Reply on RC2

In this study Alemu Yenehun et al. estimate groundwater recharge and study the spatial and temporal recharge patterns for the Lake Tana region in Ethiopia. Three (well-) established recharge estimation methods are applied, a model approach using the physically-based WetSpass model, the groundwater table fluctuation method, and the chloride mass balance method. I applaud the Authors for conducting such an extensive study in a research area where data acquisition I assume can often be challenging. I think the main contributions from this paper are 1) an improved understanding of the groundwater recharge fluxes in the case study area, and 2) the comparison of different recharge estimation methods. These contributions are valuable, as groundwater is an important source of drinking water in the region and a better understanding of the resource in this region would serve many. I found the manuscript generally well written (minor textual improvements are required), but some restructuring and changes to the figures might be required to improve the readability of the manuscript. The main concern I have for this manuscript are related to the methodology and the description thereof, discussed in detail below and in separate line comments. Additionally, while I think the study is worth publishing, I am unsure whether the contribution fits the scope of HESS (“substantial new concepts, ideas, methods, or data”) in its current form. In my opinion, it would fit much better in other journals where it can be submitted as a case study (e.g., Hydrogeology journal), and reviewed as such. If the Authors wish to publish in HESS, I think a more elaborate analysis and discussion of the uncertainties in recharge estimation could be done to better fit within the scope of the journal.

In this study Alemu Yenehun et al. estimate groundwater recharge and study the spatial and temporal recharge patterns for the Lake Tana region in Ethiopia. Three (well-) established recharge estimation methods are applied, a model approach using the physically-based WetSpass model, the groundwater table fluctuation method, and the chloride mass balance method. I applaud the Authors for conducting such an extensive study in a research area where data acquisition I assume can often be challenging. I think the main contributions from this paper are 1) an improved understanding of the groundwater recharge fluxes in the case study area, and 2) the comparison of different recharge estimation methods. These contributions are valuable, as groundwater is an important source of drinking water in the region and a better understanding of the resource in this region would serve many. I found the manuscript generally well written (minor textual improvements are required), but some restructuring and changes to the figures might be required to improve the readability of the manuscript. The main concern I have for this manuscript are related to the methodology and the description thereof, discussed in detail below and in separate line comments. Additionally, while I think the study is worth publishing, I am unsure whether the contribution fits the scope of HESS ("substantial new concepts, ideas, methods, or data") in its current form. In my opinion, it would fit much better in other journals where it can be submitted as a case study (e.g., Hydrogeology journal), and reviewed as such. If the Authors wish to publish in HESS, I think a more elaborate analysis and discussion of the uncertainties in recharge estimation could be done to better fit within the scope of the journal.
Reply: Thank you so much. We have tried to do an uncertainty analysis of the model parameters and have added it in the revised version.

Methods description
From the description of the methodology and data following in this manuscript, it is hard to reproduce the results and gain a full understanding of the modeling procedure. This could partly be solved by sharing the scripts, input, and output data used in this study. However, often a more detailed description of the modeling procedure is also required. The WetSpass model requires substantial data input, which is not always clearly described in the manuscript. From the description of the data, it seems that different time periods were used to generate the input data for WetSpass. Perhaps this could be clarified using a table that summarizes the different data sources and time series characteristics (e.g., measurement interval, period, operator). Given the high temporal variability of the different hydrometeorological variables, I assume the same time periods for all variables are used, but I could not verify this from the current manuscript. A few times it is mentioned "expert judgement" is used, but it remains unclear what values were applied and why. The calibration process is only briefly described, and the calibrated parameters are not reported. I general, I think it would be good to rewrite the methods section with reproducibility in mind.
Reply: Yes, indeed, a table containing the different data sources and time period of the WetSpass model input variables will be added in the revised final manuscript. It is true that for some of the model input variables, the data for different time periods have been considered. As the model is using long-term averages of the different variables as an input (in our case: long-term average summer and winter), hence, observing long-term timeseries temporal variations of the water balance components with the WetSpass model is impossible. The model is limited as it is simulating only long-term monthly averages as a fine time scale (time series results are not possible). In other words, the model is basically developed to simulate long-term average monthly/seasonal conditions, and most importantly the spatial variations. The model is basically developed for spatial simulation of groundwater recharge (which is later coupled with groundwater modeling-MODFLOW) whose short -time (day to day or hour to hour variations) is less important compared to the other water balance terms.
Except for the groundwater level map (which is based on recently collected data), meteorological variables (rainfall, average temp., wind speed, all meteorological parameters used to calculate PET using the Penman-Monteith method) were more or less in similar time periods. Yes, it is true that the meteorological variables have high temporal variations (most importantly the seasonal or monthly, or daily variations); year-to-year variation is relatively small. In other words, there is a small variation for a given season or month for different years. Given the objective of this paper i.e. the spatial variation of recharge and to a certain extent other water balance terms, these would not be a major problem.
The other non-meteorological (physical) parameter that changes with time is the land use. For the long-term average data (2005 to 2018), we used the land use map using the 2014 satellite image and ground truth data, for the modeling; land use of 1980 and 2000 for the evaluation of the effect of land use change on the water balance terms. The use of onetime data for the land use and long-term average for the other parameters might have slightly departed from reality and may have caused some uncertainties. However, the model is not robust enough to use different land uses for calibration. Furthermore, the change in land use is not so fast, and hence, the land use of 2014 might have represented more or less the possible land use status of the area during 2005-2018.
Explicit statements for what do we mean by "expert judgment" will be added in the revised version. We mean some adjustment for the parameters' values on the default land use parameter table has been made, for example, the root depth of forest land is changed. In the model parameter table, each land use type has given impervious, bare land, vegetation, and open water percentages. In the default parameter of the WetSpass model the vegetation area coverage for the bush and shrubland is 100%. This is based on the observation of the land use class type in the temperate zone (the Netherlands and Belgium). However, the Bush and Shrubland use in the Ethiopian (tropics) context is different: the vegetation is sometimes sparsely distributed, and is with some bare land component, during our field verification on the land use type, we tried to guess (using parcels of land on which we do some measurement), and came up with about 10% is bare land and the rest 90% is consisting of vegetation. Similarly, adjustments have been made by Gebreyohannes et al. (2013) during their application of the model for Geba catchment, in northern Ethiopia. Similarly, the sub-afro-alpine vegetation land use type found in our area (consisting of about 0.3% of the total area coverage) is not present in the default land use classes of the WetSpass model. However, we made an equivalent with wet meadow land use type, and following a similar procedure we modified the land use percentage to 80% vegetation and 20% bare land (it was 100% vegetation for wet meadow land cover type in the model parameter table.
<< insert table1: the model land use parameter table here, attached in the supplement file>> The steps followed in the calibration process, and the table for the calibrated parameters will be added in the revised version of the manuscript.
The modeling section of the methodology is more elaborately written in the revised version. However, to reduce the pages of the manuscript, all the detailed equations, and the empirical formulas developed and followed in the WetSpass model are better not to be included, we would rather cite the papers that developed the model first and improved further.

Uncertainties in recharge estimates
The authors mention in the introduction that it is important to take uncertainty in recharge estimation into account (line 47), and thus I was expecting a more elaborate analysis or discussion of the uncertainty in recharge estimation methods applied in this manuscript. As the WetSpass model is manually calibrated, no parameter uncertainties are available. It therefore remains unknown how uncertain these recharge estimates are. This could be addressed by a sensitivity analysis or a more elaborate uncertainty analysis. A discussion of the limitations of the different methods and the uncertainties of the recharge estimates at the end of section 4 would be a welcome addition to the current manuscript.
Reply: The WetSpass model has different parameters and needs input variables to give a reasonable output for the understanding of water balance components of a given river basin/watershed. Yes, to evaluate the efficiency of the WetSpass model applied in this study, the sensitivity analyses of all parameters and input variables would be a good addition of the paper. A simple sensitivity analysis is carried out and added in this revised version of the manuscript. Since the objective of doing this sensitivity analysis is to know the possible uncertainties that might be incorporated into the estimated water balance values, we did a sensitivity analysis for the calibration of the global parameters of the WetSpass model. These are alfa coefficients (it is a parameter adjusting soil moisture value), interception coefficients (adjusting interception by the plant canopy), LP coefficient (parameter adjusting evapotranspiration), rainfall intensity, and x coefficient (runoff delay factor). The other physical factors (called local parameters) such as the contribution of the slope (40%), landuse (30%) and soil (30%) in determining the balance components share, are originally developed from literature (experimental research). Thus, we kept them as they are (we have taken the default values of the model), and hence, we have not done a sensitivity analysis though their local variation might have caused some uncertainties in the estimated recharge and other water balance terms. Furthermore, we do not perform sensitivity analysis of the meteorological variables used in the model perhaps they might cause some uncertainties during measurement, interpolation, etc. They were fixed variables in our calibration similar to the physical parameters mentioned above. Among the global model parameters calibrated in the model, the most sensitive global model parameter is RF intensity. On average, for every 1 mm/hr increase of the intensity, there is a mean annual recharge increment of 38.2 mm. Fortunately, we measured the rainfall intensity at some of the meteorological stations that the BDU-IUC project (the project that sponsors this study) established (using the automatic rain gauges), and hence, we used the range of these data in our calibration.
<<insert Figure 1 attached in the supplementary file here>> In general, except for the RF intensity (which highly depends on the rainfall characteristics of an area), the other global model parameters have given a possible range of values in the WetSpass model (LP coefficient and x coefficient: 0.1 to 1; and alfa coefficient and a interception 1 to 10). Hence, the sensitivity testing for values within these ranges has been performed.  The variation in a interception is small and only for the initial values. For the relatively higher values, the recharge is almost not changing, and hence, the recharge can be considered as insensitive for the variation of a interception. This has been also shown with the low R2 value.  The groundwater recharge is not changing for the possible changing intervals of the xcoefficient, and hence, the x-coefficient is the most insensitive parameter among all the global model parameters for the Lake Tana basin.

Title
The title suggests a more general study on volcanic aquifers, while a case study is presented. I think it would be better if the title reflects the fact that it is a case study. Additionally, the title suggests that there are "point measurements" of groundwater recharge. In my view it be better to refer to these as 'point estimates', as they are empirical estimates from a recharge estimation method and not real measurements.
Reply: Thank you so much. Yes, indeed, they are point estimates. For example, the variables in the calculation of the WTF method are the change in the water level and the specific yield (Sy). The water level is based on fine-time scale measurement, however, there is estimation for Sy though it is also analyzed from slug and pumping tests (real measurements), estimation from Johnson (1967) literature compilation has been also used. Given the high aquifer variability (compared to the representativity of each test well), it is true the WTF values are also estimations. Thus, the topic is corrected accordingly.
Yes, indeed the methods are tested in the case study area, however, we do believe the evaluation part that recommends the effective application of the methods for aquifers lying at different topographical and hydrogeological conditions would make the study beyond being case study, and will attract global readers.

Line comments L31: add a comma after floodplain
Reply: thank you, added.

L72: regionalize
Reply: accepted, and corrected accordingly. L77: were all these studies in a specific study area? If so, good to mention that.
Reply: mentioned, and thus corrected accordingly.
L123: No reference to Wang et al (1996)

required?
Reply: Wang et al (1996) is about the WetSpa model. Maybe it is a good idea to add it as WetSpa is the starting point and the basis for the development of WetSpass. Hence, it is added in the new revised version. L132/135/139: Sentences introducing these equations would be nice.
Reply: thank you, added.! L133: Should there not be a change in storage term?
Reply: Yes, in the WetSpass model, the storage term is neglected. There are two ways of incorporating change in storage to the model on a seasonal basis. The first instance is that the plant available soil moisture reservoir in the summer is assumed to be filled up while it can be depleted in the model in the winter (for areas like Lake Tana basin); the second case is that in the model a different groundwater depth can be used for winter and summer. In our methodology, we followed the second option, we had prepared the groundwater table map for the summer and winter seasons using the time-series groundwater level monitoring data that we used for our study. This is explained in the methodology part of the manuscript. However, since we have Lake Tana in our study area, there is a possibility of surface water storage change. For example, the evaporation from the open lake water may cause the summation of annual AET + Runoff + Recharge to be greater than the annual RF i.e. RF < AET + runoff + groundwater recharge. Hence, there is a possibility that the storage term changes. However, change in storage in the WetSpass model is not as important as the other hydrological models, which can simulate water balance components at fine-time series (such as a daily or hourly). For such models, storage amount (variation) at each time-series highly affects the water balance components of the following time-series value. E.g. the storage change of day 1 affects the water balance values for day 2. But in the WetSpass model applied in this study (seasonal), there are only two time-series whose storage difference (especially the groundwater storage) by an independent water table map.

L142: Which evaporation equation was used?
Reply: the Penman equation is used for evaporation in WetSpass. It is discussed already in the result and discussion section.
L144: I think it would be good to state which physically-based equations (e.g., Darcy) were used and elaborate a bit on the model (e.g., finite differences).

Reply:
In general, the WetSpass model is based on known hydrological equations (conceptually and physically meaningful equations), and also some empirical equations developed from works of literature. Yes, a better-elaborated explanation of the model background will be added in the revised version of the manuscript.
L147-156: This description is rather vague, what was done exactly? How were the values changed?
Reply: In the revised manuscript, detailed changed values of the land use parameters are explained, as it is already explained above. Basically, the changes are in the percentage of bare land and vegetation area percentage of a given land use type (bush and shrubland and sub-afro-alpine grassland), and also root depths of forest land (it is already explained in the paragraph).