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Hydrological Appraisal of Climate Change Impacts on the Water Resources of the Xijiang Basin, South China Dehua Zhu 1, * 1 2

*

ID

, Samiran Das 1

ID

and Qiwei Ren 2

School of Hydrometeorology, Nanjing University of Information Science and Technology, Nanjing 210044, China; [email protected] Technology Centre, Hydraulic and Hydropower Project, Guangdong Province, Guangzhou 510635, China; [email protected] Correspondence: [email protected]

Received: 17 August 2017; Accepted: 11 October 2017; Published: 16 October 2017

Abstract: Assessing the impact of climate change on streamflow is critical to understanding the changes to water resources and to improve water resource management. The use of hydrological models is a common practice to quantify and assess water resources in such situations. In this study, two hydrological models with different structures, e.g., a physically-based distributed model Liuxihe (LXH) and a lumped conceptual model Xinanjiang (XAJ) are employed to simulate the daily runoff in the Xijiang basin in South China, under historical (1964–2013) and future (2014–2099) climate conditions. The future climate series are downscaled from a global climate model (Beijing Climate Centre-Climate System Model, BCC-CSM version 1.1) by a high-resolution regional climate model under two representative concentration pathways—RCP4.5 and RCP8.5. The hydrological responses to climate change via the two rainfall–runoff models with different mathematical structures are compared, in relation to the uncertainties in hydrology and meteorology. It is found that the two rainfall–runoff models successfully simulate the historical runoff for the Xijiang basin, with a daily runoff Nash–Sutcliffe Efficiency of 0.80 for the LXH model and 0.89 for the XAJ model. The characteristics of high flow in the future are also analysed including their frequency (magnitude–return-period relationship). It shows that the distributed model could produce more streamflow and peak flow than the lumped model under the climate change scenarios. However the difference of the impact from the two climate scenarios is marginal on median monthly streamflow. The flood frequency analysis under climate change suggests that flood magnitudes in the future will be more severe than the historical floods with the same return period. Overall, the study reveals how uncertain it can be to quantify water resources with two different but well calibrated hydrological models. Keywords: climate change; regional climate model; RCP scenarios; Liuxihe model; Xinanjiang model; Xijiang basin; China

1. Introduction Climate change is expected to cause a significant variation of hydrological and water cycle systems by altering the radiative balance of the atmosphere, which may consequently result in changes of climatic variables, such as pressure, temperature, precipitation and others [1]. For regional water management, the influence of climate change on water resources and hydrological systems can be reflected both on the contents and approaches, such as the intensity and patterns of extreme rainfall events, the magnitude and frequency of extreme floods and droughts, the quality and quantity of local water availability and the vulnerability and management of water resource systems. Although not

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all of these impacts are necessarily negative, it is be crucial for local stakeholders and management committees to evaluate them as early as possible to understand how global climate change in the future could affect regional water supplies, because of the great socio-economic importance of water and other natural resources [2]. Global climate models (GCMs) are one of the primary tools to obtain present climate and projections of future global climate change using various greenhouse gas emission scenarios. Therefore, the following three procedures are suggested to study climate change impacts on hydrology and water resources: (1) use of GCMs to produce future global climate scenarios with different emission scenarios, (2) use of downscaling techniques to match GCM output with the scales compatible with hydrological models, and (3) use of hydrological modelling systems to simulate and analyse the effects of future climate change in the study area at various scales [3]. However, using hydrologic modelling systems to study global climate change impacts still involves two challenges: (1) scale differences between GCMs and hydrological models, (2) the different response of hydrological systems due to different hydrologic model structures and catchment characteristics. It was pointed out that the GCMs cannot represent the features and dynamics at the local subgrid-scale as significantly as at the continental scale [4,5]. Although statistical downscaling and dynamic downscaling techniques have been developed and widely applied in regional climate models (RCMs) to transform and obtain finer resolutions of GCM data, the application of regional hydrology and water resource studies using GCM/RCM data at the sub-grid scale is yet difficult [6,7]. Since the GCMs accuracy decreases when the spatial and temporal resolution becomes finer and the skill drops for surface variables such as precipitation, evapotranspiration and soil moisture, all of which are key elements for hydrological models [3]. Thus more uncertainties are expected for the projected future change of precipitation sums, intensities and patterns using downscaling techniques [6,8,9]. Therefore, the number of studies focusing on regional hydrological impact assessments of climate change is relatively small, in contrast to a large number of GCM downscaling studies [10]. In terms of the different response of hydrological systems, the range of diversity of regional hydrological responses under climate change scenarios, due to different hydrological model structures, has not been widely investigated and reported in the literature. Clark et al. (2008) [11] proposed a methodology to diagnose differences in 79 hydrological model structures by combining components of four hydrological models. Although the results suggested that the choice of model structure is just as important as the choice of model parameters, further study is still required to evaluate the model structural differences in various climate regimes. Boorman and Sefton (1997) [12] investigated the range of uncertainties resulting from two climate scenarios and eight climate sensitivity tests that were applied to three catchments using two conceptual hydrological models in the UK. It was concluded that the variation of the climate change impact on flow can be significant between catchments, and the different flow response indices can change in opposite directions even for the same climate scenario. The study also suggested that more studies should be conducted to compare the impacts due to different hydrological models, which would help to quantify the uncertainties resulting from the different model strictures and parameters. Panagoulia and Dimou (1997a, b) [13,14] examined and compared the differences of two hydrological models under historical and future climate conditions. It was shown that the model’s sensitivity to greater temperature and precipitation varies in different climates. Jiang et al. (2007) [2] utilised six-monthly water balance models from the Dongjiang basin, China to assess uncertainty in hydrological responses to the climatic scenarios resulting from the use of different hydrological models. The results indicated that the climate change impacts on hydrological systems vary and depend on the hydrological model structures, even under the same future climate scenarios. Li et al. (2014) [15] used two rainfall–runoff models, Hydrologic Simulation Model (SIMHYD) and GR4J to simulate the monthly and annual runoff in the upstream catchments of the Yarlung Tsangpo River basin (YTR). The two rainfall–runoff models successfully simulate the historical runoff and show similar simulation results for the eight catchments in the YTR basins. Fischer et al. (2013) [16] used the

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standardized precipitation index (SPI-24) and the standardized discharge index (SDI-24) were used to analysis the hydrological long-term dry and wet sequences on the Xijiang River without using any hydrological models. The studies reported in the literature and discussed above have only used two or more lumped hydrological models to simulate the impact of postulated climate changes and then analyse the hydrological responses from different hydrological models, which however only represent the results of the lumped hydrological models. It is therefore desirable to compare the differences in hydrological impact of alternative climates resulting from the use of physically-based distributed hydrological models, because the spatially explicit data of elevation, land use cover, and soil properties can not only represent the catchment characteristics and reflect on the parameterisation [17], but also take full advantage of gridded GCM/RCM output to predict hydrological responses to various climate scenarios [18]. In this study, a physically-based distributed model Liuxihe (LXH) and a conceptual lumped model Xinanjiang (XAJ) are used and their capabilities in reproducing historical water balance components and in predicting the hydrological impacts of alternative climates are compared. The main objective of this study is to show how uncertain it could be to quantify and assess water resources with two different but well-calibrated models under climate change. In this context, the study is focused on quantifying how large a difference one can expect when using lumped and distributed hydrological models to simulate the impact of climate change as compared to the model capabilities in simulating historical river flow discharges. The study is performed in two steps: first, the performance of models in reproducing historical streamflow components is evaluated; and second, the differences in the simulated hydrological flows under climate change by the lumped and distributed models are evaluated and compared. 2. Data and Methodology 2.1. Methodology In this study, a physically-based distributed model, LXH, and a conceptual lumped model, XAJ, were constructed on the Xijiang Basin located in South China. The parameters for each model were calibrated and validated with historical observation data. The trial-and-error method based on the results of model parameter sensitivity analysis using global extend FAST (Fourier Amplitude Sensitivity Test) method was employed for the LXH model parameter calibration, whereas, the shuffle complex evolution algorithm developed at the University of Arizona (SCE-UA) [19] was used to optimize the parameters for the XAJ lumped model. Once calibrated, the two models were fed with the RCM historical simulations to verify the reliability of the climate data against the observations. Subsequently, two hydrological models were driven by the RCM with two emission scenarios (RCP4.5 and RCP8.5) to produce multiple runoff simulations. The RCM-based daily climate simulations were expected to capture the changes in daily climate variables that directly related to the analysis of extreme events relating to daily precipitation intensity and flood frequency. 2.2. Study Area and Hydrological Data The study area is the Xijiang (West River) basin, a tributary of the Pearl River in southern China. The main Xijiang basin is a coastal area, located in the Guangxi province in South China. It has a sub-tropical monsoon climate, characterized by large interannual variability of precipitation and widely distributed floods with high frequency, which result in massive damage to property and life. The average annual runoff recorded at the Wuzhou flow station is 224 billion cubic meters, accounting for 81.4% of the total runoff in Guangxi province. Thus the hydrological data of Wuzhou station is capable of reflecting the occurrence and the magnitude of floods in the Xijiang basin. Due to the data availability and reliability, the considered hydrological models were constructed on the Xijiang

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basin using the Wuzhou flow station as the catchment outlet. Therefore the whole drainage area is the upstream area above Wuzhou station, which is around 337,000 km2 . Although the Xijing basin is characterized by construction, like many other large rivers in China, Water 2017, 9, 793 4 of 18 the influence of the construction of reservoirs/dams on annual water discharge is much lower than thevariability variability precipitation [20], especially as regulation the regulation of hydraulic structures the main the in in precipitation [20], especially as the of hydraulic structures on theon main streamare areusually usually hourly basis instead of daily, which influence on daily/monthly stream onon anan hourly basis instead of daily, which has has less less influence on daily/monthly streamflowsimulations. simulations. streamflow The forfor model calibration andand validation include observed dailydaily evaporation, rainfall and and Thedata data model calibration validation include observed evaporation, rainfall streamflow data. AllAll thethe data were obtained fromfrom the published Yearly Hydrological BooksBooks of China streamflow data. data were obtained the published Yearly Hydrological of China for from 1960 to to 2006. In this study, the the 74 major stations shown in Figure 1, which are are forthe theperiod period from 1960 2006. In this study, 74 major stations shown in Figure 1, which almost almostevenly evenlydistributed distributedover overthe thewhole wholebasin, basin,with with60 60years yearsof of continuous continuousrecords records (1960–1988), (1960–1988), were were selected for model calibration (1 January 1973 to 1 January 1978) and validation (1 January 19791979 to selected for model calibration (1 January 1973 to 1 January 1978) and validation (1 January to3131December December1984 1984 and and 11 January January 2006/1/1 to to 3131 December 2013). 2006/1/1 December 2013).

Figure 1. Xijiang Basin (upstream of Wuzhou flow station). Figure 1. Xijiang Basin (upstream of Wuzhou flow station).

2.3. Change Scenarios andand Datasets 2.3.Climate Climate Change Scenarios Datasets The are driven driven by by the theGCM, GCM,Beijing BeijingClimate ClimateCentre CentreClimate ClimateSystem Theclimate climatedata dataused usedin in this this study study are System Model version 1.1 (BCC_CSM1.1). The projection of the climate change data on the Xijiang Model version 1.1 (BCC_CSM1.1). The projection of the climate change data on the Xijiang basin is basin is simulated by a regional climate model (RegCM4.0) under two emission scenarios of the simulated by a regional climate model (RegCM4.0) under two emission scenarios of the representative representative concentration pathways—RCP4.5 and RCP8.5. This is based on a period of transient concentration pathways—RCP4.5 and RCP8.5. This is based on a period of transient simulations from simulations from 1950 to ◦2099, with a 0.5° horizontal resolution [21]. 1950 to 2099, with a 0.5 horizontal resolution [21]. The outputs of RegCM4.0 consists of historical, RCP4.5, and RCP8.5 datasets, and each dataset The outputs of RegCM4.0 consists of historical, RCP4.5, and RCP8.5 datasets, and each provides projected mean temperature, maximum temperature, minimum temperature and dataset provides projected mean temperature, maximum temperature, minimum temperature and precipitation on a daily basis. The representative concentration pathways (RCPs) are used for climate precipitation on a daily basis. The representative concentration pathways (RCPs) are used for climate modelling and research, which describe four possible climate future scenarios depending on how modelling andgas research, which describe future scenarios depending on how much radiative is emitted in the future. four The possible RCP4.5 isclimate the intermediate emissions scenario in much radiative gas is emitted in the future. The RCP4.5 is the intermediate emissions scenario in which the radiative force is stabilised at 4.5 W/m2 by 2100 without overshoot. The RCP8.5 is the high 2 which thescenario radiative stabilised at force 4.5 W/m by8.5 2100 without overshoot. RCP8.5 is the 2 by 2100 emissions in force whichisthe radiative rises to W/m [22]. For The the projection of high climate change, the difference between the 2006–2099 period in the RCP4.5 and RCP8.5 experiment, and the 1961–2005 period in the historical experiment, will be analysed in this study.

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emissions scenario in which the radiative force rises to 8.5 W/m2 by 2100 [22]. For the projection of climate change, the difference between the 2006–2099 period in the RCP4.5 and RCP8.5 experiment, and the 1961–2005 period in the historical experiment, will be analysed in this study. Water 2017, 9,Modelling 793 3. Hydrological and Parametrisation

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3. Hydrological and Parametrisation 3.1. Lumped XinanjiangModelling Model (XAJ)

The model (XAJ) 3.1.Xinanjiang Lumped Xinanjiang Model (XAJ)is a rainfall–runoff watershed model for use in humid and semi-humidThe regions, developed in 1973 in China [23]. XAJ has been widely usedand in semiChina since Xinanjiang model (XAJ) is a rainfall–runoff watershed model for use in humid 1980, mainly flood developed simulationinand The model assumes runoff production occurs on humidfor regions, 1973forecasting. in China [23]. XAJ has been widelythat used in China since 1980, mainlyof forstorage flood simulation andvalues, forecasting. Theare model assumes runoff production occurs on the repletion to capacity which assumed tothat be distributed throughout the basin. the repletion of storage to capacity values, which are assumed to be distributed throughout the basin. The runoff is divided into three components, including surface runoff, interflow and groundwater. The runoff is divided into three components, including surface runoff, interflow and groundwater. The basin is divided into a set of sub-basins, the outflow hydrograph from each sub-basin is first The basin is divided into a set of sub-basins, the outflow hydrograph from each sub-basin is first simulated and then routed down the channels to the basin outlet. The flow chart of XAJ is shown in simulated and then routed down the channels to the basin outlet. The flow chart of XAJ is shown in Figure 2. Figure 2.

Figure 2. Xinanjiang model structure (Zhao, 1992) [23].

Figure 2. Xinanjiang model structure (Zhao, 1992) [23]. 3.2. Distributed Liuxihe Model (LXH)

3.2. Distributed Liuxihe Model (LXH)

The Liuxihe Model (LXH) is a physically-based, distributed hydrological model for river basin

flood forecasting/simulation The LXH consistsdistributed of several submodels, including The Liuxihe Model (LXH) is[24]. a physically-based, hydrological modeltheforbasin river basin digitization model (BDM), the evapotranspiration model (EM), the runoff production model (RPM), flood forecasting/simulation [24]. The LXH consists of several submodels, including the basin the runoff routing model (RRM), and the parameter deriving model (PDM). The LXH model divides digitization model (BDM), the evapotranspiration model (EM), the runoff production model (RPM), the catchment into a number of gridded cells, and each cell consists of three layers vertically, the runoff routing model (RRM), and the parameter deriving model (PDM). The LXH model divides

Xijiang Basin is a mountainous area, with a range of elevation from sea level to about 2830 m. The spatial resolution of raw DEM is about 90 m and was bilinear resampled to 500 m resolution for BDM in the LXH model. The land use data was obtained from the 1km resolution of the global land cover database Water 2017, 793 6 of 18 provided9,by the Global Observatory of Maryland University and the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences. The data was resampled to 500 m resolution using the nearest neighbour algorithm. There are 12 types of land use in Xijiang Basin, the catchment into a number of gridded cells, and each cell consists of three layers vertically, including water body, evergreen coniferous forest, evergreen broad-leaved forest, deciduous broadrepresenting the surface, unsaturated and saturated zones. The EM module is then used to calculate leaved forest, mixed forest, forested grassland, closed shrub, open shrub, and grassland, which the evapotranspiration for each cell. The RPM and RRM modules calculate the runoff produced for accounting for 0.390%, 1.617%, 0.939%, 0.326%, 0.001%, 7.407%, 25.351%, 0.133%, 0.001%, 22.507%, each cell and route runoff to the catchment outlet, respectively. The PDM module is responsible for 41.295% and 0.034%, respectively. This suggests that the cultivated land is the main land use type, model parameterisation. The detailed model structures are shown in Figure 3. accounting for 41.295% of area in Xijiang Basin.

Figure 3. 3. Liuxihe structure (Chen (Chen et et al., al., 2010) 2010) [24]. [24]. Figure Liuxihe model model structure

The soil properties used in this study were obtained from the Nanjing Soil Institute of the The DEM used in this study is derived from the 1” × 1” resolution shuttle radar topography Chinese Academy of Sciences. There are seven types of soils in Xijiang Basin, including clay, silty mission (SRTM) data, provided by the Chinese Geospatial Data Cloud (http://www.gscloud.cn). loam, loam, sandy, loam, sandy loam and sand, which account for 57.94%, 4.20%, 22.56%, 14.90%, The Xijiang Basin is a mountainous area, with a range of elevation from sea level to about 2830 m. 0.05%, respectively. It is clear that clay accounts for more than half of the catchment area. The soil The spatial resolution of raw DEM is about 90 m and was bilinear resampled to 500 m resolution for property parameters, such as soil thickness, water content at saturated condition, field capacity, BDM in the LXH model. wilting point, infiltration rate and hydraulic conductivity, were calculated by the soil water The land use data was obtained from the 1km resolution of the global land cover database characteristics hydraulic properties calculator. provided by the Global Observatory of Maryland University and the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences. The data was resampled to 500 m resolution using the nearest neighbour algorithm. There are 12 types of land use in Xijiang Basin, including water body, evergreen coniferous forest, evergreen broad-leaved forest, deciduous broad-leaved forest, mixed forest, forested grassland, closed shrub, open shrub, and grassland, which accounting for 0.390%, 1.617%, 0.939%, 0.326%, 0.001%, 7.407%, 25.351%, 0.133%, 0.001%, 22.507%, 41.295% and 0.034%, respectively. This suggests that the cultivated land is the main land use type, accounting for 41.295% of area in Xijiang Basin. The soil properties used in this study were obtained from the Nanjing Soil Institute of the Chinese Academy of Sciences. There are seven types of soils in Xijiang Basin, including clay, silty loam, loam, sandy, loam, sandy loam and sand, which account for 57.94%, 4.20%, 22.56%, 14.90%, 0.05%, respectively. It is clear that clay accounts for more than half of the catchment area. The soil property parameters, such as soil thickness, water content at saturated condition, field capacity, wilting point, infiltration rate and hydraulic conductivity, were calculated by the soil water characteristics hydraulic properties calculator.

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3.3. Model Calibration and Validation Considering the data consistency and reliability due to the relocation of the Wuzhou flow station, the observed daily hydrological data selected for the model calibration was from 1 January 1973 to 1 January 1978, and the model validations were from 1 January 1979 to 31 December 1984 and 1 January 2006 to 31 December 2013. In order to evaluate the performance of the modelling results, it is a common practice to employ some criteria for judging model performance. Five different criteria, including the Nash–Sutcliffe efficiency coefficient (NS), correlation coefficient (Cor), root mean square error (RMSE), total water balance error coefficient (Dv) and average logarithm mean square error (MSLE), were used for different assessment purposes. The M and Q represent the simulated and observed flow respectively in the equations below: The Nash–Sutcliffe efficiency coefficient (NS) is one of the most popular evaluation criteria for hydrological modelling to measure the degree of association between observed and simulated values: n

∑ ( Mi ( θ ) − Q i ) 2

NS = 1 −

i =1 n

(1)

∑ ( Q i − Q i )2

i =1

The correlation coefficient (Cor) is the linear correlation to describe the discrete degree between the observation and the simulation. The closer to one, the more correlated it is. n

∑ ( Qi − Qi )( Mi (θ ) − Mi (θ )) i =1 Cor = n q 2 2 ∑ ( Qi − Qi ) ( Mi (θ ) − Mi (θ ))

(2)

i =1

Root mean square error (RMSE) is used to evaluate the total water error, which is simply the average of the squared errors for all the simulation results, providing an objective measure of the difference between the observed and simulated values: s 1 n RMSE = ( Mi ( θ ) − Q i ) 2 (3) n i∑ =1 The total peak flow error (PF) is the ratio of total peak flow runoff error to observed peak flow runoff. The M and Q in this equation represent the sum of peak flows in each year during the simulation period: n

∑ ( Mi ( θ ) − Q i )

PF =

i =1

n

· 100%

(4)

∑ ( Qi )

i =1

The mean square logarithm error (MSLE) is high in low-flow error and small in peak-flow error, which can be used to estimate the total water error, but more to focus on low-flow and base-flow components: 1 n MSLE = ∑ (ln( Mi (θ )) − ln( Qi ))2 (5) n i =1 The statistics for the two models for the calibration and validation periods are listed in Table 1. It suggests that the distributed LXH model and lumped XAJ model have better performance on calibration (1 January 1973 to 1 January 1978) and validation one period (1 January 1979 to 31 December 1984) than the validation two period (1 January 2006 to 31 December 2013). Since the simulated peak flows from both models in the validation two period, especially the small and medium size peak flows are much less present in the observations. It may imply that the difference reflects the influence of

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human activities on the hydrological processes in the past 30 years, since the 1980s. Since a large number of water conservancy projects, water storage, and water diversion projects may have changed the natural runoff conditions in the Xijiang basin, part of the small- and medium-sized peak flows were actually blocked by water conservancy projects, which is why it was not consistent with the model simulations. Table 1. Statistics of the LXH and XAJ model calibration and validation. NS Criterions Calibration Validation 1 Validation 2

RMSE

Cor

PF

MSLE

LXH

XAJ

LXH

XAJ

LXH

XAJ

LXH

XAJ

LXH

XAJ

0.80 0.81 0.60

0.89 0.83 0.72

0.92 0.92 0.92

0.95 0.91 0.93

2898 2513 3298

2072 2365 2721

–1.9% –5.3% 15%

–3.1% –8.6% 18.1%

0.52 0.56 0.66

0.11 0.08 0.17

Although the NS and Cor criteria indicate that both the LXH and XAJ models show relatively good performance for the Xijing basin simulation, the MSLE suggests that the XAJ model outperformed the LXH model in the base flow simulation, but the LXH model is more accurate than XAJ in the peak flow simulations according to the PF statistic. However, considering the computational cost and the parametrisation process, the lumped XAJ model is much more cost-effective than the distributed LXH model. 4. Results and Discussion The reliability and accuracy of historical precipitation data produced from the climate models (RF) was first evaluated against the observed rainfall data through the two hydrological models during validation period one, in order to examine the data reliability and accuracy produced by the climate model. Then the rainfall data under the RCP4.5 and RCP8.5 scenarios obtained from the climate models were assessed against the observation data through the two hydrological models during validation period two. Finally, the projected rainfall data under the RCP4.5 and RCP8.5 scenarios from 2006 to 2099 were used as the input in the two hydrological models and the related flood frequency analysis was carried out. 4.1. Evaluation of Rainfall Products from the Climate Model According to the availability of the climate rainfall data and observation flow data, the historical rainfall climate data was assessed against the rain gauge measurements through the two hydrological models during the model validation period one (1 January 1979 to 31 December 1984). The position of the RCM grids and catchment boundaries are illustrated in Figure 4, which displays how the RCM data are used to feed the distributed LXH model, while the RCM data were averaged to the catchment mean value to feed the lumped XAJ model, according to the weighting of grid area on the catchment. Figure 5 shows that the cumulative catchment rainfall derived from historical precipitation in the climate model has fairly good agreement with the cumulative rain gauge measurements over the Xijiang basin, with only a 2.43% difference in total cumulative rainfall. However, Figure 6 indicates that the scatter is relatively high and the RF data tend to underestimate, compared to the rain gauge measurements.

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Figure4. 4.The Thedistribution distributionof ofRegional RegionalClimate ClimateModel Model(RCM) (RCM)grids gridson onthe theXijiang XijiangBasin. Basin. Figure Figure 4. The distribution of Regional Climate Model (RCM) grids on the Xijiang Basin.

Figure 5. Comparison of cumulative catchment rainfall between historical rainfall from the climate Figureand Comparison of cumulative cumulative catchment catchment rainfall rainfall between between historical historical rainfall rainfall from from the the climate climate Figure 5.5. Comparison of model rain gauge measurements. modeland andrain raingauge gaugemeasurements. measurements. model

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Figure6. 6.RF RFand andrain raingauge gaugemeasurements measurements in in scatter scatter map. map. Figure Figure 6. RF and rain gauge measurements in scatter map.

The (0.45 in in XAJ XAJand and0.83 0.83ininLXH) LXH)suggested suggested that simulation results driven by The NS statistic (0.45 that thethe simulation results driven by the The NS statistic (0.45 in XAJ and 0.83 in LXH) suggested that the simulation results driven by the RFthe in the models are slightly accurate the mean the observed although the RF in twotwo models are slightly moremore accurate thanthan the mean of theofobserved data, data, although the trend the RF in the two models are slightly more accurate than the mean of the observed data, although the trend the hydrographs the observations (see7Figures 7 and 8). Additionally, the of the of hydrographs agreed agreed with thewith observations (see Figures and 8). Additionally, the streamflow trend of the hydrographs agreed with the observations (see Figures 7 and 8). Additionally, the streamflow in the XAJ and LXH models by more RF produced more but simulations simulations in the XAJ and LXH models driven by RF driven produced peak flows butpeak muchflows less peak streamflow simulations in the XAJ and LXH models driven by RF produced more peak flows but much less peak flowobservation volume than thesimulation observation and simulation using especially rain gauge flow volume than the and the using rainthe gauge measurements, in much less peak flow volume than the observation and the simulation using rain gauge measurements, in the XAJ model. Since the LXH is fully distributed supposed the XAJ model. especially Since the LXH model is fully distributed andmodel supposed to represent the and distribution of measurements, especially in the XAJ model. Since the LXH model is fully distributed and supposed to distribution the gridded fromthe thelumped climateXAJ model better than the lumped therepresent gridded the rainfall from theof climate model rainfall better than model, it subsequently had to represent the distribution of the gridded rainfall from the climate model better than the lumped XAJ model, it subsequently performance for peak flow simulations. better performance for peakhad flowbetter simulations. XAJ model, it subsequently had better performance for peak flow simulations.

Figure Figure 7. 7. Comparison Comparisonof offlow flowsimulations simulationsin inthe theXAJ XAJmodel modeldriven drivenby byhistorical historicalrainfall rainfallfrom fromclimate climate Figure 7. Comparison of flow simulations in the XAJ model driven by historical rainfall from climate models and rain gauge measurements. models and rain gauge measurements. models and rain gauge measurements.

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Figure 8. Comparison of flow simulation in the LXH model driven by historical rainfall from climate Figure 8. Comparison of flow simulation in the LXH model driven by historical rainfall from climate models and rain gauge measurements. models and rain gauge measurements.

In summary, summary, the the historical historical precipitation precipitation data data (RF) (RF) and and RCP RCP rainfall rainfall products products from from the the climate climate model are consistent consistent with rain gauge gauge observations observations in terms of the the pattern pattern of of the the catchment catchment rainfall rainfall accumulation curve and the total precipitation over the catchment. Also, the patterns of the and the total precipitation over the catchment. the hydrographs driven by the climate models generated rainfall data in two hydrological models the climate models generated two hydrological models that that are are quite quite similar similar to the observation observation and and the hydrographs hydrographs driven driven by by the the rain rain gauges. gauges. Nevertheless, the climate model rainfall data tended to trigger more small and medium peak flows, but to underestimate climate model rainfall data tended to trigger more small and medium peak flows, but to the high peak flows during theflows two validation It implies that theIt rainfall the climate underestimate the high peak during theperiods. two validation periods. impliesdata that from the rainfall data model is possibly reliable for usemore in monthly resource and resource analysis. from the climate more model is possibly reliableand forannual use inwater monthly and simulation annual water

simulation and analysis. 4.2. Future Rainfall–Runoff Simulations 4.2. Future Rainfall–Runoff The projected rainfall Simulations data under the RCP4.5 and RCP8.5 scenarios from 2006 to 2099 were used as theThe input in the two hydrological However, the simulation were the projected rainfall data undermodels. the RCP4.5 and RCP8.5 scenariosresults from 2006 toaveraged 2099 weretoused monthly and annual time scale for analysis. as the input in the two hydrological models. However, the simulation results were averaged to the For the LXH model, the modelled flow results are listed in Table 2 and the box plots monthly anddistributed annual time scale for analysis. are illustrated in Figure 9. Both the RCP4.5 and RCP8.5 scenario the1 highest will For the distributed LXH model, the modelled flow results aresuggests listed inthat Table and the flow box plots occur in July, whereas the9.lowest flowRCP4.5 is expected in November, according to that the monthly flowflow median are illustrated in Figure Both the and RCP8.5 scenario suggests the highest will value, although the difference derived from the two scenarios is rather marginal. For the high flow in occur in July, whereas the lowest flow is expected in November, according to the monthly flow 3 /s, less than July, the average streamflow in the Xijiang basin under the RCP4.5 scenario is 12,850 m median value, although the difference derived from the two scenarios is rather marginal. For the high the modelled under RCP8.5, which 13,916 m3 /s, around difference. For the low flow in flowflow in July, the average streamflow in is the Xijiang basin under8.30% the RCP4.5 scenario is 12,850 m3/s, 3 /s, higher November, the average streamflow in the Xijiang basin under the RCP4.5 scenario is 991 m 3 less than the flow modelled under RCP8.5, which is 13,916 m /s, around 8.30% difference. For the low 3 /s, around 6.05% difference. than modelledthe under RCP8.5, which isin 931 flow flow in November, average streamflow themXijiang basin under the RCP4.5 scenario is 991 m3/s, Compared to the distributed LXH model, the simulation results achieved in the lumped XAJ higher than flow modelled under RCP8.5, which is 931 m3/s, around 6.05% difference. model show the highest flow in the Xijiang basin will occur in August, whereas the lowest is Compared to the distributed LXH model, the simulation results achieved in the lumped XAJflow model expected December, according to the mean median valuethe under the two scenarios show the in highest flow in the Xijiang basin willmonthly occur inflow August, whereas lowest flowRCP is expected in (see Figure 10). December, according to the mean monthly flow median value under the two RCP scenarios (see Figure

10).

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Table 1. Statistics for projected monthly flow simulations using the LXH model (unit: m3/s).

Min Q1 Median Min Q1 Q3 Median Max Q3 Max

Min Q1 Min Median Q1 Median Q3 Q3 Max Max

Table for projected the LXH Sep. model (unit: /s). Jan. 2. Statistics Feb. Mar. Apr.monthly Mayflow simulations Jun. Jul.using Aug. Oct. m3Nov. RCP4.5 Jan. Feb. Mar. Apr. May Jun. Jul. Aug. Sep. Oct. Nov. 429 1042 645 3410 3144 3955 4249 4764 2711 339 243 1610 3750 3782 6536 6604 RCP4.5 7232 10,512 10,712 5483 1415 583 2792 4917 5470 8545 7826 9702 12,850 12,692 7599 2282 991 429 1042 645 3410 3144 3955 4249 4764 2711 339 243 1610 3750 3782 6536 6604 7232 10,512 583 4065 6557 8807 10,619 9280 12,536 15,967 10,712 16,267 5483 9896 1415 4317 2032 2792 4917 5470 8545 7826 9702 12,850 12,692 7599 2282 991 6630 10,550 14,131 16,013 11,810 19,743 23,287 23,715 15,834 8285 3721 4065 6557 8807 10,619 9280 12,536 15,967 16,267 9896 4317 2032 6630 10,550 14,131 16,013 11,810 RCP8.5 19,743 23,287 23,715 15,834 8285 3721 208 518 1118 1934 3225 RCP8.5 3741 4982 6077 1518 430 255 1357 2831 3668 5910 6740 6544 10,607 10,680 4882 1487 603 208 518 1118 1934 3225 3741 4982 6077 1518 430 255 2437 4193 5475 7903 7870 9147 10,607 13,916 10,680 13,132 4882 7658 1487 2405 931 1357 2831 3668 5910 6740 6544 603 2437 4193 5475 7903 7870 9147 13,916 931 3705 6467 8215 9824 10,050 11,623 16,507 13,132 15,731 7658 10,835 2405 3593 1638 3705 6467 8215 9824 10,050 1638 6714 11,071 12,783 14,577 13,793 11,623 19,055 16,507 25,257 15,731 21,240 10,835 17,868 3593 6734 2884 6714

11,071

12,783

14,577

13,793

19,055

25,257

21,240

17,868

6734

2884

Dec. Dec. 268

609 1199 268 609 1857 1199 3662

1857 3662

211 688 211 1156 688 1156 1681 1681 3130 3130

(a)

(b) Figure 9. Illustration of of thethebox results driven drivenby byrepresentative representative Figure 9. Illustration boxplot plotfor formonthly monthlyflow flow simulation simulation results concentration pathways (RCP) b: RCP8.5). RCP8.5). concentration pathways (RCP)rainfall rainfallininthe theLXH LXHmodel model (a: (a: RCP4.5; RCP4.5; b:

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(a)

(b) Figure 10. Illustration of the box plot for monthly flow simulation results driven by representative Figure 10. Illustration of the box plot for monthly flow simulation results driven by representative concentration pathways (RCP) rainfall in the XAJ model (a: RCP4.5; b: RCP8.5). concentration pathways (RCP) rainfall in the XAJ model (a: RCP4.5; b: RCP8.5).

Table 2 shows that the highest average streamflow in the Xijiang basin under the RCP4.5 scenario Table that the highest the Xijiang under 1.85% the RCP4.5 scenario 3/s in August, is 10,323 3mshows less thanaverage 10,514 streamflow m3/s under in RCP8.5, whichbasin is around difference. 3 /s under RCP8.5, which is around 1.85% difference. is During 10,323 the m3 /s in August, less than 10,514 m dry season, the lowest mean monthly flow simulation found under the RCP4.5 scenario 3/s, higher 3/s,RCP4.5 During season,than the lowest mean monthlyunder flow simulation found the is 1451the mdry the flow modelled RCP8.5, which is under 1422 m aroundscenario 1.99% is 3 3 1451 m /s, higher than theassessment flow modelled which 1422 m /s, around 1.99% difference. difference. Similar to the in theunder LXH RCP8.5, model, the two isRCP scenarios produced much less variation in the average monthly flowmodel, in the Xijiang climate change much from 2006 2099. in Similar to the assessment in the LXH the twobasin RCPunder scenarios produced less to variation the average monthly flow in the Xijiang basin under climate change from 2006 to 2099. Table 2. Statistic of projected monthly flow simulation using the XAJ model (unit: m3/s).

Table Jan. 3. Statistic projected monthly simulation the XAJ model (unit: m3 /s). Feb. of Mar. Apr. MayflowJun. Jul. using Aug. Sep. Oct. Nov. Dec. Min Jan.452 Q1 1237 Median 4521977 Min 2748 Q1Q3 1237 Max 1977 4838 Median

Feb. 1085

Mar. 956

2172 3274 1085 4588 2172 8050 3274

2927 4230 956 6596 2927 10,908 4230

Apr. 2168 4550 6127 2168 8445 4550 13,647 6127

Q3 2748 4588 Max Min 4838356 8050 496

6596 10,908 819

8445 13,647 1817

2546 4209 819 2546 6138 4209 10,868

4357 6190 1817 4357 7593 6190 11,241

1175

1938

Median 3561706 Min Q1Q3 1175 2672 Median Max 1706 4816 Q3 2672 Max 4816

Q1

2690 496 1938 4286 2690 7264 4286 7264

6138 10,868

7593 11,241

RCP4.5 Jun. 2709 5342 RCP4.5 6827 2709 9266 5342 12,305 6827 7630 RCP8.5 9266 11,315 12,305 2540 2161 5014 RCP8.5 5005 6216 6940 2540 2161 5014 5005 7722 8957 6216 6940 10,965 14,122 May 1312 4776 6297 1312 7630 4776 11,315 6297

7722 10,965

8957 14,122

Jul. Aug. 2458 3146 7451 7990 9375 2458 10,323 3146 12,114 7451 13,425 7990 18,193 9375 19,545 10,323 12,114

13,425

18,193 3036 7455 10,281 3036 7455 12,449 10,281 19,467

19,545 4650 8893 10,514 4650 8893 13,145 10,514 19,285

12,449 19,467

13,145 19,285

Sep. 2179 5557 7109 2179 9556 5557 13,801 7109 9556

1384Oct. 707Nov. 534 Dec. 2561 1408 1126 366313841976707 1451 534 469825612536140820051126 771236634103197633241451 4698

2536

2005

13,801 10247712 7234103 517 3324 1989 5398 2732 1525 1106 7437 1989 362010241810723 1422 517 5398 447627322312152518041106 9762 7437 696036203231181028091422 16,082 9762 16,082

4476 6960

2312 3231

1804 2809

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Regarding the RCP rainfall performance in the two hydrological models, Figure 11 illustrates Regarding the RCP rainfall performance in the two hydrological models, Figure 11 illustrates the the comparisons of the median monthly flow simulation under two climate change scenarios in the comparisons of the median monthly flow simulation under two climate change scenarios in the LXH LXH and XAJ models. It is clear to show that the distributed LXH model produced more streamflow and XAJ models. It is clear to show that the distributed LXH model produced more streamflow than than the lumped XAJ model during January to August, especially during the high rainfall rate the lumped XAJ model during January to August, especially during the high rainfall rate seasons, but seasons, but more streamflow was modelled in the XAJ model than the LXH model during September more streamflow was modelled in the XAJ model than the LXH model during September to December. to December. Since the distributed model can reflect the spatial rainfall distribution better than the Since the distributed model can reflect the spatial rainfall distribution better than the lumped model lumped model and thus has more influence on the peak flow volume, while the lumped XAJ model and thus has more influence on the peak flow volume, while the lumped XAJ model outperformed outperformed the LXH model on the base flow in this study, which was also due to the different the LXH model on the base flow in this study, which was also due to the different rainfall–runoff rainfall–runoff mechanisms and mathematical structures of the two models. mechanisms and mathematical structures of the two models.

(a)

(b) Figure Figure11. 11.Comparisons Comparisonsofofmedian medianmonthly monthlyrainfall rainfalland andflow flowsimulation simulationunder undertwo twoclimate climatechange change scenarios scenariosinintwo twohydrological hydrologicalmodels models(a: (a:median medianmonthly monthlyrainfall; rainfall;b:b:median medianmonthly monthlystreamflow). streamflow).

In addition, the annual flow simulation shown in Figure 12 illustrates the variation between the In addition, the annual flow simulation shown in Figure 12 illustrates the variation between two scenarios in the two hydrological models. It suggests that the RCP4.5 rainfall produced the the two scenarios in the two hydrological models. It suggests that the RCP4.5 rainfall produced the smallest and the highest annual streamflow in the XAJ and LXH model, respectively, in the Xijiang basin before 2050. Nevertheless, the RCP8.5 rainfall produced the smallest annual streamflow in XAJ

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smallest and the highest annual streamflow in the XAJ and LXH model, respectively, in the Xijiang and the highest streamflow in the LXH model from 2050 to 2099.annual In general, for both basin before 2050.annual Nevertheless, the RCP8.5 rainfall produced the smallest streamflow in RCP XAJ rainfall products, more annual streamflow is modelled in the LXH model than in the XAJ model. and the highest annual streamflow in the LXH model from 2050 to 2099. In general, for both RCP rainfall products, more annual streamflow is modelled in the LXH model than in the XAJ model.

Figure 12. Comparisons of annual flow simulation under two climate change scenarios in two Figure 12. Comparisons of annual flow simulation under two climate change scenarios in two hydrological models. hydrological models.

In In order order to to analyse analyse the the flood flood magnitude magnitude under under different different scenarios scenarios in in the the future, future, this this study study used used the magnitude under climate change, which is theLog–Pearson Log–PearsonType Type33(LP3) (LP3)distribution distributiontotoestimate estimateflood flood magnitude under climate change, which the recommended distribution for for estimating return period values of flood magnitude in China [25]. is the recommended distribution estimating return period values of flood magnitude in China The has the following form with data preliminary log transformed [26]: [26]: [25].distribution The distribution has the following form the with thebeing data being preliminary log transformed

f f(xx) = 





  1  x    /  e x−ξ )/β ( xx −ξ )α−1 e−( α  β Γ(α)

 

(6) (6)

where α is  a shape; and βand is a scale a location; is a shape;  isparameter. a scale parameter. whereξ is aislocation; The method of L-moments was used to estimate of LP3 LP3 distribution distribution in in this this study. study. The method of L-moments was used to estimate the the parameters parameters of L-moments are analogous to conventional moments defined as linear combinations of probability L-moments are analogous to conventional moments defined as linear combinations of probability weighted weighted moments moments (PWMs) (PWMs) [26]. [26]. The The method method is is proven proven to to be be superior superior to to other other common common parameter parameter estimation techniques such as the maximum likelihood [27] and the method of moments [28]. estimation techniques such as the maximum likelihood [27] and the method of moments [28]. The The details details of of the the parameter parameter estimation estimationprocedure procedurebased basedon onL-moments L-momentscan canbe be found found in in Hosking Hosking and Wallis (1997) [26] and Das (2016) [28]. The above LP3 flood frequency model was applied to and Wallis (1997) [26] and Das (2016) [28]. The above LP3 flood frequency model was applied to the the annual maximum (AM) flow series derived from the observed flow data and future flow series annual maximum (AM) flow series derived from the observed flow data and future flow series simulated simulated using using hydrological hydrologicalmodels modelsbased basedon onfuture futureclimate climatedata. data. The predicted return period flood magnitudes for different The predicted return period flood magnitudes for differentseries seriesare areplotted plottedin in Figure Figure13, 13, which which indicate that the range of design values increase along with the return period. This also provides indicate that the range of design values increase along with the return period. This also provides an an uncertainty uncertainty bound bound which whichcan canbe beexpected expectedin inthe thefuture. future.

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120,000

Discharge (m3/s)

100,000

80,000

Obs RCP4.5_XAJ

60,000

RCP4.5_LXH RCP8.5_XAJ

40,000

RCP8.5_LXH Mean

20,000

0 1

10

100

1000

Return period (T) years Figure 13. 13. Comparisons undertwo two climate change scenarios inhydrological two hydrological Figure Comparisonsofofflood flood magnitude magnitude under climate change scenarios in two models based on the return period. models based on the return period.

According to the lumped XAJ model simulation results, the future flood magnitude is similar to According to the lumped XAJ model simulation results, the future flood magnitude is similar to the historical floods, and the floods under the climate change scenarios with less than 100-year return the historical floods, and the floods under the climate change scenarios with less than 100-year return periods are less severe than the historical floods, and the floods with a 1000-year return period are periods are lesstosevere than the historical floods, the floods with 1000-year period rather close the historical records. However, the and floods simulated undera same futurereturn scenarios in are rather to the historical records. However, the floods simulated under same future under scenarios theclose distributed LXH model are much more severe than the historical floods, especially the in the distributed LXH model are much more severe than the historical the13 RCP8.5 RCP8.5 scenarios. Consequently, the mean flood magnitude in thefloods, future especially illustrated under in Figure indicates that the floods under climate change are likely to be more severe than the historical floods. scenarios. Consequently, the mean flood magnitude in the future illustrated in Figure 13 indicates that

the floods under climate change are likely to be more severe than the historical floods. 5. Summary and Concluding Comments

5. Summary and Concluding Comments In this study, two hydrological models with different mathematical structures were used and their differences in reproducing historical streamflow and in predicting the hydrological of and In this study, two hydrological models with different mathematical structures impacts were used two climate change scenarios (RCP4.5 and RCP8.5) were compared. The study was performed on the their differences in reproducing historical streamflow and in predicting the hydrological impacts of Xijiang catchment, a tributary of the Pearl River, located in a subtropical humid region in southern two climate change scenarios (RCP4.5 and RCP8.5) were compared. The study was performed on the China. The main focus of the study was to test how uncertain one can expect the estimations to be Xijiang catchment, a tributary and of the Pearlrainfall–runoff River, located in a subtropical humid region in southern when using fully-distributed lumped models to simulate hydrological responses China. The main focus of the study was to test how uncertain one can expect the estimations to climate changes, as compared to their capacities in simulating historical runoff. The following to be when using fully-distributed lumped rainfall–runoff models to simulate hydrological responses conclusions are drawn from and the study:

to climate changes, as compared to their capacities in simulating historical runoff. The following (1) Both the distributed LXH model and lumped XAJ models can reproduce almost equally well the conclusions are drawn the study: historical runoff from data series. The simulated peak flows were more accurate in the LXH model, while the base flow was better simulated by the XAJ model. Both the distributed LXH model and lumped XAJ models can reproduce almost equally well the (2) The cumulative catchment daily rainfall produced from the climate model matched the rain historical runoff data series. The simulated peak flows were more accurate in the LXH model, gauge measurements very well, but they tended to produce more small and medium peak flows while base flow wasthe better by the the XAJhydrological model. andthe to underestimate high simulated peak flows during model simulations. (2) (3)TheUsing cumulative catchment daily rainfall produced the climate matched the rain RCP4.5 and RCP8.5 precipitation data as input tofrom the tested models,model marginal variations gauge very well, they tended produce more small and medium peak flows weremeasurements found in the median flow but simulation in the to same hydrological model, which may imply the difference ofthe thehigh impact from the during two climate scenarios on model the monthly streamflow andthat to underestimate peak flows the hydrological simulations. simulation this RCP8.5 study was small. (3) Using RCP4.5inand precipitation data as input to the tested models, marginal variations

(1)

were found in the median flow simulation in the same hydrological model, which may imply that the difference of the impact from the two climate scenarios on the monthly streamflow simulation in this study was small.

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(4)

(5)

(6)

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Under future climate conditions, the distributed LXH model produced more streamflow than the lumped XAJ model during January to August, but more streamflow was modelled in the XAJ model than in the LXH model from September to December. The RCP4.5 climate data could result in the smallest and the highest annual streamflow in the XAJ and LXH models respectively before 2050, but the RCP8.5 rainfall data could produce the smallest annual streamflow in the XAJ and the highest annual streamflow in the LXH model from 2050 to 2099. The flood frequency analysis suggests that the floods under climate change in the future will be more severe than the historical floods, especially under the RCP8.5 scenario in the LXH model simulations.

This study shows that the difference in the streamflow simulations depend on the climate scenarios, the season, and the hydrological model structures that span from complex to simple, reflecting a decreasing ability to specifically represent the distributed (spatial) nature of the rainfall–runoff process. In this study, the exanimated distributed model could produce more streamflow and peak flow than the lumped model under the climate change scenarios, but the lumped model outperformed in the base flow simulation. This suggests that attention must be paid when using the hydrological models to simulate hydrological responses under climate change. Comparing the median monthly modelled flow driven by the RCP4.5 and RCP8.5 scenarios, the RCP4.5 data could produce more flow during the dry season while the RCP8.5 data could trigger higher flow in the flood season. The approach of using different hydrological models with different climate scenarios could also be appropriate for the uncertainty analysis of monthly, seasonal and annual scale runoff changes. It is noted that this study does not consider the uncertainty of hydrological model parameters. Future water resource scenarios predicted by any particular hydrological model represent only the results specific to that model. More studies using different hydrological models for different catchments need to be carried out in order to provide more general conclusions. Acknowledgments: The work presented in this paper was partly supported by the National Key Research and Development Program of China (No. 2016YFA0601703). We acknowledge the modelling groups for providing their data for analysis, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the World Climate Research Programme’s (WCRP’s) Coupled Model Intercomparison Project for collecting and archiving the model output, and organizing the model data analysis activity. The regional climate model data was collected, analysed, and provided by the National Climate Centre. Author Contributions: Dehua Zhu designed research and wrote the paper. Qiwei Ren conducted data analysis and Samiran Das contributed to the discussion and writing part of the paper. Conflicts of Interest: The authors declare no conflict of interest.

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