Abstract

Tropical and subtropical regions can be particularly severely affected by flooding. Climate change is expected to lead to more intense precipitation in many regions of the world, increasing the frequency and magnitude of flood events. This paper presents a review of studies assessing the impacts of climate change on riverine flooding in the world's tropical and subtropical regions. A systematic quantitative approach was used to evaluate the literature. The majority of studies reported increases in flooding under climate change, with the most consistent increases predicted for South Asia, South East Asia, and the western Amazon. Results were more varied for Latin America and Africa where there was a notable paucity of studies. Our review points to the need for further studies in these regions as well as in Australia, in small to mid-sized catchments, and in rapidly urbanising catchments in the developing world. Adoption of non-stationary flood analysis techniques and improved site-specific socio-economic and environmental model scenarios were identified as important future directions for research. Data accessibility and mitigation of model uncertainty were recognised as the principal issues faced by researchers investigating the impacts of climate change on tropical and subtropical rivers.

INTRODUCTION

Floods are one of the most costly and widespread climate-related natural hazards (Jonkman 2005). Between 1980 and 2009, it was estimated that floods led to the death of 539,000 people and adversely affected the lives of 2.8 billion people (Doocy et al. 2013), totalling US$654 billion worth of damage (adjusted for inflation) worldwide (Munich Re 2017). Tropical and subtropical regions are often subjected to some of the worst flooding and this may be exacerbated under climate change. The United Nations International Strategy for Disaster Risk Reduction (2009) reports that the 10 countries most prone to flooding are all located in tropical South and South East Asia, with countries in South America and Africa also widely affected. As there are a number of developing countries in the tropics and subtropics, the effects of flooding may be exacerbated due to poor infrastructure and health care. In Bangladesh, for example, water-borne diseases are responsible for a greater number of flood-related deaths than drownings (Jha et al. 2012).

The intensity of extreme precipitation events is predicted to increase throughout most parts of the world under climate change (Groisman et al. 2005), potentially leading to an increase in the magnitude of extreme flows. Despite a wide consensus of increased precipitation extremes under rising temperatures, several studies have suggested that this has not led to an increase in flooding, except in small catchments where flashy flows dominate (Wasko & Sharma 2017; Sharma et al. 2018). However, trend analysis of extreme flooding events exceeding 100-year levels has shown a significant increase towards the second half of the twentieth century (Milly et al. 2002), while analysis of less extreme events has generally indicated a decreasing trend, particularly for large catchments (Do et al. 2017). Modelling on a global scale suggests that the increasing trend of the most extreme floods may continue into the future (Hirabayashi et al. 2013). Changes in the magnitude of these extreme flooding events will have major implications for landholders, flood mitigation strategies, and infrastructure design. Quantifying these effects allows engineers and managers to consider changes to urban and infrastructure design standards based on a range of possible future eventualities and scenarios.

A large number of studies have been conducted using climate change projections to quantify the effects on riverine flooding. These studies typically follow a model chain outlined by Xu et al. (2005) consisting of the following, global circulation model outputs, global climate model (GCM) downscaling and bias correction methodologies, and hydrological model applications. Selection of the most appropriate models and techniques for a given catchment can be challenging, as catchment size, topography, location, and climatic conditions must all be taken into account. Changes to sea levels and anthropogenic activities (land-use changes, urbanisation, water demand, and flood mitigation/control structures) may also be considered, adding further complexity to models. Each step in the modelling process involves assumptions, which inevitably cause some degree of error, and is compounded with each successive modelling step (Praskievicz & Chang 2009). The mitigation and quantification of this model uncertainty is a major consideration in impact studies.

Review articles assessing the impacts of climate change on riverine flooding can be found on a global (Hunt & Watkiss 2011; Kundzewicz et al. 2014), European (Kundzewicz et al. 2010; Madsen et al. 2014), and country-wide scale (Miller & Hutchins 2017) but are lacking elsewhere. This paper aims to critically evaluate the current literature in the tropical and subtropical regions of the world and examine the factors that are unique to these climates. For this purpose, a systematic quantitative literature approach has been adopted (Pickering & Byrne 2014), whereby articles have been coded into a customised database to quantitatively assess the literature. This is the first review of its kind conducted in tropical and subtropical regions and the first to adopt a quantitative approach.

To maintain a comparable standard of literature, ‘grey’ literature, including reports, conference papers, unpublished articles, theses, and book sections have been excluded from this review, rather only English language peer-reviewed journal articles have been considered. Scopus and Web of Science were searched using a defined set of search terms. The inclusion criteria specified that articles must consider climate change scenarios, the use of hydrological modelling, and analysis of extreme river flows in either tropical or subtropical regions. A total of 134 peer-reviewed journal articles were included in this review from an initial evaluation of 4,711 (Figure 1). The multitude of papers included in this review allows for a more comprehensive analysis of the literature than that found in other similar reviews. A critical evaluation of the following aspects of the literature has been included, (i) the geographic distribution of studies, (ii) the chosen methodologies in terms of downscaling, bias correction, hydrological model choice, and analysis, (iii) the key findings of the literature and, (iv) the implications of these findings and future directions for research.

Figure 1

PRISMA diagram (Moher et al. 2009) showing the number of papers included and excluded at each stage of the review process and the keywords used to find the relevant literature from the online databases.

Figure 1

PRISMA diagram (Moher et al. 2009) showing the number of papers included and excluded at each stage of the review process and the keywords used to find the relevant literature from the online databases.

DISTRIBUTION OF THE LITERATURE

An updated version of the Köppen Climate Classification (Peel et al. 2007) was used as the basis to delineate subtropical and tropical catchments (Figure 2). When river basins covered numerous climate zones, only when the majority of the catchment was within tropical or subtropical climates was it considered in this review. As such, studies on the lower Mississippi River were excluded, while those on the lower Yangtze were included. The literature was approximately evenly split between tropical and subtropical catchments, covering 5 continents and 37 countries. Most of the research (59%) was published on Asian river basins, with the remainder published in Africa (13%), the Americas (12%), and on a global scale (16%). Figure 3 presents the number of studies included in this review on a per-country basis, excluding those studies conducted on a global scale. Table S1 in the supplementary materials presents a detailed summary of the studies conducted in East Asia, Table S2 in the supplementary materials for those in South East Asia, Table S3 in the supplementary materials for South Asia, Table S4 in the supplementary materials for Africa, Table S5 in the supplementary materials for the Americas, and Table S6 in the supplementary materials for those studies conducted on a global scale. A full list of acronyms used in these tables can also be found in the supplementary materials.

Figure 2

Regions in this study considered as tropical or subtropical (highlighted) based on the Köppen Climate Classification devised by Peel et al. (2007).

Figure 2

Regions in this study considered as tropical or subtropical (highlighted) based on the Köppen Climate Classification devised by Peel et al. (2007).

Figure 3

Number of studies conducted by country in tropical and subtropical by regions. For rivers that were transboundary, only those countries that made up part of the study area and were in tropical or subtropical climates were considered.

Figure 3

Number of studies conducted by country in tropical and subtropical by regions. For rivers that were transboundary, only those countries that made up part of the study area and were in tropical or subtropical climates were considered.

It is evident from Figure 4 that there has been considerably more research published on Chinese, South Asian, and South East Asian rivers compared with other regions in the tropics/subtropics. The literature is also skewed towards studies of major river basins. Six major rivers including the Amazon, Niger, Ganges, Brahmaputra, Mekong, and Pearl River, are the focus of approximately 26% of all studies reviewed, each having an area greater than 450,000 km2. Most of the literature has been conducted on similarly large river basins, while just under 4% of the literature was published on river basins less than 1,000 km2 in area (Figure 4). This suggests that there is a need for further studies on smaller river basins as the flood-producing mechanisms are inherently different. Furthermore, studies are also required on heavily urbanising catchments in developing regions of the tropics and subtropics as they may be disproportionately affected by future flooding.

Figure 4

Number of studies conducted by continent and region (left) and number of studies conducted by catchment size (right). For the catchment size plot, <10,000 is the range between 1,000 and 10,000 km2, <100,000 is the range between 10,000 and 100,000 km2, and <1,000,000 is the range between 100,000 and 1,000,000 km2.

Figure 4

Number of studies conducted by continent and region (left) and number of studies conducted by catchment size (right). For the catchment size plot, <10,000 is the range between 1,000 and 10,000 km2, <100,000 is the range between 10,000 and 100,000 km2, and <1,000,000 is the range between 100,000 and 1,000,000 km2.

There is a marked shortage of literature focussing on parts of the Americas, Africa, and Australia. While the exclusion of non-English language journals in this review likely omitted a number of studies from Latin America and Africa, there is already low research output in these regions due to the difficulty of funding research. No studies were retrieved from Central America or the Caribbean, despite several studies being conducted in South America. This may relate to the small size of river basins in Central America and the Caribbean, as small river basins have been shown to receive considerably less research attention than larger basins (Figure 4). This assertation would seem to be supported in South America, as five of the six studies included in this review focussed on the Amazon River or its tributaries, while just one study was conducted elsewhere on the continent. In Africa, there has been a similar disproportionate focus towards the Niger River, though the literature is overall much more evenly distributed across the continent. A paucity of studies originating from central Africa including the Congo River can also be noted. The scarcity of literature originating from tropical/subtropical northern Australia is perhaps most surprising, as a large portion of the country and population reside in tropical and subtropical zones. Here, research has focussed on the temperate southwest (Evans & Schreider 2002) and southeast (Schreider et al. 1996, 2000), with only one study obtained for the tropical and subtropical northern regions. There is a clear need for further tropical and subtropical river basin research to be conducted throughout Australia, Latin America, and Africa.

RESEARCH METHODOLOGIES ADOPTED IN THE LITERATURE

Climate models

The selection of GCMs, Regional Climate Models (RCMs), emission scenarios, downscaling techniques, bias correction techniques, and hydrological models all influence the outcome of climate impact studies. Various combinations and ensembles of these components have been applied throughout the reviewed literature. In total, 99 different iterations of GCMs and numerous additional RCMs were used across all papers. This is somewhat unsurprising, given that the literature extends back approximately 17 years over which climate models have been continually updated and revised. There has been a steep upward trend in the number of studies conducted since 2010 with 90% of all reviewed literature published since this year and over half published since 2016 (Figure 5). This trend is most likely to continue as governments and researchers prioritise climate change impacts to assess past and future engineering design and planning.

Figure 5

Number of studies published and number of studies reporting use of a single or multiple GCM(s) by year, where 2019 only included part of the year.

Figure 5

Number of studies published and number of studies reporting use of a single or multiple GCM(s) by year, where 2019 only included part of the year.

Approximately 71% of the studies adopted an ensemble approach in which two or more climate models were employed to provide multiple projections as has been widely recommended (Dankers & Feyen 2009; Prudhomme & Davies 2009; Teutschbein & Seibert 2010). The remaining 29% of the reviewed literature applied just a single climate model for assessing the impacts of climate change. This is considered a major limitation as results from these studies represent just a single plausible climate outcome that may not be representative of the likely effects of climate change.

Several of the reviewed papers have reported that the largest source of uncertainty in the modelling process is from the GCM structure (Aich et al. 2014, 2016; Li et al. 2016a). Similar results have been found globally (Prudhomme et al. 2003; Kay et al. 2009), highlighting the importance of considering a range of climate models. Liu et al. (2013) reported that the relative uncertainty in predictions arising from the GCM structure was greater for projections in the mid- to late century while statistical downscaling and emission scenario selections are greater sources for predictions in the 2020s. Similar results were reported by Shen et al. (2018), suggesting that as climate projections advance further from the baseline, the divergence between model projections increases. Li et al. (2016a) found the uncertainty from GCM predictions to be spatially variable, with greater relative uncertainty in the subtropical regions to the south and east of China, compared with the more arid north and west where the uncertainty from hydrological modelling was more pronounced. Similarly, Pechlivanidis et al. (2017) reported greater relative uncertainty from GCMs in the tropical and subtropical Niger and Ganges Basins compared with the Lena and Rhine Basins. Precipitation projections from GCMs in the deep tropics, particularly over Africa and South America, have been shown to be the more uncertain than elsewhere in the world (Rowell 2012). Vetter et al. (2015) concluded that the high uncertainty introduced from climate models in the upper Niger Basin was due to the local monsoonal climate in which river runoff responds principally to high precipitation driven by the GCMs.

Emission scenarios

Over 43% of the studies employed emission scenarios from the International Panel on Climate Change Special Report on Emissions Scenarios (SRES; Nakicenovic et al. 2000), while just under 50% used the more recent Representative Concentration Pathways (RCP; Meinshausen et al. 2011). The remaining (usually older studies) made use of alternative emission scenarios. Approximately 40% of the reviewed literature applied one emission scenario, 32% applied two, 13% applied three, and 12% applied four emission scenarios (Figure 6). Compared with climate models, emission scenarios introduce only minor uncertainty, especially for projections in the mid- to late twenty-first century (Liu et al. 2013; Tian et al. 2016; Wang et al. 2017; Yuan et al. 2017).

Figure 6

Number of studies conducted by emission scenario group and number of emission scenarios used.

Figure 6

Number of studies conducted by emission scenario group and number of emission scenarios used.

Downscaling

GCMs run on a coarse resolution (typically 100–300 km) which make them unable to adequately represent local climatic features (Fowler et al. 2007), and as such, downscaling is often required. Both dynamic and statistical downscaling techniques have been widely applied throughout the literature. Approximately 35% of the studies reviewed adopted dynamic downscaling using RCMs while the remaining studies applied either statistical techniques or no downscaling at all. Bias correction is applied to correct systematic biases present in climate models including an overestimation in the number of wet days, underestimation in rainfall intensity, and inadequate year-to-year variability (Ines & Hansen 2006). The most common bias correction approaches adopted in the literature include the delta change, quantile mapping, scaling, and a trend preserving technique proposed by Hempel et al. (2013), applied in 24, 22, 13, and 14% of studies, respectively. Numerous variations of these techniques were utilised in addition to a number of alternative approaches.

Yuan et al. (2017) conducted a thorough assessment of the relative contributions to uncertainty from the emission scenario, climate model, statistical downscaling/bias correction technique, hydrological model, and flood frequency distribution. They reported that statistical downscaling and bias correction were the predominant source of uncertainty for projections involving high flows and flooding events. Chen et al. (2013) concluded that uncertainty due to downscaling and bias correction was more significant for projections of extremes than mean flows. Dobler et al. (2012) found similar results for Europe and suggested applying a range of bias correction techniques to account for this uncertainty when conducting impact studies related to extreme events. These findings highlight the importance of bias correction, especially for the modelling of flooding. In smaller to mid-sized catchments (where intense, short-duration precipitation can be a major source of flooding), bias correction is especially important. Of the bias correction techniques adopted within the literature, the quantile mapping approach appears the most suitable for flood impacts studies as it is best able to reduce systematic errors at high quantiles (Dobler et al. 2012; Chen et al. 2013).

Hydrological models

Fifty-one different hydrological, rainfall-runoff, and hydrodynamic models were applied across all studies. The most commonly used were the ‘Variable Infiltration Capacity’ (Liang 1994), ‘Hydrologiska Byråns Vattenbalansavdelning’ (Bergstrom 1976), and ‘Soil and Water Assessment Tool’ (Arnold et al. 1998) hydrological models, adopted in 16, 15, and 9% of the studies, respectively. A small subset of the research utilised hydrodynamic modelling for more accurate assessments of river flow, applying models such as SOBEK (e.g., Budiyono et al. 2016; Wei et al. 2016), MIKE 11 (e.g., Mirza 2002; Mirza et al. 2003; Kure & Tebakari 2012; Supharatid et al. 2016; Vo et al. 2016), HEC-RAS (Arunyanart et al. 2017; Shrestha & Lohpaisankrit 2017), and FLO-2D (e.g., Mishra et al. 2017). Distributed grid-based models were adopted in 37% of literature and were most widely used for global and large-scale studies. Model resolution ranged from 0.5° (approximately 55 km; e.g., Gain et al. 2013; Dankers et al. 2014; Arnell & Gosling 2016) to 200 m (e.g., Zhao et al. 2016). The remaining literature utilised semi-distributed models, and in some cases, lumped models.

The potential uncertainty derived from hydrological modelling is often overlooked in the literature. While some studies have suggested that this uncertainty is significant and cannot simply be ignored (Tian et al. 2013, 2016), others have concluded that the relative uncertainty contributed from hydrological modelling is minor, especially when compared with GCM structure (Menzel et al. 2006; Kay et al. 2009; Teng et al. 2012). Asadieh & Krakauer (2017) reported in their global study that the global hydrological models contributed more to uncertainty in streamflow changes than the GCMs. They therefore recommended that future studies adopt an ensemble of hydrological models in addition to an ensemble of GCMs.

Consideration of dams

Many studies in this review were carried out on sizeable river basins regulated by many large dams and reservoirs. The Mekong Basin has seen major dam developments over recent years for hydropower and irrigation purposes, with many more in the planning phase. When all planned dams are complete, the active storage capacity is expected to increase to 100 km3 from the 5 km3 initially available in 2010 (Johnston & Kummu 2012). Given this significant increase, several studies have considered the combined effects of climate change with future damming in the Mekong (Lauri et al. 2012; Wang et al. 2017; Whitehead et al. 2019). Lauri et al. (2012) found an increase in the annual peak discharge downstream under climate change, but a decrease when considering additional effects of future dams. Whereas Wang et al. (2017) reported that while regulation would have a significant impact on upstream flooding, it would have only minor effects on flood peaks and frequency downstream. Other studies have chosen to ignore current and future dams altogether, instead focussing on the impacts of climate change as if the system were in its natural state (Kiem et al. 2008; Västilä et al. 2010; Phi Hoang et al. 2016).

Likewise, studies in South Asia have generally considered rivers as if they were unregulated, despite numerous large dams and irrigation schemes throughout the region. A shortage of available data throughout the region, as noted by Hopson & Webster (2010), may explain why dams are often overlooked. Nonetheless, Mohammed et al. (2018) argued that the effects of regulation were minimal during the flooding season for the Ganges River as most structures are intended for use primarily during the dry season and not as flood mitigation measures. Similarly, due to the large number of dams and a lack of knowledge of the operating procedures, studies along the Yangtze River have also neglected the impacts of the numerous large dams constructed over the last 50 years (Gu et al. 2014, 2018; Yu et al. 2018). However, again it has been argued that due to the high precipitation totals in the wet season, these dams have little effect on the main channel discharge (Birkinshaw et al. 2017; Gu et al. 2018). By contrast, many studies in the Niger Basin have often considered the effects of major dams but not the effects of future planned dams (Aich et al. 2014, 2016; Thompson et al. 2016, 2017). However, this typically only involves the consideration of a few key structures which are sufficient to capture the effects of regulation, compared with the many hundreds or thousands of dams that would need to be considered throughout the Yangtze or Ganges Basins.

Data accessibility

A subset of the literature discussed difficulties in obtaining high-quality observational data for model setup, calibration, and validation. Obtaining suitable resolution in datasets for digital elevation models, land use, and bathymetry can be challenging in these regions, particularly for the analysis of small river systems. In parts of Africa, Asia, and the Americas, there is often a lack of climate observations at a sufficiently high spatial and temporal resolution to be effectively used for hydrological modelling and bias correction (Andersson et al. 2011). This was most evident for studies conducted in Africa where spatial coverage of meteorological and streamflow gauges was especially coarse. An array of reanalysed climate datasets, such as APHRODITE (Yatagai et al. 2012) and WATCH (Weedon et al. 2011), have thus been used within the literature to supplement the limited observational data. There are, however, often large discrepancies in precipitation values between the various reanalysed datasets, especially in regions with poor gauge density such as much of Africa (Fekete et al. 2004).

The application of erroneous datasets for model calibration and bias correction may result in a biased hydrological model affecting flood estimates. Stream gauge networks are also limited throughout much of the tropics and subtropics, with historical records having neither the longevity nor consistency of similar gauges in Europe and North America. Model calibration and validation is thus more complicated and historical flood frequency analysis is less accurate with limited historical streamflow records. A lack of cross-border cooperation in transboundary catchments and delays between data collection and data availability in some countries further limit data accessibility (Artan et al. 2007). There is evidently a need to improve data accessibility and for continual improvements to be made to reanalysed datasets. The development of remote sensing technology may help to improve access to quality meteorological data in remote and poorly gauged regions, which would be beneficial to modellers.

Flood analysis techniques

Several techniques were adopted in the literature for the analysis of riverine flooding, of which, flood frequency analysis was the most commonly applied, being used in 53% of the studies. This involved comparisons between historical and future projected flood magnitudes for specified return periods. Of the studies using flood frequency analysis, 90% used the Annual Maxima (AM) series, whereby yearly flow maxima's were used for flood estimation. The remaining literature utilised the Peak-Over-Threshold (POT) in which all statistically independent discharge values exceeding a selected threshold were analysed. This series is advantageous compared with the AM series as it allows for more data points to be analysed and ignores superfluous data that might have otherwise been included, a crucial advantage in highly variable climates. While the AM series dominates the literature, the POT series is potentially more appropriate for flood estimation, given the short timeframes considered (typically 20 or 30 years) for analysis of historical and future climates.

Traditional flood frequency analysis assumes stationarity, whereby the distribution of the flood frequency curves is assumed to be invariant for a given period (Prudhomme et al. 2003). However, due to continually changing climatic and hydraulic conditions (e.g., from land use, river infrastructure, and urbanisation changes), the assumption of stationarity may not always be reasonable, especially in cases where large man-made changes occur within a catchment during the time frame in question (Strupczewski et al. 2001). However, few studies have utilised non-stationary flood frequency techniques for flood impact assessments and this requires additional research attention.

Other studies have made comparisons between historic and future mean annual floods, high flows, and flood events derived from specific return period precipitation or storm events. Changes in high flows were assumed to be indicative of changes to flooding and were utilised in 26% of the reviewed papers. This method can be advantageous compared with flood frequency analysis as projections of high flows are more accurate than those of large flooding events (Aich et al. 2016). However, the rate of change predicted for high flows may not be the same as the rate of change for extreme flows, and as such, this method should only be used to provide an indication of flood changes.

Summary

There is evidently a wide array of possible approaches for assessing the impacts of climate change on extreme discharge. The choice of GCM/RCM, downscaling technique, bias correction, hydrological model, and analysis technique collectively affect the results. These choices can depend on a number of factors including computational power, budget, research domain, RCM availability, data accessibility, model familiarity, and topographic and climate variability within the spatial domain. It is largely up to the discretion of the researcher to choose adequate methods and techniques that suit their individual needs and to justify their choice appropriately.

KEY FINDINGS

Asia

Both increases and decreases in extreme flows were predicted across the literature. There was a general consensus throughout Asia towards increased flooding under climate change. In South Asia, increased flooding was projected for southern Nepal (Devkota & Gyawali 2015; Mishra & Herath 2015; Perera et al. 2015), Bangladesh (Mirza 2002; Mirza et al. 2003; Masood & Takeuchi 2016; Mohammed et al. 2018), various catchments in India (Jana et al. 2015; Mathison et al. 2015; Whitehead et al. 2018), the Mahanadi (Gosain et al. 2006; Asokan & Dutta 2008; Jin et al. 2018b), the Ganges (Whitehead et al. 2015; Tsarouchi & Buytaert 2018), and for the Brahmaputra River (Gain et al. 2011, 2013; Dutta & Ghosh 2012; Mohammed et al. 2017a, 2017b; Philip et al. 2019). Contrasting some of these findings, Gosain et al. (2011) predicted minor decreases in high flows for the Ganges, Brahmaputra, Krishna, and Cauvery Rivers but increases for the remainder of the country. Pichuka et al. (2017) predicted a decrease in the number of small flood events, but an increase in the magnitude of larger floods for the Bhadra River, while Bothale & Katpatal (2017) reported uncertain changes for the upper Wardha River. Decreased flooding was predicted for the Wainganga River (Das & Umamahesh 2017, 2018) and for two small catchments in south India (Mudbhatkal et al. 2017).

In South East Asia, increases in flooding were projected for the Mekong (Kiem et al. 2008; Västilä et al. 2010; Lauri et al. 2012; Phi Hoang et al. 2016; Edangodage Duminda Pradeep et al. 2017; Wang et al. 2017; Whitehead et al. 2019), the Yang (Shrestha & Lohpaisankrit 2017), the Trian (Dong et al. 2018), and for the Red River (Giuliani et al. 2016). Increases were also projected for catchments in Malaysia (Amin et al. 2017), the Philippines (Tolentino et al. 2016), and for the Ciliwung River in Indonesia (Emam et al. 2016; Mishra et al. 2017). Conversely, Budiyono et al. (2016) projected decreased flood risk for the Ciliwung River when considering only the effects of climate change; however, when combined with the effects of sea-level rise and land-use changes, flood risk was predicted to increase considerably. Muis et al. (2015) reported an increase in the severity of floods over large parts of Indonesia with decreases projected for Java. For the Va Gia-Thu Bon catchment in Vietnam, Vo et al. (2016) predicted an increase in flooding, while Dang et al. (2017) reported uncertain changes. Similarly, for the Chao River in Thailand, both increased flooding (Wichakul et al. 2015; Supharatid et al. 2016) and uncertain changes were reported (Hunukumbura & Tachikawa 2012; Kure & Tebakari 2012). Decreases to annual maximum flows have been predicted for the Lampao River in Thailand (Arunyanart et al. 2017).

Li et al. (2016a) projected flood magnitudes to increase throughout subtropical South China by 2100 despite a predicted decrease in annual precipitation, which was the result of a projected intensification of extreme precipitation. Increased extreme river flows were projected for Taiwan (Wei et al. 2016), the lower and middle Yangtze River (Gu et al. 2014, 2018; Ju et al. 2014; Yu et al. 2018), five river basins of Poyang Lake (Li et al. 2016b), and numerous smaller basins throughout China (Xu et al. 2011; Lu et al. 2013; Qin & Lu 2014; Kai et al. 2016; Gao et al. 2018; Shen et al. 2018; Yin et al. 2018). In the Beijiang River, both increased flooding (Wu et al. 2014, 2015) and uncertain changes were reported (Liu et al. 2017). Inconclusive results were also reported for the Lanjiang (Zhang et al. 2014) and the Jinhua Rivers (Tian et al. 2013), though more recent studies found flood magnitudes were likely to increase for the Lanjiang River (Zhang et al. 2015) and decrease for the Jinhua River (Tian et al. 2016). Increased flooding was also widely predicted for the Pearl River in south China (Liu et al. 2012, 2013; Yuan et al. 2016). Liu et al. (2018) projected an increase in the occurrence of small flooding events in the catchment and a decrease in larger events. While Yuan et al. (2017) and Zhu et al. (2017) both reported uncertain changes for the Xijiang River, a major tributary of the Pearl River.

Africa

Aich et al. (2014) predicted increases in extreme flows for the upper Blue Nile in Ethiopia, uncertain changes for the Niger River, and no changes for the Oubangui River in central Africa. Other studies have predicted increased flooding for the Niger River (Aich et al. 2016; Andersson et al. 2017) and decreased or uncertain changes were predicted for the upper Niger Basin (Vetter et al. 2015; Thompson et al. 2016, 2017; Huang et al. 2018). Elsewhere in West Africa, increased flooding was predicted for the Black Volta (Jin et al. 2018a) and the Ouémé River (Essou & Brissette 2013). Bodian et al. (2018) predicted decreased high flows for the Gambia and uncertain changes for the Senegal River. In East Africa, Taye et al. (2011) reported an increase in the magnitude of 10-year flood events for the Nyando Basin in Kenya and uncertain changes for Lake Tana Basin. Likewise, Nawaz et al. (2010) projected uncertain changes for the upper Blue Nile. Increased flooding was predicted for the Nzoia River in Kenya (Githui et al. 2009) and the Kafue River in Zambia (Ngongondo et al. 2013), whereas decreases were projected for the Pungwe River in Mozambique and Zimbabwe (Andersson et al. 2011). For the Zambezi Basin, Fant et al. (2015) reported an increase in 50-year flood events for sections in Mozambique and Zambia and insignificant changes for sections in Malawi and Zimbabwe.

Americas

In the upper Amazon River basin, increased flooding was widely predicted (Guimberteau et al. 2013; Langerwisch et al. 2013; Mora et al. 2014; Sorribas et al. 2016; Zulkafli et al. 2016), while decreases or uncertain changes were forecasted for the lower Amazon basin (Guimberteau et al. 2013; Langerwisch et al. 2013; Sorribas et al. 2016). Increases were also predicted for the Upper Grande River in Brazil (Viola et al. 2015), the Apalachicola (Chen et al. 2014), San Jacinto (Muttiah & Wurbs 2002), Yadkin-Pee Dee (Suttles et al. 2018), and Wolf Bay Basins (Wang et al. 2014) in the United States. Country-wide studies for the United States reported a likely increase in 100-year floods and high flows throughout the subtropical south east of the country (Naz et al. 2016, 2018; Wobus et al. 2017). Conversely, Risley et al. (2011) reported a likely decrease in high flows for the Flint River, while Zhao et al. (2016) and Chen et al. (2013) noted significant uncertainty in projections for the San Antonio and Chickasawhay River, respectively.

Global studies

In addition to the studies completed on catchment, regional, and national scales, several global studies have been conducted. Arora & Boer (2001) reported that reductions in flood events throughout most of the tropical and subtropical world are likely with the exception of parts of the Indian subcontinent and Brazil. Voss et al. (2002) predicted increased 10-year flood magnitudes for all tropical and subtropical rivers assessed except for the Amazon River. Similar results were reported by Hirabayashi et al. (2008) who predicted increased 100-year flood magnitudes over much of the world, with the most consistent increases in Central Africa and South Asia. Okazaki et al. (2012) and Wiel et al. (2019) also reported likely increases in flooding throughout most of the tropics. Falloon & Betts (2006) found 8 of the 10 rivers most affected by climate change to be in tropical or subtropical regions, while Alfieri et al. (2017) identified that 15 of the 20 most-affected countries were in tropical or subtropical regions. However, most of these studies based their results on the outputs of a single GCM, applying no additional downscaling or bias correction and as such, their findings may not be representative of the likely effects of climate change.

Dankers et al. (2014) applied an ensemble of GCMs predicting flood peaks to increase throughout much of the tropics, with the most consistent increases for South and South East Asia. Other multi-model ensemble studies have also consistently predicted increases for South and South East Asia (Arnell 2003; Hirabayashi et al. 2013; van Vliet et al. 2013; Koirala et al. 2014; Arnell & Gosling 2016; Winsemius et al. 2016; Döll et al. 2018). While the most consistent decreases are projected for parts of South and Central America (van Vliet et al. 2013; Koirala et al. 2014; Winsemius et al. 2016; Asadieh & Krakauer 2017). Hirabayashi et al. (2013) predicted large flooding events to increase, especially in South and South East Asia, Eastern Africa, and the northern portion of the Andes Mountains. Krysanova et al. (2017) predicted mixed results and moderate uncertainty for future high-flow occurrences over many large tropical and subtropical rivers, with high flows predicted to increase only in the Ganges River, but with moderate certainty. Paltan et al. (2018) projected the largest increase in 100-year floods to occur over north India, east China, and the southern Amazon.

Summary

The majority of studies have predicted increases in extreme flows under climate change in both the tropics and subtropics. The percentage of studies predicting increases on a per-country basis is presented in Figure 7. The most consistent changes were observed in South Asia, South East Asia, and the western Amazon, with over 70% of the reviewed literature projecting increased flooding in the future. Results from subtropical China and the United States were also highly consistent, with 67 and 70% of the literature predicting a future increase in riverine flooding, respectively. Mixed findings have been reported for most of Africa and most of South America, likely due to the small subset of literature reviewed from these regions and no consistent, meaningful conclusions can be drawn.

Figure 7

Percentage of studies (excluding studies conducted on a global scale) predicting increased river flooding on a per-country basis in tropical and subtropical regions.

Figure 7

Percentage of studies (excluding studies conducted on a global scale) predicting increased river flooding on a per-country basis in tropical and subtropical regions.

IMPLICATIONS AND FUTURE DIRECTIONS

Implications

In order for research undertaken by the scientific community to be relevant to engineers and decision makers, it is essential that uncertainty is estimated and mitigated (Andersson et al. 2011; Aich et al. 2014). Dankers et al. (2014) suggest approaching the issue of uncertainty from a risk management perspective, whereby even the most unlikely outcome that carries a high risk is considered. In doing so, the full range of plausible eventualities can be planned for and mitigated appropriately. Even so, the research processes adopted in the scientific literature are not always compatible with the legal and economic constraints placed on decision makers (Madsen et al. 2014). Any revisions made to design flood levels are likely to have a wide range of economic ramifications. Changes to the design and operation of hydraulic structures and key infrastructure can be very costly and changes to the delineation of flood hazard mapping will have consequences for insurance premiums affecting property owners. Increased pressure could be placed on water suppliers, as dams may have to operate under lower maximum storage to accommodate the increases in discharge associated with large flooding events.

Greater and more frequent floods would likely also exacerbate erosion processes, as sediment transport occurs disproportionately during extreme events (Romero et al. 2012; Gonzalez-Hidalgo et al. 2013; Boardman 2015). This could cause greater pollutant and sediment loads in rivers, affecting downstream ecosystems. Accelerated erosion processes may result in channel sedimentation, instability, and river routing changes, which can work to undermine the stability of bridges, levees, and other flood control infrastructure. The entrainment and deposition of coarse sediments in river channels may work to reduce bankfull capacity, thereby raising flood levels under future events (e.g., Lane et al. 2007).

The additional effects of sea-level rise and anthropogenic activities (e.g., hydraulic structures construction, land-use changes, and urbanisation) further complicate the issue. In some instances, these effects may be more pronounced than those of climate change (e.g., Budiyono et al. 2016; Zhao et al. 2016). The combination of these changes may be especially devastating in some developing nations of the tropics and subtropics. Bangladesh, for instance, may be jointly affected by more intense cyclones (storm surges), increased extreme river flows, and sea-level rises, all of which may exacerbate flooding. Adaptation strategies and emergency action plans are required to mitigate economic damage and fatalities from such events. These plans must be flexible and robust to account for the range of plausible scenarios and allow future adjustments to be made with advances in modelling (Mathison et al. 2013). Such plans may be more difficult to implement in the developing nations of the tropics/subtropics, as governments understandably may not prioritise them over more immediate issues.

Future directions

Continual improvements must be made to climate models, particularly in the modelling of land-surface processes if projections are to become more reliable (Okazaki et al. 2012). Increasing the availability of sub-daily climate model outputs would be advantageous especially for studies conducted on smaller catchments with flashier flows (Kiem et al. 2008). As it is widely agreed the largest source of uncertainty is from the GCM structure (Kay et al. 2009; Prudhomme & Davies 2009), future research should usefully extend the number of climate models in an ensemble modelling process. Results based on a limited number of scenarios may give a false indication of the direction of change under climate change conditions. Future studies are recommended to avoid overly simplistic bias correction techniques for precipitation, such as the delta change approach as these methods are not well suited to the modelling of extremes. Rather, a quantile mapping technique is recommended as it has been demonstrated to be more reliable in improving the projections of extreme events compared with other bias correction techniques (Dobler et al. 2012; Teutschbein & Seibert 2012). Many studies have acknowledged the need to utilise multiple climate models; however, most studies utilise a single hydrological model and downscaling/bias correction technique. Ideally, an ensemble of hydrological models, downscaling, and bias correction techniques could be employed to better account for uncertainty but this is a time-consuming process and may not always be feasible.

The majority of the reviewed literature has assumed the physical characteristics of the catchment remain the same throughout the study. However, land use, vegetation, and hydraulic structure changes all have significant impacts on the streamflow characteristics. Future studies could consider these changes through the development of site-specific socio-economic and environmental scenarios. In many of these instances, the adoption of non-stationary flood frequency analysis techniques may be preferred over traditional stationary approaches, as they allow changes in the catchment characteristics to be considered.

The research considered in this review has been geographically limited, with minimal to no research found for large portions of Africa, Latin America, and Australia. There is a need for further studies in these regions and throughout the tropics/subtropics generally. The majority of the literature has assessed the effects of climate change on large river systems and as such, additional studies on smaller to mid-sized catchments throughout the tropics are required, as the flood-producing mechanisms in these catchments are inherently different. Studies investigating flooding changes in heavily urbanising catchments in developing regions are also required. There is a need to improve the accessibility and quantity of observational data across much of the tropics. Limited historical records in stream gauge networks in the tropics/subtropics can lead to inaccurate flood frequency estimations, as they do not capture the full range of events. Enhancements in monitoring regimes are needed to improve modelling and our understanding of the extent of natural variability (Kundzewicz et al. 2008). Generally, there are limited available dynamically downscaled climate change projections for these regions compared with those in Europe (Andersson et al. 2011; Phi Hoang et al. 2016) and as such, more RCM outputs should be made available throughout the tropics and subtropics.

ACKNOWLEDGEMENTS

The first author received a Griffith University Postgraduate Research Scholarship. The authors thank the academic librarian staff at Griffith University for guidance with the literature search.

SUPPLEMENTARY DATA

The Supplementary Data for this paper is available online at http://dx.doi.org/10.2166/wcc.2019.175.

REFERENCES

REFERENCES
Abatzoglou
J. T.
,
Brown
T. J.
2012
A comparison of statistical downscaling methods suited for wildfire applications
.
International Journal of Climatology
32
(
5
),
772
780
.
doi:10.1002/joc.2312
.
Aich
V.
,
Liersch
S.
,
Vetter
T.
,
Huang
S.
,
Tecklenburg
J.
,
Hoffmann
P.
,
Koch
H.
,
Fournet
S.
,
Krysanova
V.
,
Muller
N.
,
Hattermann
F. F.
2014
Comparing impacts of climate change on streamflow in four large African river basins
.
Hydrology and Earth System Sciences
18
(
4
),
1305
1321
.
doi:10.5194/hess-18-1305-2014
.
Aich
V.
,
Liersch
S.
,
Vetter
T.
,
Fournet
S.
,
Andersson
J. C. M.
,
Calmanti
S.
,
van Weert
F. H. A.
,
Hattermann
F. F.
,
Paton
E. N.
2016
Flood projections within the Niger River Basin under future land use and climate change
.
Science of the Total Environment
562
,
666
677
.
doi:10.1016/j.scitotenv.2016.04.021
.
Alfieri
L.
,
Bisselink
B.
,
Dottori
F.
,
Naumann
G.
,
Roo
A.
,
Salamon
P.
,
Wyser
K.
,
Feyen
L.
2017
Global projections of river flood risk in a warmer world
.
Earth's Future
5
(
2
),
171
182
.
doi:10.1002/2016EF000485
.
Amin
M. Z. M.
,
Shaaban
A. J.
,
Ercan
A.
,
Ishida
K.
,
Kavvas
M. L.
,
Chen
Z. Q.
,
Jang
S.
2017
Future climate change impact assessment of watershed scale hydrologic processes in Peninsular Malaysia by a regional climate model coupled with a physically-based hydrology modelo
.
Science of the Total Environment
575
,
12
22
.
doi:10.1016/j.scitotenv.2016.10.009
.
Andersson
L.
,
Samuelsson
P.
,
Kjellström
E.
2011
Assessment of climate change impact on water resources in the Pungwe river basin
.
Tellus, Series A: Dynamic Meteorology and Oceanography
63
(
1
),
138
157
.
doi:10.1111/j.1600-0870.2010.00480.x
.
Andersson
J.
,
Ali
A.
,
Arheimer
B.
,
Gustafsson
D.
,
Minoungou
B.
2017
Providing peak river flow statistics and forecasting in the Niger River basin
.
Physics and Chemistry of the Earth
100
,
3
12
.
doi:10.1016/j.pce.2017.02.010
.
Arnell
N. W.
1999
A simple water balance model for the simulation of streamflow over a large geographic domain
.
Journal of Hydrology
217
(
3
),
314
335
.
doi:10.1016/S0022-1694(99)00023-2
.
Arnell
N. W.
2003
Effects of IPCC SRES* emissions scenarios on river runoff: a global perspective
.
Hydrology and Earth System Sciences Discussions
7
(
5
),
619
641
.
doi:10.5194/hess-7-619-2003
.
Arnell
N. W.
,
Gosling
S. N.
2016
The impacts of climate change on river flood risk at the global scale
.
Climatic Change
134
(
3
),
387
401
.
doi:10.1007/s10584-014-1084-5
.
Arnell
N. W.
,
Hudson
D. A.
,
Jones
R. G.
2003
Climate change scenarios from a regional climate model: estimating change in runoff in southern Africa
.
Journal of Geophysical Research: Atmospheres
108
(
D16
),
4519
.
doi:10.1029/2002JD002782
.
Arnold
J. G.
,
Srinivasan
R.
,
Muttiah
R. S.
,
Williams
J. R.
1998
Large area hydrologic modeling and assessment part I: model development
.
Journal of the American Water Resources Association
34
(
1
),
73
89
.
doi:10.1111/j.1752-1688.1998.tb05961.x
.
Arora
V. K.
,
Boer
G. J.
1999
A variable velocity flow routing algorithm for GCMs
.
Journal of Geophysical Research: Atmospheres
104
(
D24
),
30965
30979
.
doi:10.1029/1999JD900905
.
Arora
V. K.
,
Boer
G. J.
2001
Effects of simulated climate change on the hydrology of major river basins
.
Journal of Geophysical Research: Atmospheres
106
(
D4
),
3335
3348
.
doi:10.1029/2000JD900620
.
Artan
G.
,
Gadain
H.
,
Smith
J. L.
,
Asante
K.
,
Bandaragoda
C. J.
,
Verdin
J. P.
2007
Adequacy of satellite derived rainfall data for stream flow modeling
.
Natural Hazards
43
(
2
),
167
185
.
doi:10.1007/s11069-007-9121-6
.
Arunyanart
N.
,
Limsiri
C.
,
Uchaipichat
A.
2017
Flood hazards in the Chi River Basin, Thailand: impact management of climate change
.
Applied Ecology and Environmental Research
15
(
4
),
841
861
.
doi:10.15666/aeer/1504_841861
.
Asadieh
B.
,
Krakauer
N. Y.
2017
Global change in streamflow extremes under climate change over the 21st century
.
Hydrology and Earth System Sciences
21
(
11
),
5863
5874
.
doi:10.5194/hess-21-5863-2017
.
Asokan
S. M.
,
Dutta
D.
2008
Analysis of water resources in the Mahanadi River Basin, India under projected climate conditions
.
Hydrological Processes
22
(
18
),
3589
3603
.
doi:10.1002/hyp.6962
.
Bergstrom
S.
1976
Development and Application of a Conceptual Runoff Model for Scandinavian Catchments
.
SMHI
,
RHO No. 7
,
Norrköping
, p.
134
.
Birkinshaw
S. J.
,
Guerreiro
S. B.
,
Nicholson
A.
,
Liang
Q.
,
Quinn
P.
,
Zhang
L.
,
He
B.
,
Yin
J.
,
Fowler
H.
2017
Climate change impacts on Yangtze River discharge at the Three Gorges Dam
.
Hydrology and Earth System Sciences
21
,
1911
1927
.
doi:10.5194/hess-21-1911-2017
.
Boardman
J.
2015
Extreme rainfall and its impact on cultivated landscapes with particular reference to Britain
.
Earth Surface Processes and Landforms
40
(
15
),
2121
2130
.
doi:10.1002/esp.3792
.
Bodian
A.
,
Dezetter
A.
,
Diop
L.
,
Deme
A.
,
Djaman
K.
,
Diop
A.
2018
Future climate change impacts on streamflows of two main West Africa river Basins: Senegal and Gambia
.
Hydrology
5
(
1
),
21
.
doi:10.3390/hydrology5010021
.
Bothale
R. V.
,
Katpatal
Y. B.
2017
Impact of climate change scenarios on hydrologic response of Upper Wardha catchment, Central India
.
International Journal of Global Warming
13
(
1
),
32
56
.
doi:doi:10.1504/IJGW.2017.10006444
.
Budiyono
Y.
,
Aerts
J. C. J. H.
,
Tollenaar
D.
,
Ward
P. J.
2016
River flood risk in Jakarta under scenarios of future change
.
Natural Hazards and Earth System Sciences
16
(
3
),
757
774
.
doi:10.5194/nhess-16-757-2016
.
Chen
Y.-R.
,
Yu
B.
2015
Impact assessment of climatic and land-use changes on flood runoff in southeast Queensland
.
Hydrological Sciences Journal
60
(
10
),
1759
1769
.
doi:10.1080/02626667.2014.945938
.
Chen
X.
,
Alizad
K.
,
Wang
D. B.
,
Hagen
S. C.
2014
Climate change impact on runoff and sediment loads to the Apalachicola River at seasonal and event scales
.
Journal of Coastal Research
68
,
35
42
.
doi:10.2112/si68-005.1
.
Dang
Q. T.
,
Laux
P.
,
Kunstmann
H.
2017
Future high- and low-flow estimations for Central Vietnam: a hydro-meteorological modelling chain approach
.
Hydrological Sciences Journal
62
(
11
),
1867
1889
.
doi:10.1080/02626667.2017.1353696
.
Dankers
R.
,
Feyen
L.
2009
Flood hazard in Europe in an ensemble of regional climate scenarios
.
Journal of Geophysical Research: Atmospheres
114
(
D16
),
D16108
.
doi:10.1029/2008JD011523
.
Dankers
R.
,
Arnell
N. W.
,
Clark
D. B.
,
Falloon
P. D.
,
Fekete
B. M.
,
Gosling
S. N.
,
Heinke
J.
,
Kim
H.
,
Masaki
Y.
,
Satoh
Y.
,
Stacke
T.
,
Wada
Y.
,
Wisser
D.
2014
First look at changes in flood hazard in the Inter-Sectoral Impact Model Intercomparison Project ensemble
.
Proceedings of the National Academy of Sciences of the United States of America
111
(
9
),
3257
3261
.
doi:10.1073/pnas.1302078110
.
Das
J.
,
Umamahesh
N. V.
2017
Uncertainty and nonstationarity in streamflow extremes under climate change scenarios over a river basin
.
Journal of Hydrologic Engineering
22
(
10
),
4017042
.
doi:10.1061/(ASCE)HE.1943-5584.0001571
.
Das
J.
,
Umamahesh
N. V.
2018
Assessment of uncertainty in estimating future flood return levels under climate change
.
Natural Hazards
93
(
1
),
109
124
.
doi:10.1007/s11069-018-3291-2
.
Devkota
L. P.
,
Gyawali
D. R.
2015
Impacts of climate change on hydrological regime and water resources management of the Koshi River Basin, Nepal
.
Journal of Hydrology: Regional Studies
4
,
502
515
.
doi:10.1016/j.ejrh.2015.06.023
.
Do
H. X.
,
Westra
S.
,
Leonard
M.
2017
A global-scale investigation of trends in annual maximum streamflow
.
Journal of Hydrology
552
,
28
43
.
doi:10.1016/j.jhydrol.2017.06.015
.
Dobler
C.
,
Hagemann
S.
,
Wilby
R.
,
Stötter
J.
2012
Quantifying different sources of uncertainty in hydrological projections in an Alpine watershed
.
Hydrology and Earth System Sciences
16
(
11
),
4343
4360
.
doi:10.5194/hess-16-4343-2012
.
Döll
P.
,
Trautmann
T.
,
Gerten
D.
,
Schmied
H. M.
,
Ostberg
S.
,
Saaed
F.
,
Schleussner
C.-F.
2018
Risks for the global freshwater system at 1.5°C and 2°C global warming
.
Environmental Research Letters
13
(
4
),
44038
.
doi:10.1088/1748-9326/aab792
.
Dong
N. D.
,
Jayakumar
K. V.
,
Agilan
V.
2018
Impact of climate change on flood frequency of the Trian Reservoir in Vietnam using RCMs
.
Journal of Hydrologic Engineering
23
(
2
),
5017032
.
doi:10.1061/(ASCE)HE.1943-5584.0001609
.
Doocy
S.
,
Daniels
A.
,
Murray
S.
,
Kirsch
T. D.
2013
The human impact of floods: a historical review of events 1980–2009 and systematic literature review
.
PLoS Currents
5
.
doi:10.1371/currents.dis.f4deb457904936b07c09daa98ee8171a
.
Dutta
S.
,
Ghosh
S.
2012
Impact of climate and land use changes on the flood hazard of the middle Brahmaputra Reach, India
.
Journal of Disaster Research
7
(
5
),
573
581
.
doi:10.20965/jdr.2012.p0573
.
Edangodage Duminda Pradeep
P.
,
Sayama
T.
,
Magome
J.
,
Hasegawa
A.
,
Iwami
Y.
2017
RCP8.5-based future flood hazard analysis for the Lower Mekong River Basin
.
Hydrology
4
(
4
),
55
.
doi:10.3390/hydrology4040055
.
Emam
A. R.
,
Mishra
B. K.
,
Kumar
P.
,
Masago
Y.
,
Fukushi
K.
2016
Impact assessment of climate and land-use changes on flooding behavior in the Upper Ciliwung River, Jakarta, Indonesia
.
Water
8
(
12
).
doi:10.3390/w8120559
Essou
G. R.
,
Brissette
F.
2013
Climate change impacts on the Oueme River, Benin, West Africa
.
Journal of Earth Science & Climatic Change
4
(
6
),
1
10
.
doi:10.4172/2157-7617.1000161
.
Evans
J.
,
Schreider
S.
2002
Hydrological impacts of climate change on inflows to Perth, Australia
.
Climatic Change
55
(
3
),
361
393
.
doi:10.1023/A:1020588416541
.
Falloon
P. D.
,
Betts
R. A.
2006
The impact of climate change on global river flow in HadGEM1 simulations
.
Atmospheric Science Letters
7
(
3
),
62
68
.
doi:10.1002/asl.133
.
Fant
C.
,
Gebretsadik
Y.
,
McCluskey
A.
,
Strzepek
K.
2015
An uncertainty approach to assessment of climate change impacts on the Zambezi River Basin
.
Climatic Change
130
(
1
),
35
48
.
doi:10.1007/s10584-014-1314-x
.
Fekete
B. M.
,
Vörösmarty
C. J.
,
Roads
J. O.
,
Willmott
C. J.
2004
Uncertainties in precipitation and their impacts on runoff estimates
.
Journal of Climate
17
(
2
),
294
304
.
doi:10.1175/1520-0442(2004)017<0294:UIPATI>2.0.CO;2
.
Fowler
H. J.
,
Blenkinsop
S.
,
Tebaldi
C.
2007
Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling
.
International Journal of Climatology
27
(
12
),
1547
1578
.
doi:10.1002/joc.1556
.
Gain
A. K.
,
Immerzeel
W. W.
,
Sperna Weiland
F. C.
,
Bierkens
M. F. P.
2011
Impact of climate change on the stream flow of the Lower Brahmaputra: trends in high and low flows based on discharge-weighted ensemble modelling
.
Hydrology and Earth System Sciences
15
(
5
),
1537
1545
.
doi:10.5194/hess-15-1537-2011
.
Gain
A. K.
,
Apel
H.
,
Renaud
F. G.
,
Giupponi
C.
2013
Thresholds of hydrologic flow regime of a river and investigation of climate change impact – the case of the Lower Brahmaputra River Basin
.
Climatic Change
120
(
1–2
),
463
475
.
doi:10.1007/s10584-013-0800-x
.
Gao
C.
,
He
Z.
,
Pan
S.
,
Xuan
W.
,
Xu
Y.-P.
2018
Effects of climate change on peak runoff and flood levels in Qu River Basin, East China
.
Journal of Hydro-Environment Research.
doi:10.1016/j.jher.2018.02.005
.
Ghosh
S.
,
Dutta
S.
2012
Impact of climate change on flood characteristics in Brahmaputra basin using a macro-scale distributed hydrological model
.
Journal of Earth System Science
121
(
3
),
637
657
.
doi:10.1007/s12040-012-0181-y
.
Githui
F.
,
Gitau
W.
,
Mutua
F.
,
Bauwens
W.
2009
Climate change impact on SWAT simulated streamflow in western Kenya
.
International Journal of Climatology
29
(
12
),
1823
1834
.
doi:10.1002/joc.1828
.
Giuliani
M.
,
Anghileri
D.
,
Castelletti
A.
,
Vu
P. N.
,
Soncini-Sessa
R.
2016
Large storage operations under climate change: expanding uncertainties and evolving tradeoffs
.
Environmental Research Letters
11
(
3
),
35009
.
doi:10.1088/1748-9326/11/3/035009
.
Gonzalez-Hidalgo
J. C.
,
Batalla
R. J.
,
Cerda
A.
2013
Catchment size and contribution of the largest daily events to suspended sediment load on a continental scale
.
Catena
102
,
40
45
.
doi:10.1016/j.catena.2010.10.011
.
Gosain
A. K.
,
Rao
S.
,
Basuray
D.
2006
Climate change impact assessment on hydrology of Indian river basins
.
Current Science
90
(
3
),
346
353
.
Gosain
A. K.
,
Rao
S.
,
Arora
A.
2011
Climate change impact assessment of water resources of India
.
Current Science
101
(
3
),
356
371
.
Gosling
S. N.
,
Zaherpour
J.
,
Mount
N. J.
,
Hattermann
F. F.
,
Dankers
R.
,
Arheimer
B.
,
Breuer
L.
,
Ding
J.
,
Haddeland
I.
,
Kumar
R.
2017
A comparison of changes in river runoff from multiple global and catchment-scale hydrological models under global warming scenarios of 1 C, 2 C and 3 C
.
Climatic Change
141
(
3
),
577
595
.
doi:10.1007/s10584-016-1773-3
.
Grillakis
M. G.
,
Koutroulis
A. G.
,
Tsanis
I. K.
2013
Multisegment statistical bias correction of daily GCM precipitation output
.
Journal of Geophysical Research: Atmospheres
118
(
8
),
3150
3162
.
doi:10.1002/jgrd.50323
.
Groisman
P. Y.
,
Knight
R. W.
,
Easterling
D. R.
,
Karl
T. R.
,
Hegerl
G. C.
,
Razuvaev
V. N.
2005
Trends in intense precipitation in the climate record
.
Journal of Climate
18
(
9
),
1326
1350
.
doi:10.1175/JCLI3339.1
.
Gu
H.
,
Yu
Z.
,
Wang
G.
,
Wang
J.
,
Ju
Q.
,
Yang
C.
,
Fan
C.
2014
Impact of climate change on hydrological extremes in the Yangtze River Basin, China
.
Stochastic Environmental Research and Risk Assessment
29
(
3
),
693
707
.
doi:10.1007/s00477-014-0957-5
.
Guimberteau
M.
,
Ronchail
J.
,
Espinoza
J. C.
,
Lengaigne
M.
,
Sultan
B.
,
Polcher
J.
,
Drapeau
G.
,
Guyot
J. L.
,
Ducharne
A.
,
Ciais
P.
2013
Future changes in precipitation and impacts on extreme streamflow over Amazonian sub-basins
.
Environmental Research Letters
8
(
1
),
014035
.
doi:10.1088/1748-9326/8/1/014035
.
Hagemann
S.
,
Dümenil
L.
1997
A parametrization of the lateral waterflow for the global scale
.
Climate Dynamics
14
(
1
),
17
31
.
doi:10.1007/s003820050205
.
Hempel
S.
,
Frieler
K.
,
Warszawski
L.
,
Schewe
J.
,
Piontek
F.
2013
A trend-preserving bias correction – the ISI-MIP approach
.
Earth System Dynamics
4
(
2
),
219
236
.
doi:10.5194/esd-4-219-2013
.
Hirabayashi
Y.
,
Kanae
S.
,
Emori
S.
,
Oki
T.
,
Kimoto
M.
2008
Global projections of changing risks of floods and droughts in a changing climate
.
Hydrological Sciences Journal
53
(
4
),
754
772
.
doi:10.1623/hysj.53.4.754
.
Hirabayashi
Y.
,
Mahendran
R.
,
Koirala
S.
,
Konoshima
L.
,
Yamazaki
D.
,
Watanabe
S.
,
Kim
H.
,
Kanae
S.
2013
Global flood risk under climate change
.
Nature Climate Change
3
(
9
),
816
821
.
doi:10.1038/nclimate1911
.
Hopson
T. M.
,
Webster
P.
2010
A 1–10-day ensemble forecasting scheme for the major river basins of Bangladesh: forecasting severe floods of 2003–07
.
Journal of Hydrometeorology
11
(
3
),
618
641
.
doi:10.1175/2009JHM1006.1
.
Huang
S.
,
Kumar
R.
,
Rakovec
O.
,
Aich
V.
,
Wang
X.
,
Samaniego
L.
,
Liersch
S.
,
Krysanova
V.
2018
Multimodel assessment of flood characteristics in four large river basins at global warming of 1.5, 2.0 and 3.0 K above the pre-industrial level
.
Environmental Research Letters
13
(
12
),
124005
.
doi:10.1088/1748-9326/aae94b
.
Hunt
A.
,
Watkiss
P.
2011
Climate change impacts and adaptation in cities: a review of the literature
.
Climatic Change
104
(
1
),
13
49
.
doi:10.1007/s10584-010-9975-6
.
Hunukumbura
P.
,
Tachikawa
Y.
2012
River discharge projection under climate change in the Chao Phraya river basin, Thailand, using the MRI-GCM3. 1S dataset
.
Journal of the Meteorological Society of Japan. Ser. II
90
,
137
150
.
doi:10.2151/jmsj.2012-A07
.
Ines
A. V. M.
,
Hansen
J. W.
2006
Bias correction of daily GCM rainfall for crop simulation studies
.
Agricultural and Forest Meteorology
138
(
1
),
44
53
.
doi:10.1016/j.agrformet.2006.03.009
.
Inomata
H.
,
Takeuchi
K.
,
Fukami
K.
2011
Development of a statistical bias correction method for daily precipitation data of GCM20
.
Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering)
67
(
4
),
I_247
I_252
.
doi:10.2208/jscejhe.67.I_247
.
Iwami
Y.
,
Hasegawa
A.
,
Miyamoto
M.
,
Kudo
S.
,
Yamazaki
Y.
,
Ushiyama
T.
,
Koike
T.
2017
Comparative study on climate change impact on precipitation and floods in Asian river basins
.
Hydrological Research Letters
11
(
1
),
24
30
.
doi:10.3178/hrl.11.24
.
Jana
S.
,
Das
M.
,
Roy
D.
,
Das
S.
,
Mazumdar
A.
2015
Simulation of climate change impact in a river basin in Eastern India
.
International Journal of Hydrology Science and Technology
5
(
4
),
314
332
.
doi:10.1504/IJHST.2015.072631
.
Jha
A. K.
,
Bloch
R.
,
Lamond
J.
2012
Cities and Flooding: A Guide to Integrated Urban Flood Risk Management for the 21st Century
.
World Bank Publications, Washington DC
.
Jin
L.
,
Whitehead
P. G.
,
Appeaning Addo
K.
,
Amisigo
B.
,
Macadam
I.
,
Janes
T.
,
Crossman
J.
,
Nicholls
R. J.
,
McCartney
M.
,
Rodda
H. J. E.
2018a
Modeling future flows of the Volta River system: impacts of climate change and socio-economic changes
.
Science of the Total Environment
637–638
,
1069
1080
.
doi:10.1016/j.scitotenv.2018.04.350
.
Jin
L.
,
Whitehead
P. G.
,
Rodda
H.
,
Macadam
I.
,
Sarkar
S.
2018b
Simulating climate change and socio-economic change impacts on flows and water quality in the Mahanadi River system, India
.
Science of the Total Environment
637–638
,
907
917
.
doi:10.1016/j.scitotenv.2018.04.349
.
Johnston
R.
,
Kummu
M.
2012
Water resource models in the Mekong Basin: a review
.
Water Resources Management
26
(
2
),
429
455
.
doi:10.1007/s11269-011-9925-8
.
Jonkman
S. N.
2005
Global perspectives on loss of human life caused by floods
.
Natural Hazards
34
(
2
),
151
175
.
doi:10.1007/s11069-004-8891-3
.
Ju
Q.
,
Yu
Z.
,
Hao
Z.
,
Ou
G.
,
Wu
Z.
,
Yang
C.
,
Gu
H.
2014
Response of hydrologic processes to future climate changes in the Yangtze River Basin
.
Journal of Hydrologic Engineering
19
(
1
),
211
222
.
doi:10.1061/(ASCE)HE.1943-5584.0000770
.
Kai
D.
,
Yadong
M.
,
Liping
Z.
2016
Copula-based bivariate flood frequency analysis in a changing climate – a case study in the Huai River Basin, China
.
Journal of Earth Science
27
(
1
),
37
46
.
doi:10.1007/s12583-016-0625-4
.
Kay
A. L.
,
Davies
H. N.
,
Bell
V. A.
,
Jones
R. G.
2009
Comparison of uncertainty sources for climate change impacts: flood frequency in England
.
Climatic Change
92
(
1–2
),
41
63
.
doi:10.1007/s10584-008-9471-4
.
Kiem
A. S.
,
Ishidaira
H.
,
Hapuarachchi
H. P.
,
Zhou
M. C.
,
Hirabayashi
Y.
,
Takeuchi
K.
2008
Future hydroclimatology of the Mekong River basin simulated using the high-resolution Japan Meteorological Agency (JMA) AGCM
.
Hydrological Processes
22
(
9
),
1382
1394
.
doi:10.1002/hyp.6947
.
Kleinen
T.
,
Petschel-Held
G.
2007
Integrated assessment of changes in flooding probabilities due to climate change
.
Climatic Change
81
(
3
),
283
312
.
doi:10.1007/s10584-006-9159-6
.
Koirala
S.
,
Hirabayashi
Y.
,
Mahendran
R.
,
Kanae
S.
2014
Global assessment of agreement among streamflow projections using CMIP5 model outputs
.
Environmental Research Letters
9
(
6
),
64017
.
doi:10.1088/1748-9326/9/6/064017
.
Krysanova
V.
,
Vetter
T.
,
Eisner
S.
,
Huang
S. C.
,
Pechlivanidis
I.
,
Strauch
M.
,
Gelfan
A.
,
Kumar
R.
,
Aich
V.
,
Arheimer
B.
,
Chamorro
A.
,
van Griensven
A.
,
Kundu
D.
,
Lobanova
A.
,
Mishra
V.
,
Plotner
S.
,
Reinhardt
J.
,
Seidou
O.
,
Wang
X. Y.
,
Wortmann
M.
,
Zeng
X. F.
,
Hattermann
F. F.
2017
Intercomparison of regional-scale hydrological models and climate change impacts projected for 12 large river basins worldwide – a synthesis
.
Environmental Research Letters
12
(
10
).
doi:10.1088/1748-9326/aa8359
.
Kundzewicz
Z. W.
,
Mata
L.
,
Arnell
N. W.
,
Döll
P.
,
Jimenez
B.
,
Miller
K.
,
Oki
T.
,
Şen
Z.
,
Shiklomanov
I.
2008
The implications of projected climate change for freshwater resources and their management
.
Hydrological Sciences Journal
53
(
1
),
3
10
.
doi:10.1623/hysj.53.1.3
.
Kundzewicz
Z. W.
,
Lugeri
N.
,
Dankers
R.
,
Hirabayashi
Y.
,
Doll
P.
,
Pinskwar
I.
,
Dysarz
T.
,
Hochrainer
S.
,
Matczak
P.
2010
Assessing river flood risk and adaptation in Europe – review of projections for the future
.
Mitigation and Adaptation Strategies for Global Change
15
(
7
),
641
656
.
doi:10.1007/s11027-010-9213-6
.
Kundzewicz
Z. W.
,
Kanae
S.
,
Seneviratne
S. I.
,
Handmer
J.
,
Nicholls
N.
,
Peduzzi
P.
,
Mechler
R.
,
Bouwer
L. M.
,
Arnell
N.
,
Mach
K.
,
Muir-Wood
R.
,
Brakenridge
G. R.
,
Kron
W.
,
Benito
G.
,
Honda
Y.
,
Takahashi
K.
,
Sherstyukov
B.
2014
Flood risk and climate change: global and regional perspectives
.
Hydrological Sciences Journal
59
(
1
),
1
28
.
doi:10.1080/02626667.2013.857411
.
Kure
S.
,
Tebakari
T.
2012
Hydrological impact of regional climate change in the Chao Phraya River Basin, Thailand
.
Hydrological Research Letters
6
,
53
58
.
doi:10.3178/HRL.6.53
.
Lane
S. N.
,
Tayefi
V.
,
Reid
S. C.
,
Yu
D.
,
Hardy
R. J.
2007
Interactions between sediment delivery, channel change, climate change and flood risk in a temperate upland environment
.
Earth Surface Processes and Landforms
32
(
3
),
429
446
.
doi:10.1002/esp.1404
.
Langerwisch
F.
,
Rost
S.
,
Gerten
D.
,
Poulter
B.
,
Rammig
A.
,
Cramer
W.
2013
Potential effects of climate change on inundation patterns in the Amazon Basin
.
Hydrology and Earth System Sciences
17
(
6
),
2247
2262
.
doi:10.5194/hess-17-2247-2013
.
Lauri
H.
,
de Moel
H.
,
Ward
P. J.
,
Räsänen
T. A.
,
Keskinen
M.
,
Kummu
M. S.
2012
Future changes in Mekong River hydrology: impact of climate change and reservoir operation on discharge
.
Hydrology and Earth System Sciences
16
(
12
),
4603
4619
.
doi:10.5194/hess-16-4603-2012
.
Li
H.
,
Sheffield
J.
,
Wood
E. F.
2010
Bias correction of monthly precipitation and temperature fields from Intergovernmental Panel on Climate Change AR4 models using equidistant quantile matching
.
Journal of Geophysical Research: Atmospheres
115
(
D10
).
doi:10.1029/2009JD012882
.
Li
J. F.
,
Chen
Y. D.
,
Zhang
L.
,
Zhang
Q.
,
Chiew
F. H. S.
2016a
Future changes in floods and water availability across China: linkage with changing climate and uncertainties
.
Journal of Hydrometeorology
17
(
4
),
1295
1314
.
doi:10.1175/jhm-d-15-0074.1
.
Liang
X.
1994
A Two-Layer Variable Infiltration Capacity Land Surface Representation for General Circulation Models
.
Water Resource. Series TR140
.
University of Washington
,
Seattle
, p.
208
.
Liu
L.
,
Jiang
T.
,
Xu
J. G.
,
Zhai
J. Q.
,
Luo
Y.
2012
Responses of hydrological processes to climate change in the Zhujiang river basin in the 21st century
.
Advances in Climate Change Research
3
(
2
),
84
91
.
doi:10.3724/SP.J.1248.2012.00084
.
Liu
L.
,
Fischer
T.
,
Jiang
T.
,
Luo
Y.
2013
Comparison of uncertainties in projected flood frequency of the Zhujiang River, South China
.
Quaternary International
304
,
51
61
.
doi:10.1016/j.quaint.2013.02.039
.
Lu
G. H.
,
Xiao
H.
,
Wu
Z. Y.
,
Zhang
S. L.
,
Li
Y.
2013
Assessing the impacts of future climate change on hydrology in Huang-Huai-Hai Region in China using the PRECIS and VIC models
.
Journal of Hydrologic Engineering
18
(
9
),
1077
1087
.
doi:10.1061/(ASCE)HE.1943-5584.0000632
.
Madsen
H.
,
Lawrence
D.
,
Lang
M.
,
Martinkova
M.
,
Kjeldsen
T. R.
2014
Review of trend analysis and climate change projections of extreme precipitation and floods in Europe
.
Journal of Hydrology
519
,
3634
3650
.
doi:10.1016/j.jhydrol.2014.11.003
.
Masood
M.
,
Takeuchi
K.
2016
Climate change impacts and its implications on future water resource management in the Meghna Basin
.
Futures
78–79
,
1
18
.
doi:10.1016/j.futures.2016.03.001
.
Mathison
C.
,
Wiltshire
A.
,
Dimri
A.
,
Falloon
P.
,
Jacob
D.
,
Kumar
P.
,
Moors
E.
,
Ridley
J.
,
Siderius
C.
,
Stoffel
M.
2013
Regional projections of North Indian climate for adaptation studies
.
Science of the Total Environment
468
,
S4
S17
.
doi:10.1016/j.scitotenv.2012.04.066
.
Mathison
C.
,
Wiltshire
A. J.
,
Falloon
P.
,
Challinor
A. J.
2015
South Asia river-flow projections and their implications for water resources
.
Hydrology and Earth System Sciences
19
(
12
),
4783
4810
.
doi:10.5194/hess-19-4783-2015
.
Meinshausen
M.
,
Smith
S. J.
,
Calvin
K.
,
Daniel
J. S.
,
Kainuma
M.
,
Lamarque
J.-F.
,
Matsumoto
K.
,
Montzka
S.
,
Raper
S.
,
Riahi
K.
2011
The RCP greenhouse gas concentrations and their extensions from 1765 to 2300
.
Climatic Change
109
(
1–2
),
213
241
.
doi:10.1007/s10584-011-0156-z
.
Menzel
L.
,
Thieken
A. H.
,
Schwandt
D.
,
Bürger
G.
2006
Impact of climate change on the regional hydrology–scenario-based modelling studies in the German Rhine catchment
.
Natural Hazards
38
(
1
),
45
61
.
doi:10.1007/s11069-005-8599-z
.
Miller
J. D.
,
Hutchins
M.
2017
The impacts of urbanisation and climate change on urban flooding and urban water quality: a review of the evidence concerning the United Kingdom
.
Journal of Hydrology: Regional Studies
12
,
345
362
.
doi:10.1016/j.ejrh.2017.06.006
.
Milly
P. C. D.
,
Wetherald
R. T.
,
Dunne
K. A.
,
Delworth
T. L.
2002
Increasing risk of great floods in a changing climate
.
Nature
415
(
6871
),
514
517
.
doi:10.1038/415514a
.
Mirza
M. M. Q.
2002
Global warming and changes in the probability of occurrence of floods in Bangladesh and implications
.
Global Environmental Change
12
(
2
),
127
138
.
doi:10.1016/S0959-3780(02)00002-X
.
Mirza
M. M. Q.
,
Warrick
R. A.
,
Ericksen
N. J.
2003
The implications of climate change on floods of the Ganges, Brahmaputra and Meghna rivers in Bangladesh
.
Climatic Change
57
(
3
),
287
318
.
doi:10.1023/A:1022825915791
.
Mishra
B. K.
,
Herath
S.
2015
Assessment of future floods in the Bagmati River Basin of Nepal using bias-corrected daily GCM precipitation data
.
Journal of Hydrologic Engineering
20
(
8
),
5014027
.
doi:10.1061/(ASCE)HE.1943-5584.0001090
.
Mishra
B. K.
,
Rafiei Emam
A.
,
Masago
Y.
,
Kumar
P.
,
Regmi
R. K.
,
Fukushi
K.
2017
Assessment of future flood inundations under climate and land use change scenarios in the Ciliwung River Basin, Jakarta
.
Journal of Flood Risk Management
11
,
S1105
S1115
.
doi:10.1111/jfr3.12311
.
Mohammed
K.
,
Islam
A. S.
,
Islam
G. M. T.
,
Alfieri
L.
,
Bala
S. K.
,
Khan
M. J. U.
2017a
Extreme flows and water availability of the Brahmaputra River under 1.5 and 2°C global warming scenarios
.
Climatic Change
145
(
1
),
159
175
.
doi:10.1007/s10584-017-2073-2
.
Mohammed
K.
,
Saiful Islam
A. K. M.
,
Tarekul Islam
G. M.
,
Alfieri
L.
,
Bala
S. K.
,
Khan
M. J. U.
2017b
Impact of high-end climate change on floods and low flows of the Brahmaputra River
.
Journal of Hydrologic Engineering
22
(
10
),
04017041
.
doi:10.1061/(ASCE)HE.1943-5584.0001567
.
Mohammed
K.
,
Islam
A.
,
Islam
G. M. T.
,
Alfieri
L.
,
Khan
M. J. U.
,
Bala
S. K.
,
Das
M. K.
2018
Future floods in Bangladesh under 1.5°C, 2°C, and 4°C global warming scenarios
.
Journal of Hydrologic Engineering
23
(
12
).
doi:10.1061/(ASCE)HE.1943-5584.0001705
.
Moher
D.
,
Liberati
A.
,
Tetzlaff
J.
,
Altman
D. G.
,
Group
P.
2009
Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement
.
PLoS Medicine
6
(
7
),
e1000097
.
doi:10.1371/journal.pmed.1000097
.
Mora
D. E.
,
Campozano
L.
,
Cisneros
F.
,
Wyseure
G.
,
Willems
P.
2014
Climate changes of hydrometeorological and hydrological extremes in the Paute basin, Ecuadorean Andes
.
Hydrology and Earth System Sciences
18
(
2
),
631
648
.
doi:10.5194/hess-18-631-2014
.
Mudbhatkal
A.
,
Raikar
R. V.
,
Venkatesh
B.
,
Mahesha
A.
2017
Impacts of climate change on varied river-flow regimes of southern India
.
Journal of Hydrologic Engineering
22
(
9
),
5017017
.
doi:10.1061/(ASCE)HE.1943-5584.0001556
.
Muis
S.
,
Güneralp
B.
,
Jongman
B.
,
Aerts
J. C. J. H.
,
Ward
P. J.
2015
Flood risk and adaptation strategies under climate change and urban expansion: a probabilistic analysis using global data
.
Science of the Total Environment
538
,
445
457
.
doi:10.1016/j.scitotenv.2015.08.068
.
Munich Re
2017
NATCATSERVICE: National Catastrophe Know-how for Risk Management and Research
.
Available from
: http://natcatservice.munichre.com/
(accessed 14 December 2017)
.
Muttiah
R. S.
,
Wurbs
R. A.
2002
Modeling the impacts of climate change on water supply reliabilities
.
Water International
27
(
3
),
407
419
.
doi:10.1080/02508060208687020
.
Nakicenovic
N.
,
Alcamo
J.
,
Grubler
A.
,
Riahi
K.
,
Roehrl
R.
,
Rogner
H.-H.
,
Victor
N.
2000
Special Report on Emissions Scenarios (SRES), A Special Report of Working Group III of the Intergovernmental Panel on Climate Change
.
Cambridge University Press
,
Cambridge
,
UK
.
Nawaz
N.
,
Bellerby
T.
,
Sayed
M.
,
Elshamy
M.
2010
Blue Nile runoff sensitivity to climate change
.
Open Hydrology
4
,
137
151
.
doi:10.2174/1874378101004010137
.
Naz
B. S.
,
Kao
S.-C.
,
Ashfaq
M.
,
Rastogi
D.
,
Mei
R.
,
Bowling
L. C.
2016
Regional hydrologic response to climate change in the conterminous United States using high-resolution hydroclimate simulations
.
Global and Planetary Change
143
,
100
117
.
doi:10.1016/j.gloplacha.2016.06.003
.
Naz
B. S.
,
Kao
S.-C.
,
Ashfaq
M.
,
Gao
H.
,
Rastogi
D.
,
Gangrade
S.
2018
Effects of climate change on streamflow extremes and implications for reservoir inflow in the United States
.
Journal of Hydrology
556
,
359
370
.
doi:10.1016/j.jhydrol.2017.11.027
.
Ngongondo
C.
,
Li
L.
,
Gong
L.
,
Xu
C. Y.
,
Alemaw
B. F.
2013
Flood frequency under changing climate in the upper Kafue River basin, southern Africa: a large scale hydrological model application
.
Stochastic Environmental Research and Risk Assessment
27
(
8
),
1883
1898
.
doi:10.1007/s00477-013-0724-z
.
Okazaki
A.
,
Yeh
P. J.-F.
,
Yoshimura
K.
,
Watanabe
M.
,
Kimoto
M.
,
Oki
T.
2012
Changes in flood risk under global warming estimated using MIROC5 and the discharge probability index
.
Journal of the Meteorological Society of Japan. Ser. II
90
(
4
),
509
524
.
doi:10.2151/jmsj.2012-405
.
Paltan
H.
,
Allen
M.
,
Haustein
K.
,
Fuldauer
L.
,
Dadson
S.
2018
Global implications of 1.5°C and 2°C warmer worlds on extreme river flows
.
Environmental Research Letters
13
(
9
),
94003
.
doi:10.1088/1748-9326/aad985
.
Pechlivanidis
I. G.
,
Arheimer
B.
,
Donnelly
C.
,
Hundecha
Y.
,
Huang
S.
,
Aich
V.
,
Samaniego
L.
,
Eisner
S.
,
Shi
P.
2017
Analysis of hydrological extremes at different hydro-climatic regimes under present and future conditions
.
Climatic Change
141
(
3
),
467
481
.
doi:10.1007/s10584-016-1723-0
.
Peel
M. C.
,
Finlayson
B. L.
,
McMahon
T. A.
2007
Updated world map of the Köppen-Geiger climate classification
.
Hydrology and Earth System Sciences Discussions
4
(
2
),
439
473
.
Perera
E. D. P.
,
Hiroe
A.
,
Shrestha
D.
,
Fukami
K.
,
Basnyat
D. B.
,
Gautam
S.
,
Hasegawa
A.
,
Uenoyama
T.
,
Tanaka
S.
2015
Community-based flood damage assessment approach for lower West Rapti River basin in Nepal under the impact of climate change
.
Natural Hazards
75
(
1
),
669
699
.
doi:10.1007/s11069-014-1339-5
.
Phi Hoang
L.
,
Lauri
H.
,
Kummu
M.
,
Koponen
J.
,
Vliet
M. T. H. V.
,
Supit
I.
,
Leemans
R.
,
Kabat
P.
,
Ludwig
F.
2016
Mekong River flow and hydrological extremes under climate change
.
Hydrology and Earth System Sciences
20
(
7
),
3027
3041
.
doi:10.5194/hess-20-3027-2016
.
Philip
S.
,
Sparrow
S.
,
Kew
S. F.
,
Van Der Wiel
K.
,
Wanders
N.
,
Singh
R.
,
Hassan
A.
,
Mohammed
K.
,
Javid
H.
,
Haustein
K.
,
Otto
F. E. L.
,
Hirpa
F.
,
Rimi
R. H.
,
Saiful Islam
A. K. M.
,
Wallom
D. C. H.
,
Jan Van Oldenborgh
G.
2019
Attributing the 2017 Bangladesh floods from meteorological and hydrological perspectives
.
Hydrology and Earth System Sciences
23
(
3
),
1409
1429
.
doi:10.5194/hess-23-1409-2019
.
Pichuka
S.
,
Prasad R
R.
,
Maity
R.
,
Kunstmann
H.
2017
Development of a method to identify change in the pattern of extreme streamflow events in future climate: application on the Bhadra reservoir inflow in India
.
Journal of Hydrology: Regional Studies
9
(
C
),
236
246
.
doi:10.1016/j.ejrh.2016.12.084
.
Pickering
C.
,
Byrne
J.
2014
The benefits of publishing systematic quantitative literature reviews for PhD candidates and other early-career researchers
.
Higher Education Research & Development
33
(
3
),
534
548
.
doi:10.1080/07294360.2013.841651
.
Praskievicz
S.
,
Chang
H.
2009
A review of hydrological modelling of basin-scale climate change and urban development impacts
.
Progress in Physical Geography
33
(
5
),
650
671
.
doi:10.1177/0309133309348098
.
Prudhomme
C.
,
Jakob
D.
,
Svensson
C.
2003
Uncertainty and climate change impact on the flood regime of small UK catchments
.
Journal of Hydrology
277
(
1
),
1
23
.
doi:10.1016/S0022-1694(03)00065-9
.
Qin
X. S.
,
Lu
Y.
2014
Study of climate change impact on flood frequencies: a combined weather generator and hydrological modeling approach
.
Journal of Hydrometeorology
15
(
3
),
1205
1219
.
doi:10.1175/jhm-d-13-0126.1
.
Rammig
A.
,
Jupp
T.
,
Thonicke
K.
,
Tietjen
B.
,
Heinke
J.
,
Ostberg
S.
,
Lucht
W.
,
Cramer
W.
,
Cox
P.
2010
Estimating the risk of Amazonian forest dieback
.
New Phytologist
187
(
3
),
694
706
.
doi:10.1111/j.1469-8137.2010.03318.x
.
Reilly
J.
,
Paltsev
S.
,
Strzepek
K.
,
Selin
N. E.
,
Cai
Y.
,
Nam
K.-M.
,
Monier
E.
,
Dutkiewicz
S.
,
Scott
J.
,
Webster
M.
,
Sokolov
A.
2013
Valuing climate impacts in integrated assessment models: the MIT IGSM
.
Climatic Change
117
(
3
),
561
573
.
doi:10.1007/s10584-012-0635-x
.
Risley
J.
,
Moradkhani
H.
,
Hay
L.
,
Markstrom
S.
2011
Statistical comparisons of watershed-scale response to climate change in selected basins across the United States
.
Earth Interactions
15
(
14
),
1
26
.
doi:10.1175/2010EI364.1
.
Romero
M. E. N.
,
Martínez
T. L.
,
González-Hidalgo
J.
,
de Luis Arrillaga
M.
,
Ruiz
J. M. G.
2012
The effect of intense rainstorm events on the suspended sediment response under various land uses: the Aísa Valley Experimental Station
.
Cuadernos de Investigación Geográfica
38
,
27
47
.
doi:10.18172/cig.1274
.
Rowell
D. P.
2012
Sources of uncertainty in future changes in local precipitation
.
Climate Dynamics
39
(
7–8
),
1929
1950
.
doi:10.1007/s00382-011-1210-2
.
Schreider
S. Y.
,
Jakeman
A. J.
,
Pittock
A. B.
,
Whetton
P. H.
1996
Estimation of possible climate change impacts on water availability, extreme flow events and soil moisture in the Goulburn and Ovens basins, Victoria
.
Climatic Change
34
(
3–4
),
513
546
.
doi:10.1007/BF00139304
.
Schreider
S. Y.
,
Smith
D. I.
,
Jakeman
A. J.
2000
Climate change impacts on urban flooding
.
Climatic Change
47
(
1–2
),
91
115
.
doi:10.1023/A:1005621523177
.
Sharma
A.
,
Wasko
C.
,
Lettenmaier
D.
2018
If precipitation extremes are increasing, why aren't floods?
Water Resources Research
54
(
11
),
8545
8551
.
doi:10.1029/2018WR023749
.
Shen
M.
,
Chen
J.
,
Chen
H.
,
Zhuan
M.
,
Xu
C.-Y.
,
Xiong
L.
2018
Estimating uncertainty and its temporal variation related to global climate models in quantifying climate change impacts on hydrology
.
Journal of Hydrology
556
,
10
24
.
doi:10.1016/j.jhydrol.2017.11.004
.
Shrestha
S.
,
Lohpaisankrit
W.
2017
Flood hazard assessment under climate change scenarios in the Yang River Basin, Thailand
.
International Journal of Sustainable Built Environment
6
(
2
),
285
298
.
doi:10.1016/j.ijsbe.2016.09.006
.
Sorribas
M. V.
,
Paiva
R. C. D.
,
Melack
J. M.
,
Bravo
J. M.
,
Jones
C.
,
Carvalho
L.
,
Beighley
E.
,
Forsberg
B.
,
Costa
M. H.
2016
Projections of climate change effects on discharge and inundation in the Amazon basin
.
Climatic Change
136
(
3–4
),
555
570
.
doi:10.1007/s10584-016-1640-2
.
Strupczewski
W. G.
,
Singh
V. P.
,
Feluch
W.
2001
Non-stationary approach to at-site flood frequency modelling I. Maximum likelihood estimation
.
Journal of Hydrology
248
(
1
),
123
142
.
doi:10.1016/S0022-1694(01)00397-3
.
Su
Y.-F.
,
Cheng
C.-T.
,
Liou
J.-J.
,
Chen
Y.-M.
,
Kitoh
A.
2016
Bias correction of MRI-WRF dynamic downscaling datasets
.
Terrestrial, Atmospheric & Oceanic Sciences
27
(
5
),
649
657
.
doi:10.3319/TAO.2016.07.14.01
.
Supharatid
S.
,
Aribarg
T.
,
Supratid
S.
2016
Assessing potential flood vulnerability to climate change by CMIP3 and CMIP5 models: case study of the 2011 Thailand great flood
.
Journal of Water and Climate Change
7
(
1
),
52
67
.
doi:10.2166/wcc.2015.116
.
Suttles
K. M.
,
Singh
N. K.
,
Vose
J. M.
,
Martin
K. L.
,
Emanuel
R. E.
,
Coulston
J. W.
,
Saia
S. M.
,
Crump
M. T.
2018
Assessment of hydrologic vulnerability to urbanization and climate change in a rapidly changing watershed in the Southeast U.S
.
Science of the Total Environment
645
,
806
816
.
doi:10.1016/j.scitotenv.2018.06.287
.
Taye
M. T.
,
Ntegeka
V.
,
Ogiramoi
N. P.
,
Willems
P.
2011
Assessment of climate change impact on hydrological extremes in two source regions of the Nile River Basin
.
Hydrology and Earth System Sciences
15
(
1
),
209
222
.
doi:10.5194/hess-15-209-2011
.
Teng
J.
,
Vaze
J.
,
Chiew
F. H.
,
Wang
B.
,
Perraud
J.-M.
2012
Estimating the relative uncertainties sourced from GCMs and hydrological models in modeling climate change impact on runoff
.
Journal of Hydrometeorology
13
(
1
),
122
139
.
doi:10.1175/JHM-D-11-058.1
.
Teutschbein
C.
,
Seibert
J.
2010
Regional climate models for hydrological impact studies at the catchment scale: a review of recent modeling strategies
.
Geography Compass
4
(
7
),
834
860
.
doi:10.1111/j.1749-8198.2010.00357.x
.
Thompson
J. R.
,
Crawley
A.
,
Kingston
D. G.
2016
GCM-related uncertainty for river flows and inundation under climate change: the Inner Niger Delta
.
Hydrological Sciences Journal
61
(
13
),
2325
2347
.
doi:10.1080/02626667.2015.1117173
.
Thompson
J. R.
,
Crawley
A.
,
Kingston
D. G.
2017
Future river flows and flood extent in the Upper Niger and Inner Niger Delta: GCM-related uncertainty using the CMIP5 ensemble
.
Hydrological Sciences Journal
62
(
14
),
2239
2265
.
doi:10.1080/02626667.2017.1383608
.
Tian
Y.
,
Xu
Y. P.
,
Zhang
X. J.
2013
Assessment of climate change impacts on river high flows through comparative use of GR4 J, HBV and Xinanjiang models
.
Water Resources Management
27
(
8
),
2871
2888
.
doi:10.1007/s11269-013-0321-4
.
Tian
Y.
,
Xu
Y. P.
,
Booij
M. J.
,
Cao
L.
2016
Impact assessment of multiple uncertainty sources on high flows under climate change
.
Hydrology Research
47
(
1
),
61
74
.
doi:10.2166/nh.2015.008
.
Tolentino
P. L. M.
,
Poortinga
A.
,
Kanamaru
H.
,
Keesstra
S.
,
Maroulis
J.
,
David
C. P. C.
,
Ritsema
C. J.
2016
Projected impact of climate change on hydrological regimes in the Philippines
.
PLoS ONE
11
(
10
).
doi:10.1371/journal.pone.0163941
.
Tsarouchi
G.
,
Buytaert
W.
2018
Land-use change may exacerbate climate change impacts on water resources in the Ganges basin
.
Hydrology and Earth System Sciences
22
(
2
),
1411
1435
.
doi:10.5194/hess-22-1411-2018
.
UNISDR
2009
Global Assessment Report on Disaster Risk Reduction: Risk and Poverty in a Changing Climate Invest Today for a Safer Tomorrow
.
United Nations
,
Geneva
.
van Vliet
M. T. H.
,
Franssen
W. H. P.
,
Yearsley
J. R.
,
Ludwig
F.
,
Haddeland
I.
,
Lettenmaier
D. P.
,
Kabat
P.
2013
Global river discharge and water temperature under climate change
.
Global Environmental Change
23
(
2
),
450
464
.
doi:10.1016/j.gloenvcha.2012.11.002
.
Västilä
K.
,
Kummu
M.
,
Sangmanee
C.
,
Chinvanno
S.
2010
Modelling climate change impacts on the flood pulse in the lower Mekong floodplains
.
Journal of Water and Climate Change
1
(
1
),
67
86
.
doi:10.2166/wcc.2010.008
.
Vetter
T.
,
Huang
S.
,
Aich
V.
,
Yang
T.
,
Wang
X.
,
Krysanova
V.
,
Hattermann
F.
2015
Multi-model climate impact assessment and intercomparison for three large-scale river basins on three continents
.
Earth System Dynamics
6
(
1
),
17
43
.
doi:10.5194/esd-6-17-2015
.
Viola
M. R.
,
de Mello
C. R.
,
Chou
S. C.
,
Yanagi
S. N.
,
Gomes
J. L.
2015
Assessing climate change impacts on Upper Grande River Basin hydrology, Southeast Brazil
.
International Journal of Climatology
35
(
6
),
1054
1068
.
doi:10.1002/joc.4038
.
Vo
N. D.
,
Gourbesville
P.
,
Vu
M. T.
,
Raghavan
S. V.
,
Liong
S. Y.
2016
A deterministic hydrological approach to estimate climate change impact on river flow: Vu Gia-Thu Bon catchment, Vietnam
.
Journal of Hydro-Environment Research
11
,
59
74
.
doi:10.1016/j.jher.2015.11.001
.
Voss
R.
,
May
W.
,
Roeckner
E.
2002
Enhanced resolution modelling study on anthropogenic climate change: changes in extremes of the hydrological cycle
.
International Journal of Climatology
22
(
7
),
755
777
.
doi:10.1002/joc.757
.
Wang
R.
,
Kalin
L.
,
Kuang
W.
,
Tian
H.
2014
Individual and combined effects of land use/cover and climate change on Wolf Bay watershed streamflow in southern Alabama
.
Hydrological Processes
28
(
22
),
5530
5546
.
doi:10.1002/hyp.10057
.
Wang
W.
,
Lu
H.
,
Ruby Leung
L.
,
Li
H. Y.
,
Zhao
J.
,
Tian
F.
,
Yang
K.
,
Sothea
K.
2017
Dam construction in Lancang-Mekong River Basin could mitigate future flood risk from warming-induced intensified rainfall
.
Geophysical Research Letters
44
(
20
),
10,378
10,386
.
doi:10.1002/2017GL075037
.
Wasko
C.
,
Sharma
A.
2017
Global assessment of flood and storm extremes with increased temperatures
.
Scientific Reports
7
(
1
),
7945
.
doi:10.1038/s41598-017-08481-1
.
Weedon
G.
,
Gomes
S.
,
Viterbo
P.
,
Shuttleworth
W.
,
Blyth
E.
,
Österle
H.
,
Adam
J.
,
Bellouin
N.
,
Boucher
O.
,
Best
M.
2011
Creation of the WATCH forcing data and its use to assess global and regional reference crop evaporation over land during the twentieth century
.
Journal of Hydrometeorology
12
(
5
),
823
848
.
doi:10.1175/2011JHM1369.1
.
Wei
H. P.
,
Yeh
K. C.
,
Liou
J. J.
,
Chen
Y. M.
,
Cheng
C. T.
2016
Estimating the risk of river flow under climate change in the Tsengwen River Basin
.
Water
8
(
3
).
doi:10.3390/w8030081
.
Whitehead
P. G.
,
Sarkar
S.
,
Jin
L.
,
Futter
M. N.
,
Caesar
J.
,
Barbour
E.
,
Butterfield
D.
,
Sinha
R.
,
Nicholls
R.
,
Hutton
C.
,
Leckie
H. D.
2015
Dynamic modeling of the Ganga river system: impacts of future climate and socio-economic change on flows and nitrogen fluxes in India and Bangladesh
.
Environmental Science-Processes & Impacts
17
(
6
),
1082
1097
.
doi:10.1039/c4em00616j
.
Whitehead
P. G.
,
Jin
L.
,
Macadam
I.
,
Janes
T.
,
Sarkar
S.
,
Rodda
H. J. E.
,
Sinha
R.
,
Nicholls
R. J.
2018
Modelling impacts of climate change and socio-economic change on the Ganga, Brahmaputra, Meghna, Hooghly and Mahanadi river systems in India and Bangladesh
.
Science of the Total Environment
636
,
1362
1372
.
doi:10.1016/j.scitotenv.2018.04.362
.
Whitehead
P. G.
,
Jin
L.
,
Bussi
G.
,
Voepel
H. E.
,
Darby
S. E.
,
Vasilopoulos
G.
,
Manley
R.
,
Rodda
H.
,
Hutton
C.
,
Hackney
C.
,
Tri
V. P. D.
,
Hung
N. N.
2019
Water quality modelling of the Mekong River basin: climate change and socioeconomics drive flow and nutrient flux changes to the Mekong Delta
.
Science of the Total Environment
673
,
218
229
.
doi:10.1016/j.scitotenv.2019.03.315
.
Wichakul
S.
,
Tachikawa
Y.
,
Shiiba
M.
,
Yorozu
K.
2015
River discharge assessment under a changing climate in the Chao Phraya River, Thailand by using MRI-AGCM3.2S
.
Hydrological Research Letters
9
(
4
),
84
89
.
doi:10.3178/hrl.9.84
.
Wiel
K.
,
Wanders
N.
,
Selten
F. M.
,
Bierkens
M. F. P.
2019
Added value of large ensemble simulations for assessing extreme river discharge in a 2°C warmer world
.
Geophysical Research Letters
46
(
4
),
2093
2102
.
doi:10.1029/2019GL081967
.
Wilby
R. L.
,
Dawson
C. W.
,
Barrow
E. M.
2002
SDSM – a decision support tool for the assessment of regional climate change impacts
.
Environmental Modelling & Software
17
(
2
),
145
157
.
doi:10.1016/S1364-8152(01)00060-3
.
Winsemius
H. C.
,
Aerts
J. C. J. H.
,
van Beek
L. P. H.
,
Bierkens
M. F. P.
,
Bouwman
A.
,
Jongman
B.
,
Kwadijk
J. C. J.
,
Ligtvoet
W.
,
Lucas
P. L.
,
van Vuuren
D. P.
,
Ward
P. J.
2016
Global drivers of future river flood risk
.
Nature Climate Change
6
(
4
),
381
385
.
doi:10.1038/nclimate2893
.
Wobus
C.
,
Gutmann
E.
,
Jones
R.
,
Rissing
M.
,
Mizukami
N.
,
Lorie
M.
,
Mahoney
H.
,
Wood
A. W.
,
Mills
D.
,
Martinich
J.
2017
Climate change impacts on flood risk and asset damages within mapped 100-year floodplains of the contiguous United States
.
Natural Hazards and Earth System Sciences
17
(
12
),
2199
2211
.
doi:10.5194/nhess-17-2199-2017
.
Wood
A. W.
,
Leung
L. R.
,
Sridhar
V.
,
Lettenmaier
D. P.
2004
Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs
.
Climatic Change
62
(
1
),
189
216
.
doi:10.1023/B:CLIM.0000013685.99609.9e
.
Wu
C.
,
Huang
G.
,
Yu
H.
,
Chen
Z.
,
Ma
J.
2014
Impact of climate change on reservoir flood control in the upstream area of the Beijiang River basin, South China
.
Journal of Hydrometeorology
15
(
6
),
2203
2218
.
doi:10.1175/JHM-D-13-0181.1
.
Wu
C.
,
Huang
G. R.
,
Yu
H. J.
2015
Prediction of extreme floods based on CMIP5 climate models: a case study in the Beijiang River basin, South China
.
Hydrology and Earth System Sciences
19
(
3
),
1385
1399
.
doi:10.5194/hess-19-1385-2015
.
Xu
C.-y.
,
Widén
E.
,
Halldin
S.
2005
Modelling hydrological consequences of climate change – progress and challenges
.
Advances in Atmospheric Sciences
22
(
6
),
789
797
.
doi:10.1007/BF02918679
.
Xu
H.
,
Taylor
R. G.
,
Xu
Y.
2011
Quantifying uncertainty in the impacts of climate change on river discharge in sub-catchments of the Yangtze and Yellow River Basins, China
.
Hydrology and Earth System Sciences
15
(
1
),
333
344
.
doi:10.5194/hess-15-333-2011
.
Yatagai
A.
,
Kamiguchi
K.
,
Arakawa
O.
,
Hamada
A.
,
Yasutomi
N.
,
Kitoh
A.
2012
APHRODITE: constructing a long-term daily gridded precipitation dataset for Asia based on a dense network of rain gauges
.
Bulletin of the American Meteorological Society
93
(
9
),
1401
1415
.
doi:10.1175/BAMS-D-11-00122.1
.
Yin
J.
,
Guo
S.
,
He
S.
,
Guo
J.
,
Hong
X.
,
Liu
Z.
2018
A copula-based analysis of projected climate changes to bivariate flood quantiles
.
Journal of Hydrology
566
,
23
42
.
doi:10.1016/j.jhydrol.2018.08.053
.
Yu
Z.
,
Gu
H.
,
Wang
J.
,
Xia
J.
,
Lu
B.
2018
Effect of projected climate change on the hydrological regime of the Yangtze River Basin, China
.
Stochastic Environmental Research and Risk Assessment
32
(
1
),
1
16
.
doi:10.1007/s00477-017-1391-2
.
Yuan
F.
,
Tung
Y. K.
,
Ren
L.
2016
Projection of future streamflow changes of the Pearl River basin in China using two delta-change methods
.
Hydrology Research
47
(
1
),
217
238
.
doi:10.2166/nh.2015.159
.
Yuan
F.
,
Zhao
C.
,
Jiang
Y.
,
Ren
L.
,
Shan
H.
,
Zhang
L.
,
Zhu
Y.
,
Chen
T.
,
Jiang
S.
,
Yang
X.
,
Shen
H.
2017
Evaluation on uncertainty sources in projecting hydrological changes over the Xijiang River basin in South China
.
Journal of Hydrology
554
,
434
450
.
doi:10.1016/j.jhydrol.2017.08.034
.
Zhang
X.
,
Xu
Y.-P.
,
Fu
G.
2014
Uncertainties in SWAT extreme flow simulation under climate change
.
Journal of Hydrology
515
,
205
222
.
doi:10.1016/j.jhydrol.2014.04.064
.
Zhang
X.
,
Booij
M. J.
,
Xu
Y. P.
2015
Improved simulation of peak flows under climate change: postprocessing or composite objective calibration?
Journal of Hydrometeorology
16
(
5
),
2187
2208
.
doi:10.1175/jhm-d-14-0218.1
.
Zhang
Y.
,
You
Q.
,
Chen
C.
,
Ge
J.
2016
Impacts of climate change on streamflows under RCP scenarios: a case study in Xin River Basin, China
.
Atmospheric Research
178–179
,
521
534
.
doi:10.1016/j.atmosres.2016.04.018
.
Zhao
G.
,
Gao
H.
,
Cuo
L.
2016
Effects of urbanization and climate change on peak flows over the San Antonio River basin, Texas
.
Journal of Hydrometeorology
17
(
9
),
2371
2389
.
doi:10.1175/JHM-D-15-0216.1
.
Zulkafli
Z.
,
Buytaert
W.
,
Manz
B.
,
Rosas
C. V.
,
Willems
P.
,
Lavado-Casimiro
W.
,
Guyot
J. L.
,
Santini
W.
2016
Projected increases in the annual flood pulse of the Western Amazon
.
Environmental Research Letters
11
(
1
).
doi:10.1088/1748-9326/11/1/014013
.

Supplementary data