Landslide frequency in the Kivu Rift: impact of landscape evolution and deforestation

Author(s):  
Arthur Depicker ◽  
Gerard Govers ◽  
Liesbet Jacobs ◽  
Benjamin Campforts ◽  
Judith Uwihirwe ◽  
...  

<p>Both landscape rejuvenation through tectonic uplift and human-induced deforestation are known to increase landslide (LS) activity. Yet, the interaction between deforestation and landscape evolution has hitherto not been explicitly considered. Here, we investigate how shallow LS frequency is impacted by deforestation and landscape rejuvenation through knickpoint retreat in the Kivu Rift (East African Rift) while accounting for rock strength and slope steepness. In the past 12 Ma, the Kivu Rift has been characterized by tectonic uplift which gave rise to knickpoints in the river profiles enforcing topographic steepening. On a much shorter timescale, the rapidly growing population in the Rift has gradually expanded its cultivated and urban land leading to widespread deforestation.</p><p>We compiled an inventory of almost 8000 shallow LSs using Google Earth imagery. To quantify LS frequency, we developed a new method that accounts for the temporal and spatial inconsistency of satellite imagery coverage. To characterize long-term landscape evolution, we identified (i) 672 non-stationary knickpoints in the Rift and (ii) quantified the impact of lithology on slope threshold angles (TA). We identified two homogenous lithological groups: one group of younger/weaker lithologies (<540 Ma, TA=19.0 +/- 2.0°) and one group of older/stronger ones (>540 Ma, TA=27.9 +/- 0.3°). Further analysis focused on the latter group since it covers 85% of the study area and contained more than 95% of the observed LSs.</p><p>The overall shallow LS frequency in the rejuvenated landscapes inside the rift is 0.039 LS/km<sup>2</sup>/yr versus 0.010 LS/km<sup>2</sup>/yr in the relict landscapes outside the rift. Generally, LS frequency on recently deforested slopes increased by 200 to 800% in comparison to forested land. There is no notable difference in LS frequency on equally steep non-forested slopes (i.e. slopes deforested at least several decades ago) inside and outside of the rift. However, forest slopes of similar steepness are 2-3 times more sensitive to landsliding within the rift. We propose two mechanisms that might explain the higher frequency of landsliding on similar topographies within the rift: (i) the active undercutting by rivers may lead to slope destabilization without significantly increasing the average slope gradient as extracted from the SRTM DEM and (ii) tectonic uplift may induce rock and regolith fracturing, leading to weaker, more LS-prone slopes. The fact that we did not observe differences in LS frequency on hillslopes that were deforested long ago suggests that on such slopes a new equilibrium is established whereby these aforementioned mechanisms are no longer important.</p><p>In conclusion, one of the key factors why the rejuvenated landscape inside the rift is more sensitive to landsliding is the higher prevalence of threshold slopes due to active incision. However, the impact of rejuvenation cannot be understood by considering only its effects on overall topography. Deforestation dramatically increases LS frequency in both relict and rejuvenated landscapes, in the first decades after forest cover removal.</p>

2018 ◽  
pp. 125-141 ◽  
Author(s):  
S. M. Drobyshevsky ◽  
P. V. Trunin ◽  
A. V. Bozhechkova

The paper studies the factors of secular stagnation. Key factors of long-term slowdown in economic growth include the slowdown of technological development, aging population, human capital accumulation limits, high public debt, creative destruction process violation etc. The authors analyze key theoretical aspects of long-term stagnation and study the impact of these factors on Japanies economy. The authors conclude that most of the factors have significant influence on the Japanese economy for recent decades, but they cannot explain all dynamics. For Russia, on the contrary, we do not see any grounds for considering the decline in the economy since 2013 as an episode of secular stagnation.


2010 ◽  
Vol 278 (1712) ◽  
pp. 1661-1669 ◽  
Author(s):  
David Alonso ◽  
Menno J. Bouma ◽  
Mercedes Pascual

Climate change impacts on malaria are typically assessed with scenarios for the long-term future. Here we focus instead on the recent past (1970–2003) to address whether warmer temperatures have already increased the incidence of malaria in a highland region of East Africa. Our analyses rely on a new coupled mosquito–human model of malaria, which we use to compare projected disease levels with and without the observed temperature trend. Predicted malaria cases exhibit a highly nonlinear response to warming, with a significant increase from the 1970s to the 1990s, although typical epidemic sizes are below those observed. These findings suggest that climate change has already played an important role in the exacerbation of malaria in this region. As the observed changes in malaria are even larger than those predicted by our model, other factors previously suggested to explain all of the increase in malaria may be enhancing the impact of climate change.


2018 ◽  
Author(s):  
Xin Long ◽  
Naifang Bei ◽  
Jiarui Wu ◽  
Xia Li ◽  
Tian Feng ◽  
...  

Abstract. Although aggressive emission control strategies have been implemented recently in the Beijing–Tianjin–Hebei area (BTH), China, pervasive and persistent haze still frequently engulfs the region during wintertime. Afforestation in BTH, primarily concentrated in the Taihang and Yanshan Mountains, has constituted one of the controversial factors exacerbating the haze pollution due to its slowdown of the surface wind speed. We report here an increasing trend of forest cover in BTH during 2001–2013 based on long-term satellite measurements and the impact of the afforestation on the fine particles (PM2.5) level. Simulations using the Weather Research and Forecast model with chemistry reveal that the afforestation in BTH since 2001 generally deteriorates the haze pollution in BTH to some degree, enhancing PM2.5 concentrations by up to 6 % on average. Complete afforestation or deforestation in the Taihang and Yanshan Mountains would increase or decrease the PM2.5 level within 15 % in BTH. Our model results also suggest that implementing a large ventilation corridor system would not be effective or beneficial to mitigate the haze pollution in Beijing.


Author(s):  
P. Das ◽  
M. D. Behera ◽  
P. S. Roy

The impact of long term climate change that imparts stress on forest could be perceived by studying the regime shift of forest ecosystem. With the change of significant precipitation, forest may go through density change around globe at different spatial and temporal scale. The 100 class high resolution (60 meter spatial resolution) Indian vegetation type map was used in this study recoded into four broad categories depending on phrenology as (i) forest, (ii) scrubland, (iii) grassland and (iv) treeless area. The percentage occupancy of forest, scrub, grass and treeless were observed as 19.9&amp;thinsp;%, 5.05&amp;thinsp;%, 1.89&amp;thinsp;% and 7.79&amp;thinsp;% respectively. Rest of the 65.37&amp;thinsp;% land area was occupied by the cropland, built-up, water body and snow covers. The majority forest cover were appended into a 5&amp;thinsp;km&amp;thinsp;&amp;times;&amp;thinsp;5&amp;thinsp;km grid, along with the mean annual precipitation taken from Bioclim data. The binary presence and absence of different vegetation categories in relates to the annual precipitation was analyzed to calculate their resilience expressed in probability values ranging from 0 to 1. Forest cover observed having resilience probability (Pr) &amp;lt;&amp;thinsp;0.3 in only 0.3&amp;thinsp;% (200&amp;thinsp;km<sup>2</sup>) of total forest cover in India, which was 4.3&amp;thinsp;% &amp;lt;&amp;thinsp;0.5&amp;thinsp;Pr. Majority of the scrubs and grass (64.92&amp;thinsp;% Pr&amp;thinsp;&amp;lt;&amp;thinsp;0.5) from North East India which were the shifting cultivation lands showing low resilience, having their high tendency to be transform to forest. These results have spatial explicitness to highlight the resilient and non-resilient distribution of forest, scrub and grass, and treeless areas in India.


2021 ◽  
Author(s):  
Boris Gailleton ◽  
Luca Malatesta ◽  
Jean Braun ◽  
Guillaume Cordonnier

&lt;p&gt;Many laws have been developed to describe the different aspects of landscape evolution at large spatial and temporal scales. Natural landscapes have heterogeneous properties (lithologies, climates, tectonics, etc.) that are associated with multiple coexisting processes. In turn, this can demand different mathematical expressions to model landscape evolution as a function of time and or space. Landscape Evolution Models are mostly designed to facilitate the combination of different landscape-wide laws in a plug-and-play way and many frameworks are being developed in this aim. However, most current frameworks cannot capture important landscape processes such as lake dynamics and full sediment tracing because they are optimized for speed and handle fluxes separately. Several processes require information from more than the immediate neighboring cells within a time step and demand an integrated knowledge from the entire upstream trajectory. Lakes for example require knowledge of all upstream water and sediment fluxes to be filled. These can only be known if all the laws controlling those have been processed. Tackling these situation with a grid logic requires substantial amount of numerical refactoring from existing models.&lt;/p&gt;&lt;p&gt;We present an alternative method to tackle landscape evolution modelling in heterogeneous landscapes with a framework inspired from Lagrangian and cellular automaton methods. Our framework only relies on the assumption that upstream nodes needs to be processed before the downstream ones, including lakes with outlets, in order to process all selected governing equations on a pixel-to-pixel basis. This way, we ensure that the true content of sediment and water fluxes can be known and tracked at any points. We first utilise graph theory to (i) find the most comprehensive path to reroute water through depressions and (ii) determine a generic multiple flow topological order (any node is processed after all potential upstream ones). Particles that register and track all fluxes simultaneously can then &quot;roll&quot; on the landscape and merge between each other while interacting with the grid.&lt;/p&gt;&lt;p&gt;This formulation makes possible a number of generic features. (i) The laws can be dynamically adapted to the environment (e.g. switching from single to multiple flow function of water content, adapting erodibility function of the sediment composition and quantity), (ii) Depressions can be explicitly managed, filled (or not) and separated from the rest of the landscape (e.g. sedimentation or evaporation in lakes) as a function function of inputted fluxes and parameters, (iii) full provenance, transport time, and deposition tracking as the particle can always keep in memory where the fluxes are from and in what proportions. In this contribution, we demonstrate the impact the importance of considering these additional elements in landscape evolution. In particular, lake dynamic can significantly impact the long-term signal propagation from source to sink.&lt;/p&gt;


2020 ◽  
Author(s):  
Clairia Kankurize ◽  
Gervais Rufyikiri ◽  
Bruno Delvaux

&lt;p&gt;Located in the East African Rift Valley, western Burundi is often threatened by landslides during the rainy season. Damage can be seen both in the mountains, the sites of the landslides, and in the plain where sediments are deposited: environmental degradation, loss upstream and downstream of cultivated land, destruction of infrastructures, loss of life, waterborne diseases, floods of streams laden with sludge and stones torn off during landslides... The magnitude of these shifts justifies the need for studies to understand the factors that cause this part of Burundi to be vulnerable to landslides.&lt;/p&gt;&lt;p&gt;Here we highlight the relationship between the environmental context and the process of landslides in this region. To analyze the impact of geomorphological, geological, soil and climatic conditions as well as anthropogenic factors, we carried out an inventory of landslides in the Muhunguzi watershed, a survey of the local population and an analysis of rainfall over the period 1935-2014.&lt;/p&gt;&lt;p&gt;Of 7 Muhunguzi sub-watersheds with a total area of 21.2 km&lt;sup&gt;2&lt;/sup&gt;, 43 landslides were identified, 29 of which were on a single sub-watershed. Most landslides were shallow. Geomorphology was characterized by steep escarpments interspersed with valleys. The landslides were located on the lower slopes and most affected the rivers. The lithology was dominated by shale inclined parallel to the slope. Landslides were located on rocky, black or red soils, identified as Nitisols. The majority of landslides occurred on cultivated fields. Daily precipitations ranging between 75mm and 100mm with a return period of 5.3 years are strongly correlated to shallow landslides in the studied area. Such intense daily rain thus appears here as a major trigger to these landslides. In addition, relief, geological and soil conditions are predisposing factors while population density and the resulting land pressure worsen land instability.&lt;/p&gt;&lt;p&gt;We conclude that further studies are needed to understand the impact of soil processes and human activity in order to identify adequate management practices preventing landslides in Muhunguzi area.&lt;/p&gt;


2020 ◽  
Vol 12 (4) ◽  
pp. 638 ◽  
Author(s):  
Koen Hufkens ◽  
Thalès de Haulleville ◽  
Elizabeth Kearsley ◽  
Kim Jacobsen ◽  
Hans Beeckman ◽  
...  

Given the impact of tropical forest disturbances on atmospheric carbon emissions, biodiversity, and ecosystem productivity, accurate long-term reporting of Land-Use and Land-Cover (LULC) change in the pre-satellite era (<1972) is an imperative. Here, we used a combination of historical (1958) aerial photography and contemporary remote sensing data to map long-term changes in the extent and structure of the tropical forest surrounding Yangambi (DR Congo) in the central Congo Basin. Our study leveraged structure-from-motion and a convolutional neural network-based LULC classifier, using synthetic landscape-based image augmentation to map historical forest cover across a large orthomosaic (~93,431 ha) geo-referenced to ~4.7 ± 4.3 m at submeter resolution. A comparison with contemporary LULC data showed a shift from previously highly regular industrial deforestation of large areas to discrete smallholder farming clearing, increasing landscape fragmentation and providing opportunties for substantial forest regrowth. We estimated aboveground carbon gains through reforestation to range from 811 to 1592 Gg C, partially offsetting historical deforestation (2416 Gg C), in our study area. Efforts to quantify long-term canopy texture changes and their link to aboveground carbon had limited to no success. Our analysis provides methods and insights into key spatial and temporal patterns of deforestation and reforestation at a multi-decadal scale, providing a historical context for past and ongoing forest research in the area.


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