Application of artificial neural networks in regional flood frequency analysis: a case study for Australia

Regional flood frequency analysis (RFFA) is widely used in practice to estimate flood quantiles in ungauged catchments. Most commonly adopted RFFA methods such as quantile regression technique (QRT) assume a log-linear relationship between the dependent and a set of predictor variables. As non-linear models and universal approximators, artificial neural networks (ANN) have been widely adopted in rainfall runoff modeling and hydrologic forecasting, but there have been relatively few studies involving the application of ANN to RFFA for estimating flood quantiles in ungauged catchments. This paper thus focuses on the development and testing of an ANN-based RFFA model using an extensive Australian database consisting of 452 gauged catchments. Based on an independent testing, it has been found that ANN-based RFFA model with only two predictor variables can provide flood quantile estimates that are more accurate than the traditional QRT. Seven different regions have been compared with the ANN-based RFFA model and it has been shown that when the data from all the eastern Australian states are combined together to form a single region, the ANN presents the best performing RFFA model. This indicates that a relatively larger dataset is better suited for successful training and testing of the ANN-based RFFA models.     

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.     

Water balance modeling over variable time scales based on the Budyko framework – Model development and testing

Partitioning of precipitation into evapotranspiration and runoff is controlled by climate and catchment characteristics. The degree of control exerted by these factors varies with the spatial and temporal scales of processes modeled. The Budyko framework or the “limits” concept was used to model water balance at four temporal scales (mean annual, annual, monthly and daily). The method represents a top-down approach to hydrologic modeling and is expected to achieve parsimony of model parameters. Daily precipitation, potential evapotranspiration, and streamflow from 265 catchments in Australia were used. On a mean annual basis, the index of dryness defined as the ratio of potential evapotranspiration to precipitation was confirmed to be a dominant factor in determining the water balance with one model parameter. Analysis of the data, however, suggested increased model complexity is necessary on finer time scale such as monthly. In response, the Budyko framework for mean annual water balance was extended to include additional factors and this resulted in a parsimonious lumped conceptual model on shorter-time scale. The model was calibrated and tested against measured streamflow at variable time scales and showed promising results. The strengths of the model are consistent water balance relationships across different time scales, and model parsimony and robustness. As result, the model has the potential to be used to predict streamflow for ungauged catchments.     

Spatio-temporal changes of hydrological processes and underlying driving forces in Guizhou region, Southwest China

Understanding the changes in streamflow and associated driving forces is crucial for formulating a sustainable regional water resources management strategy in the environmentally fragile karst area of the southwest China. This study investigates the spatio-temporal changes in streamflow of the Guizhou region and their linkage with meteorological influences using the Mann–Kendall trend analysis, singular-spectrum analysis (SSA), Lepage test, and flow duration curves (FDCs). The results demonstrate that: (1) the streamflow in the flood-season (June–August) during 1956–2000 increased significantly (confidence level ≥95%) in most catchments, closely consistent with the distinct increasing trend of annual rainfall over wet-seasons. The timings of abrupt change for streamflow in most catchments are found to occur at 1986; (2) streamflow in the Guizhou region experiences significant seasonal changes prior/posterior to 1986, and in most catchments the coefficient of variation of monthly streamflow increases; (3) spatial changes in streamflow indicate that monthly streamflow in the north-west decreases but increases in other parts; (4) the spatial high- and low-flow map (Q ₅ and Q ₉₅) reveals an increase in the extremely large streamflow in the five eastern catchments but a decrease in the extremely low streamflow in the four eastern catchments and three western catchments during 1987–2000. An increase in streamflow, particularly extreme flows, during the flood season would increase the risk of extreme flood events, while a decrease in streamflow in the dry season is not beneficial to vegetation restoration in this ecologically fragile region.     

Streamflow response of Belgian catchments to IPCC climate change scenarios

The IRMB (Integrated Runoff Model—F. Bultot) daily step conceptual model has been applied to eight Belgian catchments with areas ranging from 100 to 1200 km². These catchments are characterized by various infiltration rates and ground water storage capacities. The outputs of six GCMs (General Circulation Model) distributed by the IPCC (Intergovernmental Panel on Climate Change) and an earlier scenario have been used to perturb time series of hydrometeorological input data relevant to simulate the water cycle. This paper focuses on the impacts on streamflow and its surface and underground components, as well as on the occurrence of flood days and low flow days. Impacts are shown to be catchment and scenario dependent. Due to the scenario diversity, streamflow impacts are found to be either positive or negative. The trends are common to scenarios with the same patterns or to catchments with similar characteristics. For all but two scenarios, all the catchments present an increase of flood frequency. Nevertheless, for all the scenarios, catchments with prevailing surface flow are undergoing an increase in flood frequency during winter months.     

How can a few streamflow measurements help to predict daily hydrographs at almost ungauged sites?

Abstract

Available data from nearby gauging stations can provide a great source of hydrometric information that is potentially transferable to an ungauged site. Furthermore, streamflow measurements may even be available for the ungauged site. This paper explores the potential of four distance-based regionalization methods to simulate daily hydrographs at almost ungauged pollution-control sites. Two methods use only the hydrological information provided by neighbouring catchments; the other two are new regionalization methods parameterized with a limited number of streamflow data available at the site of interest. Based on a network of 149 streamgauges and 21 pollution-control sites located in the Upper Rhine-Meuse area, the comparative assessment demonstrates the benefit of making available point streamflow measurements at the location of interest for improving quantitative streamflow prediction. The advantage is moderate for the prediction of flow types (stormflow, recession flow, baseflow) and pulse shape (duration of rising limb and falling limb).

Editor Z.W. Kundzewicz; Associate editor A. Viglione     

Regionalization of constraints on expected watershed response behavior for improved predictions in ungauged basins

Approaches to modeling the continuous hydrologic response of ungauged basins use observable physical characteristics of watersheds to either directly infer values for the parameters of hydrologic models, or to establish regression relationships between watershed structure and model parameters. Both these approaches still have widely discussed limitations, including impacts of model structural uncertainty. In this paper we introduce an alternative, model independent, approach to streamflow prediction in ungauged basins based on empirical evidence of relationships between watershed structure, climate and watershed response behavior. Instead of directly estimating values for model parameters, different hydrologic response behaviors of the watershed, quantified through model independent streamflow indices, are estimated and subsequently regionalized in an uncertainty framework. This results in expected ranges of streamflow indices in ungauged watersheds. A pilot study using 30 UK watersheds shows how this regionalized information can be used to constrain ensemble predictions of any model at ungauged sites. Dominant controlling characteristics were found to be climate (wetness index), watershed topography (slope), and hydrogeology. Main streamflow indices were high pulse count, runoff ratio, and the slope of the flow duration curve. This new approach provided sharp and reliable predictions of continuous streamflow at the ungauged sites tested.     

Simulating Groundwater-Streamflow Connections in the Upper Colorado River Basin

In mountain, snow driven catchments, snowmelt is supposed to be the primary contribution to river streamflows during spring. In these catchments the contribution of groundwater is not well documented because of the difficulty to monitor groundwater in such complex environment with deep aquifers. In this study we use an integrated hydrologic model to conduct numerical experiments that help quantify the effect of lateral groundwater flow on total annual and peak streamflow in predevelopment conditions. Our simulations focus on the Upper Colorado River Basin (UCRB; 2.8 × 10⁵ km²) a well-documented mountain catchment for which both streamflow and water table measurements are available for several important sub-basins. For the simulated water year, our results suggest an increase in peak flow of up to 57% when lateral groundwater flow processes are included—an unexpected result for flood conditions generally assumed independent of groundwater. Additionally, inclusion of lateral groundwater flow moderately improved the model match to observations. The correlation coefficient for mean annual flows improved from 0.84 for the no lateral groundwater flow simulation to 0.98 for the lateral groundwater flow one. Spatially we see more pronounced differences between lateral and no lateral groundwater flow cases in areas of the domain with steeper topography. We also found distinct differences in the magnitude and spatial distribution of streamflow changes with and without lateral groundwater flow between Upper Colorado River Sub-basins. A sensitivity test that scaled hydraulic conductivity over two orders of magnitude was conducted for the lateral groundwater flow simulations. These results show that the impact of lateral groundwater flow is as large or larger than an order of magnitude change in hydraulic conductivity. While our results focus on the UCRB, we feel that these simulations have relevance to other headwaters systems worldwide.     

Flood variability east of Australia’s Great Dividing Range

The variability of flow in river channels influences the spatial and temporal variability of many biophysical processes including the transport of sediment and waterborne pollutants and the recruitment of aquatic animals and plants. In this study, inter- and intra-basin patterns of flood variability are examined for catchments east of Australia’s Great Dividing Range. Three measures of flood variability are explored with uncertainty quantified using bootstrap resampling. The two preferred measures of flood variability (namely a flood quantile ratio and a power law scaling coefficient) produced similar results. Catchments in the wet tropics of far north Queensland experience low flood variability. Flood variability increased southwards through Queensland, reaching a maximum in the vicinity of the Fitzroy and Burnett River basins. The small near-coast catchments of southern Queensland and northern New Wales experience low flood variability. Flood variability is also high in the southern Hunter River and Hawkesbury–Nepean basins. Using L-moment ratio diagrams with data from 424 streamflow stations, we also conclude that the Generalised Pareto distribution is preferable for modelling flood frequency curves for this region. These results provide a regional perspective that can be used to develop new hypotheses about the effects of hydrologic variability on the biophysical characteristics of these Australian rivers.     

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.     

Application of L‐moments and Bayesian inference for low‐flow regionalization in Sefidroud basin,Iran

Reliable estimation of low flows at ungauged catchments is one of the major challenges in water‐resources planning and management. This study aims at providing at‐site and ungauged sites low‐flow frequency analysis using regionalization approach. A two‐stage delineating homogeneous region is proposed in this study. Clustering sites with similar low‐flow L‐moment ratios is initially conducted, and L‐moment‐based discordancy and heterogeneity measures are then used to detect unusual sites. Based on the goodness‐of‐fit test statistic, the best‐fit regional model is identified in each hydrologically homogeneous region. The relationship between mean annual 7‐day minimum flow and hydro‐geomorphic characteristics is also constructed in each homogeneous region associated with the derived regional model for estimating various low‐flow quantiles at ungauged sites. Uncertainty analysis of model parameters and low‐flow estimations is carried out using the Bayesian inference. Applied in Sefidroud basin located in northwestern Iran, two hydrologically homogeneous regions are identified, i.e. the east and west regions. The best‐fit regional model for the east and west regions are generalized logistic and Pearson type III distributions, respectively. The results show that the proposed approach provides reasonably good accuracy for at‐site as well as ungauged‐site frequency analysis. Besides, interval estimations for model parameters and low flows provide uncertainty information, and the results indicate that Bayesian confidence intervals are significantly reduced when comparing with the outcomes of conventional t‐distribution method. Copyright © 2013 John Wiley & Sons, Ltd.     

Climate change in Ireland from precipitation and streamflow observations

On the basis of General Circulation Model (GCM) experiments with increased CO₂, many parts of the northern latitudes including western Europe, are expected to have enhanced hydrologic cycles. Using observations of precipitation and streamflow from Ireland, we test for climatic and hydrologic change in this maritime climate of the northeast Atlantic. Five decades of hourly precipitation (at eight sites) and daily streamflow at four rivers in Ireland were investigated for patterns of climate variability. An increase in annual precipitation was found to occur after 1975. This increase in precipitation is most noticeable on the West of the island. Precipitation increases are significant in March and October and are associated with increases in the frequency of wet hours with no change in the hourly intensities. Analysis of streamflow data shows the same trends. Furthermore, analysis of extreme rainfall events show that a much greater proportion of extremes have occurred in the period since 1975. A change also occurred in the North Atlantic Oscillation (NAO) index around 1975. The increased NAO since 1975 is associated with increased westerly airflow circulation in the Northeast Atlantic and is correlated with the wetter climate in Ireland. These climatic changes have implications for water resources management particularly flood analysis and protection.     

The formation of groups for regional flood frequency analysis

Abstract

A new technique is developed for identifying groups for regional flood frequency analysis. The technique uses a clustering algorithm as a starting point for partitioning the collection of catchments. The groups formed using the clustering algorithm are subsequently revised to improve the regional characteristics based on three requirements that are defined for effective groups. The result is overlapping groups that can be used to estimate extreme flow quantiles for gauged or ungauged catchments. The technique is applied to a collection of catchments from India and the results indicate that regions with the desired characteristics can be identified using the technique. The use of the groups for estimating extreme flow quantiles is demonstrated for three example sites.     

Use of a nonstationary copula to predict future bivariate low flow frequency in the Connecticut river basin

Frequency analysis of streamflow provides an essential ingredient in our understanding of hydrologic events and provides needed guidance in the design and management of water resources infrastructure. However, traditional hydrologic approaches often fail to include important external effects that can result in unpredictable or unforeseen changes in streamflow. Moreover, previous studies investigating multiple characteristics of streamflow do not address a nonstationary approach. This study explores nonstationary frequency analysis of bivariate characteristics, including occurrence and severity, of annual low flow in the Connecticut River Basin, United States. To investigate bivariate low flow frequency, copulas and their marginal distributions are constructed by using stationary and nonstationary approaches. Our study results indicate that streamflow used in this study demonstrate significant nonstationarity. Over time, the occurrence and severity of low flows are shown to be lower with the same probability based on the results of nonstationary copulas. Bivariate low flow frequencies in the years 1970, 2000, and 2030, and their joint return periods are estimated under the nonstationary copulas. Copyright © 2016 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.