Comparative evaluation of conceptual and physical rainfall–runoff models

Jaiswal, R. K.; Ali, Sohrat; Bharti, Birendra

doi:10.1007/s13201-019-1122-6

Cited by 114 publications

(47 citation statements)

References 52 publications

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“…In some cases, the lumped conceptual models have been found to perform better than physically based models. For instance in the study by Jaiswal et al [98], for calibration, AWBM and TANK yield NSE values of 0.76 and 0.84, respectively, while SWAT had NSE of 0.75. However, in other areas, some rainfall-runoff models can exhibit unsatisfactory performances.…”

Section: Model Performance For the Calibration Validation And Entirementioning

confidence: 99%

Performance of rainfall–runoff models in reproducing hydrological extremes: a case of the River Malaba sub-catchment

2021

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Daily River Malaba flows recorded from 1999 to 2016 were modelled using seven lumped conceptual rainfall–runoff models including AWBM, SACRAMENTO, TANK, IHACRES, SIMHYD, SMAR and HMSV. Optimal parameters of each model were obtained using an automatic calibration strategy. Mismatches between observed and modelled flows were assessed using a total of nine “goodness-of-fit” metrics. Capacity of the models to reproduce historical hydrological extremes was assessed through comparison of amplitude–duration–frequency (ADF) relationships or curves constructed based on observed and modelled flow quantiles. Generally, most of the hydrological models performed better for high than low flows. ADF curves of both high and low flows for various return periods from 5 to 100 years were well reproduced by AWBM, SAC, TANK and HMSV. ADF curves for high and low flows were poorly reproduced by SIMHYD and SMAR, respectively. Overall, AWBM performed slightly better than other models if both high and low flows are to be considered simultaneously. The deviations of these models were larger for high than low return periods. It was found that the choice of a “goodness-of-fit” metric affects how model performance can be judged. Results from this study also show that when focusing on hydrological extremes, uncertainty due to the choice of a particular model should be taken into consideration. Insights from this study provide relevant information for planning of risk-based water resources applications.

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Section: Model Performance For the Calibration Validation And Entirementioning

confidence: 99%

Performance of rainfall–runoff models in reproducing hydrological extremes: a case of the River Malaba sub-catchment

2021

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“…Examples of these include the TANK model [ 264 ], Hydrologic Engineering Center's Hydraulic Modeling System (HEC-HMS) [ 265 , 266 ], TOPMODEL, Système Hydrologique Européen (SHE), Soil and Water Assessment Tool (SWAT) [ 267 , 268 ] and complex conceptual models such as MODified HYDROLOG (MODHYDROLOG) [ 269 ]. A review of the pros and cons of these models by Sivapalan et al [ 270 ] and Jaiswal et al [ 271 ] revealed that distributed physically based models have the advantage of accounting for spatial heterogeneities and provide detailed description of the hydrological processes in a catchment with limited demands of input data hence their widespread use in numerous hydrological studies [ 266 , 272 – 276 ]. The same notion was confirmed by the World Meteorological Organization [ 181 ] in their inter-comparison of conceptual hydrological models for operational hydrological forecasting.…”

Section: Hydrological Modellingmentioning

confidence: 99%

Climate change/variability and hydrological modelling studies in Zimbabwe: a review of progress and knowledge gaps

Maviza

Ahmed

2021

SN Appl. Sci.

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This paper reviews developments in climate science and hydrological modelling studies in Zimbabwe over the past 29 years in an effort to expose knowledge gaps within this research domain. We initially give a global and regional overview and then follow a systematic thematic approach in reviewing specifically online published, peer-reviewed journal articles on climate change/variability and hydrological modelling in Zimbabwe. The state and progress towards advanced integrated climate and hydrological modelling research are assessed, tracking benchmarks in the research methodologies (tools and techniques) used therein including geographic information systems and remote sensing. We present descriptive summaries of key findings, highlighting the main study themes (categories) and general conclusions arising from these studies while examining their implications for future climate and hydrological modelling research in Zimbabwe. Challenges associated with climate and hydrological modelling research in Zimbabwe are also briefly discussed and the main knowledge gaps in terms of research scope and methodologies employed in the reviewed studies also exposed. We conclude by presenting plausible potential areas of focus in updating and advancing scientific knowledge to better understand the climate-land use-hydrology nexus in Zimbabwe. While this paper is primarily relevant for researchers, the general findings are also important for policy-makers since it exposes potential areas for policy intervention or agenda setting in as far as climate and hydrology science research is concerned so as to effectively address pertinent questions in this domain in Zimbabwe.

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“…One of the main effects of urban green infrastructure on runoff water quality is the overall reduction in runoff quantity, thereby reducing the quantity of pollutants reaching urban waterways and the sewer infrastructure (Gooré Bi et al, 2015). Research on urban rainfall and runoff pollution mainly includes the following aspects: the spatial and temporal distribution of urban stormwater runoff pollutants (Brezonik and Stadlemann, 2002;Jeffrey et al, 2005;Geonha et al, 2007); the pollution characteristics of stormwater runoff produced by different underlying surfaces such as urban asphalt pavement (Ellis, 2000) and green pavement (Huang et al, 2006); the controlling effect of urban greening on pollutants (Ren et al, 2005;Chen et al, 2009;Ren et al, 2020;Xu et al, 2020); and the erosive effect of stormwater runoff and the development on urban rainfall-runoff models (Murakami et al, 2008;Ouyang et al, 2010;;Jaiswal et al, 2020). Among them, studying the characteristics of urban rainfall and runoff products is the basis for in-depth research on urban areal source pollution.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Migration Characteristics of Stormwater Runoff Pollutants on Different Underlying Surfaces in Guangzhou, China

Pan¹,

Li²,

Gao³

et al. 2021

Front. Earth Sci.

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Stormwater runoff is identified as urban nonpoint source pollution that increasingly introduces contaminants to urban water bodies and impedes sustainable development. The pollution load of runoff varies due to the interception of different land cover types during the urban hydrological process. During the rainy season (June–August) in 2018, five different underlying surfaces (green roof, parking lot, urban road, parkway, and grassland) were selected in Guangzhou to analyze the migration characteristics of stormwater runoff pollutants. The concentrations of heavy metals, such as chromium (Cr), cadmium (Cd), lead (Pb), and mercury (Hg), as well as total nitrogen (TN), total phosphorus (TP), and polycyclic aromatic hydrocarbons (PAHs) were collected and analyzed on different underlying surfaces with the rainfall data at the beginning of a stormwater runoff event. The results showed that PAHs, heavy metals, and TP existed mainly in the form of particles; nitrogen was mainly present as ammonia and nitrate; and the TN, TP, PAHs, and heavy metal were significantly different in the stormwater runoff on each underlying surface. The pollutant concentration in urban road runoff was the highest, accounting for 40–70% of the total pollutant concentration in the stormwater runoff, and the pollutant concentration in green roof runoff was the lowest, accounting for 10–40% of the total pollutant concentration in the runoff. An obvious effect of initial rainfall erosion was observed during stormwater runoff from urban roads and parking lots, and the scouring effect on grasslands and green roofs was mainly due to the many factors affecting the underlying surface during the middle and late stages. The rates of reduction of heavy metals were the most significant. The effect of water purification was positively correlated with stormwater runoff duration. The rates of reduction of TN, heavy metals (Cr, Cd, and Hg), and PAHs in the grassland areas were 35.20 ± 26.28, 0.24 ± 10.13, 71.77 ± 10.97,32.62 ± 110.48, and 41.30 ± 8.78%, respectively. This study could provide a theoretical basis for preventing and managing pollutants in urban stormwater runoff.

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Comparative evaluation of conceptual and physical rainfall–runoff models

Cited by 114 publications

References 52 publications

Performance of rainfall–runoff models in reproducing hydrological extremes: a case of the River Malaba sub-catchment

Performance of rainfall–runoff models in reproducing hydrological extremes: a case of the River Malaba sub-catchment

Climate change/variability and hydrological modelling studies in Zimbabwe: a review of progress and knowledge gaps

Analysis of the Migration Characteristics of Stormwater Runoff Pollutants on Different Underlying Surfaces in Guangzhou, China

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