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The US Department of Agriculture (USDA), Soil Conservation Services (SCS) rainfall-runoff model has been applied worldwide since 1954 and adopted by Malaysian government agencies. Malaysia does not have regional specific curve numbers (CN) available for the use in rainfall-runoff modelling, and therefore a SCS-CN practitioner has no option but to adopt its guideline and handbook values which are specific to the US region. The selection of CN to represent a watershed becomes subjective and even inconsistent to represent similar land cover area. In recent decades, hydrologists argue about the accuracy of the predicted runoff results from the model and challenge the validity of the key parameter, initial abstraction ratio coefficient (λ) and the use of CN. Unlike the conventional SCS-CN technique, the proposed calibration methodology in this chapter discarded the use of CN as input to the SCS model and derived statistically significant CN value of a specific region through rainfall-runoff events directly under the guide of inferential statistics. Between July and October of 2004, the derived λ was 0.015, while λ = 0.20 was rejected at alpha = 0.01 level at Melana watershed in Johor, Malaysia. Optimum CN of 88.9 was derived from the 99% confidence interval range from 87.4 to 96.6 at Melana watershed. Residual sum of square (RSS) was reduced by 79% while the runoff model of Nash–Sutcliffe was improved by 233%. The SCS rainfall-runoff model can be calibrated quickly to address urban runoff prediction challenge under rapid land use and land cover changes.

Rainfall–runoff relationship is one of the most complex hydrological phenomena. A conventional neural network (NN) with backpropagation algorithm has successfully modelled various non-linear hydrological processes in recent years. However, the convergence rate of the backpropagation NN is relatively slow, and solutions may trap at local minima. Therefore, a new metaheuristic algorithm named as cuckoo search optimisation was proposed to combine with the NN to model the daily rainfall–runoff relationship at Sungai Bedup Basin, Sarawak, Malaysia. Two-year rainfall–runoff data from 1997 to 1998 had been used for model training, while one-year data in 1999 was used for model validation. Input data used are current rainfall, antecedent rainfall and antecedent runoff, while the targeted output is current runoff. This novel NN model is evaluated with the coefficient of correlation (R) and the Nash–Sutcliffe coefficient (E²). Results show that cuckoo search optimisation neural network (CSONN) is able to yield R and E² to 0.99 and 0.94, respectively, for model validation with the optimal configuration of number of nests (n) = 20, initial discovery rate of alien eggs (painitial) = 0.6, hidden neuron (HN) = 100, iteration number (IN) = 1,000 and learning rate (LR) = 1 for CSONND4 model. The results revealed that the newly developed CSONN is able to simulate runoff accurately using only precipitation and runoff data.

Rainfall simulators are used on experimental hydrology, in areas such as, e.g., urban drainage and soil erosion, with important timesaving when compared to real scale hydrological monitoring. The purpose of this paper is to contribute to increase the quality of rainfall simulation, namely, for its use with scaled physical models.

Two pressurized rainfall simulators are considered. M1 uses three HH-W 1/4 FullJet nozzles under an operating pressure of 166.76 kPa and was tested over a 4.00 m length by 2.00 m width V-shaped surface. M2 was prepared to produce artificial rainfall over an area of 10.00 m length by 10.00 m width. The spatial distribution of rainfall produced from a single nozzle was characterized in order to theoretically find the best positioning for nozzles to cover the full 100 m² area with the best possible rainfall uniformity.

Experiments with M1 led to an average rainfall intensity of 76.77-82.25 mm h⁻¹ with a 24.88 per cent variation coefficient and a Christiansen Uniformity Coefficient (CUC) of 78.86 per cent. The best result with M2 was an average rainfall intensity of 75.12-76.83 mm h⁻¹ with a 21.23 per cent variation coefficient and a CUC of 83.05 per cent.

This study contributes to increase the quality of artificial rainfall produced by pressurized rainfall simulators.

M2 is the largest rainfall simulator known by the authors worldwide. Its use on rainfall-runoff studies (e.g. urban areas, erosion, pollutant transport) will allow for a better understanding of complex surface hydrology processes.

Time of concentration (T_c) is one of the main inputs in rainfall–runoff model which depends on catchment length, slope, soil properties and surface cover. Factor such as floodplain also has a significant contribution on the flood wave travel time. Floodplain which influences the flow and the travelling time is not possible to be calculated using common T_c formulae. One approach to handle this complex behaviour is to deploy the hydrodynamic model as part of the rainfall–runoff model. This chapter explains the application of hydrodynamic approach to determine T_c for large catchment with the effect of floodplain. A hydrodynamic river model for Sg Relai was developed as part of the rainfall–runoff model covering 460 km² catchment area. It includes channels covering 90 km long which is extended to the floodplain based on the digital terrain model. The simulation results show that once the flood water spill to the floodplain, the channel travelling time is delayed by several hours. The delay of the travel time increases as the rainfall intensity increase which demonstrates that hydrodynamic modelling with the integration of floodplain is capable to compute the variation of T_c.

The present study aims to evaluate the effect of climate change on the flood hazard potential in the Kelantan River Basin using current and future scenarios.

The intensity-duration-frequency (IDF) was used to estimate the current 50- and 100-year return period 24-h design rainfall, and the climate change factor (CCF) was used to compute the future design rainfall. The CCF was calculated from the rainfall projections of two global climate models, CGCM1 and CCSM3, with different pre-processing steps applied to each. The IDF data were used in the rainfall-runoff-inundation model to simulate current and future flood inundation scenarios.

The estimated CCF values demonstrate a contrast, whereby each station had a CCF value greater than one for CGCM1, while some stations had a CCF value of less than one for CCSM3. Therefore, CGCM1 projected an aggravation and CCSM3 a reduction of flood hazard for future scenarios. The study reveals that topography plays an essential role in calculating the CCF.

To the best of the author’s knowledge, this is the first study to examine flood projections in the Kelantan River Basin. It is, therefore, hoped that these results could benefit local managers and authorities by enabling them to make informed decisions regarding flood risk mitigation in a climate change scenario.

Climate change is expected to alter the major components of hydrological regime such as streamflow and water availability. The magnitude and their impacts are still uncertain. Therefore, it is highly required to study streamflow and flood vulnerability in tropical river basins particularly urbanised basin such as Langat River Basin. This study aims to model the future streamflow of Langat River Basin due to climate change using Rainfall-Runoff Inundation (RRI) model. Daily rainfall data obtained from Department of Irrigation and Drainage Malaysia and topographic data from HydroSHEDS at 15-second resolution were used. The projected future rainfall (2075–2099) is extracted from MRI-AGCM3.2s under the worst carbon emission scenario, RCP8.5. The annual maximum series of 1-day rainfall is selected for statistical bias correction using Quantile Mapping. The General Circulation Model data were found to be greatly corrected with reasonable Nash–Sutcliffe efficiency, Percent bias and Root Mean Square Error values. The mean of maximum 1-day future rainfall in Langat River Basin is found to be inconsistent where parts of the upstream will experience an increment at about 7% while other parts decrease at 8%. Meanwhile, the rainfall at downstream area are expected to decrease at 40%. Based on RRI simulation, the future streamflow can achieve up to 92% increment.

The rapid and ongoing expansion of urbanised impervious areas could lead to more frequent flood inundation in urban flood-prone regions. Nowadays, urban flood inundation induced by rainstorm is an expensive natural disaster in many countries. In order to reduce the flooding risk, eco-roof systems (or green roof systems) could be considered as an effective mechanism of mitigating flooding disasters through their rainwater retention capability. However, there is still a lack of examining the stormwater management tool. The purpose of this paper is to evaluate the effects on flooding disaster from extensive green roofs.

Based on geographical information system (GIS) simulation, this research presents a frame of assessing eco-roof impacts on urban flash floods. The approach addresses both urban rainfall-runoff and underground hydrologic models for traditional impervious and green roofs. Deakin University’s Geelong Waurn Ponds campus is chosen as a study case. GIS technologies are then utilised to visualise and analyse the effects on flood inundation from surface properties of building roofs.

The results reveal that the eco-roof systems generate varying degrees of mitigation of urban flood inundation with different return period storms.

Although the eco-roof technology is considered as an effective stormwater management tool, it is not commonly adopted and examined in urban floods. This study will bring benefits to urban planners for raising awareness of hazard impacts and to construction technicians for considering disaster mitigation via roof technologies. The approach proposed here could be used for the disaster mitigation in future urban planning.

Under the background of rapid urbanization, all kinds of urban water problems have gradually come into being: local flooding frequently happens, water environment is deteriorated, water-supply is in tension, etc. Meanwhile, with rapid development of higher education in China, campus area and scale are gradually expanding, but traditional campus construction has many drawbacks. In order to promote sponge campus planning and construction of universities in hilly areas and provide demonstration windows for sponge city construction, based on deficiencies of campus construction of Hunan City University in the aspect of water resource utilization, we used ArcGis spatial analysis method, simulation method and comparative analysis method on Storm Water Management Model (SWMM) to establish sponge campus construction indexes, content system and optimal design strategies with objectives of campus water safety, water environment and water resource utilization. Results indicate that: difference between sponge campus planning and traditional campus planning mainly lies in rainfall management. We combed the design process of sponge campus planning in hilly areas from the perspective of rainfall management, and simulated the process of sponge facilities controlling the rainfall in the campus via computer model to verify reasonability of sponge facility planning and select the optimal planning and construction plan. This study has defined design process of sponge campus planning in hilly areas to a certain degree and provided a research basis for sponge campus planning and construction of universities, setting up a typical example and driving effects on solving urban local flooding problem and rainfall resource utilization in hilly areas.

Global change results both from climatic and land practice evolution in response to anthropogenic needs for development and human safety. The purpose of this paper is to describe a method to assess the respective effect of both sources of change on the flood regimes.

The research takes place in the Western periurban part of Lyon (France), which is characterized by a rapidly expanding, scattered urban development since the 1970s. An increase in frequency of large floods is reported. At the same time, a long daily rainfall time series exhibits sensitive changes in rainfall durations and intensities. Independent analysis of global change components is performed using observed rainfall and land‐use data from two disconnected decades. A marked difference in natural climatic regime variability between decades is used as a surrogate to study effect of climate change.

Anthropogenic activity at the observed rate of land use change, in particular urban change, mainly influences the frequent flood distribution. The observed large flood increase results both of longer rainfall events and more heavy daily rainfall. From simulations, a 43 percent urban cover as planned from 2025 projection would have a very sensitive effect also on larger floods, giving the hand to a more anthropogenic‐based flood control.

Despite an expected increase in rainfall and flow variability regimes as a result of climate change and a projected growing of the world urban population, there is a lack of methodology to address combination of both processes on the flood regimes. A method is proposed to judge on the respective importance and interplay of these processes.

The purpose of this study is to address a highly heterogeneous rift margin environment and exhibit considerable spatiotemporal hydro-climatic variations. In spite of limited, random and inaccurate data retrieved from rainfall gauging stations, the recent advancement of satellite rainfall estimate (SRE) has provided promising alternatives over such remote areas. The aim of this research is to take advantage of the technologies through performance evaluation of the SREs against ground-based-gauge rainfall data sets by incorporating its applicability in calibrating hydrological models.

Selected multi satellite-based rainfall estimates were primarily compared statistically with rain gauge observations using a point-to-pixel approach at different time scales (daily and seasonal). The continuous and categorical indices are used to evaluate the performance of SRE. The simple scaling time-variant bias correction method was further applied to remove the systematic error in satellite rainfall estimates before being used as input for a semi-distributed hydrologic engineering center's hydraulic modeling system (HEC-HMS). Runoff calibration and validation were conducted for consecutive periods ranging from 1999–2010 to 2011–2015, respectively.

The spatial patterns retrieved from climate hazards group infrared precipitation with stations (CHIRPS), multi-source weighted-ensemble precipitation (MSWEP) and tropical rainfall measuring mission (TRMM) rainfall estimates are more or less comparably underestimate the ground-based gauge observation at daily and seasonal scales. In comparison to the others, MSWEP has the best probability of detection followed by TRMM at all observation stations whereas CHIRPS performs the least in the study area. Accordingly, the relative calibration performance of the hydrological model (HEC-HMS) using ground-based gauge observation (Nash and Sutcliffe efficiency criteria [NSE] = 0.71; R² = 0.72) is better as compared to MSWEP (NSE = 0.69; R² = 0.7), TRMM (NSE = 0.67, R² = 0.68) and CHIRPS (NSE = 0.58 and R² = 0.62).

Calibration of hydrological model using the satellite rainfall estimate products have promising results. The results also suggest that products can be a potential alternative source of data sparse complex rift margin having heterogeneous characteristics for various water resource related applications in the study area.

This research is an original work that focuses on all three satellite rainfall estimates forced simulations displaying substantially improved performance after bias correction and recalibration.