Individual and combined impacts of future land-use and climate conditions on extreme hydrological events in a representative basin of the Yangtze River Delta, China

Atmospheric Research 236 (2020) 104805

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Individual and combined impacts of future land-use and climate conditions on extreme hydrological events in a representative basin of the Yangtze River Delta, China

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Qiang Wanga, Youpeng Xua, , Yuefeng Wangb, Yuqing Zhangc, Jie Xianga, Yu Xua, Jie Wanga a

School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China School of Geography and Tourism, Chongqing Normal University, Chongqing 401331, China c School of Urban and Environmental Science, Huaiyin Normal University, Huai'an 223300, China b

A R T I C LE I N FO

A B S T R A C T

Keywords: Hydrological extremes Climate change LUCC Statistical downscaling method CA-Markov XRB

Land use/cover changes (LUCC) as well as climate change have the potential to significantly alter the characteristics of extreme hydrological events under changing environments. This study evaluates the individual and combined impacts that LUCC and climate change have on extreme hydrological events using Soil and Water Assessment Tool (SWAT) applied in the Xitiaoxi River Basin (XRB). Two future land-use conditions, projected by CA-Markov model, and 21 climate scenarios, downscaled from three general circulation models, are considered. The results show that urban area would expand dramatically from 4.4% to 12.63%, which mainly occupy the forest-grass and agriculture land. The LUCC will decrease low flow index as well as increase high flow index, annual maximum 1-day and 5-day streamflow. Extreme precipitation indices all exhibit trends toward an increase in future occurrence. Furthermore, in testing the response of extreme hydrological events to climate change, most extreme hydrological indices are predicted to become greater than they were throughout the 1970s. The combined effects of LUCC and climate change would increase all extreme hydrological indices. Moreover, the climate change would contribute > 50% of these hydrological impacts. These findings indicate that the XRB will experience more severe extreme hydrological events in both flooding and drought due to climate and land-use change. It is advised that disaster mitigation measures must be updated accordingly to respond to this changing situation.

1. Introduction In past decades, extreme hydrological events have been observed with increasing frequency, and these events have been characterized by growing costs in life and property (Chou et al., 2013; IPCC, 2014). Changes in climate and underlying surface characteristics have been recognized as two primary factors (Mittal et al., 2016; Saghafian et al., 2008; Wang et al., 2018; Zhang et al., 2016). The land use/cover changes (LUCC) caused by human activity, such as urbanization, have opened the gates for more severe flooding (Zhou et al., 2013). Moreover, climate change and human activity also work to intensify regional hydrological drought (Zhang et al., 2018). Due to the increasing frequency of such extreme hydrological events, it has become even more important than ever to detect the response of extreme hydrological events to future climate and land-use conditions. Climate change has been found to be a major contributor to extreme

meteorological events, and its impacts on hydrological processes have been analyzed in past studies (Gosling et al., 2011; Meaurio et al., 2017; Zhang et al., 2016). The most common method for evaluating climate impacts is to apply General Circulation Models (GCMs) to drive hydrological models, such as the soil and water assessment tool (SWAT), the variable infiltration capacity model (Liang et al., 1994), the water and energy transfer process model (Jia et al., 2001), as well as others. GCMs can simulate the general circulation of the planet's atmosphere, providing sound and credible information on past, present, and future climate (Zhang et al., 2016). Representative Concentration Pathways (RCPs), new climate change simulation scenarios, have been established in the fifth phase of the Coupled Model Intercomparison Project (CMIP5), and can be utilized to depict various possible future climate scenarios (van Vuuren et al., 2011). Statistical or dynamic downscaling methods usually are used due to the relatively coarse spatial resolution of GCMs (Fowler and Kilsby, 2007). Specifically, the Statistical

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Corresponding author at: Nanjing University, No. 163 Xianlin Avenue, Nanjing, Jiangsu Province, PR China. E-mail addresses: [email protected] (Q. Wang), [email protected] (Y. Xu), [email protected] (Y. Wang), [email protected] (Y. Zhang), [email protected] (J. Xiang), [email protected] (Y. Xu), [email protected] (J. Wang). https://doi.org/10.1016/j.atmosres.2019.104805 Received 3 February 2019; Received in revised form 4 December 2019; Accepted 10 December 2019 Available online 16 December 2019 0169-8095/ © 2019 Elsevier B.V. All rights reserved.

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Fig. 1. The location and topography of the Xitiaoxi River Basin (XRB) with hydrological, meteorological and rainfall station.

corpus of research literature on the topic is based exclusively on historical climate and land-use scenarios. Moreover, research built around evaluating the individual and combined impacts of LUCC and climate change on extreme hydrological events is very limited. Extreme hydrological events can cause tremendous damage to human society, including economic loss and environmental degradation, in addition to loss of human life (Hu et al., 2017; Zhou et al., 2002). Regardless, recent research in the field has merely investigated the trends and changing properties of such extreme hydrological events, but only using historical materials (Burn et al., 2010; Zhang et al., 2010). Greater knowledge and insight can be achieved for the research community by investigating the driving mechanisms behind this phenomenon, as well as by adopting improved quantitative methodologies with integrated frameworks, by pairing hydrological models with different climate and future land-use scenarios. The Yangtze River Delta is one of the most developed regions in all of China, and its underlying surface has undergone considerable change over the past few decades. In addition, the area's precipitation intensity has seen a noticeable increase over the past 57 years, resulting a corresponding increase in the region's risk of flash floods (Han et al., 2015; Wang et al., 2016). Due to the economic development and changing environment in the area, it comes as no surprise that it has become more vulnerable to natural disasters in general, and extreme hydrological events specifically. With this in mind, the overarching objective of this study is to detect the individual and combined effects of climate change and LUCC on extreme hydrological events. To achieve this objective, the SWAT model (specifically a physically-based, semi-distributed, basin-scale

Downscaling Method (SDSM) model serves as an effective tool for bridging both the spatial and temporal resolution gaps that exist between what hydrological models demand and what GCMs are able to provide (Wilby et al., 2002). SDSM is widely used to downscale climate data in the research conducted on the hydrological impacts in various regions around the world (Wilby and Dawson, 2007). Another trigger factor found to cause change in hydrological regimes is LUCC (de Paulo Rodrigues Da Silva et al., 2018; Gashaw et al., 2018; Rajib and Merwade, 2017; Zope et al., 2016). This is especially the case in the process of urbanization, where a permeable vegetated land surface has be replaced by an impervious cityscape. Past work on the subject has shown that the LUCC caused by urbanization can work to increase peak runoff and runoff volume (Zhou et al., 2013; Zope et al., 2016). It must be noted that most studies on the topic are based on historical land-use datasets and have placed comparatively less emphasis on the combined effects of climate change and LUCC. As such, projecting future land-use scenarios and identifying its hydrological impacts is sorely needed in the field. The Markov chain model is based on a simple probabilistic process which can be described as the current state depends only on its most adjacent previous period, and the transition can be presents by transition probability matrices (Iacono et al., 2012; Yang et al., 2019). A Cellular Automata-Markov (CA-Markov) combines CA and the Markov Chain and has been widely used to predict both quantitative and spatial change of land use (Halmy et al., 2015; Subedi et al., 2013). However, past studies on the hydrological effects of LUCC and climate change have focused primarily on variation within long-term hydrological processes rather than extreme events. In addition, the 2

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survey data on a scale of 1:10,000. Landsat images were used to extract land-use data for 2008 and 2019 using supervised classification, and the results were found to fall within an acceptable level of precision. These spatial data were compiled into raster-based formats and unified into 30 m resolution.

hydrological model) was constructed to simulate the hydrological cycle within the case study area. The CA-Markov model was used to predict the future land use scenarios. The accuracy of the SDSM model was assessed and a calibrated model was subsequently used to downscale climate variables. Lastly, the impacts of LUCC and climate change on extreme hydrological events were evaluated driven by two future landuse and 18 climate projections with one historical land-use and 3 climate scenarios. The results of this study could provide a scientific basis for better understanding and implementing flood control, disaster reduction, and drought adaptation in the Yangtze River Delta region.

2.2.3. Climate data Daily reanalysis data for the years from 1961 to 2015 were derived from the National Centers for Environment Prediction (NCEP), served as a set of observed large-scale atmospheric variables. These reanalysis data include large-scale atmospheric observation variables with a resolution of 2.5° (longitude) × 2.5° (latitude). The climate scenario data with daily interval were obtained from three GCMs outputs taken from CMIP5's three most widely used RCPs. Specifically, they are a very low forcing scenario (RCP 2.6), a medium stabilization scenario (RCP 4.5), and very high emission scenario (RCP 8.5). In the RCP 2.6 scenario, radiative forcing peaks at ~3 W/m2 (~490 ppm CO2) before 2100 and then drops to 2.6 W/m2 by 2100. The RCP 4.5 scenario is characterized by medium stabilization without overshoot pathways, and rises to 4.5 W/m2 (~650 ppm CO2) at stabilization after 2100. On the contrary, the RCP 8.5 scenario represents a rising radiative forcing pathway that grows to 8.5 W/m2 (~1370 ppm CO2) by 2100 (van Vuuren et al., 2011). Although there are many GCMs available, most models have a certain area range and accuracy tailored for specific applications. The BCC-CSM 1.1 (Beijing Climate Center, Climate System Model, version 1.1), the CanESM2 (Second Generation Canadian Earth System Model), and the NorESM1-M (Norwegian Earth System Model, version 1) models have been found in past research to be capable of simulating the climate over Eastern China sufficiently (Chen and Frauenfeld, 2014; Siew et al., 2014; Xin et al., 2013; Zhang et al., 2016). In addition, note that multi-model ensemble climate simulations can reduce the uncertainty introduced by utilizing GCMs (Meaurio et al., 2017; Reshmidevi et al., 2018). Hence, the three aforementioned GCMs (i.e. BCC-CSM 1.1, CanESM2, and NorESM1-M) were selected to generate the climate scenarios for this study. Specifically, the spatial resolutions for the BCC-CSM1.1, CanESM2, and NorESM1-M models are 2.8125° (longitude) × 2.8125° (latitude), 2.8125° (longitude) × 2.8125° (latitude), 2.5° (longitude) × 1.875° (latitude), respectively. All GCM datasets were uniformed to the same resolution to match the study's reanalysis data taken from the NCEP to avoid any biases caused by differences in scale (Zhang et al., 2016). All datasets with daily interval for each of the RCPs' historical periods (1961–2000) as well as future scenarios (2006–2099) were downloaded from the website of the Earth System Grid Federation (https://esgf-node.llnl.gov/).

2. Materials and methodology 2.1. The study area The Xitiaoxi River Basin (XRB), an area that encompasses 1191 km2 in all, is located within China's Yangtze River Delta between the longitudes 119°14′E-119°45′E and latitudes 30°23′N-31°11′N (Fig. 1). The relief of the basin is found in a hilly topography, with elevations ranging from 5 to 1580 m above sea level. The regional climate is influenced by the East Asian monsoon, with annual average temperatures and precipitation of about 15.5 °C and 1584 mm, respectively. The Xitiaoxi River is one of the most important upstream tributaries from the Taihu Lake, supplying about 26.8 × 108 m3 (approximately 27.7%) water into Taihu Lake annually. The XRB was selected as representative basin of the Yangtze River Delta, a region that has experienced rapid urbanization over the past few decades. Due the dynamic changes in land use and land cover, as well as the specific climate conditions of the area, the mechanisms of extreme hydrological events therein may well have been influenced. Thus, the XRB is investigated specifically as a representative region of the Yangtze River Delta to explore the impacts that climate change and LUCC will have on extreme hydrological events. 2.2. Datasets 2.2.1. Hydro-meteorological data The study's hydro-meteorological data include daily streamflow, precipitation, and weather measurements, which were supplied by the Anji Hydrology and Water Resources Investigation Bureau. The daily streamflow data, taken from 1994 to 2015, were collected from three hydrological stations. More specifically, the streamflow data with daily interval were collected from the Hengtangcun station at the outlet of the XRB and actual flow measurements taken from two local reservoirs. The daily precipitation data, taken from 1972 to 2015, were collected from 16 rainfall gauge stations. The weather data, taken from 1961 to 2015, were collected from the Anji meteorological station, which consist of daily maximum and minimum temperature, relative humidity, mean wind speed, and solar radiation measurements. The hydro-meteorological data taken from 1994 to 2015 were used for constructing, calibrating, and validating the SWAT model. The daily precipitation and weather data were selected as predictors for downscaling and used for calibrating as well as validating the SDSM model. The locations for these hydrological and meteorological stations are presented in Fig. 1.

2.3. Methodology For this study, an integrated approach with SWAT, CA-Markov, and SDSM models was used to detect the individual and combined impacts that climate change and LUCC will have on extreme hydrological events. The hydrological processes were simulated using the SWAT model for the areas of upstream at the outlet station of XRB. Future land-use scenarios were projected by CA-Markov model. Climate scenarios from the study's collection of three GCMs, under its three RCPs, and for its three periods (i.e. 1961–1990 for the baseline scenario, 2036–2065 for the mid-century scenario, and 2070–2099 for the latecentury scenario), were downscaled using SDSM. Variation in extreme hydrological events under different climate and land-use scenarios was then analyzed. The general metrological framework is presented in Fig. 2.

2.2.2. Geospatial data Spatial data for topography, soils, and land use, were used to represent watershed heterogeneity for hydrological simulations. Topography was attained using a digital elevation model obtained from the ASTER GDEM datasets provided by the Computer Network Information Center at the Chinese Academy of Sciences (http://www. gscloud.cn). The spatial data for soil were obtained from the Second National General Soil Survey data and has a scale of 1:10,000. Soil property information was provided by the Anji Bureau of Agriculture. The land-use data span over four years, namely 1985, 2002, 2008 and 2019. The data for 1985 and 2002 were obtained by digitizing land

2.3.1. The hydrological model The SWAT model is originally developed by United States Department of Agriculture's Agriculture Research Service (Arnold et al., 1998) and wildly used in simulating and investigating the hydrological 3

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Fig. 2. Schematic representation of the methodology in this study.

2.3.2. CA-Markov model The Markov process is a random process that a system undergoes transitions from one state to another. The basic premise of the Markov chain is that land use at some point in the future t + 1 can be determined as a function of current land use t (Iacono et al., 2012), which can be describe as:

processes in various watershed regions around the world (see the SWAT literature database at https://www.card.iastate.edu/swat_articles/). The model divides a watershed into sub-basins connected to a river network that is based on digital elevation model. Each of these subbasins is then divided into hydrological response units based on a unique combination of land use, soil, and slope characteristics (Neitsch et al., 2011). Hydrological processes are firstly simulated in each subbasin. Thereafter, the simulated hydrological components are aggregated toward the outlet of the basin through its network of streams. Many different physical processes involved (e.g., evapotranspiration, surface runoff, infiltration, lateral flow, and groundwater flow) can be simulated in such a watershed study. The components in the hydrological cycle, as simulated by the SWAT model, are based on the water balance equation (Arnold et al., 2012). The SWAT model was honed using SWAT Calibration and Uncertainty Programs (SWAT-CUP). SWAT-CUP can provide parameter calibration and verification procedures to enable sensitivity analysis, calibration, validation, and uncertainty analysis (Abbaspour, 2014). The sequential uncertainty fitting (SUFI-2) algorithm was selected to calibrate and validate the parameters within the SWAT-CUP (Abbaspour et al., 2004). In the SUFI-2 algorithm, the uncertainty exhibited in the model's parameters accounts for all sources of uncertainty, such as the uncertainty found in driving (e.g., rainfall), the conceptual model, its parameters, as well as the measured data. The uncertainties found in the model output variables are expressed in a 95% probability distribution (95PPU), calculated at the 2.5% and 97.5% levels of the cumulative distribution for an output variable generated by the propagation of parameter uncertainties using Latin hypercube sampling. The model's results covering most observations at 95PPU were found to be satisfied. The P-factor and R-factor are used to quantify fit between simulation results (at 95PPU) and sample observations. Specifically, P-factor denotes the percentage of observed data enveloped at 95PPU, and a value of > 70% is considered desirable. R-factor represents the thickness of 95PPU in enveloping, and a value of < 1 is considered desirable (Abbaspour et al., 2004). The P-factor, Rfactor, coefficient of determination (R2), the Nash-Sutcliffe coefficient (NS), and the percent bias (PBIAS) are used to describe the performance of SWAT model (Abbaspour, 2014; Moriasi et al., 2007; Nie et al., 2011).

P × Lt = Lt + 1

(1)

where Lt and Lt+1 are the land use at time t and t + 1, respectively. P is the transition probability matrix, which governs the probability of transition between each pair of land use datasets. The P follows that:

Pij =

Aij Ai

(2)

m

∑ Pij = 1 j=1

(3)

where Pij represents the probability of land use i transformed into land use j. Ai is the total transformed area of land use i over transition period, and Aij is the area of land use i transformed into land use j, and m is the number of the land use types. The Markov model can quantitatively predict the dynamic changes of landscape pattern. However, the model is not good at dealing with the spatial pattern of land-use change. Cellular Automata (CA) can predict any transition among any number of categories (Pontius and Malanson, 2005). CA-Markov model combine the advantages of CA theory and the space layout forecast of Markov model, and thus can perform better in modelling land-use change in both time and spatial dimension (Li et al., 2015). The CA-Markov model was conducted in IDRISI software and must be validated before prediction. The land-use map of 2019 was first predicted with the land-use maps of 1985 and 2002, and then the accuracy of CA-Markov was evaluated by Kappa coefficient which was calculated based on the predicted and actual land-use maps. The value of Kappa coefficient above 80% indicates good performance of prediction (Yang et al., 2019). Finally, the land-use maps of 2053 and 2087 were predicted to present the land-use patterns of 2050s and 2080s, respectively. 2.3.3. The SDSM model The SDSM model is an effective tool for downscaling climate 4

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applied widely in hydro-meteorological research because of their robustness as well as their fairly straightforward calculation and interpretation (Sillmann et al., 2013). All these indices for extreme hydrological events can be divided into two types, namely flood indicators and drought indicators. Flood indicators typically include maximum 1day rainfall (RX1day), maximum 5-day rainfall (RX5day), high flow index (QH), consecutive high-flow days (CDH), maximum 1-day streamflow (SX1day), and maximum 5-day streamflow (SX5day); and drought indicators include low flow index (QL), consecutive low-flow days (CDS), and consecutive dry days (CDD). All of these indices were calculated by selecting either the annual maximum or all of the events that exceed a certain threshold.

variables from simulations generated using GCMs, which can be in and of themselves unrealistic at the temporal and spatial scales of interest (Wilby and Dawson, 2007; Zhang et al., 2016). The model originally developed in the research of Wilby et al. (2002) has been wildly applied for meteorological, hydrological, and environmental assessment in many different regions around the world (Wilby and Dawson, 2007). SDSM takes a predictand along with a set of predictor variables, and computes the parameters of multiple regression equations via an optimization algorithm (either dual simplex of ordinary least squares) (Wilby and Dawson, 2007). Compared to dynamical downscaling methodologies, SDSM model can better facilitate the rapid development of multiple, low-cost, single-site scenarios for daily surface weather variables under both present and future climate forcing. With these considerations in mind, the SDSM version 5.2 was used in this study to downscale RCP scenarios in historic and future periods of the XRB. Ten large-scale atmospheric variables were selected as predictors for downscaling. Namely, they are mlsp (mean seal level pressure), p500 and p850 (geopotential height at 500 and 850 hPa, respectively), p5_u and p8_u (zonal velocity component at 500 and 850 hPa, respectively), r500 and r850 (relative humidity at 500 and 850 hPa, respectively), rhum (near surface relative humidity), shum (near surface specific humidity), and temp (mean temperature at 2 m). The daily maximum temperature, minimum temperature, relative humidity, and solar radiation for one meteorological station, as well as the daily precipitation for all of the 16 rainfall gauge stations, were selected as predictands in the XRB simulation model. The years from 1961 to 1988 and from 1989 to 2015 served as calibration and validation periods for downscaling daily maximum temperature, minimum temperature, relative humidity, and solar radiation. Due to the limitations in the precipitation measurements for the study's time series, the periods from 1972 to 1993 and from 1994 to 2015 served as calibration and validation periods for daily precipitation, respectively. In addition, correlation coefficient (R), bias (BIAS) and percent bias (PBIAS) were used to describe the performance of the baseline scenarios that were generated by SDSM models.

2.3.6. Decomposing the effects of climate change and LUCC To assess the individual impacts of climatic and land-use changes on these extreme hydrological events, we defined the contributions of future climatic and land-use conditions on hydrological changes using the following expressions,

∆HSL, C = HSL, C − HSbasiline

(4)

where ΔHSL, C is total change in each hydrological index under future climate and land-use conditions compared with baseline scenario (i.e., the climate conditions in 1970s and land-use condition in 1985), S represents the each GCM, L is the future land-use scenarios (i.e., 2053 and 2087), HSbasiline is the value of each hydrological index under baseline period.

ηSL = ηSC =

HsL,1970s − Hsbasiline ∆HSL, C

(5)

− Hsbasiline ∆HSL, C

(6)

HS1985, C

where ηS and ηS are the contributions of future land-use and climatic conditions for changes of each hydrological index compared with baseline scenario, respectively. HsL, 1970s represents the value of each hydrological index under the scenario of future land use and 1970s climate. HS1985, C represents the value of each hydrological index under the scenario of future climate and 1985 land-use condition. L

2.3.4. Scenarios The calibrated SWAT model was run for three land-use scenarios (i.e. 1985, 2053 and 2087) with 21 climate scenarios (including 18 future and 3 historical scenarios). Future climate scenarios compiled from its three GCMs (i.e. BCC-CSM1.1, CanESM2, and NorESM1-M), under its three RCPs (i.e. RCP 2.6, RCP 4.5, and RCP8.5) for two future periods, i.e., 2050s (2036–2065) and 2080s (2070–2099), and baseline climate scenarios compiled from three GCMs for the period of 1970s (1961–1990).

C

3. Results and discussion 3.1. The performance of SWAT model To accurately simulate the hydrological processes that may occur in the XRB under changing environments, the parameters of the SWAT model were calibrated for the period from 1996 to 2005 under the land use conditions of 2002, and then validated for the period from 2006 to 2015 under the land use conditions of 2008. The model was run using the ArcSWAT extension for ArcGIS 10.3 and conducted on a daily time scale within the XRB. The SUFI-2 algorithm was selected to calibrate

2.3.5. Extreme indices Nine indices for extreme hydrological events, made up of three precipitation indices and six river discharge indices, were selected to evaluate the variation in these extremes in the XRB. Details regarding these indices are presented in Table 1. Similar indices have been Table 1 Definitions of extreme indices used in this study.

Precipitation indices

River discharge indices

Index

Descriptive name

Definitions

Units

CDD RX1day RX5day QL QH CDS

Consecutive dry days Maximum 1-day rainfall Maximum consecutive 5-day rainfall Low flow index High flow index Consecutive low-flow days

days mm mm m3/s m3/s days

CDH

Consecutive high-flow days

SX1day SX5day

Maximum 1-day streamflow Maximum consecutive 5-day streamflow

Annual maximum number of consecutive days with daily precipitation < 1 mm Annual maximum 1-day precipitation Annual maximum average consecutive 5-day precipitation The multi-annual average of 10th percentiles of daily flow The multi-annual average of the 90th flow percentiles of daily flow Annual maximum number of consecutive days with daily flow < 10th percentile of the flow data Annual maximum number of consecutive days with daily flow > 90th percentile of the flow data Annual highest daily flow Annual highest average flow in consecutive 5 days

5

days m3/s m3/s

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Fig. 3. Comparison of the simulated and observed streamflow daily timescale during calibration (1996–2005) and validation (2006–2015) periods. 95PPU represents the range between 2.5% and 97.5% levels of the cumulative distribution of model simulation outputs.

squares regression analysis, as shown in Fig. 4. Here it can be seen that the R2 for annual maximum 1-day and 5-day flows are 0.85 and 0.81, respectively. Both values are statistically significant (p < .01). This indicates that there is a strong correlation between the simulated and observed annual maximum streamflow values. Thus it can be concluded that the study's SWAT model can capture extreme streamflow events accurately. The uncertainties in the model output variables, expressed as values on 95PPU, were also analyzed, as seen in Fig. 3. It is found that the 95PPU enveloped most of the observed flow. In addition, the P-factor values of 0.62 during the calibration periods and 0.86 during the validation periods show the SWAT model's simulations enveloped > 80% of observed data during validation, and > 60% of observed data during calibration (Table 2). Moreover, the R-factor values for both calibration and validation were all found to be lower than 1, indicating a small amount of uncertainty in the range of simulation results (Abbaspour, 2014). From the results for model performance, it is found that the SWAT model on the daily scale exhibits sufficient accuracy with a small amount of uncertainty in simulating the hydrological processes. Thus, SWAT model constructed with optimal parameters were utilized to evaluate the responses of extreme hydrological events to changes in land use and climate conditions.

Table 2 Daily performance statistics of SWAT model for calibration and validation periods.

P-factor R-factor R2 NS PBIAS (%)

Calibration (1996–2005)

Validation (2006–2015)

0.62 0.85 0.68 0.68 −4.50

0.86 0.91 0.68 0.65 −7.98

and validate the parameters within the SWAT-CUP uncertainty programs, and the NS was selected as objective function. Comparisons between the simulated and observed streamflow at daily interval through the calibration and validation periods are presented in Fig. 3. It can be seen that the simulated streamflow series matches well with the observed data. The statistics for the model performance criteria are listed in Table 2. It is found that R2 and NS are all higher than 0.65, for both the calibration and validation periods. In addition, the absolute values for PBIAS are found to be lower than 10%. As per the evaluation criteria of the SWAT model, the model's performance herein can be considered sufficient for the XRB (Moriasi et al., 2007). Furthermore, the correlations of annual maximum 1-day and 5-day flows between the simulated and observed streamflow during the calibration and validation periods were examined using an ordinary least

Fig. 4. Observed and simulated annual maximum 1-day (a) and 5-day (b) flow during the calibration and validation periods. 6

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Fig. 5. Observed and downscaled daily maximum temperature (a), minimum temperature (b), monthly precipitation (c), daily relative humidity (d) and daily solar radiation (e) during calibration period.

Fig. 6. Observed and downscaled daily maximum temperature (a), minimum temperature (b), monthly precipitation (c), daily relative humidity (d) and daily solar radiation (e) during validation period.

3.2. The performance evaluation of SDSM model

BIAS lower than 0.02 °C in calibration periods and 0.72 °C in validation periods, see Figs. 5a, b and 6a, b. Variables such as precipitation, relative humidity, and solar radiation did not fare better than temperature with all of their values for R higher than 0.7 at statistical significance (p < .01), see Figs. 5c–e and 6c–e. In addition, the PBIAS values for precipitation, relative humidity, and solar radiation were all

Figs. 5 and 6 present the performance of the SDSM model in both the calibration and validation periods. It can be seen that the SDSM models downscale the daily maximum temperature as well as minimum temperature very well, with all values for R over 0.9 and all values for

7

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events, the land-use scenarios in 2053 and 2087 were projected based on CA-Markov model. The overall Kappa coefficient for the predicted and actual land-use maps in 2019 is 80.12%, which indicating a good performance of CA-Markov. LUCC between 1985 and 2087 is shown in Fig. 7. Forest-grass land is the main land-use type in XRB, and will decrease slightly of entire area. Agriculture land would decrease from 17.45% in 1985 to 11.26% in 2053, and to 13.53% in 2087. The water bodies occupy a small proportion of the area, but would decrease slightly from 1985 to 2087 as well. Conversely, the urbanization in the area is found to expand dramatically, from 4.4% in 1985 to 11.63% in 2053 and to 12.63% in 2087. The impacts of LUCC on extreme hydrological events are examined using SWAT model under different land-use scenarios with same climate conditions. Keeping the climate conditions constant, the variations found for QL, QH, SX1day, SX5day, CDS, and CDH under the landuse changes processes between 1985 and 2053 and 1985–2087 are presented in Fig. 8. Consistent results are found in the response of extreme hydrological events to LUCC under the different climate scenarios. For the drought indicators, LUCC was found to decrease QL by about 0–1 m3/s (Fig. 8a, b). For the flood indicators (i.e. QH, SX1day, and SX5day), LUCC was found to increase the values for QH, SX1day, and SX5day by about 0–2 m3/s, 0–2 m3/s, and 0–3 m3/s, respectively (Fig. 8c–h). However, LUCC was found to have little impacts on CDS and CDH, seeing an increase of about 1–2 days for CDH (Fig. 8i–l). LUCC, especially under the rapid processes of urbanization that occur in developing countries, can lead to a substantial increase proportion of impervious area. In the XRB specifically, such impervious area saw a significant increase, encroaching on forest-grass and agriculture land (Wang et al., 2018). Previous studies have found that an increase in such impervious area typically leads to a decrease in infiltration, canopy interception, as well as regional water holding capacity (Rose and Peters, 2001; Zhou et al., 2013). As a result, regional hydrological processes, and especially rainfall-runoff processes, are ultimately influenced greatly by local urbanization. The extreme flood indices (i.e. QH, SX1day, and SX5day) were found to increase corresponding to a growth in the runoff coefficient. This means that rainfall events of similar magnitude will produce more massive flooding caused

Fig. 7. Area extent of land-use classes in the years from 1985 to 2087 for XRB (%). Total area of XRB is 1191 km2. The land-use maps of 1985, 2002 and 2019 is historical data, and the land-use maps of 2053 and 2087 are projected by CAMarkov model.

found to be lower than 15% for the calibration and validation periods, with the sole exception of precipitation during the calibration period. Similar to the findings from related studies in the field (Wang et al., 2015; Zhang et al., 2016; Zhou et al., 2015), performance in this study was found to be relatively poor for precipitation. This could be attributed to the fact that precipitation is more complex and discrete than temperature. Basically, the simulated and downscaled variables are generally consistent with the observed data, and the SDSM model appears to be sufficiently capable of generating regional climate scenarios for the XRB. 3.3. Changes in extreme hydrological events under changing environments 3.3.1. LUCC and its impacts on extreme hydrological events To project the impacts of future LUCC on extreme hydrological

Fig. 8. Variations of (a) QL, (b) QH, (c) SX1day, (d) SX5day, (e) CDS and (f) CDH during (a) 1985–2053 and 1985–2087 land use conditions under each climate scenarios. 8

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Fig. 9. Box plot of the extreme precipitation indices under different scenarios for BCC-CSM1.1, CanESM2, NorESM1-M and Ensemble mean. The limits of the box represent the 0.25 and 0.75 percentiles, while the line and point inside represent the median and average values. The whiskers indicate the minimum and maximum values not considered outliers, and the outliers are plotted individually using the ‘+’ symbol.

3.3.2. Changes in the extreme precipitation indices The climate scenarios for the periods of 1970s, 2050s and 2080s from its three GCMs (i.e. BCC-CSM1.1, CanESM2 and NorESM1-M), and under its three RCP scenarios (i.e. RCP 2.6, RCP 4.5, and RCP 8.5) were downscaled by the calibrated SDSM model. The extreme precipitation indices (i.e. RX1day, RX5day and CDD) under scenarios in the past (1970s) and future (2050s and 2080s) periods are shown in Fig. 9. As for the results for RX1day and RX5day, potential triggers for extreme floods, it can be seen that most of the median and average values in the future climate scenarios are greater than the values in 1970s scenario. Fig. 9 also shows that the median and average values for RX1day and RX5day in the 2080s are higher than the values in the 2050s under the RCP4.5 and RCP8.5 scenarios. Conversely, the projected RX1day and RX5day in the 2080s is found to be lower than the same values in the 2050s under the scenario RCP2.6. This is largely due to the radiative forcing that will peak before 2100, and then decline to 2.6 W/m2 by 2100, under the scenario. From the ensemble mean of the three GCMs, the RX1day under the future RCP scenarios exhibits a larger interquartile range as well as a higher 0.25 percentile than in the 1970s scenario, suggesting that the XRB is indeed at risk for future extreme rainfall events. With respect to CDDs, an indicator for drought, the results show

Fig. 10. Variations of (a) QL and (b) QH between different climate scenarios and 1970s under 1985 land use conditions.

by increased impervious area as a result of urbanization. The decrease seen in QL specifically may been caused by regional deforestation as well as shortages in precipitation during non-flooding seasons. 9

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Fig. 11. The flow duration curves for SX1day and SX5day under different climate scenarios for BCC-CSM 1.1, CanESM2, NorESM1-M and Ensemble mean.

With respect to the study's drought indicators, Fig. 12 shows that the future CDS generated by the CanESM2 and NorESM1-M models were found to be greater than their values for the 1970s. According to the ensemble mean of the study's GCMs, it can be seen that almost all future CDS exhibit obvious increases when compared to their values in the 1970s. These results demonstrate that climate change works to increase the frequency and magnitude of extreme weather events, such as extreme rainstorms as well as high temperature, and that the magnitude-frequency relationship for extreme streamflow events will shift accordingly (Deal et al., 2017). Past studies also support this study's results that future peaks in high streamflow are likely to increase as a result of increases in extreme rainfall events (Shkolnik et al., 2018; Zhang et al., 2016).

there are differences between the different GCMs. Specifically, BCCCSM 1.1 and CanESM2 project a greater number of CDDs than for the 1970s, while NorESM1-M is found to produce lower values for the median and 0.25 percentile when compared with those for the 1970s under the RCP 4.5 scenario. On the whole, and as indicated by the ensemble mean, the number of CDDs in the future RCP scenarios are found to be greater than that for the 1970s, indicating that more severe drought can be expected in future climate conditions.

3.3.3. The impacts of climate change on extreme hydrological events The response of extreme hydrological events to climate change were also detected. Details therein are presented in Figs. 10–12. The land use conditions were for 1985 and kept constant in these simulations specifically to avoid influence from LUCC. Fig. 10 indicates that, in most future climate scenarios, QL and QH will see an increase when compared with the values for the 1970s, with the average values for ensemble mean at about 2–5 m3/s and 4–15 m3/ s, respectively. As for the study's other flood indicators, comparative analysis for the flow duration curve in Fig. 11 demonstrates that the response of SX1day and SX5day in the future scenarios were similar to that for the 1970s. The future SX1day and SX5day for the BCC-CSM 1.1 and CanESM2 models were projected to be higher than those for 1970s. However, the future SX1day and SX5day for the NorESM1-M model were not all actually found to be higher than for 1970s. According to the ensemble mean of the study's three GCMs, these two flood indicators (i.e. SX1day and SX5day) were much higher than in the 1970s for the vast majority of RCPs, especially in the 2080s under the RCP 8.5 scenario. Moreover, the box-and-whisker plots presented in Fig. 12 reveal that the values for future CDH were nearly the same with those for the 1970s with the exception of the RCP 8.5 scenario for the 2050s.

3.3.4. The combined impacts of future LUCC and climate change on extreme hydrological events The combined impacts of LUCC and climate change as well as their contribution have also been evaluated respectively by compared ensemble mean values under future climate and land-use scenarios with the values under baseline period. The ensemble mean values of each hydrological extreme index under future scenarios and theirs variations compared with baseline period are shown in Fig. 13. Table 3 shows the each contribution of climate change and LUCC to variations of each hydrological extreme index under future climate and land-use scenarios compared with baseline scenario. The results show that all hydrological extreme indices would increase under the combined effects of LUCC and climate change in future (Fig. 13 and Table 3), which are higher affected by individual effect of LUCC or climate change. The CDH has little change under future 10

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Fig. 12. Box plots of CDS and CDH under different scenarios for BCC-CSM1.1, CanESM2, NorESM1-M and Ensemble mean. The limits of the box represent the 0.25 and 0.75 percentiles, while the line and point inside represent the median and average values, respectively. The whiskers indicate the minimum and maximum values not considered outliers, and the outliers are plotted individually using the ‘+’ symbol.

climate and land-use scenarios. QL and QH will increase 2–5 m3/s and 4–20 m3/s in future compared with baseline period, respectively, of which climate change would influence > 50%. In addition, different from the positive impacts of climate change on both QL and QH, LUCC would have negative effect on QL while positive effect on QH. With regards to SX1day and SX5day, their values would increase 3–35 m3/s and 4–30 m3/s, respectively, of which climate change would also contribute > 50%. The future CDS would increase about 2–5 days, which suggests the XRB may well face a more severe threat from water shortages in years to come. The combined effects of climate change and LUCC would exert an even greater influence on extreme hydrological events. The XRB was chosen as a case for this study specifically as a representative region of the Yangtze River Delta, and thus these findings may also hold true for the other areas in the surrounding regions characterized similarly by shifting climate conditions as well as comparable underlying surface manipulation. Due to both LUCC and climate change, it can be concluded that the Yangtze River Delta may also experience more severe extreme events in flooding and drought. Thus, adaptation strategies should be updated and improved in order to minimize the adverse impacts that a changing environment will have on agricultural production and domestic water consumption in the area. 3.4. Uncertainty and limitations The uncertainties inherent in the analysis of hydrological impacts can be attributed primarily to hydrological models as well as their input data, such as those for downscaling climate measures (Fenta Mekonnen and Disse, 2018; Marchane et al., 2017; Minville et al., 2008). In this study, the SUFI-2 algorithm method was adopted in SWAT-CUP programs to assess the uncertainties inherent in the study's SWAT model. Moreover, multi-GCM approaches were applied to investigate whether these uncertainties came from specific climate models. Note that a study's model structure as well as its model

Fig. 13. The (a) ensemble mean values of each hydrological extreme indices under baseline and future scenarios and (b) theirs variances compared with baseline period. The units of y-axis for QL, QH, SX1day and SX5day are m3/s, and the units of y-axis for CDS and CDH are days.

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Table 3 Each contribution of climate change and LUCC to variations of each hydrological extreme indices under future climate and land-use scenarios compared with baseline scenario. Scenarios RCP2.6 2050s - land use 2053

RCP2.6 2080s -land use 2087

RCP 4.52050s - land use 2053

RCP 4.52080s - land use 2087

RCP 8.52050s - land use 2053

RCP 8.52080s - land use 2087

Variation Caused by Caused by Variation Caused by Caused by Variation Caused by Caused by Variation Caused by Caused by Variation Caused by Caused by Variation Caused by Caused by

climate LUCC climate LUCC climate LUCC climate LUCC climate LUCC climate LUCC

QL

QH

SX1day

SX5day

CDS

CDH

3.54 116.38% −16.38% 3.34 126.42% −26.42% 3.61 114.04% −14.04% 2.04 139.12% −39.12% 3.52 115.23% −15.23% 4.03 123.57% −23.57%

6.07 90.44% 9.56% 6.09 78.04% 21.96% 4.81 85.16% 14.84% 5.64 73.11% 26.89% 7.35 89.02% 10.98% 19.12 90.87% 9.13%

8.70 100.27% −0.27% 10.62 82.23% 17.77% 3.34 95.04% 4.96% 4.69 59.73% 40.27% 8.04 99.05% 0.95% 34.48 93.96% 6.04%

8.81 93.86% 6.14% 10.41 78.46% 21.54% 14.70 29.48% 70.52% 4.99 76.00% 24.00% 9.18 92.52% 7.48% 29.62 92.48% 7.52%

4.16 104.28% −4.28% 3.43 96.76% 3.24% 4.86 97.48% 2.52% 2.58 102.16% −2.16% 3.88 79.66% 20.34% 2.98 120.15% −20.15%

1.24 92.86% 7.14% 1.51 105.15% −5.15% 1.16 93.27% 6.73% 0.61 140.00% −40.00% 6.98 96.82% 3.18% 0.70 101.59% −1.59%

The units for variations of QL, QH, SX1day and SX5day are m3/s, and the units for variation CDS and CDH are days.

well as 21 climate scenarios was utilized to quantitatively assess the individual and combined impacts that LUCC and climate change will have on extreme hydrological events in the XRB. Two future land-use maps of 2053 and 2087 were predicted by CA-Markov model based on historical land-use data in 1985, 2002 and 2019. The climate scenarios for the periods from 1961 to 1990, from 2036 to 2065, and from 2070 to 2099, were downscaled from three GCMs, and under three distinct RCP scenarios. To summarize, the primary results are as follows:

parameterizations also represent major sources of uncertainty in hydrological models (Yin et al., 2017). In this study, the SWAT model was found to perform well, with a P-factor value of 0.62 (< 0.7) during calibration periods and a value of 0.86 (> 0.7) during validation periods. Moreover, all R-factor values were therein were found to be lower than 1. These results indicate that the study's SWAT simulations carry only a small range of uncertainty when applied to the XRB (Abbaspour, 2014). In addition, the simulated annual maximum 1-day and 5-day flows were found to be consistent with historically measured values, with an R2 of 0.85 and 0.81 for the annual maximum 1-day and 5-day flows, respectively. The input land-use maps during calibration or validation period can only represent the land-use pattern for one year, which might cause by the underestimation of the land-use intensity due to urban expansion during calibration and validation period. Thus the uncertainty caused by the input land-use data during calibration and validation periods could also be further studied. It is accepted that downscaled future climate scenarios will always present a considerable degree of uncertainty (Reshmidevi et al., 2018). Regardless, simulating such scenarios using multi-model ensemble climate simulations is a very practical method for reducing the uncertainties when applied to evaluate hydrological responses to climate change (Fenta Mekonnen and Disse, 2018; Liersch et al., 2018; Zhang et al., 2016). In this study, three GCMs were used to generate its climate scenarios, and the performance of the study's SDSM model suggests that the models capture hydro-meteorological variables well (Figs. 5 and 6). In addition, results showed that the response of extreme hydrological events to climate change as well as LUCC under the three GCMs were consistent in finding that all flooding and drought indicators were likely to increase in the future. As with any study in the field, there are several bounds and limitations to this study that must be noted here. A first limitation is that it was only the impacts of LUCC and climate change that were analyzed in this study. Other factors of human intervention, such as construction on water projects, will also alter the regional mechanisms of such hydrological processes, but are beyond the scope of this study. However, consideration for these uninvestigated factors in future research will increase the community's understanding regarding the changing mechanisms of extreme hydrological events in China as well as in other countries around the world.

(1) The SWAT model performs with satisfactory in simulating the hydrological processes in the XRB, with all values for R2 and NS higher than 0.65 during the calibration and validation periods on a daily scale. All absolute values for PBIAS are found to be lower than 10 for both the calibration and validation periods. In addition, the SWAT model is found to capture well the region's annual extreme flow. The uncertainty analysis shows that there is a small range of uncertainty in SWAT simulations. (2) The SDSM model performance shows that downscaling the temperature variables works well. The other variables such as precipitation, relative humidity, and solar radiation are found not to perform better than the temperature variable, but are all still within acceptable ranges. In general, the simulated and downscaled variables are consistent with the sample observation data, and the SDSM model appear more than capable of generating accurate regional climate scenarios for the XRB. (3) The future land-use scenarios in 2053 and 2087 are projected using CA-Markov model which's accuracy show a good performance. The LUCC that would take place from 1985 to 2087 in the XRB shows that, on the one hand, the region's forest-grass land and agriculture land decrease. On the other hand, the urban area is found to have expanded dramatically from 4.4% in 1985 to 11.63% in 2053 and to 12.63% in 2087. The LUCC is characterized by a decreased QL by 0–1 m3/s, and an increased for QH, SX1day and SX5day at about 0–2 m3/s, 0–2 m3/s, and 0–3 m3/s, respectively. (4) The extreme precipitation indices (i.e., RX1day, RX5day and CDDs) shows that most values in the future climate scenarios are bigger than those for the past climate scenario. The response of extreme hydrological events to climate changes shows that future SX1day and SX5day values in the vast majority of RCPs will be much higher than the levels observed in the 1970s, especially in the 2080s under the RCP 8.5 scenario. With respect to the drought indicators, almost all future CDS have obvious increases compared to observations made in the 1970s.

4. Conclusions In this study, SWAT model driven by three land-use conditions as 12

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(5) The combined effects of LUCC and climate change would increase QL, QH, SX1day, SX5day, and CDS by 2–5 m3/s, 4–20 m3/s, 3–35 m3/s, 4–30 m3/s, and 2–5 days. In addition, the climate change would contribute > 50% of these hydrological impacts.

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These finding indicate that LUCC and climate change in the XRB would increase the gap between flooding and drought. More specifically, it is revealed that the XRB will experience more severe extreme hydrological events in the future, and thus it can be concluded that disaster mitigation measures should be updated and improved in order to best respond to the regional flooding and drought that would likely become more severe in years to come. Declaration of Competing Interest We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of the manuscript. Acknowledgements This work was supported the National Key Research and Development Program of China (No. 2018YFC1508201), National Natural Science Foundation of China (No. 41771032). References Abbaspour, K.C., 2014. SWAT-CUP 2012: SWAT Calibration and Uncertainty Programs A User Manual. Abbaspour, K.C., Johnson, C.A., Genuchten, M.T.V., 2004. Estimating uncertain flow and transport parameters using a sequential uncertainty fitting procedure. Vadose Zone J. 3 (4), 1340–1352. Arnold, J.G., Srinivasan, R., Muttiah, R.S., Williams, J.R., 1998. Large area hydrologic modeling and assessment part I: Model development. Jawra J. Am. Water Resour. Assoc. 34 (1), 73–89. Arnold, J.G., Kiniry, J.R., Srinivasan, R., Williams, J.R., Neitsch, S.L., 2012. Soil and Water Assessment Tool Theoretical Documentation, (Version 2012). Burn, D.H., Sharif, M., Zhang, K., 2010. Detection of trends in hydrological extremes for Canadian watersheds. Hydrol. Process. 24 (13), 1781–1790. Chen, L., Frauenfeld, O.W., 2014. Surface air temperature changes over the Twentieth and Twenty-first centuries in China simulated by 20 CMIP5 models. J. Clim. 27 (11), 3920–3937. Chou, C., et al., 2013. Increase in the range between wet and dry season precipitation. Nat. Geosci. 6 (4), 263–267. De Paulo Rodrigues Da Silva, V., et al., 2018. Simulation of stream flow and hydrological response to land-cover changes in a tropical river basin. Catena 162, 166–176. Deal, E., Favre, A., Braun, J., 2017. Rainfall variability in the Himalayan orogen and its relevance to erosion processes. Water Resour. Res. 53 (5), 4004–4021. Fenta Mekonnen, D., Disse, M., 2018. Analyzing the future climate change of Upper Blue Nile River basin using statistical downscaling techniques. Hydrol. Earth Syst. Sci. 22 (4), 2391–2408. Fowler, H.J., Kilsby, C.G., 2007. Using regional climate model data to simulate historical and future river flows in Northwest England. Clim. Chang. 80 (3–4), 337–367. Gashaw, T., Tulu, T., Argaw, M., Worqlul, A.W., 2018. Modeling the hydrological impacts of land use/land cover changes in the Andassa watershed, Blue Nile Basin, Ethiopia. Sci. Total Environ. 619–620, 1394–1408. Gosling, S.N., Taylor, R.G., Arnell, N.W., Todd, M.C., 2011. A comparative analysis of projected impacts of climate change on river runoff from global and catchment-scale hydrological models. Hydrol. Earth Syst. Sci. 15 (1), 279–294. Halmy, M.W.A., Gessler, P.E., Hicke, J.A., Salem, B.B., 2015. Land use/land cover change detection and prediction in the north-western coastal desert of Egypt using MarkovCA. Appl. Geogr. 63, 101–112. Han, L., Xu, Y., Yang, L., Deng, X., 2015. Changing structure of precipitation evolution during 1957–2013 in Yangtze River Delta, China. Stoch. Env. Res. Risk A 29 (8), 2201–2212. Hu, M., et al., 2017. Assessment of hydrological extremes in the Kamo River Basin, Japan. Hydrol. Sci. J. 62 (8), 1255–1265. Iacono, M., Levinson, D., El-Geneidy, A., Wasfi, R., 2012. A markov chain model of land use change in the Twin Cities, 1958–2005. J. Land Use Mobil. Environ. 8 (3), 1–14. IPCC, 2014. IPCC, 2014: Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the IPCC, Geneva, Switzerland, pp. 151. Jia, Y., Ni, G., Kawahara, Y., Suetsugi, T., 2001. Development of WEP model and its

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