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• A deep learning algorithm can provide more skilful nowcasting for medium and strong rainfall cases than for a weak one. • A deep learning algorithm trained by the dataset for one region can be skilfully used to nowcast rainfall for another region with the similar weather system. • For a deep learning algorithm, there is an optimal number of iterations for training the algorithm. It is a critical need to provide timely and valuable alerts of rainstorms and floods to the public. However, it still remains a world-class challenge to achieve serviceable nowcasting rainstorms with even a short lead time of one hour. Different deep learning algorithms have been adopted to improve nowcasting accuracy. Unfortunately, it is still a question which algorithm is more suitable and how to interpret the rainstorm nowcasting results from deep learning. To this end, this paper focuses on modelling the evolution of rainstorm clouds using deep learning algorithms that can be applied to nowcast rainstorms for the next few hours. Adopting three deep learning algorithms, the study provides a detailed analysis of the nowcasting results of three typical cases of different rainfall intensities from a radar echo mosaic image dataset. The dataset was collected in Guangdong, China, and the analysis interprets the performance differences. The analysis further discloses that an AI-based method can provide more skilful nowcasting for medium and strong rainfall cases than for weak ones. Moreover, a deep learning algorithm trained by the dataset for one region can be skilfully used to nowcast rainfall for another region with a similar weather system. This explains the nowcasting capability of deep learning algorithms as well as their robustness. Besides, experiments on the number of iterations reveal that more iterations do not achieve higher nowcasting accuracy. With improved interpretability of deep learning from the perspective of real-world application in the study, it is expected that the algorithms producing higher accuracy and longer lead time nowcasts will be made possible. [ABSTRACT FROM AUTHOR]

Highlights • Interception accounted for 24.0% of total rainfall. • Throughfall accounted for 69.8% of total rainfall, increasing towards subhumid sites. • Stemflow accounted for 6.2% of total rainfall, decreasing towards subhumid sites. • Shrubs presented higher stemflow and lower throughfall percentages than trees. • Higher stemflow percentage of dry sites was explained by higher abundance of shrubs. Abstract The net amount of rainfall entering into the soil and its spatial distribution at the patch scale are key drivers of ecosystem processes in drylands. The spatial distribution of water is mainly controlled by vegetation canopy which determines the partitioning of rainfall into interception, throughfall and stemflow. In this paper, we synthesized and analyzed rainfall partitioning for 68 woody plant species in drylands (delimited by a rainfall-potential evapotranspiration ratio <0.65; 46 papers). We explored the role of rainfall inputs and plant morphological attributes (life form, phenology, leaf type and bark type) by considering a rainfall gradient from 145 to 805 mm year−1. On average, interception, throughfall and stemflow accounted for 24.0, 69.8 and 6.2% of total rainfall, respectively. Stemflow was the most variable flux (coefficient of variation = 107.8%), while interception and throughfall were less variable fluxes (coefficient of variation = 39.2 and 20.4%, respectively). Along the increasing rainfall gradient, interception showed a tendency to decrease from 27.1 to 18.9% (p = 0.12), throughfall increased from 61.4 to 81.2% (p < 0.01) and stemflow decreased from 10.0 to 1.6% (p < 0.0001). Shrubs presented higher stemflow than trees (9.4% vs. 3.5%, respectively; p < 0.0001), while trees presented higher throughfall than shrubs (72.3% vs. 63.0%, respectively; p < 0.05). Species with smooth barks presented higher stemflow than species with rough barks (8.3% vs. 4.2%, respectively; p < 0.0001). Both phenology and leaf type had no effect on interception, throughfall and stemflow (p > 0.05 for all cases). Shrubs were more abundant towards the dry edge of the rainfall gradient (145–500 mm year−1), while trees were more abundant towards the subhumid edge (500–805 mm year−1). These results suggested that higher stemflow found towards the dry edge of the rainfall gradient was caused by the higher abundance of shrubs, which generated more stemflow than trees. Our findings highlighted the ecohydrological key role of vegetation life form and rainfall inputs affecting the amount of water entering into the soil and its spatial distribution in drylands. [ABSTRACT FROM AUTHOR]

Highlights • Proposing soil mulching for enhancing groundwater resources and a model to assess it. • Assessing the use of various mulches in increasing deep percolation of rainfalls. • Deep percolation rate is higher in precipitations with shorter time intervals. Abstract In this paper, a model is proposed for the assessment of the effectiveness of various mulches in increasing deep percolation of rainfalls for enhancing arid zones aquifers. For this purpose, 8 precipitations were selected from the Intensity-Duration-Frequency (IDF) curves of the study area with a return period of 2 and 5 years. The deep percolation of these precipitations were examined in lysimeters with gravel, sand and mixed mulches and without any mulch. Then 192 data of soil moisture, daily maximum air-temperature and deep percolation were measured for driving empirical equations. Moreover, the efficiency and accuracy of these empirical equations were investigated using Coefficient of Determination (CD) and Nash–Sutcliffe Efficiency Coefficient (NSEC). The results showed that the obtained empirical equations could estimate deep percolation and evaporation with an acceptable accuracy. Using these equations and the soil-water balance equation, a soil moisture model was developed for the assessment of deep percolation. Then it was examined to evaluate the efficiency of the mulched soils in increasing soil moisture and aquifer recharging of three years' precipitations in Shahrekord plain, Iran. The results showed that the deep percolation increased in all examined mulched soils compared to unmulched soil, its maximum and cumulative increase were in gravel mulch with 21.3% and 30%, respectively. Furthermore, groundwater modeling results showed that the mulching could improve groundwater level as 0.34 m over a three year period. Finally, this paper proposes soil mulching for enhancing groundwater resources and a model to assess it. [ABSTRACT FROM AUTHOR]

Highlights • Innovative trend analysis (ITA) and Mann-Kendall (MK) determine average trend. • Double-ITA, Triple-ITA, and partial MK are proposed for stability test of trends. • ITA should be used together D-ITA or T-ITA for better trend analysis. • MK should be used together partial MK for better trend analysis. • Comparisons of ITA, D-ITA, T-ITA, classical MK, and partial MK are presented. Abstract The climate change is an important event that affects hydrological, agricultural and water resources planning variables, and therefore, the hydrologists and meteorologists frequently try to identify trend possibilities especially in rainfall, runoff and temperature time series. For this purpose, the classical Mann-Kendall (MK), Spearman's rho (SR), Sen's slope, and linear regression approaches are applied frequently in the literature. Recently, innovative trend analysis (ITA) provides visual inspection and identification of categorical trends, which is one of the main concerns in this paper. On the basis of ITA methodology, several improvements namely double-ITA (D-ITA) and triple-ITA (T-ITA) procedures are suggested using with simple ITA together. These methods are attractive for the trend stability assessment by comparing partial trend components during different sub-periods of a given record series. Furthermore, partial MK test approach is proposed in this paper for the same purpose. These procedures and approach are applied to a set of annual rainfall records at many stations in different regions of Turkey. As a result, the comparison of the suggested methods based on partial sub-series of the same time series helps to improve trend detection with stability identification. [ABSTRACT FROM AUTHOR]

Highlights • Worst flash flood in Spain in 1962 caused more than 800 deaths. • Mostly grey literature on the catastrophe is raised to international audience. • Example of highest vulnerability and role of sediment on water levels. Abstract The 1962 Rubí flood was a severe flash flood, the worst to ever take place in Spain, claiming the lives of more than 800 people following 200 mm of rainfall in 2 h. Such a high number of casualties can be explained by the very high vulnerability of people who lived in the floodplains of a wandering, ephemeral stream (a wadi) prone to flash floods. Many publications to commemorate the 50th anniversary of the flood have been used as proxy data for the event, especially in terms of social aspects. The meteorological and pluviometric information is reviewed, while other information is only found in grey literature. Recently, the event has been considered an outlier in the panorama of European flash floods. Because of the above, this paper aims to convey all the information to readers and show that it deserves to be raised to an international level as an example of an extreme flash flood. After addressing some misunderstandings, it can be concluded that the event was not an outlier, nor was it extreme, in terms of total rainfall, return period, discharge, discharge per unit basin area, unit stream power and flow velocity. It may be extraordinary because the flood reached very high levels by transporting large amounts of both fine and coarse sediment particles. The stream is steep, ephemeral, lacking an armour layer, and prone to torrential events, in which the large sediment transport played a role in how high the flood levels rose. The use of flooding marks to compute discharges is also discussed. In addition, the paper presents a torrential calculation based on the momentum principle in a control volume. [ABSTRACT FROM AUTHOR]

A modified Green-and-Ampt model is formulated to quantify infiltration on sloping terrain underlain by homogeneous soil wetted by surficial water application. This paper’s theory for quantifying infiltration relies on the mathematical statement of the coupled partial differential equations (pdes) governing infiltration and runoff. These pdes are solved by employing an explicit finite-difference numerical method that yields the infiltration, the infiltration rate, the depth to the wetting front, the rate of runoff, and the depth of runoff everywhere on the slope during external wetting. Data inputs consist of a water application rate or the rainfall hyetograph of a storm of arbitrary duration, soil hydraulic characteristics and antecedent moisture, and the slope’s hydraulic and geometric characteristics. The presented theory predicts the effect an advancing wetting front has on slope stability with respect to translational sliding. This paper’s theory also develops the 1D pde governing suspended sediment transport and slope degradation caused by runoff influenced by infiltration. Three examples illustrate the application of the developed theory to calculate infiltration and runoff on a slope and their role on the stability of cohesive and cohesionless soils forming sloping terrain. [ABSTRACT FROM AUTHOR]

• Nationwide streamflow projections for Australia informed by CMIP5 and CMIP6. • Vast majority of CMIP5 and CMIP6 GCMs project less winter rainfall across Australia. • Lower winter rainfall translates to a significant reduction in annual runoff. • Modelled median projection of runoff shows a 50% reduction in far southwest Australia. • Modelled median projection of runoff shows a 20% reduction in far southeast Australia. Projections of streamflow under future climate are essential for developing adaptation strategies in the water and related sectors. This paper presents nationwide streamflow projections for Australia informed by climate change signals in CMIP6 GCMs and compares the projections with those from CMIP5 GCMs. The modelled future runoff projections driven by CMIP5 and CMIP6 GCMs are relatively similar for far southern Australia. The median projection is a 50% reduction in mean annual runoff in far southwest Australia and 20% reduction in far southeast Australia by 2046–2075 relative to 1976–2015 under the high SSP5-8.5/RCP8.5 global warming scenario. The vast majority of CMIP5 and CMIP6 GCMs project less winter rainfall across Australia. As most of the runoff in far southern Australia occurs in winter and spring, the lower winter rainfall translates to a significant reduction in annual runoff. It is therefore prudent to plan and manage for a reduction in future water resources, particularly in the densely populated and important agricultural regions in southeast Australia. There is less agreement between GCMs in the summer rainfall projection, with the CMIP6 GCMs generally projecting a wetter future (or smaller decrease in rainfall) than the CMIP5 GCMs. The modelled median projection for mean annual runoff is a 10% reduction in the south-east coast, 5% reduction in the north-east coast and little change in northern Australia. More GCMs project an increase rather than a decrease in the interannual variability of rainfall, and this would further amplify multi-year hydrological droughts. [ABSTRACT FROM AUTHOR]

• A new method was proposed to merge multiple satellite-based precipitation products (SPPs) • The method estimates grid scale errors of SPPs based on water budget closure. • The method was verified by comparing with original SPPs and three merging methods. • The performance of SPPs was slightly reduced when enforcing water budget closure. Merging multiple satellite/reanalysis-based precipitation products (SPPs) is a critical way to improve the accuracy of spatial precipitation (P) estimation. In this paper, a new method was proposed to merge multiple SPPs by considering rain gauge-based observations and water budget closure. Based on the errors of SPPs at gauged regions, the proposed method first estimates the mesh-based errors of SPPs as the intermediate variable using the error in water budget closure and then calculates the respective merging weights of SPPs in data merging. The performance of this proposed method was verified by comparing its results with the input of original SPPs and the data merged by five existing merging methods in mainland China at mesh scale and in nine major basins. Two scenarios were designed for the period from 2003.02 to 2016.12 at a monthly temporal resolution − with and without water budget closure constraints. Seven popular SPPs were considered, including GPM IMERG, PERSIANN, GSMaP, CHIRPS, ERA5, CFSV2, and TerraClimate. The results showed that the proposed method improved the P estimates in terms of CC, RMSE, MAE, PBIAS and KGE in both scenarios, and that closing the terrestrial water budget would slightly reduce the accuracy of SPPs against observations. The reduction percentage is about 5% in term of CC and KGE, and 12% in term of RMSE. This study is the first attempt to merge SPPs and gauge-based rain observation with consideration of water budget closure. It provides not only an effective and robust tool for the merging of multiple SPPs, but also important insights into the further development of SPP merging methods by considering water budget closure. [ABSTRACT FROM AUTHOR]

• We propose a new method to quantify Climate Change Effects at hydrological scales. • The method allows to assimilate climatic projections in CDFs at rain gauge scale. • The initial (i.e., before any change scenario begins) skewness plays a crucial role. • A strong increase of outliers frequency is expected for series EV1 alike skewed. Quantifying the potential effects of Climate Change (CC) on hydrological scales is a topic with a more and more increasing interest for the scientific community, given the CC impact on agriculture, industry, economy, human health, ecosystems, among the others. In this context, the paper focuses on the sub-daily Annual Maxima (AM) of rainfall height time series, and specifically on the crucial role played by initial skewness, i.e. the skewness of the observed rainfall series used to evaluate potential parametric trends. Here a quick and user-friendly methodology is proposed, that is aimed at quantifying plausible future changes in terms of probability distributions assumed at rain gauge scale, from projections of any climatic model. In detail, the Generalized Extreme Value (GEV) and the Two-Component Extreme Value (TCEV) distributions are adopted as probability functions suitable for modelling observed rainfall AM series, which could increase their frequency and magnitude into future horizons under CC. EURO-CORDEX projections for Europe are considered, under the hypothesis that the values of the change factor, i.e. the ratio between the values of a specific quantile at two specific time horizons, are invariant when moving from an areally-averaged scale (typical for any climate model) to a point rain gauge scale, which induces that future changes are provided (in terms of frequency and magnitude of extreme events) without the need of any spatial downscaling from the assumed projections. The proposed methodology can contribute to hazard quantification associated to potential climate changes, and thus it can play a crucial role in the assessment of hydraulic structures resilience; the obtained results, specific for the study area of Italy, but easily extendable on a global scale, showed that larger increases in frequency of future heavy events are expected for time series with "EV1 alike" values of initial skewness. [ABSTRACT FROM AUTHOR]

The paper presents issues relating to the influence of time distribution of rainfall on the required storage capacity of stormwater reservoirs. The research was based on data derived from simulations of existing drainage systems. The necessary models of catchments and the drainage system were prepared using the hydrodynamic modelling software SWMM 5.0 (Storm Water Management Model). The research results obtained were used to determine the critical rainfall distribution in time which required reserving the highest capacity of stormwater reservoir. In addition, it can be confirmed based on the research that dimensioning of enclosed structures should rely on using the critical precipitation generated as the characteristics of a synthetically developed rainfall vary dynamically in time. In the final part of the paper, the results of the analyses are compared and followed with the ensuing conclusions. The results of the research will have impact on the development of methodologies for dimensioning retention facilities in drainage systems. [ABSTRACT FROM AUTHOR]

Farm dams that are not managed properly at the individual level can create water storage equity and safety threats to downstream communities and the environment that aggregate at the catchment level: a potential ‘dual-extreme cumulative’ problem. The paper provides indicative evidence and develops understanding of this novel phenomenon and associated policy needs within the Australian setting comprising dual hydrologic extremes of floods and droughts, further exacerbated by climate change. This is achieved through comparative case studies involving surveys of both dam owner perceptions and dam management practices in four States representing a complete range of integrated policy approaches from weak to strong. Survey results find most farmers do not believe dam maintenance is important, will undertake spillway blocking and do not plan for emergencies. These results are supported by physical on-site findings of farmers neglecting dams and blocking or under-designing spillways, in turn storing more water than they are entitled and creating unsafe dams at both the individual and cumulative levels. From detailed cross-case comparative assessment against policy context, it emerges that on-farm perceptions and practices form a range of ‘acceptability’ of dam management that directly reflects policy strength and integration in each setting. The paper advances the international small dams policy, agricultural water management and hydrology literatures, evidencing the need for effective integrated policy to mitigate dual extreme cumulative threats. Importantly, guidance is provided to jurisdictions internationally with high inter-annual rainfall variation on how best to design integrated policy that can achieve both water storage equity and safety in agricultural catchments. [ABSTRACT FROM AUTHOR]

Summary The aim of this paper is to analyse the potential relationship between flash flood events and convective precipitation in Catalonia, as well as any related trends. The paper starts with an overview of flash floods and their trends in the Mediterranean region, along with their associated factors, followed by the definition of, identification of, and trends in convective precipitation. After this introduction the paper focuses on the north-eastern Iberian Peninsula, for which there is a long-term precipitation series (since 1928) of 1-min precipitation from the Fabra Observatory, as well as a shorter (1996–2011) but more extensive precipitation series (43 rain gauges) of 5-min precipitation. Both series have been used to characterise the degree of convective contribution to rainfall, introducing the β parameter as the ratio between convective precipitation versus total precipitation in any period. Information about flood events was obtained from the INUNGAMA database (a flood database created by the GAMA team), with the aim of finding any potential links to convective precipitation. These flood data were gathered using information on damage where flood is treated as a multifactorial risk, and where any trend or anomaly might have been caused by one or more factors affecting hazard, vulnerability or exposure. Trend analysis has shown an increase in flash flood events. The fact that no trends were detected in terms of extreme values of precipitation on a daily scale, nor on the associated ETCCDI (Expert Team on Climate Change Detection and Indices) extreme index, could point to an increase in vulnerability, an increase in exposure, or changes in land use. However, the summer increase in convective precipitation was concentrated in less torrential events, which could partially explain this positive trend in flash flood events. The β parameter has been also used to characterise the type of flood event according to the features of the precipitation. The highest values correspond to short and local events, usually with daily β values above 0.5, while the minimum threshold of daily β for catastrophic flash floods is 0.31. [ABSTRACT FROM AUTHOR]

• Exploring the urban inundation response law based on designed rainstorms by coupled hydrodynamic and rainfall-tracking model. • Source tracking analysis method is novelty proposed to excavate the response law of the connectivity among source areas to characteristics of rainstorms. • The analysis of source areas contribution further illustrates the reason why the study area would undergo inundation damage when the precipitation exceeds 100 mm. • The study is helpful to further understand the response law of inundation and relationship among source areas to characteristics of rainstorms, provide novel insights for constructing integrated flooding intervention practices. Extreme stormwater and high-impervious area, caused by climate change and high-urbanization, are widely considered as the essential factors for causing urban flood. Understanding the response law of urban inundation to characteristics of rainstorms is helpful to implement integrated flood mitigation measures. Therefore, this paper is focused on exploring the urban inundation response law based on designed rainstorms by coupled hydrodynamic and rainfall-tracking model. Pilot area of Guyuan sponge city, which is divided into 5 source areas, is adopted to conduct 36 types of rainfall scenario simulations with the combination of different rainfall amounts and durations as well as peak coefficients. Inundation analysis methods is used to obtain the relationship between maximum inundation maps and characteristics of rainstorms, and source tracking analysis method is novelty proposed to excavate the response law of the connectivity among source areas to characteristics of rainstorms. First, Results of inundation analysis reveal that rainfall amount, duration and peak value are the first, second and third critical rainstorm characteristics that determine the severity of disasters, the variation of inundation volume are 140 %, 30 % and 4.2 % when precipitation and duration as well as peak value double. Second, the inundation area that water depth more than 0.5 m increases quickly is the major reason that inundation volume varies with 2 times as the increasing of precipitation; the transferring runoff volume between source areas and land use reaches 0–6 % and 0–10 % of inundation volume. Third, SA 1 and 2 are the critical sub-areas to manage the runoff, when precipitation equals to 200 mm, SA 1 contributes 19.5–33.7 % of inundation volume in SA 2, and SA 2 contributes 28–30 % of inundation volume in SA 3. The study is helpful to further understand the response law of inundation and relationship among source areas to characteristics of rainstorms, provide novel insights for constructing integrated flooding intervention practices. [ABSTRACT FROM AUTHOR]

• Drought followed by extreme rain/flooding exposed vulnerabilities in Ceará, Brazil. • Adaptative strategies in dry periods may become maladaptive during extreme rain. • Impacts of and responses to drought, extreme rain, and flooding are interrelated. • Hydrometereological extremes should be considered together in semi-arid regions. As climates continue to change, semi-arid rainfall regimes are projected to become increasingly more extreme, with longer dry periods and fewer, but more intense, heavy rainfall events. This process of hydroclimatic intensification increases the risk associated with successive extreme climate hazards, such as drought and then heavy rainfall, in the same area. However, the development of effective adaptation strategies to drought, heavy rainfall, and flood hazards is hampered by the lack of integrated analyses that consider the interactions among these hazards and responses to them. To increase understanding of these interactions, this paper examines impacts of and adaptive actions taken in response to prolonged drought (2012–2018) and subsequent extreme rainfall and flooding (March and April 2023) in the semi-arid region of Ceará, Northeast Brazil. We find that policies and adaptive measures focus primarily on drought hazard, while preparation for extreme rainfall events and flooding is scant. Management plans and long-term adaptive strategies are being developed for drought and not other extreme hydrometeorological events. Additionally, emergency measures for extreme rainfall and flooding are much less structured than those for drought. As such, the paradigm "Convivência com o semiárido" ("Living with the semi-arid region") remains limited to adapting to drought hazard, ultimately hindering adequate extreme rainfall and drought responses. To promote more effective adaptive strategies in the context of hydroclimatic intensification in semi-arid regions, drought, heavy rainfall, and flood responses must be considered in a more integrated manner to create management plans to prepare for the potential impacts of these hazards into the future. [ABSTRACT FROM AUTHOR]

• A single IDF model valid for various return periods and durations is developed. • New scaling models of design rainfall curves are presented. • Adjusted rainfall event durations are considered in the context of scale invariance. • Parsimonious intensity–duration–frequency (IDF) structure is obtained. • Good fit to quantile predictions of extreme rainfall data for specified durations. This paper deals with intensity–duration–frequency (IDF) curves, which express the relationship between average rainfall intensity and event duration for various probabilities of non-exceedance (or return periods) for the design/analysis of hydraulic interventions and infrastructures in riverine and urban drainage contexts. New scaling models are proposed to develop a single IDF model valid for all durations, from below 1 h to 24 h. In these models, the scale invariance is applied to rainfall intensity to obtain a parsimonious IDF structure capable of defining a family of IDF curves at various return periods. The main novelty consists of the formulation of simple and multiple scale invariance based on adjusted design event durations. The parameterization is carried out in two phases: in the first phase, the adjustment size is searched for iteratively while the other parameters of the IDF structure are directly obtained in cascade; in the second phase, which enables considering different reliability levels for extreme rainfall data as a function of sample length at different durations, parameter refinement is carried out by means of a local optimization algorithm. The second step accommodates data samples of different extension as a function of available data at different durations. The application to four case studies at various latitudes in Europe, namely Helsinborg (Sweden), Frauenwald (Germany), Pavia and Erice (Italy), proves the IDF structure to fit well the quantile predictions of extreme rainfall data for specified durations below and above one hour. [ABSTRACT FROM AUTHOR]

• A framework is proposed to analyze compound floods with multiple driving factors. • Local rainfall has a larger impact on the flooding levels of the GBA, China. • Flooding area is about 50 km2 and 1.3 m flooded depth for the worst scenario. • Findings have important implications to GBA's flooding control and management. Low-lying coastal cities are vulnerable to compound floods caused by many factors including river flows, tides and local rainfall. Many previous studies focus on the impacts of rainfall and tidal levels (two driving factors) on estuaries or regions near the main single river, while research about the three influencing factors on the floods for complex urban river networks remain relatively scarce. This paper proposes a framework to simulate the coastal compound flooding process, with a series of scenarios designed to explore the main flooding drivers and their worst possible combination. The approach is used to quantify the induced flooding consequences, in which a two-dimensional model is employed to conduct a case study for a city with complex river networks. The Qianshan River basin in Zhuhai City, located in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) of China is used as the case study. Application results show that: (i) the occurrence and properties of flooding in this coastal area are jointly affected by upstream river flows, downstream tidal levels and local rainfall intensity with different contribution levels, (ii) compared to tidal levels, local rainfall intensity has a larger impact on the flooding levels of the study region, and (iii) the worst scenario where a 50-year return period upstream river flow co-occurring with high tide and a 100-year return period rainfall can produce a flooding area up to 50 km2 for the study region, with an average of 1.3 m flooded depth. The proposed framework with its findings offers an in-depth understanding and quantification of coastal urban flood characteristics within the GBA of China. [ABSTRACT FROM AUTHOR]

• Developed the M-XAJ model incorporating minor impoundment effects to improve flood forecasting precision. • Derived aggregated reservoir discharge capacity curves for data-scarce small water bodies. • Computed aggregated reservoir releases via multi-bottom sills graded weir flow pattern. To improve flood prediction accuracy in the basin, this paper constructs a modified Xin'anjiang (M-XAJ) model to account for the regulatory and storage effects of small-scale water storage structures on surface runoff, based on the original Xin'anjiang (XAJ) model. In the M-XAJ model, the regulatory and storage effects of small water storage are calculated by utilizing an aggregated reservoir using multi-bottom sills graded weir flow pattern (MBSP). The discharge processes of the aggregated reservoir is combined with the processes of runoff production and convergence over the natural area to form the overall discharge processes at the outlet section. Using the two subbasins of the Xianghongdian Reservoir as case studies, 13 flood events were selected to validate the predictive capabilities of the M-XAJ model. The results indicate that (1) the qualification rate for flood peak (Q p) and the qualification rate flood volume (Q v) substantially increased for the two sub-basins during the training and verification periods. Additionally, the mean relative error of peak lag time (M E tp ) was considerably reduced. Specifically, the mean Nash efficiency coefficient (MNS) for floods at the Qingshan section improved by 0.18 and 0.24 respectively. (2) the M-XAJ model improved prediction accuracy for the first flood event following an extended drought period without rainfall, as well as for the initial flood preceding a sequence of flooding events. However, the prediction accuracy for continuous flooding events was not noticeably enhanced by the M-XAJ model. (3) the M-XAJ model demonstrated improved simulation capabilities for low- and moderate-magnitude flood events; however, there were no noticeable enhancements for high-magnitude flooding simulations. [ABSTRACT FROM AUTHOR]

Summary Increasing demands from emergency responders for accurate flood prediction, particularly in cities, have motivated consistent increases in the resolution of urban drainage models. Such models are now primarily limited by the accuracy and resolution of the initialising rainfall field. Surface rainfall estimates from radar, traditionally derived at scales of order 1 km, are now requested at grid lengths of 100 m to drive improvements in the outputs of these models. Deriving radar precipitation products on grids at the sub-kilometre scale introduces new requirements for the processing of reflectivity measurements into surface rainfall rates. A major source of uncertainty is the physical distance between the radar measurement and the surface onto which precipitation falls. Whilst adjustments to account for inhomogeneity in the vertical reflectivity profile have been extensively investigated, the effects of horizontal displacement have not. This paper discusses the issue of wind drift, first by outlining the need for correction, and then by evaluating the corrections available for impact at the required scale. One correction is detailed and its sensitivity evaluated with respect to the assumptions necessary in its derivation. These sensitivities are verified by trials on the Met Office operational radar processing system, where errors on wind drift displacement estimates are shown to be of order 1 km or more. This is significantly greater than the grid length desired by hydrological users. The paper therefore concludes by suggesting further research necessary to ensure the accuracy of radar precipitation estimates at sub-kilometre resolution. [ABSTRACT FROM AUTHOR]

• Developed a hybrid statistical-numerical model which predicts that without intervention the compound flood risk will increase in Haikou City under the influence of future climate change. • Under the influence of climate change, both rainfall and sea water level will increase. • Climate change will increase the probability and the failure probability of compound flood, and will intensify inundation (increase flood depths, extend the flood extent and longer flood durations). • The combined impact of future changes in rainfall and water level on the magnitude of flooding is greater than the sum of their individual impacts. Compound flooding caused by a combination of rainfall and water level may occur in coastal cities. Under the influence of climate change, compound flooding is likely to be intensified. In this paper, using the main urban area of Haikou City as an example, we developed an event-based statistical-numerical hybrid framework to investigate the effects of sea level rise (SLR) and future rainfall changes on compound flood risk. Future rainfall was predicted by using five global climate models (GCMs) in CMIP5. A combination of GCMs and IPCC AR5 was used to predict the future SLR. The copula was then used to calculate the probabilistic risk of compound flooding (OR joint probability and failure probability) for historical and future scenarios, and the numerical model was utilized to evaluate the risk of inundation under historical and future scenarios. The probabilistic risk results show that both the probability and the failure probability of compound flooding will increase in the future. Output from GCMs indicate that when the OR event of the 5-year event (the compound flooding when at least one of the 5-year rainfall and the 5-year water level happens) occurs, the joint probability of future compound flooding will likely increase to 37.5%. The numerical model results indicate that the flood extent, flood depth and flood duration will increase under the influence of future rainfall and SLR. Under the impact of climate change, the flood depth in the FS 5 case will increase by 22.85% and the flood extent in the FS 5 case will increase by 41.42% compared to HS 5. By controlling for variables, we also find that the combined impact of future changes in rainfall and water level on the magnitude of flooding is greater than the sum of their individual impacts. Finally, we find that water storage facilities and tide gates could be set up to mitigate compound flooding under climate change. [ABSTRACT FROM AUTHOR]

Summary: Accurate flood forecasts are critical to an efficient flood event management strategy. Until now, hydro-meteorological forecasts have mainly been used to establish early-warnings in France (meteorological and flood vigilance maps) or over the world (flash-flood guidances). These forecasts are typically limited either to the main streams covered by the flood forecasting services or to watersheds with specific assets like check dams, which in most cases are well gauged river sections, thus leaving aside large parts of the territory. This paper presents a distributed hydro-meteorological forecasting approach, which makes use of the high spatial and temporal resolution rainfall estimates that are now available, to provide information at ungauged sites. The proposed system intended to detect road inundation risks had initially been developed and tested in areas of limited size. This paper presents the extension of such a system to an entire region (i.e. the Gard region in Southern France), including over 2000 crossing points between rivers and roads and its validation with respect to a large data set of actual reported road inundations observed during recent flash flood events. These initial validation results appear to be most promising. The eventual proposed tool would provide the necessary information for flood event management services to identify the areas at risk and adopt appropriate safety and rescue measures: i.e. pre-positioning of rescue equipment, interruption of the traffic on the exposed roads and determination of safe access or evacuation routes. Moreover, beyond the specific application to the supervision of a road network, the research undertaken herein also provides results for the performance of hydro-meteorological forecasts on ungauged headwaters. [Copyright &y& Elsevier]

Summary: Catchment hydrological models are widely used in climate change impact studies. The considered climate scenarios often involve increases in rainfall intensities. These increases might go beyond the range of historical events used in model calibration and validation. The hydrological impact results of climate change consequently are under these circumstances highly uncertain. This paper presents a method to test the validity of hydrological models for that type of impact analysis. The method is based on the evaluation of peak flow increases due to different levels of rainfall increases. The method is demonstrated, based upon the results of three conceptual rainfall–runoff models (NAM, PDM, VHM) for three catchments in Belgium. It is shown how the performance of the model in predicting impacts of rainfall increases can be integrated in the calibration process and how this method can increase the reliability of climate change impact results on peak flows. The paper addresses the importance of the relation between the soil moisture content and the overland flow coefficient, as it controls the generation of peak flows in the considered rainfall–runoff models. [ABSTRACT FROM AUTHOR]

• Raindrop size distribution for unit area and time is useful for erosion studies. • The ratio kinetic power P n /intensity I is estimated by DSD and terminal velocity. • Relationship for estimating kinetic power is available for some theoretical DSD. • The relationship between P n / I and rainfall intensity I is site specific. • The relationship between P n / I and the median volume diameter is not site specific. The knowledge of the rainfall drop size distribution (DSD) at the land surface is essential for understanding precipitation mechanisms affecting soil erosion processes. Rainfall erosivity is defined as the potential of rain to cause erosion and it can be evaluated by rainfall kinetic power, which is determined by DSD and raindrop terminal velocity. This paper firstly deals with the raindrop terminal velocity estimate. Then the most widely used DSD are reviewed highlighting the difference between the raindrop size distribution per unit volume of air and that per unit area and time. The reliability of the available kinetic power-rainfall intensity relationships and their application in several part of the world is discussed, highlighting that the use of rainfall intensity is not sufficient to determine the rainfall kinetic power everywhere. Finally, the influence of seasonality on both raindrop size distribution and rainfall energy characteristics is investigated using DSD measurements carried out by an optical disdrometer placed at Palermo experimental area. [ABSTRACT FROM AUTHOR]

• The quality of OPERA radar precipitation composites is assessed during 2015–2017. • SYNOP gauges records over Europe are used as reference and to adjust OPERA rainfall. • 3 years analysis shows improvement in the radar composites after rain gauge adjustment. • The daily monitoring shows gradual improvements of the OPERA composite itself. • Adjusted OPERA composites are useful for pan-European flash flood hazard assessment. Monitoring continental precipitation over Europe with high resolution (2 km, 15 min) has been possible since the operational production of the OPERA composites from the European weather radar networks. The OPERA data are the essential input to a hazard assessment tool for identifying localized rainfall-induced flash floods at European scale, and their quality determines the performance of the tool. This paper analyses the OPERA data quality during the warm seasons of 2015–2017 by comparing the estimated rainfall accumulations with the SYNOP rain gauge records over Europe. To compensate the OPERA underestimation, a simple spatially-variable bias adjustment method has been applied. The long-term comparison between the OPERA and gauge point daily rainfall accumulations at the gauge locations shows the benefit of the bias adjustment. Additionally, the daily monitoring shows gradual improvement of the OPERA data year by year. The impact of the quality of the OPERA data for effective flash flood identification is demonstrated for the case of the flash floods that occurred from 29 May to 3 June 2016 in central Europe. [ABSTRACT FROM AUTHOR]

• Effects of interpolation methods on rainfall data scarcity were compared. • Impacts of of data scarcity on model uncertainty were investigated. • The effects of the different imputed data sets on H/NPS results were quantified. • The EMB algorithm was proved to be superior to the traditional methods. • The threshold effect and monitoring during high-flow periods were highlighted. Rainfall data scarcity has caused enormous problems in hydrologic and non-point pollution (H/NPS) predictions, as rainfall data represent the key input to watershed models. In this study, the effects of different imputation methods such as the data augmentation (DA) and the expectation maximization with bootstrap (EMB) algorithms on rainfall data scarcity were compared. The effects of different data scarcity rates and periods on model performance and prediction uncertainty were then quantified. Finally, the effects of different imputed data sets on H/NPS results were evaluated with the soil and water assessment tool (SWAT). A real case study in the Daning River watershed, Three Gorges Reservoir Region, China, was evaluated. The results indicated that rainfall data scarcity during low flow periods would result in poorer model performance and larger prediction uncertainty, especially to some minimum values, and the time when the maximum values are more susceptible is the rainfall data scarcity during high flow periods. The repair of rainfall data and the H/NPS model performance obtained by the EMB algorithm are superior to the traditional DA and weather generator performances. This advantage of the EMB algorithm would be more definitive if a specific threshold of data scarcity is reached. It is noted that even if the best algorithm is used, the imputed value is always lower than the peak observed value. This paper reports important implications for the choice of imputation methods and the use of H/NPS models for solving data scarcity problems for watershed studies. [ABSTRACT FROM AUTHOR]

Highlights • Monthly precipitation was predicted using soft computing methods including ANFIS with GA, ACOR, PSO and DE. • Suggested algorithms improved the ANFIS performance for modeling the precipitation. • Suggested methods decreased the event of trapping in local optima. • ANFIS-ACOR model with least error and most correlation showed highest performance in monthly precipitation forecast. Abstract Existent algorithms to train adaptive neuro-fuzzy inference system (ANFIS) such as gradient descent and least-square methods are useful, but they have problems such as trapping in local optimum as well as high-volume of computations. This paper examines the application of genetic algorithm (GA), ant colony optimization for the continuous domain (ACO R), particle swarm optimization (PSO) and differential evolution (DE) for improving the performance of ANFIS models in simulating monthly rainfall magnitudes. Monthly rainfall records of nine foothills to arid weather stations of Isfahan province, central Iran were used for evaluating the proposed methodology. The correlation coefficient and non-linear sensitivity analysis were performed on the available rainfall records. Monthly rainfall modelling was carried out based on three input groups and used separately in five applied models (e.g. ANFIS, ANFIS-PSO, ANFIS-ACO R , ANFIS-DE, and ANFIS-GA). The obtained results showed that proposed hybrid models had better accuracy than the simple ANFIS model in escaping local optima. The best model was ANFIS-ACO R (with R2 = 0.92, RMSE = 2.73 mm and SI = 0.26), while sole ANFIS model (with R2 = 0.33, RMSE = 9.98 mm and SI = 0.90) presented the worst output. Moreover, the obtained results showed that: (1) Coupling the optimization algorithms with ANFIS can improve its performance in modeling the monthly rainfall time series; (2) the correlation coefficient and sensitivity analysis of input data provided a valid input selection that could improve the model accuracy with neglecting the redundant input parameters. [ABSTRACT FROM AUTHOR]

Highlights • An early warning system (EWS) for heavy rainfall nowcasting is proposed. • Discretizing input variables selectively can improve the prediction quality of the EWS. • Principal component analysis matches well with the selective discretization. • A comparative analysis was conducted on various classifiers. • Logistic regression is well suited for heavy rainfall nowcasting. Abstract The purpose of an early warning system (EWS) is to issue warning signals prior to extreme events. Extreme weather events, however, are hard to predict due to their chaotic behavior. This paper suggests a method for an effective EWS for very short-term heavy rainfall with machine learning techniques. The EWS produces a warning signal when it is expected to reach the criterion for a heavy rain advisory within the next 3 h. We devised a selective discretization method that converts a subset of continuous input variables to nominal ones. Meteorological data obtained from automatic weather stations are preprocessed by the selective discretization and principal component analysis. As a classifier, logistic regression is used to predict whether or not a warning is required. A comparative evaluation was performed on the EWS models generated by various classifiers. The tests were run for 652 locations in South Korea from 2007 to 2012. The empirical results showed that the preprocessing methods improved the prediction quality and logistic regression works well on heavy rainfall nowcasting in terms of F-measure and equitable threat score. [ABSTRACT FROM AUTHOR]

Highlights • Main controlling factors for groundwater recharge among 88 metrics were identified. • Same annual rainfall with a seasonal shift could result in a decline of recharge. • Lower groundwater depth could lead to a positive feedback of declining recharge. • Spatially the most significant factors for recharge were soil and land attributes. • Findings could serves as a reference for climate change impacts on groundwater. Abstract This paper demonstrated the benefits of statistical methods when investigating the climatic and non-climatic drivers responsible for variations in groundwater recharge with a series of up to 43 years of annual recharge for 426 bores in South-East South Australia. We identified the factors influencing groundwater recharge based on 71 climatic metrics and 17 non-climatic metrics (including groundwater abstraction). The results showed: (1) Rainfall during April to October was the most important variable influencing recharge temporal variation, with its decline identified as the most significant factor related to recharge reduction; (2) In contrast, a negative correlation between rainfall during December to February (DJF) and annual groundwater recharge was found. This suggests that a seasonal shift in rainfall (such as decreasing rainfall during April to October and an increase during DJF) can result in a decline in recharge even when the annual rainfall remains unchanged; (3) The length of wet spells (consecutive rain days) and increasing potential evapotranspiration (PET) were additional significant predictors for recharge temporal variation. It demonstrated that a simple empirical relationship (such as recharge as a fixed percentage of rainfall) is not a reliable estimation of renewable groundwater resources under changing climatic conditions; (4) There is a statistically significant correlation between mean groundwater depth and recharge, which implies that if groundwater level fall due to rainfall declines then a positive feedback loop can lead to further recharge declines; (5) Spatially the most statistically significant factors influencing groundwater recharge were soil types and land attributes. The findings of this study can identify which stressors should be included when investigating the impact of climate change on groundwater recharge. [ABSTRACT FROM AUTHOR]

Highlights • The rainfall Areal Reduction Factor (ARF) was investigated using radar rainfall. • The ARF decreases with increasing area while increases with duration. • Within-storm rainfall coefficient of variation is a very strong predictor of ARF. • Different storm orientation made approximately 20% difference of the ARF value. • Current design framework should consider the rainfall spatial variability. Abstract For the last several decades, great efforts have been put into converting point precipitation into mean areal precipitation in the design of hydraulic and hydrologic infrastructures. The ratio between point and areal precipitation, called the Areal Reduction Factor (ARF), has been identified to vary significantly depending on a variety of factors, with some factors remaining to be undiscovered. In this paper, we highlight the influence of internal spatial variability of storms on the ARF value. For this, we employ a storm identification algorithm on the radar composite data to identify a total of 54,758 elliptically-shaped extreme storms over the six-year study period. Then, we investigate the relationship between the various storm characteristics to their ARF value. Our findings are as follow: First, we confirm a widely-accepted notion that ARF generally increases with the duration, and it is inversely related to the storm area. Second, we discover that spatial variability within storm, e.g., the coefficient of variation of radar image pixel rainfall values, is a very strong predictor of the ARF value along with area and duration. Last, the difference of ARF values between storms that have elliptical shapes and those that are circular over the same area is about 20% on average. These findings inform that the current design framework of areal rainfall estimation will be improved by incorporating the information on the rainfall spatial variability and storm shape. [ABSTRACT FROM AUTHOR]

Highlights • High resolution catchment scale rainfall predictions were generated from 8 GCMs. • Multiple GCMs is preferable over a best model to account for the future variability. • Simulated climate change has limited impact on pollutant treatment performance. • Larger WSUD systems are recommended to account for the future variability. • Bigger rainwater tank is suggested for same harvesting reliability in the future. Abstract Water Sensitive Urban Design (WSUD) stormwater systems, also known as Low Impact Development (LID) systems or Nature Based Solutions (NBS), are currently implemented based on the underlying assumption of statistical stationarity of rainfall, which threatens to become outdated under climatic uncertainty. This paper applies a new downscaling method to examine the implications of climate change on future rainfall and evaluate the reliability of WSUD stormwater infrastructure in pollution reduction, flow frequency mitigation and reliability as an alternative water supply. A variety of future atmospheric scenarios are considered as part of this comprehensive assessment by analysing an ensemble of eight different downscaled General Circulation Models (GCMs). High resolution catchment-scale rainfall projections for Melbourne, Australia were generated using a scheme called High-resolution Downscaling of Rainfall Using STEPS (HiDRUS) at a fine 1 km and 6-min scale for more precise analysis with uncertainty estimates. Statistical analyses show that, in general, the climate models predict a drier future with fewer rainfall events and longer dry periods when comparing the simulated near future (2040–2049) periods against the base-line period (1995–2004). The difference simulated between historical and future rainfall projections show minimum difference of WSUD performance in pollution removal and flow frequency reduction, with slightly lower harvesting reliability (<3%) observed under future climate; high variabilities, however, were observed across GCM simulations, indicating big uncertainties of system reliability under various conditions, e.g. design wetland sizes may vary from 2.5% to 4.0% of the impervious catchment area according to different future projects across GCMs. Larger WSUD systems are recommended to ensure reliable performance of pollution removal, as well as harvesting reliability under simulated future conditions. The significance of considering an ensemble of different GCMs as opposed to many scenarios generated by a single 'best' climate model was also demonstrated for the robust estimation of uncertainty in future WSUD reliability. This work highlights important considerations for the future design, management and quantitative evaluation of WSUD reliability. [ABSTRACT FROM AUTHOR]

Summary: This paper assesses the potential of several calibration strategies to meet two objectives: good discharge simulations and the ability to reproduce one functional characteristic. Indeed, although classical rainfall–discharge models are often calibrated based on their efficiency in simulating discharge time series, this does not warrant an optimal representation of certain of the system’s hydrodynamic properties, since these properties are used for water management purposes. Therefore, this paper investigates the trade-off between two objectives: (i) good discharge simulations in terms of the least mean square errors and (ii) the ability to reproduce the autocorrelation function of the discharge time series. For this purpose, we applied two rainfall–discharge models on the Baget karst system, an extensively studied system located in the French Pyrenees. The results show that a single-objective calibration based on the classical Nash and Sutcliffe efficiency (NSE) coefficient gives relatively satisfying modelling results, but the autocorrelation function is systematically overestimated. The proposed multi-objective approach improves the ability of the model to mimic the autocorrelation function without greatly altering the model’s NSE efficiency. Last, the multi-objective framework reduces parameter uncertainty and increases the robustness of the two rainfall–discharge models. [ABSTRACT FROM AUTHOR]

Summary: Hillslope hydrological response to precipitation is extremely complex and poorly modeled. One possible approach for reducing the complexity of hillslope response and its mathematical parameterization is to look for macroscale hydrological behavior. Hillslope threshold response to storm precipitation is one such macroscale behavior observed at field sites across the globe. Nevertheless, the relative controls on the precipitation–discharge threshold poorly known. This paper presents a combined model development, calibration and testing experiment study to investigate the primary controls on the observed precipitation–discharge threshold relationship. We focus on the dominant hydrological processes revealed in part one of this two-part paper and with our new numerical model, replicate the threshold response seen in the discharge record and other hydrometric and tracer data available at the site. We then present a series of virtual experiments designed to probe the controls on the threshold response. We show that the threshold behavior is due to a combination of environmental (storm spacing and potential evapotranspiration) and geologic (bedrock permeability and bedrock topography) factors. The predicted precipitation–discharge threshold subsumes the complexity of plot-scale soil water response. We then demonstrate its use for prediction of whole-catchment storm discharge at other first order catchments at Maimai and the HJ Andrews Experimental Forest in Oregon. [ABSTRACT FROM AUTHOR]

Abstract: The importance of hydrological uncertainty analysis has been emphasized in recent years and there is an urgent need to incorporate uncertainty estimation into water resources assessment procedures used in the southern Africa region. The region is characterized by a paucity of accurate data and limited human resources, but the need for informed development decisions is critical to social and economic development. One of the main sources of uncertainty is related to the estimation of the parameters of hydrological models. This paper proposes a framework for establishing parameter values, exploring parameter inter-dependencies and setting parameter uncertainty bounds for a monthly time-step rainfall-runoff model (Pitman model) that is widely used in the region. The method is based on well-documented principles of sensitivity and uncertainty analysis, but recognizes the limitations that exist within the region (data scarcity and accuracy, model user attitudes, etc.). Four example applications taken from different climate and physiographic regions of South Africa illustrate that the methods are appropriate for generating behavioural stream flow simulations which include parameter uncertainty. The parameters that dominate the model response and their degree of uncertainty vary between regions. Some of the results suggest that the uncertainty bounds will be too wide for effective water resources decision making. Further work is required to reduce some of the subjectivity in the methods and to investigate other approaches for constraining the uncertainty. The paper recognizes that probability estimates of uncertainty and methods to include input climate data uncertainties need to be incorporated into the framework in the future. [Copyright &y& Elsevier]

Summary: This paper presents and assesses the model MedREM, in which annual-based climate and rainstorm data are used to estimate the long-term (R)Universal Soil Loss Equations and its Revisions (USLE) annual rainfall erosivity over a large region. Two known models of rainfall erosivity (named after the first author of the original paper, Torri and Yang) were also assessed and compared with the MedREM. Yang and Torri models are both based on the annual precipitation models. MedREM also takes annual maximum daily precipitation data into account, and incorporates a longitude-dependent coefficient. The test area was a large region centered on the Mediterranean basin, in which 66 weather stations were available – 43 Italian and 23 out-of-Italy sites (12 countries, about 30–50° latitude North, and 10–50° longitude East) – with multi-year data of (R)USLE annual erosivity. The three models were calibrated against (R)USLE rainfall erosivity data from 55 stations and evaluated over a validation dataset from 11 Italian stations. On the validation dataset, the MedREM estimates generally compared well with the (R)USLE data according to Nush–Sutcliffe coefficient (0.87 against 0.76 and 0.73 with Torri and Yang model, respectively). Implications for erosivity modelling were discussed in the context of climatic features concluding that accurate estimations of site-specific annual erosivity for the Mediterranean region require process-based model with spatially-explicit parameterization. [Copyright &y& Elsevier]

Summary: An important tool in the management of floods is the use of rainfall–runoff models to predict the arrival of discharge peaks. These models generally use rainfall and potential evapotranspiration rates as input, and relate these to the catchment discharge through a number of conceptual equations. The parameters of these equations are estimated through a comparison of the modeled discharge to the observations. Only one variable, catchment discharge, is thus generally used to calibrate and validate these models. The objective of this paper is to validate the internal model dynamics of two widely used rainfall–runoff models using baseflow estimates. The baseflow time series used in this paper are obtained through the use of a physically-based digital baseflow filter. These models, the Hydrologiska Byråns Vattenbalansavdelning (HBV) model and the Probability Distributed Model (PDM), were calibrated using 1year of hourly discharge data. The HBV model uses a linear reservoir for the modeling of groundwater flow, while the PDM uses a cubic reservoir for this purpose. In order to assess the impact of the type of reservoir choice, the cubic reservoir in the PDM was also replaced by a linear reservoir. Two different parameter estimation algorithms were used for model calibration. The Shuffled Complex Evolution (SCE-UA) algorithm minimizes the Root Mean Square Error between the model simulations and the observations, while Multistart Weight-Adaptive Recursive Parameter Estimation (MWARPE) uses the Extended Kalman Filter equations in an iterative, Monte-Carlo framework. MWARPE was found to lead to the best discharge simulations, but the differences with the results obtained from the SCE-UA algorithm were relatively small. When only the modeled discharge was analyzed, no clear picture emerged of which model produced the best results. However, it has been found that the replacement of the cubic groundwater reservoir in the PDM by a linear reservoir resulted in a strongly improved model performance with respect to the baseflow. Further, MWARPE consistently led to the best baseflow estimates for the three models, while the HBV model resulted in the best simulations of the baseflow, regardless of the calibration algorithm. The overall conclusion of this paper is that, even though it may be difficult to assess which model and calibration algorithm resulted in the best discharge estimates, the MWARPE calibration algorithm and the HBV model consistently led to the best internal model dynamics. [Copyright &y& Elsevier]

Summary: One motivation for setting up the CATCH (Couplage de l’Atmosphère Tropicale et du Cycle Hydrologique) project at the end of the 1990s, was to contribute to documenting the Sahelian rainfall variability at the interannual scale and to provide a fine monitoring of possible long-term trends of the rainfall regime. This paper is a first attempt at characterising the Sahelian rainfall regime of the two last decades (1990–2007) by comparison to the rainfall regime of the previous decades, namely the 20-year wet period 1950–1969 and the 20-year dry period 1970–1989. While the rainfall deficit remained unabated in the Western Sahel (1990–2007 mean equal to the 1970–1989 mean, both being lower than the 1950–1969 mean), the Central Sahel progressively recorded wetter years from the end of the 1990s, but this recovery is limited (1990–2007 average larger by 10% than the 1970–1989 average, but still lower than the 1950–1989 average). There are also significant differences between the Western Sahel and the Central Sahel when looking at the interannual variability pattern and at the seasonal cycle. The low-frequency rainfall patterns are similar between the Western Sahel and the Central Sahel, but the interannual year-to-year variability is weakly related to each other. In the Central Sahel, the major modification of the seasonal cycle in the most recent decades was the disappearance of the well marked August peak observed during the wet period. In the Western Sahel the rainfall deficit is more or less evenly distributed all along the rainy season. The second part of the paper makes use of the CATCH-Niger recording rain gauge network in order to compare several ways of defining rainy events. The statistical properties of these various populations of rainy events are compared. It is shown that a simple CPP model allows for retrieving the statistical characteristics of point rainy events from daily rainfall series. It is also confirmed that in this area, the interannual rainfall variability is primarily linked to the year-to-year fluctuation of the number of large mesoscale rainfall events. [Copyright &y& Elsevier]

Summary: Stochastic generation of hydrological time series is a useful tool for the design and management of water resources systems. One of the shortcomings of many available models for stochastic generation of daily rainfall data is that they are unable to preserve satisfactorily key statistical properties simultaneously at daily, monthly and annual time scales. In this paper, a method for coupling two different time scales of stochastic hydrological time series models is introduced. The key feature of the method is to first generate two resembling time series, one preserving key statistical properties at a finer time scale and the other at a coarser time scale. Adjustment is then made to the finer time scale series so that this series becomes consistent with the coarser time scale series. Because the initial two time series resemble each other, the adjustment is kept small. In the paper, the technique for generating two resembling time series is described. The implementation of the method for coupling daily and monthly rainfall series is demonstrated. Test results of the method using rainfall data from a number of sites around Australia showed that the coupling method was able to generate daily rainfall time series that preserved satisfactorily some key statistical properties at daily, monthly and even annual time scales. [Copyright &y& Elsevier]

Summary: Calibration and prediction in conceptual rainfall-runoff (CRR) modelling is affected by the uncertainty in the observed forcing/response data and the structural error in the model. This study works towards the goal of developing a robust framework for dealing with these sources of error and focuses on model error. The characterisation of model error in CRR modelling has been thwarted by the convenient but indefensible treatment of CRR models as deterministic descriptions of catchment dynamics. This paper argues that the fluxes in CRR models should be treated as stochastic quantities because their estimation involves spatial and temporal averaging. Acceptance that CRR models are intrinsically stochastic paves the way for a more rational characterisation of model error. The hypothesis advanced in this paper is that CRR model error can be characterised by storm-dependent random variation of one or more CRR model parameters. A simple sensitivity analysis is used to identify the parameters most likely to behave stochastically, with variation in these parameters yielding the largest changes in model predictions as measured by the Nash–Sutcliffe criterion. A Bayesian hierarchical model is then formulated to explicitly differentiate between forcing, response and model error. It provides a very general framework for calibration and prediction, as well as for testing hypotheses regarding model structure and data uncertainty. A case study calibrating a six-parameter CRR model to daily data from the Abercrombie catchment (Australia) demonstrates the considerable potential of this approach. Allowing storm-dependent variation in just two model parameters (with one of the parameters characterising model error and the other reflecting input uncertainty) yields a substantially improved model fit raising the Nash–Sutcliffe statistic from 0.74 to 0.94. Of particular significance is the use of posterior diagnostics to test the key assumptions about the data and model errors. The assumption that the storm-dependent parameters are log-normally distributed is only partially supported by the data, which suggests that the parameter hyper-distributions have thicker tails. The results also indicate that in this case study the uncertainty in the rainfall data dominates model uncertainty. [Copyright &y& Elsevier]

Abstract: The first part of this two-part paper demonstrated the feasibility of the direct use of data from a high resolution (25km) Regional Climate Model (RCM) to provide inputs for a rainfall-runoff model, in order to obtain estimates of flood frequency. This paper uses data from a climate change experiment with the same RCM (HadRM3H) to provide estimates of change in flood frequency between the 1970s and 2080s, for 15 catchments across Great Britain. This experiment is a rerun, at twice the horizontal resolution, of one of those used by the UK Climate Impacts Programme (UKCIP) in the construction of its UKCIP02 climate change scenarios for the UK. It thus allows an exploration of the implication of these scenarios using a resolution more suited to the detailed hydrological application addressed in this paper. Despite decreases in annual average rainfall in all but one catchment, eight show an increase in flood frequency at most return periods whereas two show substantial decreases. As part I of this paper showed a distinct positive correlation between errors in annual rainfall and errors in flood frequency, the fact that flood frequency can increase despite an overall decrease in rainfall implies a marked change in the distribution of rainfall, either in terms of the probability of rainfall events and/or its seasonal cycle. Decreases in flood peaks are shown for a number of the catchments in the south and east of England, despite an increase in winter mean and extreme rainfall. Increased summer and autumn soil moisture deficits are thought to be the reason for this. Other catchments, further north or west, show an increase in flood peaks, in some cases of over 50% at the 50-year return period. Care needs to be taken when interpreting these results, as they are based on a single RCM experiment (using driving data from one GCM under a single emissions scenario). Other RCM experiments may give quite different results, and ensemble runs would ideally be required to limit sample error. [Copyright &y& Elsevier]

The drought in the African Sahel, persisting since 1968, emphasizes the vulnerability of local water resources to climatic variations and land use changes in this semiarid environment. Following the Hapex–Sahel experiment (1992–1994), research aims at understanding and modeling the impact on the hydrological cycle of these changing climatic and environmental conditions over a 1500-km2 area east of Niamey, Niger. The hydrological landscape consists of a myriad of small endoreic catchments feeding temporary or permanent pools from which water percolates to an unconfined aquifer. As the first step in a local-to-regional scale approach to the water resource recharge in the area, a physically based rainfall–runoff model is developed for a typical, 1.9-km2 endoreic system named Wankama, which is monitored continuously since 1992. This first of two companion papers describes the key elements of the hydrological landscape, the experimental setup with which the Wankama catchment and pool are monitored, the r.water.fea distributed hydrological model and its application to the construction of the Wankama catchment model. Because of the difficulties of long-term field experimentation in the region, not all observed events can be considered equally well recorded; in particular, detection of exogenous water inflow occurrences that may alter Wankama runoff estimates is needed. The paper describes the careful, model-aided data analysis performed in order to select the observation set with which the model can most reliably be tuned and operated. In this rainfall–runoff data analysis phase, the model is used prior to any calibration. Model calibration and verification, including output uncertainty, are the subject of the second paper [J. Hydrol. (2003)]. All steps of analysis and modeling were performed on the 1992–1998 data only, before the 1999–2000 data became available, and are tested a posteriori against the latter. [Copyright &y& Elsevier]

• A novel data-based modelling approach, (EOF) is introduced for riverine management. • The results showed significant similarity between simulated and observed patterns. • The natural forces were the main driving factor for the sediment load before 1988. • Anthropogenic pressure emerged as an important driver to the declining of sediment load since the end of 1988. This paper introduces a novel data-based modelling approach, Empirical Orthogonal Function (EOF), to characterize the sediment load variability at the downstream section of the Red River system in Vietnam. We modified the original EOF and performed it on the demeaned datasets of monthly total sediment load. Rating curves between the discharge and sediment load were used to validate the performance of EOF analysis. The results showed significant similarity between simulated and observed patterns. A dramatic decrease in sediment load has been observed via both the EOF and rating curve methods across the observation period (∼1958–2021), with two dramatic decreasing phases identified (1988–2008 and 2009-present). The variation in the sediment load was mainly related to the seasonal and inter-annual hydrological conditions in the basin, as well as the dam-reservoir impact. Before 1988, river discharge and rainfall were well correlated to the first EOF mode, indicating that the natural forces were the main driving factor for the sediment load. However, anthropogenic pressure (dam-reservoir impact) emerged as an important driver to the declining of sediment load since the end of 1988 and became more severe towards the end of 2008. We conclude that the modified EOF method developed in this study successfully demonstrates a simple and efficient means of reconstructing and evaluating the sediment load variability over time in data-poor regions, influenced by complex natural and anthropogenic – drivers. It can, thus, be useful for water resources management in ungauged basins in the region. [ABSTRACT FROM AUTHOR]

[Display omitted] • An enhanced degree-day model is developed based on a flexible modeling approach. • Calibration and control were based on local and remotely-sensed snow data, and runoff. • The development resulted in a one-free parameter snow- and ice-accounting routine. • The model is statistically equivalent or better than the alternative models tested. This paper evaluates the degrees of freedom and complexity warranted in temperature-index models to jointly simulate local snow measurements, remotely-sensed snow cover and runoff in mountainous areas. To address this issue, the snow- and ice-accounting routine (SIAR) on top of the GR4J model was developed on a dataset covering 17 mountainous catchments (45 to 3580 km2) in the French Alps and Pyrenees. Model calibration and control were based on streamflow series, fractional snow cover (FSC) computed from MODIS snow products and at least one chronicle of local measurements of snow water equivalent (SWE) acquired in each catchment for the period 2004–2016. SIAR was applied according to an adaptable number of elevation bands and different parametrizations ranging from 11 free parameters (precipitation orographic correction, temperature lapse rate, variation in the temperature lapse rate, snowfall adjustment, rainfall lapse rate, thermal inertia of the snow pack, constant and variable part of the degree-day snow melt factor, degree-day ice melt factor, 2-parameter hysteresis between SWE and FSC), to only fixed parameters. Results showed that the one-free-parameter SIAR is as efficient as more parametrized versions in simulating both local and distributed snow dynamics as well as runoff. Interestingly, using SIAR without any free parameters by fixing the snowfall adjustment to a median value of 60% only led to slight impairment of local SWE dynamics. Certain processes represented in SIAR (glacier-component, sublimation, simple energy balance, snowpack cold-content, variable melt factor) were then alternatively turned off to justify those retained in its final version. The modeling performances were also compared by applying SIAR with different distribution options ranging from full distribution according to 0.25 km2 cells to lumped mode. A number of equal-area elevation bands according to the catchment hypsometry proved to be a good compromise as it allowed snow and runoff simulations of similar accuracy to the full distribution mode, while limiting computational time. Finally, SIAR was compared with the Cemaneige snow routine, which showed its modeling performance was better. These findings suggest that it is possible, and even advisable, to limit the number of free parameters in temperature-index models in order to reduce problems of over-parameterization and equifinality. [ABSTRACT FROM AUTHOR]

Rainfall-induced shallow landslide is a common geological hazard around the world that can pose serious threat to both lives and property. Previous studies suggested that rainfall induced shallow landslides always occurred either above the bottom of a downward wetting front in infiltration, or below a rising water table. This paper proposed a different mechanism of shallow landslides in a modelling approach. The comprehensive physics-based Integrated Hydrology Model (InHM) and the infinite slope stability model were employed on a virtual slope to simulate the hydrologic response and estimate the slope stability. Different failure mechanisms with various characteristics were investigated under diverse rainfall scenarios (i.e., various combinations of rainfall depths, durations and temporal patterns). The results showed a novel mechanism of shallow landslides: a significant vertical change in saturated hydrologic conductivity causes the accumulation of infiltrated water, and subsequently leading to an increase pore pressures and landslides in unsaturated soil layer. More importantly, this kind of landslide would bring larger landslide volume even with smaller total rainfall depths. This study expanded our understanding about landslides and had important application in alleviating the loss of lives and property. [ABSTRACT FROM AUTHOR]

This paper presents a case study on an ancient drainage system in the Hetao old urban center, Ganzhou, China, which is playing a key role in the prevention of urban flooding. The system includes ancient city walls, a Fushou ditch, and ponds. The ancient city walls were constructed to defend the river water intrusion. The Fushou ditch and ponds primarily served for the drainage and storage of the rainwater, respectively. The process of rainwater management in the Ganzhou ancient drainage system, including the guidance, storage, infiltration, purification and utilization, and drainage of rainwater, confirms the feasibility of the design concept of modern sponge city. Based on the lessons learnt from this ancient drainage system, the guideline for the rainwater management and design of sponge city is proposed. It is suggested that the sponge city should be planned before the new urban development. During the construction of sponge city, the dispersed ponds and the ditches made of durable and permeable materials in Ganzhou ancient drainage system are recommended to increase the amount of infiltration, storage and utilization of rainwater. [ABSTRACT FROM AUTHOR]

Diffuse agricultural pollution is widely recognized as a significant threat to the quality of water resources. Metaldehyde is a soluble synthetic aldehyde pesticide used globally in agriculture which has caused recent concern due to high observed levels (exceeding the European and UK standards for pesticides in drinking water value of 0.1 µg/l) in surface waters utilized for potable water supply. This paper describes the development of a new travel time based physically distributed metaldehyde prediction model which aims to describe the short term fluctuations of metaldehyde concentrations in surface waters caused by rainfall runoff events. This will enable water infrastructure operators to consider informed control decisions in order to improve the quality of abstracted surface water. The methodology is developed and trailed within a case study catchment in the UK. The new approach integrates spatially and temporally disaggregated surface runoff generation, routing and build-up/wash-off concepts using a simple structure in a GIS environment to build a metaldehyde concentration prediction model. The use of 1 km 2 resolution radar rainfall data and identification of high risk areas in the catchment provide an approach which considers the spatio-temporal variations of pollutant generation and transport in the catchment. The model is calibrated and validated using available catchment flow and a new metaldehyde concentration dataset acquired using automatic samplers over four rainfall events. An average coefficient of determination and model efficiency of 0.75 and 0.46 respectively have been obtained for the rainfall events used to validate the model. This shows the capability of the model for the intended purpose of predicting the arrival of peak metaldehyde concentrations at surface water abstraction sites and informing abstraction decisions. [ABSTRACT FROM AUTHOR]

Rainwater harvesting (RWH) systems are an effective low impact development practice that provides both water supply and runoff reduction benefits. A stochastic modelling approach is proposed in this paper to quantify the water supply reliability and stormwater capture efficiency of RWH systems. The input rainfall series is represented as a marked Poisson process and two typical water use patterns are analytically described. The stochastic mass balance equation is solved analytically, and based on this, explicit expressions relating system performance to system characteristics are derived. The performances of a wide variety of RWH systems located in five representative climatic regions of the United States are examined using the newly derived analytical equations. Close agreements between analytical and continuous simulation results are shown for all the compared cases. In addition, an analytical equation is obtained expressing the required storage size as a function of the desired water supply reliability, average water use rate, as well as rainfall and catchment characteristics. The equations developed herein constitute a convenient and effective tool for sizing RWH systems and evaluating their performances. [ABSTRACT FROM AUTHOR]

This paper examines the frequency, distribution tails, and peak-over-threshold (POT) of extreme floods through analysis that centers on the October 2015 flooding in North Carolina (NC) and South Carolina (SC), United States (US). The most striking features of the October 2015 flooding were a short time to peak (T p ) and a multi-hour continuous flood peak which caused intensive and widespread damages to human lives, properties, and infrastructure. The 2015 flooding was produced by a sequence of intense rainfall events which originated from category 4 hurricane Joaquin over a period of four days. Here, the probability distribution and distribution parameters (i.e., location, scale, and shape) of floods were investigated by comparing the upper part of empirical distributions of the annual maximum flood (AMF) and POT with light- to heavy- theoretical tails: Fréchet, Pareto, Gumbel, Weibull, Beta, and Exponential. Specifically, four sets of U.S. Geological Survey (USGS) gauging data from the central Carolinas with record lengths from approximately 65–125 years were used. Analysis suggests that heavier-tailed distributions are in better agreement with the POT and somewhat AMF data than more often used exponential (light) tailed probability distributions. Further, the threshold selection and record length affect the heaviness of the tail and fluctuations of the parent distributions. The shape parameter and its evolution in the period of record play a critical and poorly understood role in determining the scaling of flood response to intense rainfall. [ABSTRACT FROM AUTHOR]

Extreme precipitation can cause flooding and may result in great economic losses and deaths. The return level is a commonly used measure of extreme precipitation events and is required for hydrological engineer designs, including those of sewerage systems, dams, reservoirs and bridges. In this paper, we propose a two-step method to estimate the return level and its uncertainty for a study region. In the first step, we use the generalized extreme value distribution, the L-moment method and the stationary bootstrap to estimate the return level and its uncertainty at the site with observations. In the second step, a spatial model incorporating the heterogeneous measurement errors and covariates is trained to estimate return levels at sites with no observations and to improve the estimates at sites with limited information. The proposed method is applied to the daily rainfall data from 273 weather stations in the Haihe river basin of North China. We compare the proposed method with two alternatives: the first one is based on the ordinary Kriging method without measurement error, and the second one smooths the estimated location and scale parameters of the generalized extreme value distribution by the universal Kriging method. Results show that the proposed method outperforms its counterparts. We also propose a novel approach to assess the two-step method by comparing it with the at-site estimation method with a series of reduced length of observations. Estimates of the 2-, 5-, 10-, 20-, 50- and 100-year return level maps and the corresponding uncertainties are provided for the Haihe river basin, and a comparison with those released by the Hydrology Bureau of Ministry of Water Resources of China is made. [ABSTRACT FROM AUTHOR]

Hydrological and non-point source pollution (H/NPS) predictions in ungagged basins have become the key problem for watershed studies, especially for those large-scale catchments. However, few studies have explored the comprehensive impacts of rainfall data scarcity on H/NPS predictions. This study focused on: 1) the effects of rainfall spatial scarcity (by removing 11%–67% of stations based on their locations) on the H/NPS results; and 2) the impacts of rainfall temporal scarcity (10%–60% data scarcity in time series); and 3) the development of a new evaluation method that incorporates information entropy. A case study was undertaken using the Soil and Water Assessment Tool (SWAT) in a typical watershed in China. The results of this study highlighted the importance of critical-site rainfall stations that often showed greater influences and cross-tributary impacts on the H/NPS simulations. Higher missing rates above a certain threshold as well as missing locations during the wet periods resulted in poorer simulation results. Compared to traditional indicators, information entropy could serve as a good substitute because it reflects the distribution of spatial variability and the development of temporal heterogeneity. This paper reports important implications for the application of Distributed Hydrological Models and Semi-distributed Hydrological Models, as well as for the optimal design of rainfall gauges among large basins. [ABSTRACT FROM AUTHOR]

The high solubility and conservative behaviour of chloride make it ideal for use as an environmental tracer of water and salt movement through the hydrologic cycle. For such use the spatial distribution of chloride deposition in rainfall at a suitable scale must be known. A number of authors have used point data acquired from field studies of chloride deposition around Australia to construct relationships to characterise chloride deposition as a function of distance from the coast; these relationships have allowed chloride deposition to be interpolated in different regions around Australia. In this paper we took this a step further and developed a chloride deposition map for all of Australia which includes a quantification of uncertainty. A previously developed four parameter model of chloride deposition as a function of distance from the coast for Australia was used as the basis for producing a continental scale chloride deposition map. Each of the four model parameters were made spatially variable by creating parameter surfaces that were interpolated using a pilot point regularisation approach within a parameter estimation software. The observations of chloride deposition were drawn from a literature review that identified 291 point measurements of chloride deposition over a period of 80 years spread unevenly across all Australian States and Territories. A best estimate chloride deposition map was developed from the resulting surfaces on a 0.05 degree grid. The uncertainty in the chloride deposition map was quantified as the 5th and 95th percentile of 1000 calibrated models produced via Null Space Monte Carlo analysis and the spatial variability of chloride deposition across the continent was consistent with landscape morphology. The temporal variability in chloride deposition on a decadal scale was investigated in the Murray-Darling Basin, this highlighted the need for long-term monitoring of chloride deposition if the uncertainty of the continental scale map is to be reduced. Use of the derived chloride deposition map was demonstrated for a probabilistic estimation of groundwater recharge for the southeast of South Australia using the chloride mass balance method. [ABSTRACT FROM AUTHOR]

Subsurface flow is often recognized as a dominant runoff generation process. However, observing subsurface properties, and understanding how they control flow pathways, remains challenging. This paper investigates how surface slope and bedrock cleavage control subsurface flow pathways in a slate bedrock headwater catchment in Luxembourg, characterised by a double-peak streamflow response. We use a range of experimental techniques, including field observations of soil and bedrock characteristics, and a sprinkling experiment at a site located 40 m upslope from the stream channel. The sprinkling experiment uses Br − as a tracer, which is measured at a well downslope from the plot and at various locations along the stream, together with well and stream hydrometric responses. The sprinkling experiment is used to estimate velocities and celerities, which in turn are used to infer flow pathways. Our results indicate that the single or first peak of double-peak events is rainfall-driven (controlled by rainfall) while the second peak is storage-driven (controlled by storage). The comparison between velocity and celerity estimates suggests a fast flowpath component connecting the hillslope to the stream, but velocity information was too scarce to fully support such a hypothesis. In addition, different estimates of celerities suggest a seasonal influence of both rainfall intensity rate and residual water storage on the celerity responses at the hillslope scale. At the catchment outlet, the estimated of the total mass of Br − recovered in the stream was about 2.5% of the application. Further downstream, the estimate mass of Br − was about 4.0% of the application. This demonstrates that flowpaths do not appear to align with the slope gradient. In contrast, they appear to follow the strike of the bedrock cleavage. Our results have expanded our understanding of the importance of the subsurface, in particular the underlying bedrock systems, and the importance of cleavage orientation, as well as topography, in controlling subsurface flow direction in this catchment. [ABSTRACT FROM AUTHOR]