the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Assessing Future Hydrological Impacts of Climate Change on High-Mountain Central Asia: Insights from a Stochastic Soil Moisture Water Balance Model
Abstract. We use a new set of data available to compute 21st century climate impacts on the hydrology of 221 catchments in high-mountain Central Asia. For each of these subcatchments, a parsimonious steady state stochastic soil moisture water balance model was set up and the partitioning of available water from precipitation into runoff and evaporation computed for different climate futures using the Budyko framework. Climate change sensitivity coefficients are analytically derived for the first time using the total differential method. Relative changes in discharge for three future periods 2011–2040, 2041–2070, and 2071–2100 were computed in relation to the baseline period from 1979–2011. For the baseline observation period, climate data from a global high-resolution climatology data set (CHELSA V21) were used to extract mean daily subcatchment-specific temperature and precipitation values. Data from the coupled model intercomparison project phase 6 (CMIP6) were used to compute catchment mean future climate data using 4 GCM models with 4 scenario runs each. CMIP6 data were bias corrected with CHELSA V21 observation data. For the spatial distribution of soil parameters, different global products were utilized. The robustness of the soil water balance model results was assessed using a comprehensive sensitivity analysis in relation to variations of these soil parameters over typically observed ranges for each subcatchment.
The analysis of climate change suggests increasing precipitation over the three periods (+4.44 %, +5.89 %, and +8.51 % relative increases in median total precipitation averaged over subcatchment and scenarios). Median values of temperatures changes between periods relative to the baseline are +1.33 °C, +2.44 °C, and +3.55 °C. Results of the hydrological soil water balance model runs suggest a median increase of discharge of +4.71 %, +7.44 % and +10.87 % for the corresponding periods. This is a strong indication of a wetter and hotter future in Central Asia, relative to today’s hydroclimate. Modelling results suggest that decreasing contributions from glacier melt over the course of the 21st century will be offset by increases in discharge consistently throughout the region, despite increasing potential evapotranspiration. Increases in relative discharge will be most pronounced in the Afghan Murghab-Harirud basin and in the Amu Darya. Changes in precipitation characteristics in terms of frequency and event depth also indicate possible impacts on hydrological extremes which remains a heavily under researched topic in Central Asia.
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RC1: 'Comment on egusphere-2023-520', Anonymous Referee #1, 15 Jun 2023
egusphere-2023-520 “Assessing Future Hydrological Impacts of Climate Change on High-Mountain Central Asia: Insights from a Stochastic Soil Moisture Water Balance Model” T.Siegfried, AU.Mujahid, BS.Marti, P.Molnar, DN.Karger and A.Yakovlev
This article describes the implementation of an analytic steady-state water balance on 221 sub-catchments in central Asia. It is very well put together, reads easily from start to finish, has extensive useful figures showing the results, a careful splitting of the maths into “just the important results” in the main text and all the “tedious derivation” in the Appendix, and some thoughtful caveats in the Discussion.
It is nit-picking to point out on line 169 that you only need dx(t)/dt=0 in Eq(2), as a value of w0=0 would indicate an impermeable land surface where P=Q, or a volume with no storage such that the evaporative process has no opportunity to occur. Figure 8 does show very low values of w0, and linear fits crossing the x-axis, but there is no indication in the text that w0 ever reduced to zero (or negative); Zr as a component of w0 was positively correlated to temperature which always increased in the future periods used.
Given that the solution to the water balance is a single line equation that is solved explicitly, why do the authors go down the path of using differentials to estimate changes in Q? Such an approach is usually taken when the computation is much more complex or requires some iterative solution, so for small enough perturbations the differential is a good estimate of change in output. You already need to calculate new values of E and P and all their components for the three future periods, so the new Q value could be calculated directly and compared to the baseline period. The only indication of the values (or magnitude) of the sensitivity coefficients is Figure 11 which visually indicates that, in this environment, change in Q is a simple tug-of-war between increasing precipitation event depth (alpha) and increasing active soil depth (Zr), with both Ep and Lambda of little importance.
The caption for Figure 15 is wrong. According to the map key, these are “relative changes in” precipitation frequency and depth.
Figures B2-4 show the same parameter, Zr, but with three different ranges on the same continuous rainbow colour scale. They cannot be easily visually compared and require a non-linear bin-style scale that is the same for all three. Continuous colour ramps are most useful in remote sensing applications particularly, where the individual items being coloured are relatively small, the same shape and size, and there are very many of them (the false shaded DEM under the figures is a perfect example). For only 221 irregularly shaped, sized and arranged polygons this is wasted. I would also suggest moving Figure B5 to follow B1 as they are the saturation and wilting points of the soil, or potentially just show a single map with their difference which is the input (s1-sw) to w0.
Perhaps the most important follow-on, and unfortunately it may no longer be a simple analytic solution, is to consider inter-annual variation (see eg. Zhang et al, 2008 using a Budyko framework). It may be possible to remain analytic on an annual basis with storage change zero over a year, but storage carryover between quarterly or monthly sub-annual intervals in a matrix equation. From the human perspective, it is not only important to know how much more flow we can expect but also when. If the water is used for irrigation of crops but will arrive in a different month in the future, then which crops are most appropriate is an issue. If most of the future flow is concentrated over a short period, then this could lead to flooding or loss of opportunity for use in irrigation or domestic water consumption. This also has implications for management with regard to storage in-stream via dams, off-stream with other engineered structures, or some form of managed aquifer recharge to mitigate flow seasonality.
Zhang L, Potter N, Hickel K, Zhang Y and Shao Q (2008) Water balance modeling over variable time scales based on the Budyko framework – Model development and testing. Journal of Hydrology, 360, 117-131, doi: 10.1016/j.jhydrol.2008.07.021
Citation: https://doi.org/10.5194/egusphere-2023-520-RC1 - AC1: 'Reply on RC1', Tobias Siegfried, 07 Aug 2023
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RC2: 'Comment on egusphere-2023-520', Anonymous Referee #2, 10 Jul 2023
The manuscript is very well written and easy to follow.
A very simplified water balance model is applied with numerous assumptions that require more discussions and comparisons with data sets like:
- Evaporation (including transpiration) depends only on potential evaporation, what about water availability?
- The partitioning of runoff and evaporation is it dependent on the topography?
- Parameters k1 and k2 (equation (17)) are set to 100 and 5. In Oudin et al. (2005) quoted by the authors, these parameters have to be calibrated with values depending on the hydrological model... Values of 100 and 5 are based on the water catchments they studied, which are different from the catchements addressed here.
- Concerning the rooting depth, it is assumed that data from Fan et al. (2017) are a good compromise, but it is not explained why ….
- Equation (18) is an oversimplified model that needs to be checked versus data.
The authors mentioned most of these simplifications.
Because these simplifications are very strong and sometimes not consistent with our knowledge, they must be checked by comparisons with existing data (at least, at the scale of one water catchment). Without that checking, there is no possibility to verify the plausibility of the results and the paper appears like a modelling exercise not suitable for HESS.
Citation: https://doi.org/10.5194/egusphere-2023-520-RC2 - AC2: 'Reply on RC2', Tobias Siegfried, 07 Aug 2023
-
RC3: 'Comment on egusphere-2023-520', Anonymous Referee #3, 18 Jul 2023
This study evaluate the hydrological impact on the central Aisa. The results are interesting. However, this study make many assumption to simplify the water balance model, which need extra explanation and work to make sure the results are accurate.
Line 140, water balance equation. this equation is only for closed system. what happens between the water interchaning between subcatchements and glacier melt? Please use the term evapotranspiration to represent both transpiration and evaporation.
Line 165, evapotranspiration takes up a large poration of the water balance. This study made assumption that Em is not dependent on time. However, the E could vary a lot seasonally, e.g. serveral times higher in summer than winter. Besides, the Em is also dependent on the vegetation type, soil moisture (another important factor in this study) and temperature. This study project 3 degree climate change which could significantly impact the Em term. How do you make sure this assumption will not affect the final result? I recommend to re-model the evapotranspiration term. Please refer study: Zhou, Z., & Guo, Q. (2022). Drainage alternatives for rain gardens on subsoil of low permeability: Balance among ponding time, soil moisture, and runoff reduction. Journal of Sustainable Water in the Built Environment, 8(3), 05022002.
Citation: https://doi.org/10.5194/egusphere-2023-520-RC3 - AC3: 'Reply on RC3', Tobias Siegfried, 07 Aug 2023
Status: closed
-
RC1: 'Comment on egusphere-2023-520', Anonymous Referee #1, 15 Jun 2023
egusphere-2023-520 “Assessing Future Hydrological Impacts of Climate Change on High-Mountain Central Asia: Insights from a Stochastic Soil Moisture Water Balance Model” T.Siegfried, AU.Mujahid, BS.Marti, P.Molnar, DN.Karger and A.Yakovlev
This article describes the implementation of an analytic steady-state water balance on 221 sub-catchments in central Asia. It is very well put together, reads easily from start to finish, has extensive useful figures showing the results, a careful splitting of the maths into “just the important results” in the main text and all the “tedious derivation” in the Appendix, and some thoughtful caveats in the Discussion.
It is nit-picking to point out on line 169 that you only need dx(t)/dt=0 in Eq(2), as a value of w0=0 would indicate an impermeable land surface where P=Q, or a volume with no storage such that the evaporative process has no opportunity to occur. Figure 8 does show very low values of w0, and linear fits crossing the x-axis, but there is no indication in the text that w0 ever reduced to zero (or negative); Zr as a component of w0 was positively correlated to temperature which always increased in the future periods used.
Given that the solution to the water balance is a single line equation that is solved explicitly, why do the authors go down the path of using differentials to estimate changes in Q? Such an approach is usually taken when the computation is much more complex or requires some iterative solution, so for small enough perturbations the differential is a good estimate of change in output. You already need to calculate new values of E and P and all their components for the three future periods, so the new Q value could be calculated directly and compared to the baseline period. The only indication of the values (or magnitude) of the sensitivity coefficients is Figure 11 which visually indicates that, in this environment, change in Q is a simple tug-of-war between increasing precipitation event depth (alpha) and increasing active soil depth (Zr), with both Ep and Lambda of little importance.
The caption for Figure 15 is wrong. According to the map key, these are “relative changes in” precipitation frequency and depth.
Figures B2-4 show the same parameter, Zr, but with three different ranges on the same continuous rainbow colour scale. They cannot be easily visually compared and require a non-linear bin-style scale that is the same for all three. Continuous colour ramps are most useful in remote sensing applications particularly, where the individual items being coloured are relatively small, the same shape and size, and there are very many of them (the false shaded DEM under the figures is a perfect example). For only 221 irregularly shaped, sized and arranged polygons this is wasted. I would also suggest moving Figure B5 to follow B1 as they are the saturation and wilting points of the soil, or potentially just show a single map with their difference which is the input (s1-sw) to w0.
Perhaps the most important follow-on, and unfortunately it may no longer be a simple analytic solution, is to consider inter-annual variation (see eg. Zhang et al, 2008 using a Budyko framework). It may be possible to remain analytic on an annual basis with storage change zero over a year, but storage carryover between quarterly or monthly sub-annual intervals in a matrix equation. From the human perspective, it is not only important to know how much more flow we can expect but also when. If the water is used for irrigation of crops but will arrive in a different month in the future, then which crops are most appropriate is an issue. If most of the future flow is concentrated over a short period, then this could lead to flooding or loss of opportunity for use in irrigation or domestic water consumption. This also has implications for management with regard to storage in-stream via dams, off-stream with other engineered structures, or some form of managed aquifer recharge to mitigate flow seasonality.
Zhang L, Potter N, Hickel K, Zhang Y and Shao Q (2008) Water balance modeling over variable time scales based on the Budyko framework – Model development and testing. Journal of Hydrology, 360, 117-131, doi: 10.1016/j.jhydrol.2008.07.021
Citation: https://doi.org/10.5194/egusphere-2023-520-RC1 - AC1: 'Reply on RC1', Tobias Siegfried, 07 Aug 2023
-
RC2: 'Comment on egusphere-2023-520', Anonymous Referee #2, 10 Jul 2023
The manuscript is very well written and easy to follow.
A very simplified water balance model is applied with numerous assumptions that require more discussions and comparisons with data sets like:
- Evaporation (including transpiration) depends only on potential evaporation, what about water availability?
- The partitioning of runoff and evaporation is it dependent on the topography?
- Parameters k1 and k2 (equation (17)) are set to 100 and 5. In Oudin et al. (2005) quoted by the authors, these parameters have to be calibrated with values depending on the hydrological model... Values of 100 and 5 are based on the water catchments they studied, which are different from the catchements addressed here.
- Concerning the rooting depth, it is assumed that data from Fan et al. (2017) are a good compromise, but it is not explained why ….
- Equation (18) is an oversimplified model that needs to be checked versus data.
The authors mentioned most of these simplifications.
Because these simplifications are very strong and sometimes not consistent with our knowledge, they must be checked by comparisons with existing data (at least, at the scale of one water catchment). Without that checking, there is no possibility to verify the plausibility of the results and the paper appears like a modelling exercise not suitable for HESS.
Citation: https://doi.org/10.5194/egusphere-2023-520-RC2 - AC2: 'Reply on RC2', Tobias Siegfried, 07 Aug 2023
-
RC3: 'Comment on egusphere-2023-520', Anonymous Referee #3, 18 Jul 2023
This study evaluate the hydrological impact on the central Aisa. The results are interesting. However, this study make many assumption to simplify the water balance model, which need extra explanation and work to make sure the results are accurate.
Line 140, water balance equation. this equation is only for closed system. what happens between the water interchaning between subcatchements and glacier melt? Please use the term evapotranspiration to represent both transpiration and evaporation.
Line 165, evapotranspiration takes up a large poration of the water balance. This study made assumption that Em is not dependent on time. However, the E could vary a lot seasonally, e.g. serveral times higher in summer than winter. Besides, the Em is also dependent on the vegetation type, soil moisture (another important factor in this study) and temperature. This study project 3 degree climate change which could significantly impact the Em term. How do you make sure this assumption will not affect the final result? I recommend to re-model the evapotranspiration term. Please refer study: Zhou, Z., & Guo, Q. (2022). Drainage alternatives for rain gardens on subsoil of low permeability: Balance among ponding time, soil moisture, and runoff reduction. Journal of Sustainable Water in the Built Environment, 8(3), 05022002.
Citation: https://doi.org/10.5194/egusphere-2023-520-RC3 - AC3: 'Reply on RC3', Tobias Siegfried, 07 Aug 2023
Data sets
Assessing Future Hydrological Impacts of Climate Change on High-Mountain Central Asia: Insights from a Stochastic Soil Moisture Water Balance Model - Data and Code Tobias Siegfried https://doi.org/10.5281/zenodo.7753626
Model code and software
Assessing Future Hydrological Impacts of Climate Change on High-Mountain Central Asia: Insights from a Stochastic Soil Moisture Water Balance Model - Data and Code Tobias Siegfried https://doi.org/10.5281/zenodo.7753626
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