the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 05 Sep 2024
Projections of streamflow intermittence under climate change in European drying river networks
Abstract. Climate and land use changes, as well as human water use and flow alterations, are causing worldwide shifts in river flow dynamics. During the last decades, low-flows, flow intermittence, and drying have increased in many regions of the world, including Europe. This trend is projected to continue and exacerbate in the future, resulting in more frequent and intense hydrological droughts. However, due to a lack of data and studies on temporary rivers in the past, little is known about the processes governing the development of flow intermittence and drying, their timing and frequency, as well as their long-term evolution under climate change. Moreover, understanding the impact of climate change on the drying up of rivers is crucial to assess the impact of climate change on aquatic ecosystems, biodiversity and functional integrity of freshwater systems.
This study is one of the first to present future projections of drying in intermittent river networks, and to analyze future changes in the drying patterns at high resolution spatial and temporal scale. The flow intermittence projections were produced using a hybrid hydrological model forced with climate projection data from 1985 until 2100 under three climate scenarios in six European drying river networks. The watersheds areas are situated in different biogeographic regions, located in Spain, France, Croatia, Hungary, Czechia, and Finland, and their areas range from 150 km2 to 350 km2. Additionally, flow intermittence indicators were developed and calculated to assess changes in the characteristics of the drying spells at the reach scale, and changes in the spatial extent of drying in the river network at various time intervals.
The results show that drying patterns are projected to increase and expand in time and space in all three climate scenarios, despite differences in the amplitude of changes. Temporally, in addition to the average frequency of drying events, the duration also increases over the year. Seasonal changes are expected to result in an earlier onset and longer persistence of drying throughout the year. Summer drying maxima are likely to shift to earlier in the spring, with extended drying periods or additional maxima occurring in autumn, and in some regions extending into the winter season. A trend analysis of extreme events shows that the extreme dry spells observed in recent years could become regular by the end of the century. Additionally, we observe transitions from perennial to intermittent reaches in the future.
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RC1: 'Comment on hess-2024-272', Anonymous Referee #1, 02 Oct 2024
The authors using a hybrid model to predict the flow intermittence which is important for water security in the changing climate. i think the idea and approach are novel and the manuscript is well written and results are well presented. it looks like a good work at my first glance. However, I have a first of all question which determine if such a work is necessary. Authors used a physical model to get the simulated hydrologic variables such as streamflow and baseflow. Then authors mentioned, the RF model was built as the JAMS-J2000 model cannot simulate the drying up of rivers. This is the key issue. There are so many surface-subsurface integrated hydrologic models in nowadays, e.g., ParFlow, HGS, MIKESHE, so why don't you use a physical model that has such an ability. Then the drying and wetting of stream networks are pretty easy to get through the magnitude of streamflow. Authors spent a lot of time to build a physical model with limited capability and then spend extra work to build the RF model. The target used for training and predicting in RF is also much harder to observe and quantify than streamflow. So I am not quite sure the purpose/significance of authors to do so.
Citation: https://doi.org/10.5194/hess-2024-272-RC1 -
CC1: 'Reply on RC1', Louise Mimeau, 08 Oct 2024
The authors would like to thank Referee#1 for his/her positive feedback on the quality of the paper.
The question about the value of using a hybrid model is indeed a legitimate one. We provided the first elements of an answer in the previous paper (Mimeau et al, 2024) in which we described in detail the J2000-RF hybrid flow intermittence model and its implementation in 3 basins. Here is an extract from the introduction to this paper explaining the reasons for developing such a model: « Studies have already looked at modelling intermittent rivers with a physical hydrological model (Jaeger et al., 2014; Tzoraki et al., 2016; Llanos-Paez et al., 2023). One major difficulty in modelling flow intermittence is that hydrological models have difficulties in simulating zero flows (Shanafield et al., 2021). First there is a numerical challenge: the flow routing scheme implemented in the models to propagate the streamflow across the river networks cannot represent sudden transitions from wet to dry. Second, the origins of intermittence are multiple (disconnection between the river and the water table, drying up following a long period without precipitation, infiltration from the riverbed into a fault or a karstic subsoil, drying up following anthropic withdrawals, etc.) (Datry et al., 2016) and sometimes very local. Representing all these processes in the models is thus complex and requires a large amount of data. A more common approach to modelling intermittent rivers is the use of artificial neural networks (ANNs) (Daliakopoulos and Tsanis, 2016; Beaufort et al., 2019) and random forest (RF) (González-Ferreras and Barquín, 2017; Beaufort et al., 2019; Belemtougri, 2022; Jaeger et al., 2023) models. These models are easier to implement, do not require a priori knowledge of the origins of drying, and show good performances in predicting the spatial distribution of flow regimes (perennial or intermittent) in the river networks. »
Physically-based models such as ParFlow, HGS and MIKESHE explicitly represent the groundwater-river connection and are used to model river networks with intermittent or ephemeral regimes. However, these models are to be used in specific contexts. Gutierrez-Jurado et al. (2021) list 11 studies using fully integrated surface–subsurface hydrologic models (including ParFlow, HGS, tRIBS) to simulate runoff and streamflow processes in non-perennial systems in semi-arid regions or Mediterranean climate regions. The authors state that « the required level of information to adequately parameterise boundary value problems has restricted the use of fully integrated surface–subsurface hydrologic models (ISSHMs) in non-perennial river catchments to mostly small-scale hillslope or headwater catchments (0.001–0.9 km2) ». In these regions, flow intermittence can be a very local phenomenon and generally occurs on small streams at the head of a basin. Taking account of the groundwater-river connection in modelling these systems therefore requires a very fine spatial discretisation and very precise topographical data, which prevents the use of this type of model for larger catchment areas. In another context, Herzog et al. (2021) were able to use ParFlow to model the hydrology of ephemeral rivers in a large (14,000 km²) West African basin at a 1 × 1-km2 resolution, because in this region flow intermittence occurs at a much larger spatial scale (streamflow is mainly controlled by perched aquifers discharging into inland valleys during the rainy season).
The 6 European basins in our study are characterised by local flow intermittence, which generally occurs in small headwater streams. The representation of intermittence in these basins, covering between 150 and 300 km², would therefore be very complicated for the reasons given by Gutierrez-Jurado et al. (2021). In addition, each of the 6 studied catchments has its own particular characteristics, with processes that are difficult to represent with ISSHMs. For example, in the Albarine catchment (France), intermittence in the upstream part of the catchment is caused by infiltration of the river into a karstic soil. We know that this karstic soil leads to exchanges of groundwater with adjacent basins, but the current knowledge of this karstic system is insufficient for it to be represented in a physical model (lack of data to represent the karst with its underground flow paths and directions). Another example is the Genal catchment (Spain), which is characterised by intermittent water flow caused by both a semi-arid climate and water abstraction for irrigation. It is quite possible to represent water abstraction in a hydrological model (surface and groundwater abstraction modules are implemented in J2000), however, in the case of the Genal basin, data on the volumes of water abstracted are not available and it is therefore impossible to represent these abstractions explicitly in the models.
Finally, another limitation is the computational power. Gutierrez-Jurado et al. (2021) indicate that the 11 studies listed which sought to simulate intermittence in small semi-arid basins using SIHM models were able to carry out simulations over periods ranging from a few hours to almost 1 year. Herzog et al. (2021) indicate that a 2-year hydrological simulation with the ParFlow model in their basin can be carried out in 5 hours of calculations. Our study focuses on the evolution of intermittence over the long term. To do this, we carried out 15 simulations for the 6 catchment areas studied, from 1985 to 2100 (3 greenhouse gas emission scenarios x 5 climate models), i. e. more than 10k model-year. These long-term simulations, that crucially take into account both the uncertainty related to the emissions scenarios and the uncertainty of the climate models, would not have been possible with an ISSHM.
The association of the J2000 process-oriented hydrological model with a random forest model therefore makes it possible to take account of flow intermittence in different climatic, geological and anthropogenic contexts and in medium-sized basins over the long term, which would be very difficult to achieve with an ISSHM.
We hope that these elements have answered your questions and we look forward to reading your detailed comments on our study.
Louise Mimeau, on behalf the co-authors.
References :
Herzog, A., Hector, B., Cohard, J. M., Vouillamoz, J. M., Lawson, F. M. A., Peugeot, C., and de Graaf, I.: A parametric sensitivity analysis for prioritizing regolith knowledge needs for modeling water transfers in the West African critical zone, Vadose Zone Journal, 20(6), e20163, https://doi.org/10.1002/vzj2.20163, 2021.
Gutierrez-Jurado, K. Y., Partington, D., and Shanafield, M.: Taking theory to the field: streamflow generation mechanisms in an intermittent Mediterranean catchment, Hydrol. Earth Syst. Sci., 25, 4299–4317, https://doi.org/10.5194/hess-25-4299-2021, 2021.
Mimeau, L., Künne, A., Branger, F., Kralisch, S., Devers, A., and Vidal, J.-P.: Flow intermittence prediction using a hybrid hydrological modelling approach: influence of observed intermittence data on the training of a random forest model, Hydrol. Earth Syst. Sci., 28, 851–871, https://doi.org/10.5194/hess-28-851-2024, 2024.
Citation: https://doi.org/10.5194/hess-2024-272-CC1 -
AC1: 'Reply on RC1', Annika Künne, 16 Oct 2024
The authors would like to thank Referee#1 for his/her positive feedback on the quality of the paper.
The question about the value of using a hybrid model is indeed a legitimate one. We provided the first elements of an answer in the previous paper (Mimeau et al, 2024) in which we described in detail the J2000-RF hybrid flow intermittence model and its implementation in 3 basins. Here is an extract from the introduction to this paper explaining the reasons for developing such a model: « Studies have already looked at modelling intermittent rivers with a physical hydrological model (Jaeger et al., 2014; Tzoraki et al., 2016; Llanos-Paez et al., 2023). One major difficulty in modelling flow intermittence is that hydrological models have difficulties in simulating zero flows (Shanafield et al., 2021). First there is a numerical challenge: the flow routing scheme implemented in the models to propagate the streamflow across the river networks cannot represent sudden transitions from wet to dry. Second, the origins of intermittence are multiple (disconnection between the river and the water table, drying up following a long period without precipitation, infiltration from the riverbed into a fault or a karstic subsoil, drying up following anthropic withdrawals, etc.) (Datry et al., 2016) and sometimes very local. Representing all these processes in the models is thus complex and requires a large amount of data. A more common approach to modelling intermittent rivers is the use of artificial neural networks (ANNs) (Daliakopoulos and Tsanis, 2016; Beaufort et al., 2019) and random forest (RF) (González-Ferreras and Barquín, 2017; Beaufort et al., 2019; Belemtougri, 2022; Jaeger et al., 2023) models. These models are easier to implement, do not require a priori knowledge of the origins of drying, and show good performances in predicting the spatial distribution of flow regimes (perennial or intermittent) in the river networks. »
Physically-based models such as ParFlow, HGS and MIKESHE explicitly represent the groundwater-river connection and are used to model river networks with intermittent or ephemeral regimes. However, these models are to be used in specific contexts. Gutierrez-Jurado et al. (2021) list 11 studies using fully integrated surface–subsurface hydrologic models (including ParFlow, HGS, tRIBS) to simulate runoff and streamflow processes in non-perennial systems in semi-arid regions or Mediterranean climate regions. The authors state that « the required level of information to adequately parameterise boundary value problems has restricted the use of fully integrated surface–subsurface hydrologic models (ISSHMs) in non-perennial river catchments to mostly small-scale hillslope or headwater catchments (0.001–0.9 km2) ». In these regions, flow intermittence can be a very local phenomenon and generally occurs on small streams at the head of a basin. Taking account of the groundwater-river connection in modelling these systems therefore requires a very fine spatial discretisation and very precise topographical data, which prevents the use of this type of model for larger catchment areas. In another context, Herzog et al. (2021) were able to use ParFlow to model the hydrology of ephemeral rivers in a large (14,000 km²) West African basin at a 1 × 1-km2 resolution, because in this region flow intermittence occurs at a much larger spatial scale (streamflow is mainly controlled by perched aquifers discharging into inland valleys during the rainy season).
The 6 European basins in our study are characterised by local flow intermittence, which generally occurs in small headwater streams. The representation of intermittence in these basins, covering between 150 and 300 km², would therefore be very complicated for the reasons given by Gutierrez-Jurado et al. (2021). In addition, each of the 6 studied catchments has its own particular characteristics, with processes that are difficult to represent with ISSHMs. For example, in the Albarine catchment (France), intermittence in the upstream part of the catchment is caused by infiltration of the river into a karstic soil. We know that this karstic soil leads to exchanges of groundwater with adjacent basins, but the current knowledge of this karstic system is insufficient for it to be represented in a physical model (lack of data to represent the karst with its underground flow paths and directions). Another example is the Genal catchment (Spain), which is characterised by intermittent water flow caused by both a semi-arid climate and water abstraction for irrigation. It is quite possible to represent water abstraction in a hydrological model (surface and groundwater abstraction modules are implemented in J2000), however, in the case of the Genal basin, data on the volumes of water abstracted are not available and it is therefore impossible to represent these abstractions explicitly in the models.
Finally, another limitation is the computational power. Gutierrez-Jurado et al. (2021) indicate that the 11 studies listed which sought to simulate intermittence in small semi-arid basins using SIHM models were able to carry out simulations over periods ranging from a few hours to almost 1 year. Herzog et al. (2021) indicate that a 2-year hydrological simulation with the ParFlow model in their basin can be carried out in 5 hours of calculations. Our study focuses on the evolution of intermittence over the long term. To do this, we carried out 15 simulations for the 6 catchment areas studied, from 1985 to 2100 (3 greenhouse gas emission scenarios x 5 climate models), i. e. more than 10k model-year. These long-term simulations, that crucially take into account both the uncertainty related to the emissions scenarios and the uncertainty of the climate models, would not have been possible with an ISSHM.
The association of the J2000 process-oriented hydrological model with a random forest model therefore makes it possible to take account of flow intermittence in different climatic, geological and anthropogenic contexts and in medium-sized basins over the long term, which would be very difficult to achieve with an ISSHM.
We hope that these elements have answered your questions and we look forward to reading your detailed comments on our study.
Louise Mimeau, on behalf the co-authors.
References :
Herzog, A., Hector, B., Cohard, J. M., Vouillamoz, J. M., Lawson, F. M. A., Peugeot, C., and de Graaf, I.: A parametric sensitivity analysis for prioritizing regolith knowledge needs for modeling water transfers in the West African critical zone, Vadose Zone Journal, 20(6), e20163, https://doi.org/10.1002/vzj2.20163, 2021.
Gutierrez-Jurado, K. Y., Partington, D., and Shanafield, M.: Taking theory to the field: streamflow generation mechanisms in an intermittent Mediterranean catchment, Hydrol. Earth Syst. Sci., 25, 4299–4317, https://doi.org/10.5194/hess-25-4299-2021, 2021.
Mimeau, L., Künne, A., Branger, F., Kralisch, S., Devers, A., and Vidal, J.-P.: Flow intermittence prediction using a hybrid hydrological modelling approach: influence of observed intermittence data on the training of a random forest model, Hydrol. Earth Syst. Sci., 28, 851–871, https://doi.org/10.5194/hess-28-851-2024, 2024.
Citation: https://doi.org/10.5194/hess-2024-272-AC1
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CC1: 'Reply on RC1', Louise Mimeau, 08 Oct 2024
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RC2: 'Comment on hess-2024-272', Anonymous Referee #2, 01 Nov 2024
The study presents an interesting examination of the impacts of climate change on river network dynamics through a hybrid modeling approach that combines a physical-based model with random forests. The relevance of the topic to hydrology and ecology is clear, and the conclusions align with expectations. However, I believe there are opportunities to enhance the credibility of the experimental framework and the discussion of results. Specific recommendations are as follows:
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Data Scarcity and Model Validation: As mentioned in section 4.1, the scarcity of data presents challenges in validating the accuracy of the model. While the authors propose potential approaches, a clearer description of the methods and the data used is necessary. For instance, how many gauging stations, the duration of the observations, and how they are utilized for validation is not clear. Since the model operates at the reach scale, Table 1 should include the number of reaches for each study catchment or other relevant quantitative parameters. Are the parameters used for POD and FAR based on the number of reaches or temporal averages?
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Justification for Site Selection: The rationale behind selecting the six study catchments is unclear. The introduction suggests an intention to explore outcomes under varying climatic conditions across Europe; however, the results and discussions seems do not adequately address the characteristics of these climatic regions or the influence of hydrological processes (e.g., dominant rainfall or snow dynamics). If the focus is more on the responses of different tributaries within a single catchment, the significance of the chosen regions becomes ambiguous.
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Geometry of River Networks: The seasonal dynamics of river networks could be significantly influenced by their geometry (e.g., Horton’s law, Roy et al., 2020). I recommend that the authors include some relevant geometric parameters for the regions studied and conduct a corresponding analysis or at least a discussion.
- Model Accuracy Assessment: The assessment of model accuracy presented in Figure 3 appears to rely on cross-validation of the average proportion of dry reaches over time. If my understanding is correct, the persuasive power of this validation remains relatively weak. It may be beneficial to present the uncertainty in model simulations (not just the uncertainty associated with climate change projections). Additionally, while the authors note in lines 166-167 that two catchments exhibit lower model efficiency due to data scarcity, the paper does not specify the available sample sizes for each catchment. Furthermore, could the differences in model efficiency be attributed to variations in river network structure? I encourage the authors to address this point.
Roy, J., Tejedor, A., & Singh, A. (2022). Dynamic Clusters to Infer Topologic Controls on Environmental Transport of River Networks. Geophysical Research Letters, 49, 1–11. https://doi.org/10.1029/2021GL096957
Citation: https://doi.org/10.5194/hess-2024-272-RC2 -
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