the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 04 Feb 2021
Climatic expression of rainfall on soil moisture dynamics in drylands
Abstract. In drylands, characterised by water scarcity and frequent meteorological droughts, knowledge of soil moisture dynamics and its drivers (evapotranspiration, soil physical properties and the timing and sequencing of precipitation events) is fundamental to understanding changes in water availability to plants and human society, especially under a nonstationary climate. Given the episodic and stochastic nature of rainfall in drylands and the limited availability of data in these regions, we sought to explore what effects the temporal resolution of precipitation data has on soil moisture and how soil moisture distributions might evolve under different scenarios of climate change. Such information is critical for anticipating the impact of a changing climate on dryland communities across the globe, especially those that depend on rainfed agriculture and groundwater wells for drinking water for humans and livestock. A major challenge to understanding soil moisture in response to climate is the availability of precipitation datasets for dryland regions across the globe. Gridded precipitation data may only be available for daily or weekly time periods, even though rainstorms in drylands often occur on much shorter time scales, but it is currently unknown how this timescale mismatch might affect our understanding of soil moisture. Numerical modelling enables retrodiction or prediction of how climate translates into dynamically evolving moisture within the soil profile. It can be used to explore how climate data at different temporal resolutions affect these soil moisture dynamics, as well as to explore the influence of shifts in rainfall characteristics (e.g., storm intensity) under potential scenarios of climate change. This study uses Hydrus 1-D, to investigate the dynamics of soil moisture over a period of decades in response to the same underlying rainfall data resolved at hourly, daily, and weekly resolutions, as well as to step changes in rainfall delivery, which is expected under a warming atmosphere. We parameterised the model using rainfall, evaporative demand, and soils data from the semi-arid Walnut Gulch Experimental Watershed (WGEW) in SE Arizona, but we present the results as a generalized study of how rainfall resolution and shifts in rainfall intensity may affect dryland soil moisture at different depths. Our results indicate that hourly or better rainfall resolution captures the dynamics of soil moisture in drylands, and that critical information on soil water content, moisture availability to vegetation, actual evapotranspiration, and deep percolation of infiltrated water is lost when soil moisture modelling is driven by rainfall data at coarser temporal resolutions (daily, weekly). We further show that modest changes in rainfall intensity dramatically shift soil water content and the overall water balance. These findings are relevant to the prediction of soil moisture for crop yield forecasts, for adaptation to climate-related risks, and for anticipating the challenges of water scarcity and food insecurity in dryland communities around the globe, where available datasets are of low spatial and temporal resolution.
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RC1: 'Comment on hess-2021-48', Anonymous Referee #1, 22 Feb 2021
This paper makes an obvious point, and one that is quite well-known, namely that the temporal resolution of precipitation data can have an important effect on simulated hydrological response. However, this is an issue that is often overlooked in evaluating issues such as climate change, where the models used to represent future climate scenarios may report data on relatively coarse time scales. Hence a paper that reminds the community of these issues and in particular attempts to quantify the associated effects for drylands is in principle to be welcomed.
There are however, important limitations of the present paper. The authors seem to have a very limited understanding of the hydrology of the arid systems they are simulating, and ignore important effects. A general issue for arid climates such as Walnut Gulch, the case study on which the paper is based, is that summer precipitation is based on thunderstorm rainfall, and is therefore highly localized. This is a major challenge for simulating hydrological response, and one that has been quantified for Walnut Gulch in several publications (e.g. Michaud, J.D. and Sorooshian, S., 1994, Effect of rainfall-sampling errors on simulations of desert flash floods. Water Resour. Res., 30, 10, pp. 2765-2775). This effect is also well described in various books on arid zone hydrology (for example the 2 CUP books, Hydrological Modeling in Arid and Semi-Arid Areas, and Groundwater Modelling in Arid and Semi-Arid areas). A major problem therefore arises when grid-square averaged precipitation from a relatively coarse spatial resolution climate model is used. The effects of spatial smoothing of thunderstorm precipitation will bias the simulations in similar ways to temporal smoothing, and is likely to be at least as important an issue. This is not mentioned, let alone addressed, in the current paper. In fact the paper is quite unclear about its treatment of spatial precipitation. line 213 states that an ensemble average of precipitation from 15 grid locations was used, but the details are not specified, and it is unclear why this approach was used, since the soil moisture simulations seem to be a single site and 1D.
It is also well-known that overland flow can be an important process in these areas, and the major mode of runoff generation. This runoff is focussed in the normally dry river channels, and subsequent channel bed infiltration is often a key process for groundwater recharge (and its use by rural communities). However, the conceptual diagram for the HYDRUS model used in the paper (Figure 2) has no representation of overland flow – so it is unclear whether or not this process was represented in the simulations. In addition, intense precipitation can lead to surface crusting (see Morin, J. and Benyamini, Y. ,1977, Rainfall infiltration into bare soils. Water. Resour. Res., 13, 5, 813-817). In the paper, a 1D vertically uniform soil profile is used, and the aggregate soil water response presented in the paper (Fig 6) does not appear to be a particularly good representation of the observations. The 1D assumption may or may not be valid, but more detail of the vertical soil moisture response is needed to convince the reader that the model, on which the whole paper is based, is in fact able to simulate the dynamics of the observed response.
In conclusion, I regret that in my opinion this paper does not warrant publication in its present form. The authors need to demonstrate a better understanding of the hydrology of the region on which their data is based, and hence do a better job in defining and addressing the key issues involved in the approximations inherent in 1D climate models. Temporal resolution of precipitation is an important issue, but one that cannot be addressed in isolation of the other aspects mentioned above.
A few specific comments follow:
Abstract: Note that data resolution does not change soil moisture – it does change simulated soil moisture.
line 46 but is expressed – improper sentence
line 88 insert ‘and’ soil moisture
line 132 ‘we divided reported event precipitation depth (mm) by event duration (min), and then aggregated the resulting set of events into hourly precipitation data’ not sure what this means in terms of resolution – presumably nothing sub-hourly?
line 193 – note a uniform soil profile was used
line 203 STORM stochastic model used for climate perturbations – method not described
line 205 what is meant by a high resolution grid?
Fig 6 – significant differences in the distribution of soil moisture between observed and simulated – these are not mentioned or discussed
line 213 ensemble average from 15 grid locations was used. Not clear why, since soil moisture simulations seem to be a single site and 1D??
Citation: https://doi.org/10.5194/hess-2021-48-RC1 -
AC1: 'Reply on RC1', Kipkemoi Isaac, 12 Mar 2021
Reviewer 1
We thank Reviewer#1 for their useful comments. Below in bold are our response to the each of the comments.
This paper makes an obvious point, and one that is quite well-known, namely that the temporal resolution of precipitation data can have an important effect on simulated hydrological response.
Although this point may seem obvious to this reviewer, the temporal resolution of rainfall data into hydrological models is not well treated in the literature in the same way spatial resolution has been treated. We believe that both impacts of changing temporal and spatial resolutions must be carefully investigated, especially as hydrological models continue to be employed at much finer spatial scales. There are numerous examples in which rainfall at coarse temporal resolution is used to assess hydrological response in catchments. The issue is more critical to get right in drylands due to the strong sensitivity of rainfall partitioning at that surface to the intensity and duration of driving rainfall events. In this paper, we explore how using precipitation data resolved at different temporal resolutions (hourly, daily, weekly) affects estimates of soil moisture and plant-water availability particularly in dryland regions. This is because datasets currently being used by governments and humanitarian agencies in drylands of developing nations are Globally available (gridded) precipitation data that are typically resolved at daily, weekly, and monthly temporal resolutions. We believe that as a scientific community, it is imperative for us to share accurate information of how the datasets used to drive models that are used for decision making in those regions may not represent inherent hydrological processes of drylands, Hence our broader goals are to investigate the impacts of precipitation on soil water based on the characteristic hydrological processes that occur in drylands (Blöschl and Sivapalan, 1995), including rainfall intermittence, high intensity and short duration storm events, and to identify the critical timescale of precipitation data needed to assess water availability to human society for these vulnerable regions.
Numerical models such as Hydrus can be used to give get insights of natural world processes while achieving computing efficiency and with lower costs.
However, this is an issue that is often overlooked in evaluating issues such as climate change, where the models used to represent future climate scenarios may report data on relatively coarse time scales. Hence a paper that reminds the community of these issues and in particular attempts to quantify the associated effects for drylands is in principle to be welcomed.
We would like to thank Reviewer#1 for highlighting the usefulness of our contribution. Our paper explores an overlooked area in evaluating climate change response reminds the community temporal resolution issues. We think an attempt to systematically quantify effects of rainfall data at temporal scales consistent with the driving hydrology of drylands needs more attention.
There are, however, important limitations of the present paper. The authors seem to have a very limited understanding of the hydrology of the arid systems they are simulating and ignore important effects. A general issue for arid climates such as Walnut Gulch, the case study on which the paper is based, is that summer precipitation is based on thunderstorm rainfall, and is therefore highly localized. This is a major challenge for simulating hydrological response, and one that has been quantified for Walnut Gulch in several publications (e.g. Michaud, J.D. and Sorooshian, S., 1994, Effect of rainfall-sampling errors on simulations of desert flash floods. Water Resour. Res., 30, 10, pp. 2765-2775). This effect is also well described in various books on arid zone hydrology (for example the 2 CUP books, Hydrological Modeling in Arid and Semi-Arid Areas, and Groundwater Modelling in Arid and Semi-Arid areas).
Yes, it is true that rainfall is highly localized at WGEW, a subject that our team has published on (Singer and Michaelides 2017). However, while other studies have emphasized the hydrological response over the entire catchment (e.g, capturing the streamflow signal in the mainstem), our aim here was instead to explore the effects of rainfall resolution on infiltration and AET in a 1D framework. This simple modelling framework, using a well-established and well-tested infiltration model, allowed us to put our focus on the direct impacts of changing rainfall resolution on resulting soil moisture. To accomplish this goal, we have employed two separate analyses. First, we used local rainfall information for a single rainfall gauge (digital gauging station number #82), which is part of the WGEW network and which is co-located with the COSMOS soil moisture probe (http://cosmos.hwr.arizona.edu/Probes/StationDat/010/index.php).The analysis of rainfall at this location and the modelling of soil moisture arising from it, enabled us to directly compare the measured COSMOS soil moisture data to modelled values from Hydrus over the period 2000-2019. We then modified the input rainfall resolution to explore how temporal averaging of input rainfall affects soil moisture.
Next, once we gained confidence of the plausible (hourly) soil moisture series over this historical time period, we explored the potential effects of a systematic changes to the intensity of individual rainstorms over the catchment, without modifying the overall distribution of seasonal rainfall totals. Here, we used the STOchastic Rainstorm Model (STORM) (Singer and Michaelides, 2017; Singer et al., 2018), which explicitly simulates storm events at high spatial and temporal resolution. We used output from this stochastic rainstorm model averaged at hourly resolution and over an area of 15 km2, which represents the contribution of 15 simulated rainfall locations on a 1-km grid. The purpose of the spatial averaging of STORM output over 10 simulations each of 20 years, was to develop a robust characterization of rainfall inputs to Hydrus to explore the effects of systematic changes to rainfall distributions. To benchmark the effect of these changes to rainfall intensity, we have also simulated stochastic rainfall under current climate conditions (‘historical’). Based on the apparent confusion, we will clarify these points in the manuscript.
A major problem therefore arises when grid-square averaged precipitation from a relatively coarse spatial resolution climate model is used. The effects of spatial smoothing of thunderstorm precipitation will bias the simulations in similar ways to temporal smoothing and is likely to be at least as important an issue. This is not mentioned, let alone addressed, in the current paper. In fact, the paper is quite unclear about its treatment of spatial precipitation. line 213 states that an ensemble average of precipitation from 15 grid locations was used, but the details are not specified, and it is unclear why this approach was used, since the soil moisture simulations seem to be a single site and 1D.
We thank reviewer#1 for raising this issue. We have provided more detail in the response above about rainfall inputs under our climate change scenarios and have updated the text to reflect these points. The most critical point to highlight here is that any stochastic model (representing processes that vary in space and time) may yield biased results for a particular location (based on the simulated expression of rainstorm areas, etc), so it is important to spatially average the results over an area around the site of interest. Ultimately, we want to point out that STORM preserves the inherent intensity characteristics during individual rainstorms, rainfall sequencing, and even the time series of the driving evaporative demand. Additionally, our aim was not necessarily to derive an accurate representation of future climate change on soil moisture at this location, but rather to explore the potential effects of changes in rainfall intensity. Our modelling framework served that purpose.
In Line 213, we will revise the paragraph to read ‘we used an ensemble average from 15 rainfall grid locations (Figure 1), covering an area of WGEW where soil moisture has been well monitored. To ensure that our simulations were robust, we used a simulation bounding area around gauge #82 at the Kendall site of ~16 km2. This is made up of 15 rainfall locations, from which we computed an average rainfall as input to HYDRUS. From the resultant dataset we produced 10 realisations for each grid location per climate scenario, each set of data per climate scenario is used to drive Hydrus resulting to an equivalent of 200 years’ worth of simulation time. The model soil properties were kept the same for the all the simulations.
It is also well-known that overland flow can be an important process in these areas, and the major mode of runoff generation. This runoff is focussed in the normally dry river channels, and subsequent channel bed infiltration is often a key process for groundwater recharge (and its use by rural communities). However, the conceptual diagram for the HYDRUS model used in the paper (Figure 2) has no representation of overland flow – so it is unclear whether or not this process was represented in the simulations. In addition, intense precipitation can lead to surface crusting (see Morin, J. and Benyamini, Y. ,1977, Rainfall infiltration into bare soils. Water. Resour. Res., 13, 5, 813-817). In the paper, a 1D vertically uniform soil profile is used, and the aggregate soil water response presented in the paper (Fig 6) does not appear to be a particularly good representation of the observations. The 1D assumption may or may not be valid, but more detail of the vertical soil moisture response is needed to convince the reader that the model, on which the whole paper is based, is in fact able to simulate the dynamics of the observed response.
Yes, indeed overland flow, transmission losses into dry channel beds, focused recharge, surface crusting, sediment transport etc. are all important processes in drylands. Our team has published on these dryland processes extensively – including in Walnut Gulch (e.g. Chen et al., 2019; Michaelides et al., 2018; Singer & Michaelides, 2017; Jaeger et al., 2017; Michaelides & Singer, 2014; Singer & Michaelides, 2014; Michaelides et al., 2012; Michaelides & Martin, 2012; Michaelides et al., 2009 etc.). However, this paper focuses on the 1D vertical distribution of rainfall into the soil profile only – there is no representation of 2D processes of water flow over hillslopes and in channels. We are interested in understanding how soil moisture dynamics vary with rainfalls of different temporal resolutions. Therefore, we are simplifying the representation of this problem in a 1D model of a 1m deep soil profile on a flat surface (similarly to a column experiment performed using typical land surface models). We are using a well-published soil infiltration model (Hydrus – over 3000 publications) to isolate the effect of rainfall and PET on soil moisture dynamics. We believe that the use of Hydrus is justified, for example, over more simplified soil hydrology representation in typical 1D land surface models. Figure 6 demonstrates that we are able to capture the median value of the measured soil moisture response in the top 30cm of the soil profile in the Kendall basin, so we are confident that this model can simulate the broad dynamics of the soil moisture redistribution in this area. There are no long timeseries of soil moisture observations available deeper than 30cm. Also, we want to clarify that this is not a predictive case study. We are using WGEW because of the rich data availability within a dryland site. We are seeking to understand relative differences in soil moisture due to rainfall temporal resolution.
A few specific comments follow:
Abstract: Note that data resolution does not change soil moisture – it does change simulated soil moisture.
We will add ‘modelled soil water’ in line 34.
line 46 but is expressed – improper sentence
we will delete ‘but’ and add that.
line 88 insert ‘and’ soil moisture
We will insert and
line 132 ‘we divided reported event precipitation depth (mm) by event duration (min), and then aggregated the resulting set of events into hourly precipitation data’ not sure what this means in terms of resolution – presumably nothing sub-hourly?
The highest resolution we use in this study is hourly (as explained in data section 3.2), so yes, we are not using sub-hourly data.
line 193 – note a uniform soil profile was used
We will re-write the sentence as ‘e represented the 1D soil profile for the Kendall in Hydrus-1D as a single soil layer with uniform soil properties’
The soil column properties we are using has uniform soil properties along vertical (z) axis.
line 203 STORM stochastic model used for climate perturbations – method not described
Please refer back to the first two responses above. These will be reflected in the updated manuscript methods.
line 205 what is meant by a high-resolution grid?
1km resolution grid - please refer back to the first two responses above.
Fig 6 – significant differences in the distribution of soil moisture between observed and simulated – these are not mentioned or discussed
There is already a statement about this in the manuscript. In Figure 6 we show boxplots of the modelled (Hydrus) and in situ observed (COSMOS) soil moisture.
The medians of the modelled and observed soil moisture distributions are statistically similar (Mann-Whitney U test, p = 0.5774) while the distributions are statistically different (Kolmogorov-Smirnov statistic, p< 2.2e-16). This is expected and likely due to the nature of multi-year, hourly resolution datasets. Particularly, hourly data from the COSMOS sensor tend to be very noisy than traditional point-scale sensors.
line 213 ensemble average from 15 grid locations was used. Not clear why since soil moisture simulations seem to be a single site and 1D??
Please refer back to the first two responses above that distinguish between our methods for historical analysis of soil moisture versus the future climate change scenarios. These clarifications will be reflected in the updated manuscript methods.
Citations used above:
Chen, S-A., Michaelides, K., Grieve, S.W.D. and Singer, M.B. (2019) Aridity is expressed in river topography globally. Nature 573–577, doi.org/10.1038/s41586-019-1558-8.
Michaelides, K., Hollings, R., Singer, M.B., Nichols, M., Nearing, M. (2018) Spatial and temporal analysis of hillslope-channel coupling and implications for the longitudinal profile in a dryland basin. Earth Surface Processes and Landforms doi:10.1002/esp.4340
Singer, M.B. and Michaelides, K., (2017) Deciphering the expression of climate change within the Lower Colorado River basin by stochastic simulation of convective rainfall. Environmental Research Letters, 12,104011 doi:10.1088/1748-9326/aa8e50.
Jaeger, K., Sutfin, N., Tooth, S.E., Michaelides, K. and Singer, M.B. (2017) Geomorphology and sediment regimes of intermittent rivers; in Datry, T., Bonada, N., Boulton, A. (eds.), Intermittent Rivers: Ecology and Management, Elsevier.
Singer, M.B. and Michaelides, K. (2014) How is topographic simplicity maintained in ephemeral, dryland channels? Geology, doi:10.1130/G36267.1.
Michaelides, K. and Singer, M.B. (2014) Impact of coarse sediment supply from hillslopes to the channel in runoff-dominated, dryland fluvial systems. Journal of Geophysical Research–Earth Surface, doi:10.1002/2013JF002959, 119 (6) 1205 – 1221.
Michaelides, K., Lister, D., Wainwright, J. and Parsons, A.J. (2012) Linking runoff and erosion dynamics to nutrient fluxes in a degrading dryland landscape. Journal of Geophysical Research-Biogeosciences, doi:10.1029/2012JG002071, 117, G00N15.
Michaelides, K. and Martin, G.J. (2012) Sediment transport by runoff on debris-mantled dryland hillslopes. Journal of Geophysical Research-Earth Surface, doi:10.1029/2012JF002415, 117, F03014.
Michaelides, K., Lister, D., Wainwright, J. and Parsons, A.J. (2009) Vegetation controls on small-scale runoff and erosion dynamics in a degrading dryland environment. Hydrological Processes, doi:10.1002/hyp.7293, 23: 1617 – 1630.
Citation: https://doi.org/10.5194/hess-2021-48-AC1
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AC1: 'Reply on RC1', Kipkemoi Isaac, 12 Mar 2021
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CC1: 'Comment on hess-2021-48', Innocent Ngare, 31 Mar 2021
1. The paper is well researched and concisely written. It gives critical climate variability dynamics in drylands that surge extremes and applications for forage and agriculture.
2. The authors have keenly selected a widely applied model that gives discourse on soil moisture and temporal rainfall resolution.
3. However, the authors mention Richards equation model that informs the Hydrus-ID yet the explanation is limited and doesn’t come out clearly, I would appreciate the authors to give some of the underlying equations.
Based on the contribution of this paper to dryland hydrology and the Author’s responses to first referee, I recommend this paper for publication.Citation: https://doi.org/10.5194/hess-2021-48-CC1 -
AC2: 'Reply on CC1', Kipkemoi Isaac, 13 Apr 2021
We thank the community reviewer for their useful comments. Below in bold are our response to the each of the comments.
The paper is well researched and concisely written. It gives critical climate variability dynamics in drylands that surge extremes and applications for forage and agriculture.
The authors have keenly selected a widely applied model that gives discourse on soil moisture and temporal rainfall resolution.
We thank the reviewer for taking time to give the above two comments. Indeed, our goal was to explore how using precipitation data resolved at different temporal resolutions (hourly, daily, weekly) affects estimates of soil moisture and plant-water availability particularly in dryland regions. As also highlighted by one of the reviewers, the issue of how temporal resolution of precipitation data can impact simulated hydrological responses (i.e., soil moisture dynamics) especially in climate change studies is often overlooked. The reason for initiating this research was to underscore importance of using datasets that are representative of dryland hydrology (Blöschl and Sivapalan, 1995). This included identifying the critical timescale of precipitation data needed to assess water availability to human society in these vulnerable regions.
However, the authors mention Richard’s equation model that informs the Hydrus-1D, yet the explanation is limited and doesn’t come out clearly, I would appreciate the authors to give some of the underlying equations.
Thank you for this comment. We have left out widely published and well-known equations in this manuscript so that we could only directly address the problem at hand. This helps the readers to focus on our main contribution to the paper (i.e., the impact of precipitation resolution on soil moisture dynamics and consequences for different storminess scenarios in the future). The inclusion of previously published material would have made this paper unnecessarily long and redundant. We direct this and other readers to the widely published material on Hydrus-1D. In our paper we merely mention the key equations. Hydrus-1D solves the Richards equation and includes Darcy’s Law within the sink term in Richard’s equation (Šimůnek et al., 2012; Wang et al., 2009).
Based on the contribution of this paper to dryland hydrology and the Author’s responses to first referee, I recommend this paper for publication.
Thank you.
References
Blöschl, Günter, and Murugesu Sivapalan. "Scale issues in hydrological modelling: a review." Hydrological processes 9.3‐4 (1995): 251-290.
Šimůnek, J., Van Genuchten, M. T., and Šejna, M.: The HYDRUS software package for simulating the two-and three-dimensional movement of water, heat, and multiple solutes in variably-saturated porous media, Technical manual, 2012.
Wang, P., Quinlan, P., and Tartakovsky, D. M.: Effects of spatio-temporal variability of precipitation on contaminant migration in the vadose zone, Geophys. Res. Lett., 36, 10.1029/2009gl038347, 2009.
Citation: https://doi.org/10.5194/hess-2021-48-AC2
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AC2: 'Reply on CC1', Kipkemoi Isaac, 13 Apr 2021
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RC2: 'Comment on hess-2021-48', Anonymous Referee #2, 16 May 2021
This research is interesting, but at the same time this was a very mature research area. It does not make much sense to continue to use this HYDRUS model for research on HAD, for there are lots of related studies.
1 There are some errors in Figure 2, generally speaking, the roots can absorb water Approximately below the root layer with a capillary water holding height depth, it seems that the authors do not understand the basic principles of ecological hydrology in arid areas.
2 The author did not consider the runoff confluence process during precipitation.
3 The results of using Penman’s formula to calculate ET have not been verified by measured plot data, which may lead to the converse results.
Citation: https://doi.org/10.5194/hess-2021-48-RC2 -
AC3: 'Reply on RC2', Kipkemoi Isaac, 22 May 2021
Reviewer 2
We welcome the comments provided by Reviewer#2. However, we would like to note that the Reviewer does not comment on the rationale for the study or the results and implications (impact of rainfall resolution on soil moisture). Their three comments refer to minor points (one of which we addressed in our response to Reviewer 1) and the other two are brief comments which lack clarity and justification. We address all three comments below in bold.
This research is interesting, but at the same time this was a very mature research area. It does not make much sense to continue to use this HYDRUS model for research on HAD, for there are lots of related studies.
We feel that the reviewer has misrepresented our study. It was not simply a case study use of HYDRUS, but rather an investigation of the potential interactions between rainfall and soil moisture, which are inherently nonlinear. HYDRUS is indeed commonly used in the literature because it has been shown in numerous studies to capture the essence of vertical flow through porous unsaturated media. We disagree with the reviewer that ‘there are lots of related studies’ on investigating the impact of temporal resolution of rainfall delivery in drylands on soil moisture. In these environments, it is important to characterise the effects of rainfall delivery (rainfall intermittence, high intensity, and short duration storm events)
- There are some errors in Figure 2, generally speaking, the roots can absorb water Approximately below the root layer with a capillary water holding height depth, it seems that the authors do not understand the basic principles of ecological hydrology in arid areas.
In Fig 2, we have simply provided the conceptual model as originally outlined in the HYDRUS manual. While capillarity may be an important consideration in locations where the water table is close to the surface, we have assumed a deep-water table, well below the full soil depth. Therefore, we assume no vertical interaction between water table fluctuations and capillarity. Thus, capillary water is NOT available to roots. This is often the case in dryland environments, especially at locations distant from channels. We can clarify this point in the figure caption.
- The author did not consider the runoff confluence process during precipitation.
We addressed this issue in our responses to Reviewer 1. To summarise here: indeed, overland flow, transmission losses into dry channel beds, focused recharge, surface crusting, sediment transport etc. are all important processes in drylands. Our team has published on these dryland processes extensively – including in Walnut Gulch (e.g., Chen et al., 2019; Michaelides et al., 2018; Singer & Michaelides, 2017; Jaeger et al., 2017; Michaelides & Singer, 2014; Singer & Michaelides, 2014; Michaelides et al., 2012; Michaelides & Martin, 2012; Michaelides et al., 2009 etc.). However, this paper focuses on the 1D vertical distribution of rainfall into the soil profile only – there is no representation of 2D processes of water flow over hillslopes and in channels. We are interested in understanding how soil moisture dynamics vary with rainfalls of different temporal resolutions. Therefore, we are simplifying the representation of this problem in a 1D model of a 1m deep soil profile on a flat surface (similarly to a column experiment performed using typical land surface models).
- The results of using Penman’s formula to calculate ET have not been verified by measured plot data, which may lead to the converse results.
The comment from the Reviewer is not clear. Converse to what? In this study, we used AZMET ETO data calculated by a well-established organization by a well-respected method using regionally available data. This data was downloaded from the link provided in table 1 (https://cals.arizona.edu/AZMET/). We also provided the formulae used to calculate ETo, which is based on the Penman-Monteith Equation (Eq. 1). The equation derivation and calculation procedures for the standardized ETo including testing and validation is provided in Brown (2005). This method is a well-established one for characterising evaporative demand from the atmosphere for a wide range of hydrological analyses (Vicente‐Serrano, et al 2020.).
References
Brown, P. (2005). Standardized reference evapotranspiration: A new procedure for estimating reference evapotranspiration in Arizona.
Chen, S-A., Michaelides, K., Grieve, S.W.D. and Singer, M.B. (2019) Aridity is expressed in river topography globally. Nature 573–577, doi.org/10.1038/s41586-019-1558-8.
Jaeger, K., Sutfin, N., Tooth, S.E., Michaelides, K. and Singer, M.B. (2017) Geomorphology and sediment regimes of intermittent rivers; in Datry, T., Bonada, N., Boulton, A. (eds.), Intermittent Rivers: Ecology and Management, Elsevier.
Michaelides, K. and Martin, G.J. (2012) Sediment transport by runoff on debris-mantled dryland hillslopes. Journal of Geophysical Research-Earth Surface, doi:10.1029/2012JF002415, 117, F03014.
Michaelides, K. and Singer, M.B. (2014) Impact of coarse sediment supply from hillslopes to the channel in runoff-dominated, dryland fluvial systems. Journal of Geophysical Research–Earth Surface, doi:10.1002/2013JF002959, 119 (6) 1205 – 1221.
Michaelides, K., Hollings, R., Singer, M.B., Nichols, M., Nearing, M. (2018) Spatial and temporal analysis of hillslope-channel coupling and implications for the longitudinal profile in a dryland basin. Earth Surface Processes and Landforms doi:10.1002/esp.4340
Michaelides, K., Lister, D., Wainwright, J. and Parsons, A.J. (2012) Linking runoff and erosion dynamics to nutrient fluxes in a degrading dryland landscape. Journal of Geophysical Research-Biogeosciences, doi:10.1029/2012JG002071, 117, G00N15.
Michaelides, K., Lister, D., Wainwright, J. and Parsons, A.J. (2009) Vegetation controls on small-scale runoff and erosion dynamics in a degrading dryland environment. Hydrological Processes, doi:10.1002/hyp.7293, 23: 1617 – 1630.
Singer, M.B. and Michaelides, K. (2014) How is topographic simplicity maintained in ephemeral, dryland channels? Geology, doi:10.1130/G36267.1.
Singer, M.B. and Michaelides, K., (2017) Deciphering the expression of climate change within the Lower Colorado River basin by stochastic simulation of convective rainfall. Environmental Research Letters, 12,104011 doi:10.1088/1748-9326/aa8e50.
Vicente‐Serrano, S.M., McVicar, T.R., Miralles, D.G., Yang, Y. and Tomas‐Burguera, M., 2020. Unraveling the influence of atmospheric evaporative demand on drought and its response to climate change. Wiley Interdisciplinary Reviews: Climate Change, 11(2), p.e632, https://doi.org/10.1002/wcc.632, 2020.
Citation: https://doi.org/10.5194/hess-2021-48-AC3
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AC3: 'Reply on RC2', Kipkemoi Isaac, 22 May 2021
Status: closed
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RC1: 'Comment on hess-2021-48', Anonymous Referee #1, 22 Feb 2021
This paper makes an obvious point, and one that is quite well-known, namely that the temporal resolution of precipitation data can have an important effect on simulated hydrological response. However, this is an issue that is often overlooked in evaluating issues such as climate change, where the models used to represent future climate scenarios may report data on relatively coarse time scales. Hence a paper that reminds the community of these issues and in particular attempts to quantify the associated effects for drylands is in principle to be welcomed.
There are however, important limitations of the present paper. The authors seem to have a very limited understanding of the hydrology of the arid systems they are simulating, and ignore important effects. A general issue for arid climates such as Walnut Gulch, the case study on which the paper is based, is that summer precipitation is based on thunderstorm rainfall, and is therefore highly localized. This is a major challenge for simulating hydrological response, and one that has been quantified for Walnut Gulch in several publications (e.g. Michaud, J.D. and Sorooshian, S., 1994, Effect of rainfall-sampling errors on simulations of desert flash floods. Water Resour. Res., 30, 10, pp. 2765-2775). This effect is also well described in various books on arid zone hydrology (for example the 2 CUP books, Hydrological Modeling in Arid and Semi-Arid Areas, and Groundwater Modelling in Arid and Semi-Arid areas). A major problem therefore arises when grid-square averaged precipitation from a relatively coarse spatial resolution climate model is used. The effects of spatial smoothing of thunderstorm precipitation will bias the simulations in similar ways to temporal smoothing, and is likely to be at least as important an issue. This is not mentioned, let alone addressed, in the current paper. In fact the paper is quite unclear about its treatment of spatial precipitation. line 213 states that an ensemble average of precipitation from 15 grid locations was used, but the details are not specified, and it is unclear why this approach was used, since the soil moisture simulations seem to be a single site and 1D.
It is also well-known that overland flow can be an important process in these areas, and the major mode of runoff generation. This runoff is focussed in the normally dry river channels, and subsequent channel bed infiltration is often a key process for groundwater recharge (and its use by rural communities). However, the conceptual diagram for the HYDRUS model used in the paper (Figure 2) has no representation of overland flow – so it is unclear whether or not this process was represented in the simulations. In addition, intense precipitation can lead to surface crusting (see Morin, J. and Benyamini, Y. ,1977, Rainfall infiltration into bare soils. Water. Resour. Res., 13, 5, 813-817). In the paper, a 1D vertically uniform soil profile is used, and the aggregate soil water response presented in the paper (Fig 6) does not appear to be a particularly good representation of the observations. The 1D assumption may or may not be valid, but more detail of the vertical soil moisture response is needed to convince the reader that the model, on which the whole paper is based, is in fact able to simulate the dynamics of the observed response.
In conclusion, I regret that in my opinion this paper does not warrant publication in its present form. The authors need to demonstrate a better understanding of the hydrology of the region on which their data is based, and hence do a better job in defining and addressing the key issues involved in the approximations inherent in 1D climate models. Temporal resolution of precipitation is an important issue, but one that cannot be addressed in isolation of the other aspects mentioned above.
A few specific comments follow:
Abstract: Note that data resolution does not change soil moisture – it does change simulated soil moisture.
line 46 but is expressed – improper sentence
line 88 insert ‘and’ soil moisture
line 132 ‘we divided reported event precipitation depth (mm) by event duration (min), and then aggregated the resulting set of events into hourly precipitation data’ not sure what this means in terms of resolution – presumably nothing sub-hourly?
line 193 – note a uniform soil profile was used
line 203 STORM stochastic model used for climate perturbations – method not described
line 205 what is meant by a high resolution grid?
Fig 6 – significant differences in the distribution of soil moisture between observed and simulated – these are not mentioned or discussed
line 213 ensemble average from 15 grid locations was used. Not clear why, since soil moisture simulations seem to be a single site and 1D??
Citation: https://doi.org/10.5194/hess-2021-48-RC1 -
AC1: 'Reply on RC1', Kipkemoi Isaac, 12 Mar 2021
Reviewer 1
We thank Reviewer#1 for their useful comments. Below in bold are our response to the each of the comments.
This paper makes an obvious point, and one that is quite well-known, namely that the temporal resolution of precipitation data can have an important effect on simulated hydrological response.
Although this point may seem obvious to this reviewer, the temporal resolution of rainfall data into hydrological models is not well treated in the literature in the same way spatial resolution has been treated. We believe that both impacts of changing temporal and spatial resolutions must be carefully investigated, especially as hydrological models continue to be employed at much finer spatial scales. There are numerous examples in which rainfall at coarse temporal resolution is used to assess hydrological response in catchments. The issue is more critical to get right in drylands due to the strong sensitivity of rainfall partitioning at that surface to the intensity and duration of driving rainfall events. In this paper, we explore how using precipitation data resolved at different temporal resolutions (hourly, daily, weekly) affects estimates of soil moisture and plant-water availability particularly in dryland regions. This is because datasets currently being used by governments and humanitarian agencies in drylands of developing nations are Globally available (gridded) precipitation data that are typically resolved at daily, weekly, and monthly temporal resolutions. We believe that as a scientific community, it is imperative for us to share accurate information of how the datasets used to drive models that are used for decision making in those regions may not represent inherent hydrological processes of drylands, Hence our broader goals are to investigate the impacts of precipitation on soil water based on the characteristic hydrological processes that occur in drylands (Blöschl and Sivapalan, 1995), including rainfall intermittence, high intensity and short duration storm events, and to identify the critical timescale of precipitation data needed to assess water availability to human society for these vulnerable regions.
Numerical models such as Hydrus can be used to give get insights of natural world processes while achieving computing efficiency and with lower costs.
However, this is an issue that is often overlooked in evaluating issues such as climate change, where the models used to represent future climate scenarios may report data on relatively coarse time scales. Hence a paper that reminds the community of these issues and in particular attempts to quantify the associated effects for drylands is in principle to be welcomed.
We would like to thank Reviewer#1 for highlighting the usefulness of our contribution. Our paper explores an overlooked area in evaluating climate change response reminds the community temporal resolution issues. We think an attempt to systematically quantify effects of rainfall data at temporal scales consistent with the driving hydrology of drylands needs more attention.
There are, however, important limitations of the present paper. The authors seem to have a very limited understanding of the hydrology of the arid systems they are simulating and ignore important effects. A general issue for arid climates such as Walnut Gulch, the case study on which the paper is based, is that summer precipitation is based on thunderstorm rainfall, and is therefore highly localized. This is a major challenge for simulating hydrological response, and one that has been quantified for Walnut Gulch in several publications (e.g. Michaud, J.D. and Sorooshian, S., 1994, Effect of rainfall-sampling errors on simulations of desert flash floods. Water Resour. Res., 30, 10, pp. 2765-2775). This effect is also well described in various books on arid zone hydrology (for example the 2 CUP books, Hydrological Modeling in Arid and Semi-Arid Areas, and Groundwater Modelling in Arid and Semi-Arid areas).
Yes, it is true that rainfall is highly localized at WGEW, a subject that our team has published on (Singer and Michaelides 2017). However, while other studies have emphasized the hydrological response over the entire catchment (e.g, capturing the streamflow signal in the mainstem), our aim here was instead to explore the effects of rainfall resolution on infiltration and AET in a 1D framework. This simple modelling framework, using a well-established and well-tested infiltration model, allowed us to put our focus on the direct impacts of changing rainfall resolution on resulting soil moisture. To accomplish this goal, we have employed two separate analyses. First, we used local rainfall information for a single rainfall gauge (digital gauging station number #82), which is part of the WGEW network and which is co-located with the COSMOS soil moisture probe (http://cosmos.hwr.arizona.edu/Probes/StationDat/010/index.php).The analysis of rainfall at this location and the modelling of soil moisture arising from it, enabled us to directly compare the measured COSMOS soil moisture data to modelled values from Hydrus over the period 2000-2019. We then modified the input rainfall resolution to explore how temporal averaging of input rainfall affects soil moisture.
Next, once we gained confidence of the plausible (hourly) soil moisture series over this historical time period, we explored the potential effects of a systematic changes to the intensity of individual rainstorms over the catchment, without modifying the overall distribution of seasonal rainfall totals. Here, we used the STOchastic Rainstorm Model (STORM) (Singer and Michaelides, 2017; Singer et al., 2018), which explicitly simulates storm events at high spatial and temporal resolution. We used output from this stochastic rainstorm model averaged at hourly resolution and over an area of 15 km2, which represents the contribution of 15 simulated rainfall locations on a 1-km grid. The purpose of the spatial averaging of STORM output over 10 simulations each of 20 years, was to develop a robust characterization of rainfall inputs to Hydrus to explore the effects of systematic changes to rainfall distributions. To benchmark the effect of these changes to rainfall intensity, we have also simulated stochastic rainfall under current climate conditions (‘historical’). Based on the apparent confusion, we will clarify these points in the manuscript.
A major problem therefore arises when grid-square averaged precipitation from a relatively coarse spatial resolution climate model is used. The effects of spatial smoothing of thunderstorm precipitation will bias the simulations in similar ways to temporal smoothing and is likely to be at least as important an issue. This is not mentioned, let alone addressed, in the current paper. In fact, the paper is quite unclear about its treatment of spatial precipitation. line 213 states that an ensemble average of precipitation from 15 grid locations was used, but the details are not specified, and it is unclear why this approach was used, since the soil moisture simulations seem to be a single site and 1D.
We thank reviewer#1 for raising this issue. We have provided more detail in the response above about rainfall inputs under our climate change scenarios and have updated the text to reflect these points. The most critical point to highlight here is that any stochastic model (representing processes that vary in space and time) may yield biased results for a particular location (based on the simulated expression of rainstorm areas, etc), so it is important to spatially average the results over an area around the site of interest. Ultimately, we want to point out that STORM preserves the inherent intensity characteristics during individual rainstorms, rainfall sequencing, and even the time series of the driving evaporative demand. Additionally, our aim was not necessarily to derive an accurate representation of future climate change on soil moisture at this location, but rather to explore the potential effects of changes in rainfall intensity. Our modelling framework served that purpose.
In Line 213, we will revise the paragraph to read ‘we used an ensemble average from 15 rainfall grid locations (Figure 1), covering an area of WGEW where soil moisture has been well monitored. To ensure that our simulations were robust, we used a simulation bounding area around gauge #82 at the Kendall site of ~16 km2. This is made up of 15 rainfall locations, from which we computed an average rainfall as input to HYDRUS. From the resultant dataset we produced 10 realisations for each grid location per climate scenario, each set of data per climate scenario is used to drive Hydrus resulting to an equivalent of 200 years’ worth of simulation time. The model soil properties were kept the same for the all the simulations.
It is also well-known that overland flow can be an important process in these areas, and the major mode of runoff generation. This runoff is focussed in the normally dry river channels, and subsequent channel bed infiltration is often a key process for groundwater recharge (and its use by rural communities). However, the conceptual diagram for the HYDRUS model used in the paper (Figure 2) has no representation of overland flow – so it is unclear whether or not this process was represented in the simulations. In addition, intense precipitation can lead to surface crusting (see Morin, J. and Benyamini, Y. ,1977, Rainfall infiltration into bare soils. Water. Resour. Res., 13, 5, 813-817). In the paper, a 1D vertically uniform soil profile is used, and the aggregate soil water response presented in the paper (Fig 6) does not appear to be a particularly good representation of the observations. The 1D assumption may or may not be valid, but more detail of the vertical soil moisture response is needed to convince the reader that the model, on which the whole paper is based, is in fact able to simulate the dynamics of the observed response.
Yes, indeed overland flow, transmission losses into dry channel beds, focused recharge, surface crusting, sediment transport etc. are all important processes in drylands. Our team has published on these dryland processes extensively – including in Walnut Gulch (e.g. Chen et al., 2019; Michaelides et al., 2018; Singer & Michaelides, 2017; Jaeger et al., 2017; Michaelides & Singer, 2014; Singer & Michaelides, 2014; Michaelides et al., 2012; Michaelides & Martin, 2012; Michaelides et al., 2009 etc.). However, this paper focuses on the 1D vertical distribution of rainfall into the soil profile only – there is no representation of 2D processes of water flow over hillslopes and in channels. We are interested in understanding how soil moisture dynamics vary with rainfalls of different temporal resolutions. Therefore, we are simplifying the representation of this problem in a 1D model of a 1m deep soil profile on a flat surface (similarly to a column experiment performed using typical land surface models). We are using a well-published soil infiltration model (Hydrus – over 3000 publications) to isolate the effect of rainfall and PET on soil moisture dynamics. We believe that the use of Hydrus is justified, for example, over more simplified soil hydrology representation in typical 1D land surface models. Figure 6 demonstrates that we are able to capture the median value of the measured soil moisture response in the top 30cm of the soil profile in the Kendall basin, so we are confident that this model can simulate the broad dynamics of the soil moisture redistribution in this area. There are no long timeseries of soil moisture observations available deeper than 30cm. Also, we want to clarify that this is not a predictive case study. We are using WGEW because of the rich data availability within a dryland site. We are seeking to understand relative differences in soil moisture due to rainfall temporal resolution.
A few specific comments follow:
Abstract: Note that data resolution does not change soil moisture – it does change simulated soil moisture.
We will add ‘modelled soil water’ in line 34.
line 46 but is expressed – improper sentence
we will delete ‘but’ and add that.
line 88 insert ‘and’ soil moisture
We will insert and
line 132 ‘we divided reported event precipitation depth (mm) by event duration (min), and then aggregated the resulting set of events into hourly precipitation data’ not sure what this means in terms of resolution – presumably nothing sub-hourly?
The highest resolution we use in this study is hourly (as explained in data section 3.2), so yes, we are not using sub-hourly data.
line 193 – note a uniform soil profile was used
We will re-write the sentence as ‘e represented the 1D soil profile for the Kendall in Hydrus-1D as a single soil layer with uniform soil properties’
The soil column properties we are using has uniform soil properties along vertical (z) axis.
line 203 STORM stochastic model used for climate perturbations – method not described
Please refer back to the first two responses above. These will be reflected in the updated manuscript methods.
line 205 what is meant by a high-resolution grid?
1km resolution grid - please refer back to the first two responses above.
Fig 6 – significant differences in the distribution of soil moisture between observed and simulated – these are not mentioned or discussed
There is already a statement about this in the manuscript. In Figure 6 we show boxplots of the modelled (Hydrus) and in situ observed (COSMOS) soil moisture.
The medians of the modelled and observed soil moisture distributions are statistically similar (Mann-Whitney U test, p = 0.5774) while the distributions are statistically different (Kolmogorov-Smirnov statistic, p< 2.2e-16). This is expected and likely due to the nature of multi-year, hourly resolution datasets. Particularly, hourly data from the COSMOS sensor tend to be very noisy than traditional point-scale sensors.
line 213 ensemble average from 15 grid locations was used. Not clear why since soil moisture simulations seem to be a single site and 1D??
Please refer back to the first two responses above that distinguish between our methods for historical analysis of soil moisture versus the future climate change scenarios. These clarifications will be reflected in the updated manuscript methods.
Citations used above:
Chen, S-A., Michaelides, K., Grieve, S.W.D. and Singer, M.B. (2019) Aridity is expressed in river topography globally. Nature 573–577, doi.org/10.1038/s41586-019-1558-8.
Michaelides, K., Hollings, R., Singer, M.B., Nichols, M., Nearing, M. (2018) Spatial and temporal analysis of hillslope-channel coupling and implications for the longitudinal profile in a dryland basin. Earth Surface Processes and Landforms doi:10.1002/esp.4340
Singer, M.B. and Michaelides, K., (2017) Deciphering the expression of climate change within the Lower Colorado River basin by stochastic simulation of convective rainfall. Environmental Research Letters, 12,104011 doi:10.1088/1748-9326/aa8e50.
Jaeger, K., Sutfin, N., Tooth, S.E., Michaelides, K. and Singer, M.B. (2017) Geomorphology and sediment regimes of intermittent rivers; in Datry, T., Bonada, N., Boulton, A. (eds.), Intermittent Rivers: Ecology and Management, Elsevier.
Singer, M.B. and Michaelides, K. (2014) How is topographic simplicity maintained in ephemeral, dryland channels? Geology, doi:10.1130/G36267.1.
Michaelides, K. and Singer, M.B. (2014) Impact of coarse sediment supply from hillslopes to the channel in runoff-dominated, dryland fluvial systems. Journal of Geophysical Research–Earth Surface, doi:10.1002/2013JF002959, 119 (6) 1205 – 1221.
Michaelides, K., Lister, D., Wainwright, J. and Parsons, A.J. (2012) Linking runoff and erosion dynamics to nutrient fluxes in a degrading dryland landscape. Journal of Geophysical Research-Biogeosciences, doi:10.1029/2012JG002071, 117, G00N15.
Michaelides, K. and Martin, G.J. (2012) Sediment transport by runoff on debris-mantled dryland hillslopes. Journal of Geophysical Research-Earth Surface, doi:10.1029/2012JF002415, 117, F03014.
Michaelides, K., Lister, D., Wainwright, J. and Parsons, A.J. (2009) Vegetation controls on small-scale runoff and erosion dynamics in a degrading dryland environment. Hydrological Processes, doi:10.1002/hyp.7293, 23: 1617 – 1630.
Citation: https://doi.org/10.5194/hess-2021-48-AC1
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AC1: 'Reply on RC1', Kipkemoi Isaac, 12 Mar 2021
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CC1: 'Comment on hess-2021-48', Innocent Ngare, 31 Mar 2021
1. The paper is well researched and concisely written. It gives critical climate variability dynamics in drylands that surge extremes and applications for forage and agriculture.
2. The authors have keenly selected a widely applied model that gives discourse on soil moisture and temporal rainfall resolution.
3. However, the authors mention Richards equation model that informs the Hydrus-ID yet the explanation is limited and doesn’t come out clearly, I would appreciate the authors to give some of the underlying equations.
Based on the contribution of this paper to dryland hydrology and the Author’s responses to first referee, I recommend this paper for publication.Citation: https://doi.org/10.5194/hess-2021-48-CC1 -
AC2: 'Reply on CC1', Kipkemoi Isaac, 13 Apr 2021
We thank the community reviewer for their useful comments. Below in bold are our response to the each of the comments.
The paper is well researched and concisely written. It gives critical climate variability dynamics in drylands that surge extremes and applications for forage and agriculture.
The authors have keenly selected a widely applied model that gives discourse on soil moisture and temporal rainfall resolution.
We thank the reviewer for taking time to give the above two comments. Indeed, our goal was to explore how using precipitation data resolved at different temporal resolutions (hourly, daily, weekly) affects estimates of soil moisture and plant-water availability particularly in dryland regions. As also highlighted by one of the reviewers, the issue of how temporal resolution of precipitation data can impact simulated hydrological responses (i.e., soil moisture dynamics) especially in climate change studies is often overlooked. The reason for initiating this research was to underscore importance of using datasets that are representative of dryland hydrology (Blöschl and Sivapalan, 1995). This included identifying the critical timescale of precipitation data needed to assess water availability to human society in these vulnerable regions.
However, the authors mention Richard’s equation model that informs the Hydrus-1D, yet the explanation is limited and doesn’t come out clearly, I would appreciate the authors to give some of the underlying equations.
Thank you for this comment. We have left out widely published and well-known equations in this manuscript so that we could only directly address the problem at hand. This helps the readers to focus on our main contribution to the paper (i.e., the impact of precipitation resolution on soil moisture dynamics and consequences for different storminess scenarios in the future). The inclusion of previously published material would have made this paper unnecessarily long and redundant. We direct this and other readers to the widely published material on Hydrus-1D. In our paper we merely mention the key equations. Hydrus-1D solves the Richards equation and includes Darcy’s Law within the sink term in Richard’s equation (Šimůnek et al., 2012; Wang et al., 2009).
Based on the contribution of this paper to dryland hydrology and the Author’s responses to first referee, I recommend this paper for publication.
Thank you.
References
Blöschl, Günter, and Murugesu Sivapalan. "Scale issues in hydrological modelling: a review." Hydrological processes 9.3‐4 (1995): 251-290.
Šimůnek, J., Van Genuchten, M. T., and Šejna, M.: The HYDRUS software package for simulating the two-and three-dimensional movement of water, heat, and multiple solutes in variably-saturated porous media, Technical manual, 2012.
Wang, P., Quinlan, P., and Tartakovsky, D. M.: Effects of spatio-temporal variability of precipitation on contaminant migration in the vadose zone, Geophys. Res. Lett., 36, 10.1029/2009gl038347, 2009.
Citation: https://doi.org/10.5194/hess-2021-48-AC2
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AC2: 'Reply on CC1', Kipkemoi Isaac, 13 Apr 2021
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RC2: 'Comment on hess-2021-48', Anonymous Referee #2, 16 May 2021
This research is interesting, but at the same time this was a very mature research area. It does not make much sense to continue to use this HYDRUS model for research on HAD, for there are lots of related studies.
1 There are some errors in Figure 2, generally speaking, the roots can absorb water Approximately below the root layer with a capillary water holding height depth, it seems that the authors do not understand the basic principles of ecological hydrology in arid areas.
2 The author did not consider the runoff confluence process during precipitation.
3 The results of using Penman’s formula to calculate ET have not been verified by measured plot data, which may lead to the converse results.
Citation: https://doi.org/10.5194/hess-2021-48-RC2 -
AC3: 'Reply on RC2', Kipkemoi Isaac, 22 May 2021
Reviewer 2
We welcome the comments provided by Reviewer#2. However, we would like to note that the Reviewer does not comment on the rationale for the study or the results and implications (impact of rainfall resolution on soil moisture). Their three comments refer to minor points (one of which we addressed in our response to Reviewer 1) and the other two are brief comments which lack clarity and justification. We address all three comments below in bold.
This research is interesting, but at the same time this was a very mature research area. It does not make much sense to continue to use this HYDRUS model for research on HAD, for there are lots of related studies.
We feel that the reviewer has misrepresented our study. It was not simply a case study use of HYDRUS, but rather an investigation of the potential interactions between rainfall and soil moisture, which are inherently nonlinear. HYDRUS is indeed commonly used in the literature because it has been shown in numerous studies to capture the essence of vertical flow through porous unsaturated media. We disagree with the reviewer that ‘there are lots of related studies’ on investigating the impact of temporal resolution of rainfall delivery in drylands on soil moisture. In these environments, it is important to characterise the effects of rainfall delivery (rainfall intermittence, high intensity, and short duration storm events)
- There are some errors in Figure 2, generally speaking, the roots can absorb water Approximately below the root layer with a capillary water holding height depth, it seems that the authors do not understand the basic principles of ecological hydrology in arid areas.
In Fig 2, we have simply provided the conceptual model as originally outlined in the HYDRUS manual. While capillarity may be an important consideration in locations where the water table is close to the surface, we have assumed a deep-water table, well below the full soil depth. Therefore, we assume no vertical interaction between water table fluctuations and capillarity. Thus, capillary water is NOT available to roots. This is often the case in dryland environments, especially at locations distant from channels. We can clarify this point in the figure caption.
- The author did not consider the runoff confluence process during precipitation.
We addressed this issue in our responses to Reviewer 1. To summarise here: indeed, overland flow, transmission losses into dry channel beds, focused recharge, surface crusting, sediment transport etc. are all important processes in drylands. Our team has published on these dryland processes extensively – including in Walnut Gulch (e.g., Chen et al., 2019; Michaelides et al., 2018; Singer & Michaelides, 2017; Jaeger et al., 2017; Michaelides & Singer, 2014; Singer & Michaelides, 2014; Michaelides et al., 2012; Michaelides & Martin, 2012; Michaelides et al., 2009 etc.). However, this paper focuses on the 1D vertical distribution of rainfall into the soil profile only – there is no representation of 2D processes of water flow over hillslopes and in channels. We are interested in understanding how soil moisture dynamics vary with rainfalls of different temporal resolutions. Therefore, we are simplifying the representation of this problem in a 1D model of a 1m deep soil profile on a flat surface (similarly to a column experiment performed using typical land surface models).
- The results of using Penman’s formula to calculate ET have not been verified by measured plot data, which may lead to the converse results.
The comment from the Reviewer is not clear. Converse to what? In this study, we used AZMET ETO data calculated by a well-established organization by a well-respected method using regionally available data. This data was downloaded from the link provided in table 1 (https://cals.arizona.edu/AZMET/). We also provided the formulae used to calculate ETo, which is based on the Penman-Monteith Equation (Eq. 1). The equation derivation and calculation procedures for the standardized ETo including testing and validation is provided in Brown (2005). This method is a well-established one for characterising evaporative demand from the atmosphere for a wide range of hydrological analyses (Vicente‐Serrano, et al 2020.).
References
Brown, P. (2005). Standardized reference evapotranspiration: A new procedure for estimating reference evapotranspiration in Arizona.
Chen, S-A., Michaelides, K., Grieve, S.W.D. and Singer, M.B. (2019) Aridity is expressed in river topography globally. Nature 573–577, doi.org/10.1038/s41586-019-1558-8.
Jaeger, K., Sutfin, N., Tooth, S.E., Michaelides, K. and Singer, M.B. (2017) Geomorphology and sediment regimes of intermittent rivers; in Datry, T., Bonada, N., Boulton, A. (eds.), Intermittent Rivers: Ecology and Management, Elsevier.
Michaelides, K. and Martin, G.J. (2012) Sediment transport by runoff on debris-mantled dryland hillslopes. Journal of Geophysical Research-Earth Surface, doi:10.1029/2012JF002415, 117, F03014.
Michaelides, K. and Singer, M.B. (2014) Impact of coarse sediment supply from hillslopes to the channel in runoff-dominated, dryland fluvial systems. Journal of Geophysical Research–Earth Surface, doi:10.1002/2013JF002959, 119 (6) 1205 – 1221.
Michaelides, K., Hollings, R., Singer, M.B., Nichols, M., Nearing, M. (2018) Spatial and temporal analysis of hillslope-channel coupling and implications for the longitudinal profile in a dryland basin. Earth Surface Processes and Landforms doi:10.1002/esp.4340
Michaelides, K., Lister, D., Wainwright, J. and Parsons, A.J. (2012) Linking runoff and erosion dynamics to nutrient fluxes in a degrading dryland landscape. Journal of Geophysical Research-Biogeosciences, doi:10.1029/2012JG002071, 117, G00N15.
Michaelides, K., Lister, D., Wainwright, J. and Parsons, A.J. (2009) Vegetation controls on small-scale runoff and erosion dynamics in a degrading dryland environment. Hydrological Processes, doi:10.1002/hyp.7293, 23: 1617 – 1630.
Singer, M.B. and Michaelides, K. (2014) How is topographic simplicity maintained in ephemeral, dryland channels? Geology, doi:10.1130/G36267.1.
Singer, M.B. and Michaelides, K., (2017) Deciphering the expression of climate change within the Lower Colorado River basin by stochastic simulation of convective rainfall. Environmental Research Letters, 12,104011 doi:10.1088/1748-9326/aa8e50.
Vicente‐Serrano, S.M., McVicar, T.R., Miralles, D.G., Yang, Y. and Tomas‐Burguera, M., 2020. Unraveling the influence of atmospheric evaporative demand on drought and its response to climate change. Wiley Interdisciplinary Reviews: Climate Change, 11(2), p.e632, https://doi.org/10.1002/wcc.632, 2020.
Citation: https://doi.org/10.5194/hess-2021-48-AC3
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AC3: 'Reply on RC2', Kipkemoi Isaac, 22 May 2021
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