Assessing Climate Change Impacts on Streamflow and Baseflow in the Karnali River Basin, Nepal: A CMIP6 Multi-Model Ensemble Approach Using SWAT and Web-Based Hydrograph Analysis Tool

Round 1

Reviewer 1 Report

The paper discusses assessing the Assessing Climate Change Impacts on Streamflow and Baseflow in the Karnali River Basin, Nepal: A CMIP6 Multi-Model Ensemble Approach Using SWAT and Web-Based Hydrograph Analysis Tool, which seems technically sound. However, a few potential areas might require clarification and improvement.

How does the study's research question align with current scientific understanding and gaps in climate change impacts on hydrology, particularly in the context of the Karnali River Basin (KRB) in Nepal?

The study employs thirteen statistical bias correction methods for precipitation and nine for temperatures. How do the authors determine the best bias correction methods for their analysis, and are these methods adequately validated against observed data?

The paper mentions the use of four GCMs selected based on performance metrics. However, it is unclear how these models compare with other available GCMs in terms of their ability to simulate the specific climatic conditions of the Karnali River Basin. Were any other models considered, and why were they not selected?

The paper identifies Bernoulli-Weibull, linear parametric transformation function, and smoothing spline as the best bias correction methods for precipitation, maximum, and minimum temperatures. It would be beneficial to understand the criteria for determining the "best" methods and whether they have been validated against independent datasets.

Considering the study's use of a multi-model ensemble approach, how do the authors address the potential uncertainties and biases associated with the selected CMIP6 models? Is there a systematic approach to model selection and ensemble formation?

The paper mentions the use of SWAT for simulating future river discharge and WHAT for baseflow analysis. How comprehensive are these models' calibration and validation processes, especially in capturing the hydrological processes within the KRB?

How do the findings regarding the increasing trends in precipitation, temperatures, and river discharge under future climate scenarios compare with existing literature and observed trends in the region?

What novel insights or contributions does the paper offer regarding assessing climate change impacts on hydrology using a CMIP6 multi-model ensemble approach? How does this study advance our understanding of hydrological responses to climate change in high-altitude river basins like the KRB?

Given the importance of baseflow in river systems, how did the authors conduct the baseflow separation analysis, and what tools or models were used to differentiate baseflow from streamflow?

Climate change is known to alter precipitation and temperature distributions. How did the authors integrate these changes into their hydrological modeling, and what approaches were taken to evaluate the temporal and spatial changes in water balance within the KRB?

The paper should compare its findings with other relevant studies to contextualize its results within the broader body of research.

Author Response

Reviewer 1

Dear reviewer,

Thank you for your time and effort in reviewing our manuscript. We have addressed each of your comments point by point, and we hope that these revisions have substantially improved the overall quality of our manuscript.

How does the study's research question align with current scientific understanding and gaps in climate change impacts on hydrology, particularly in the context of the Karnali River Basin (KRB) in Nepal?

Response:

Thank you for your comments and suggestion. We have addressed this question in

Line 106 to 112:

Further, mountainous river basins are highly susceptible to the impacts of climate change. Previous studies on the KRB have predicted an increase in river discharge in the future. However, these forecasts were made based on a hydrological model calibrated at a single location and integrated with climate variable projections from CMIP5 (Dahal et al., 2020). A multi-site calibration would provide a better understanding of streamflow dynamics, particularly in geographically diverse basins such as the KRB.

The study employs thirteen statistical bias correction methods for precipitation and nine for temperatures. How do the authors determine the best bias correction methods for their analysis, and are these methods adequately validated against observed data?

Response: thank you for your valuable suggestion. This is addressed in line 284 to 290, and line 539 to 542 as

Various techniques have been employed for the bias correction of precipitation and temperature data. A study conducted in the Himalayan region of India demonstrated that quantile mapping effectively minimized biases in both precipitation and temperature [41]. Similarly, research in the Karkheh River basin, an area with diverse topography, showed that several quantile mapping methods successfully reduced biases in these climatic variables (Enayati et al., 2021). Our study area, also situated at a high altitude, employs quantile mapping for bias correction of raw GCMs.

The study compared the bias-corrected GCM data with the observed data to evaluate how well it reproduces. The results showed that the bias-corrected GCM data is more consistent with the observed data than the raw GCM data. Figures 6, 7, and 8 illustrate how the selected bias correction methods bring the climatic variables closer to the observed values.

The paper mentions the use of four GCMs selected based on performance metrics. However, it is unclear how these models compare with other available GCMs in terms of their ability to simulate the specific climatic conditions of the Karnali River Basin. Were any other models considered, and why were they not selected?

Response: Thank you again for your comment. Line 497-498 addresses this comment. 

Out of 10 GCMs, six were discarded due to their high biases while comparing with the historical observed data (1980-2014).

The paper identifies Bernoulli-Weibull, linear parametric transformation function, and smoothing spline as the best bias correction methods for precipitation, maximum, and minimum temperatures. It would be beneficial to understand the criteria for determining the "best" methods and whether they have been validated against independent datasets.

Response: Please refer to line 330 to 336.

To assess the performance of these bias correction methods, each of these bias correction methods was applied to four selected raw GCMs, and the performance of each method was evaluated based on four established criteria: the RSR, R², PBIAS, and NSE (Moriasi et al., 2007). These criteria constitute a performance rating system for each bias correction method. Table 3 presents the performance rating criterion for the performance indicators.

However, We did not utilize historical data when applying the bias correction methods; it was only used to compare the outcomes from the bias-corrected raw Global Climate Models (GCMs). Therefore, no validation was necessary in this context.

Considering the study's use of a multi-model ensemble approach, how do the authors address the potential uncertainties and biases associated with the selected CMIP6 models? Is there a systematic approach to model selection and ensemble formation?

Response: Thank you for bringing this out. Please refer to line 592 to 609 as

There are several things to consider while using a multi-model ensemble approach; for example, individual models exhibit higher uncertainties; therefore, the results derived from such models may not be reliable……………………………………………………………. This was accomplished using a straightforward mean technique, calculating the average daily bias-corrected outputs from these top four GCMs (Ahmed et al., 2019).

The paper mentions the use of SWAT for simulating future river discharge and WHAT for baseflow analysis. How comprehensive are these models' calibration and validation processes, especially in capturing the hydrological processes within the KRB?

Response: Thank you again for this suggestion. Please refer to line 895 to 902.

Previous studies have shown that the SWAT model effectively assesses the impact of climate change on streamflow (Dhami et al., 2018; Dahal et al., 2020). However, these studies have relied on calibrations at single sites. Our research employed a multi-site calibration approach to improve model performance and reduce uncertainties. Additionally,  previous studies have not explored base flow separation in the KRB. We used a WHAT to filter the base flow to address this gap. This method was appropriate for isolating the base flow in areas near the KRB with similar geographical characteristics (Bastola et al., 2018).

How do the findings regarding the increasing trends in precipitation, temperatures, and river discharge under future climate scenarios compare with existing literature and observed trends in the region?

Response: We would like to thank to the reviewer for reviewing specific detail again.

Please refer to line 619 to 630 and line 1033 to 1047.

Our study utilized a multi-model ensemble of four selected GCMs to project precipitation and maximum and minimum temperatures in the KRB for 2022-2100. The analysis indicates an increasing trend in precipitation and maximum and minimum temperatures over time, aligning with previous research in the KRB region (Dahal et al., 2020; Pandey et al., 2020). Additionally, our results reveal a more rapid increase in minimum temperature than maximum temperature across the Himalayan, Hilly, and Plains regions. This trend is consistent with findings from other river basins in Nepal (Devkota et al., 2017; Rajbhandari et al., 2017; Sigdel et al., 2022). However, this contradicts the result of a study that reported a faster increase in maximum temperature in the Himalayan region (Pandey et al., 2020). Furthermore, our analysis suggests a higher trend in precipitation and maximum and minimum temperatures in highland areas compared to lowland regions. This observation corroborates the findings from (Agarwal et al., 2016) and (Shrestha and Aryal, 2011) studies. This divergence highlights the complex and varied climatic patterns across different topographical zones within the region.

A previous study investigated how climate change affects water availability in the Karnali River Basin, utilizing CMIP5 model outputs and single-site calibration. The study found that the atmospheric temperature across the basin has increased, but there is a significant variability in precipitation patterns depending on the location. Their finding anticipates that there will be an increase in annual precipitation and river discharge compared to baseline levels (Zhu et al., 2021). Our research, which leverages CMIP6 model outputs and multi-site calibration, reveals that minimum temperatures are rising more rapidly than maximum temperatures, with a uniform temperature increase across all monitoring stations. Precipitation patterns vary depending on the season and location. Historically, precipitation peaks at gauge stations have trailed river discharge peaks by approximately one month. This delay is attributed to snowmelt in the Himalayas, which sustains river flows even after decreased precipitation. However, with projected increases in maximum and minimum temperatures, snowmelt may accelerate, leading to future peaks of precipitation and river discharge occurring within the same month (Dahal et al., 2020).

What novel insights or contributions does the paper offer regarding assessing climate change impacts on hydrology using a CMIP6 multi-model ensemble approach? How does this study advance our understanding of hydrological responses to climate change in high-altitude river basins like the KRB?

Response:

Thank you again for your valuable comments and suggestion. Please refer to Line: 123 to 136 as below;

This paper makes significant progress in assessing the impact of climate change on the hydrology of high-altitude river basins like the KRB using a CMIP6 multi-model ensemble approach. It offers new insights into how temperature changes, especially the pronounced increase in minimum temperatures, impact these sensitive areas. Our findings reveal that rising temperatures lead to increased snowmelt and higher river discharge levels, altering water availability and the timing of peak discharges. Such changes have important implications for water resource management, agriculture, and disaster risk reduction. This research enhances our ability to address the challenges climate change poses on water resources by providing a robust framework for predicting hydrological changes. It presents comprehensive assessments for future scenarios, aiding policymakers, stakeholders, and researchers in sustainable water management strategies. Ultimately, this study equips local authorities with the knowledge to better prepare for and adapt to climate-induced changes, offering valuable insights for mitigating flood and drought risks in critical ecosystems.

Given the importance of baseflow in river systems, how did the authors conduct the baseflow separation analysis, and what tools or models were used to differentiate baseflow from streamflow?

Response: We employed a simple filter through a WHAT to distinguish base flow from stream flow. This tool was chosen as the optimal base flow separation technology for river basins with similar geographical locations (Bastola et al., 2018) (Line 476 to 478).

Climate change is known to alter precipitation and temperature distributions. How did the authors integrate these changes into their hydrological modeling, and what approaches were taken to evaluate the temporal and spatial changes in water balance within the KRB?

Response: We agreed with your statement on climate change. Please refer to section 2.6 (line 453 to 463). 

The paper should compare its findings with other relevant studies to contextualize its results within the broader body of research.

Response: Thank you again for your valuable suggestions. We have added adequate relevant studies as per your suggestions. 

Author Response File: Author Response.pdf

Reviewer 2 Report

The paper demonstrates a commendable scientific quality through its meticulous application of a physically based, semi-distributed SWAT model for multi-site calibration across the diverse Karnali River Basin (KRB) region. The choice of this methodology, specifically tailored for accurately assessing the impact of climate change on streamflow, reflects a pertinent and well-thought-out approach. Integrating outputs from CMIP6 GCMs into the SWAT model showcases a robust framework, ensuring the reliability of the analysis. The incorporation of critical future scenarios (SSP245 and SSP585) across distinct time zones provides a comprehensive and nuanced understanding of potential outcomes, showcasing the paper's scientific rigor and relevance in addressing the complexities of climate change impact assessment in the KRB.

1. The research strategically selected General Circulation Models (GCMs) based on their relevance to South Asian climate change, utilizing nine for precipitation and ten for temperature extremes. Evaluation criteria included RSR, R², PBIAS, and NSE, forming a performance rating system. The top-performing GCMs for each variable were chosen, subjected to bias correction, and integrated into a multi-model ensemble for robust climate projections. Questions for consideration: How did the chosen GCMs perform in the South Asian context? How effective was the performance rating system in identifying reliable models? What impact does the multi-model ensemble approach have on the reliability of climate projections?

2-The study advocates reducing uncertainties in climate variable projections by utilizing the mean time series from a Multi-Model Ensemble (MME) of superior-performing GCMs. Various approaches, from simple arithmetic means to machine learning algorithms, have been documented in the literature. This research opts for a simple mean method, applied to precipitation, maximum, and minimum temperatures, using an ensemble of the four top-ranked GCMs. Questions: How does the simplicity of the chosen mean method impact the accuracy of the mean time series? What comparative advantages does the simple mean approach offer over more complex algorithms in terms of uncertainty reduction? Can the effectiveness of the selected mean time series methodology be validated through comparisons with other advanced techniques?

3.How does the geographical diversity of the Karnali River Basin influence the spatial distribution of projected precipitation patterns, and what are the implications for regional climate resilience?

 4.What specific characteristics of the mountain, hilly, and plain regions, represented by stations at different elevations, contribute to variations in the projected changes of maximum and minimum temperatures?

 5. In what ways do the observed variations in precipitation and temperature changes at the selected stations diverge from the baseline period average (1980-2014), and how might these variations impact the unique physiographic regions within the basin?

6. How can the study's analysis of average annual precipitation, maximum temperature, and minimum temperature changes over the near, mid, and far future provide actionable insights for climate adaptation and mitigation strategies in the Karnali River Basin?

7.What are the key findings from the comprehensive analysis of the rate of precipitation and temperature changes throughout the year, particularly when considering the distinct seasons (pre-monsoon, monsoon, post-monsoon, and winter)?

8.How can the identified trends and patterns in precipitation and temperature changes during different seasons inform policymakers and local communities about potential vulnerabilities and opportunities for climate resilience?

 9. Considering the physiographic variability, what are the specific challenges and opportunities posed by climate change in the mountain, hilly, and plain regions of the Karnali River Basin, and how can adaptation strategies be tailored accordingly?

10.What are the key GCMs identified as the top performers based on performance metrics for precipitation, maximum temperature, and minimum temperature, and how do these models contribute to the reliability of climate projections in the Karnali River Basin?

11.How do the chosen bias correction methods, specifically Bernoulli-Weibull, linear parametric transformation function, and smoothing spline, enhance the accuracy of precipitation, maximum temperature, and minimum temperature projections, and what implications do these corrections hold for water resource management and environmental planning?

12.What are the observed annual and seasonal changes in precipitation, maximum temperature, and minimum temperature under the SSP245 and SSP585 scenarios, and how might these variations impact the vulnerability and resilience of the Karnali River Basin to climate change?

 13.In light of the projected increases in minimum temperatures outpacing maximum temperatures, how does this asymmetry influence the overall climate trends and potential ecological consequences in the study area, and what adaptive measures might be recommended based on these findings?

remarks :

-the conclusion is well presented with details. 

-the abstract must be improved. 

- the methodologies must be described to show and highlight the links between steps and the redlexions. 

the methodologies must be introduced before collecting data and describing meteorological data , because the first step of the methodologies is the data collection.

Author Response

Reviewer 2

Dear reviewer, I am writing to express my gratitude for your invaluable comments and suggestions regarding our manuscript. We truly appreciate your efforts in helping us enhance the quality of our paper. We have carefully considered each of your comments and addressed them in detail. We hope that our revisions have significantly improved the overall quality of the paper. Thank you once again for your time and support.

The paper demonstrates a commendable scientific quality through its meticulous application of a physically based, semi-distributed SWAT model for multi-site calibration across the diverse Karnali River Basin (KRB) region. The choice of this methodology, specifically tailored for accurately assessing the impact of climate change on streamflow, reflects a pertinent and well-thought-out approach. Integrating outputs from CMIP6 GCMs into the SWAT model showcases a robust framework, ensuring the reliability of the analysis. The incorporation of critical future scenarios (SSP245 and SSP585) across distinct time zones provides a comprehensive and nuanced understanding of potential outcomes, showcasing the paper's scientific rigor and relevance in addressing the complexities of climate change impact assessment in the KRB.

1. The research strategically selected General Circulation Models (GCMs) based on their relevance to South Asian climate change, utilizing nine for precipitation and ten for temperature extremes. Evaluation criteria included RSR, R², PBIAS, and NSE, forming a performance rating system. The top-performing GCMs for each variable were chosen, subjected to bias correction, and integrated into a multi-model ensemble for robust climate projections. Questions for consideration: How did the chosen GCMs perform in the South Asian context? How effective was the performance rating system in identifying reliable models? What impact does the multi-model ensemble approach have on the reliability of climate projections?

Response: Thank you for your comments and suggestions. Please refer to line 252 to 258 and line 592 to 609.

The study advocates reducing uncertainties in climate variable projections by utilizing the mean time series from a Multi-Model Ensemble (MME) of superior-performing GCMs. Various approaches, from simple arithmetic means to machine learning algorithms, have been documented in the literature. This research opts for a simple mean method, applied to precipitation, maximum, and minimum temperatures, using an ensemble of the four top-ranked GCMs. Questions: How does the simplicity of the chosen mean method impact the accuracy of the mean time series? What comparative advantages does the simple mean approach offer over more complex algorithms in terms of uncertainty reduction? Can the effectiveness of the selected mean time series methodology be validated through comparisons with other advanced techniques?

Response: Thank you for your time and valuable suggestions. We have addressed your queries in line

593 to 605 and line 353 to 362.

3.How does the geographical diversity of the Karnali River Basin influence the spatial distribution of projected precipitation patterns, and what are the implications for regional climate resilience?

Response: Please refer to line 615 to 618 and line 659 to 673.

 4.What specific characteristics of the mountain, hilly, and plain regions, represented by stations at different elevations, contribute to variations in the projected changes of maximum and minimum temperatures?

Response: Thank you for your concerns. Please refer to line 628 to 630.

Furthermore, our analysis suggests a higher trend in precipitation and maximum and minimum temperatures in highland areas compared to lowland regions. This observation corroborates the findings from (Agarwal et al., 2016) and (Shrestha and Aryal, 2011) studies.

5. In what ways do the observed variations in precipitation and temperature changes at the selected stations diverge from the baseline period average (1980-2014), and how might these variations impact the unique physiographic regions within the basin?

Response: Thank you again for your suggestions and concerns. We have addressed your queries in  line 620 to 626.

Our study utilized a multi-model ensemble of four selected GCMs to project precipitation and maximum and minimum temperatures in the KRB for 2022-2100. The analysis indicates an increasing trend in precipitation and maximum and minimum temperatures over time, aligning with previous research in the KRB region (Dahal et al., 2020; Pandey et al., 2020). Additionally, our results reveal a more rapid increase in minimum temperature than maximum temperature across the Himalayan, Hilly, and Plains regions. This trend is consistent with findings from other river basins in Nepal (Devkota et al., 2017; Rajbhandari et al., 2017; Sigdel et al., 2022).

6. How can the study's analysis of average annual precipitation, maximum temperature, and minimum temperature changes over the near, mid, and far future provide actionable insights for climate adaptation and mitigation strategies in the Karnali River Basin?

Response: Please refer to line 826 to 874.

7.What are the key findings from the comprehensive analysis of the rate of precipitation and temperature changes throughout the year, particularly when considering the distinct seasons (pre-monsoon, monsoon, post-monsoon, and winter)?

Response: Please refer to line 842 to 848.

8.How can the identified trends and patterns in precipitation and temperature changes during different seasons inform policymakers and local communities about potential vulnerabilities and opportunities for climate resilience?

Response: Please refer to line 826 to 841.

9. Considering the physiographic variability, what are the specific challenges and opportunities posed by climate change in the mountain, hilly, and plain regions of the Karnali River Basin, and how can adaptation strategies be tailored accordingly?

Response: Thank you for your concerns. We have addressed your concerns in line 1154 to 1171.

10.What are the key GCMs identified as the top performers based on performance metrics for precipitation, maximum temperature, and minimum temperature, and how do these models contribute to the reliability of climate projections in the Karnali   River Basin?

Response: Thank you again for your questions. Please refer to line 498 to 505, and regarding reliability, please refer to line 524 to 532.

11.How do the chosen bias correction methods, specifically Bernoulli-Weibull, linear parametric transformation function, and smoothing spline, enhance the accuracy of precipitation, maximum temperature, and minimum temperature projections, and what implications do these corrections hold for

Response: Please refer to line 551 to 562 concerning chose bias correction methods.

12.What are the observed annual and seasonal changes in precipitation, maximum temperature, and minimum temperature under the SSP245 and SSP585 scenarios, and how might these variations impact the vulnerability and resilience of the Karnali River Basin to climate change?

Response: Thank you again for concerns. We have addressed your concerns in line 842 to 874.

 13.In light of the projected increases in minimum temperatures outpacing maximum temperatures, how does this asymmetry influence the overall climate trends and potential ecological consequences in the study area, and what adaptive measures might be recommended based on these findings?

Response: Thank you for bringing out this point. Please refer to line 659 to 673.

remarks : 

-the conclusion is well presented with details. 

-the abstract must be improved. 

- the methodologies must be described to show and highlight the links between steps and the redlexions. 

the methodologies must be introduced before collecting data and describing meteorological data , because the first step of the methodologies is the data collection.

We have revised our abstract. Regarding methodologies, we have addressed your concerns and incorporated suggestions. We believe this has improved the overall quality of our manuscript.

Author Response File: Author Response.pdf

Reviewer 3 Report

Dear authors,

Please check the attached file.

Comments for author File: Comments.pdf

Author Response

Reviewer 3

Dear reviewer,

Thank you for your time and effort in reviewing our manuscript. We appreciate your support in improving the quality of our manuscript. We have addressed your concerns by going through them point by point, as you suggested.

 Line 44: Please give the comparison with similar mountainous regions across the world in term of air surface temperature (on global and regional scale)?

Response: Thank you for your comments. Please refer to line 46 to 54 for your query.

Line 57: “The Karnali River Basin (KRB), situated in the western regions of Nepal, has been the focus of many studies previously investigating the effects of climate change on river flows, with a particular emphasis on high-flow events.” Please provide the studies that can confirm these sentences. Also, provide some data about high-flow events as hydro-climate extremes.

Response: We have provided studies regarding your comments. Please refer to line 65 to 69.

Line 101: What is the aim of this research? Also, highlight the novelty of this study?

Response: Thank you for your concerns. Please refer to lines 113 to 136, which address your concerns.

Line 118: Based on the Figure 1. it is not clear what type of stations are selected? Is it ordinary meteorological stations, precipitation stations? You must indicate and clear this on the map, because the legend is confusing.

Response: Figure 1 has been improved.

Line 130: Please describe the climate type in defined study area according to Köppen climate classification?

Response: Climate type according to Koppen climate classification has been define in line 157 to 159.

Line 137 to line 139: These sentences are too confusing. Please give better explanation.

Response: We have revised our sentences as per your suggestion. Please see line 169 to 179.

Line 207: Figure 3 is too poor resolution. Can you produce better map of soil types?

Response: We have produced comparatively better map (Figure 3).

Line 210: Methodology is too long, please check it and rephrase it and reduce text as much as possible. 

Response: Thank you for your comments and suggestions. This is a more methodological-based paper, so it is important to describe it in detail. However, as per your suggestions, we have revised it.

Line 438: In this section, you must compare presented results with similar regions on a global scale. I didn’t notice you, did it?

Response: We have compared our findings with the relevant studies throughout our paper.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Given that the authors have addressed all of my comments and implemented significant corrections based on my feedback, the paper can now be deemed suitable for publication by the Sustainability Journal.

Author Response

N/A.

Reviewer 2 Report

the report (answers) was clear.

the revised version is ready to be published 

Author Response

N/A.