Runoff Characteristics Research Articles

Validating the Effect of Topography and Geology on Rainfall–Runoff in Mountainous Catchments Using the Improved HYdrologic CYcle Model

ABSTRACTRainfall–runoff characteristics of mountainous catchments are affected by many factors, such as topography and geology. Traditionally, the effects of geology on rainfall–runoff characteristics have been explained using geology, but the differences in runoff characteristics within the same geological settings have not been examined. These differences can be expressed as differences between the hydrological model parameters. However, the effects of geology on the model calculations have not yet been clarified. Thus, this study aims to clarify the effects of topography and geology on model calculations using an improved HYdrologic CYcle (HYCY) model that considers bedrock infiltration. Runoff observations were conducted for approximately 3 years in 19 catchments at 2 sites located in sedimentary rock and granite mountains. Rainfall was recorded at each site. The observed hydrographs were used to optimise the parameters for each catchment using the least‐squares method. The relationship between parameter m and the soil layer storage was calculated using the optimised parameters, representing the percentage of the area contributing to runoff. Furthermore, these results were compared with observational analysis results. The improved HYCY model accurately represented all 19 runoffs. When the total precipitation in 1 event exceeded 200 mm, parameter m became ~1 and ~0.3–0.4 in sedimentary rock and granitic catchments, respectively, which shows the effect of geology. The effects of topography on the parameters were exhibited in Kc and Kb, which calculated the storm flow from the channels and baseflow hydrographs, respectively. However, the parameter distributions exhibited geological differences, namely in parameter Kh, Kb and m. The parameter Kh calculates the overland flow hydrograph. This implies that geological differences affect the probability of the overland flow generation rate and the recession hydrographs of the overland flow and baseflow.

Determining an optimal run-off coefficient method for estimating peak discharge in the Lesti River catchment

The run-off coefficients provide vital hydrological data used for river discharge forecasts and flood risk management. Selecting an appropriate method to determine this coefficient is essential for accurately estimating peak discharge. This study compared the effectiveness of the Hassing, Cook, and U.S. Forest Service methods integrating GIS in estimating run-off coefficients in the Lesti River catchment area from 2013 to 2019. The findings revealed that the run-off coefficient was determined to be 0.188–0.243 using the U.S. Forest Service method, 0.194–0.213 using the Hassing method, and 0.466–0.480 using the Cook method. These results showed a rapid increase in the run-off coefficient within the Lesti River catchment area, signifying a heightened susceptibility to flooding. This is particularly concerning as the Lesti River is a primary tributary to the Brantas River. The comparison of estimated versus observed peak discharge emphasised the superiority of the runoff coefficient associated with the Hassing method over alternative methodologies when utilised as input data for peak discharge estimation. This was evident by the notable measurement error values of 11% for MAPE and 0.58 for MAE. The Hassing method emerged as the most appropriate and reliable for reflecting run-off characteristics in the Lesti River catchment area. Additionally, it proved to be the most accurate for estimating run-off coefficients in the Nakayasu process for peak discharge estimation. Consequently, applying the Hassing method offers a viable strategy for effectively mitigating flood risks in the Lesti catchment area.

Bayesian estimates for changes of the Russian river runoff in the 21st century as based on the CMIP6 model ensemble simulations

Based on ensemble calculations with the CMIP6 (Coupled Model Intercomparison Project, phase 6) climate models and using Bayesian averaging, an analysis was conducted on the changes in the 21st century runoff of several Russian rivers – the Volga, Ob, Yenisei, Lena, Amur, and Selenga. Bayesian weights considered the quality of models’ reproduction of runoff (long-term average runoff, linear runoff trend over the time interval with available runoff observations, interannual and interdecadal variability). The quality of runoff characteristics reproduction by individual models in the CMIP6 ensemble varies most significantly for the long-term average runoff, runoff trend, and, to a lesser extent, for interannual variability. In the 21st century, the ensemble average runoff increases for most of the analyzed rivers, except for the Volga. This increase is more pronounced under scenarios with larger anthropogenic impacts. It is especially significant for the SSP5-8.5 scenario (Shared Socioeconomic Pathways, 5-8.5), under which the runoff increase trend from 2015 to 2100 relative to its current long-term average is (10 ± 4)% for the Ob, (16 ± 3)% for the Yenisei, (39 ± 7)% for the Lena, (36 ± 7)% for the Amur, and (18 ± 6)% for the Selenga. The primary reason for the change in ensemble average runoff in the 21st century in models under all SSP scenarios is the change in precipitation. Accounting for differences in model quality in reproducing river runoff on average for 2015–2100 reduces inter-model deviations relative to the corresponding values with uniform weighting of model results by 6–26%, depending on the SSP scenario and river basin.

Land use transformations in the Brazilian Savanna: A decade of soil erosion and runoff measurements

Changes in land cover and land use (LULC) are one of the main drivers of erosion and runoff. How ever, most research has relied on short-term observations and only focused on one or two land cover types. We investigated the long-term trade-off between common agricultural land covers (sugarcane, pasture, and soybean), runoff, and soil loss rates. We compared these to native forest (wooded Cerrado) and bare soil. The field observations were done in 100 m2 experimental plots in Brazil maintained during the past 10 years. The paper provides three main contributions: (1) long-term runoff and soil loss rates of plots under different LULCs, (2) comparison of runoff, soil loss, and pedological characteristics between plots constructed 10 years apart, and (3) analysis of the trade-off between different LULCs. When ranking land covers based on runoff and soil loss rates, there is a shift in ranking positions, making it difficult to determine which one is more environmentally harmful. However, it is evident that whatever agricultural practice is used, there is a significant impact when compared to native forest. For example, the area converted to pasture resulted in almost 20 times higher runoff, while conversion to sugarcane resulted in 5 times higher soil loss. Not only land cover plays a major influencing factor, but also weathering exposing time. Areas under the same land cover and environmental conditions had different rates of soil loss and runoff due to long-term exposure effects such as soil crusting. Our findings have high relevance for the hydrological and agricultural community by demonstrating (i) the magnitude of trade-off in terms of soil loss and runoff due to land cover changes and (ii) that soil loss should not be assumed to be a linear process over time, as it is commonly assumed.

Evolution characteristics and attribution analysis of runoff in the coal mining area in northwest China: A case study of the Kuye River Basin

ABSTRACT The Kuye River Basin is the main coal-producing area in northwest China, and it is also a typical arid and semi-arid area. Under the combined influence of climate change and human activities, the runoff of the basin decreases noticeably. The Mann–Kendall trend test, the Hurst index, cumulative anomaly, and wavelet analysis were used to analyze the runoff evolution characteristics of the Kuye River, and the contributions of climate change and human activities to the runoff reduction were quantitatively separated by the slope-changing ratio of the cumulative quantity. The results showed that the runoff decreased significantly from 1970 to 2020, and there was an obvious abrupt change in 1996, while the change trends in precipitation and evaporation were not significant. The contributions of climate change and human activities to runoff reduction were 22.71 and 77.29%, respectively, and human activities have become the dominant factor in runoff variations. Coal mining and the implementation of soil and water conservation measures are considered to be the main human activities contributing to the reduction in runoff. In particular, mining activities reduce groundwater recharge to rivers, which provides a profound understanding of the effect of human activities in mining areas on runoff variation.

Runoff and infiltration characteristics of rainfall and snowmelt water on a model embankment slope composed of sandy soil simulating vegetation works.

Runoff and infiltration characteristics of rainfall and snowmelt water on a model embankment slope composed of sandy soil simulating vegetation works.

Transport Pathways of Nitrate in Stormwater Runoff Inferred from High-Frequency Sampling and Stable Water Isotopes.

Storm events can mobilize nitrogen species from landscapes into streams, exacerbating eutrophication and threatening aquatic ecosystems as well as human health. However, the transport pathways and storm responses of different nitrogen forms remain elusive. We used high-frequency chemical and isotopic sampling to partition sources of stormwater runoff and determine transport pathways of multiple nitrogen forms in an agricultural catchment. Bayesian mixing modeling reveals shallow subsurface water as the dominant source of stormwater runoff, contributing 74% of the water flux and 72, 71, and 79% of total nitrogen (TN), total dissolved nitrogen (TDN), and nitrate (NO3-N), respectively. Groundwater, by contrast, contributed 11% of stormwater runoff and 21, 22, and 17% of TN, TDN, and NO3-N, respectively. The remaining 14% of stormwater runoff can be attributed to rainwater, which contains much less TN, TDN, and NO3-N. Surprisingly, during storm events, the dominant nitrogen form was NO3-N rather than dissolved organic nitrogen. Antecedent conditions and runoff characteristics have an important influence on nitrogen loads during storm events. Our results provide insight into hydrological mechanisms driving nitrogen transport during storm events and may help in developing catchment management practices for reducing nitrogen pollution in aquatic ecosystems.

Analysis of Surface Runoff Characteristics in Zhengzhou City under Extreme Rainfall Conditions

Analysis of Surface Runoff Characteristics in Zhengzhou City under Extreme Rainfall Conditions

Prediction of runoff characteristics in permeable pavements using experimental data and intelligent models.

Considering the growing use of permeable pavements, the prediction of runoff passing through this pavement model is of considerable importance. The prediction of rainfall-runoff relationships can be a challenge because of several factors including data uncertainty, non-linear relationships, and high temporal and spatial variability. To deal with these challenges, intelligent algorithms are often used to predict such complex phenomena. In this research, runoff control parameters were investigated in two types of permeable pavements (permeable interlocking concrete pavement (PICP) and high strength clogging resistant permeable pavement (CRP)) using support vector machine (SVM), support vector machine-bat (SVM-BA) and support vector machine-grasshopper (SVM-GOA). Variables used in the models included percentage of coverage by permeable pavement (A), rainfall intensity (I), slope (S), and pavement type coefficient (K) as input data, and runoff coefficient (C), time to runoff (Tr), and peak discharge (Qp) as output data. In this research, from the total of 108 data extracted from the experimental results, 86 data were used in the training period, and 22 data were used in the test period. The results of the test period show that the SVM-BA model has the best performance with values of MAE = 0.010 in predicting C, MAE = 1.330 min in predicting Tr, and MAE = 0.029 lit/min in predicting Qp. The SVM-GOA model is ranked second with values of MAE = 0.051 in predicting C, MAE = 3.285 min in predicting Tr, and MAE = 0.097 lit/min in predicting Qp. Also, the SVM model is ranked third with values of MAE = 0.063 in predicting C, MAE = 4.470 min in predicting Tr, and MAE = 0.121 lit/min in predicting Qp. In summary, the SVM-BA algorithm showed the best performance and the SVM algorithm showed the weakest performance in predicting runoff characteristics in permeable pavements.

Restored vegetation dominates the decrease in surface and subsurface runoff on the Loess Plateau

Understanding the runoff generation response to environmental changes at the basin scale can provide insights into the evolution of hydrological processes. The objective of this study is to detect and attribute the changes in surface and subsurface runoff in the Chinese Loess Plateau, a region with significant environmental and hydrological changes in the past several decades, to understand how and to what extent the runoff generation can be changed. Taking 13 basins (comprising 56 hydrological stations) on the Chinese Loess Plateau as the study area, we identified rainfall-runoff events and established indicators (event surface runoff coefficient, event timescale, normalized event peak discharge, baseflow, and baseflow index) to characterize the runoff generation, and further attributed the changes in runoff generation characteristics before (1961–2000) and after (2001–2019) the revegetation project. In particular, we inversely estimated the parameters of Soil Conservation Service Curve Number (SCS-CN) to analyze the initial abstraction and potential maximum retention under changing environment. Surface and subsurface runoff significantly decreased, with the surface runoff coefficient and baseflow decreasing by 34 % ± 24 % and 26 % ± 23 %, respectively. The surface runoff hydrograph became flatter with a 75 % decrease in the normalized event peak discharge and 82 % increase in the timescale of surface runoff, respectively. Normalized Difference Vegetation Index (32 %) and population (34 %) dominated the increase in the potential maximum retention, with 84 % of stations experiencing growth exceeding 100 %. The reduction in surface runoff can be primarily attributed to the enhanced soil infiltration due to revegetation and other human activities. Higher evapotranspiration resulting from revegetation was likely the dominant factor leading to the decrease in subsurface runoff. This study detailed the impacts of revegetation on surface and subsurface runoff generation, which can enhance our understanding of hydrological changes.

Assessment of urban inundation and prediction of combined flood disaster in the middle reaches of Yellow river basin under extreme precipitation

Watershed Cities often face the dual flood risks of upstream floods and urban waterlogging. This compound disaster is caused by the interaction of multiple factors (topography, storms, upstream water and urban drainage, etc.), and is particularly affected by extreme rainfall events and the large and small drainage carrying capacity. To explore the combined effects of extreme rainfall and drainage systems, this study applied the Chicago rainfall hyetograph method and constructed design under different return periods. A comprehensive model combining the verified SWMM and LISFLOOD hydraulic models was utilised to build one-dimensional and two-dimensional coupled watershed urban flood models, simulating inundation depth and duration. By considering urban rainfall, runoff, topography, and drainage system characteristics, the flood processes under different extreme rainfall scenarios were simulated, and the node overflow and inundation depth were obtained. A flood risk assessment of watershed cities was conducted by combining methods such as the HR threshold method, evaluation level, and threshold division. In this study, Huyi District of Xi’an City, China, was used as the research area. The main research findings show: (1) the precipitation design values for different return periods were calculated based on the proposed method, with amount for a 100-year event being 87.196 mm, suitable for composite flood simulation; (2) applying the SWMM-LISFLOOD coupled model constructed for midstream urban areas ensured the accurate simulation of composite flood processes. Under different extreme rainfall return period scenarios, the percentage of flood inundation depth exceeding 1 m caused by multiple disaster factors increased from 0.73 % to 5.19 %; (3) extreme rainfall leads to different levels of internal flooding risk. When the return period of extreme rainfall is 5 years, the area inundated by composite floods in the research area is 67.21 km2. In comparison, when the return period of extreme rainfall is 50 years, the area inundated by composite floods in the research area significantly increases to a total inundation area of 286.85 km2. This study is expected to provide scientific references for flood prevention planning, composite disaster risk management, and urban resilience building in watershed urban areas while significantly impacting modern economic growth, social stability, and the safety of watershed cities.

Precipitation input increases biodiversity of planktonic communities in the Qinghai-Tibet Plateau

Planktonic communities in aquatic ecosystems are crucial water quality indicators, with their growth dependent on runoff chemical and hydraulic characteristics (e.g., nutrient availability and turbidity). Previous studies have indicated that runoff components (i.e., proportions of precipitation, groundwater, snowmelt, etc.) play a vital role in regulating runoff characteristics, potentially affecting planktonic communities. However, the response of these communities to runoff components, particularly in mountainous regions, remains underexplored. In this study, we conducted four sampling campaigns from 2017 to 2020 in a watershed on the Qinghai-Tibet Plateau. Combined with laboratory incubation experiments, we examined the impact of various runoff components on the diversity and abundance of phytoplankton and zooplankton. We found that a higher proportion of precipitation in runoff contributed to an increase in the diversity of plankton communities. Laboratory experiments with unified water samples incubated with different runoff components demonstrated that the significant influence of precipitation on planktonic diversity primarily stems from the influx of abundant exogenous particulate material into rivers. Using a path analysis, we further confirmed that the impact of precipitation on diversity is primarily through chemical pathways, notably by increasing nutrient concentrations. Our study enhances our understanding of the interactions between the hydrological cycle and aquatic ecosystems, offering valuable insights for effectively maintaining and managing these natural environments.

SENSITIVITY OF THE KOLYMA RIVER RUNOFF TO MODERN CLIMATE CHANGE

The Kolyma River is a large river in the continuous permafrost zone. Its basin is subject to significant climatic changes. An information-modeling complex ECOMAG is employed for the study of water regime features and dynamics. ECOMAG calculates daily water flow rates at specified points based on meteorological data with a one-day resolution.This study analyzes the actual dynamics of water runoff and runoff-forming factors during the period from 1979 to 2013. A physical-mathematical model is adapted for the Kolyma River basin. Furthermore, the study examines the sensitivity of water regime characteristics to changing climate parameters. Calibration and verification demonstrated the ECOMAG’s ability to accurately reproduce observed water discharges for two points on the Kolyma River and the satisfactory performance on its tributary, the Bolshoy Anyui River. The ECOMAG model was for the first time employed for the Kolyma River basin, thus allowing the investigation of climate-related runoff changes. The preliminary statistical analysis revealed that if the average hydrological characteristics in the Kolyma River basin from 2000 to 2013 are compared with those from 1979 to 1999 a 5,1% rise of annual river runoff is evident for the Kolymskoye river section 283 km upstream the Kolyma River mouth (from 99,4 km3 in 1979-1999 up to 104,5 km3 in 2000-2013). The ECOMAG model also effectively captures changes in the annual water runoff and its intra-annual distribution.A rise in the mean annual air temperature by 1,3°C is characteristic of the modern time period. According to weather station data, the air temperature in March, April, November, and December increased by more than 2,5°C. The total annual precipitation increased by 8,3%, with September and March experiencing the most pronounced growth.To gain a more comprehensive understanding of the mechanisms governing the Kolyma River’s response to climate changes, the sensitivity of water runoff characteristics to air temperature and precipitation changes was analyzed using the ECOMAG model to generate artificial meteorological data series. The study demonstrates that water discharge is more sensitive to a 5% increase in precipitation than to a 1ºС rise in air temperature.

Overflow Simulation and Optimization of a Drainage System in an Urban Area in the Northern Anhui Plain

Quantitative simulation of urban waterlogging using computer models is an effective technical means for urban storm water management, especially for predicting and preventing waterlogging. In this study, a city in the northern Anhui Plain, China, was selected as the study site. The Storm Water Management Model was applied to simulate the dynamic changes in the pipeline overload, node overflow, and discharge port runoff characteristics from three perspectives: surface runoff, pipe network transmission, and flow control of low-impact development. The operation of the rainwater pipe network under different return periods and the real-time operation of the rainwater pipe network were simulated to seek solutions to urban waterlogging problems caused by flat terrain and slow drainage. The results revealed that surface runoff is the primary source of rainfall in the study area, with a runoff coefficient of 0.599. The drainage pipe network was optimized by expanding the diameter of the pipe from ≤1.5 mm to ≥2 mm. The water reduction rate was more than 50%, and overload did not occur after optimization. Therefore, sinking green space technology and optimization methods for expanding a pipe diameter can reduce urban waterlogging.

A comparison of several cyclo-stationary and fractionally differenced time-varying autoregressive models in runoff simulation and prediction

Due to the far-reaching effects of climate change and human activities, hydrological processes exhibit cyclo-stationarity and long-term memory. Neither the traditional autoregressive (AR) nor the time-varying autoregressive (TVAR) model can comprehensively characterize these runoff characteristics, leading to poor model prediction performance. We propose a novel fractionally differenced TVAR model based on the de-seasonality method (FDTVARD) to better capture the above runoff characteristics and achieve improved runoff simulation and prediction accuracy. We first compare the effects of two covariates, time and lagged target variables, on the performance of the FDTVARD model. The FDTVARD model is compared with the AR model based on the de-seasonality method (ARD), fractionally differenced ARD (FDARD) and TVAR model based on the de-seasonality method (TVARD), and the model performance is evaluated using daily runoff data from seven hydrological stations in the Yellow River Basin, China. The results show that (1) the FDTVARD model has the simplest model form and yields the best simulation and forecasting performance, and the optimal covariate is time. (2) Considering only the time-varying nature of the model coefficients improves the ARD model prediction performance more than considering only the long-term memory of the runoff series. Considering both factors improve the simulation and prediction performance of the ARD model more than considering only one factor. The study findings could facilitate improvements in the prediction accuracy of stochastic hydrological models.

Quantitative evaluation of runoff drivers based on the MLLR-Budyko framework

ABSTRACT At the request of the authors, the article ‘Quantitative evaluation of runoff drivers based on the MLLR-Budyko framework’ has been withdrawn from Water Supply.

Assessing future urban flood hazard: A comprehensive approach to estimating the implications of future rainfall scenarios

AbstractEstimating potential changes in future flood patterns based on anticipated changes in hydrological characteristics within the basin is crucial for mitigating flood damage and managing flood risk. In this study, nonparametric probability models are used to estimate future rainfall patterns in Seoul under the GCM‐based climate change scenarios (CCS), and the estimated future daily rainfall data was temporally downscaled to hourly units using the KNNR‐GA technique. Changes in flood hazard and runoff characteristics of the target area based on the estimated future rainfall data are quantitatively assessed. The results highlight that under CCS, flood runoff may increase further into the future, resulting in more significant changes in flood patterns and accelerating the increase in flood hazard. The delta change factor of flood risk indicators increased relatively significantly in more severe CCS. This study also proposed a process to estimate future flood runoff and mitigation effects according to CCS by reflecting various flood mitigation measures in the urban drainage system model. These findings can offer valuable insights for setting the direction of current and future mitigation measures.

Spatial distribution of rainfall–runoff characteristics and peak lag time in high‐relief meso‐scale mountain catchments where observations are scarce

AbstractClarifying the relationship between rainfall and runoff characteristics in mountainous areas is crucial to improving flood and sediment disaster prediction. This study investigates that relationship, focusing on meso‐scale catchments of 1–10 km2 in high‐relief mountainous regions where conventional observational data are limited. Such research requires observations in multiple adjoining watersheds due to the variability of runoff patterns in mountainous river systems. However, observations are challenging in meso‐scale catchments due to equipment loss in rivers. This study included seven observation sites with catchment areas of 0.16–9.01 km2 and relative elevations exceeding 1000 m within the Higashigochi River basin, a sub‐branch of the Oi River basin in Shizuoka Prefecture, Japan. Water level gauges were strategically installed at each of these locations to prevent loss. Peak lag time (the time difference between the rainfall and water level peaks), which is crucial to flood hydrograph definition and disaster preparedness, was assessed. The results indicate a general trend of increasing peak lag time with larger catchment area, although spatial heterogeneity was observed at small sites. This finding aligns with prior studies in meso‐scale catchments within mountainous regions with relative elevations of 450 m or less, suggesting that in such meso‐scale catchments, larger catchment area tends to result in longer peak lag times, regardless of topographic undulation. Overall, the impact of topographic undulations on peak lag time appears surprisingly modest, indicating that catchment area plays a more significant role in determining peak lag time. The observed heterogeneity of peak lag time could be attributable to the influence of bedrock groundwater discharge.

A copula-based approach to instream ecological flow determination considering inter- and intra-annual runoff variability

ABSTRACT Instream ecological flow (IEF) provides a flow reference value for maintaining the stability of river ecosystems and enriching biodiversity. Existing methods for determining the IEF often consider only one of the intra-annual runoff distribution characteristics or the inter-annual runoff distribution characteristics. In this study, a copula-based ecological runoff determination method is proposed to consider both annual and monthly runoff magnitudes. The marginal distribution functions of annual and monthly runoff are first determined using the maximum likelihood method. Then the copula function is used to construct the joint distribution function of annual and monthly runoff series. Using the flow duration curve method, the IEF under different annual and monthly runoff exceedance probabilities is calculated. The probability values are combined with the initial IEF values to determine the final IEF process. A case study is conducted using real-world data from the Tongtian River in China. The results show that the total annual basic ecological water demands of the Tongtian River in abundant, flat, and dry water years are 138, 102 and 72.2 billion m3. The proposed method effectively avoids the disturbance of extreme runoff.

Runoff characteristics of a small watershed of a Japanese cedar plantation during three cold seasons, including a rare warmer and less snowy year

Runoff characteristics of a small watershed of a Japanese cedar plantation during three cold seasons, including a rare warmer and less snowy year