Runoff plays an essential part in the hydrological cycle, as it regulates the quantity of water which flows into streams and returns surplus water into the oceans. Runoff modelling may assist in ...understanding, controlling, and monitoring the quality and amount of water resources. The aim of this article is to discuss various categories of rainfall–runoff models, recent developments, and challenges of rainfall–runoff models in flood prediction in the modern era. Rainfall–runoff models are classified into conceptual, empirical, and physical process-based models depending upon the framework and spatial processing of their algorithms. Well-known runoff models which belong to these categories include the Soil Conservation Service Curve Number (SCS-CN) model, Storm Water Management model (SWMM), Hydrologiska Byråns Vattenbalansavdelning (HBV) model, Soil and Water Assessment Tool (SWAT) model, and the Variable Infiltration Capacity (VIC) model, etc. In addition, the data-driven models such as Adaptive Neuro Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN), Deep Neural Network (DNN), and Support Vector Machine (SVM) have proven to be better performance solutions in runoff modelling and flood prediction in recent decades. The data-driven models detect the best relationship based on the input data series and the output in order to model the runoff process. Finally, the strengths and downsides of the outlined models in terms of understanding variation in runoff modelling and flood prediction were discussed. The findings of this comprehensive study suggested that hybrid models for runoff modeling and flood prediction should be developed by combining the strengths of traditional models and machine learning methods. This article suggests future research initiatives that could help with filling existing gaps in rainfall–runoff research and will also assist hydrological scientists in selecting appropriate rainfall–runoff models for flood prediction and mitigation based on their benefits and drawbacks.
Land surface and global hydrological models are often used to characterize global water and energy fluxes and stores and to model their future trajectories. This study evaluates estimates of ...streamflow and evapotranspiration (ET) obtained with a priori parameterization from a land surface model CSIRO Atmosphere Biosphere Land Exchange (CABLE) and a global hydrological model (H08) against a global dataset of streamflow from 644 largely unregulated catchments and ET from 98 flux towers and benchmarks their performance against two lumped conceptual daily rainfall–runoff models modèle du Génie Rural à 4 paramètres Journalier (GR4J) and a simplified version of the HYDROLOG model (SIMHYD). The results show that all four models perform poorly in simulating the monthly and annual runoff values, with the rainfall–runoff models outperforming both CABLE and H08. The model biases in runoff are generally reflected as a complementary opposite bias in ET. All models can generally reproduce the observed seasonal and interannual runoff variability. The correlations between the modeled and observed runoff time series are reasonable, with the rainfall–runoff models performing slightly better than CABLE and H08 at the monthly time scale and all four models performing similarly at the annual time scale. The results suggest that while the land surface and global hydrological models cannot adequately simulate the actual runoff time series and long-term average volumes, they can reasonably simulate the monthly and interannual runoff variability and trends and can therefore be reliably used for broadscale or comparative regional and global water and energy balance assessments and simulations of future trajectories. They can be improved through validating the models or calibrating some of the more sensitive and less physically based parameters.
We report an empirical analysis of the hydrologic response of three small, highly impervious urban watersheds to pulse rainfall events, to assess how traditional stormwater management (SWM) alters ...urban hydrographs. The watersheds vary in SWM coverage from 3% to 61% and in impervious cover from 45% to 67%. By selecting a set of storm events that involved a single rainfall pulse with >96% of total precipitation delivered in 60 min, we reduced the effect of differences between storms on hydrograph response to isolate characteristic responses attributable to watershed properties. Watershed‐average radar rainfall data were used to generate local storm hyetographs for each event in each watershed, thus compensating for the extreme spatial and temporal heterogeneity of short‐duration, intense rainfall events. By normalizing discharge values to the discharge peak and centring each hydrograph on the time of peak we were able to visualize the envelope of hydrographs for each group and to generate representative composite hydrographs for comparison across the three watersheds. Despite dramatic differences in the fraction of watershed area draining to SWM features across these three headwater tributaries, we did not find strong evidence that SWM causes significant attenuation of the hydrograph peak. Hydrograph response for the three watersheds is remarkably uniform despite contrasts in SWM, impervious cover and spatial patterns of land cover type. The primary difference in hydrograph response is observed on the recession limb of the hydrograph, and that change appears to be associated with higher storm‐total runoff in the watersheds with more area draining to SWM. Our findings contribute more evidence to the work of previous authors suggesting that SWM is less effective at attenuating urban hydrographs than is commonly assumed. Our findings also are consistent with previous work concluding that percent impervious cover may have greater influence on runoff volume than percent SWM coverage.
We report on a comparative analysis of urban runoff response to pulse rainfall events in three high‐impervious cover headwater tributaries with large differences in area draining to stormwater management (SWM) features. Characteristic runoff response was remarkably similar across all three watersheds and results do not support the assumption that SWM causes significant attenuation of the urban hydrograph. Differences are most apparent on the receding limb where the watershed with highest SWM coverage had the slowest recession.
Runoff prediction in ungauged catchments is a significant hydrological challenge. The common approach is to calibrate hydrological models against streamflow data from gauged catchments, and then ...regionalize or transfer parameter values from the gauged calibration to predict runoff in the ungauged catchments. This paper explores the potential for using parameter values from hydrological models calibrated solely against readily available remotely sensed evapotranspiration data to estimate runoff time series. The advantage of this approach is that it does not require observed streamflow data for model calibration and is therefore particularly useful for runoff prediction in poorly gauged or ungauged regions. The modeling experiments are carried out using data from 222 catchments across Australia. The results from the remotely sensed evapotranspiration runoff‐free calibration are encouraging, particularly in simulating monthly runoff and mean annual runoff in the wetter catchments. However, results for daily runoff and in the drier regions are relatively poor, and further developments are needed to realize the benefit of direct model calibration against remotely sensed data to predict runoff in ungauged catchments.
Key Points
Direct calibration of hydrological models solely against remotely sensed ET (RS‐ET) data is used to predict runoff in ungauged regions
The RS‐ET runoff‐free calibration approach is tested on 222 catchments across Australia
The RS‐ET runoff‐free calibration can estimate mean annual runoff and monthly runoff series in wet catchments reasonably well
A water harvesting system for research purposes has been established in the Lisan Peninsula of the Dead Sea in the middle of the Jordan Rift Valley, where no authorized guideline is available for ...designing water harvesting systems. Rainfall and runoff, which occurred as flash floods, were observed at the downstream end of a gorge with a 1.12 km2 barren catchment area from October 2014 through July 2019. Due to the extremely arid environment, runoff from the catchment is ephemeral, and the flash flood events can be clearly distinguishable from each other. Thirteen flash flood events with a total runoff volume of more than 100 m3 were successfully recorded during the five rainy seasons. Pearson and Spearman correlations between duration, total rainfall depths at two points, total runoff volume, maximum runoff discharge, bulk runoff coefficient, total variation in runoff discharge and maximum variation in runoff discharge of each flash flood event were examined, revealing no straightforward relationship between rainfall and runoff. The performance of the conventional SCS runoff curve number method was also deficient in reproducing any rainfall–runoff relationship. Therefore, probability distribution fitting was performed for each random variable, focusing on the lognormal distribution with three parameters and the generalized extreme value distribution. The maximum goodness‐of‐fit estimation turns out to be a more rational and efficient method in obtaining the parameter values of those probability distributions rather than the standard maximum likelihood estimation, which has known disadvantages. Results support the design of the water harvesting system and provide quantitative information for designing and operating similar systems in the future.
Rainfall and surface runoff are the two most important components, which control the groundwater recharge of the basin. The long-term groundwater recharge of an aquifer gets affected by the ...population growth, irregular agriculture activities and industrialization. Hence, estimation of rainfall-surface runoff is very much essential for proper groundwater management practices. In the present study, Soil Conservation Service Curve Number (SCS-CN) model was employed in combination with geospatial techniques to estimate rainfall-surface runoff for the Lower Bhavani River basin in South India. To develop the SCS-CN model, rainfall data were obtained for 33 years (1983–2015) from 22 rain gauge stations spread over the basin. IRS LISS-IV satellite data of 5.8 m spatial resolution were used to analyze the land use/land cover (LU/LC) behavior. Based on the soil properties, four Hydrological Soil Groups (HSG) were identified in the basin which is most significant for surface runoff estimation. Curve Number (CN) values were obtained for various Antecedent Moisture Conditions (AMC) such as dry condition (AMC I), average condition (AMC II) and wet condition (AMC III). Spatial distribution of CN values was plotted using Geographical Information System (GIS) for the entire Lower Bhavani Basin to assess the surface runoff potential. The results indicate that the annual rainfall varies from 267 mm (2002) to 1528.6 mm (2005), and the annual surface runoff varies from 102.04 mm (1985) to 463.02 mm (2010). The SCS-CN model outputs predict that the average surface runoff of the basin is 211.99 mm, and the average surface runoff volume is 81,995,380 m
3
. The study also indicates that nearly 53% of the basin area is dominated by high to very high surface runoff potential. Finally, the output of surface runoff potential was validated with the Average Groundwater Level Fluctuation (AGLF) observed in 57 wells spread over the entire basin. The basin AGLF ranges from 2.32 to 21.72 m. The surface runoff potential categories are satisfactorily matching with the AGLF categories. Moderate surface runoff as well as moderate AGLF zones mostly occupy the central portion of the basin, which possess good groundwater potential. However, the high surface runoff zones in the basin lead more surface water flow into the river channels, which reduce the infiltration rate and decline the water table. This problem can be solved by constructing suitable artificial groundwater recharge structures across the river channels in the high surface runoff potential areas.
We examine the extent to which the parameters of different types of catchments are sensitive to calibration criteria selection (i.e. parameter agreement), and explore possible connections with ...overall model performance and model complexity. To this end, we calibrate the lumped GR4J, GR5J and GR6J hydrological models - coupled with the CemaNeige snow module - in 95 catchments spanning a myriad of hydroclimatic and physiographic characteristics across Chile, using 12 streamflow-oriented objective functions. The results show that (i) the choice of objective function has smaller effects on parameter values in catchments with low aridity index and high mean annual runoff ratio, in contrast to drier climates; and (ii) catchments with better parameter agreement also provide better performance across model structures and simulation periods. More generally, this work provides insights on the type of catchments where it is more challenging to find sub-domains in the parameter space that satisfy multiple streamflow criteria.
Soil moisture is a key control on runoff generation and biogeochemical processes on hillslopes. Precipitation events can evoke different soil moisture responses with depth through the soil profile, ...and responses can differ among landscape positions along a hillslope. We sought to elucidate the nature of these responses by estimating changes in water content, response time between peak precipitation and peak soil moisture, and wetting front velocities for 43 storms at 45 locations on three adjacent hillslopes within a headwater catchment of the southern Appalachian Mountains (NC, USA). We used a multivariate modeling approach to quantify the relative influences and the predictability of soil moisture responses by a combination of landscape and storm characteristics. We quantified the lag correlations between hillslope mean soil moisture and catchment runoff to demonstrate how storm properties and hillslope‐scale characteristics may influence runoff at the catchment outlet. Soil moisture responses varied widely, and no consistent patterns were observed among response metrics laterally or vertically along hillslopes. In contrast to other studies, we found that the relative influence of hillslope properties and storm characteristics varied with soil moisture responses and during storms. Antecedent conditions and storm depths influenced the strength of lag correlations between soil moisture and runoff, whereas storm mean intensity was correlated with the lag times. These results highlight the utility of intensive observations for characterizing heterogeneity in soil moisture responses, suggesting, among other things, a need for better representation of the subsurface processes in rainfall‐runoff models. Identifying the relative importance of drivers can be beneficial in building parsimonious hydrological models.
Key Points
No consistent patterns in soil moisture responses among landscape positions laterally along hillslopes
Dominant controls on soil moisture responses to storms varied with individual response metrics and during storms
Storm depth, mean intensity, and antecedent conditions mediated the soil moisture‐runoff relationships in space and time
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