CHANS-SD-YRB V1.0: A System Dynamics model of the coupled human-natural systems for the Yellow River Basin

Sang, Shan; Li, Yan; Zong, Shuang; Yu, Lu; Wang, Shuai; Liu, Yanxu; Wu, Xutong; Song, Shuang; Zhao, Wenwu; Wang, Xuhui; Fu, Bojie

doi:10.5194/egusphere-2025-5597

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https://doi.org/10.5194/egusphere-2025-5597

Preprints

19 Nov 2025

| 19 Nov 2025

CHANS-SD-YRB V1.0: A System Dynamics model of the coupled human-natural systems for the Yellow River Basin

Shan Sang, Yan Li, Shuang Zong, Lu Yu, Shuai Wang, Yanxu Liu, Xutong Wu, Shuang Song, Wenwu Zhao, Xuhui Wang, and Bojie Fu

Abstract. Modeling the coupled human–natural systems (CHANS) is vital for understanding human–natural interactions and achieving regional sustainability, offering a powerful tool to alleviating human–water conflicts, ensuring food security, thereby supporting the region’s pathway toward sustainable development. However, the scarcity of regional-scale CHANS models constrains progress in practical applications for regional sustainability. The Yellow River basin (YRB) is an ideal region for modeling regional CHANS due to its closely coupled human and natural systems, which are stressed by water and ecosystem fragility. Here, we developed the CHANS-SD-YRB model using the System Dynamics approach, integrating 10 sectors essential for modeling human-water interactions of the basin, including five human sectors (Population, Economy, Energy, Food, and Water Demand) and five natural sectors (Water Supply, Sediment, Land, Carbon, and Climate). The model can simulate evolution and feedbacks of the YRB CHANS annually at provincial and sub-basin scales, while conserving hydrological connectivity between sub-basins. The model can accurately reproduce historical CHANS dynamics, achieving strong quantitative agreement with historical data (R > 0.95 for human sectors and R > 0.7 for natural sectors), which supports its applicability for scenario analyses and future projections. We applied the model to explore human–natural system dynamics under a future baseline scenario, assuming the continuation of existing policies and climate projection under middle of the road scenario (SSP–RCP 2-4.5). The future projections (2021–2100) indicate that achieving sustainable development in the YRB will remain challenging, though economic growth and food security are expected to improve. Emerging issues, such as ecological–human water trade-offs, labor shortages, reduced sediment load, and limited carbon absorption capacity, may hinder regional long-term sustainability.

Received: 12 Nov 2025 – Discussion started: 19 Nov 2025

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11 Mar 2026

CHANS-SD-YRB V1.0: a system dynamics model of the coupled human-natural systems for the Yellow River Basin

Shan Sang, Yan Li, Shuang Zong, Lu Yu, Shuai Wang, Yanxu Liu, Xutong Wu, Shuang Song, Wenwu Zhao, Xuhui Wang, and Bojie Fu

Geosci. Model Dev., 19, 2039–2058, https://doi.org/10.5194/gmd-19-2039-2026,https://doi.org/10.5194/gmd-19-2039-2026, 2026

Short summary

Shan Sang, Yan Li, Shuang Zong, Lu Yu, Shuai Wang, Yanxu Liu, Xutong Wu, Shuang Song, Wenwu Zhao, Xuhui Wang, and Bojie Fu

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2025-5597', Anonymous Referee #1, 18 Dec 2025
This paper introduces CHANS-SD-YRB v1.0, a system dynamics CHANS model of the Yellow River Basin and operates at provincial (human processes) and sub-basin (natural processes) scales. The manuscript reads well and a supplement with more elaborate model descriptions. By coupling the human and natural processes within a regional CHANS framework, this model description paper lays the foundations for policy-oriented modelling for the Yellow River Basin. Overall, the model is an ambitious synthesis, but several methodological choices and transparency gaps need some tightening. Find my specific comments and suggestions below.
1. Introduction
The first two paragraphs of the introduction goes back and forth between regional and global CHANS research and modelling, which breaks the flow and is a little disorientating. It would be clearer if CHANS research and modelling is first introduced more generally. After introducing the main models, I suggest that the authors identify the overrepresentation of global scale CHANS models. And then bring in the distinction and need for more regional scale modelling, which situates this work within.
L66-69: The sentence on CHANS theories and models applies to both regional and global scale. I suggest moving this above, prior to the delineation between global vs. regional CHANS models for a better flow.
L70-71: I suggest rephrasing it as “growing regional environmental and societal pressures”
L75-81: The sentence mixes model classes (e.g. IAMs, iESMs) and specific methodologies (e.g., system dynamics), which makes it slightly misleading – since the SD models can also be considered IAMs. I recommend revising this sentence to clearly distinguish model classes from methodologies and examples.

For instance: “Numerous integrated modelling approaches have been developed to simulate human–natural interactions at the global scale. These include system dynamics-based integrated assessment models (IAMs), such as ANEMI (Breach & Simonovic, 2021), FeliX (Rydzak et al., 2013; Ye et al., 2024), and FRIDA (Rajah et al., 2025); process-based and optimization-based IAMs (Vaidyanathan, 2021), which dominate traditional policy scenario analysis; integrated Earth system models (iESMs) (Jain et al., 2022), which couple socio-economic and biophysical components within Earth system frameworks; and Earth system models with synchronously coupled human components, such as E3SM-GCAM (Di Vittorio et al., 2025).”
2. Model description
L162: The citation provided for the sentence is a model description paper of a specific SD model. Given that the sentence is on SD method more generally, I would suggest references to more foundational works (e.g., Forrester, 1968; Richardson, 2011)
Forrester, J. W.: Principles of systems, Pegasus Communications, Inc., Waltham, MA, 1968.

Richardson, G. P.: Reflections on the foundations of system dynamics: Foundations of System Dynamics, Syst. Dyn. Rev., 27, 219–243, https://doi.org/10.1002/sdr.462, 2011.

2.2.1 Population
In Figure 3, the only direct cross-sectoral feedback is between the Economy and Population, through life expectancy. Total fertility is kept exogenous to the model, yet in most SD models fertility rate tends to be endogenized with a function from GDP per capita – assumption being that fertility rate is negatively correlated with higher literacy levels and access to contraceptives etc. What is the justification for excluding this relationship in the model?
Migration rate is also depicted as an exogenous input to the model. In the model, this appears to be an endogenous variable as a function of GDP difference between national and YSB, which scales the exogenous parameter, migration rate other effects. Also, the migration rate other effects is a time-dependent parameter (lookup table) that increases migration slightly between 2000 and 2005, sharp rise by 2010, and then a decline by 2015. Thereafter, this parameter is held constant. How was this determined? How does this affect future projections of migration? The modelling choices and assumptions here should be clearly documented in the model description.
L237-238: Births, deaths, and migration are “determined by a series of social and economic factors” is very vague. What and how are these factors conceptualised and represented in the model? Given that life expectancy is endogenous and part of the key inter-sectoral feedback, I would expect more descriptions of how life expectancy is modelled.
2.2.2. Economy
L267-268: Capital elasticities are obtained from T21-China model. Given that they are time-varying, it would useful for readers to understand the assumptions in that model for deriving the shape. The lookup table stops at 2020. Is the parameter value then held constant for future projections? If so, is that a valid assumption for projecting future production?
L264: TFP is an important yet endogenous variable in the model, which should also be documented as an equation in the manuscript given that it includes several effects (infrastructure, education, health). The elasticities for these effects, unlike labour and capital, are constant parameters in the model. How were these elasticities determined? Education (avg years of schooling) is derived from census yearbooks for the historical period, but what is the strategy for future projections? Infrastructure is also an exogenous variable, however, the data and its source does not seem to be documented in the manuscript or the supplement.
L270: Crop and livestock prices are exogenous inputs from China statistical yearbook up to 2020. Are prices then held constant for the future period?
Within the Economy sector of the model, there is internal feedback between GDP and the Capital stocks through private and government savings as a fraction of GDP. I recommend including this link in Figure 3 with a brief description in the manuscript. Currently, in the supplement, the formulations surrounding private and governmental investments are not fully described. There are several lookup tables here (e.g., investment share tables, saving share of income table), which would benefit from some elaboration in the supplement at the very least.
2.2.3. Energy
Most of the endogenous variables in the energy sector are formulated as linear functions of GDP per capita. Though this was simply stated as such in the manuscript and supplement, there is no justification as to why linear relationships were chosen. More concerning, is the use of IF THEN ELSE functions to specify two different sets of constants and slopes before and after 2010 within a few of these relationships. These assumptions are not documented either.
2.2.4. Food
L293-297: Per capita meat demand is derived from a nonlinear regression of GDP per capita and historical per capita production. In the model, there is an IF THEN ELSE function which specifies a different relationship for time 1981. Why is that so?
L303-305: Crop yields are influenced by precipitation, temperature and CO2 concentration from the Climate sector with a reference to the supplement. The supplement, however, does not describe the influence of CO2 concentrations. There is also a mismatch between the model and the supplement creating confusions. In the model, there are two different variables: ‘YRB food production’ and ‘province crop production’ determined by ‘province yield’ and ‘province yield future’ respectively. Precipitation and temperature influences only ‘province yield’ whereas CO2 concentration influences ‘province yield future’. The supplement does not sufficiently describe this formulations for me to understand the conceptualization here. Please clarify.
Food demand formulations (e.g., S34 and S36) rely on exogenous inputs for daily requirements and dietary proportions from the historical period. How is future demand projected beyond the historical period?
2.2.8. Land
The transfer matrix stock is used to calculate the ‘final historical transfer matrix’ in the model. There is a separate ‘future transfer matrix’ variable with multiple subscripts having the value 0 while others simply take the final historical transfer matrix value. This warrants elaboration in the supplement, since it’s not clear how land use changes are determined beyond data replication for the historical period.
3 Model validation and application
I appreciate that the authors have provided the scenario specification in the supplement S5 along with Table 2 that lists some of the data sources and assumptions for future baseline of key variables. I understand that there are over a 100 exogenous variables in the model. However, it is quite disorienting that there is an excel file for time-varying data inputs and several other time-varying inputs hidden within lookup tables (i.e., data points that are interpolated). And, as alluded to in my above comments in the model description, it is unclear how many of these time-varying inputs are formulated for the future baseline (most stop at historical period). I strongly recommend that an additional supplement file is included for ALL exogenous time-varying inputs, including those hidden in table functions. For clarity, the file should list all the historical data points used to drive model behaviour along with a column for data source, followed by all the future baseline data points with another column for data source/assumptions.
Table S2 lists quite a number of exogenous inputs. I would like for a distinction between exogenous variables (time-varying) and parameters (constants). While the above file should contain all the historical and future data points for exogenous variables, another file or table should be provided for all model parameters along with the estimated value and data source (literature or model calibration). Having these files would considerably improve the transparency of the model – especially for readers unfamiliar with Vensim software.
The main text for description of simulation results should explicitly refer to figures to guide the reader.
The authors have emphasized high correlations for historical fits. However I am slightly skeptical since the current approach risks overfitting: several relationships are fitted to historical to the historical period being validated and many exogenous time series drive the dynamics of the model. To build confidence in the behavioural validity, the authors should use out-of-sample validation: model fit to a subset of historical time series (e.g. 1981 - 2005) and use the remains sample points to validate the model outputs.
Sensitivity analyses to quantify parametric uncertainty is also a validation standard in system dynamics. No such analysis appears in the manuscript or supplement.
L436: What are some plausible explanations for Shanxi exhibiting lower accuracy?
L504: What is the red line? I do not see a red line in Figure 14(a) land area. Why does cropland area “need” to stay above the red line?
4 Discussion
L635-638: Although the model is presented as a CHANS framework, the Climate sector functions as an exogenous driver rather than a dynamically coupled component. The absence of climate feedbacks limits the scope of coupling. The model cannot represent second-order feedback dynamics such as how afforestation or carbon emissions might alter local hydrology or climate, critical for CHANS theory. Therefore, the authors should explicitly acknowledge this limitation and avoid overstating its scope, especially in L597-597 where they state the “model’s comprehensive coupling broadens its scope of application.” They should also discuss implications of climate feedback omissions for scenario analysis.
The authors have justified using CMIP6 data instead of observed records to ensure temporal consistency, but they do not discuss the potential bias introduced by replacing observed data. In the model, it appears that the authors have considered SSP1.26, 2.45 and 5.85. Showing and discussing the sensitivity of results to alternative climate pathways could help demonstrate robustness.
Citation: https://doi.org/10.5194/egusphere-2025-5597-RC1
- AC3: 'Reply on RC1', Shan Sang, 09 Feb 2026
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-5597/egusphere-2025-5597-AC3-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2025-5597-AC3
RC2:
'Comment on egusphere-2025-5597', Anonymous Referee #2, 20 Dec 2025

Sang et al develops a regional system dynamics model to represent coupled human natural systems in the Yellow River Basin, integrating population, economy, energy, food, water, land, carbon, and climate processes to explore historical dynamics and future baseline projections. Overall it looks good, with extensive explaination of the model structure and some illustrative results. However, my main concern is that the human dimension, especially the energy sector, is relatively weakly specified to support the strong claims made about cross sector dynamics in this coupled framework. Because energy use is tightly linked with economic activity, water demand, food production, and carbon emissions, a simplified or poorly validated energy representation risks propagating bias across the entire system. So it’s unclear to me whether the projected interactions among energy, water, food, and carbon are internally consistent or robust, especially in the future projections that are central to the paper’s conclusions.
A few specific comments:
First, A key issue is the system boundary of the energy sector. Although an energy module is included, the model appears to explicitly represent only electricity, while other major energy uses, particularly in industry, are treated in a highly aggregated manner. This is problematic because non electric energy use and structural change in industry are major drivers of both emissions and water demand. In addition, important indirect effects are not clearly represented. For example, electrification trends such as electric vehicle deployment would increase electricity demand while reducing oil consumption, yet such substitution dynamics are not discussed. Similarly, changes in industrial energy structure could feed back to economic output and water use, but it is unclear how these interactions are captured. Given the stated goal of representing coupled human natural systems, it is important for the authors to clarify how these cross sector linkages are treated and how sensitive the results are to the simplified energy boundary.
Second, the description of the energy sector in Section 2.2.3 is unclear. The statement that “coal, oil, and gas consumption are derived from linear relationships between historical sectoral GDP” and corresponding consumption is ambiguous. It is not clear whether this refers to total final energy consumption or only to fuels used for electricity generation. More importantly, a linear extrapolation based on historical GDP trends is unlikely to be appropriate for long term projections under ongoing structural change, efficiency improvements, and decarbonization. The manuscript also does not clearly explain how total energy demand evolves over time or how energy intensity changes are represented. Besides, electricity generation shares are imposed exogenously based on Li et al. 2024, which makes the future pathway highly deterministic. The underlying policy assumptions and narratives are not clearly described. So Figure 16 would benefit from validation against recent historical data before 2025 and comparison with ranges reported in the literature. In particular, the emerging nuclear capacity around 2030 requires stronger justification in terms of feasibility and policy assumptions in China’s context.
Third, the paper emphasizes the regional and spatially explicit nature of the CHANS SD YRB model, but this advantage is not fully demonstrated in the results. Although Figure 1 highlights spatial resolution across provinces and sub basins, many subsequent figures are conceptual diagrams rather than quantitative outputs. Model validation for energy variables ends in 2020, and maybe there are more recent to validate, such as 2021-2024? It would be important to show how near term projections after 2020 compare with observations, particularly for electricity generation and fuel consumption. Also, author may consider presenting and validating provincial level energy results beyond more aggregated models.
Lastly, there are several clarity issues that should be addressed. The meaning of “fire” in the energy sector in Figure 2 is unclear. In addition, the term “per capital” appears repeatedly in the text and figures and should be corrected to “per capita” throughout the manuscript.

Citation: https://doi.org/10.5194/egusphere-2025-5597-RC2
- AC2: 'Reply on RC2', Shan Sang, 09 Feb 2026
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-5597/egusphere-2025-5597-AC2-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2025-5597-AC2
RC3:
'Comment on egusphere-2025-5597', Anonymous Referee #3, 23 Dec 2025

This manuscript develops a regional coupled human–natural systems model for the Yellow River basin (CHANS SD-YRB) using a System Dynamics framework that integrates 10 interacting sectors at annual, provincial, and sub-basin scales while explicitly conserving hydrological connectivity among sub-basins. The model reproduces historical dynamics with strong reported agreement and is then used for baseline projections under continued policies and SSP245. The key novelty is the basin-specific, multi-sector CHANS platform that couples socio-economic dynamics with water, sediment, land, carbon, and climate processes in a single connected sub-basin framework, enabling diagnosis of cross-sector feedbacks and trade-offs relevant for long-term sustainability planning. However, some revisions are required before the manuscript can be considered for publication.

Major:
Could the authors clarify the key advantages of this study (and the CHANS SD-YRB model) compared with Sang et al. (2025)?
Figure 4: The model appears not to include a price response to market changes. Under scenarios with carbon pricing, this assumption may miss some impacts of the carbon price. Could the authors discuss this limitation?
Lines 284: Electricity generation shares are treated as exogenous and sourced from the yearbook. Do these shares account for future changes? If not, given that the SSP2 baseline is above 6.0 W/m² while this study uses SSP245 (which still implies future climate policy), would assuming fixed shares remain reasonable?
Minor:
Line 92: Could the authors please double-check the citation format here?
Line 178: Could the authors clarify how these variables are used as proxies for disaggregation?
Equations (5) and (6): Could the authors explain why the functions are specified in this form?
Lines 467: Could the authors elaborate on why SSP245 aligns more closely with the climate trends in the YRB?

Citation: https://doi.org/10.5194/egusphere-2025-5597-RC3
- AC1: 'Reply on RC3', Shan Sang, 09 Feb 2026
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-5597/egusphere-2025-5597-AC1-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2025-5597-AC1

Interactive discussion

Status: closed

RC1:
'Comment on egusphere-2025-5597', Anonymous Referee #1, 18 Dec 2025
This paper introduces CHANS-SD-YRB v1.0, a system dynamics CHANS model of the Yellow River Basin and operates at provincial (human processes) and sub-basin (natural processes) scales. The manuscript reads well and a supplement with more elaborate model descriptions. By coupling the human and natural processes within a regional CHANS framework, this model description paper lays the foundations for policy-oriented modelling for the Yellow River Basin. Overall, the model is an ambitious synthesis, but several methodological choices and transparency gaps need some tightening. Find my specific comments and suggestions below.
1. Introduction
The first two paragraphs of the introduction goes back and forth between regional and global CHANS research and modelling, which breaks the flow and is a little disorientating. It would be clearer if CHANS research and modelling is first introduced more generally. After introducing the main models, I suggest that the authors identify the overrepresentation of global scale CHANS models. And then bring in the distinction and need for more regional scale modelling, which situates this work within.
L66-69: The sentence on CHANS theories and models applies to both regional and global scale. I suggest moving this above, prior to the delineation between global vs. regional CHANS models for a better flow.
L70-71: I suggest rephrasing it as “growing regional environmental and societal pressures”
L75-81: The sentence mixes model classes (e.g. IAMs, iESMs) and specific methodologies (e.g., system dynamics), which makes it slightly misleading – since the SD models can also be considered IAMs. I recommend revising this sentence to clearly distinguish model classes from methodologies and examples.

For instance: “Numerous integrated modelling approaches have been developed to simulate human–natural interactions at the global scale. These include system dynamics-based integrated assessment models (IAMs), such as ANEMI (Breach & Simonovic, 2021), FeliX (Rydzak et al., 2013; Ye et al., 2024), and FRIDA (Rajah et al., 2025); process-based and optimization-based IAMs (Vaidyanathan, 2021), which dominate traditional policy scenario analysis; integrated Earth system models (iESMs) (Jain et al., 2022), which couple socio-economic and biophysical components within Earth system frameworks; and Earth system models with synchronously coupled human components, such as E3SM-GCAM (Di Vittorio et al., 2025).”
2. Model description
L162: The citation provided for the sentence is a model description paper of a specific SD model. Given that the sentence is on SD method more generally, I would suggest references to more foundational works (e.g., Forrester, 1968; Richardson, 2011)
Forrester, J. W.: Principles of systems, Pegasus Communications, Inc., Waltham, MA, 1968.

Richardson, G. P.: Reflections on the foundations of system dynamics: Foundations of System Dynamics, Syst. Dyn. Rev., 27, 219–243, https://doi.org/10.1002/sdr.462, 2011.

2.2.1 Population
In Figure 3, the only direct cross-sectoral feedback is between the Economy and Population, through life expectancy. Total fertility is kept exogenous to the model, yet in most SD models fertility rate tends to be endogenized with a function from GDP per capita – assumption being that fertility rate is negatively correlated with higher literacy levels and access to contraceptives etc. What is the justification for excluding this relationship in the model?
Migration rate is also depicted as an exogenous input to the model. In the model, this appears to be an endogenous variable as a function of GDP difference between national and YSB, which scales the exogenous parameter, migration rate other effects. Also, the migration rate other effects is a time-dependent parameter (lookup table) that increases migration slightly between 2000 and 2005, sharp rise by 2010, and then a decline by 2015. Thereafter, this parameter is held constant. How was this determined? How does this affect future projections of migration? The modelling choices and assumptions here should be clearly documented in the model description.
L237-238: Births, deaths, and migration are “determined by a series of social and economic factors” is very vague. What and how are these factors conceptualised and represented in the model? Given that life expectancy is endogenous and part of the key inter-sectoral feedback, I would expect more descriptions of how life expectancy is modelled.
2.2.2. Economy
L267-268: Capital elasticities are obtained from T21-China model. Given that they are time-varying, it would useful for readers to understand the assumptions in that model for deriving the shape. The lookup table stops at 2020. Is the parameter value then held constant for future projections? If so, is that a valid assumption for projecting future production?
L264: TFP is an important yet endogenous variable in the model, which should also be documented as an equation in the manuscript given that it includes several effects (infrastructure, education, health). The elasticities for these effects, unlike labour and capital, are constant parameters in the model. How were these elasticities determined? Education (avg years of schooling) is derived from census yearbooks for the historical period, but what is the strategy for future projections? Infrastructure is also an exogenous variable, however, the data and its source does not seem to be documented in the manuscript or the supplement.
L270: Crop and livestock prices are exogenous inputs from China statistical yearbook up to 2020. Are prices then held constant for the future period?
Within the Economy sector of the model, there is internal feedback between GDP and the Capital stocks through private and government savings as a fraction of GDP. I recommend including this link in Figure 3 with a brief description in the manuscript. Currently, in the supplement, the formulations surrounding private and governmental investments are not fully described. There are several lookup tables here (e.g., investment share tables, saving share of income table), which would benefit from some elaboration in the supplement at the very least.
2.2.3. Energy
Most of the endogenous variables in the energy sector are formulated as linear functions of GDP per capita. Though this was simply stated as such in the manuscript and supplement, there is no justification as to why linear relationships were chosen. More concerning, is the use of IF THEN ELSE functions to specify two different sets of constants and slopes before and after 2010 within a few of these relationships. These assumptions are not documented either.
2.2.4. Food
L293-297: Per capita meat demand is derived from a nonlinear regression of GDP per capita and historical per capita production. In the model, there is an IF THEN ELSE function which specifies a different relationship for time 1981. Why is that so?
L303-305: Crop yields are influenced by precipitation, temperature and CO2 concentration from the Climate sector with a reference to the supplement. The supplement, however, does not describe the influence of CO2 concentrations. There is also a mismatch between the model and the supplement creating confusions. In the model, there are two different variables: ‘YRB food production’ and ‘province crop production’ determined by ‘province yield’ and ‘province yield future’ respectively. Precipitation and temperature influences only ‘province yield’ whereas CO2 concentration influences ‘province yield future’. The supplement does not sufficiently describe this formulations for me to understand the conceptualization here. Please clarify.
Food demand formulations (e.g., S34 and S36) rely on exogenous inputs for daily requirements and dietary proportions from the historical period. How is future demand projected beyond the historical period?
2.2.8. Land
The transfer matrix stock is used to calculate the ‘final historical transfer matrix’ in the model. There is a separate ‘future transfer matrix’ variable with multiple subscripts having the value 0 while others simply take the final historical transfer matrix value. This warrants elaboration in the supplement, since it’s not clear how land use changes are determined beyond data replication for the historical period.
3 Model validation and application
I appreciate that the authors have provided the scenario specification in the supplement S5 along with Table 2 that lists some of the data sources and assumptions for future baseline of key variables. I understand that there are over a 100 exogenous variables in the model. However, it is quite disorienting that there is an excel file for time-varying data inputs and several other time-varying inputs hidden within lookup tables (i.e., data points that are interpolated). And, as alluded to in my above comments in the model description, it is unclear how many of these time-varying inputs are formulated for the future baseline (most stop at historical period). I strongly recommend that an additional supplement file is included for ALL exogenous time-varying inputs, including those hidden in table functions. For clarity, the file should list all the historical data points used to drive model behaviour along with a column for data source, followed by all the future baseline data points with another column for data source/assumptions.
Table S2 lists quite a number of exogenous inputs. I would like for a distinction between exogenous variables (time-varying) and parameters (constants). While the above file should contain all the historical and future data points for exogenous variables, another file or table should be provided for all model parameters along with the estimated value and data source (literature or model calibration). Having these files would considerably improve the transparency of the model – especially for readers unfamiliar with Vensim software.
The main text for description of simulation results should explicitly refer to figures to guide the reader.
The authors have emphasized high correlations for historical fits. However I am slightly skeptical since the current approach risks overfitting: several relationships are fitted to historical to the historical period being validated and many exogenous time series drive the dynamics of the model. To build confidence in the behavioural validity, the authors should use out-of-sample validation: model fit to a subset of historical time series (e.g. 1981 - 2005) and use the remains sample points to validate the model outputs.
Sensitivity analyses to quantify parametric uncertainty is also a validation standard in system dynamics. No such analysis appears in the manuscript or supplement.
L436: What are some plausible explanations for Shanxi exhibiting lower accuracy?
L504: What is the red line? I do not see a red line in Figure 14(a) land area. Why does cropland area “need” to stay above the red line?
4 Discussion
L635-638: Although the model is presented as a CHANS framework, the Climate sector functions as an exogenous driver rather than a dynamically coupled component. The absence of climate feedbacks limits the scope of coupling. The model cannot represent second-order feedback dynamics such as how afforestation or carbon emissions might alter local hydrology or climate, critical for CHANS theory. Therefore, the authors should explicitly acknowledge this limitation and avoid overstating its scope, especially in L597-597 where they state the “model’s comprehensive coupling broadens its scope of application.” They should also discuss implications of climate feedback omissions for scenario analysis.
The authors have justified using CMIP6 data instead of observed records to ensure temporal consistency, but they do not discuss the potential bias introduced by replacing observed data. In the model, it appears that the authors have considered SSP1.26, 2.45 and 5.85. Showing and discussing the sensitivity of results to alternative climate pathways could help demonstrate robustness.
Citation: https://doi.org/10.5194/egusphere-2025-5597-RC1
- AC3: 'Reply on RC1', Shan Sang, 09 Feb 2026
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-5597/egusphere-2025-5597-AC3-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2025-5597-AC3
RC2:
'Comment on egusphere-2025-5597', Anonymous Referee #2, 20 Dec 2025

Sang et al develops a regional system dynamics model to represent coupled human natural systems in the Yellow River Basin, integrating population, economy, energy, food, water, land, carbon, and climate processes to explore historical dynamics and future baseline projections. Overall it looks good, with extensive explaination of the model structure and some illustrative results. However, my main concern is that the human dimension, especially the energy sector, is relatively weakly specified to support the strong claims made about cross sector dynamics in this coupled framework. Because energy use is tightly linked with economic activity, water demand, food production, and carbon emissions, a simplified or poorly validated energy representation risks propagating bias across the entire system. So it’s unclear to me whether the projected interactions among energy, water, food, and carbon are internally consistent or robust, especially in the future projections that are central to the paper’s conclusions.
A few specific comments:
First, A key issue is the system boundary of the energy sector. Although an energy module is included, the model appears to explicitly represent only electricity, while other major energy uses, particularly in industry, are treated in a highly aggregated manner. This is problematic because non electric energy use and structural change in industry are major drivers of both emissions and water demand. In addition, important indirect effects are not clearly represented. For example, electrification trends such as electric vehicle deployment would increase electricity demand while reducing oil consumption, yet such substitution dynamics are not discussed. Similarly, changes in industrial energy structure could feed back to economic output and water use, but it is unclear how these interactions are captured. Given the stated goal of representing coupled human natural systems, it is important for the authors to clarify how these cross sector linkages are treated and how sensitive the results are to the simplified energy boundary.
Second, the description of the energy sector in Section 2.2.3 is unclear. The statement that “coal, oil, and gas consumption are derived from linear relationships between historical sectoral GDP” and corresponding consumption is ambiguous. It is not clear whether this refers to total final energy consumption or only to fuels used for electricity generation. More importantly, a linear extrapolation based on historical GDP trends is unlikely to be appropriate for long term projections under ongoing structural change, efficiency improvements, and decarbonization. The manuscript also does not clearly explain how total energy demand evolves over time or how energy intensity changes are represented. Besides, electricity generation shares are imposed exogenously based on Li et al. 2024, which makes the future pathway highly deterministic. The underlying policy assumptions and narratives are not clearly described. So Figure 16 would benefit from validation against recent historical data before 2025 and comparison with ranges reported in the literature. In particular, the emerging nuclear capacity around 2030 requires stronger justification in terms of feasibility and policy assumptions in China’s context.
Third, the paper emphasizes the regional and spatially explicit nature of the CHANS SD YRB model, but this advantage is not fully demonstrated in the results. Although Figure 1 highlights spatial resolution across provinces and sub basins, many subsequent figures are conceptual diagrams rather than quantitative outputs. Model validation for energy variables ends in 2020, and maybe there are more recent to validate, such as 2021-2024? It would be important to show how near term projections after 2020 compare with observations, particularly for electricity generation and fuel consumption. Also, author may consider presenting and validating provincial level energy results beyond more aggregated models.
Lastly, there are several clarity issues that should be addressed. The meaning of “fire” in the energy sector in Figure 2 is unclear. In addition, the term “per capital” appears repeatedly in the text and figures and should be corrected to “per capita” throughout the manuscript.

Citation: https://doi.org/10.5194/egusphere-2025-5597-RC2
- AC2: 'Reply on RC2', Shan Sang, 09 Feb 2026
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-5597/egusphere-2025-5597-AC2-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2025-5597-AC2
RC3:
'Comment on egusphere-2025-5597', Anonymous Referee #3, 23 Dec 2025

This manuscript develops a regional coupled human–natural systems model for the Yellow River basin (CHANS SD-YRB) using a System Dynamics framework that integrates 10 interacting sectors at annual, provincial, and sub-basin scales while explicitly conserving hydrological connectivity among sub-basins. The model reproduces historical dynamics with strong reported agreement and is then used for baseline projections under continued policies and SSP245. The key novelty is the basin-specific, multi-sector CHANS platform that couples socio-economic dynamics with water, sediment, land, carbon, and climate processes in a single connected sub-basin framework, enabling diagnosis of cross-sector feedbacks and trade-offs relevant for long-term sustainability planning. However, some revisions are required before the manuscript can be considered for publication.

Major:
Could the authors clarify the key advantages of this study (and the CHANS SD-YRB model) compared with Sang et al. (2025)?
Figure 4: The model appears not to include a price response to market changes. Under scenarios with carbon pricing, this assumption may miss some impacts of the carbon price. Could the authors discuss this limitation?
Lines 284: Electricity generation shares are treated as exogenous and sourced from the yearbook. Do these shares account for future changes? If not, given that the SSP2 baseline is above 6.0 W/m² while this study uses SSP245 (which still implies future climate policy), would assuming fixed shares remain reasonable?
Minor:
Line 92: Could the authors please double-check the citation format here?
Line 178: Could the authors clarify how these variables are used as proxies for disaggregation?
Equations (5) and (6): Could the authors explain why the functions are specified in this form?
Lines 467: Could the authors elaborate on why SSP245 aligns more closely with the climate trends in the YRB?

Citation: https://doi.org/10.5194/egusphere-2025-5597-RC3
- AC1: 'Reply on RC3', Shan Sang, 09 Feb 2026
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-5597/egusphere-2025-5597-AC1-supplement.pdf
  
  Citation: https://doi.org/10.5194/egusphere-2025-5597-AC1

Peer review completion

AR – Author's response | RR – Referee report | ED – Editor decision | EF – Editorial file upload

AR by Shan Sang on behalf of the Authors (09 Feb 2026) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (10 Feb 2026) by Dalei Hao

RR by Anonymous Referee #2 (10 Feb 2026)

RR by Anonymous Referee #1 (16 Feb 2026)

Suggestions for revision or reasons for rejection

Thank you for the revised manuscript and sufficiently addressing my comments. The manuscript is now much stronger and makes the model description more transparent. I have a few remaining minor suggestions for the authors to consider:

In the Supporting Information Section S2.3, I suggest that the revised text where you have justified piecewise linear regression, you elaborate what you mean by socioeconomic shifts:
e.g. “Notably, these parameters were estimated using a piecewise linear regression with distinct coefficients for the pre- and post-2010 periods to capture the structural changes in China’s socio-economic development and energy policies starting around 2010 (e.g., improved energy efficiency and industrial restructuring).”

On circularity in per capita meat demand, I would like to point out that the circular dependency is due to the lack of a stock variable. Feedback loops in SD are closed by at least one stock, otherwise we end up with unsolvable simultaneous equations. In this case, I would have made ‘per capita meat demand’ a smth (where you define the initial value) that adjust to changes in GDP per capita with a short adjustment time. This would’ve solved the issue without resorting to the IF THEN ELSE function.

On out of sample test: Perhaps including sample points beyond 2020 in the historical simulation (as was done in Figure 1 in the response letter to reviewer 2) would really benefit building confidence in the calibration of the model.

L521-524: The sentence still guides the reader to assume there is a red line in the graph – it’s not clear that red line is a specific policy terminology. For clarity, I suggest rephrasing the sentence as such:
“ Under the future baseline scenario, land use patterns remain relatively stable based on historical trends (Fig. 14(a)), as no new land use policies are introduced and cropland area in each province has to stay above a mandatory minimum threshold for food security (China’s Cropland Red Line policy).”

L646-650: The authors could consider the recent article from Wells et al. published in GMD (https://doi.org/10.5194/gmd-19-1229-2026) on the representation of climate impacts as part of human-climate feedback loops, consistent with the system dynamics framework, that can be adapted to future versions of the model.

On CMIP data: Thank you for your response and clarification of the bias correction. In the response you mentioned that “Exploring the sensitivity to other pathways (SSP1-2.6 and SSP5-8.5) is a valuable direction for future scenario comparisons.“ The authors should also consider including this notion in the discussion section as part of future research.

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RR by Anonymous Referee #3 (23 Feb 2026)

Suggestions for revision or reasons for rejection

I thank the authors for their careful and thorough responses to the previous review comments, as well as for the substantial revisions made to the manuscript. The paper has improved considerably as a result. I have two follow-up points that would benefit from further clarification before the manuscript can be accepted for publication

Re: “Could the authors clarify the key advantages of this study (and the CHANS SD-YRB model) relative to Sang et al. (2025)?”
I appreciate the authors’ explanation that Sang et al. (2025) proposed the overall framework and that the present study implements it. However, in Sang et al. (2025), Section 3 (“Implementation of the modeling framework with a system dynamics approach”, https://www.sciencedirect.com/science/article/pii/S2666683925000331#sec0003:~:text=the%20external%20factors.-,3.%20Implementation%20of%20modeling%20framework%20with%20system%20dynamics%20approach,-Inspired%20by%20existing), the authors state that they implemented the framework shown in their Figure 3 and present historical and future simulations using a YRB system-dynamics model spanning 1981–2100.
Given this apparent overlap in both framework implementation and temporal coverage, I encourage the authors to more explicitly articulate the key methodological or analytical or … advances of the present study relative to Sang et al. (2025), and to clearly state what new insights this study provides beyond that earlier work.

Re: “ Line 467: Could the authors elaborate on why SSP2-4.5 aligns more closely with the observed and projected climate trends in the YRB?”
I appreciate the detailed explanation for selecting SSP2-4.5. However, I would like to raise a related question regarding its consistency with the stated policy framing. The response notes that “this trajectory aligns closely with China’s current national strategy, specifically the goal to peak carbon emissions before 2030 and achieve deep reductions thereafter.”
This description seems more consistent with a lower forcing pathway (e.g., RCP2.6), which features earlier emissions peaking and sustained, deep post-peak reductions. In contrast, RCP4.5 is generally characterized as a stabilization pathway, with later peaking and more gradual declines, rather than deep reductions.
While I do not question the rationale for adopting SSP2 as the socioeconomic pathway, I encourage the authors to clarify why RCP4.5 is preferred over a lower forcing level (e.g., RCP2.6, or a slightly higher level than that, RCP3.7) that may better align with the stated goals of early peaking and deep emissions reductions. For example, are there data limitations, scenario availability issues, or region-specific considerations that motivate this choice? Clarifying this point would strengthen the internal consistency of the scenario justification.

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ED: Publish subject to minor revisions (review by editor) (23 Feb 2026) by Dalei Hao

AR by Shan Sang on behalf of the Authors (01 Mar 2026) Author's response Author's tracked changes Manuscript

ED: Publish as is (02 Mar 2026) by Dalei Hao

AR by Shan Sang on behalf of the Authors (04 Mar 2026)

Journal article(s) based on this preprint

11 Mar 2026

CHANS-SD-YRB V1.0: a system dynamics model of the coupled human-natural systems for the Yellow River Basin

Shan Sang, Yan Li, Shuang Zong, Lu Yu, Shuai Wang, Yanxu Liu, Xutong Wu, Shuang Song, Wenwu Zhao, Xuhui Wang, and Bojie Fu

Geosci. Model Dev., 19, 2039–2058, https://doi.org/10.5194/gmd-19-2039-2026,https://doi.org/10.5194/gmd-19-2039-2026, 2026

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Shan Sang, Yan Li, Shuang Zong, Lu Yu, Shuai Wang, Yanxu Liu, Xutong Wu, Shuang Song, Wenwu Zhao, Xuhui Wang, and Bojie Fu

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https://doi.org/10.5194/egusphere-2025-5597-supplement

Shan Sang, Yan Li, Shuang Zong, Lu Yu, Shuai Wang, Yanxu Liu, Xutong Wu, Shuang Song, Wenwu Zhao, Xuhui Wang, and Bojie Fu

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Regional coupled human–natural systems models are essential for regional sustainability. We developed a new model, CHANS-SD-YRB, using System Dynamics for the Yellow River Basin in China, which faces severe human-water conflicts. The model links 10 components, including Population, Economy, Energy, Food, Water, Sediment, Land, Carbon, and Climate to simulate basin’s key human-natural interactions. The model is applicable for sustainable development through scenario analyses and predictions.


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