the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Climatic, topographic, and groundwater controls on runoff response to precipitation: evidence from a large-sample data set
Abstract. Understanding the factors that influence catchment runoff response is essential for effective water resource management. Runoff response to precipitation can vary significantly, depending on the dynamics of hillslope water storage and release, and on the transmission of hydrological signals through the channel network. Here, we use Ensemble Rainfall-Runoff Analysis (ERRA) to characterize the runoff response of 211 Iranian catchments with diverse landscapes and climates. ERRA quantifies the increase in lagged streamflow attributable to each unit of additional precipitation, while accounting for nonlinearities in catchment behavior. Peak runoff response, as quantified by ERRA across Iran, is higher in more humid climates, in steeper and smaller catchments, and in catchments with shallower water tables. The direction and approximate magnitude of these effects persist after correlations among the drivers (e.g., deeper water tables are more common in more arid regions) are accounted for. These findings highlight the importance of catchment attributes in shaping runoff behavior, particularly in arid and semi-arid regions, where climatic variability and groundwater dynamics are crucial factors in sustainable water resource management and effective flood risk mitigation.
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CC1: 'Comment on egusphere-2025-35', Nima Zafarmomen, 19 Jan 2025
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1) While ERRA is introduced as a novel approach to estimate impulse response functions in nonlinear, nonstationary, and heterogeneous systems, the paper could benefit from a more detailed explanation of how ERRA compares to or improves upon traditional models. Could you clarify the specific advantages of using ERRA over simpler regression-based methods, especially in terms of its handling of nonlinearity and nonstationarity?
2) The study discards certain catchments due to unreasonable Q/P ratios or the presence of dams. How does the exclusion of these data points affect the generalizability of the results, particularly in arid regions where water scarcity issues might be more prominent? Could this exclusion introduce a bias towards more typical hydrological conditions?
3) The study highlights the interactions between groundwater depth, topography, and climate but doesn't fully explore potential synergistic or antagonistic effects between these factors. Could you provide more in-depth analysis on how these interactions vary across different climatic zones, particularly in extreme arid versus humid regions?
4) The paper states that larger catchment areas tend to have lower peak heights of runoff response. However, the correlation is weak, suggesting that catchment area is not the dominant factor. Could further sensitivity analysis be done to isolate the contribution of catchment area to the runoff response, especially considering regional variations in topography?
5) While the study acknowledges the role of groundwater over-extraction in shaping runoff responses, it could expand on the anthropogenic effects in the regions studied. Could the authors include a more detailed discussion on how land use changes, agricultural practices, or urbanization might affect runoff response in the context of groundwater depletion?
6) The study uses temporal averages of groundwater depth but does not account for potential seasonal or inter-annual variations in groundwater levels. How might fluctuations in groundwater depth over time influence runoff response, and would incorporating temporal variability improve model accuracy?
I highly recommend the authors consider citing recent work on hydrological modeling for irrigation, specifically studies integrating satellite-based vegetation indices to improve runoff prediction accuaract. For example, studies like 'Assimilation of Sentinel‐based Leaf Area Index for Modeling Surface‐Groundwater Interactions in Irrigation Districts'.
Citation: https://doi.org/10.5194/egusphere-2025-35-CC1 -
AC1: 'Reply to CC1', James Kirchner, 03 Feb 2025
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We thank Nima Zafarmomen for these comments. Please see the original comments below in plain type, and our responses in bold.
1) While ERRA is introduced as a novel approach to estimate impulse response functions in nonlinear, nonstationary, and heterogeneous systems, the paper could benefit from a more detailed explanation of how ERRA compares to or improves upon traditional models. Could you clarify the specific advantages of using ERRA over simpler regression-based methods, especially in terms of its handling of nonlinearity and nonstationarity?
Our study does not introduce ERRA, per se; that was done by Kirchner (HESS 28, 4427-4454, 2024), as a specific application of a more general approach to impulse response functions in nonlinear, nonstationary, and heterogeneous systems (Kirchner, Sensors 22, 3291, 2022). Sections 4 and 5 of Kirchner (2022) present benchmark tests of the handling of nonlinearity and nonstationarity, and illustrates the consequences of ignoring nonlinearity and nonstationarity, using synthetic data. Sections 3 and 4 of Kirchner (2024) illustrate similar principles using applications to real-world hydrological data. Together, these sections comprise over 20 pages in print, so it would not be appropriate to repeat them here. We can, however, refer readers to the relevant sections and figures in these previous publications.
2) The study discards certain catchments due to unreasonable Q/P ratios or the presence of dams. How does the exclusion of these data points affect the generalizability of the results, particularly in arid regions where water scarcity issues might be more prominent? Could this exclusion introduce a bias towards more typical hydrological conditions?
These catchments were excluded for quality control reasons. In any of the climatic regions of Iran, if the average measured Q is greater than 0.8 times the average measured P – implying that evapotranspiration is less than 0.2 times P – this is a clear indication that there must be substantial errors in the measurements of Q, P, or both. Thus there should be no impact on the generalizability of the results, because one should not be trying to generalize from clearly erroneous data. In any case, this criterion only excluded 154 basins out of 1155, so the overall impact on our results should be small (but again, excluding erroneous data is clearly the right thing to do).
We excluded catchments that were substantially affected by dams (as revealed in satellite imagery), because discharge below dams reflects both dam management decisions (which are not trying to study) and natural hydrological processes (which are the focus of our investigation). We need to exclude records below dams if we want to see the behavior of the physical system, without the confounding effects of dam operations. We don't think this affects the generalizability of our results, because as far as we know, the physical processes relating precipitation to streamflow presumably are not different in basins where people subsequently decide to build dams, and basins where they don't.
3) The study highlights the interactions between groundwater depth, topography, and climate but doesn't fully explore potential synergistic or antagonistic effects between these factors. Could you provide more in-depth analysis on how these interactions vary across different climatic zones, particularly in extreme arid versus humid regions?
In the discussion section, we already provide a detailed analysis of how shallow groundwater in humid regions leads to higher runoff peaks, while deeper groundwater in arid regions promotes infiltration and reduces runoff, highlighting the synergistic effects of aridity and groundwater levels. We emphasize the role of the aridity index (AI) in influencing these interactions, with the combination of AI, topography, and groundwater depth shaping runoff behavior in different climatic contexts.
Statistically, one can test for interactions by including interaction terms in regression models like those reported in Table 1. We tested for such interactions, and none were statistically significant, which is why we didn't report them. We can of course add a comment that explains this.
4) The paper states that larger catchment areas tend to have lower peak heights of runoff response. However, the correlation is weak, suggesting that catchment area is not the dominant factor. Could further sensitivity analysis be done to isolate the contribution of catchment area to the runoff response, especially considering regional variations in topography?
It is intuitive that larger catchments have lower runoff-response peak heights, because different precipitation events from different parts of the catchment with different travel times are ultimately mixed at the gage. What our multiple regression analysis exactly does is to isolate the contribution of catchment area to streamflow response by removing linear trends of confounding factors. Our analysis shows that catchment size does not seem to be as important as initially thought, with slope being a more important topographic feature (at least in this data set). Some potentially important factors, such as subsurface permeability or available water storage capacity, cannot be taken into account because we have no data for them. This may account for some of the scatter in the regression results.
5) While the study acknowledges the role of groundwater over-extraction in shaping runoff responses, it could expand on the anthropogenic effects in the regions studied. Could the authors include a more detailed discussion on how land use changes, agricultural practices, or urbanization might affect runoff response in the context of groundwater depletion?
Usually, changes in agriculture/landuse affect only a very small portion of most catchments on decadal time scales, and thus do not alter the rainfall-runoff relations significantly. For the relatively short time period between 2000 and 2018 we don't expect drastic changes in landuse.
Broadly our conceptual model indicates the direction of expected effects of groundwater decline, however a quantitative analysis of how groundwater decline impacted the presented relations is out of the scope for the current analysis.
6) The study uses temporal averages of groundwater depth but does not account for potential seasonal or inter-annual variations in groundwater levels. How might fluctuations in groundwater depth over time influence runoff response, and would incorporating temporal variability improve model accuracy?
In many locations, the seasonal and inter-annual variations in groundwater levels are a small fraction of the average groundwater depth. Thus we would expect their effects to be difficult to detect, particularly given the seasonal and interannual variations in other forcing factors, such as precipitation and vapor pressure deficit. Such an analysis is beyond the scope of the current work.
I highly recommend the authors consider citing recent work on hydrological modeling for irrigation, specifically studies integrating satellite-based vegetation indices to improve runoff prediction accuaract. For example, studies like 'Assimilation of Sentinel‐based Leaf Area Index for Modeling Surface‐Groundwater Interactions in Irrigation Districts'
The goal of our analysis is not to predict runoff, but to understand runoff generation processes and to characterize the relationship between precipitation and streamflow, as modulated by landscape characteristics (including groundwater levels). The challenge with using vegetation indices such as LAI and NDVI is that they are strongly influenced by variations in climatic aridity, which is also a direct driver of rainfall-runoff relationships. Thus in our analysis we consider variations in climatic aridity to be the root driver of both vegetation patterns and the sensitivity of runoff to rainfall (which is of course also modulated by vegetation, as an intermediary in the climate-vegetation-streamflow causal chain).
Citation: https://doi.org/10.5194/egusphere-2025-35-AC1 -
CC2: 'Reply on AC1', Nima Zafarmomen, 04 Feb 2025
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Thank you for your responses. Below are the second set of comments I provided for your review:
1) The authors note that statistical tests for interaction effects did not produce significant results, suggesting limited “synergistic” or “antagonistic” interplay among aridity, groundwater depth, and slope. However, in highly heterogeneous catchments—or in regions experiencing rapid anthropogenic alteration—interaction terms can become obscured. I recommend you dividing catchments into clusters (e.g., extreme-arid vs. semi-arid vs. humid) and re-running a separate regression or partial correlation for each climate cluster. This might reveal region-specific interactions between groundwater depth, slope, and aridity that are masked when grouping all basins together.
2) The authors excluded basins with dams and those with suspect Q/P data ratios, defending that choice to remove clearly erroneous or management-driven data. However, large-scale dam construction and data anomalies often co-occur with water scarcity or over-exploitation. I suggest provide a short subsection or appendix explaining how many catchments were removed in each climatic zone, along with summary statistics. This detail can help readers assess whether certain regions or aridity classes became underrepresented.
3) The manuscript treats declining groundwater primarily as a static or background phenomenon, with the authors explaining that assessing interannual variability is out of scope. However, in many arid and semi-arid regions of Iran, agricultural water demand (and thus groundwater pumping) can fluctuate seasonally. Even a concise paragraph on how seasonal groundwater extraction might alter infiltration/runoff partitioning would be valuable. The authors could highlight existing studies or anecdotal evidence (e.g., “dry-season pumping draws down water tables, potentially diminishing baseflow and altering subsequent runoff peaks once the next rainy spell arrives”).4) The current study uses daily time steps and annual-mean groundwater depths. Some of Iran’s rainfall occurs in short bursts during transitional seasons, while over-extraction of groundwater might be strongest in the growing season. Suggest a follow-up framework in which the ERRA approach is applied to seasonal subsets of data to assess whether monsoon-like precipitation events or dry-season extraction produce distinct RRD signatures. Even if the authors have limited time series to do this robustly, a short mention of how seasonal segmentation might provide additional clarity on the processes would strengthen the broader relevance of the work.
5) ERRA gives an empirical characterization of rainfall-to-runoff “impulse response,” but readers might also want a conceptual link to classic infiltration, saturation excess, and variable source area processes. Add a brief conceptual figure or paragraph illustrating how deeper vs. shallower groundwater can shift the infiltration capacity or the areal extent of saturated areas, and how that shift emerges quantitatively in the RRD peaks. Even a high-level conceptual depiction would help tie the empirical results back to established hydrological theory.
Citation: https://doi.org/10.5194/egusphere-2025-35-CC2 -
AC2: 'Reply to CC2', James Kirchner, 08 Feb 2025
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We thank Nima Zafarmomen for these additional comments, which we will consider in any eventual revision of the manuscript.
Citation: https://doi.org/10.5194/egusphere-2025-35-AC2
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AC2: 'Reply to CC2', James Kirchner, 08 Feb 2025
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CC2: 'Reply on AC1', Nima Zafarmomen, 04 Feb 2025
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AC1: 'Reply to CC1', James Kirchner, 03 Feb 2025
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