the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Multiscalar and nonlinear controls of drought impacts in Spain revealed from media-reported data
Abstract. This study assesses the multiscalar and nonlinear relationships between drought severity, characterized by the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI), and media-reported hydrological and agricultural impacts across Spain during 1976–2023. Drought indices were derived from a high-resolution gridded climate dataset (1.1 km), while impact data were obtained from standardized monthly frequencies of drought-related newspaper articles at the provincial scale. Results demonstrate robust temporal coherence between drought conditions and impacts, with major drought episodes associated with anomalies exceeding +1 to +2 standard deviations. Drought–impact relationships strengthen markedly with accumulation timescale, reaching maximum correlations at 12–24 months (|r| ≈ 0.6–0.8), while short timescales (1–3 months) show weak associations (|r| < 0.3). Hydrological impacts are primarily associated with accumulated medium- and long-term moisture deficits (12–48 months), whereas agricultural systems respond more rapidly to short- and intermediate-term drought conditions (3–12 months). However, both sectors shift to longer time scales as drought severity increases. Sensitivity analyses reveal pronounced nonlinear responses, with impacts increasing disproportionately during severe drought conditions with a peak at 12–36 months. The consistently stronger association of SPEI relative to SPI, especially in case of agricultural impacts, suggesting a dominant contribution of atmospheric evaporative demand. Our results indicate that drought impacts are governed by the accumulation and nonlinear propagation of moisture deficits modulated by temperature-dependent processes and provide a framework for improved impact-based drought monitoring in a warming climate.
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Status: open (until 17 Aug 2026)
- RC1: 'Comment on egusphere-2026-3003', Anonymous Referee #1, 26 Jul 2026 reply
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- 1
This paper uses a dataset of news articles about drought (Lopez-Otal 2025) to compare drought indicators with reported hydrologic and agricultural drought impacts. For impact data, the authors use standardized monthly frequencies of news articles, by province. For climate data, they use 48 different lengths (1-48 months) of Standardized Precipitation-Evapotranspiration Index and Standardized Precipitation Index. Comparing differences in the relationships between SPI and impacts and SPEI and impacts, they assess the contributions to impacts of temperature-driven processes vs. precipitation-driven processes. They find stronger relationships over longer time periods, 3-12 months for agricultural impacts and 12-48 months for hydrologic impacts.
Their findings are consistent with what one would expect, but/and provide a novel way to quantify the effects of SPI and SPEI, including which timescales are most relevant. Using a dataset of media articles with counts standardized by month and location is a novel and substantial methodological contribution.
The authors find extensive spatial heterogeneity between provinces in the relationships between drought indicators and drought impacts. I believe this study provides a robust methodological approach that could be used to begin exploring the contributions of different social and environmental variables to local resilience.
Specific items
On line 90 it says news articles published from May 1976 to December 2023 were analyzed. That would be 572 months. But line 144 refers to 576 months. Please reconcile the difference.
Starting on line 94, drought impact extraction – are the bins of agricultural or hydrologic impact articles mutually exclusive, or how much overlap is there? I ask because Fig 1c illustrates a fairly tight relationship between agricultural and hydrologic impacts, or, as described on line 211, “coherent cross-sector drought propagation.” It makes sense that they would be occurring at the same time, but differences would be masked if a substantial portion is being reported in the same articles. News framing could be “drought is causing problems such as [ag impact], [hydro impact].”
Line 148 refers to a covariance matrix, but I am not finding what variables are being analyzed. Please explicitly describe them. Is it the 48 timescales of SPI and SPEI, hydro vs ag, and province? I found myself looking for socio-economic variables, particularly in light of the references to non-linear responses and spatial heterogeneity.
Fig. 3: It would be helpful to have a little more contrast between the two colors.
Line 441: The discussion of using media coverage as data beginning on line 61 is good and nuanced. It stands in contrast with the sentence beginning on line 441.
Line 442: What does “also” refer to? If you are comparing drought impacts reported by media to drought impacts that are physical effects derived from climatological indicators, please define your terms in much greater detail. Same comment re/ “another” on line 443.
Lines 443 & 446: You mention media bias without going into any depth about what you mean or what it may mean for your analysis. I urge researchers using news as data to avoid routinely mentioning bias without explaining what they are referring to. News media are indeed constrained and shaped by cultural and institutional norms and are sometimes designed to express explicit viewpoints. (Science is also constrained and shaped by cultural and institutional norms, including the methods, preferences and interests of various disciplines.) But more germane than “bias” is the reality that news is not intended to be a comprehensive record of drought impacts. It is broader-brush, focusing societal attention where it’s needed, on what’s novel or problematic, serving an issue-identification function (Dow 2007). Using news as drought impact data is valuable in that it helps us understand drought’s effects at specific places and times. But in calibrating drought indices, news-derived impacts can be thought of as “best-available” data or “reasonably proxy” data, rather than the data that would exist if researchers or policymakers had the resources to collect data that was tailored for purpose. To call that “bias” seems a bit off-point.
Line 443: “… and subject to varied media coverage” … the sequence of clauses in this sentence leaves the object of this thought in question.
On line 444, instead of “consumed,” do you mean “experienced” or “framed” or “interpreted”? People are said to consume news but are not said to consume drought.
A possible rewrite of this important sentence:
Although news articles are written for purposes other than systematically documenting drought impacts, they provide a credible representative record of drought’s effects at specific places and times. News articles go beyond the physical effects of drought, providing historic documentation of how drought affected people, livelihoods, ecosystems and socio-economic systems.
References
Dow, K. (2010). News coverage of drought impacts and vulnerability in the US Carolinas, 1998–2007. Natural hazards, 54(2), 497-518.