Although rainfall-triggered landslides are initiated by subsurface hydro-mechanical processes related to the loading, weakening, and eventual failure of slope materials, most landslide early warning systems (LEWSs) have relied solely on rainfall event information. In previous decades, several studies demonstrated the value of integrating proxies for subsurface hydrologic information to improve rainfall-based forecasting of shallow landslides. More recently, broader access to commercial sensors and telemetry for real-time data transmission has invigorated new research into hydrometeorological thresholds for LEWSs. Given the increasing number of studies across the globe using hydrologic monitoring, mathematical modeling, or both in combination, it is now possible to make some insights into the advantages versus limitations of this approach. The extensive progress demonstrates the value of in situ hydrologic information for reducing both failed and false alarms through the ability to characterize infiltration during – as well as the drainage and drying processes between – major storm events. There are also some areas for caution surrounding the long-term sustainability of subsurface monitoring in landslide-prone terrain, as well as unresolved questions in hillslope hydrologic modeling, which relies heavily on the assumptions of diffuse flow and vertical infiltration but often ignores preferential flow and lateral drainage. Here, we share a collective perspective based on our previous collaborative work across Europe, North America, Africa, and Asia to discuss these challenges and provide some guidelines for integrating knowledge of hydrology and climate into the next generation of LEWSs. We propose that the greatest opportunity for improvement is through a measure-and-model approach to develop an understanding of landslide hydro-climatology that accounts for local controls on subsurface storage dynamics. Additionally, new efforts focused on the subsurface hydrology are complementary to existing rainfall-based methods, so leveraging these with near-term precipitation forecasts is a priority for increasing lead times.

Vegetation plays a critical role in regulating the catchment water balance and enhancing soil stability through root reinforcement. The dynamic nature of vegetation, particularly its seasonal change, significantly affects the magnitude of this influence. However, quantifying the long-term impacts of dynamic vegetation on both flood and landslide occurrences at the catchment scale remains challenging due to the complexity of root structures and the varying dimensions of landslides. In this study, we improved the coupled hydrological-geotechnical model iHydroSlide3D v1.0 by incorporating key vegetation components, such as Leaf Area Index (LAI), root characteristics, and their seasonal dynamics. The improved model was validated using historical observations and applied to a 100-years simulation driven by a weather generator. Three computational scenarios were employed to assess the influence of vegetation on key hydrological and slope-stability variables. Results show that vegetation reduces soil moisture and runoff during low to moderate rainfall events but has a limited impact during larger rainfall events. Additionally, slope stability is found to be more influenced by root reinforcement than soil water uptake. The dynamic nature of vegetation plays a decisive role in modulating its effects on hydrological processes and soil stability, depending on the growth or decay trend of vegetation. This modeling framework offers a robust tool for assessing long-term flood and landslide risks in vegetated catchments.

With the availability of an increased number of ground-based weather radars, the development of composite radar rainfall estimates has become common practice. In mountainous terrain, weather radar measurements often encounter beam blockage effects, resulting in erroneous estimates of rainfall. This study introduces a novel relative radar quality index based on the radar reflectivity fraction to enhance the radar composite product. Additionally, we develop an improved mean field bias adjustment technique by including the spatial variability of the bias adjustment factors associated with the quality of radar observations. Radar reflectivity data from a network of single-polarization S-band radars, the Sattahip and Phimai radar stations in Thailand, and automatic rain gauges within the composite area, were used for the analysis. Three independent datasets were employed: (1) 51 storm events (2016–2022) for evaluating radar composite performance and QI-based bias adjustment; (2) hourly data from August–October 2020 to assess bias factor uncertainty; and (3) three heavy storms (2016, 2017, and 2020) to examine the QI method's effectiveness in beam-blocked basins. Our analysis explored seven combinations of hourly radar composite products. Subsequently, the performance of radar rainfall estimates obtained from applying the proposed mean field bias was evaluated by comparing them with the conventional technique. Results show the potential of integrating combined multiple quality indices to improve rainfall estimates, particularly for heavy rainfall events in mountainous regions.

Organisms perpetually release genetic material in their surroundings, referred to as environmental DNA (eDNA), which can be captured and subsequently analyzed to detect biodiversity across the tree of life. In lotic, dynamic environments, little is known about the specific factors that affect the concentration of eDNA between release by the host and its dissemination into the environment. This gap in knowledge introduces significant uncertainty when applying eDNA as a monitoring tool. Our objective is to provide insight on the factors that affect the eDNA concentrations in ecosystems representative of rivers and streams. To this end, we conducted a series of laboratory experiments in a rotating circular (annular) flume, which allows for extended degradation experiments under conditions of flow. Here, we show that flow velocity impacts the observed eDNA concentration over time. Our results suggest that flow-induced transport keeps eDNA in suspension, reducing eDNA removal from the water column, which increased the observed concentration of eDNA. We observed a temporary increase in eDNA concentration over the early phase of the flume experiment with the highest flow velocity. This increase in eDNA concentration seems to be due to a combination of low eDNA degradation rates and high shear stress, which fragment and subsequently homogenize eDNA particles over the water column. The results of our study show the importance of better understanding and assessing the detection probability of eDNA, both in controlled laboratory and larger-scale environmental conditions.

Sand filtration systems (SF) are a well-established approach in ensuring the availability of clean water. Understanding the transport properties of colloidal particles within SF systems is of paramount importance for optimizing their performance. This study investigated the potential utilization of silica-encapsulated DNA particles, equipped with a magnetic core to enhance particle separation and quantification efficiency (SiDNAMag). These particles were evaluated as tracers for delineating complex pathways and conducting source tracking within sand filtration (SF) systems for particulate substances. The study focused on exploring the sensitivity of SiDNAMag to solution chemistry, while elucidating the underlying mechanisms governing their transport and retention in sand filtration systems. Laboratory columns and HYDRUS-1D modeling were employed to analyze a range of water chemistry solutions, encompassing NaCl, NaHCO₃, CaCl₂, and MgCl₂, with ionic strengths ranging from 0.1 mM to 20 mM. The results revealed that the transport of DNA-tagged silica particles could be described by a first-order kinetic attachment and detachment rate coefficient. Elevated ionic strengths consistently led to increased particle adhesion and decreased rates of detachment. The sticking efficiencies of SiDNAMag particles exhibited a range of 0.7 to 1. The remarkable adhesive effectiveness can be ascribed to the comparatively low negative charge exhibited by SiDNAMag particles. This leads to the creation of unstable colloids and encourages the aggregation of these colloidal particles, thereby limiting the potential application of these particles as a tracer. In conclusion, this work underlines the potential of SiDNAMag particles as a potential subsurface tracer. However, further research is warranted to investigate strategies for reducing the interaction between these particles and sand, particularly in response to the chemistry of the infiltrated water.

The new scientific decade (2023-2032) of the International Association of Hydrological Sciences (IAHS) aims at searching for sustainable solutions to undesired water conditions–whether it be too little, too much or too polluted. Many of the current issues originate from global change, while solutions to problems must embrace local understanding and context. The decade will explore the current water crises by searching for actionable knowledge within three themes: global and local interactions, sustainable solutions and innovative cross-cutting methods. We capitalise on previous IAHS Scientific Decades shaping a trilogy; from Hydrological Predictions (PUB) to Change and Interdisciplinarity (Panta Rhei) to Solutions (HELPING). The vision is to solve fundamental water-related environmental and societal problems by engaging with other disciplines and local stakeholders. The decade endorses mutual learning and co-creation to progress towards UN sustainable development goals. Hence, HELPING is a vehicle for putting science in action, driven by scientists working on local hydrology in coordination with local, regional, and global processes.

Enhanced and effective hydrological monitoring plays a crucial role in understanding water-related processes in a rapidly changing world. Within this context, image-based river monitoring has been shown to significantly enhance data collection, improve analysis and accuracy, and support effective and timely decision making. The integration of remote and proximal sensing technologies with citizen science and artificial intelligence may revolutionize monitoring practices. Therefore, it is crucial to evaluate the quality of current research and ongoing initiatives to envision the potential trajectories for research activities within this specific field. The evolution of monitoring strategies is progressing in multiple directions that should converge to build a critical mass around relevant challenges to find innovative solutions that overcome limitations of traditional approaches. The present study reviews examples and good practices of enhanced hydrological monitoring in different applications, reflecting on the strengths and limitations of new approaches.

The dynamics of the bifurcating Karnali river in the western plains of Nepal and India is governed by the geomorphological processes in an alluvial fan. The dynamic branches showcase a notable degree of braiding, dominant channel switching and unequal discharge partitioning. Since recent switching of the dominant channel of Karnali system occurred after an intense monsoon in 2009, the eastern Geruwa branch of the system, which used to be dominant channel passing through the Bardiya National Park, is now receiving a lower share of discharge. This situation exacerbates in the low flow periods when there is very small flow in the Geruwa branch. This decreasing discharge has been associated with depleting diversity of wildlife habitat in Bardiya National Park (Bijlmakers et al., 2023). For sustainable habitat management in the Bardiya National Park, there is a necessity to study the dynamic Karnali river and its two branches, the eastern Geruwa branch and the western Kauriala branch. Activities such as sediment mining, construction of irrigation and hydropower and inter-basin water transfer projects will potentially influence the system dynamics. Our objective is to understand the switching behaviour of the Karnali system to the natural dynamics such as bend sorting (Baar et al., 2020; Parker & Andrews, 1985) of sediments at the location where water from the main Karnali enters the Geruwa branch, and offer understanding of system response to human interventions especially with regards to the distribution of discharge between the Geruwa and Kauriala branches. We combine the technique of field observations and numerical modelling to study the system.

Particle tracers are sometimes used to track sources and sinks of riverine particulate and contaminant transport. A potentially new particle tracer is ~200 nm sized superparamagnetic silica encapsulated DNA (SiDNAFe). The main objective of this research was to understand and quantify the settling and aggregation behaviour of SiDNAFe in river waters based on laboratory settling experiments. Our results indicated, that in quiescent conditions, more than 60% of SiDNAFe settled within 30 h, starting with a rapid settling phase followed by an exponential-like slow settling phase in the three river waters we used (Meuse, Merkske, and Strijbeek) plus MilliQ water. In suspensions of 1000× higher particle concentrations, the hydrodynamic diameter (D_h-DLS) of SiDNAFe increased over time, with its polydispersity index (PDI) positively correlated with particle size. From these observations, we inferred that the rapid SiDNAFe settling was mainly due to homo-aggregation and not due to hetero-aggregation (e.g., with particulate matter present in river water). Incorporating a first-order mass loss term which mimics the exponential phase of the settling in quiescent conditions seems to be an adequate step forward when modelling the transport of SiDNAFe in river injection experiments. Furthermore, we validated the applicability of magnetic separation and up-concentration of SiDNAFe in real river waters, which is an important advantage for carrying out field-scale SiDNAFe tracing experiments.

Recently, superparamagnetic silica encapsulated DNA microparticles (SiDNAFe) were designed and in various experiments used as a hydrological tracer. We investigated the effect of bed characteristics on the transport behaviour and especially the mass loss of SiDNAFe in open channel injection experiments. Hereto, a series of laboratory injection experiments were conducted with four channel bed conditions (no sediment, fine river sediment, coarse sand, and goethite-coated coarse sand) and two water qualities (tap water and Meuse water). Breakthrough curves (BTCs) were analysed and modelled. Mass loss of SiDNAFe was accounted for as a first-order decay process included in a 1-D advection and dispersion model with transient storage (OTIS). SiDNAFe BTCs could be adequately described by advection and dispersion with or without a first-order decay process. SiDNAFe mass recoveries exhibited a wide range, varying from 50% to 120% from sediment-free conditions to coarse (coated) sediment. In 6 out of 8 cases, SiDNAFe mass recovery was complete. Retention of SiDNAFe was 1–2 orders of magnitude greater than gravitational settling rates, as determined in Tang et al. (Hydrological Processes, e14801, 2023). We reason this was due to grain-scale hyporheic flows and coupled water-sediment-particle interactions. The dispersive behaviour of SiDNAFe generally mimicked that of NaCl tracer. We concluded that SiDNAFe can be used in tracing experiments. However, water quality and sediment characteristics may affect the fate of SiDNAFe in river environments. SiDNAFe is a promising tool for particulate multi-tracing in large rivers.

Occurrence of rainfall-induced landslides is increasing worldwide, owing to land use and climate changes. Although the connection between hydrology and rainfall-induced landslides might seem obvious, hydrological processes have been only marginally considered in landslide research for decades. In 2016, an advanced review paper published in WIREs Water [Bogaard and Greco (2016), WIREs Water, 3(3), 439–459] pointed out several challenging issues for landslide hydrology research: considering large-scale hydrological processes in the assessment of slope water balance; including antecedent hydrological information in landslide hazard assessment; understanding and quantifying the feedbacks between deformation and infiltration/drainage processes; overcoming the conceptual mismatch of soil mechanics models and hydrological models. While little progress has been made on the latter two issues, a variety of studies have been published, focusing on the role of hydrological processes in landslide initiation and prediction. The importance of the identification of the origin of water to understand the processes leading to landslide activation is largely acknowledged. Techniques and methodologies for the definition of landslide catchments and for the assessment of landslide water balance are progressing fast, often considering the hydraulic effect of vegetation. The use of hydrological information in landslide prediction models has also progressed enormously. Empirical predictive tools, to be implemented in early warning systems for shallow landslides, benefit from the inclusion of antecedent soil moisture, extracted from different sources depending on the scale of the prediction, leading to significant improvement of their predictive skill. However, this kind of information is generally still missing in operational LEWS. This article is categorized under: Science of Water > Hydrological Processes.

Preferential flow induced by desiccation cracks (PF-DC) has been proven to be an important hydrological effect that could cause various geotechnical engineering and ecological environment problems. Investigation on the PF-DC remains a great challenge due to the soil shrinking–swelling behavior. This work presents an experimental and numerical study of the PF-DC considering the dynamic changes of desiccation cracks. A soil column test was conducted under wetting–drying cycles to investigate the dynamic changes of desiccation cracks and their hydrological response. The ratios between the crack area and soil matrix area (crack ratio), crack aperture and depth were measured. The soil water content, matrix suction and water drainage were monitored. A new dynamic dual-permeability preferential flow model (DPMDy) was developed, which includes physically consistent functions in describing the variation of both porosity and hydraulic conductivity in crack and matrix domains. Its performance was compared to the single-domain model (SDM) and rigid dual-permeability model (DPM) with fixed crack ratio and hydraulic conductivity. The experimental results showed that the maximum crack ratio and aperture decreased when the evaporation intensity was excessively raised. The self-closure phenomenon of cracks and increased surficial water content was observed during low-evaporation periods. The simulation results showed that the matrix evaporation modeled by the DPMDy is lower than that of the SDM and DPM, but its crack evaporation is the highest. Compared to the DPM, the DPMDy simulated a faster pressure head building-up process in the crack domain and higher water exchange rates from the crack to the matrix domain during rainfall. Using a fixed crack ratio in the DPM, whether it is the maximum or the average value from the experiment data, will overestimate the infiltration fluxes of PF-DC but underestimate its contribution to the matrix domain. In conclusion, the DPMDy better described the underlying physics involving crack evolution and hydrological response with respect to the SDM and DPM. Further improvement of the DPMDy should focus on the hysteresis effect of the soil water retention curve and soil deformation during wetting–drying cycles.

Landslides are one of the major weather-related geohazards. To assess their potential impact and design mitigation solutions, a detailed understanding of the slope processes is required. Landslide modelling is typically based on data-rich geomechanical models. Recently, machine learning has shown promising results in modelling a variety of processes. Furthermore, slope conditions are now also monitored from space, in wide-area repeat surveys from satellites. In the present study we tested if use of machine learning, combined with readily available remote sensing data, allows us to build a deformation nowcasting model. A successful landslide deformation nowcast, based on remote sensing data and machine learning, would demonstrate effective understanding of the slope processes, even in the absence of physical modelling. We tested our methodology on the Vögelsberg, a deep-seated landslide near Innsbruck, Austria. Our results show that the formulation of such a machine learning system is not as straightforward as often hoped for. The primary issue is the freedom of the model compared to the number of acceleration events in the time series available for training, as well as inherent limitations of the standard quality metrics such as the mean squared error. Satellite remote sensing has the potential to provide longer time series, over wide areas. However, although longer time series of deformation and slope conditions are clearly beneficial for machine-learning-based analyses, the present study shows the importance of the training data quality but also that this technique is mostly applicable to the well-monitored, more dynamic deforming landslides.

The growing coincidence of occurrence of natural hazards and vulnerable societies, leading to economic damages and fatalities, has triggered more studies on benefits of prevention measures. This chapter describes a study that aims to demonstrate benefits of risk-prevention measures by applying the KULTURisk methodology (see Chapter 6). The demonstration was implemented in the Ubaye Valley (Barcelonnette town), France. Our findings show that the methodology is an adaptable decision-making tool that may be used to support the analysis of alternative scenarios for flood-risk reduction.

In the terrestrial environment, interactions between natural organic matter (NOM) and colloids can lead to the formation of an environmental corona around colloids, influencing their transport behaviour and, ultimately, their ecotoxicity. We used a synthetically designed colloid tagged with DNA (DNAcol) as a surrogate for natural colloids and investigated its transport in saturated sand columns. We varied the concentrations of NOM and ionic strength (CaCl₂), to better understand the transport and release of DNAcol in porous media under both steady and transient porewater chemistry conditions. In addition, we aimed to understand the main factors that control deposition and release of DNAcol under tested conditions. To induce transient chemistry, we replaced the injection solution containing NOM and/or CaCl₂ with Milli-Q water. The results showed that the deposition rate of DNAcol was inversely proportional to the concentration of NOM. The deposition rate increased significantly even under low ionic strength (CaCl₂) conditions of tested conditions. Notably, the influence of NOM on the transport of DNAcol was most pronounced at the lowest range of [Ca²⁺]/DOC ratios, and the attachment of DNAcol to the sand grains was negligible. Moreover, the results showed while the DLVO theory captured the general trend of experimental results, it significantly underestimated the deposition of DNAcol in the presence of CaCl₂. Under transient porewater chemistry conditions, colloid remobilization was observed upon flushing the column with Milli-Q water, leading to a secondary peak in the breakthrough curves. We observed that under transient porewater chemistry conditions, when the ionic strength of the solution was 10 mM, the magnitude of the remobilization peak was more significant compared to conditions with 1 mM ionic strength. Our work emphasized the complex interplay between water quality on the one hand and deposition and release of colloidal matter in saturated porous media on the other hand.

Rivers flowing from the highlands to the lowlands, characterize the Terai Arc Landscape (TAL) in Nepal and India, but also the neighbouring region in Bhutan. The sudden change in slope results in deposition of nutrient-rich sediment, which forms large alluvial or fluvial fans that enhance biodiversity and economic activities in the region. Our objective is to identify similarities and differences in hydro-morphodynamics between the TAL rivers, with a focus on the Karnali and Koshi systems in Nepal. It is expected that such insight in river dynamics will aid the sustainability and management of the TAL rivers. To this end, we investigate and compare discharge characteristics, historical flow paths, and morphodynamic characteristics such as channel shifting, braiding, and flow partitioning to identify and understand the fan scale dynamics of the two systems. Hereto we used optical remote sensing images and river discharge time series from Chisapani (Karnali) and Chatara (Koshi) gauging stations. In addition, we conducted a field campaign in November 2022.

Continuous and slow-moving deep-seated landslides entail challenges for the effective planning of mitigation strategies aiming at the reduction of landslide movements. Given that the activity of most of these landslides is governed by pore pressure variations within the shear zone, profound knowledge about their hydrogeological control is required. In this context, the present study presents a new approach for the spatial assessment of probable recharge areas to better understand a slope's hydrogeological system. The highly automated geo-statistical approach derives recharge probability maps of groundwater based on stable isotope monitoring and a digital elevation model (DEM). By monitoring stable isotopes in both groundwater and precipitation, mean elevations of recharge areas can be determined and further constrained in space with the help of the DEM. The approach was applied to the Vögelsberg landslide, an active slab of a deep-seated gravitational slope deformation (DSGSD) in the Watten valley (Tyrol, Austria). Resulting recharge probability maps indicate that shallow groundwater emerging at springs on the landslide recharges between 1000 and 1650 a.s.l. In contrast, groundwater encountered in wells up to 49ĝ€¯m below the landslide's surface indicates a mean recharge elevation of up to 2200 a.s.l. matching the highest parts of the catchment. Further inferred proxies, including flow path length, estimated recharge area sizes, and mean transit times of groundwater, resulted in a profound understanding of the hydrogeological driver of the landslide. It is shown that the new approach can provide valuable insights into the spatial pattern of probable recharge areas where mitigation measures aiming at reducing groundwater recharge could be most effective.

Landslides are a major geohazard in hilly and mountainous environments. In-situ inspection of downslope motion is costly, sometimes dangerous and, requires prior knowledge of the existence of a landslide. Remote sensing from space is a way to detect and characterize landslides systematically at large scale. Interferometric Synthetic Aperture Radar (InSAR) has shown to be a valuable resource of deformation information, but it requires expert knowledge and considerable computational efforts. Moreover, the successful application of InSAR for landslides requires a favorable acquisition geometry relative to the landslide deformation pattern. Consequently, there is a need for a widely applicable tool to assess the potential of InSAR at a particular location a priori. Here we present a novel, generic approach to assess the potential of InSAR-based deformation tracking, providing a standardised and automated method applicable on any slope. We define the detection potential as the sensitivity of InSAR to detect downslope displacement combined with the presence of coherently scattering surfaces. We show that deformation can be detected on at least 91% of the global landslide-prone slopes, and provide an open source Google Earth Engine tool for the quick assessment of the availability of potential coherent scatterers. This tool enables any person interested in applying InSAR to routinely assess the potential for monitoring landslide deformation in their region of interest.

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 %.