High Precipitation Variability Research Articles

Harnessing the Radio Frequency Power Level of Cellular Terminals for Weather Parameter Sensing

In light of recent extreme weather events, it is imperative to explore innovative methodologies for promptly and accurately measuring various meteorological parameters. The high spatial and temporal variability in precipitation often surpasses the resolution capabilities of traditional rain gauge measurements and satellite estimation algorithms. Therefore, exploring alternative methods to capture this variability is crucial. Research on the correlation between signal attenuation and precipitation could offer valuable insights into these alternative approaches. This study investigates (a) the feasibility of the classification of precipitation rate using signal power measurements in cellular terminals and (b) the impact of atmospheric humidity as well as other meteorological parameters on the signal. Specifically, signal power data were collected remotely through a specialized Android application designed for this research. During the time of analysis, the power data were processed alongside meteorological parameters obtained from the meteorological station of the Physics Department at the University of Ioannina gathered over one semester. Having in mind the radio refractivity of the air as a fascinating concept affecting the way radio waves travel through the atmosphere, the processed results revealed a correlation with signal attenuation, while a correlation between the latter and absolute humidity was also observed. Moreover, a precipitation rate classification was attained with an overall accuracy exceeding 88%.

Spatial and temporal patterns of forest fires in the Central Monte: relationships with regional climate

BackgroundNatural and anthropogenic wildfires burn large areas of arid and semi-arid forests with significant socio-economic and environmental impacts. Fire regimes are controlled by climate, vegetation type, and anthropogenic factors such as ignition sources and human-induced disturbances. Projections of climate and land-use change suggest that these controlling factors will change, altering fire regimes in the near future. In the southern Central Monte, Mendoza, Argentina, the factors that modulate the fire temporal and spatial variability are poorly understood. We reconstructed the fire history of southeast of Mendoza from 1984 to 2023 and investigated the relationships between fire extent and climate variability at seasonal and interannual scales. Burned areas were determined using Google Earth Engine by processing Landsat 5-TM, Landsat 7-ETM+ , and Landsat 8-OLI-TIRS sensor imagery.ResultsThe region exhibited high spatial and temporal variability in fire occurrence, being a mosaic of areas with different fire histories and recovery times. Between 1985 and 2023, fire recurrence ranged from sites unburned to sites with up to 14 fires. The occurrence of large fires was strongly favored by a combination of a year with abundant spring–early summer precipitation, which favors fuel accumulation, followed by a year of low spring–early summer precipitation. Precipitation and burnt area showed a very pronounced 6–7 year cycle, suggesting a dominant climatic control on fire occurrence.ConclusionsFire distribution in southeastern Mendoza forests is not homogeneous, resulting in a mosaic of patches with different fire histories. This heterogeneity may be related to vegetation patterns and land use. The temporal variability of fires is strongly influenced by climate variability, which would promote fuel production and subsequent drying. Large fires are concentrated in periods of high interannual precipitation variability. Climate change scenarios predict an increase in temperature and precipitation variability in the region, suggesting future changes in fire dynamics. Our results contribute to the development of fire guidelines for southeastern Mendoza forests, focusing on periods of wet years followed by dry years that favor fire occurrence and spread.

The effect of shrub cover on conifer water-use patterns, growth and response to precipitation variability in the southern Sierra Nevada.

As wildfires increase in size and severity, large areas of forest are undergoing substantial increases in shrub cover. In forests where water is the limiting resource, the partitioning of soil water between shrubs and young trees may determine how shrubs affect tree growth and water-stress. Here we evaluated juvenile trees (average age=32years) of two dominant conifer species in the southern Sierra Nevada of California (Abies concolor (white fir) and Pinus jeffreyi (Jeffrey pine)) growing in the presence or absence of shrubs. The two shrub species included Arctostaphylos patula and Ceanothus cordulatus, a nitrogen-fixing species. We analyzed the δ2H and δ18O values of xylem water for both tree and shrub species to assess how shrub cover affects the water-uptake patterns of conifers and whether there is niche partitioning between trees and shrubs. We found that growing near shrubs did not have a significant effect on the water source dynamics of either tree species, with similar source water contributions calculated for conifers growing in both the presence and absence of shrubs. Using a tree-ring analysis of growth and δ13C from 2016 to 2021, a period of high precipitation variability, we found that shrub cover had a positive effect on tree growth while decreasing carbon discrimination, which may be due to increased nitrogen availability from Ceanothus cordulatus. Overall, our results suggest that growing in the presence of shrubs does not alter the water uptake patterns of white fir and Jeffrey pine and instead may have a positive effect on the growth rates of these species during both wet and dry years.

Positive asymmetric responses indicate larger carbon sink with increase in precipitation variability in global terrestrial ecosystems

<p>Climate changes have caused high inter-annual variability in precipitation. However, how the terrestrial ecosystem responds to precipitation variability remains unclear. Using global remote sensing data and a meta-analysis by synthesizing 800 pairwise observations of experimental manipulations worldwide, we quantified the responses of the terrestrial ecosystem net carbon productivity (NEP) to precipitation variability. The results indicate that NEP displays a positive asymmetry in response to precipitation change, e.g., the magnitude of the increase in NEP (33.4%) under water-addition treatments is larger than that of the decline in NEP (-24.62%) under water-reduction treatments. The positive asymmetry of NEP in arid regions (< 500 mm) is larger than that in humid regions (> 500 mm). The former is mainly due to the positive asymmetry in vegetation productivity, while the latter results from the respiration process, i.e., the decrease in soil respiration in water-reduction treatments is stronger than in water-addition treatments. Furthermore, land models reproduce a positive NEP asymmetry in response to precipitation change, but display poor performance in ecosystem respiration (ER) responses owing to uncertainties in simulating soil water content (SWC). The positive asymmetry of NEP in this study implies that the increase in precipitation variability (except extreme anomalies) is conducive to high carbon sink in the global terrestrial ecosystem. Meanwhile, the performance of the models when simulating SWC in response to precipitation in humid regions needs to be further improved to better predict the carbon sink in the terrestrial ecosystem.</p>

Plastic mulch increases dryland wheat yield and water-use productivity, while straw mulch increases soil water storage

Amplifying drought stress and high precipitation variability impair dryland wheat production. These problems can potentially be minimized by using plastic mulch (PM) or straw mulch (SM). Therefore, wheat grain yield, soil water storage, soil temperature and water-use productivity (WUP) of PM and SM treatments were compared with no mulch (CK) treatment on dryland wheat over a period of eight seasons. Compared to the CK treatment, PM and SM treatments on average significantly increased grain yield by 12.6 and 10.5%, respectively. Compared to the CK treatment, SM treatment significantly decreased soil daily temperature by 0.57, 0.60 and 0.48°C for the whole seasons, growing periods and summer fallow periods, respectively. In contrast, compared to the CK treatment, PM treatment increased soil daily temperature by 0.44, 0.51 and 0.27°C for the whole seasons, growing periods and summer fallow periods, respectively. Lower soil temperature under SM allowed greater soil water storage than under PM. Pre-seeding soil water storage was 17% greater under the SM than under the PM treatment. Soil water storage post-harvest was similar for the PM and SM treatments, but evapotranspiration (ET) was 4.5% higher in the SM than in the PM treatment. Consequently, WUP was 6.6% greater under PM than under the SM treatment. Therefore, PM treatment increased dryland wheat yield and water-use productivity, while straw mulch increased soil water storage.

Vegetation response to climate change and human activity in southwestern China since the Last Glacial Maximum

Southwestern China, a biodiversity hotspot, underwent dramatic climate change following the Last Glacial Maximum and intensive human activity also exerted a major impact since the late Holocene. In this paper, we report a pollen record from Yilong Lake in Yunnan Province, southwestern China, to understand vegetation response to long-term climatic variation and human activities at a regional-level over the last 27,000 years. Findings show that temperate and coniferous taxa were dominant before 18 cal kyr BP reflecting the glacial climate. Between 18 and 14 cal kyr BP, thermophilic taxa increased in response to global deglaciation. A notable shift from deciduous to evergreen forests occurred at approximately 14 cal kyr BP, after which pollen concentration increased but pollen diversity decreased. Pollen assemblages indicated dense forests, primarily composed of evergreen oaks developed under warm and strong summer monsoon conditions from 13 to 8 cal kyr BP. High rates of vegetation change occurred during the middle Holocene before vegetation diversity gradually increased during the Holocene Thermal Optimum, with high variability of precipitation in the Yilong Lake region. Anthropogenic forcing progressively became the main factor affecting vegetation composition and vegetation change rates over the past three millennia.

Effects of extra-extreme precipitation variability on multi-year cumulative nitrous oxide emission in a semiarid grassland

High temporal precipitation variability, characterized by less frequent but larger-magnitude precipitation events, is increasing. However, how precipitation variability affects N2O emissions and the underlying mechanisms remain unclear, especially at multiple-year scales. We conducted a 3-year manipulative experiment in which the same long-term mean growing season precipitation total was repackaged into events of inversely varying magnitude and frequency to simulate four levels of precipitation variability (extra-extreme, extreme, medium, and normal) in a semiarid grassland. Cumulative N2O emissions was the smallest under the extra-extreme precipitation variability scenario (6 very large rainfall events), 26% less than emission under the other three treatments (10, 16, and 24 rainfall events) over the three growing seasons. However, this difference was almost entirely due to three sampling events in the third year. Plant community (biomass and biodiversity), soil abiotic properties (water, dissolved organic carbon and pH), soil microbial biomass (carbon, nitrogen and the ratio), and soil functional genes (archaeal and bacterial amoA, nirS, nirK, narG, and nosZ) explained 61% of the variation in N2O emissions in response to the precipitation variability. Structural equation modelling indicated that the precipitation variability had direct positive effects, and indirect negative effects via soil abiotic properties, on soil functional genes that ultimately had positive effects on N2O emissions. The reduction in N2O emission in the extra-extreme precipitation variability scenario in the third year was due to low levels of soil water content, soil pH, aboveground biomass, and microbial biomass carbon simultaneously. Our results suggest that at the multi-year timescale, semiarid grasslands may have negative feedbacks to future precipitation regimes with higher variability.

Dynamic and Thermodynamic Drivers of Severe Sub-Hourly Precipitation Events in Mainland Portugal

Sub-hourly heavy precipitation events (SHHPs) associated with regional low-pressure (RegL) systems in Portugal are a natural hazard that may have significant socioeconomic implications, namely in agriculture. Therefore, in this paper, their dynamic and thermodynamic drivers are analysed. Three weather stations were used to isolate SHHPs from 2000 to 2022. Higher precipitation variability is found in southern Portugal, with a higher ratio of extreme events on fewer rainy days. This study shows that these SHHP events are associated with low-pressure systems located just to the west of the Iberian Peninsula. These systems exhibit a cold core, particularly strong at mid-levels, and a positive vorticity anomaly, which is stronger in the upper troposphere, extending downward to low levels. These conditions drive differential positive vorticity advection and, therefore, rising motion to the east of the low-pressure systems. Moreover, at low levels, these systems promote moisture advection over western Iberia, also generating instability conditions, which are assessed by instability indices (Convective available potential energy, the Total-Totals index, and the K-index). The combination of these conditions drives heavy precipitation events. Lastly, the total column cloud ice water revealed higher values for the heavier precipitation events, suggesting that it may be a useful predictor of such events.

Development of Monthly Scale Precipitation-Forecasting Model for Indian Subcontinent using Wavelet-Based Deep Learning Approach

In the present work, a wavelet-based multiscale deep learning approach is developed to forecast precipitation using the lagged monthly rainfall, local climate variables, and global teleconnections such as IOD, PDO, NAO, and Nino 3.4 as predictors. The conventional methods are limited by their inability to capture the high precipitation variability in time and space. The proposed multiscale method was tested and validated over the Krishna River basin in India. The results from the proposed methods were compared with contemporary models based on Multiple Linear Regression and Neural Networks. Overall, the forecasting accuracy was higher using the wavelet-based hybrid models than the single-scale models. The wavelet-based methods yielded results with 13–34% reduced error when compared with the best single-scale models. The proposed multi-scale model was then applied to the different climatic regions of the country, and it was shown that the model could forecast rainfall with reasonable accuracy for different climate zones of the country.

Mean age at menarche and climate variables on a global scale.

Cross-population variation in age at menarche is related to many factors. The purpose of this study was to examine climate variables in relation to mean age at menarche among 87 modern human populations. We hypothesized a later age at menarche among populations living in areas with high precipitation variability, heavy seasonal rainfall, and high temperatures year-round due to water-borne diseases and periods of resource scarcity. Using a comparative dataset, we examined geospatial distribution and climate variables in relation to age at menarche for 87 modern human populations. We found the strongest predictor of a later age at menarche was higher fertility followed by a later mean age at death. In addition, higher annual rainfall, higher precipitation seasonality, and lower annual mean temperature were moderate predictors of age at menarche. We propose that later ages at menarche in countries with high fertility may be a life-history strategy developed in response to climatic conditions that have resulted in higher immunological load. In these conditions, females may prioritize growth rather than reproduction. Shifts in climate and global population growth may change the future biological landscape of age at menarche.

Modeling periodicity in equid serial enamel isotopes as a proxy for precipitation seasonality

Seasonality shapes environments by driving vegetation patterns and faunal community composition, and identifying seasonality patterns in the past can provide unique insight into paleoenvironments and how they changed over time. Many environmental proxies operate on too large a time scale to glean data about intra-annual seasonality. However, serial enamel isotope samples from the teeth of high-crowned ungulates show promise for providing this temporal resolution of data. Interpreting these data remains a challenge due to the complexity of factors related to animal physiology and the isotopic values of environmental inputs. This study seeks to establish a method for interpreting intra-tooth isotopic change in modern equid teeth as a signal of seasonality by applying a linear mixed effect model to the isotope data to detect periodicity in serial enamel isotope values. Using a modern assemblage of zebra (Equus quagga) from Ol Pejeta Conservancy in Kenya, where long-term climate data has been collected, allows us to compare known climatic seasonality patterns to periodicity in the zebra tooth enamel isotopes. The results of the linear mixed effect modeling, in tandem with the climate data from Ol Pejeta, demonstrate the impact high precipitation variability can have on an environment. While our isotope results point towards measured precipitation seasonality patterns at Ol Pejeta, noise in our data and the variation identified by the models illustrate the challenges of working with serial enamel isotope data. Future refinement and application of this model will allow us to better characterize seasonality in both modern and fossil enamel datasets.

Soil surface grass phytolith morphotypes across bioclimatic gradients and biomes at about latitude 29°S in South Africa

This study examines the distribution patterns of grass subfamilies and their diagnostic silica phytoliths along a transect running approximately at latitude 28° S in South Africa. The transect encompasses 77 surface samples collected along moisture and temperature gradients, across 6 biomes and 39 ecozones, and spanning elevations between near-sea level and over 2253 m. This study tests the relation between the distribution and density of grass subfamilies and the proportions of their diagnostic grass silica short cell phytoliths (GSSCP) representing them, and how these proportions correlate with five climatic variables (mean temperature of the wettest quarter, annual precipitation, precipitation of the wettest quarter, precipitation variability, and the aridity index). The results suggest that C3-grass diagnostic GSSCP morphotypes increase with a decline in temperature in the wettest quarter, occurring in areas receiving mostly winter rain or at high elevations in areas of summer rain. Of the C4 grass subfamilies, the Panicoideae increase with summer precipitation and low precipitation variability, while the Aristidoideae-Stipagrostis the opposite. The distribution of Chloridoideae depends on complex changes in all climatic variables, suggesting aridity gradients only in the eastern half of the transect. In view of the data, this study reviews the usefulness of the phytolith humidity-aridity index (Iph) and the water stress index (Fs) and proposes the Panicoideae index (PI) and Stipagrostis index (SI) as indicator of aridity and high precipitation variability in southern Africa.

Spatio-temporal rainfall variability and its impacts on the hydrological response of nature-based solutions

ABSTRACT The high spatio-temporal variability of precipitation makes the hydrological responses of nature-based solutions (NBS) indeterminate. Hence, this study investigated the hydrological responses of a semi-urban catchment and three NBS scenarios concerning different spatio-temporal rainfall data from both C-band radar and X-band radar. Multi-Hydro, the fully-distributed and physically-based hydrological model, is employed to study the spatio-temporal rainfall variability and the resulting hydrological impacts on the NBS scenarios. The scale-independent indicator of maximum probable singularity (γs ) within the Universal Multifractals (UM) framework is used to quantify the spatio-temporal variability of rainfall and catchment responses across scales. The main results show that the hydrological performances of the NBS scenarios can be largely underestimated or overestimated because of the spatio-temporal rainfall variability, with a relative error ranging from −91% to 199%, and NBS can effectively reduce surface runoff across scales, which is characterised by a lower γs .

TPHiPr: a long-term (1979–2020) high-accuracy precipitation dataset (1∕30°, daily) for the Third Pole region based on high-resolution atmospheric modeling and dense observations

Abstract. Reliable precipitation data are highly necessary for geoscience research in the Third Pole (TP) region but still lacking, due to the complex terrain and high spatial variability of precipitation here. Accordingly, this study produces a long-term (1979–2020) high-resolution (1/30∘, daily) precipitation dataset (TPHiPr) for the TP by merging the atmospheric simulation-based ERA5_CNN with gauge observations from more than 9000 rain gauges, using the climatologically aided interpolation and random forest methods. Validation shows that TPHiPr is generally unbiased and has a root mean square error of 5.0 mm d−1, a correlation of 0.76 and a critical success index of 0.61 with respect to 197 independent rain gauges in the TP, demonstrating that this dataset is remarkably better than the widely used datasets, including the latest generation of reanalysis (ERA5-Land), the state-of-the-art satellite-based dataset (IMERG) and the multi-source merging datasets (MSWEP v2 and AERA5-Asia). Moreover, TPHiPr can better detect precipitation extremes compared with these widely used datasets. Overall, this study provides a new precipitation dataset with high accuracy for the TP, which may have broad applications in meteorological, hydrological and ecological studies. The produced dataset can be accessed via https://doi.org/10.11888/Atmos.tpdc.272763 (Yang and Jiang, 2022).

Asymmetric response of primary productivity to precipitation anomalies in Southwest China

Southwest China has been the largest terrestrial carbon sink in China over the past 30 years, but has recently experienced a succession of droughts caused by high precipitation variability, potentially threatening vegetation productivity in the region. Yet, the impact of precipitation anomalies on the vegetation primary productivity is poorly known. We used an asymmetry index (AI) to explore possible asymmetric productivity responses to precipitation anomalies in Southwest China from 2003 to 2018, using a precipitation dataset, combined with gross primary productivity (GPP), net primary productivity (NPP), and vegetation optical depth (VOD) products. Our results indicate that the vegetation primary productivity of Southwest China shows a negative asymmetry, suggesting that the increase of vegetation primary productivity during wet years exceeds the decrease during dry years. However, this negative asymmetry of vegetation primary productivity was shifted towards a positive asymmetry during the period of analysis, suggesting that the resistance of vegetation to drought, has increased with the rise in the occurrence of drought events. Among the different biomes, grassland vegetation primary productivity had the highest sensitivity to precipitation anomalies, indicating that grasslands are more flexible than other biomes and able to adjust primary productivity in response to precipitation anomalies. Furthermore, our results showed that the asymmetry of vegetation primary productivity was influenced by the effects of temperature, precipitation, solar radiation, and anthropogenic and topographic factors. These findings improve our understanding of the response of vegetation primary productivity to climate change over Southwest China.

Responses of nutrient resorption to interannual precipitation variability and nitrogen addition in a pine plantation

AbstractPlant nutrient concentration and resorption play a major role in determining primary production and development of forests. Yet how plant nutrient concentration and resorption respond to altered interannual precipitation and how interannual precipitation variability mediates the responses of plant nutrient concentration and resorption to N addition remain uncertain. Here, we examined the responses of needle nutrient (N and P) concentrations of all aged classes, nutrient resorption efficiency, and proficiency to N addition (10 g N m−2 year−1) over a 4‐year period (from 2016 to 2019) that experienced high variability in precipitation in a Mongolian pine plantation in a semiarid region of northeast China. There were significant interannual variations in N and P concentrations in all aged needles and litters, N and P resorption efficiency, as well as soil inorganic N and available P concentrations in the control plots. Increased precipitation resulted in increased needle nutrient concentrations across the four consecutive years. However, needle nutrient concentrations did not covary with soil available nutrients. The responses of needle N resorption efficiency (NRE) to N addition differed across the four years, with N addition decreasing NRE in wet years, but not in dry years. Taken together, our results showed that foliar nutrient concentrations are sensitive to altered annual precipitation and that different interaction effects exist between N addition and sampling year for different nutrient variables. Such differential responses highlight the challenge of predicting effects of N addition on nutrient cycling over temporal scales.

Simulating the Impacts of Changes in Precipitation Timing and Intensity on Tree Growth

AbstractThe timing and intensity of precipitation varies from year‐to‐year and is expected to change in the future. Assessing the impacts of this moisture delivery variability on tree growth is important both for future forest health and for our interpretation of pre‐instrumental tree‐ring records. Here, we used the Vaganov‐Shashkin model to investigate how changes in precipitation delivery impact tree growth at five sites representing four species in two North American river basins with high precipitation variability but different seasonal cycles. Evenly distributed precipitation increased tree growth in the Lower Sacramento watershed, while the water‐limited South Platte benefited from concentrated precipitation early in the growing season. Although most experimental simulations retained the pattern of high‐ and low‐growth years, tree growth was reduced with fewer, more intense precipitation events, which could affect interpretation of past climate extremes. Under the RCP4.5 scenario, projected warming offset the potential benefits of increased precipitation on tree growth.

Spatial mapping of annual rainfall in the São Francisco River Basin

Precipitation is an important object of study and plays an important role in the dynamics of rainfall distribution in a region. This study investigated the spatial and temporal variation of precipitation in the São Francisco River Basin (SFRB). A historical series of data from 1989 to 2018 was analyzed, and a random function was decomposed into trend and residual components for analysis of precipitation. Interpolation techniques were used to analyze precipitation spatial behavior over time, using high-resolution precipitation maps. Our results showed that the exponential model prevailed in four periods. The findings also showed a high precipitation variability in the SFRB and enabled us to monitor precipitation behavior over the years, as well as in the different sub-regions in SFRB. Finally, important information was obtained, enabling, for instance, the identification of vulnerable areas suffering from lack of rainfall.
 Keywords: kriging, R software, variogram.

Spatiotemporal estimation of 6-hour high-resolution precipitation across China based on Himawari-8 using a stacking ensemble machine learning model

Precipitation plays a significant role in the hydrological cycle and atmospheric circulation. However, estimating precipitation is a challenging task due to the high spatiotemporal variability of precipitation. The development of machine learning has marked a new approach to precipitation estimation. In this study, a stacking ensemble machine learning model was developed to estimate 6-hour precipitation at a high spatial resolution of 5 km based on Himawari-8 and ground stations data over China. Metrological and topographic factors are considered ancillary data. The stacking model consists of two levels. Three sub-models, namely, extremely randomized trees, extreme gradient boosting, and deep neural networks, were trained respectively in level-1 of the stacking model. Level-2 linearly combines sub-models to achieve the final estimation. Evaluation results over China indicate that the stacking model outperforms the individual model, with a correlation coefficient of 0.630, a mean absolute error of 1.431 mm/6hr, and a root mean square error of 4.248 mm/6hr. The performance of the proposed model is better than several widely used precipitation products (IMERG, GSMaP, and ERA5). In particular, the detection rate of heavy precipitation (≥16.9 mm/6hr) in our model, with a probability of detection of 0.97, is distinctly superior to that of the other products. The precipitation estimations are accumulated and plotted at different temporal scales, contrasting with these products. The spatial patterns appear to be relatively coincident at annual and daily scales. More importantly, the spatial patterns of our model are more reliable than those of the products due to its smaller mean bias. The study provided a novel alternative for producing improved high-resolution precipitation datasets with high accuracy.

A novel Budyko-based approach to quantify post-forest-fire streamflow response and recovery timescales

Recent increases in the incidences of wildfires have necessitated the development of methodologies to quantify the effect of these fires on streamflows. Climate variability has been cited as a major challenge in revealing the true contribution of disturbance to streamflow changes. To address this, we developed an annual Budyko “decomposition” method for (1) statistical change detection of hydrologic signatures post-fire, (2) separating climate-driven and fire-driven changes in streamflow, and (3) estimating hydrologic recovery timescales after fire. We demonstrate the use of this methodology for 17 watersheds in Southern California with high interannual variability in precipitation. We show that while traditional metrics like changes in flow or runoff ratio might not detect a disturbance effect due to confounding climate signals, the Budyko framework can be used successfully for statistical change detection. The Budyko approach was also found to be robust in detecting changes in 5 highly burned catchments (>40% burned area ratio), while changes in less burned (2) and unburned catchments (10) were insignificant. We further used the Budyko approach to quantify the contribution of fire-driven versus climate driven changes in streamflow and found that fire contributed to an average increase in streamflow on the order of 80 mm yr−1, though the effect varied greatly between years. Finally, we estimated hydrologic recovery timescales that varied between 5 and 45 years for four burned catchments. We found a significant linear relationship between recovery time and burned area at medium and high severity for our study catchments, with about 4 years of recovery time per 10% of the watershed burned.