the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A novel method for correcting water budget components and reducing their uncertainties by optimally distributing the imbalance residual without full closure
Abstract. Closing the water budget improves the consistency of water budget component datasets, including precipitation (P), evapotranspiration (ET), streamflow (Q) and terrestrial water storage change (TWSC), thereby enhancing the understanding of basin-scale water cycle processes. Existing water budget closure correction (BCC) methods typically redistribute the entire water imbalance error (ΔRes) to achieve perfect water budget closure but often neglect the trade-off between achieving closure and the errors introduced into budget components as a result of this redistribution. This study quantifies the uncertainties introduced by existing BCC methods (CKF, MCL, MSD, and PR) across 84 basins representing diverse climate zones. We then propose a novel method, IWE-Res, to identify the optimal balance for redistributing ΔRes. This method minimizes the combined error from both introduced budget component errors and the remaining ΔRes error, while reducing the occurrence of negative values. The results indicate: (1) Existing BCC methods can lead to negative values in corrected budget components, with negative values comprising approximately 0–10 % (mostly below 5 %) of the time series; (2) Compared to existing BCC methods, the proposed IWE-Res method improves the accuracy of corrected P by 29.5 %, corrected ET by 24.7 %, corrected Q by 69.0 %, and corrected TWSC by 6.8 % based on the root mean square error (RMSE); and (3) In most basins, except in cold regions, the optimal balance is reached when 40 %–90 % of ΔRes is redistributed. By offering a more balanced approach to water budget closure, this study improves the accuracy and reliability of corrected budget component datasets.
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Status: open (until 11 Jun 2025)
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RC1: 'Comment on egusphere-2025-990', Anonymous Referee #1, 11 May 2025
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The manuscript is focused on numerical techniques to 'distribute' residuals of a water balance equation over the contributing terms, avoiding negative values. The manuscript seems focused on the numerical techniques, with limited efforts for a hydrological interpretation. It could be more convincing if authors bring a bit more on the 'explanation' side.
It is not immediately evident to this reviewer that negative values are a problem, especially for the soil storage term (TWSC). In fact one may expect this term to be symmetric around zero, and maybe the same holds for the errors?
One would assume, based on considerations of the various terms of the water balance that a comparison between the negative (or positive) residuals over time will help identifying which term may be primarily responsible: the soil term can dominate in the short term (e.g. days), but will be small for annual comparisons and may become negligible at decadal scale (except for the long-term desiccation discourse). Spatial patterns are also expected, as frontal rainfall patterns are much easier to represent correctly than thunderstorms (much of tropics and arid zone rainfall) -- indeed your later results (Fig. 6) seem to match this expectation.
Maybe further reference can be made to the 'Budyko' literature that looks at an annual balance, while your current analysis takes a monthly perspective.
The abstract could become more attractive to readers if the time unit (monthly balance calculations) is made explicit, as results for daily or annual balance calculations will likely be different.
Details:
The Highlights should be understandable for a non-technical expert -- at the moment they are too full of jargon to attract readers.Line 57 Indeed a closed budget gives some confidence in the underlying estimates, but not if the closure is obtained by 'fudging' the data, without 'understanding'. So I disagree that 'closing the budget' helps with 'understanding'.
Line 66. Before delving into the details it will be good for the reader to be reminded of the physical aspects of uncertainty in the various terms, as these are of different natures:
P precipitation input -- the typically are fairly reliable point data from rainfall gauge data, often with some need t gap fill missing data. The main uncertainty here is in the spatial distribution and representativeness of rainfall gauges, in relation to rainfall types (for frontal rains the spatial uncertainty is low, for local storms it can be high). The distribution of rainfall gauges is often determined in part by accessibility and convenience, and overall uncertainty of daily rainfall may be easily underestimated. More recent satellite based estimates of rainfall appear to perform well for frontal rains, but not in other rainfall types.
ET Evapotranspiration equations have been fairly well calibrated, but there can be uncertainty over the advection term especially in small catchments. For larger areas energy balance equations may be sufficient.
Q monitoring of outflow can have low uncertainty if ;rating curves' are frequently calibrated. However, the delineation of the watershed (and area used for the calculations) can be off where groundwater flows don't necessarily follow surface catchment delineations and can be underestimated.
TWSC can become negligible if a multi-year balance is considered (verifying the P and Q estimates) but can dominate the balance at a daily time-scale. A major challenge is the depth over which TWSC is to be assessed, as changes in the topsoil can be more easily assessed than that deeper in the soil.Line 98 Negative ET is possible under 'dew formation' conditions... (be it in only part of a daily temperature cycle)
Line 244 There can be 'bias' (systematic error, e.g. if groundwater flows mean that the basin is not closed and part of outflowing Q is missed; the area of the basin can also be incorrect), part 'measurement error'. As you focus on relatively large basins, the bias term may be relatively small, but for smaller watersheds the bias terms cannot be ignored. Standard techniques such as plotting cumulative Q vs cumulative P give indications, especially if nested Q data exist beyond outflow data.
Line 702-714, Figure 7 - would it make sense to compensate S Hemisphere data for a 6 month shift in seasons? Or even more flexibly to use a hydrological year concept with a standardized month for maximum P.
Citation: https://doi.org/10.5194/egusphere-2025-990-RC1
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