Helen Brindley’s research while affiliated with Imperial College London and other places

What is this page?


This page lists works of an author who doesn't have a ResearchGate profile or hasn't added the works to their profile yet. It is automatically generated from public (personal) data to further our legitimate goal of comprehensive and accurate scientific recordkeeping. If you are this author and want this page removed, please let us know.

Publications (1)


Study area subdivided into 100 km grid cells, showing the average annual burned area percentage in each cell, averaged over 2001–2020 (a) and the corresponding dominant international geosphere‐biosphere program land cover type classification for the same period (b), both based on Moderate Imaging Spectroradiometer (MODIS) data. MODIS tiles shown for reference.
Total burned area during the fire season by year, corresponding to the total area of pixels identified as burned in the MCD64A1 data set. In northern hemisphere Africa (NHA, blue), a decline of 2.1×104 $2.1\times 1{0}^{4}$ km2 ${\text{km}}^{2}$ per year is observed p=1.2×10−6 $\left(p=1.2\times 1{0}^{-6}\right)$. In southern hemisphere Africa (SHA, red), opposing trends are observed before and after 2011, with an increase of 2.1×104 $2.1\times 1{0}^{4}$ km2 ${\text{km}}^{2}$/year (p=0.018) $(p=0.018)$ and a decrease of 2.7×104 $2.7\times 1{0}^{4}$ km2 ${\text{km}}^{2}$/year (p=0.003) $(p=0.003)$ respectively. Trends are determined using an ordinary least squares algorithm.
Calculation of albedo anomaly for a given 500 m Moderate Imaging Spectroradiometer pixel. Top panel shows the climatology derived from non‐fire years (blue), with the same pattern repeating every 12 months, and the actual pixel albedo values (red). Bottom panel shows the difference between the values ‐ the albedo anomaly. Orange vertical lines indicate a fire event.
Average albedo anomaly following a fire over the whole study period and region. Day of burn uncertainty (yellow shading) refers to the ±8 $\pm 8$ day uncertainty on albedo values. Albedo uncertainty (gray shading) represents the spatial variation. Exponential fit, found using the SciPy Trust Region Reflective algorithm (Virtanen et al., 2020), follows Equation 7, with Δα=0.019±0.001 ${\Delta }\alpha =0.019\pm 0.001$, αb=(9.5±0.2)×10−4 ${\alpha }_{b}=(9.5\pm 0.2)\,\times 1{0}^{-4}$ and τ=34.0±0.4 $\tau =34.0\pm 0.4$ days.
Average radiative forcing of albedo changes following fires at t=0 $t=0$ across all burn locations, calculated according to Equation 3. Gray shading represents the spatial standard deviation. Day of burn uncertainty (yellow shading) refers to the ±8 $\pm 8$ day uncertainty on albedo values.

+5

Two Decades of Fire‐Induced Albedo Change and Associated Short‐Wave Radiative Effect Over Sub‐Saharan Africa
  • Article
  • Full-text available

January 2025

·

22 Reads

Michaela Flegrová

·

Helen Brindley

We present an analysis of 20 years of fire and albedo data in Africa. We show that, in the mean, the sub‐Saharan Africa post‐fire surface albedo anomaly can be parameterized using an exponential recovery function, recovering from a decrease of 0.019±0.001 0.019±0.0010.019\pm 0.001 immediately after a fire with a time constant of 34.0±0.4 34.0±0.434.0\pm 0.4 days. Although the magnitude of albedo changes shows large spatial and temporal variations and a strong land cover type (LCT) dependency, exponential recovery is observed in the majority of LCTs. We show that fires cause long‐term surface brightening, with an Africa‐wide albedo increase of (9.5±0.2)×10−4 (9.5±0.2)×104(9.5\pm 0.2)\times 1{0}^{-4} 10 months after a fire, but we find this is driven almost exclusively by slow vegetation recovery in the Kalahari region, confirming previous findings. Using downward surface shortwave flux (DSSF) estimates, we calculate the fire‐induced surface radiative forcing (RF), peaking at 5±2 5±25\pm 2 Wm⁻² in the burn areas, albeit with a significantly smaller effect when averaged temporally and spatially. We find that the long‐term RF in months 5–10 after a burn averaged over the continent is negative because of the brightening observed. Despite a well‐documented reduction in burning in Africa in the recent decades, our temporal analysis does not indicate a decrease in the overall fire‐induced RF likely due to large interannual variability in albedo anomaly and DSSF data. However, we observe a decline in the short‐term RF in southern hemisphere Africa, driven by both a reduction in fires and changes in LCT distributions.

Download