ALBERT

Description: Changes in land use and land cover are major drivers of hydrological alteration in the tropical Andes. However, quantifying their impacts is fraught with difficulties because of the extreme diversity in meteorological boundary conditions, which contrasts strongly with the lack of knowledge about local hydrological processes. Although local studies have reduced data scarcity in certain regions, the complexity of the tropical Andes poses a big challenge to regional hydrological prediction. This study analyses data generated from a participatory monitoring network of 25 headwater catchments covering three of the major Andean biomes ( páramo , jalca , and puna ), and link their hydrological responses to main types of human interventions (cultivation, afforestation and grazing). A paired catchment setup was implemented to evaluate the impacts of change using a “trading space-for-time” approach. Catchments were selected based on regional representativeness and contrasting land use types. Precipitation and discharge have been monitored and analysed at high temporal resolution for a time period between 1 and 5 years. The observed catchment responses clearly reflect the extraordinarily wide spectrum of hydrological processes of the tropical Andes. They range from perennially humid páramos in Ecuador and northern Peru with extremely large specific discharge and baseflows, to highly seasonal, flashy catchments in the drier punas of southern Peru and Bolivia. The impacts of land use are similarly diverse and their magnitudes are a function of catchment properties, original and replacement vegetation, and management type. Cultivation and afforestation consistently affect the entire range of discharges, particularly low flows. The impacts of grazing are more variable, but have the largest effect on the catchment hydrological regulation. Overall, anthropogenic interventions result in increased streamflow variability and significant reductions in catchment regulation capacity and water yield, irrespective of the hydrological properties of the original biome. This article is protected by copyright. All rights reserved.

Description: As a consequence of the remote location of the Andean páramo is knowledge on their hydrologic functioning limited, notwithstanding this alpine tundra ecosystem act as water towers for a large fraction of the society. Given the harsh environmental conditions in this region is year-round monitoring cumbersome, and it would be beneficially if the monitoring needed for the understanding of the rainfall-runoff response could be limited in time. To identify the hydrological response and the effect of temporal monitoring a nested (n = 7) hydrological monitoring network was set up in the Zhurucay catchment (7.53 km 2 ), south Ecuador. The research questions were: (1) can event sampling provide similar information in comparison to continuous monitoring, and (2) if so, how many events are needed to achieve a similar degree of information? A subset of 34 rainfall runoff events was compared to monthly values derived from a continuous monitoring scheme from December 2010 to November 2013. Land cover and physiographic characteristics were correlated with eleven hydrological indices. Results show that despite some distinct differences between event and continuous sampling, both datasets reveal similar information; more in particular the monitoring of a single event in the rainy season provides the same information as continuous monitoring, while during the dry season 10 events ought to be monitored. This article is protected by copyright. All rights reserved.

Description: 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.

Description: Abstract High‐elevation tropical grassland systems, called Páramo, provide essential ecosystem services such as water storage and supply for surrounding and lowland areas. Páramo systems are threatened by climate and land use changes. Rainfall generation processes and moisture transport pathways influencing precipitation in the Páramo are poorly understood but needed to estimate the impact of these changes, particularly during El Niño conditions which largely affect hydrometeorological conditions in tropical regions. To fill this knowledge gap, we present a stable isotope analysis of rainfall samples collected on a daily to weekly basis between January 2015 and May 2016 during the strongest El Niño event recorded in history (2014‐2016) in two Páramo regions of Central America (Chirripó, Costa Rica) and the northern Andes (Cajas, south Ecuador). Isotopic compositions were used to identify how rainfall generation processes (convective and orographic) change seasonally at each study site. HYSPLIT air mass back trajectory analysis was used to identify preferential moisture transport pathways to each Páramo site. Our results show the strong influence of northeast trade winds to transport moisture from the Caribbean Sea to Chirripó and the South American low‐level jet to transport moisture from the Amazon forest to Cajas. These moisture contributions were also related to the formation of convective rainfall associated with the passage of the Intertropical Convergence Zone over Costa Rica and Ecuador during the wetter seasons and to orographic precipitation during the transition and drier seasons. Our findings provide essential baseline information for further research applications of water stable isotopes as tracers of rainfall generation processes and transport in the Páramo and other montane ecosystems in the tropics.

Description: The scarcity of water resources in mountain areas can distort normal water application patterns with among other effects, a negative impact on water supply and river ecosystems. Knowing the probability of droughts might help to optimize a priori the planning and management of the water resources in general and of the Andean watersheds in particular. This study compares Markov chain- (MC) and Bayesian network- (BN) based models in drought forecasting using a recently developed drought index with respect to their capability to characterize different drought severity states. The copula functions were used to solve the BNs and the ranked probability skill score (RPSS) to evaluate the performance of the models. Monthly rainfall and streamflow data of the Chulco River basin, located in Southern Ecuador, were used to assess the performance of both approaches. Global evaluation results revealed that the MC-based models predict better wet and dry periods, and BN-based models generate slightly more accurately forecasts of the most severe droughts. However, evaluation of monthly results reveals that, for each month of the hydrological year, either the MC- or BN-based model provides better forecasts. The presented approach could be of assistance to water managers to ensure that timely decision-making on drought response is undertaken.

Description: Understanding of evapotranspiration (ET) processes over Andean mountain environments is crucial, particularly due to the importance of these regions to deliver water-related ecosystem services. In this context, the detection of spatio-temporal changes in ET remains poorly investigated for specific Andean ecosystems, like the páramo. To overcome this lack of knowledge, we implemented the energy-balance model METRIC with Landsat 7 ETM+ and MODIS-Terra imagery for a páramo catchment. The implementation contemplated adjustments for complex terrain in order to obtain daily, monthly and annual ET maps (between 2013 and 2014). In addition, we compared our results to the global ET product MOD16. Finally, a rigorous validation of the outputs was conducted with residual ET from the water balance. ET retrievals from METRIC (Landsat-based) showed good agreement with the validation-related ET at monthly and annual steps (mean bias error 〈8 mm·month−1 and annual deviation 〈17%). However, METRIC (MODIS-based) outputs and the MOD16 product were revealed to be unsuitable for our study due to the low spatial resolution. At last, the plausibility of METRIC to obtain spatial ET retrievals using higher resolution satellite data is demonstrated, which constitutes the first contribution to the understanding of spatially-explicit ET over an alpine catchment in the neo-tropical Andes.

Description: Mountain regions worldwide present a pronounced spatiotemporal precipitation variability, which added to scarce monitoring networks limits our understanding of the generation processes involved. To improve our understanding of clouds and precipitation dynamics and cross-scale generation processes in mountain regions, we analyzed spatiotemporal rainfall patterns using satellite cloud products (SCP) in the Paute basin (900–4200 m a.s.l. and 6481 km2) in the Andes of Ecuador. Precipitation models, using SCP and GIS data, reveal the spatial extension of three regimes: a three-modal (TM) regime present across the basin, a bimodal (BM) regime, along sheltered valleys, and a unimodal (UM) regime at windward slopes of the eastern cordillera. Subsequently, the spatiotemporal analysis using synoptic information shows that the dry season of the BM regime during boreal summer is caused by strong subsidence inhibiting convective clouds formation. Meanwhile, in UM regions, low advective shallow cap clouds mainly cause precipitation, influenced by water vapor from the Amazon and enhanced easterlies during boreal summer. TM regions are transition zones from UM to BM and zones on the windward slopes of the western cordillera. These results highlight the suitability of satellite and GIS data-driven statistical models to study spatiotemporal rainfall seasonality and generation processes in complex terrain, as the Andes.

Description: Flash-flood forecasting has emerged worldwide due to the catastrophic socio-economic impacts this hazard might cause and the expected increase of its frequency in the future. In mountain catchments, precipitation-runoff forecasts are limited by the intrinsic complexity of the processes involved, particularly its high rainfall variability. While process-based models are hard to implement, there is a potential to use the random forest algorithm due to its simplicity, robustness and capacity to deal with complex data structures. Here a step-wise methodology is proposed to derive parsimonious models accounting for both hydrological functioning of the catchment (e.g., input data, representation of antecedent moisture conditions) and random forest procedures (e.g., sensitivity analyses, dimension reduction, optimal input composition). The methodology was applied to develop short-term prediction models of varying time duration (4, 8, 12, 18 and 24 h) for a catchment representative of the Ecuadorian Andes. Results show that the derived parsimonious models can reach validation efficiencies (Nash-Sutcliffe coefficient) from 0.761 (4-h) to 0.384 (24-h) for optimal inputs composed only by features accounting for 80% of the model’s outcome variance. Improvement in the prediction of extreme peak flows was demonstrated (extreme value analysis) by including precipitation information in contrast to the use of pure autoregressive models.

Description: Few high-elevation tropical catchments worldwide are gauged and even fewer are studied using combined hydrometric and isotopic data. Consequently, we lack information needed to understand processes governing rainfall-runoff dynamics and to predict their influence on downstream ecosystem functioning. To address this need, we present a combination of hydrometric and water stable isotopic observations in the wet Andean páramo ecosystem of the Zhurucay Ecohydrological Observatory (7.53 km 2 ). The catchment is located in the Andes of south Ecuador between 3400 and 3900 m a.s.l. Water samples for stable isotopic analysis were collected during 2 years (May 2011 – May 2013), while rainfall and runoff measurements were continuously recorded since late 2010. The isotopic data reveal that Andosol soils predominantly situated on hillslopes drain laterally to Histosols (Andean páramo wetlands) mainly located at the valley bottom. Histosols, in turn, feed water to creeks and small rivers throughout the year, establishing hydrologic connectivity between wetlands and the drainage network. Runoff is primarily comprised of pre-event water stored in the Histosols, which is replenished by rainfall that infiltrates through the Andosols. Contributions from the mineral horizon and the top of the fractured bedrock are small and only seem to influence discharge in small catchments during low flow generation (non-exceedance flows 〈 Q 35 ). Variations in source contributions are controlled by antecedent soil moisture, rainfall intensity, and duration of rainy periods. Saturated hydraulic conductivity of the soils, higher than the year-round low precipitation intensity, indicates that Hortonian overland flow rarely occurs during high intensity precipitation events. Deep groundwater contributions to discharge seem to be minimal. These results suggest that, in this high-elevation tropical ecosystem: 1) subsurface flow is a dominant hydrological process and 2) (Histosols) wetlands are the major source of stream runoff. Our study highlights that detailed isotopic characterization during short time periods provides valuable information about ecohydrological processes in regions where very few basins are gauged.

Description: Water, Vol. 10, Pages 1169: Impact of Rain Gauges Distribution on the Runoff Simulation of a Small Mountain Catchment in Southern Ecuador Water doi: 10.3390/w10091169 Authors: Adrián Sucozhañay Rolando Célleri In places with high spatiotemporal rainfall variability, such as mountain regions, input data could be a large source of uncertainty in hydrological modeling. Here we evaluate the impact of rainfall estimation on runoff modeling in a small páramo catchment located in the Zhurucay Ecohydrological Observatory (7.53 km2) in the Ecuadorian Andes, using a network of 12 rain gauges. First, the HBV-light semidistributed model was analyzed in order to select the best model structure to represent the observed runoff and its subflow components. Then, we developed six rainfall monitoring scenarios to evaluate the impact of spatial rainfall estimation in model performance and parameters. Finally, we explored how a model calibrated with far-from-perfect rainfall estimation would perform using new improved rainfall data. Results show that while all model structures were able to represent the overall runoff, the standard model structure outperformed the others for simulating subflow components. Model performance (NSeff) was improved by increasing the quality of spatial rainfall estimation from 0.31 to 0.80 and from 0.14 to 0.73 for calibration and validation period, respectively. Finally, improved rainfall data enhanced the runoff simulation from a model calibrated with scarce rainfall data (NSeff 0.14) from 0.49 to 0.60. These results confirm that in mountain regions model uncertainty is highly related to spatial rainfall and, therefore, to the number and location of rain gauges.