Steep Mountainous Terrain Research Articles

Characteristics and Estimation of the Time of Concentration for Small Forested Catchments in Steep Mountainous Terrain

Accurate modeling of flood flow hydrographs for small forested catchments in steep mountainous terrain is challenging because of large errors in the estimation of response time using existing empirical equations. The time of concentration (TC) for a catchment is a widely used time parameter for estimating peak discharges in hydrological designs. In this study, we developed an estimated TC using readily available mountain catchment variables, a small catchment, steep slope, and narrow valley, using empirical equations. For our approach, we used directly measured data from 39 forested catchments (area: 0.02–9.69 km2) during 3648 observed rainfall events over a 10-year observation period. Based on the uncertainties inherent in the empirical equation, the estimated TC values were compared and analyzed through multiple regression and two different modified empirical modelling equations using our observed catchment parameters. The mean TC was significantly correlated with catchment size and stream length but negatively correlated with stream slope (p < 0.01). As a result, the mean TC estimated using the three modelling equations with catchment variables was qualitatively similar and had relative differences ranging from −12.5 to 15.5 min (−49 to 56%). Therefore, the models (particularly modeling equations with multiple regression, a modified empirical formula, and modified SCS Lag) can efficiently determine the TC and can be used in any small forested catchment in steep mountainous terrain.

Assessment and mapping of landslides in steep mountainous terrain using PS-InSAR: A case study of Karimabad Valley in Chitral

Assessing and mapping landslides in rugged, mountainous terrain presents considerable challenges due to the high cost, risk, and complexity involved. High-resolution assessments of landslides are essential for mitigating landslide-relevant damage and understanding landslide kinematics. Recent studies have utilized satellite-based Synthetic Aperture Radar Interferometry (InSAR) to achieve millimeter-scale detection of ground surface deformation. Among these InSAR techniques, Persistent Scatterers Interferometry (PS-InSAR) is particularly useful in identifying landslide-prone areas, offering a means to map and detect slope movements and characterize the stability of potentially unstable slopes. This study focuses on the slow-moving unstable hillslopes of Karimabad Valley in Chitral. We conducted an analysis using Sentinel-1 SLC data over a three-year period, obtained in interferometric wide swath mode with vertical-vertical polarization. Our results, derived from both ascending and descending satellite passes, reveal that the hillslopes of Karimabad Valley exhibit an average velocity of −25 to +28 mm/yr. Based on the deformation pattern identified, we detected seven deformation zones, with particular concern for the Beshgram village area. To better understand terrain steepness, four topographic profile lines (AA', BB', CC', and DD') were extracted and correlated with the deformation time series. Furthermore, the deformation time series were examined in relation to rainfall, earthquake, and snowfall data. These analyses highlight the complex interplay of multiple factors contributing to the observed variations in deformation, including the region's topography characterized by steep slopes, the freezing and thawing of snow, and seismic events.

Bridging the gap between airborne and spaceborne imaging spectroscopy for mountain glacier surface property retrievals

Observations of glacier albedo are sparse, despite being a first-order control on net solar radiation and melt rates. Recent and forthcoming airborne and satellite imaging spectrometer missions increase our ability to record changes in albedo over time and space but will require an understanding of uncertainties, especially in areas of rugged terrain. Some of these uncertainties arise from the timing of acquisition and resolution of digital elevation models (DEM) that are used for topographic correction. We use the fine-resolution data (2 m) from the Airborne Coastal Observatory (ACO), an imaging spectroscopy and lidar remote sensing platform, to quantify the expected error from other airborne and satellite scale platforms. First, we describe our retrieval workflow - the Imaging Spectroscopy Snow and Ice Algorithm (ISSIA) that yields spatial retrievals of broadband albedo (BBA), optical grain size, and radiative forcing by light absorbing particles (RFLAP) using the coincident 2 m lidar DEM over mountain glaciers. We use two acquisitions over Place Glacier, British Columbia, Canada that represent typical spring and summer conditions as a baseline from which synthetic datasets were generated to represent data from coarser-scale imaging spectroscopy platforms. Each synthetic dataset was motivated by specific airborne and satellite platform scenarios and quantifies four sources of error: (1) DEM spatial resolution and elevation accuracy, (2) coincident vs non-coincident DEM collection, (3) imagery spatial resolution, and (4) spectral resolution. Use of a 30 m DEM introduced the most error in steep mountain terrain, which primarily increased error in the BBA and RFLAP retrievals. The timing of DEM acquisition mattered most for high-resolution imagery and in areas surrounding the glacier toe where mass loss occurred more rapidly. Coarse-resolution imagery did not fully capture spatial variability in surface properties and topography, which lowered variance and increased overall errors. Coarsening of spectral resolution from 5 nm to 10 nm had minimal impact on surface property retrieval and associated errors were independent of DEM or imagery resolution. A simple energy analysis showed that surface albedo error, due to topographic correction with coarse and non-coincident DEMs, resulted in melt rate errors ranging from 6 to 20% for airborne and 11–20% for satellite scenarios.

ProForM: A simulation model for the management of mountain protection forests

Protection from gravitational natural hazards such as snow avalanches and rockfall is among the most important ecosystem services provided by forests in steep mountain terrain such as the European Alps. These so-called “protection forests” often have to be actively managed to ensure a sustainable ecosystem service provision in time and space. For studying these slowly developing ecosystems, dynamic forest models are important tools to assess the effect of management on the protective function. However, existing models focus on the current protective effect and in many cases neglect aspects of stand resistance and resilience to large disturbances, which are important factors for maintaining the long-term protective function. Therefore, we developed ProForM, a new dynamic forest model aimed at (1) reproducing stand dynamics of managed temperate mountain forests and (2) assessing their protective function in detail. ProForM is a structurally simple model that contains mechanistic formulations of basic demographic processes but was developed mainly with empirical data. It is spatially explicit and was developed with a focus on regeneration dynamics. The model is parameterized for four tree species (Picea abies, Abies alba, Fagus sylvatica, and Acer pseudoplatanus) and four elevational zones, ranging from mixed species submontane to spruce-dominated subalpine forests. ProForM was calibrated with and validated against independent data from long-term forest inventories from Switzerland. It exhibited a satisfactory performance in reproducing measured basal area, stem number, diameter distribution and mean crown ratio at the stand level, comparable to the performance of other, typically more complex models. Illustrative example simulations demonstrate the assessment of the protective function of a stand against gravitational hazards according to multiple criteria used in forestry practice in Alpine countries. ProForM is a valuable new tool to study the impacts of management on the protective effect of mountain forests, and to inform forest managers.

Climate resilience of farming systems in steep mountain terrain of selected regions in South Asia

This study assessed the climate resilience and characterized the existing farming systems in steep terrain in the hilly regions in South Asia. The farming systems considered were at an elevation ≥300 m in the mountain regions of two sites from Sri Lanka (Hatton and Welimada) and one site each from Bangladesh (Chittagong) and Nepal (Jhikhu Khola). A Climate Resilience Index (CRIi) score, varying from 0 (negligible resilience) and 1 (very high resilience), was calculated for each household using 31 parameters under Adaptive Capacity (ADC), Absorptive Capacity (ABC) and Transformative Capacity (TC). To spatially represent the CRIi, the four study locations were mapped using Inverse Distance Weighted (IDW) interpolation technique of GIS. All 424 households in the study sites scored a CRIi between 0.36 and 0.76, while the average CRI was the highest in Hatton (0.67), followed by Welimada (0.60), Jhikhu Khola (0.59) and Chittagong (0.48). Different demographic, socioeconomic and environmental parameters have contributed to the level of climate resilience of farming system units. Identification of good management practices of the climate-resilient farming systems and implementing those practices in vulnerable systems would increase the resilience and well-being of farming communities in steep terrain of mountain regions in South Asia.

A study on spring rejuvenation and springshed characterization in Mussoorie, Garhwal Himalaya using an integrated geospatial-geophysical approach

In the Himalayan region, drinking water has always been an issue. A total of six gravity springs and springsheds are selected at Mussoorie Hills, Garhwal (Lesser) Himalaya that prioritized the augmentation of water resources through spring rejuvenation. LISS-IV and Cartosat-I data are used to study topography, delineate springshed boundary, analyzing structural setting, surface water flow pattern. This geospatial analysis is validated further by the hydrogeological survey and Electrical Resistivity Tomography (Wenner configuration; especially carried out in Kandighat (<45° slope angle)). They inferred that discharge is structurally controlled by Mussoorie Syncline (along its limbs) and impervious Krol Limestone. The high lineament density, gentle slope and positive NDVI determined the springsheds potential for groundwater resources augmentation and development. The Guru Nanak Fifth Centenary School, Mussoorie, became the protagonist for solving the drinking water issue, using an integrated geospatial (LISS IV and Cartosat I data) and geophysical survey (Vertical Electrical Sounding and Electrical Resistivity Tomography). The ERT survey shows a low resistivity zone (~10–100 Ωm); subsequently, the VES results are plotted in a logarithmic graph, and the curve obtained (matched in the IPI2 software by inverse slope method) shows sharp peaks that correspond with ERT results very well. Therefore, these low resistivity value confirms the presence of water saturation zones. In December 2017, final drilling took place, thereby discovering groundwater yield at a surplus rate of 180 L/m surfed up to 130 m below the ground level. This expedition became the first successful scientific study in the Mussoorie hills on drinking water extraction from aquifers by integrating Geospatial and Geophysical methods. Therefore, it is clear proof of the efficiency and effectiveness of the technology and can be adopted elsewhere in the steep mountainous terrains of the Himalayan region.

Active Landslides Detection in Zhouqu County, Gansu Province Using InSAR Technology

Objectives Zhouqu County in Gansu Province is highly prone to landslides. On July 12, 2018, a landslide blocked the Bailong River near the county, posing a serious threat to the life and property of local residents and the safety of infrastructure. The early identification of potential landslides around this area is of great significance to protect the life and property of local residents and the security of its infrastructure. Methods Firstly, small baseline subset interferometry technology (SBAS-InSAR) was adopted to identify the potential active landslides in the surrounding area of Zhouqu County, with the Sentinel-1A satellite ascending and descending images from October 2017 to December 2018. Then, a combination of optical remote sensing image interpretation and field investigation is used to determine and identify potential landslides. The visual interpretation method can be used to identify landslides that have occurred or are occurring on the optical images. The field investigation was used to verify the landslide results identified by InSAR and optical imagery to increase the effectiveness and accuracy of landslide detection. Results A total of 23 active landslides were detected in the study area, with 16 active landslides detected on the ascending images and 11 on the descending images, with 4 active landslides having valid deformation information detected on both the ascending and descending images. Combined with the visual interpretation of optical remote sensing images and field investigation, the deformation characteristics of 4 typical landslides, including Jiangdingya, Mentouping, Suoertou, and Xieliupo Landslides, were analyzed. It was found that the deformation rates of Jiangdingya, Mentouping, and Xieliupo Landslides were significantly higher during the rainy season than during the non-rainy season, and the Suoertou Landslide did not show a significant increase in the deformation rate during the rainy season but showed fluctuations in the deformation rate during the rainy season. Conclusions The use of single-orbit radar satellite data for landslide detection can easily lead to the missed detection of landslide hazards, and the combined use of ascending and descending radar images can improve the accuracy of landslide detection in steep mountainous terrain. The deformation rates of active landslides in the study area have a good correlation with rainfall. The results can provide supports for the geohazard mitigation and prevention in Zhouqu County. The strategy which combines optical imagery, field investigation, and time-series InSAR can efficiently and accurately detect and monitor landslides over a wide area.

Southeastern Tibetan Plateau serves as the dominant sand contributor to the Yangtze River: Evidence from Pb isotopic compositions of detrital K‐feldspar

AbstractThere is as yet no consensus as to the dominant source of sediment transported to the oceans: the steep tectonically active highlands, or the gently sloping lowlands? In this study, new Pb isotopic analyses of detrital K‐feldspar have been combined with published data to constrain sand supply in the Yangtze River, one of the largest fluvial sediment transport systems on Earth. Tributaries draining southeastern Tibet have predominantly unradiogenic K‐feldspar sand grains. K‐feldspar in the middle Yangtze comprises c. 50% of grains of this kind, confirming that the southeastern Tibetan tributaries are the dominant sediment contributors. K‐feldspars characteristic of tributaries of the middle‐lower reaches are not strongly represented in the trunk Yangtze, indicating that they contribute little sediment. Our study demonstrates that tectonic uplift in southeastern Tibet is the primary control on sand supply. This is consistent with quartz 10Be data and with bulk‐sand petrography, indicating a major contribution of detritus from steep mountainous terrain.

Characteristic atmospheric states during mid-summer droughts over Central America and Mexico

Annual precipitation over Central America and large areas of Mexico is typically characterised by its bimodal distribution, with a precipitation minimum in July to August that occurs between two separate maxima from May to July and August to October. Several theories have been proposed to explain this phenomenon, which is often termed the mid-summer drought (MSD), but most fail to address the different characteristics associated with individual MSD events. Here, a regression-based approach is used to detect and quantify the annual and climatological MSD signature over Central America and Mexico. This approach has been evaluated and shown to be robust for various datasets with different spatial resolutions. It was found that in the southeast of the Mexico/Central America region, MSDs start earlier and end later than elsewhere, and are thus longer in duration. However, the coast of the Gulf of Mexico, Cuba, and large areas of Central America, exhibit climatologically stronger MSDs. Changes in precipitation, brought about by the interaction between reversals of the onshore/offshore winds and orographic forcing associated with the steep mountainous terrain, have also been shown to be significant factors in the timing of MSD occurrences, offering support for a combined theory of large-scale dynamics and regional forcing. Using self-organising maps (SOMs) as an analysis tool, it was found that MSD events over the domain display strong spatial variability. The MSDs over the domain also generate distinct signatures and may be forced by particular mechanisms. We found that El Nino-Southern Oscillation (ENSO) could be a potential classifier for the SOM identified atmospheric states, based on the correspondence of MSD occurrences with ENSO phases.

Landslide-controlled soil erosion rate in the largest tableland on the Loess Plateau, China

Hillslopes in the steep mountainous terrains are eroded by landslides, however the quantitative characterization of the interaction between the soil erosion and landslide is rare. Here, taking the largest tableland in the Loess Plateau of China as an example, we calculated the area and volume of 5,420 shallow loess landslides and compared these against the Chinese Soil Loss Equation (CSLE) derived soil erosion rate of 15 sub-catchments. The results on the spatial variability of soil erosion suggest that the average soil erosion modulus in the study area is 50.54 tons·ha−1·year−1, but the value varies widely across the whole research area. Overall, soil erosion is more severe in the north, which is similar to the distribution of landslide scars extracted from Google imagery. The volume of the landslides in the northern watershed was approximately 4-fold that of the landslides in the southern watershed. Our analysis reveals a satisfactory linear fitting result (R2 = 0.68) between the area of landslides and soil erosion. Based on the results, we believe that restoring vegetation cover in landslide scars should become the most urgent action to mitigate soil erosion.

First insights into the feeding habits of the Critically Endangered black snub-nosed monkey, Rhinopithecus strykeri (Colobinae, Primates).

Since its initial discovery in 2010 in the Gaoligong Mountains on the Sino-Myanmar border, there remains no direct information on the feeding habits of the black snub-nosed monkey (Rhinopithecus strykeri). This species is on the verge of extinction, with an estimated remaining population of < 400 individuals. Due to difficulties in following these monkeys across steep mountainous terrain, during 203 observation days (September 2015-January 2017) we recorded 80h of behavioral records of a wild population (Luoma group). Our preliminary results identified 14 plant species and four lichen species consumed by the monkeys. In addition, we provided the only two captive individuals of this species with a cafeteria diet composed of > 600 wild-collected plant species that were gathered from known R. strykeri habitats to determine which plant species and food items were considered palatable. Our results indicate that the captive monkeys freely consumed young and mature leaves, fruits/seeds, buds, flowers, twigs, and bark from 170 different species of trees, bushes, and herbs representing 76 genera and 41 plant families, as well as 15 species of lichen. All foods consumed by the wild monkeys were also consumed by the captive individuals. Food plants consumed by R. strykeri were found principally in intact subtropical evergreen broadleaf forests and hemlock-broadleaf mixed forests at an altitude of 2200-3000m. Strict enforcement of habitat protection and access to resources across this elevation zone appear to be essential for the conservation and survivorship of this critically endangered primate.

Geomorphic, morphometric and structural analysis of North West Benin Owena River Basin, Nigeria: implications for groundwater development

An approach detailing geomorphic, morphological and structural characteristics of a basin provides insight into groundwater development in data scarce regions. Remote sensing and GIS aids the identification, extraction and mapping of these characteristics. In this study, the geomorphic, morphological and structural parameters were delineated for North West Benin Owena River Basin (NWBORB), Nigeria towards its groundwater development. The basin was delineated from topographic maps. Five major sub-basins (SB I–V) were generated. Basin geomorphic characteristics were obtained from fundamental topographic information. Each sub-basin was quantitatively assessed of for their geomorphic, morphometric (linear, areal, and relief) and structural aspects. Geomorphic, morphological and structural parameters relevant to groundwater were utilized for sub-basin prioritization and identification of deficit/surplus groundwater zones. NWBORB is a sixth order basin. Dominated by rain fed lower order streams, it reflects late youth to early mature stage of development of the fluvial geomorphic cycle. It has a low stream frequency (Fs) and moderate drainage density (Dd) of 1.149. Bifurcation ratio (Rb) shows that the lower stream orders are surging through highly dissected, steep mountainous terrain that creates a seasonal low groundwater prospect, with exceptions occurring in the pediplains and flood plain areas. A comparison of the predominant orientations of the second, third and fourth stream orders with the lineament directions indicates structural control within the basin. Sustainable water conservation techniques are suggested locally within the drainage basin. SB-II is the most deficit zone, while SB-V is found to be surplus zone of groundwater potential.

Estimating aboveground biomass using Pléiades satellite image in a karst watershed of Guizhou Province, Southwestern China

Biomass in karst terrain has rarely been measured because the steep mountainous limestone terrain has limited the ability to sample woody plants. Satellite observation, especially at high spatial resolution, is an important surrogate for the quantification of the biomass of karst forests and shrublands. In this study, an artificial neural network (ANN) model was built using Pleiades satellite imagery and field biomass measurements to estimate the aboveground biomass (AGB) in the Houzhai River Watershed, which is a typical plateau karst basin in Central Guizhou Province, Southwestern China. A back-propagation ANN model was also developed. Seven vegetation indices, two spectral bands of Pleiades imagery, one geomorphological parameter, and land use/land cover were selected as model inputs. AGB was chosen as an output. The AGB estimated by the allometric functions in 78 quadrats was utilized as training data (54 quadrats, 70%), validation data (12 quadrats, 15%), and testing data (12 quadrats, 15%). Data-model comparison showed that the ANN model performed well with an absolute root mean square error of 11.85 t/ha, which was 9.88% of the average AGB. Based on the newly developed ANN model, an AGB map of the Houzhai River Watershed was produced. The average predicted AGB of the secondary evergreen and deciduous broadleaved mixed forest, which is the dominant forest type in the watershed, was 120.57 t/ha. The average AGBs of the large distributed shrubland, tussock, and farmland were 38.27, 9.76, and 11.69 t/ha, respectively. The spatial distribution pattern of the AGB estimated by the new ANN model in the karst basin was consistent with that of the field investigation. The model can be used to estimate the regional AGB of karst landscapes that are distributed widely over the Yun-Gui Plateau.

Seismic monitoring of small alpine rockfalls – validity, precision and limitations

Abstract. Rockfall in deglaciated mountain valleys is perhaps the most important post-glacial geomorphic process for determining the rates and patterns of valley wall erosion. Furthermore, rockfall poses a significant hazard to inhabitants and motivates monitoring efforts in populated areas. Traditional rockfall detection methods, such as aerial photography and terrestrial laser scanning (TLS) data evaluation, provide constraints on the location and released volume of rock but have limitations due to significant time lags or integration times between surveys, and deliver limited information on rockfall triggering mechanisms and the dynamics of individual events. Environmental seismology, the study of seismic signals emitted by processes at the Earth's surface, provides a complementary solution to these shortcomings. However, this approach is predominantly limited by the strength of the signals emitted by a source and their transformation and attenuation towards receivers. To test the ability of seismic methods to identify and locate small rockfalls, and to characterise their dynamics, we surveyed a 2.16 km2 large, near-vertical cliff section of the Lauterbrunnen Valley in the Swiss Alps with a TLS device and six broadband seismometers. During 37 days in autumn 2014, 10 TLS-detected rockfalls with volumes ranging from 0.053 ± 0.004 to 2.338 ± 0.085 m3 were independently detected and located by the seismic approach, with a deviation of 81−29+59 m (about 7 % of the average inter-station distance of the seismometer network). Further potential rockfalls were detected outside the TLS-surveyed cliff area. The onset of individual events can be determined within a few milliseconds, and their dynamics can be resolved into distinct phases, such as detachment, free fall, intermittent impact, fragmentation, arrival at the talus slope and subsequent slope activity. The small rockfall volumes in this area require significant supervision during data processing: 2175 initially picked potential events reduced to 511 potential events after applying automatic rejection criteria. The 511 events needed to be inspected manually to reveal 19 short earthquakes and 37 potential rockfalls, including the 10 TLS-detected events. Rockfall volume does not show a relationship with released seismic energy or peak amplitude at this spatial scale due to the dominance of other, process-inherent factors, such as fall height, degree of fragmentation, and subsequent talus slope activity. The combination of TLS and environmental seismology provides, despite the significant amount of manual data processing, a detailed validation of seismic detection of small volume rockfalls, and revealed unprecedented temporal, spatial and geometric details about rockfalls in steep mountainous terrain.

A close‐ridge small‐scale atmospheric flow field and its influence on snow accumulation

AbstractThe rough, steep, and complex terrain in the alpine environment causes a variety of flow patterns such as blocking, speed‐up, or flow separation, which influence precipitation, snow deposition, and ultimately snow distribution on the ground. Cloud‐terrain interactions, flow‐particle interactions, and snow transport affect snow accumulation patterns, but the relative importance of these processes is not fully understood, in particular, in complex mountainous terrain. A unique combination of measurements and model simulations is used in a local case study during a 2 day snowfall event to demonstrate the current understanding of snow accumulation in very steep alpine terrain. Doppler wind lidar measurements show an eddy‐like structure on the leeward side of the Sattelhorn ridge (in the Dischma valley near Davos, Switzerland), which could partly be replicated by Advanced Regional Prediction System (ARPS) flow simulations. Snow deposition measurements with a terrestrial laser scanner show a complex deposition pattern, which is only partially captured by Alpine3D deposition simulations driven by the ARPS flow fields. This shows that additional processes such as avalanches may play a role or that a more refined simulation of flow or flow‐particle interactions is required to fully understand snow distribution in very steep mountainous terrain.

COASTAL MODELING FOR REGIONAL SEDIMENT BUDGET IN WEST MAUI, HAWAII

The West Maui Region incorporates a thin coastal margin backed by steep mountainous terrain that has been vastly altered by agricultural and urbanized development. Coastline includes headlands and reefs with a very limited supply of sediment. Shoreline was found to be erosional chronically based on average rates. The dynamics of the area are complex with a wave climate affected by intricate bathymetry, wind, and island sheltering. Longshore currents vary locally and temporally from nearshore to offshore. Wave and current modeling indicates that large waves in the summer and winter have driven the majority of sediment transport along the coast. The littoral transport is essentially northward in summer and southward in winter. The net transport of longshore sediment is overall small. The nearshore eddy formation with wave breaking nearshore over narrow sandy bed and wide reefs may increase the complexity of sediment movement within the region.

Topographic correction method for steep mountain terrain images

Topographic correction method for steep mountain terrain images

The waterfall paradox: How knickpoints disconnect hillslope and channel processes, isolating salmonid populations in ideal habitats

Waterfalls create barriers to fish migration, yet hundreds of isolated salmonid populations exist above barriers and have persisted for thousands of years in steep mountainous terrain. Ecological theory indicates that small isolated populations in disturbance-prone landscapes are at greatest risk of extirpation because immigration and recolonization are not possible. On the contrary, many above-barrier populations are currently thriving while their downstream counterparts are dwindling. This quandary led us to explore geomorphic knickpoints as a mechanism for disconnecting hillslope and channel processes by limiting channel incision and decreasing the pace of base-level lowering. Using LiDAR from the Oregon Coast Range, we found gentler channel gradients, wider valleys, lower gradient hillslopes, and less shallow landslide potential in an above-barrier catchment compared to a neighboring catchment devoid of persistent knickpoints. Based on this unique geomorphic template, above-barrier channel networks are less prone to debris flows and other episodic sediment fluxes. These above-barrier catchments also have greater resiliency to flooding, owing to wider valleys with greater floodplain connectivity. Habitat preference models further indicate that salmonid habitat is present in greater quantity and quality in these above-barrier networks. Therefore the paradox of the persistence of small isolated fish populations may be facilitated by a geomorphic mechanism that both limits their connectivity to larger fish populations yet dampens the effect of disturbance by decreasing connections between hillslope and channel processes above geomorphic knickpoints.

Flood forecasting and alert system for Arda River basin

Summary The paper presents the set-up and functioning of a flood alert system based on SURFEX–TOPODYN platform for the cross-border Arda River basin. The system was built within a Bulgarian-Greek project funded by the European Territorial Cooperation (ETC) Programme and is in operational use since April 2014. The basin is strongly influenced by Mediterranean cyclones during the autumn–winter period and experiences dangerous rapid floods, mainly after intensive rain, often combined with snow melt events. The steep mountainous terrain leads to floods with short concentration time and high river speed causing damage to settlements and infrastructure. The main challenge was to correctly simulate the riverflow in near-real time and to timely forecast peak floods for small drainage basins below 100 km 2 but also for larger ones of about 1900 km 2 using the same technology. To better account for that variability, a modification of the original hydrological model parameterisation is proposed. Here we present the first results of a new model variant which uses dynamically adjusted TOPODYN river velocity as function of the computed partial streamflow discharge. Based on historical flooding data, river sections along endangered settlements were included in the river flow forecasting. A continuous hydrological forecast for 5 days ahead was developed for 18 settlements in Bulgaria and for the border with Greece, thus giving enough reaction time in case of high floods. The paper discusses the practical implementation of models for the Arda basin, the method used to calibrate the models’ parameters, the results of the calibration-validation procedure and the way the information system is organised. A real case of forecasted rapid floods that occurred after the system’s finalisation is analysed. One of the important achievements of the project is the on-line presentation of the forecasts that takes into account their temporal variability and uncertainty. The web presentation includes a comparison of the forecasted river flow to three predefined alert levels.

Optimizing the number of training areas for modeling above-ground biomass with ALS and multispectral remote sensing in subtropical Nepal

Remote sensing-based inventories of above-ground forest biomass (AGB) require a set of training plots representative of the area to be studied, the collection of which is the most expensive part of the analysis. These are time-consuming and costly because the large variety in forest conditions requires more plots to adequately capture this variability. A field campaign in general is challenging and is hampered by the complex topographic conditions, limited accessibility, steep mountainous terrains which increase labor efforts and costs. In addition it is also depend on the ratio between size of study area and number of training plots. In this study, we evaluate the number of training areas (sample size) required to estimate AGB for an area in the southern part of Nepal using airborne laser scanning (ALS), RapidEye and Landsat data. Three experiments were conducted: (i) AGB model performance, based on all the field training plots; (ii) reduction of the sample size, based on the ALS metrics and the AGB distribution; and (iii) prediction of the optimal number of training plots, based on the correlation between the remote sensing and field data. The AGB model was fitted using the sparse Bayesian method. AGB model performance was validated using an independent validation dataset. The effect of the strategies for reducing the sample size was readily apparent for the ALS-based AGB prediction, but the RapidEye and Landsat sensor data failed to capture any such effect. The results indicate that adequate coverage of the variability in tree height and density was an important condition for selecting the training plots. In addition, the ALS-based AGB prediction required the smallest number of training plots and was also quite stable with a small number of field plots.