Runoff Signatures Research Articles

Estimating Vegetation Greening Influences on Runoff Signatures Using a Log‐Based Weighted Ensemble Method

AbstractVegetation greening profoundly impacts the water cycle, and recent concerns about greening impacts have focused on various hydrological cycle components. However, the impacts of greening on catchment runoff signatures reflecting magnitude, low/high flow frequency, low/high flow duration and flow dynamics remain poorly understood. To properly simulate these runoff signatures, we use five modified hydrological models incorporating vegetation dynamics and further derive three ensemble approaches to obtain eight runoff time series outputs in a major tributary of the Yellow River Basin. Multiple validations suggest that the log‐based weighted ensemble (LWE) approach is robust for depicting the impact of greening on selected runoff signatures. This is especially true for the low flow part of the runoff time series and the overall performance of the selected signatures since LWE explicitly reduces the low flow bias. With this approach, five experiments were designed to isolate the impact of vegetation greening on runoff signatures, and the comparisons among the experiments indicate that greening noticeably decreases runoff magnitude, increases low flow frequency/duration and decreases high flow frequency/duration signatures. However, greening has little influence on runoff dynamic signatures. Each percent increase in leaf area index results in (a) changes of −0.2 ± 0.1% for magnitude signatures; (b) changes of −0.34 ± 0.30% and 0.56 ± 0.28% with wide ranges for annual high flow days and annual low flow days, respectively; and (c) marginal change on flow dynamic signatures. This study provides new insights by disentangling greening impacts on various runoff signatures using a trade‐off ensemble method.

The effects of satellite soil moisture data on the parametrization of topsoil and root zone soil moisture in a conceptual hydrological model

Abstract In a previous study, the topsoil and root zone ASCAT satellite soil moisture data were implemented into three multi-objective calibration approaches of the TUW hydrological model in 209 Austrian catchments. This paper examines the model parametrization in those catchments, which in the validation of the dual-layer conceptual semi-distributed model showed improvement in the runoff simulation efficiency compared to the single objective runoff calibration. The runoff simulation efficiency of the three multi-objective approaches was separately considered. Inferences about the specific location and the physiographic properties of the catchments where the inclusion of ASCAT data proved beneficial were made. Improvements were primarily observed in the watersheds with lower slopes (median of the catchment slope less than 15 per cent) and a higher proportion of farming land use (median of the proportion of agricultural land above 20 per cent), as well as in catchments where the runoff is not significantly influenced by snowmelt and glacier runoff. Changes in the mean and variability of the field capacity parameter FC of the soil moisture regime were analysed. The values of FC decreased by 20 per cent on average. Consequently, the catchments’ water balance closure generally improved by the increase in catchment evapotranspiration during the validation period. Improvements in model efficiency could be attributed to better runoff simulation in the spring and autumn month. The findings refine recommendations regarding when hydrological modelling could consider satellite soil moisture data added to runoff signatures in calibration useful.

Global Runoff Signatures Changes and Their Response to Atmospheric Environment, GRACE Water Storage, and Dams

Runoff signatures (RS), a special set of runoff indexes reflecting the hydrological process, have an important influence on many fields of both human and natural systems by flooding, drought, and available water resources. However, the global RS changes and their causes remain largely unknown. Here, we make a comprehensive investigation of RS changes and their response to total water storage anomalies (TWSA) from GRACE satellites, atmospheric circulation, and reservoir construction by using daily runoff data from 21,955 hydrological stations during 1975–2017. The global assessment shows that (1) in recent years, the global extreme flow signatures tend to decrease, while the low and average flow signatures are likely to increase in more regions; (2) the spatial patterns of trends are similar for different RS, suggesting that the runoff distribution tends to entirely upward in some regions, while downward in other regions; (3) the trends in RS are largely consistent with that in TWSA over most regions in North America and eastern South America during 1979–2017, indicating that the GRACE-based TWSA have great potential in hydrological monitoring and attribution; (4) atmospheric circulation change could partly explain the global spatiotemporal variation patterns of RS; (5) dams have important influences on reducing the high flow signature in the catchments including dams built during 1975–2017. This study provides a full picture of RS changes and their possible causes, which has important implications for water resources management and flood and drought disaster assessment.

Impacts of El Niño–southern oscillation on global runoff: Characteristic signatures and potential mechanisms

AbstractRunoff signatures, including low flow, high flow, mean flow and flow variability, have important implications on the environment and society, predominantly through drought, flooding and water resources. Yet, the response of runoff signatures has not been previously investigated at the global scale, and the influencing mechanisms are largely unclear. Hence, this study makes a global assessment of runoff signature responses to the El Niño and La Niña phases using daily streamflow observations from 8217 gauging stations during 1960–2015. Based on the Granger causality test, we found that ~15% of the hydrological stations of multiple runoff signatures show a significant causal relationship with El Niño–southern oscillation (ENSO). The quantiles of all runoff signatures were larger during the El Niño phase than during the La Niña phase, implying that the entire flow distribution tends to shift upward during El Niño and downward during La Niña. In addition, El Niño has different effects on low and high flows: it tends to increase the low and mean flow signatures but reduces the high flow and flow variability signatures. In contrast, La Niña generally reduces all runoff signatures. We highlight that the impacts of ENSO on streamflow signatures are manifested by its effects on precipitation (P), potential evaporation (PET) and leaf area index (LAI) through ENSO‐induced atmospheric circulation changes. Overall, our study provides a comprehensive picture of runoff signature responses to ENSO, with valuable insights for water resources management and flood and drought disaster mitigation.

Future changes in annual, seasonal and monthly runoff signatures in contrasting Alpine catchments in Austria

Abstract. Hydrological regimes of alpine catchments are expected to be strongly affected by climate change, mostly due to their dependence on snow and ice dynamics. While seasonal changes have been studied extensively, studies on changes in the timing and magnitude of annual extremes remain rare. This study investigates the effects of climate change on runoff patterns in six contrasting Alpine catchments in Austria using a process-based, semi-distributed hydrological model and projections from 14 regional and global climate model combinations for two representative concentration pathways, namely RCP4.5 and RCP8.5. The study catchments represent a spectrum of different hydrological regimes, from pluvial–nival to nivo-glacial, as well as distinct topographies and land forms, characterizing different elevation zones across the eastern Alps to provide a comprehensive picture of future runoff changes. The climate projections are used to model river runoff in 2071–2100, which are then compared to the 1981–2010 reference period for all study catchments. Changes in the timing and magnitude of annual maximum and minimum flows, as well as in monthly runoff and snowmelt, are quantified and analyzed. Our results indicate a substantial shift to earlier occurrences in annual maximum flows by 9 to 31 d and an extension of the potential flood season by 1 to 3 months for high-elevation catchments. For low-elevation catchments, changes in the timing of annual maximum flows are less pronounced. Magnitudes of annual maximum flows are likely to increase by 2 %–18 % under RCP4.5, while no clear changes are projected for four catchments under RCP8.5. The latter is caused by a pronounced increase in evaporation and decrease in snowmelt contributions, which offset increases in precipitation. In the future, minimum annual runoff will occur 13–31 d earlier in the winter months for high-elevation catchments, whereas for low-elevation catchments a shift from winter to autumn by about 15–100 d is projected, with generally larger changes for RCP8.5. While all catchments show an increase in mean magnitude of minimum flows by 7–30% under RCP4.5, this is only the case for four catchments under RCP8.5. Our results suggest a relationship between the elevation of catchments and changes in the timing of annual maximum and minimum flows. For the magnitude of the extreme flows, a relationship is found between catchment elevation and annual minimum flows, whereas this relationship is lacking between elevation and annual maximum flow.

The Role of Hydrological Signatures in Calibration of Conceptual Hydrological Model

Determining an optimal calibration strategy for hydrological models is essential for a robust and accurate water balance assessment, in particular, for catchments with limited observed data. In the present study, the hydrological model Bilan was used to simulate hydrological balance for 20 catchments throughout the Czech Republic during the period 1981–2016. Calibration strategies utilizing observed runoff and estimated soil moisture time series were compared with those using only long-term statistics (signatures) of runoff and soil moisture as well as a combination of signatures and time series. Calibration strategies were evaluated considering the goodness-of-fit, the bias in flow duration curve and runoff signatures and uncertainty of the Bilan model. Results indicate that the expert calibration and calibration with observed runoff time series are, in general, preferred. On the other hand, we show that, in many cases, the extension of the calibration criteria to also include runoff or soil moisture signatures is beneficial, particularly for decreasing the uncertainty in parameters of the hydrological model. Moreover, in many cases, fitting the model with hydrological signatures only provides a comparable fit to that of the calibration strategies employing runoff time series.

Improved Understanding of the Link Between Catchment‐Scale Vegetation Accessible Storage and Satellite‐Derived Soil Water Index

AbstractThe spatiotemporal dynamics of water volumes stored in the unsaturated root zone are a key control on the response of terrestrial hydrological systems. Robust, catchment‐scale root‐zone soil moisture estimates are thus critical for reliable predictions of river flow, groundwater recharge, or evaporation. Satellites provide estimates of near‐surface soil moisture that can be used to approximate the moisture content in the entire unsaturated root zone through the Soil Water Index (SWI). The characteristic time length (T, in days), as only parameter in the SWI approach, characterizes the temporal variability of soil moisture. The factors controlling T are typically assumed to be related to soil properties and climate; however, no clear link has so far been established. In this study, we hypothesize that optimal T values (Topt) are linked to the interplay of precipitation and evaporation during dry periods, thus to catchment‐scale vegetation accessible water storage capacities in the unsaturated root zone. We identify Topt by matching modeled time series of root‐zone soil moisture from a calibrated process‐based hydrological model to SWI from several satellite‐based near‐surface soil moisture products in 16 contrasting catchments in the Meuse river basin. Topt values are strongly and positively correlated with vegetation accessible water volumes that can be stored in the root zone, here estimated for each study catchment both as model calibration parameter and from a water‐balance approach. Differences in Topt across catchments are also explained by land cover (% agriculture), soil texture (% silt), and runoff signatures (flashiness index).

Source Identification of Particulate Metals/Metalloids Deposited in the San Juan River Delta of Lake Powell, USA

Whereas mining and non-mining sourced particulate metal/metalloids (PM) (> 0.45 μm) are present in the tributaries of the San Juan River, USA, the individual contributions of PM from the San Juan River tributaries to the sediment of the San Juan River Delta of Lake Powell were previously unknown. Suspended PM signatures, including enrichment factors (ratios of PM concentrations to ubiquitous metals such as aluminum), lead isotopes, color, and particle size, were used to tie layers in a San Juan River sediment core to upstream tributary sources. Tributary PM concentrations and loads were compared to Lower San Juan River suspended PM loads to estimate the relative contribution of tributary PM (both mining and non-mining sourced) directly upstream of Lake Powell. Results suggest elevated enrichment factors of lead, cadmium, copper, and zinc in deposited sediment were sourced from mined tributaries, whereas elevated manganese in deposited sediment was sourced from unmined tributaries. Sediment Pb isotope ratios reflected the depleted signature of the mineralized vein (present in the headwaters of mined tributaries), the enriched signature of the Chinle Sandstone (predominant underlying geology of unmined tributaries), or a mixture of these two endmembers. These independent lines of evidence were used to link probable tributary source and runoff category to sediment layers, where ~ 10% and 5% of the overall PM deposited in the sediment core was attributed to mining versus non-mining sources, respectively. Because traditional dating methods were not possible, runoff category signatures were used to estimate that the 3.37 m San Juan River Delta core was deposited over ~ 1.3 years.

Improved Process Representation in the Simulation of the Hydrology of a Meso-Scale Semi-Arid Catchment

The variability of rainfall and climate, combined with land use and land cover changes, and variation in geology and soils makes it a difficult task to accurately describe the key hydrological processes in a catchment. With the aim to better understand the key hydrological processes and runoff generation mechanisms in the semi-arid meso-scale Kaap catchment in South Africa, a hydrological model was developed using the open source STREAM model. Dominant runoff processes were mapped using a simplified Height Above the Nearest Drainage approach combined with geology. The Prediction in Ungauged Basins (PUB) framework of runoff signatures was used to analyse the model results. Results show that in the headwater sub-catchments of Noordkaap and Suidkaap, plateaus dominate, associated with slow flow processes. Therefore, these catchments have high baseflow components and are likely the main recharge zone for regional groundwater in the Kaap. In the Queens sub-catchment, hillslopes associated with intermediate and fast flow processes dominate. However, this catchment still has a strong baseflow component, but it seems to be more impacted by evaporation depletion, due to different soils and geology, especially in drier years. At the Kaap outlet, the model indicates that hillslopes are important, with intermediate and fast flow processes dominating and most runoff being generated through direct runoff and shallow groundwater components, particularly in wetter months and years. There is a high impact of water abstractions and evaporation during the dry season, affecting low flows in the catchment. Results also indicate that the root zone storage and the parameters of effective rainfall separation (between unsaturated and saturated zone), quickflow coefficient and capillary rise, were very sensitive in the model. The inclusion of capillary rise (feedback from the saturated to unsaturated zone) greatly improved the simulation results.

Predicting Runoff Signatures Using Regression and Hydrological Modeling Approaches

AbstractAccurate prediction of runoff signatures is important for numerous hydrological and water resources applications. However, there are lack of comprehensive evaluations of various approaches for predicting hydrological signatures. This study, for the first time, introduces regression tree ensemble approach and compares it with other three widely used approaches (multiple linear regression, multiple log‐transformed linear regression, and hydrological modeling) for assessing prediction accuracy of 13 runoff characteristics or signatures, using a large data set from 605 catchments across Australia. The climate, in particular, mean annual precipitation and aridity index, has the most significant influence on the runoff signatures. Physical catchment attributes including forest ratio, slope, and soil water holding capacity also have significant influence (p < 0.05) on the runoff signatures. All four approaches can predict the long‐term average and high flow signatures accurately. The regression approaches can also well predict majority of the other runoff signatures, with the Nash‐Sutcliffe Efficiency larger than 0.60. The regression tree ensemble outperforms the two linear regressions in predicting signatures of flow dynamics. The hydrological models, calibrated to one specific objective criterion, cannot predict many of the runoff signatures, particularly those reflecting low flows and flow dynamics. This is because in most hydrological model applications, the simulations allow satisfactory predictions of long‐term average and high flow signatures. In applications where a specific runoff signature is needed, regression relationships that directly relate that runoff signature to catchment attributes give the best predictions. Here the regression tree ensemble is overall best and offers significant potential, being able to predict most of the runoff signatures very well.

TopREML: a topological restricted maximum likelihood approach to regionalize trended runoff signatures in stream networks

Abstract. We introduce topological restricted maximum likelihood (TopREML) as a method to predict runoff signatures in ungauged basins. The approach is based on the use of linear mixed models with spatially correlated random effects. The nested nature of streamflow networks is taken into account by using water balance considerations to constrain the covariance structure of runoff and to account for the stronger spatial correlation between flow-connected basins. The restricted maximum likelihood (REML) framework generates the best linear unbiased predictor (BLUP) of both the predicted variable and the associated prediction uncertainty, even when incorporating observable covariates into the model. The method was successfully tested in cross-validation analyses on mean streamflow and runoff frequency in Nepal (sparsely gauged) and Austria (densely gauged), where it matched the performance of comparable methods in the prediction of the considered runoff signature, while significantly outperforming them in the prediction of the associated modeling uncertainty. The ability of TopREML to combine deterministic and stochastic information to generate BLUPs of the prediction variable and its uncertainty makes it a particularly versatile method that can readily be applied in both densely gauged basins, where it takes advantage of spatial covariance information, and data-scarce regions, where it can rely on covariates, which are increasingly observable via remote-sensing technology.

Parameter uncertainty and identifiability of a conceptual semi-distributed model to simulate hydrological processes in a small headwater catchment in Northwest China

Abstract Introduction Conceptual hydrological models are useful tools to support catchment water management. However, the identifiability of parameters and structural uncertainties in conceptual rainfall-runoff modeling prove to be a difficult task. Here, we aim to evaluate the performance of a conceptual semi-distributed rainfall-runoff model, HBV-light, with emphasis on parameter identifiability, uncertainty, and model structural validity. Results The results of a regional sensitivity analysis (RSA) show that most of the model parameters are highly sensitive when runoff signatures or combinations of different objective functions are used. Results based on the generalized likelihood uncertainty estimation (GLUE) method further show that most of the model parameters are well constrained, showing higher parameter identifiability and lower model uncertainty when runoff signatures or combined objective functions are used. Finally, the dynamic identifiability analysis (DYNIA) shows different types of parameter behavior and reveals that model parameters have a higher identifiability in periods where they play a crucial role in representing the predicted runoff. Conclusions The HBV-light model is generally able to simulate the runoff in the Pailugou catchment with an acceptable accuracy. Model parameter sensitivity is largely dependent upon the objective function used for the model evaluation in the sensitivity analysis. More frequent runoff observations would substantially increase the knowledge on the rainfall-runoff transformation in the catchment and, specifically, improve the distinction of fast surface-near runoff and interflow components in their contribution to the total catchment runoff. Our results highlight the importance of identifying the periods when intensive monitoring is critical for deriving parameter values of reduced uncertainty.

Comparative assessment of predictions in ungauged basins – Part 3: Runoff signatures in Austria

Abstract. This is the third of a three-part paper series through which we assess the performance of runoff predictions in ungauged basins in a comparative way. Whereas the two previous papers by Parajka et al. (2013) and Salinas et al. (2013) assess the regionalisation performance of hydrographs and hydrological extremes on the basis of a comprehensive literature review of thousands of case studies around the world, in this paper we jointly assess prediction performance of a range of runoff signatures for a consistent and rich dataset. Daily runoff time series are predicted for 213 catchments in Austria by a regionalised rainfall–runoff model and by Top-kriging, a geostatistical estimation method that accounts for the river network hierarchy. From the runoff time-series, six runoff signatures are extracted: annual runoff, seasonal runoff, flow duration curves, low flows, high flows and runoff hydrographs. The predictive performance is assessed in terms of the bias, error spread and proportion of unexplained spatial variance of statistical measures of these signatures in cross-validation (blind testing) mode. Results of the comparative assessment show that, in Austria, the predictive performance increases with catchment area for both methods and for most signatures, it tends to increase with elevation for the regionalised rainfall–runoff model, while the dependence on climate characteristics is weaker. Annual and seasonal runoff can be predicted more accurately than all other signatures. The spatial variability of high flows in ungauged basins is the most difficult to estimate followed by the low flows. It also turns out that in this data-rich study in Austria, the geostatistical approach (Top-kriging) generally outperforms the regionalised rainfall–runoff model.

Classification of Ontario watersheds based on physical attributes and streamflow series

• We investigate NLPCA and SOM for gauged/ungauged catchment classification. • We identify hydrologically homogenous clusters of basins using runoff indices. • We use classification based on PCA and runoff indices as benchmark classification. • NLPCA indicates reliable performance in gauged/ungauged catchment classification. • NLPCA can be used for ungauged basins classification prior to regionalization. Nonlinear cluster analysis techniques including Self Organizing Maps (SOMs), standard Non-Linear Principal Component Analysis (NLPCA) and Compact Non-Linear Principal Component Analysis (Compact-NLPCA) are investigated for the identification of hydrologically homogeneous clusters of watersheds across Ontario, Canada. The results of classification based on catchment attributes and streamflow series of Ontario watersheds are compared to those of two benchmarks: the standard Principal Component Analysis (PCA) and K -means classification based on recently proposed runoff signatures. The latter classified the 90 watersheds into four homogeneous groups used as a reference classification to evaluate the performance of the nonlinear clustering techniques. The similarity index between the first largest group of the reference classification and the one from the NLPCA based on streamflow, is about 0.58. For the Compact-NLPCA the similarity is about 0.56 and for the SOM it is about 0.52. Furthermore, those results remain slightly the same when the watersheds are classified based on watershed attributes – suggesting that the nonlinear classification methods can be robust tools for the classification of ungauged watersheds prior to regionalization. Distinct patterns of flow regime characteristics and specific dominant hydrological attributes are identified in the clusters obtained from the nonlinear classification techniques – indicating that the classifications are sound from the hydrological point of view.

Dioxin in Storm-Water Runoff in Houston, Texas

Concentrations of polychlorinated dibenzo-p-dioxins and dibenzofurans in runoff were measured at 10 small flood control drainage channels in the Houston area. Total toxicity equivalent concentrations in runoff ranged from 0.01 to 0.11 pg/L for the dissolved phase and from 0.02 to 0.88 pg/L for the suspended phase. The dissolved concentrations were lower than their respective suspended concentrations, with average suspended/dissolved ratios between 5 and 152 for individual congeners. Average observed logs of organic-carbon (OC)-normalized suspended sediment-dissolved partitioning coefficients (log K OC ) varied between 5.47 and 7.83 L/kg OC. Dioxin concentrations in runoff were generally at the same level or lower than those measured in the receiving water body. Principal component analyses indicated that the signatures for dissolved runoff match those observed in dissolved ambient water in the channel, while the suspended-sediment runoff signatures are similar to those observed in dry air deposition in the Houston area.