Runoff Response Research Articles

Response of Runoff, Sediment Yield, and Runoff‐Related Dissolved Organic Carbon Loss to Variable Straw Mulching Rates on Sloping Lands of Regosols

ABSTRACTDissolved organic carbon (DOC), an organic carbon fraction with high activeness and mobility, migrated by runoff is a key part in carbon cycle. A rational straw mulching rate can be regulated to obtain maximum benefits while controlling runoff and sediment yield on sloping lands. However, little remains known about the optimal straw mulching rates required for effectively reducing the loss of DOC in runoff. Therefore, to overcome the existing limitations, this study investigated the effects of modified maize straw mulching rates on the loss of DOC during runoff, utilizing indoor rainfall simulation. Five mulching rates, including 0, 0.2, 0.4, 0.6, and 0.8 kg m−2 [control (CK) and treatments (T1, T2, T3, and T4), respectively], were tested in combination with three slope gradients (10°, 15°, and 20°) to evaluate how straw mulching rate influences runoff, sediment yield, and runoff‐related DOC loss under a heavy rainfall intensity of 90 mm h−1. Our results showed that various straw mulching rates did not significantly differ runoff rates; however, straw mulching significantly reduced sediment concentration and yield. Moreover, the reduction in sediment yield increased with an increase in mulching rate. Compared to the CK, T1 resulted in a 63% increase in DOC loss at a slope of 20°. Additionally, T2 caused an 8% and 7.2% increase in DOC loss at both 10° and 15° slopes. Conversely, T3 and T4 reduced DOC loss by 54.1%–80.8% and 51.1%–65.2%, respectively, across all slope gradients. These results suggested that mulching rates of 0.2–0.4 kg m−2 may potentially increase DOC loss in runoff on the sloping lands. Our results hold significant importance in optimizing the use of straw mulching for sustainable management practices in agricultural lands.

Spatial Differences in the Response of Runoff to Climate and Land Use Changes in the Yiluo River Basin

Spatial Differences in the Response of Runoff to Climate and Land Use Changes in the Yiluo River Basin

Characterizing nonlinear, nonstationary, and heterogeneous hydrologic behavior using ensemble rainfall–runoff analysis (ERRA): proof of concept

Abstract. A classical approach to understanding hydrological behavior is the unit hydrograph and its many variants, but these often assume linearity (runoff response is proportional to effective precipitation), stationarity (runoff response to a given unit of rainfall is identical, regardless of when it falls), and spatial homogeneity (runoff response depends only on spatially averaged precipitation). In the real world, by contrast, runoff response is typically nonlinear, nonstationary, and spatially heterogeneous. Quantifying this nonlinearity, nonstationarity, and spatial heterogeneity is essential to unraveling the mechanisms and subsurface properties controlling hydrological behavior. Here, I present proof-of-concept demonstrations illustrating how nonlinear, nonstationary, and spatially heterogeneous rainfall–runoff behavior can be quantified, directly from data, using ensemble rainfall–runoff analysis (ERRA), a data-driven, model-independent method for quantifying rainfall–runoff relationships across a spectrum of time lags. I show how ERRA uses nonlinear deconvolution to quantify how catchments' runoff responses vary with precipitation intensity and to estimate their precipitation-weighted runoff response distributions. I further illustrate how ERRA combines nonlinear deconvolution with de-mixing techniques to reveal how runoff response depends jointly on precipitation intensity and nonstationary ambient conditions, including antecedent wetness and vapor pressure deficit. I demonstrate how ERRA's de-mixing techniques can be used to quantify spatially heterogeneous runoff responses in different parts of a catchment, even if those subcatchments are not separately gauged. I also illustrate how ERRA's broken-stick deconvolution capabilities can be used to quantify multiscale runoff responses that combine hydrograph peaks lasting for hours and recessions lasting for weeks, well beyond the average spacing between storms. ERRA can unscramble these multiple effects on runoff response even if they are overprinted on each other through time and even if they are corrupted by autoregressive moving average (ARMA) noise. Results from this approach may be informative for catchment characterization, process understanding, and model–data comparisons; they may also lead to a better understanding of storage dynamics and landscape-scale connectivity. An R script is provided to perform the necessary calculations, including uncertainty analysis.

Hydrogeological mapping of fracture networks using earth observation data to improve rainfall–runoff modeling in arid mountains, Saudi Arabia

Abstract Rainfall–runoff modeling is essential for the hydrological analysis of basins; however, the traditional modeling approach does not incorporate geological features such as fractures and fissures in the modeling task. These features are significant in the water loss during a rainstorm, which should be incorporated to obtain realistic rainfall–runoff results. A novel approach is presented here in to quantify the geological features and link them to the curve number (CN) method. The proposed methodology has not been applied in the literature. This approach is validated on five gauged basins, namely, Yiba, Al Lith, Liyya, Habawnah, and Tabalah, in the southwest part of Saudi Arabia. Four major stages are conducted. The first stage is the extraction of the geological lineaments using remote sensing and geographical information system technology; the second stage is estimating CN from rainfall–runoff data; the third stage is developing a relationship between CN and lineament density (LD); and the final stage is evaluating the developed equations on hydrological response. The least-squares method is employed to minimize the difference between observed and predicted runoff and determine the optimum range of CN. The research provides a comprehensive understanding of hydrological processes in fractured geologic systems and explores the influence of fractures on curve number. This study identifies two major lineament trends aligned with the Arabian trend direction, namely, north-northwest (NNW)–south-southeast (SSE) and north-northeast (NNE)–south-southwest (SSW). Furthermore, a moderate inverse correlation is established between LD and CN, highlighting the significance of geologic fractures on the hydrological response. The findings of this study provide insight into how the geological fissures in the mountainous region affected the rainfall–runoff response that leads to a low value of CN due to the water loss in the fissures and faults. As a result, this study clearly demonstrates the importance of the geological structures on rainfall–runoff responses.

The potential to reduce runoff generation through improving cropping and tillage practices in a sub-humid continental climate

Agricultural sustainability is threatened by both water deficit and water excess, especially at the presence of extreme meteorological events resulting from climate change. However, there has been lack of demonstrations on management options with long-term values for agricultural adaptation to runoff. Using 20 years of monitoring data (1993‒2012) for two experimental fields in the Canadian Prairies as a case study, we quantified the effects of rainfall characteristics, crop type and biomass, and tillage on growing-season runoff generation using regression analyses and thereafter scenario comparisons. With growing-season gross rainfall ranging between 183 and 456 mm, runoff responses varied between 0 and 59 mm. Over the 20-year study period, 70%‒74% of the growing-season runoff was generated by rainfall events > 100 mm. Compared to high-intensity tillage, long-term conservation tillage reduced both overall runoff and runoff in large events likely by improving water infiltration. Under both tillage methods, growing-season runoff significantly increased with increasing rainfall but decreased with increasing biomass (R2 range: 0.40‒0.58; p range: 0.0007‒0.02). At the event level, the rainfall-runoff relationship followed a piecewise regression model (Cd = 0.82; p < 0.0001; “breakpoint” rainfall event = 105 mm), in which runoff increased slowly before reaching the “breakpoint” but rose sharply afterwards. Due to a greater biomass, canola resulted in less runoff than wheat. Scenario analyses showed that increasing crop biomass by 50% under the current average rainfall conditions could reduce runoff by 81‒86% in wheat and 100% in canola. The reduction may be attributed to the combined effects of crop on interception, evapotranspiration, and infiltration. In conclusion, although in a sub-humid continental climate like the Canadian Prairies there are generally low amounts of rainfall runoff, this study demonstrates significant runoff in some years, especially following large rainfall events. Runoff generation can be significantly reduced through improving cropping and tillage practices, and such effects on regional water retention should be further assessed by considering the past and future changes in climate and management.

Snow runoff modelling in the upper Indus River Basin and its implication to energy water food nexus

Pakistan's hydropower sector depends heavily on glacier and snowmelt water that originates from the Upper Indus Basin (UIB). It is expected that climate change may adversely affect future hydropower generation capacity as a result of fluctuations in the magnitude, seasonality and hydrological extremes of the Indus River flow. This study employed the Degree-Day Snowmelt Runoff Model alongside the Moderate Resolution Imaging Spectroradiometer MODIS and daily ground-based hydro-meteorological data to model the snowmelt runoff response in the UIB. The results indicated a significant increase in the annual and seasonal runoff under both RCP4.5 and RCP8.5 scenarios, suggesting more water availability for hydropower and irrigation. By the end of the century, annual river flow is projected to increase by 28 % to 69 % under the RCP4.5 and RCP8.5 climate scenarios. Consequently, rise in annual river flow is expected to increase the electricity generation capacity of future hydropower projects by 93 % to 167 % under the RCP4.5 and RCP8.5 scenarios, respectively. The construction of robust multipurpose dams may potentially reduce flood risks in downstream areas during peak flows, while also supplying water for hydropower generation and irrigation during low flows. This, in turn, may enhance the resilience of both the hydropower and agriculture sectors in the face of climate change.

Season-dependent climate sensitivity of the surface runoff of major rivers in Changbai Mountain

Due to the sensitivity of runoff to changes in seasonal snowpack, the changes in surface runoff within high-latitude and high-elevation areas are more complex than in other areas. However, this sensitivity is often overlooked when correlating changes in runoff with meteorological variables. Here, we analyze changes in surface runoff during snowmelt and snowless periods and their sensitivity to climate change from 1960 to 2019 in the upstream watersheds of the Songhua, Yalu, and Tumen Rivers in Changbai Mountain. Most runoff indices, except for low flow in the Yalu River, exhibited decreasing trends during snowmelt and snowless periods. Precipitation was the primary variable responsible for surface runoff during snowless and snowmelt periods. Notably, rainfall replenished all runoff indices during snowless periods, particularly under high flow conditions. The average and high flows were also primarily supplemented by rainfall during snowmelt periods. However, low flow was positively correlated with snowmelt in the Songhua and Tumen Rivers and with temperature in the Songhua, Yalu and Tumen Rivers during snowmelt periods. Additionally, extreme runoff events were closely associated with extreme precipitation. These findings demonstrate the seasonal and index-dependent climate sensitivity of surface runoff within Changbai Mountain, offering insights into the surface runoff response mechanism to climate change in high-altitude mountainous areas. Under future climate change conditions, frequent and intense extreme precipitation events may increase the risk of flooding, owing to the high sensitivity of high flows to precipitation, posing a serious threat to regional ecological security.

Impact of Urbanization-Driven Land Use Changes on Runoff in the Upstream Mountainous Basin of Baiyangdian, China: A Multi-Scenario Simulation Study

Urbanization in the Haihe River Basin in northern China, particularly the upstream mountainous basin of Baiyangdian, has significantly altered land use and runoff processes. The runoff is a key water source for downstream areas like Baiyangdian and the Xiong’an New Area, making it essential to understand these changes’ implications for water security. However, the exact implications of these processes remain unclear. To address this gap, a simulation framework combining SWAT+ and CLUE-S was used to analyze runoff responses under different land use scenarios: natural development (ND), farmland protection (FP), and ecological protection (EP). The model simulation results were good, with NSE above 0.7 for SWAT+. The Kappa coefficient for CLUE-S model validation was 0.83. The further study found that from 2005 to 2015, urban construction land increased by 11.50 km2 per year, leading to a 0.5–1.3 mm rise in annual runoff. Although urban expansion continued, the other scenarios, which emphasized farmland and forest preservation, slowed this growth. Monthly runoff changes were most significant during the rainy season, with annual runoff in ND, FP, and EP varying by 8.9%, 10.9%, and 7.7%, respectively. While the differences in annual runoff between scenarios were not dramatic, these findings provide a theoretical foundation for future water resource planning and management in the upstream mountainous area of Baiyangdian and offer valuable insights for the sustainable development of Xiong’an New Area. Additionally, these results contribute to the broader field of hydrology by highlighting the importance of considering multiple land use scenarios in runoff change analysis.

Using Physics-Encoded GeoAI to Improve the Physical Realism of Deep Learning′s Rainfall-Runoff Responses under Climate Change

Recent research has shown that deep learning (DL) faces physical realism challenges in predicting runoff responses under climate change, mainly due to DL’s data dependence and lack of process understanding. In this study, a physics-encoded neural network model (dNN) was developed to adress this. dNN enables a fully process-based way to training and prediction by encoding process-based modeling knowledge into the DL architecture, including the water balance principle and causal linkages of catchment hydrological processes. To examine whether dNN can produce reliable runoff responses under warming scenarios, we first conducted regional training for dNN on daily runoff in 29 catchment in California. Two process-based models, EXP-HYDRO and HBV, were then developed as benchmarks. Both dNN and a pure data-driven LSTM were forced under warming scenarios, and the monthly hydrographs and total runoff ratios metrics were evaluated relative to the benchmarks. The results demonstrated: (1) For monthly hydrographs, dNN exhibited advantages in capturing cold-season runoff increase and warm-season recession than LSTM, effectively predicting the changes and trends in monthly runoff under warming scenarios; (2) For total runoff ratios, dNN predicted fewer catchments with increased runoff, indicating it can better maintain the total water budget under warming scenarios; (3) Through the synergy with physics, dNN was able to reasonably infer unobserved snowpack dynamics under warming scenarios. These results highlight the credibility and necessity of considering physics for DL in predicting runoff responses under climate change. Overall, this study provides a promising solution for considering physics in DL to further improve the process understanding in changing environments.

Deforestation-induced runoff changes dominated by forest-climate feedbacks.

Large-scale deforestation alters water availability through its direct effect on runoff generation and indirect effect through forest-climate feedbacks. However, these direct and indirect effects and their spatial variations are difficult to separate and poorly understood. Here, we develop an attribution framework that combines the Budyko theory and deforestation experiments with climate models, showing that widespread runoff reductions caused by the indirect effect of forest-climate feedbacks can largely offset the direct effect of reduced forest cover on runoff increases. The indirect effect dominates the hydrological responses to deforestation over 63% of deforested areas worldwide. This indirect effect arises from deforestation-induced reductions in precipitation and potential evapotranspiration, which decrease and increase runoff, respectively, leading to complex patterns of runoff responses. Our findings underscore the importance of forest-climate feedbacks for improved understanding and prediction of climate and hydrological changes caused by deforestation, with profound implications for sustainable management of forests and water resources.

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.

Reconceptualizing threshold‐mediated runoff responses: A case study from the Humber River watershed, Ontario, Canada

AbstractWatershed‐scale runoff responses are driven by various factors including climate, geology, soils, topography and landcover. They are often threshold‐mediated, expressing significant changes in hydrologic behaviour at critical moments in time or points in space. The influence of multiple explanatory variables on rainfall‐runoff relationships is not adequately captured by commonly applied approaches portraying runoff responses as a function of one variable related to watershed storage. In this case study, a novel approach was borrowed from ecological research to quantify and better understand threshold‐mediated runoff responses. Modelled three‐dimensional surfaces depicting metrics of event runoff responses as a function of rainfall amount and intensity were analysed to quantify both the abruptness of potential thresholds (i.e., threshold strength) and the simultaneous influence of different rainfall characteristics on the response (i.e., diagonality). The approach was applied to sub‐watersheds of the Humber River (Ontario, Canada), which have a nested configuration and a strong land use gradient, providing an opportunity to explore how the interplay between rainfall amount and intensity in determining runoff response is affected by sub‐watershed physical features. The study revealed that threshold strengths and the simultaneous influence of rainfall amount and intensity varied, depending on the sub‐watershed and event‐specific conditions. There was evidence that sub‐watershed slope and imperviousness along with the watershed position relative to prevailing weather patterns influences threshold strength and diagonality. This research extends threshold analyses in hydrology to encompass multiple explanatory variables: it aligns more closely with perceptual models of runoff generation and encourages a reimagining of thresholds as discontinuities in response across various combinations of explanatory variables. The threshold strength and diagonality parameters facilitate objective comparisons of thresholds across space and time and may be valuable tools for watershed classification and inter‐comparison, and for evaluating and/or calibrating rainfall‐runoff models. These promising lines of inquiry would be best served by applying this methodology across a broader range of spatial scales and hydroclimatic conditions.

What control the spatial patterns and predictions of runoff response over the contiguous USA?

What control the spatial patterns and predictions of runoff response over the contiguous USA?

Optimization of the SWAT+ model to adequately predict different segments of a managed streamflow hydrograph

ABSTRACT Complete representation of rainfall–runoff responses in complex, large watersheds using a single-objective parameterization approach in watershed models is often unachievable. In this study we present a calibration approach for the SWAT+ model that independently fits model parameters for different flow segments of the hydrograph. The approach is demonstrated for the Feather River, California, USA, using daily streamflow from the Lake Oroville Reservoir outlet gage. Results show that when model parameters were independently fitted for different flow segments the KGE, NSE, PBIAS, and RSR values improved to 0.96, 0.99, −3.3, and 0.10, respectively, compared to 0.72, 0.66, −9.30, and 0.53, respectively, achieved under a multiobjective and full hydrograph (average hydrograph) calibration. The results highlight that when considering the average hydrograph and flow duration curves, a more balanced representation of both poorly and well-performing segments is achieved, emphasizing the importance of segment-specific parameterization and multi-objective evaluation for accurately representing different flow conditions.

Flexible Permeable-Pavement System Sustainability: A Methodology for Stormwater Management Based on PM Granulometry

Permeable-pavement design methodologies can improve the hydrologic and therefore the environmental benefits of rural and urban roadway systems. By contrast, conventional impervious pavements perturb the hydrologic cycle, altering the relationship between the rainfall loading and runoff response. Impervious pavements create a hydraulically conductive interface for the transport of traffic-generated chemicals and particulate matter (PM), deleteriously impacting their proximate environments. Permeable-pavement systems are countermeasures to mitigate hydrologic, chemical, and PM impacts. However, permeable pavements are not always equally implementable due to costs, PM loadings, and design constraints. A potential solution to facilitate environmental benefits while meeting the traffic load capacity is the combination of two filtration systems placed at the pavement shoulders and/or pedestrian sidewalks: a bituminous-pavement open-graded friction course (BPFC) and an aggregate-filled infiltration trench. This solution is presented in this manuscript together with the methodological framework and the first results of the investigations into designing and validating such a combined system. The research was conducted at the laboratories of the Polytechnic University of Bari and the University of Florida, while an operational and full-scale physical model was constructed in Bari, Italy. The first results presented characterize the PM deposition on public roads based on granulometry (particle size distributions (PSDs) and particle number densities (PNDs)). Samples (n = 16) were collected and analyzed at eight different sites with different land uses, traffic, and pavements from different cities (Bari and Taranto, Italy). The PM analysis showed similar distributions (PSDs and PNDs), except for two samples. The gravimetric-based PSDs of the PM had granulometric distributions in the sand-size range. In contrast, the PNDs, modeled by a Power Law Model (PLM) (R2 ≥ 0.92), illustrated an exponentially increasing number of particles in the fine silt and clay-size range, representing less than 10% of the PSD mass. Moreover, the results indicate that PM sourced from permeable-pavement systems has differing impacts on the pavement service life.

Toward Flash Flood Modeling Using Gradient Resolving Representative Hillslopes

AbstractIt is increasingly acknowledged that the acceleration of the global water cycle, largely driven by anthropogenic climate change, has a disproportionate impact on sub‐daily and small‐scale hydrological extreme events such as flash floods. These events occur thereby at local scales within minutes to hours, typically in response to high‐intensity rainfall events associated with convective storms. In the present work, we show that by employing physically based representative hillslope models that resolve the main gradients controlling overland flow hydrology and hydraulics, we can get reliable simulations of flash flood response in small data‐scarce catchments. To this end, we use climate reanalysis products and transfer soil parameters previously obtained for hydrological predictions in an experimental catchment in the same landscape. The inverted mass balance of flood reservoirs downstream is employed for model evaluation in these nearly ungauged basins. We show that our approach using representative hillslopes and climate data sets can provide reasonable uncalibrated estimates of the overland runoff response (flood magnitude, storm volume, and event runoff coefficients) in three of the four catchments considered. Given that flash floods typically occur at scales of a few km2 and in ungauged places, our results have implications for operational flash flood forecasting and open new avenues for using gradient resolving physically based models for the design of small and medium flood retention basins around the world.

Understanding the role of the spatial-temporal variability of catchment water storage capacity and its runoff response using deep learning networks

The land surface of a watershed acts as a large reservoir, with its catchment water storage capacity (CWSC) influencing rainfall-runoff relationship. Estimating CWSC at global grid scale is challenging due to calibration complexity, limited spatial continuity, and data scarcity. To address this, a deep learning-based approach incorporates spatial reconstruction and temporal transfer for capturing spatio-temporal variations in watershed characteristics. The study focuses on the Global Runoff Data Centre dataset and presents a grid-based hydrological model. Findings demonstrate accurate identification of CWSC distribution, with the model achieving an R 2 of 0.92 and the runoff Kling–Gupta efficiency of 0.71 during validation. According to the CMIP6 projections, the global CWSC is anticipated to undergo a significant increase at a rate of 1.7 mm per decade under the SSP5-8.5 emission scenario. Neglecting spatio-temporal CWSC variability globally overestimates climate change’s impact on runoff, potentially reducing the projected long-term increase by up to 41%.

Runoff generation in a semiarid environment: The role of rainstorm intra-event temporal variability and antecedent soil moisture

Rainfall intensity and antecedent soil moisture are key variables affecting the initiation and accumulation of runoff, and therefore, impact on the occurence flood events. We used time series of rainfall intensities characterizing rainstorms with different mean, standard deviation, and skewness to simulate runoff response of a sealed loamy soil profile under various initial soil moisture conditions. We found that shifting from wet to dry initial soil moisture leads to a 300 % increase in ponding time and a 350 % decrease in total runoff. Intense rainfall events catalyze runoff development and runoff occurs earlier with increasing mean, maximum intensity, and variance of rainfall. Higher variance in intra-event storm-intensity temporal distribution yields more runoff. Earlier runoff formation aligned with negatively skewed storm structures, but largest cumulative runoff resulted from storms displaying near-zero skewness values. The combination between rainfall intensification and extended dry periods between storms might reduce potential runoff. These insights improve our understanding of anticipated impacts of climate variations on floods and may also contribute to ecosystem health evaluations, and bolster predictive capabilities of surface processes.

Catchment-Scale Hydrologic Effectiveness of Residential Rain Gardens: A Case Study in Columbia, Maryland, USA

To mitigate the adverse impacts of urban stormwater on streams, watershed managers are increasingly using low-impact development and green infrastructure (LID-GI) stormwater control measures, such as rain gardens—vegetated depressional areas that collect and infiltrate runoff from rooftops and driveways. Their catchment-scale performance, however, can vary widely, and few studies have investigated the cumulative performance of residential rain gardens for event runoff control in intermediate-sized (i.e., 1–10 km2) suburban catchments. We modeled three years of continuous rainfall-runoff from a 3.1 km2 catchment in Columbia, MD, USA, using the Storm Water Management Model (SWMM). Various extents of rain garden implementation at residential houses were simulated (i.e., 25%, 50%, 75%, and 100% coverage) to determine the effects on peak flow, runoff volume, and lag time. On average, treating 100% of residential rooftops in the catchment reduced peak flows by 14.3%, reduced runoff volumes by 11.4%, and increased lag times by 3.2% for the 223 rainfall events during the simulation period. Peak flow reductions were greater for smaller storm events (p &lt; 0.01). Our results show that residential rain gardens can significantly improve the runoff response of suburban catchments, and that they represent an effective and relatively low-cost option for urban watershed management and restoration.

DTM resolution controls the accuracy of estimating surface runoff indicators in flat, lowland landscapes

AbstractSurface runoff plays an important role in contaminant transport, nutrient loss, soil erosion and peak discharges in streams and rivers. Because it is the result of a variety of complex hydrological processes, estimating surface runoff using physically based hydrological models is challenging. Upscaling of physical soil properties is necessary to cope with the limits of computational power in surface runoff modelling. In flat landscapes, the (micro)topographic surface controls the onset and progression of surface runoff on saturated soils during rain events. Therefore, its proper representation is crucial when attempting to model and predict surface runoff. In this study, the influence of microtopography (centimetre scale) on estimations of maximum depression storage (MDS), random roughness (RR) and the connectivity threshold (CT) is explored. These properties are selected because they often serve as surface runoff indicators in hydrological modelling. To characterize microtopography, a terrestrial laser scanner (TLS) is used to generate a digital terrain model (DTM) of the study site with a horizontal spatial resolution of 5 cm. MDS, RR and CT are then calculated and compared to the values generated from the publicly available Dutch national DTM dataset with a resolution of 50 cm. Our results show considerable differences in MDS, RR and CT when calculated for the different input resolution datasets. Using DTMs that do not sufficiently capture microtopography leads to underestimation of MDS and RR, and to overestimation of CT. Our findings indicate that surface runoff indicators, and thereby the surface runoff response of a saturated surface to rainfall events, are defined at scales smaller than the scales of typically available DTMs. Understanding surface runoff through modelling studies therefore requires a framework that accounts for this lack of information arising from using coarser resolution DTMs. We demonstrate a linear relationship between MDS values generated from the different resolution DTMs. This opens the possibility of using empirical scaling relationships between high‐ and lower‐resolution DTMs to account for microtopography. Repetition of our measurements on similar surfaces would contribute to establishing such empirical scaling relationships. Our results should be seen as indicative of flat landscapes and surfaces where centimetre scale microtopography is relevant.