Satellite and hydrological model-based technologies provide estimates of rainfall and soil moisture over larger spatial scales and now cover multiple decades, sufficient to explore their value for the development of landslide early warning systems in data-scarce regions. In this study, we used statistical metrics to compare gauge-based and satellite-based precipitation products and assess their performance in landslide hazard assessment and warning in Rwanda. Similarly, the value of high-resolution satellite and hydrological model-derived soil moisture was compared to in situ soil moisture observations at Rwandan weather station sites. Based on statistical indicators, rainfall data from Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM_IMERG) showed the highest skill in reproducing the main spatiotemporal precipitation patterns at the study sites in Rwanda. Similarly, the satellite- and model-derived soil moisture time series broadly reproduce the most important trends of in situ soil moisture observations. We evaluated two categories of landslide meteorological triggering conditions from IMERG satellite precipitation: first, the maximum rainfall amount during a multi-day rainfall event, and second, the cumulative rainfall over the past few day(s). For each category, the antecedent soil moisture recorded at three levels of soil depth, the top 5 cm by satellite-based technologies as well as the top 50 cm and 2 m by modelling approaches, was included in the statistical models to assess its potential for landslide hazard assessment and warning capabilities. The results reveal the cumulative 3 d rainfall to be the most effective predictor for landslide triggering. This was indicated not only by its highest discriminatory power to distinguish landslide from no-landslide conditions (AUC ∼ 0.72), but also the resulting true positive alarms (TPRs) of ∼80 %. The modelled antecedent soil moisture in the 50 cm root zone Seroot(t−3) was the most informative hydrological variable for landslide hazard assessment (AUC ∼ 0.74 and TPR 84 %). The hydro-meteorological threshold models that incorporate the Seroot(t−3) and following the cause–trigger concept in a bilinear framework reveal promising results with improved landslide warning capabilities in terms of reduced rate of false alarms by ∼20 % at the expense of a minor reduction in true alarms by ∼8 %.@en

Door klimaatverandering verandert het afvoerregime van onze grote rivieren. Hoogwaters worden hoger en frequenter, laagwaters lager en langduriger. Hoe we daarop reageren hangt af van hoe we klimaatverandering zien: als opgave, of als kans om onvolkomenheden aan te pakken. In dit artikel presenteren we aanzetten voor een meer geo-ecologisch gefundeerde inrichting, of – naar McHarg – voor design with nature.

Climate change is expected to significantly influence the discharge regime of the Rhine and Meuse rivers. Floods may increase in magnitude and occur more frequently; low discharges are likely to become even smaller and last longer. How to respond to those changes? Can all river functions be fulfilled into the future and which interventions are then needed to keep the country safe and the rivers navigable? The answer to these questions depends on whether climate change is primarily considered as a challenge, or rather as an opportunity to, at the same time, redress a few deficiencies and detrimental geo-ecological developments that are being provoked by how we straightjacketed our floodplains and engineered our rivers in behalf of navigation but negatively impact biodiversity. We propose a more geo-ecologically grounded approach to our rivers’ spatial design and management – inspired by McHarg’s design with nature –, which encompasses making available much more room for safe flood discharge and reduced hydrodynamics for ecosystem development in the floodplains, as well as more nature-based river training to allow for morphological developments and natural rejuvenation. @en