Assessing watershed hydrological response to climate change based on signature indices

Due to the fact that one of the important ways of describing the performance of basins is to use the hydrological signatures, the present study investigates the effects of climate change using the hydrological signatures in Azarshahr Chay basin, Iran. To this end, the Canadian Earth system model (CanESM2) is first used to predict future climate change (2030–2059) under two Representative Concentration Pathways (RCP2.6 and RCP8.5). Six signature indices were extracted from flow duration curve (FDC) as follows: runoff ratio (RR), high-segment volume (FHV), low-segment volume (FLV), mid-segment slope (FMS), mid-range flow (FMM), and maximum peak discharge (DiffMaxPeak). These signature indices act as sorts of fingerprints representing differences in the hydrological behavior of the basin. The results indicate that the most significant changes in the future hydrological response are related to the FHV and FLV and FMS indices. The BiasFHV index indicates an increase in high discharge rates under the RCP8.5 scenario, compared to the baseline period, and also the RCP2.6 scenario. The mean annual discharge rate, however, is lower than the discharge rate under this scenario. Generally, for the RCP8.5 scenario, the changes in the signature indices in both high discharges and low discharges are significant.


GRAPHICAL ABSTRACT INTRODUCTION
The world is presently facing rapid climate changes. One of the main concerns of hydrologists is comprehension and prediction of related hydrologic changes. Therefore, it is necessary to precisely map the changes in hydrologic response of catchments (Casper et al. ). (1) investigation of the climate change effects on streamflow in Azarshahr Chay catchment in the future  under Representative Concentration Pathways (RCP2.6 and RCP8.5) and (2)

Study area
Azarshahr Chay basin is one of the fourth-order sub-basins in northwestern Iran. This basin covers an area of 702 km 2 and is situated southwest of Tabriz city. Figure 1 shows the location of the basin. The basin is characterized by an average annual precipitation of 266.21 mm and an average annual temperature of about 13.22 C. Tmin, Tmax, and precipitation obtained from two meteorological stations  were used in the present study. Solar radiation, wind speed, and relative humidity which were simulated by the SWAT model were among other meteorological data used in the study. Azarshahr hydrometric station and the observed streamflow changes in the basin were used for calibration and verification purposes ( Figure 1 and Table 1). These scenarios are designed based on results of socio-economic and technological studies as well as the concentration of some gases in the decades to come. In the RCP2.6 and RCP8.5 scenarios, the effect of lowest CO 2 emissions (490 ppm) and highest GHG emissions (1,370 ppm) on radiation retention is projected to be 2.6 W/m 2 and 8.5 W/m 2 by 2100, respectively (IPCC ; Khan & Koch ).

Climate change and downscaling
The output of GCMs cannot, due to their large scale or high spatial resolution, be used on a regional or local scale. Therefore, it is necessary to establish a quantitative relationship between large-scale variables of general circulation model and the small-scale observational variables (local or regional) (Wilby et al. ). This relationship is presented in the following equation (Dibike & Coulibaly ): where Y is the predictor variable, X is the predictand vari- where SWt is the final soil water content (mm), SW o is the initial soil water content (mm) (up to a depth of 60 cm), t The SUFI2 algorithm was used in the SWAT-CUP software to improve the calibration quality and analysis of uncertainty in the model results. The SUFI2 algorithm combines calibration and uncertainty and tries to determine the uncertainty parameters in such a way that the observational data mostly fall within the pre-determined uncertainty zone.
In the meantime, it tries to create the narrowest possible uncertainty spectrum. Therefore, optimal conditions are realized when: (1) most of the observational data are bracketed by 95% prediction uncertainty (95-PPU) band (P-factor → 1) and (2)  In the present study, meteorological information including daily precipitation, as well as daily Tmax andTmin (1981-1989) were introduced into the model and other meteorological information was simulated by the model.

The curve number method, variable storage method, and
Hargreaves-Samani method were used to estimate surface runoff, carry out flow routing, and determine evapotranspiration, respectively. Azarshahr hydrometric station was

Signature indices
Signature indices provide information that can be used to diagnose and isolate the causes of model inadequacy thereby providing guidance towards model improvement The first signature index focuses on the long-term inputoutput behavior of the system (volume balance), and therefore measures the percent bias in overall runoff (% BiasRR). This index is strongly controlled by the evapotranspiration process and any factor that influences the quantity of water available for evapotranspiration. The index should, therefore, be sensitive to the model components and parameters that control these processes (Herbst et al. ).
BiasRR presented in Equation (3) where o and s represent the observed and simulated discharges, respectively.
Output indices of the FDC are recognized as watershed characteristics that operate on short-to medium-timescale.  Table 3.

Model performance
Root mean square error (RMSE), determination coefficient (R 2 ), and Nash-Sutcliffe efficiency (NSE) are used to check the reliability of the simulated data in SDSM and SWAT models.
where O̅ denotes the mean observed data, S̅ is the mean simulated data, O i represents the observed data in the entire period and S i denotes the simulated data in the entire period, n is the total number of data.

RESULTS AND DISCUSSION
The SDSM model and climate change results  Table 4 The model performance results obtained using R 2 , NSE, and RMSE coefficients are presented in Table 5 for both calibration and validation periods. As Table 5 shows, R 2 and NSE coefficients obtained in calibration and validation periods are highly consistent and close to 1, which shows optimal performance and high simulation accuracy of the model. In the calibration period, R 2 , NSE, and RMSE coefficients were in the 0.86-0.99, 0.82-0.99, and 0.68-1.83 range, respectively. In the validation period, however, these coefficients were in the 0.78-0.98, 0.72-0.98, and 0.56-2.04 range, respectively. Once a reasonable relation between the model and obtained data was achieved, the future simulation was performed.
In the next step, the SDSM downscaling accuracy was first evaluated and then the future climate was simulated using the CanESM2 model under RCP2.6 and RCP8.5 scenarios. The range of future temperature and precipitation variations are presented in Table 6. Figure 2 shows the monthly temperature changes in Azarshahr station (1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005) and precipitation changes in Azarshahr and Ghermezigol stations  in the baseline period as compared to the future periods. According to Figure 2, the highest rise in Tmax (2.61) has occurred in July under the RCP8.5 scenario. The figure   Figure 2, the rise in precipitation becomes less significant as we shift from RCP2.6 to RCP8.5.

The SWAT model and discharge variation results
The required data including DEM maps, land use, basin soil type, daily precipitation data, temperature as well as the infor-

Signature indices analysis and changes
Before analyzing the basin signatures, it is necessary to plot the FDC. Therefore, the 30-year runoff data were used to V_: 'value', this means the existing parameter value will be replaced by the given value.
plot the FDC. The FDC in the baseline period and under RCP2.6 and RCP8.5 scenarios is presented in Figure 5.
This curve serves as the premise of basin signature analysis.
Every single signature was evaluated against the five intrinsic features of a good signature (identifiability, robustness, consistency, representativeness, and discriminatory power) and the scores of the signatures used for the basin in the study area were calculated according to Table 3.
Since there are two scenarios for the future, the aforementioned equations were used twice. Taking into account   therefore, this index is significantly affected by high flows.
Taking into account the difference of 25.68% between the two scenarios, it can be argued that the conclusion is con- higher than other scenarios. This means that, in the optimistic scenario, the low flow will be higher in the future and the runoff distribution will be uniform over the RCP8.5 scenario. Then, in the RCP2.6 scenario, fewer extreme events have occurred. Therefore, the reason why the average

CONCLUSION
Development of industrial activities which is followed by overlooking of environmental issues has made the climate change effects more obvious than ever before and that is why this phenomenon is recognized as a global concern.
Variations in temperature and precipitation patterns can significantly affect the quantity and quality of water resources, under the RCP2.6 and RCP8.5 scenarios, respectively, and since this signature is provided for high flows, it can be argued that the number of high flows is higher under the pessimistic scenario (RCP8.5). This finding indicates that consideration of pessimistic conditions can lead to severe hydrological responses and extreme events such as floods in this signature. In other words, the average annual flow rate increases in comparison with the base period (the RCP8.5 scenario is þ2.1% which is less than the RCP2.6 (þ3.2%)). However, the average annual flow rate is lower in RCP8.5, the examination of the FHV index (percent bias in FDC high-segment volume) increases to a higher percentage in RCP8.5 (94.55%) than the base period and RCP2.6 (31.18%). BiasFLV (low discharges) was found to be 120.61% under the RCP2.6 scenario and 62.88% under the RCP8.5 scenario. This means that the low-flow volume is higher under the optimistic scenario, so it can be con- The results showed that signatures are useful instruments that can be used to evaluate changes in the future hydrological response of basins or to compare the basins with each other. The results also suggested that average annual and monthly changes (temperature, precipitation, runoff) alone cannot provide us with an inclusive account of future changes. Therefore, indices can be used as valuable instruments to address high to low flow changes, flood and drought, as well as the hydrological behavior of basins.
However, it should be noted that more studies are needed for better understanding of the response of the basin, such as investigating unusually wet or dry periods.

DATA AVAILABILITY STATEMENT
Data cannot be made publicly available; readers should contact the corresponding author for details.