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Floods cause significant damage to human life, infrastructure, agriculture, and the economy. Predicting peak runoffs is crucial for hazard assessment, but it is challenging in remote areas like the Andes due to limited hydrometeorological data. We utilized a 300 km2 catchment over the period 2015–2021 to develop runoff forecasting models exploiting precipitation information retrieved from an X-band weather radar. For the modeling task, we employed the Random Forest (RF) algorithm in combination with a Feature Engineering (FE) strategy applied to the radar data. The FE strategy is based on an object-based approach, which derives precipitation characteristics from radar data. These characteristics served as inputs for the models, distinguishing them as "enhanced models" compared to "referential models" that incorporate precipitation estimates from all available pixels (1210) for each hour. From 29 identified events, enhanced models achieved Nash-Sutcliffe efficiency (NSE) values ranging from 0.94 to 0.50 for lead times between 1 and 6 h. A comparative analysis between the enhanced and referential models revealed a remarkable 23% increase in NSE-values at the 3 h lead time, which marks the peak improvement. The enhanced models integrated new data into the RF models, resulting in a more accurate representation of precipitation and its temporal transformation into runoff. [ABSTRACT FROM AUTHOR]

The tropical forests in the Amazon store large amounts of carbon and are still considered a carbon sink. There is evidence that deforestation can turn a forest landscape into a carbon source due to land use and forest degradation. Deforestation causes fragmented forest landscapes. It is known from field experiments that forest dynamics at the edge of forest fragments are altered by changes in the microclimate and increased tree mortality ("edge effects"). However, it is unclear how this will affect large fragmented forest landscapes, and thus the entire Amazon region. The aim of this study is to investigate different forest attributes in edge and core forest areas at high resolution, and thus to identify the large-scale impacts of small-scale edge effects. Therefore, a well-established framework combining forest modelling and lidar-generated forest structure information was combined with radar-based forest cover data. Furthermore, forests were also analyzed at the landscape level to investigate changes between highly fragmented and less-fragmented landscapes. This study found that the aboveground biomass in forest edge areas is 27% lower than in forest core areas. In contrast, the net primary productivity is 13% higher in forest edge areas than in forest core areas. In the second step, whole fragmented landscapes were analyzed. Nearly 30% of all forest landscapes are highly fragmented, particularly in the regions of the Arc of Deforestation, on the edge of the Andes and on the Amazon river banks. Less-fragmented landscapes are mainly located in the central Amazon rainforest. The aboveground biomass is 28% lower in highly fragmented forest landscapes than in less-fragmented landscapes. The net primary productivity is 13% higher in highly fragmented forest landscapes than in less-fragmented forest landscapes. In summary, fragmentation of the Amazon rainforest has an impact on forest attributes such as biomass and productivity, with mostly negative effects on forest dynamics. If deforestation continues and the proportion of highly fragmented forest landscapes increase, the effect may be even more intense. By combining lidar, radar and forest modelling, this study shows that it is possible to map forest structure, and thus the degree of forest degradation, over a large area and derive more detailed information about the carbon dynamics of the Amazon region. [ABSTRACT FROM AUTHOR]

A vertically pointing Ka-band radar (Metek MIRA-35C) installed at the Instituto Geof'ısico del Per'u, Atmospheric Microphysics and Radiation Laboratory (LAMAR) Huancayo Observatory, which is located at an elevation of 3.3 km MSL in the Andes Mountains of Peru, is used to investigate the effects of terrain on satellite-based precipitation measurement in the Andes. We compare the vertical structure of precipitation observed by theMIRA-35C with Ka-band radar measurements from the Dual-Frequency Precipitation Radar (DPR) on board the Global Precipitation Measurement (GPM) mission core satellite using an approach based on Taylor's hypothesis of frozen turbulence that attempts to reduce the impact of spatiotemporal offsets between these two radar measurements. From 3 April 2014 to 20 May 2018, the DPR measured precipitation near LAMAR during 15 of its 157 coincident overpasses. There were six simultaneous observations with MIRA-35C. We found that the average of the DPR's lowest clutter-free bin is 1.62 km AGL, but the presence of precipitation worsens the situation, causing a 0.4-km-deeper algorithm-detected blind zone for the DPR at the Huancayo Observatory. In the study area, the depth of the clutter layer observed with DPR often extends above the melting layer but can be highly variable, extending even as high as 5 km AGL. These results suggest that DPR estimates of stratiform precipitation over the AndesMountains are likely underestimated because of the terrain effects on the satellite measurements and problems in its blind zone detection algorithms, highlighting the difficulty in estimating precipitation in mountainous terrain from spaceborne radar. [ABSTRACT FROM AUTHOR]

On 18 May 2015, a severe rainfall event triggered a flash flood in the municipality of Salgar, located in the northwestern Colombian Andes. This work aims to reconstruct the main hydrological features of the flash flood to better understand the processes modulating the occurrence of the event. Radar quantitative precipitation estimates (QPEs), satellite information, and post-event field visits are used to reconstruct the Salgar flash flood, in an ungauged basin, addressing the relationship among rainfall spatiotemporal structure, soil moisture, and runoff generation during successive rainfall events by using a conceptual modeling framework including landslide and hydraulic submodels. The hydrological model includes virtual tracers to explore the role of runoff and subsurface flow and the relative importance of convective and stratiform precipitation in flash flood generation. Despite potential shortcomings due to the lack of data, the modeling results allow an assessment of the impact of the interactions between runoff, subsurface flow, and convective–stratiform rainfall on the short-term hydrological mechanisms leading to the flash flood event. The overall methodology reproduces the magnitude and timing of the La Liboriana flash flood peak discharge considerably well, as well as the areas of landslide occurrence and flood spots, with limitations due to the spatial resolution of the available digital elevation model. Simulation results indicate that the flash flood and regional landslide features were strongly influenced by the antecedent rainfall, which was associated with a northeasterly stratiform event. The latter recharged the gravitational and capillary storages within the model, moistening the entire basin before the occurrence of the flash flood event and impacting the subsurface–runoff partitioning during the flash flood event. Evidence suggests that the spatial structure of the rainfall is at least as important as the geomorphological features of the basin in regulating the occurrence of flash flood events. [ABSTRACT FROM AUTHOR]

Abstract: Experimental satellite and radar data and numerical simulations at constant pressure levels were analyzed to estimate the relative importance of mountain waves during the development of 3 severe deep convection events with abundant hail precipitation in Mendoza, Argentina. The mesoscale numerical simulation model WRF 3.0 was used to determine the regional circulation previous to the deep convection. GPS radio occultation data were used to validate the simulations. It was found that updrafts generated by mountain waves constitute a possible triggering mechanism during the storms initiation. A continuous wavelet transform analysis was applied to the vertical velocity. In all cases, large amplitude stationary waves were observed just before the initiation of convection, possibly triggering it. The horizontal direction of propagation and the corresponding horizontal and vertical wavelengths of both main modes were identified. Vertical velocity and convective inhibition index were found to be potential storm forecasting flags in this region. [Copyright &y& Elsevier]

Abstract: The Andes of Ecuador show an extreme heterogeneity of spatial and temporal distribution of precipitation. The existing operational network of the national weather service is not capable of reproducing these complex patterns. By using a cost-efficient rain radar and a network of high-resolution rain gauges, the real complexity of the rainfall distribution and the meteorological processes of rainfall formation can be assessed. A blending method encompassing geostatistical tools allows to derive a comprehensive rainfall climatology for the study area. Precipitation is predominantly of the advective type, associated with humid air masses from the Amazon basin transported by the tropical easterlies. The typical form is light to heavy drizzle with long duration but lower rain rates. However, in contrast to former knowledge there is no single mechanism of rain formation for any given place. Several processes interact like small and large-scale convective cloud systems, local and regional valley/mountain breeze systems and terrain-lines of preferred moisture transport interact on various time scale. This leads to complex patterns of rainfall in space and time. Several types of characteristic weather situations are revealed by the study. They are characterized by specific combinations of local and regional atmospheric processes and interactions with the topographical configuration. They are modified by mesoscale and continental circulation patterns as the annual shift of pressure cells, the east Andean low-level Jet and katabatic flows. [ABSTRACT FROM AUTHOR]

Abstract: Two cases of severe convection have been studied by means of a set of mesoscale numerical simulations using the Pennsylvania State University-National Center for Atmospheric Research model. These events were caused by two convective systems developing during 23–24 December 2004 and 4–5 January 2005 in the province of Mendoza, Argentina, in the eastern Andes. Two S-band radars registered maximum reflectivities of over 60 dBZ during more than 5 h and the hailpads in the study zone registered hailstones with a diameter of up to 3 cm on one of the study days. The synoptic patterns were similar in both cases. The arrival of a trough from the west, crossing the border with Chile, and the presence of the Northwestern Argentinean Low outlined a favorable environment for the development of convection on the lee side of the Andes. The high diurnal temperatures and the topography of the area favored the formation of thermal mesolows on the lee side of the mountain range. Model output fields suggest that a low-level jet stream carrying warm and moist air from the north impinging on the eastern side of the Andes, together with a mid and high-level cold advection associated to the trough, generated a convergence line with areas of convective instability and water vapor flux convergence. In both cases, the mesoscale model forecasts the spatial distribution of the precipitation field relatively accurately but underestimates the total precipitation estimated by radars. The Andes Mountain Range and the solar radiation could have played an important role in triggering convection in the study zone. A factor separation technique is used to explore this issue. For both events the combined effect of topography and solar radiation is decisive to explain the distribution of precipitation in the study area. [Copyright &y& Elsevier]

Ground based rainfall information is hardly available in most high mountain areas of the world due to the remoteness and complex topography. Thus, proper understanding of spatio-temporal rainfall dynamics still remains a challenge in those areas. Satellite-based rainfall products may help if their rainfall assessment are of high quality. In this paper, microwave-based integrated multi-satellite retrieval for the Global Precipitation Measurement (GPM) (IMERG) (MW-based IMERG) was assessed along with the random-forest-based rainfall (RF-based rainfall) and infrared-only IMERG (IR-only IMERG) products against the quality-controlled rain radar network and meteorological stations of high temporal resolution over the Pacific coast and the Andes of Ecuador. The rain area delineation and rain estimation of each product were evaluated at a spatial resolution of 11 km2 and at the time of MW overpass from IMERG. The regionally calibrated RF-based rainfall at 2 km2 and 30 min was also investigated. The validation results indicate different essential aspects: (i) the best performance is provided by MW-based IMERG in the region at the time of MW overpass; (ii) RF-based rainfall shows better accuracy rather than the IR-only IMERG rainfall product. This confirms that applying multispectral IR data in retrieval can improve the estimation of rainfall compared with single-spectrum IR retrieval algorithms. (iii) All of the products are prone to low-intensity false alarms. (iv) The downscaling of higher-resolution products leads to lower product performance, despite regional calibration. The results show that more caution is needed when developing new algorithms for satellite-based, high-spatiotemporal-resolution rainfall products. The radar data validation shows better performance than meteorological stations because gauge data cannot correctly represent spatial rainfall in complex topography under convective rainfall environments. [ABSTRACT FROM AUTHOR]

Topography has an important role in shaping regional and global climate systems, as it acts as a mechanical barrier to the low-level moisture flow. Thus, a complex spatial pattern of rainfall can exist over the mountainous region. Moreover, it is critical to advance our understanding of the relationship between rainfall and topography in terms of rainfall timing, frequency, and magnitude. In this study, characteristics of austral summer (December–February) precipitation are analyzed using 17-year (1998–2014) high-spatial-resolution (0.05° × 0.05°) data obtained from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) over the Andean region focusing on topographic impact. We observe an interaction between precipitation patterns and topography, with clear precipitation–elevation relationships in the Andes regions. The rainfall maxima zone was observed over the higher terrain of the central and southern Andes, and the zone is attributed to frequency and intensity of rainfall, respectively. In the foothills of the central Andes, we find there was a persistent rain system when a moist, low-level flow was lifted due to topography. In contrast, steep mountain slopes and a relatively dry atmosphere modulate deep convection in the foothills of southern Andes. [ABSTRACT FROM AUTHOR]

Lack of rainfall information at high temporal resolution in areas with a complex topography as the Tropical Andes is one of the main obstacles to study its rainfall dynamics. Furthermore, rainfall types (e.g., stratiform, convective) are usually defined by using thresholds of some rainfall characteristics such as intensity and velocity. However, these thresholds highly depend on the local climate and the study area. In consequence, these thresholds are a constraining factor for the rainfall class definitions because they cannot be generalized. Thus, this study aims to analyze rainfall-event types by using a data-driven clustering approach based on the k-means algorithm that allows accounting for the similarities of rainfall characteristics of each rainfall type. It was carried out using three years of data retrieved from a vertically pointing Micro Rain Radar (MRR) and a laser disdrometer. The results show two main rainfall types (convective and stratiform) in the area which highly differ in their rainfall features. In addition, a mixed type was found as a subgroup of the stratiform type. The stratiform type was found more frequently throughout the year. Furthermore, rainfall events of short duration (less than 70 min) were prevalent in the study area. This study will contribute to analyze the rainfall formation processes and the vertical profile. [ABSTRACT FROM AUTHOR]

Discharge forecasting is a key component for early warning systems and extremely useful for decision makers. Forecasting models require accurate rainfall estimations of high spatial resolution and other geomorphological characteristics of the catchment, which are rarely available in remote mountain regions such as the Andean highlands. While radar data is available in some mountain areas, the absence of a well distributed rain gauge network makes it hard to obtain accurate rainfall maps. Thus, this study explored a Random Forest model and its ability to leverage native radar data (i.e., reflectivity) by providing a simplified but efficient discharge forecasting model for a representative mountain catchment in the southern Andes of Ecuador. This model was compared with another that used as input derived radar rainfall (i.e., rainfall depth), obtained after the transformation from reflectivity to rainfall rate by using a local Z-R relation and a rain gauge-based bias adjustment. In addition, the influence of a soil moisture proxy was evaluated. Radar and runoff data from April 2015 to June 2017 were used. Results showed that (i) model performance was similar by using either native or derived radar data as inputs (0.66 < NSE < 0.75; 0.72 < KGE < 0.78). Thus, exhaustive pre-processing for obtaining radar rainfall estimates can be avoided for discharge forecasting. (ii) Soil moisture representation as input of the model did not significantly improve model performance (i.e., NSE increased from 0.66 to 0.68). Finally, this native radar data-based model constitutes a promising alternative for discharge forecasting in remote mountain regions where ground monitoring is scarce and hardly available. [ABSTRACT FROM AUTHOR]

Despite many efforts of the radar community, quantitative precipitation estimation (QPE) from weather radar data remains a challenging topic. The high resolution of X-band radar imagery in space and time comes with an intricate correction process of reflectivity. The steep and high mountain topography of the Andes enhances its complexity. This study aims to optimize the rainfall derivation of the highest X-band radar in the world (4450 m a.s.l.) by using a random forest (RF) model and single Plan Position Indicator (PPI) scans. The performance of the RF model was evaluated in comparison with the traditional step-wise approach by using both, the Marshall-Palmer and a site-specific Z–R relationship. Since rain gauge networks are frequently unevenly distributed and hardly available at real time in mountain regions, bias adjustment was neglected. Results showed an improvement in the step-wise approach by using the site-specific (instead of the Marshall-Palmer) Z–R relationship. However, both models highly underestimate the rainfall rate (correlation coefficient < 0.69; slope up to 12). Contrary, the RF model greatly outperformed the step-wise approach in all testing locations and on different rainfall events (correlation coefficient up to 0.83; slope = 1.04). The results are promising and unveil a different approach to overcome the high attenuation issues inherent to X-band radars. [ABSTRACT FROM AUTHOR]