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The event runoff coefficient ( ) and the recession coefficient ( ) are of theoretical importance for understanding catchment response and of practical importance in hydrological design. We analyse 57 event periods in the period 2013 to 2015 in the 66 ha Austrian Hydrological Open Air Laboratory (HOAL), where the seven subcatchments are stratified by runoff generation types into wetlands, tile drainage and natural drainage. Three machine learning algorithms (Random forest (RF), Gradient Boost Decision Tree (GBDT) and Support vector machine (SVM)) are used to estimate and from 22 event based explanatory variables representing precipitation, soil moisture, groundwater level and season. The model performance of the SVM algorithm in estimating and is generally higher than that of the other two methods, measured by the coefficient of determination , and the performance for is higher than that for . The relative importance of the explanatory variables for the predictions, assessed by a heatmap, suggests that of the tile drainage systems is more strongly controlled by the weather conditions than by the catchment state, while the opposite is true for natural drainage systems. Overall, model performance strongly depends on the runoff generation type.

This study proposes using a Turing-like test for model evaluations and invalidations based on evidence of epistemic uncertainties in event runoff coefficients. Applying the consequent “limits of acceptability” results in all the 100 000 model parameter sets being rejected. However, applying the limits, together with an allowance for timing errors, to time steps ranked by discharge, results in an ensemble of 2064 models that can be retained for predicting discharge peaks. These do not include any of the models with the highest (> 0.9) efficiencies. The analysis raises questions about the impact of epistemic errors on model simulations, and the need for both better observed data and better models.

The regulation and spatial differences of urban runoffs are of great concern in contemporary hydrological research. However, owing to a shortage of basic data sources and restrictions on urban hydrological simulation functions, simulating and investigating the regulation mechanism behind rainfall-runoff processes remain significantly challenging. In this study, the Time Variant Gain Model (TVGM), a hydrological nonlinear system model, was extrapolated to the hydrodynamic model of an urban drainage network system by integrating it with the widely used Stormwater Management Model (SWMM) to adequately simulate urban runoff events while considering various underlying surfaces and runoff routing modes, such as surface, drainage network and river runoff, in urban regions (i.e., TVGM-SWMM). Moreover, runoff events were characterized using the following four runoff regime metrics: runoff coefficient, capture ratio of annual runoff volume, standardized flood timescale, and the ratio of occurrence time differences between flow and rainfall peak to event duration (peak flow delay time). The characteristics and spatial differences of urban runoff regulations were investigated, and the key impact factors and their relative contributions were identified using multivariate statistical analyses. Four communities were selected as our study areas, consisting of communities from Beijing, Shenzhen, Wuhan, and Chongqing. Our results showed that the TVGM-SWMM performed considerably better than SWMM alone. The comprehensive simulation accuracy of 60% of the events (12/20) improved by 486%, with the bias improving the most, followed by the efficiency coefficient. Barring the runoff coefficient, significant spatial differences were identified at the patch scale for the runoff regime metrics, with differences of 0.43, 0.22, and 0.16 ( p <0.05). The key impact factors were the pipe length ( r =0.51) in the drainage network system and the forest area ratios ( r =0.56), sponge measures ( r =0.52), grassland ( r =0.48), and impervious surface ( r =0.46) in the underlying surfaces. The contributions of the drainage network system and the underlying surfaces were 4.27% and 37.83%, respectively. Regulation in the Beijing community, dominated by grassland regulation, delayed and reduced the peak flow and total runoff volume. In the Shenzhen community, sharp and thin runoff events were mainly generated by impervious surfaces and were not adequately regulated. Forest regulation was the dominant regulation type in the Wuhan community, which reduced the total runoff volume and delayed the peak flow. Waterbody regulation was the primary regulation type in the Chongqing community, which reduced the total runoff volume and peak flow. This study aims to introduce a comprehensive theoretical and technical assessment of the hydrological effects of urbanization and the performance of sponge city construction and provide a reference for urban hydrological model improvements in China.

This paper aimed to investigate the dominant runoff processes (DRP’s) at plot-scale in the Curvature Subcarpathians under natural rainfall conditions characteristic for Romania’s temperate environment.The study was based on 32 selected rainfall-runoff events produced during the interval April–September (2014–2017). By comparing water balance on the analyzed Luvisol plots for two types of land use (grassland vs. bare soil), we showed that DRP’s are mostly formed by Hortonian Overland Flow (HOF), 47% vs. 59% respectively. On grassland, HOF is followed by Deep Percolation (DP, 31%) and Fast Subsurface Flow (SSF, 22%), whereas, on bare soil, DP shows a higher percentage (38%) and SSF a lower one (3%), which suggests that the soil-root interface controls the runoff generation.Concerning the relationship between antecedent precipitation and runoff, the study indicated the nonlinearity of the two processes, more obvious on grassland and in drought conditions than on bare soil and in wet conditions (as demonstrated by the higher runoff coefficients). Moreover, the HOF appeared to respond differently to rainfall events on the two plots - slightly longer lag-time, lower discharge and lower volume on grassland - which suggests the hydrologic key role of vegetation in runoff generation processes.

The presented study investigates the dynamics of DOC concentrations in headwater peatbog areas with respect to the extreme rainfall-runoff (R-R) events hydrometeorological catchment preconditions (23 variables in total). The main data sources were automatic devices for monitoring of groundwater level, discharges and rainfalls providing data in 10 min steps installed in the Vydra River catchment and one automatic water sampler ISCO in sub-catchment of the Rokytka River basin in the Šumava (Bohemian Forest) National Park. The study period was 2018–2021, in which 18 R-R events were analysed. Data of DOC variability and catchment conditions were analysed using Spearman’s correlation coefficient, Principal Component Analysis and DOC/Q hysteresis loops. Changes in groundwater level and discharges had the greatest influence on DOC concentrations. Higher mean and maximum DOC were measured during events after a longer period without an extreme R-R event. The greater lag time of maximum DOC after peak flow and the higher mean DOC during the event were primarily due to hydrometeorological preconditions of the catchment. The highest DOC was in autumn after the previous summer period with low discharges and low groundwater levels. DOC was also positively correlated with air and water temperatures.

In this study, a hybrid approach has been used to increase the predictive efficiency of the SCS-CN model. A recently proposed Ajmal model (developed after randomized configuration) that ignored initial abstraction and maximum potential retention has been given the conceptual framework of the SCS-CN model and a new outcome-based hybrid model (Miv) was formulated. A total of 78 watersheds (7817 events) were used for calibration and the remaining 36 watersheds (3967 events) for validation to develop this hybrid model. The numerical value of hybrid model parameters Lc, λ and S were calibrated using calibration dataset and a simple non-linear one-parameter model has been developed. The performance of the Ajmal (Miii) and hybrid model (Miv) was compared with the original SCS-CN method (λ = 0.2 as Mi and λ = 0.05 as Mii). The performance of models was compared by using four statistical error indices i.e. RMSE, NSE, PBIAS, and n(t) and applying ranking and grading system (RGS). The mean RMSE, NSE, PBIAS, and n(t) values were found superior for Miv (5.60 mm, 0.71, 6.97%, 1.15) model followed by Miii (5.98 mm, 0.65, 16.52%, 1.01), Mii (6.27 mm, 0.61, 20%, 0.90) and Mi (6.98 mm, 0.46, 24.2%, 0.72) model for tested watersheds. The hybrid model (Miv) exhibited consistently well performance for all size watersheds. On the basis of the agreement between watershed runoff coefficient (C) and calibrated model parameter (Lc or CN), R2 value was found relatively higher for hybrid model (Miv) than other models.

Purpose Relatively little is known in the Mediterranean environment about changes in streamflow during urban development in partially urbanized peri-urban catchments. This paper explores the modification of streamflow regime as a consequence of the construction of an enterprise park, a major road, and expansion of residential areas, leading to urban areas increase from 32 to 40 % in a small catchment (6.2 km 2 ), located in the periphery of one of the main cities in central mainland Portugal. Materials and methods The study was carried out over five hydrological years (October 2008–September 2009 to October 2012–September 2013), including two initial years of pre- and three following years of post-additional urban development. Streamflow was recorded by a V-notch weir at the catchment outlet at 5-min intervals. Rainfall was recorded at a weather station 0.5 km north of the catchment and by five tipping-bucket raingauges installed in January 2011 within the study catchment. Streamflow was converted into runoff and split into baseflow and stormflow components by applying a mathematical low-pass digital filter. Streamflow differences were investigated through changes in (i) annual runoff coefficients, (ii) annual baseflow index, (iii) seasonal baseflow index and stormflow coefficient, and (iv) storm event analysis. Results and discussion Annual runoff coefficient ranged from 14 to 21 % and storm runoff coefficient from 9 to 14 %, both between the driest 2011/12 and wettest 2012/13. Although these differences were influenced by inter-annual weather variability, a comparison between years with similar rainfall before and after additional urban development showed a 43 % increase in storm runoff. Impacts on streamflow were also noticed through changes on hydrograph: (i) regression lines of storm runoff against rainstorm parameters exhibited higher vertical positions in 2012/13 than 2008/09, (ii) gradual increase in peak flow but with a clear distance between pre- and post- additional urbanization, (iii) quicker response time from 60–75 min to 40–45 min between both periods, and (iv) decrease in recession time from 21–29 h to 7–9 h for the same periods. Conclusions The dispersed urban pattern and permeable soils provide many overland flow sinks, favouring relatively low storm runoff of the catchment. Nevertheless, the enlargement of impervious surfaces (from 12.8 to 17.0 %) and particularly the storm drainage system installed in new urban areas led to great changes on rainfall–runoff event responses. Urban planning should consider the landscape mosaic of peri-urban areas in order to maximize water infiltration and minimize the impacts on streamflow regime.

Event-based rainfall-runoff models are practical tools commonly used to predict catchments’ response to a rainfall event. However, one of the main concerns is that the characteristics of rain events are neglected in the model development. This paper develops a novel event-based rainfall-runoff equation to incorporate rainfall characteristics into account. The performance of the new equation is evaluated based on the root mean square error, Nash–Sutcliffe efficiency coefficient, and per cent bias for 13,339 rainfall-runoff events between 2005 and 2020 over 23 catchments across New Zealand and Australia with oceanic, mediterranean, tropical, subtropical, and semiarid climates. Compared to the previous event-based models, the new equation shows an improvement in runoff estimation in almost all case studies. Furthermore, considering the new equation is simple, efficient, and takes the rain event duration into account, the new equation has the potential to become a robust alternative method to the conventional curve number method in hydrological engineering projects.

Process-based hydrological models are of great importance to understand hydrological processes and support decision making. The LImburg Soil Erosion Model (LISEM) requires information on soil and land-use-related attributes to represent the transformation of rainfall into runoff for isolated rainfall events. This study aimed at evaluating LISEM for estimation of direct surface runoff (DSR) hydrographs in a watershed in Southern Brazil under the predominance of long-duration rainfall events, dominated by Argisols and with availability of a high-density rain gauge network. In addition, this study sought to: (i) suggest and evaluate a procedure for definition of initial soil moisture from antecedent 5-day rainfall depth; (ii) reduce the degree of subjectivity involved in the determination of some vegetation-related parameters by using remote sensing; and (iii) recommend a validation procedure. The saturated soil hydraulic conductivity and the Manning’s surface roughness coefficient were calibrated considering 11 rainfall–runoff events, whereas the validation was performed for 4 events from the average calibrated parameters. The Nash–Sutcliffe coefficient was used to assess both calibration and validation, resulting in average values of 0.64 and 0.58, respectively. It can be inferred from the results that the use of remote sensing to derive some LISEM parameters, along with the suggested schemes for definition of initial soil moisture and validation, was effective and provided sound results even for long-duration rainfall events. The results of this study and its methodological procedures can serve as a basis for other professionals who intend to use LISEM for both conducting detailed analyses of DSR hydrographs and supporting water resources management.

In this study, we analyzed the suitability of using the CHIRPS, CMORPH and TRMM platforms in monitoring extreme precipitation events, precipitation–runoff relationships, and seasonal/year-to-year variability in the Saltito semiarid sub-basin in the Mexican state of Durango. Satellite precipitation products (SPP) in 16 sites were contrasted point to point with data from rainfall gauge stations and with a daily temporal resolution for the period of four years (2015–2019). Using this information, we constructed Rx1d, Rx2d, R25mm, and RR95 extreme rainfall indices. For the precipitation–runoff relationships, a runoff model based on the Storm Water Management Model (SWMM) was calibrated and validated with gauge data, and we obtained the Qx1d, Qx2d, and Qx3d runoff indices. We used the bias volume (%), MSE, correlation coefficient, and median bias to evaluate the ability of satellite products to detect and analyze extreme precipitation and run flow events. Although these sensors tend to overestimate both precipitation levels and the occurrence of extreme precipitation events, their high spatial and temporal resolutions make them a reliable tool for the analysis of trends in climate change indices. As a result, they serve as a useful resource in evaluating the intensity of climate change in the region, particularly in terms of precipitation patterns. They also allow hydrological modeling and the observation of precipitation–runoff relationships. This is relevant in the absence of precipitation and hydrometric information, which is usually common in vast regions of the developing world.