•In a case study at hand, in San Antonio, the infill urban development strategy benefits more from runoff control than the sprawl urban development strategy.•In non-flood season, four LID practices ...(permeable pavements, bioretention cells, vegetated swales, and green roofs) can reduce basin runoff effectively.•In extremely wet conditions, the implementation of LID practices delays urban peak runoff and causes stacking of rural and urban sub-flows, leading to larger basin peaks.
Low Impact Development (LID) was promoted as an alternative to conventional urban drainage methods. The effects of LID at the site or urban scales have been widely evaluated. This project aims to investigate the impact of LID implementation on basin runoff at a regional scale in a half-urbanized catchment, particularly the overlap of urban and rural sub-flows at peak times. A SUPERFLEX conceptual model framework is adapted as a semi-distributed model to simulate the rainfall-runoff relationship in the catchment for San Antonio, Texas, as a case study. Scenario analyses of both urban development and LID implementation are conducted. Results show that (1) the infill urban development strategy benefits more from runoff control than the sprawl urban development; (2) in non-flood season, permeable pavements, bioretention cells, and vegetated swales decrease peak runoff significantly, and permeable pavements, bioretention cells, and green roofs are good at runoff volume retention; (3) contrary to the general opinion about the peak reduction effect of LID, for a partly urbanized, partly rural basin, the LID implementation delays urban peaks and may cause larger stacking of rural and urban peak runoffs, leading to larger basin peaks under extremely wet conditions.
•Hybrid modelling approaches increase the accuracy of peak flow forecasting.•Aggregating fine resolution simulations improves the characterisation of peak flows.•Wavelets and variograms are useful ...diagnostics of models’ performance across scales.
Accurate prediction of extreme flow events is important for mitigating natural disasters such as flooding. We explore and refine two modelling approaches (both separately and in combination) that have been demonstrated to improve the prediction of daily peak flow events. These two approaches are firstly, models that aggregate fine resolution (sub-daily) simulated flow from a process-based model (PBM) to daily, and secondly, hybrid models that combine PBMs with statistical and machine learning methods. We propose the use of variography and wavelet analyses to evaluate these models across temporal scales. These exploratory methods are applied to both measured and modelled data in order to assess the performance of the latter in capturing variation, at different scales, of the former. We compare change points detected by the wavelet analysis (measured and modelled) with the extreme flow events identified in the measured data. We found that combining the two modelling approaches improves prediction at finer scales, but at coarser scales advantages are less pronounced. Although aggregating fine-scale model outputs improved the partition of wavelet variation across scales, the autocorrelation in the signal is less well represented as demonstrated by variography. We demonstrate that exploratory time-series analyses, using variograms and wavelets, provides a useful assessment of existing and newly proposed models, with respect to how they capture changes in flow variance at different scales and also how this correlates with measured flow data – all in the context of extreme flow events.
•New method for design quantile assessment based on few largest historical records.•Maximum likelihood with left censoring type II estimation method is applied.•The total bias proved to be acceptable ...for censored sample.•It is even smaller when estimated on the whole sample, if the model is misspecified.•Promising results are obtained also for the samples where all elements are known.
The common practice to improve the assessments of design (upper) quantiles of annual flow maxima distribution is incorporating historical data into flood frequency estimates, which in general are greater than the systematic flood maxima records. A big body of literature was dedicated to this issue showing the advantages and constraints of this approach, but the research was not concentrated on the largest floods which for many reasons cannot be treated in the same way as less important ones. Assuming that only few (k) largest flood peaks in the specified time period (M) are known we applied maximum likelihood censoring type II method to estimate a hundred-year flow quantile. The relative bias supported by the relative root mean square error of the estimates were evaluated in numerical simulation experiments for different values of k and M performed under true and false distribution assumptions. The main and surprising result of the experiments is that in case of a false model assumption the estimation of the upper tail quantiles based on few largest floods in a given period can provide comparable or even better estimates in the sense of the relative bias and relative root mean square error than the estimation for the whole sample.
Encouraged by the results of numerical experiments in the aspect of historical heavily censored samples we found that it might be reasonable to use this approach in a situation where all elements of the series of observation are known, but the upper tail of the distribution (in our case, F = 0.99 quantile) will be assessed using only the largest records. The results are presented in the case study research.
•Diagnosis of covariation of projections for evapotranspiration and rainfall.•Statistically downscaled climate scenarios using frequency and intensity quantile perturbations.•Construction of ...surrogate indicative scenarios to replicate range of extreme impacts.
To account for the high uncertainty in climate change scenarios, it is advisable to include the maximum possible amount of climate model simulations. Since this is not always feasible, impact assessments are inevitably performed with a limited set of scenarios. The development of few synthesised scenarios is a challenge that needs more attention as the number of available climate change simulations grows. Whether these scenarios are representative enough for future climate change is a question that needs addressing. There is thus a vital need for techniques which can carefully examine the climate model simulations and extract representative climate scenarios that facilitate impact studies. This study presents a methodology of constructing tailored scenarios for assessing runoff flows including extreme conditions (peak flows) from an array of future climate change signals of rainfall and potential evapotranspiration (ETo) derived from the climate model simulations. The aim of the tailoring process is to generate few scenarios that can optimally represent the spectrum of climate scenarios. These tailored scenarios have the advantage of being few in number as well as having a clear description of the seasonal variation of the climate signals, hence allowing easy interpretation of the implications of future changes. The tailoring process begins with an analysis of the hydrological impacts of the climate change signals from all available climate model simulations in a simplified (computationally less expensive) impact model. The climate change signals are transferred to the rainfall and ETo input series of the impact model based on a quantile perturbation technique that accounts for the changes in extremes. The climate model simulations are then subdivided into high, mean and low hydrological impacts using a quantile change analysis. From this impact classification, the corresponding rainfall and ETo change factors are back-tracked on a seasonal basis to determine rainfall–ETo covariation. The established rainfall–ETo variations are used to inform the scenario construction process. Additionally, the ‘back-tracking’ of extreme flows from driving scenarios is a useful diagnostic of the physical responses to climate change scenarios. The method is demonstrated through the application of 28 RCM runs and a selected catchment in central Belgium.
Decades of research has concluded that the percent of impervious surface cover in a watershed is strongly linked to negative impacts on urban stream health. Recently, there has been a push by ...municipalities to offset these effects by installing structural stormwater control measures (SCMs), which are landscape features designed to retain and reduce runoff to mitigate the effects of urbanisation on event hydrology. The goal of this study is to build generalisable relationships between the level of SCM implementation in urban watersheds and resulting changes to hydrology. A literature review of 185 peer‐reviewed studies of watershed‐scale SCM implementation across the globe was used to identify 52 modelling studies suitable for a meta‐analysis to build statistical relationships between SCM implementation and hydrologic change. Hydrologic change is quantified as the percent reduction in storm event runoff volume and peak flow between a watershed with SCMs relative to a (near) identical control watershed without SCMs. Results show that for each additional 1% of SCM‐mitigated impervious area in a watershed, there is an additional 0.43% reduction in runoff and a 0.60% reduction in peak flow. Values of SCM implementation required to produce a change in water quantity metrics were identified at varying levels of probability. For example, there is a 90% probability (high confidence) of at least a 1% reduction in peak flow with mitigation of 33% of impervious surfaces. However, as the reduction target increases or mitigated impervious surface decreases, the probability of reaching the reduction target also decreases. These relationships can be used by managers to plan SCM implementation at the watershed scale.
Improved knowledge of watershed-scale spatial and temporal variability of sediment yields (SY) is needed to design erosion control strategies, particularly in the most severely eroded areas. The ...present study was conducted to provide this knowledge for the humid tropical highlands of Ethiopia using the Akusity and Kasiry paired watersheds in the Guder portion of the Upper Blue Nile basin. Discharge and suspended sediment concentration data were monitored during the rainy season of 2014 and 2015 using automatic flow stage sensors, manual staff gauges and a depth-integrated sediment sampler. The SY was calculated using empirical discharge–sediment curves for different parts of each rainy season. The measured mean daily sediment concentration differed greatly between years and watersheds (0.51gL−1 in 2014 and 0.92gL−1 in 2015 for Kasiry, and 1.04gL−1 in 2014 and 2.20gL−1 in 2015 for Akusity). Sediment concentrations at both sites decreased as the rainy season progressed, regardless of the rainfall pattern, owing to depletion of the sediment supply and limited transport capacity of the flows caused by increased vegetation cover. Rainy season SYs for Kasiry were 7.6tha−1 in 2014 and 27.2tha−1 in 2015, while in Akusity SYs were 25.7tha−1 in 2014 and 71.2tha−1 in 2015. The much larger values in 2015 can be partly explained by increased rainfall and larger peak flow events. The magnitude and timing of peak flow events are major determinants of the amount and variability of SYs. Thus, site-specific assessment of such events is crucial to reveal SY dynamics of small watersheds in tropical highland environments.
Instantaneous peak flows (IPFs) are often required to derive design values for sizing various hydraulic structures, such as culverts, bridges, and small dams/levees, in addition to informing several ...water resources management-related activities. Compared to mean daily flows (MDFs), which represent averaged flows over a period of 24 h, information on IPFs is often missing or unavailable in instrumental records. In this study, conventional methods for estimating IPFs from MDFs are evaluated and new methods based on the nonlinear regression framework and machine learning architectures are proposed and evaluated using streamflow records from all Canadian hydrometric stations with natural and regulated flow regimes. Based on a robust model selection criterion, it was found that multiple methods are suitable for estimating IPFs from MDFs, which precludes the idea of a single universal method. The performance of machine learning-based methods was also found reasonable compared to conventional and regression-based methods. To build on the strengths of individual methods, the fusion modeling concept from the machine learning area was invoked to synthesize outputs of multiple methods. The study findings are expected to be useful to the climate change adaptation community, which currently heavily relies on MDFs simulated by hydrologic models.
Our study focuses on the hydrologic implications of resolving and modeling atmospheric processes at different spatial scales. Here we use heavy precipitation events from an atmospheric model that was ...run at different horizontal grid spacings (i.e., 250 m, 500 m, 1 km, 2 km 4 km, and 12 km) and able to resolve different processes. Within an idealized simulation framework, these rainfall events are used as input to an operational distributed hydrologic model to evaluate the sensitivity of the hydrologic response to different forcing grid spacings. We consider the finest scale (i.e., 250 m) as reference, and compute event peak flows and volumes across a wide range of basin sizes. We find that the use of increasingly-coarser inputs leads to changes in the distribution of event peak flows and volumes, with the strongest sensitivity at the smallest catchment sizes. Our results show the compromise between computational cost and hydrologic performance, providing basic information for future endeavors geared towards regional downscaling.
The distribution of peak river flows was studied over the period 1960–2000 in the watershed of Purapel River (Maule Region, Chile) in order to evaluate the relationship of these flows to vegetation ...cover. Maximum annual and summer flows, between decades and between periods, monthly and yearly were compared with multi-temporal analysis for 1955, 1978 and 1997. The evolution of peak flows over the period was irregular, and did not show significant differences in the majority of comparisons. The variation in vegetation cover during the study period was caused by migration from native forests to commercial plantation of
Pinus radiata (D. Don). The data from this watershed does not permit the conclusion that variation in peak flows is due to variation in vegetative cover. The peak flows are more highly dependant on precipitation values. For the watershed and period of study the native forest and plantations of
P. radiata (D. Don) have a similar hydrological behavior.
Determining wildland fire impacts on streamflow can be problematic as the hydrology in burned watersheds is influenced by post‐fire weather conditions. This study presents a quantile‐based analytical ...framework for assessing fire impacts on low and peak daily flow magnitudes, while accounting for post‐fire weather influences. This framework entails (a) the bootstrap method to compute the relative change in the post‐fire annual flow and weather statistics, (b) double mass analysis to detect if post‐fire baseflow and quick‐flow yield ratios are significantly altered, and (c) a quantile regression method to parse fire effects on flow at a specific quantile. We illustrate the applicability of this analytical framework using 44 western US streams with at least 5% of their watershed area burned. Results indicate that large, high‐severity burns in upland watersheds can raise the streamflow magnitude at the 0.05th and 0.95th quantiles for at least the five post‐fire years. Quantile regression results show that the median fire‐related increase in flow for the five post‐fire years can be up to 5,000% (Standard Error; S.E.< 2%) at the 0.05th quantile and 161% (S.E.< 10%) at the 0.95th quantile. The fire‐related increase in flow was often pronounced at the 0.05th quantile for streams in the Pacific Northwest and California regions. The difference in fire effects on flow (at both quantiles) across streams was related to post‐fire weather, pyrology, physiography, and land cover. The proposed analytical framework can be useful for detecting and quantifying fire effects on the low and peak stream flows in burned watersheds without overlapping disturbances.
Key Points
Evaluating the impact of watershed burning on streamflow can be problematic due to the confounding effect of post‐fire weather influences
A quantile‐based analytical framework is presented for assessing fire effect on low & peak flow while accounting for weather influences
This analytical framework can be useful for inferring fire effect on low & peak flows in burned watersheds without overlapping disturbances
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