments. The averaging methods were tested on 267 catchments in the province of Québec, Canada, in a leave-one-out cross-validation approach. It was found that the best hydrological model was practically always better than the others used individually or in a multi-model framework, thus no averaging scheme performed statistically better than the best single member. It was also found that the robustness and adaptability of the models were highly influential on the models’ performance in cross-verification. The results show that multi-model averaging techniques are not necessarily suited for regionalization applications, and that models selected in such studies must be chosen carefully to be as robust as possible on the study site.

Editor M.C. Acreman; Associate editor S. Grimaldi     

Data time-step dependency of conceptual rainfall—streamflow model parameters: an empirical study with implications for regionalisation

Abstract

Employing 1-, 2-, 4-, 6-, 12- and 24-hourly data sets for two catchments (10.6 and 298 km²) in Wales, the calibrated parameters of a unit hydrograph-based model are shown to change substantially over that range of data time steps. For the smaller basin, each model parameter reaches, or approaches, a stable value as the data time step decreases, providing a straightforward method of estimating time-step independent model parameter values. For the larger basin, the model parameters also reach, or approach, stable values using hourly data, but, for reasons given in the paper, interpretation of the results is more difficult. Model parameter sensitivity analyses are presented that give insights into the relative precision on the parameters for both catchments. The paper discusses the importance of accounting for model parameter data time-step dependency in pursuit of a reduction in the uncertainty associated with estimates of flow in ungauged basins, and suggests that further work along these lines be undertaken using different catchments and models.     

Characterizing and understanding the variability of streamflow drought indicators within the USA

ABSTRACT

Low streamflow conditions can have adverse consequences for society and river ecology. The variability and drivers of streamflow drought indicators within the USA were investigated using observed streamflow records from 603 gauges across the USA. The analysis was based on two main approaches: (i) low-flow magnitude indicators, and (ii) streamflow deficit indicators. First, we examined how streamflow drought indicators vary spatially across the USA. Second, we used a data-driven clustering method to identify spatial clusters for each indicator. Finally, we assessed the association with regional climate drivers. The results show that the spatial variability of low-flow magnitude indicators is significantly different from the deficit indicators. Further, our clustering approach identifies regions of spatial homogeneity, which can be linked to the extreme regional climate drivers and land–atmosphere interactions. The influence of regional climate on streamflow drought indicators varies more between clusters than between indicators.     

Estimation of rainfall elasticity of streamflow in Australia

Abstract

Estimates of rainfall elasticity of streamflow in 219 catchments across Australia are presented. The rainfall elasticity of streamflow is defined here as the proportional change in mean annual streamflow divided by the proportional change in mean annual rainfall. The elasticity is therefore a simple estimate of the sensitivity of long-term streamflow to changes in long-term rainfall, and is particularly useful as an initial estimate of climate change impact in land and water resources projects. The rainfall elasticity of streamflow is estimated here using a hydrological modelling approach and a nonparametric estimator. The results indicate that the rainfall elasticity of streamflow (? _P ) in Australia is about 2.0–3.5 (observed in about 70% of the catchments), that is, a 1% change in mean annual rainfall results in a 2.0–3.5% change in mean annual streamflow. The rainfall elasticity of streamflow is strongly correlated to runoff coefficient and mean annual rainfall and streamflow, where streamflow is more sensitive to rainfall in drier catchments, and those with low runoff coefficients. There is a clear relation-ship between the ? _P values estimated using the hydrological modelling approach and those estimated using the nonparametric estimator for the 219 catchments, although the values estimated by the hydrological modelling approach are, on average, slightly higher. The modelling approach is useful where a detailed study is required and where there are sufficient data to reliably develop and calibrate a hydrological model. The nonparametric estimator is useful where consistent estimates of the sensitivity of long-term streamflow to climate are required, because it is simple to use and estimates the elasticity directly from the historical data. The nonparametric method, being model independent, can also be easily applied in comparative studies to data sets from many catchments across large regions.     

Evaluation of the Glugla method for estimating evapotranspiration and groundwater recharge

Abstract

Estimates of groundwater recharge are often needed for a variety of groundwater resource evaluation purposes. A method for estimating long-term groundwater recharge and actual evapotranspiration not known in the English literature is presented. The method uses long-term average annual precipitation, runoff, potential evaporation, and crop-yield information, and uses empirical parameter curves that depend on soil and crop types to determine long-term average annual groundwater recharge (GWR). The method is tested using historic lysimeter records from 10 lysimeters at Coshocton, Ohio, USA. Considering the coarse information required, the method provides good estimates of groundwater recharge and actual evapotranspiration, and is sensitive to a range of cropping and land-use conditions. Problems with practical application of the technique are mentioned, including the need for further testing using given parameter curves, and for incorporating parameters that describe current farming practices and other land uses. The method can be used for urban conditions, and can be incorporated into a GIS framework for rapid, large-area, spatially-distributed estimations of GWR. An example application of the method is given.     

A comparison of methods for low streamflow estimation from spot measurements

Low streamflow statistic estimators at ungauged river sites generally have large errors and uncertainties. This can be due to many reasons, including lack of data, complex hydrologic processes, and the inadequate or improper characterization of watershed hydrogeology. One potential solution is to take a small number of streamflow measurements at an ungauged site to either estimate hydrogeologic indices or transfer information from a nearby site using concurrent streamflow measurements. An analysis of four low streamflow estimation techniques, regional regression, regional plus hydrogeologic indices, baseflow correlation, and scaling, was performed within the Apalachicola–Chattahoochee–Flint watershed, a U.S. Geological Survey WaterSMART region in the south‐eastern United States. The latter three methods employ a nominal number of spot measurements at the ungauged site to improve low streamflow estimation. Results indicate that baseflow correlation and scaling methods, which transfer information from a donor site, can produce improved low streamflow estimators when spot measurements are available. Estimation of hydrogeologic indices from spot measurements improves regional regression models, with the baseflow recession constant having more explanatory power than the aquifer time constant, but these models are generally outperformed by baseflow correlation and scaling.     

Practical aspects of calibrating and selecting unit hydrograph-based models for continuous river flow simulation

Abstract

A continuous simulation rainfall-streamflow modelling approach that identifies unit hydrographs for total streamflow has been applied to an 11-year record from a national hydrometric monitoring network catchment in the UK. The model is of the parametrically parsimonious conceptual model (PPCM) type that can make efficient use of rainfall, streamflow and air temperature data readily available from established national and regional monitoring networks. A multiple split-sample model calibration and simulation analysis is presented that reveals some guiding principles for calibrating and applying PPCMs generally. The inadequacy of a one-dimensional objective function for calibrating best PPCMs is demonstrated. A two-dimensional objective function approach is superior but is shown to be unreliable in some cases, confirming the need for additional critical inspection of other model performance statistics, model parameters and time series plots as an integral part of the model calibration process. A strong tendency evident from the multiple split-sample analysis is that, for the catchment studied, models that fit relatively well in calibration mode perform relatively poorly in simulation mode.     

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.     

Incorporating uncertainty in hydrological predictions for gauged and ungauged basins in southern Africa

Abstract

The increasing demand for water in southern Africa necessitates adequate quantification of current freshwater resources. Watershed models are the standard tool used to generate continuous estimates of streamflow and other hydrological variables. However, the accuracy of the results is often not quantified, and model assessment is hindered by a scarcity of historical observations. Quantifying the uncertainty in hydrological estimates would increase the value and credibility of predictions. A model-independent framework aimed at achieving consistency in incorporating and analysing uncertainty within water resources estimation tools in gauged and ungauged basins is presented. Uncertainty estimation in ungauged basins is achieved via two strategies: a local approach for a priori model parameter estimation from physical catchment characteristics, and a regional approach to regionalize signatures of catchment behaviour that can be used to constrain model outputs. We compare these two sources of information in the data-scarce region of South Africa. The results show that both approaches are capable of uncertainty reduction, but that their relative values vary.

Editor D. Koutsoyiannis

Citation Kapangaziwiri, E., Hughes, D.A., and Wagener, T., 2012. Incorporating uncertainty in hydrological predictions for gauged and ungauged basins in southern Africa. Hydrological Sciences Journal, 57 (5), 1000–1019.     

Balancing trade‐off issues in land use change and the impact on streamflow and salinity management

The south‐west region of the Goulburn–Broken catchment in the south‐eastern Murray–Darling Basin in Australia faces a range of natural resource challenges. A balanced strategy is required to achieve the contrasting objectives of remediation of land salinization and reducing salt export, while maintaining water supply security to satisfy human consumption and support ecosystems. This study linked the Catchment Analysis Tool (CAT), comprising a suite of farming system models, to the catchment‐scale CATNode hydrological model to investigate the effects of land use change and climate variation on catchment streamflow and salt export. The modelling explored and contrasted the impacts of a series of different revegetation and climate scenarios. The results indicated that targeted revegetation to only satisfy biodiversity outcomes within a catchment is unlikely to have much greater impact on streamflow and salt load in comparison with simple random plantings. Additionally, the results also indicated that revegetation to achieve salt export reduction can effectively reduce salt export while having a disproportionately smaller affect on streamflows. Furthermore, streamflow declines can be minimized by targeting revegetation activities without significantly altering salt export. The study also found that climate change scenarios will have an equal if not more significant impact on these issues over the next 70 years. Uncertainty in CATNode streamflow predictions was investigated because of the effect of parameter uncertainty. Copyright © 2012 John Wiley & Sons, Ltd.     

Quantification of uncertainty in the assessment of future streamflow under changing climate conditions

Climate change has a significant influence on streamflow variation. The aim of this study is to quantify different sources of uncertainties in future streamflow projections due to climate change. For this purpose, 4 global climate models, 3 greenhouse gas emission scenarios (representative concentration pathways), 6 downscaling models, and a hydrologic model (UBCWM) are used. The assessment work is conducted for 2 different future time periods (2036 to 2065 and 2066 to 2095). Generalized extreme value distribution is used for the analysis of the flow frequency. Strathcona dam in the Campbell River basin, British Columbia, Canada, is used as a case study. The results show that the downscaling models contribute the highest amount of uncertainty to future streamflow predictions when compared to the contributions by global climate models or representative concentration pathways. It is also observed that the summer flows into Strathcona dam will decrease, and winter flows will increase in both future time periods. In addition to these, the flow magnitude becomes more uncertain for higher return periods in the Campbell River system under climate change.     

Using raw regional climate model outputs for quantifying climate change impacts on hydrology

General circulation model outputs are rarely used directly for quantifying climate change impacts on hydrology, due to their coarse resolution and inherent bias. Bias correction methods are usually applied to correct the statistical deviations of climate model outputs from the observed data. However, the use of bias correction methods for impact studies is often disputable, due to the lack of physical basis and the bias nonstationarity of climate model outputs. With the improvement in model resolution and reliability, it is now possible to investigate the direct use of regional climate model (RCM) outputs for impact studies. This study proposes an approach to use RCM simulations directly for quantifying the hydrological impacts of climate change over North America. With this method, a hydrological model (HSAMI) is specifically calibrated using the RCM simulations at the recent past period. The change in hydrological regimes for a future period (2041–2065) over the reference (1971–1995), simulated using bias‐corrected and nonbias‐corrected simulations, is compared using mean flow, spring high flow, and summer–autumn low flow as indicators. Three RCMs driven by three different general circulation models are used to investigate the uncertainty of hydrological simulations associated with the choice of a bias‐corrected or nonbias‐corrected RCM simulation. The results indicate that the uncertainty envelope is generally watershed and indicator dependent. It is difficult to draw a firm conclusion about whether one method is better than the other. In other words, the bias correction method could bring further uncertainty to future hydrological simulations, in addition to uncertainty related to the choice of a bias correction method. This implies that the nonbias‐corrected results should be provided to end users along with the bias‐corrected ones, along with a detailed explanation of the bias correction procedure. This information would be especially helpful to assist end users in making the most informed decisions.     

Comparison of design peak flow estimation methods for ungauged basins in Iran

ABSTRACT

Determining the catchment design peak flow is crucial in hydrological practice. In this paper, the conceptual rainfall–runoff model EBA4SUB (Event-Based Approach for Small and Ungauged Basins) was applied in six catchments in Iran. The aims were to test EBA4SUB in reconstructing runoff hydrographs for the investigated case studies and to provide a suitable alternative for the review and updating of design peak flow estimation in Iran. The EBA4SUB output was compared with previous studies on selected catchments. The results show, for all case studies, a large variability in the peak flow values; the EBA4SUB model gave flow values similar to the other methodologies. The EBA4SUB model can be recommended for the following reasons: (i) it minimizes the subjectivity of the modeller. (ii) its modules are based on state-of-the-art procedures, which have been appropriately optimized for ungauged basins; and (iii) it furnishes the whole design flood hydrograph.     

Estimating daily streamflow in the Congo Basin using satellite-derived data and a semi-distributed hydrological model

ABSTRACT

The application of remotely-sensed data for hydrological modeling of the Congo Basin is presented. Satellite-derived data, including TRMM precipitation, are used as inputs to drive the USGS Geospatial Streamflow Model (GeoSFM) to estimate daily river discharge over the basin from 1998 to 2012. Physically-based parameterization was augmented with a spatially-distributed calibration that enables GeoSFM to simulate hydrological processes such as the slowing effect of the Cuvette Centrale. The resulting simulated long-term mean of daily flows and the observed flow at the Kinshasa gauge were comparable (40 631 and 40 638 m³/s respectively), in the 7-year validation period (2004–2010), with no significant bias and a Nash-Sutcliffe model efficiency coefficient of 0.70. Modeled daily flows and aggregated monthly river outflows (compared to historical averages) for additional sites confirm the model reliability in capturing flow timing and seasonality across the basin, but sometimes fails to accurately predict flow magnitude. The results of this model can be useful in research and decision-making contexts and validate the application of satellite-based hydrological models driven for large, data-scarce river systems such as the Congo.     

Water and nutrient predictions in ungauged basins: set-up and evaluation of a model at the national scale

Abstract

A dynamic water quality model, HYPE, was applied to a large, data-sparse region to study whether reliable information on water quantity and water quality could be obtained for both gauged and ungauged waterbodies. The model (called S-HYPE) was set up for all of Sweden (～450 000 km²), divided into sub-basins with an average area of 28 km². Readily available national databases were used for physiographic data, emissions and agricultural practices, fixed values for representative years were used. Daily precipitation and temperature were used as the dynamic forcing of the model. Model evaluation was based on data from several hundred monitoring sites, of which approximately 90% had not been used in calibration on a daily scale. Results were evaluated using the Nash-Sutcliffe efficiency (NSE), correlation and relative errors: 92% of the spatial variation was explained for specific water discharge, and 88% and 59% for total nitrogen and total phosphorus concentrations, respectively. Day-to-day variations were modelled with satisfactory results for water discharge and the seasonal variation of nitrogen concentrations was also generally well captured. In 20 large, unregulated rivers the median NSE for water discharge was 0.84, and the corresponding number for 76 partly-regulated river basins was 0.52. In small basins, the NSE was typically above 0.6. These major achievements relative to previous similar experiments were ascribed to the step-wise calibration process using representative gauged basins and the use of a modelling concept, whereby coefficients are linked to physiographic variables rather than to specific sites.

Editor D. Koutsoyiannis

Citation Strömqvist, J., Arheimer, B., Dahné, J., Donnelly, C. and Lindström, G., 2012. Water and nutrient predictions in ungauged basins: set-up and evaluation of a model at the national scale. Hydrological Sciences Journal, 57 (2), 229–247.