Browsing by Author "Horton, Alexander J."

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    The Cambodian Mekong floodplain under future development plans and climate change
    (EUROPEAN GEOSCIENCES UNION, 2022-03-22) Horton, Alexander J.; Triet, Nguyen V.K.; Hoang, Long P.; Heng, Sokchhay; Hok, Panha; Chung, Sarit; Koponen, Jorma; Kummu, Matti; Department of Built Environment; Water and Environmental Eng.; Helmholtz Centre Potsdam - German Research Centre for Geosciences; Wageningen University and Research Centre; Institute of Technology of Cambodia; EIA Finland Ltd.
    Water infrastructure development is considered necessary to drive economic growth in the Mekong region of mainland Southeast Asia. Yet the current understanding of hydrological and flood pattern changes associated with infrastructural development still contains several knowledge gaps, such as the interactions between multiple drivers, which may have serious implications for water management, agricultural production, and ecosystem services. This research attempts to conduct a cumulative assessment of basin-wide hydropower dam construction and irrigation expansion, as well as climate change, implications on discharge, and flood changes in the Cambodian Mekong floodplain. These floodplains offer important livelihoods for a considerable part of the 6.4 million people living on them, as they are among the most productive ecosystems in the world - driven by the annual flood pulse. To assess the potential future impacts, we used an innovative combination of three models: Mekong basin-wide distributed hydrological model IWRM-VMod, with the Mekong delta 1D flood propagation model MIKE-11 and 2D flood duration and extent model IWRM-Sub enabling detail floodplain modelling. We then ran scenarios to approximate possible conditions expected by around 2050. Our results show that the monthly and seasonal hydrological regimes (discharges, water levels, and flood dynamics) will be subject to substantial alterations under future development scenarios. Projected climate change impacts are expected to decrease dry season flows and increase wet season flows, which is in opposition to the expected alterations under development scenarios that consider both hydropower and irrigation. The likely impact of decreasing water discharge in the early wet season (up to -30 %) will pose a critical challenge to rice production, whereas the likely increase in water discharge in the mid-dry season (up to +140 %) indicates improved water availability for coping with drought stresses and sustaining environmental flows. At the same time, these changes would have drastic impacts on total flood extent, which is projected to decline by around 20 %, having potentially negative impacts on floodplain productivity and aquaculture, whilst reducing the flood risk to more densely populated areas. Our findings demonstrate the substantial changes that planned infrastructural development will have on the area, potentially impacting important ecosystems and people's livelihoods, calling for actions to mitigate these changes as well as planning potential adaptation strategies.
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    Flood severity along the Usumacinta River, Mexico : Identifying the anthropogenic signature of tropical forest conversion
    (Elsevier, 2021-01-01) Horton, Alexander J.; Nygren, Anja; Diaz-Perera, Miguel A.; Kummu, Matti; Department of Built Environment; Water and Environmental Eng.; University of Helsinki; Colegio de la Frontera Sur
    Anthropogenic activities are altering flood frequency-magnitude distributions along many of the world's large rivers. Yet isolating the impact of any single factor amongst the multitudes of competing anthropogenic drivers is a persistent challenge. The Usumacinta River in southeastern Mexico provides an opportunity to study the anthropogenic driver of tropical forest conversion in isolation, as the long meteorological and discharge records capture the river's response to large-scale agricultural expansion without interference from development activities such as dams or channel modifications. We analyse continuous daily time series of precipitation, temperature, and discharge to identify long-term trends, and employ a novel approach to disentangle the signal of deforestation by normalising daily discharges by 90-day mean precipitation volumes from the contributing area in order to account for climatic variability. We also identify an anthropogenic signature of tropical forest conversion at the intra-annual scale, reproduce this signal using a distributed hydrological model (VMOD), and demonstrate that the continued conversion of tropical forest to agricultural land use will further exacerbate large-scale flooding. We find statistically significant increasing trends in annual minimum, mean, and maximum discharges that are not evident in either precipitation or temperature records, with mean monthly discharges increasing between 7% and 75% in the past decades. Model results demonstrate that forest cover loss is responsible for raising the 10-year return peak discharge by 25%, while the total conversion of forest to agricultural use would result in an additional 18% rise. These findings highlight the need for an integrated basin-wide approach to land management that considers the impacts of agricultural expansion on increased flood prevalence, and the economic and social costs involved.
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    Identifying Key Drivers of Peatland Fires Across Kalimantan's Ex-Mega Rice Project Using Machine Learning
    (AMERICAN GEOPHYSICAL UNION, 2021-12) Horton, Alexander J.; Virkki, Vili; Lounela, Anu; Miettinen, Jukka; Alibakhshi, Sara; Kummu, Matti; Department of Built Environment; Water and Environmental Eng.; University of Helsinki; VTT Technical Research Centre of Finland
    Throughout Indonesia ecological degradation, agricultural expansion, and the digging of drainage canals has compromised the integrity and functioning of peatland forests. Fragmented landscapes of scrubland, cultivation, degraded forest, and newly established plantations are then susceptible to extensive fires that recur each year. However, a comprehensive understanding of all the drivers of fire distribution and the conditions of initiation is still absent. Here we show the first analysis in the region that encompasses a wide range of driving factors within a single model that captures the inter-annual variation, as well as the spatial distribution of peatland fires. We developed a fire susceptibility model using machine learning (XGBoost random forest) that characterizes the relationships between key predictor variables and the distribution of historic fire locations. We then determined the relative importance of each predictor variable in controlling the initiation and spread of fires. The model included land-cover classifications, a forest clearance index, vegetation indices, drought indices, distances to infrastructure, topography, and peat depth, as well as the Oceanic Niño Index (ONI). The model performance consistently scores highly in both accuracy and precision across all years (>75% and >67.5% respectively), though recall metrics are much lower (>25%). Our results confirm the anthropogenic dependence of extreme fires in the region, with distance to settlements and distance to canals consistently weighted the most important driving factors within the model structure. Our results may help target the root causes of fire initiation and propagation to better construct regulation and rehabilitation efforts to mitigate future fires.
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    Identifying post-earthquake debris flow hazard using Massflow
    (Elsevier Science B.V., 2019-08-14) Horton, Alexander J.; Hales, Tristram C.; Ouyang, Chaojun; Department of Built Environment; Water and Environmental Eng.; Cardiff University; Chinese Academy of Sciences
    Catastrophic debris flows are common after large earthquakes and pose a significant risk for recovering communities. The depositional volume of these large debris flows is often much greater than the initiation volume, suggesting that bulking of the flow plays an important role in determining their volume, speed, and runout distance. Observations from recent earthquakes have driven progress in understanding the relationship between triggering rainfall events and the timing of post-earthquake debris flows. However, we lack an adequate mechanism for quantifying bulking and applying it within a hazard context. Here we apply a 2D dynamic debris flow model (Massflow) that incorporates a process-based expression of basal entrainment to understand how debris flow bulking may occur within post-earthquake catchments and develop hazard maps. Focussing on catchments in the epicentral area of the 2008 Mw 7.9 Wenchuan Earthquake, we first parameterised the model based on a large debris flow that occurred within the Hongchun catchment, before applying the calibrated model to adjoining catchments. A model sensitivity analysis identified three main controls on debris flow bulking; the saturation level of entrainable material along the flow pathway, and the size and position of initial mass failures. The model demonstrates that the difference between small and very large debris flows occur across a narrow range of pore-water ratios (λ). Below λ = 0.65 flows falter at the base of hillslopes and come to rest in the valley bottom, above λ = 0.70 they build sufficient mass and momentum to sustain channelised flow and transport large volumes of material beyond the valley confines. Finally, we applied the model across different catchments to develop hazard maps that demonstrate the utility of Massflow in post-earthquake planning within the Wenchuan epicentral region.
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    Targeted land management strategies could halve peatland fire occurrences in Central Kalimantan, Indonesia
    (SPRINGER, 2022-09-08) Horton, Alexander J.; Lehtinen, Jaakko; Kummu, Matti; Department of Built Environment; Department of Computer Science; Water and Environmental Eng.; Computer Science Professors; Computer Science - Visual Computing (VisualComputing); Computer Science - Artificial Intelligence and Machine Learning (AIML); Professorship Lehtinen Jaakko
    Indonesian peatlands and their large carbon stores are under threat from recurrent large-scale fires driven by anthropogenic ecosystem degradation. Although the key drivers of peatland fires are known, a holistic methodology for assessing the potential of fire mitigation strategies is lacking. Here, we use machine learning (convolutional neural network) to develop a model capable of recreating historic fire observations based on pre-fire season parameters. Using this model, we test multiple land management and peatland restoration scenarios and quantify the associated potential for fire reduction. We estimate that converting heavily degraded swamp shrubland areas to swamp forest or plantations can reduce fires occurrence by approximately 40% or 55%, respectively. Blocking all but major canals to restore these degraded areas to swamp forest may reduce fire occurrence by 70%. Our findings suggest that effective land management strategies can influence fire regimes and substantially reduce carbon emissions associated with peatland fires, in addition to enabling sustainable management of these important ecosystems. Land management scenarios that restore degraded swamp shrubland areas to swamp forest through blocking minor canal systems could substantially reduce peatland fire occurrence and associated greenhouse gas emissions, according to a machine learning and numerical modelling study.