In January 2016, a high-precipitation event (HPE) affected northwestern Ecuador, leading to devastating flooding in the Esmeraldas River Basin. The HPE appeared in the aftermath of the 2015/16 El Niño as an early onset of heavy rainfalls, normally expected in the peak rainy season between March and April. We investigate the local HPE atmospheric setting and the regional “weather-within-climate” characteristics of the growing-season rainfall between December and January using gauge data, satellite imagery, and reanalysis. The unusual convective environment in late January 2016 involved local and synoptic drivers leading the development of a mesoscale convective complex (MCC) during the nighttime of 24th January. The genesis of the MCC was related to an early-arriving thermal weather state and orographic lifting; the Andean ranges acted as both a channel boosting upslope flow and convective updrafts and as a heavy rain divide for inner valleys. The synoptic controls were associated with 1) a southern boundary of the inter-tropical convergence zone, abnormally displaced to 4°N as response to the 2015/16 El Niño where eastward air surges merge upward vertical mass fluxes; 2) the arrival to the Ecuadorian coast of an equatorially propagating Kelvin wave; and 3) a low-mid level moisture influx coming from the Amazon associated with ascent due to changes in the Walker circulation. Lastly, we suggest that the convective environment in late January was also favored by cross-time-scale interference of the very strong El Niño event and a strong and persistent Madden-Julian oscillation (MJO) in the central Pacific.

Extreme weather events have devastating impacts on human health, economic activities, ecosys tems, and infrastructure. It is therefore crucial to anticipate extremes and their impacts to allow for preparedness and emergency measures. There is indeed potential for probabilistic subseasonal prediction on timescales of several weeks for many extreme events. Here we provide an overview of subseasonal predictability for case studies of some of the most prominent extreme events across the globe using the ECMWF S2S prediction system: heatwaves, cold spells, heavy precipitation events, and tropical and extratropical cyclones. The considered heatwaves exhibit predictability on timescales of 3-4 weeks, while this timescale is 2-3 weeks for cold spells. Precipitation extremes are the least predictable among the considered case studies. Tropical cyclones, on the other hand, can exhibit probabilistic predictability on timescales of up to 3 weeks, which in the presented cases was aided by remote precursors such as the Madden-Julian Oscillation. For extratropical cyclones, lead times are found to be shorter. These case studies clearly illustrate the potential for event - dependent advance warnings for a wide range of extreme events. The subseasonal predictability of extreme events demonstrated here allows for an extension of warning horizons, provides advance information to impact modelers, and informs communities and stakeholders affected by the impacts of extreme weather events.

In January 2016, a high precipitation event (HPE) affected the northern coast of Ecuador leading to devastating flooding in the Esmeraldas’ river basin. The HPE appeared in the aftermath of the 2015/2016 El Niño as an early onset of heavy rainfalls otherwise expected in the core rainy season (Mar-Apr). Using gauge data, satellite imagery and reanalysis we investigate the daily and ‘weather-within-climate’ characteristics of the HPE and its accompanying atmospheric conditions. The convective storms developed into a mesoscale convective complex (MCC) during nighttime on 24th January. The scale size of the heavy rainfall system was about 250 km with a lifecycle lasting 16 hours for the complete storm with 6 hours of convective showers contributing to the HPE. The genesis of the MCC was related to above-normal moisture and orographic lifting driving convective updrafts; the north-south mountain barrier acted as both a channel boosting upslope flow when it moves over hillslopes; and, as a heavy-rain divide for inner valleys. The above normal moisture conditions were favored by cross-time-scale interactions involving the very strong El Niño 2015/2016 event, an unusually persistent Madden–Julian oscillation (MJO) in phases 3 and 6, remotely forced by tropical synoptic scale disturbances. In the dissipation stage, a moderate low-level easterly shear with wind velocity of about 10 m/s moved away the unstable air and the convective pattern disappear on the shore of the Esmeraldas basin. We use ECMWF re-forecast from the Sub-seasonal to Seasonal (S2S) prediction project dataset and satellite observations to investigate the predictability of the HPE. Weekly ensemble-mean rainfall anomaly forecasts computed from raw (uncorrected) S2S reforecast initialized on 31st Dec 2015, 7th, 14th and 21st Jan 2016 are used to assess the occurrence of rainfall anomalies over the region. The reforecast represents consistently, over all lead times, the spatial pattern of the HPE. Also, the ensemble-mean forecast shows positive rainfall anomalies at times scales of 1-3 weeks (0-21 days) at nearly all initialization dates and lead times, predicting this way successfully the timing and amplitude of the highest HPE leading the 25th January flood.

Streamflow is the runoff response integrated in space and time over a complex system involving climatic and catchment physiographic factors. In the Andes, accelerating runoff process understanding is hampered by the inability to quantify heterogeneity of surface and subsurface catchment properties. Here, we present a statistical approach based on regression models and correlation analysis that links hydrological signatures and catchment properties to unveil processes in a set of volcanic mountain catchments (latitude 0°30'N) in Ecuador. The catchments represent form and function diversity in the same hydrological unit. We found that despite of similar atmospheric‐water inputs the water yield in the north‐east region is about 5× larger than in the south‐west region and their flow regimes are asymmetric. The soil‐bedrock interface and lithology exert a first‐order control on hydrologic partitioning, and this allowed us to hypothesize two hydrological mechanisms. Firstly, in the north‐east region, the perennial streamflow is associated with seasonal rainfall patterns, and subsequent drainage processes taking place at the surface and subsurface level. The amount of streamflow is related to landform characteristics, high canopy density and root development of forest as well as water holding capacity of organic soils. From a mechanistic standpoint, the low concentration time, steep slopes and shallow infiltration limited by high‐consolidated deposits of sedimentary and volcanics suggest a lateral movement of the flow. Secondly, in the south‐west region the streamflow regime is mostly groundwater‐dependent and it becomes seasonally enhanced by rainfall. Larger seasonal variations of precipitation and temperature result into enhanced evapotranspiration in the drier months, limiting shallow soil infiltration. Under the soil layers, highly permeable pyroclastic deposits and andesitic lavas promote deep percolation. The results highlight the degree of dissimilarity of hydrological processes in Andean settings, but unravelling their complexity seems plausible using streamflow signatures and causal explanatory models.

Advances in open data science serve large-scale model developments and, subsequently, hydroclimate services. Local river flow observations are key in hydrology but data sharing remains limited due to unclear quality, or to political, economic or infrastructure reasons. This presentation provides methods for quality checking openly accessible river-flow time series. Availability, outliers, homogeneity and trends were assessed in 21 586 time series from 13 data providers worldwide. We found a decrease in data availability since the 1980s, scarce open information in southern Asia, the Middle East and North and Central Africa, and significant river-flow trends in Africa, Australia, southwest Europe and Southeast Asia. We distinguish numerical outliers from high-flow peaks, and integrate all investigated quality characteristics in a composite indicator.Some 5338 gauges from these river flow time series (> 10 years) were used in the evaluation of the Worldwide-HYPE (WWH) hydrological model at the global scale (half for calibration and half for independent validation), resulting in a median monthly KGE of 0.4. However, WWH performance varies widely spatially and with the target flow signature. The model performs best (KGE > 0.6) in Eastern USA, Europe, South-East Asia, and Japan, as well as in parts of Russia, Canada, and South America. It also shows overall good potential to capture flow signatures of monthly high flows, spatial variability of high flows, duration of low flows, and constancy of daily flow. Nevertheless, there remains large potential for model improvements and we suggest both redoing the parameter estimation and reconsidering parts of the model structure for the next WWH version.References:Crochemore, L., Isberg, K., Pimentel, R., Pineda, L., Hasan, A., Arheimer, B., (2019). Lessons learnt from checking the quality of openly accessible river flow data worldwide. Hydrological Sciences Journal. https://doi.org/10.1080/02626667.2019.1659509Arheimer, B., Pimentel, R., Isberg, K., Crochemore, L., Andersson, J. C. M., Hasan, A., Pineda, L., (accepted). Global catchment modelling using World-Wide HYPE (WWH), open data and stepwise parameter estimation. Hydrology and Earth System Sciences Discussions. In press. https://doi.org/10.5194/hess-2019-111

Modern hydrology relies on multiple sources of information combined with climatological, hydrological and glaciological data. These data can be collected through various sources such as private initiatives by companies, research programs, and both national and international organisms. They also vary by types, e.g., in situ measurements, satellite, reanalysis and simulated data. Recently the ANDEX research project, as a GEWEX regional program, was created to understand the processes related to the hydrological cycle and energy fluxes in the Andean region from Colombia to Patagonia. It is quite challenging to carry out this program given the complex orography and diversity of climates from tropical to sub-polar climates. This review article is a compilation of the various databases that are useful for hydrometeorological research in the South American Andes. The National Meteorological and Hydrological Services in Bolivia, Chile, Colombia, Ecuador, Peru, Venezuela and Argentina provide a large amount of data however the high-elevation areas are poorly instrumented and the number of stations varies greatly between the countries. National databases are only partially shared with the international bodies responsible for summarizing the existing data; this causes problems in term of data product assimilation. Across the entire continent, too few radiosondes are being used despite the fact that these data are crucial for validating and identifying problems in the atmospheric models. An increasing number of satellite data are available but it is difficult to assimilate them into the hydroclimatological models suited to the adjusted spatial and temporal resolutions. Specifically, for precipitation, we recommend merged products that account for the high spatial and temporal variability across the Andes. Finally, the international ANDEX program could be an excellent opportunity to increase the knowledge of the hydrological processes in the Andes.

Recent advancements in catchment hydrology (such as understanding catchment similarity, accessing new data sources, and refining methods for parameter constraints) make it possible to apply catchment models for ungauged basins over large domains. Here we present a cutting-edge case study applying catchment-modelling techniques with evaluation against river flow at the global scale for the first time. The modelling procedure was challenging but doable, and even the first model version showed better performance than traditional gridded global models of river flow. We used the open-source code of the HYPE model and applied it for >130 000 catchments (with an average resolution of 1000 km(2)), delineated to cover the Earth's landmass (except Antarctica). The catchments were characterized using 20 open databases on physiographical variables, to account for spatial and temporal variability of the global freshwater resources, based on exchange with the atmosphere (e.g. precipitation and evapotranspiration) and related budgets in all compartments of the land (e.g. soil, rivers, lakes, glaciers, and floodplains), including water stocks, residence times, and the pathways between various compartments. Global parameter values were estimated using a stepwise approach for groups of parameters regulating specific processes and catchment characteristics in representative gauged catchments. Daily and monthly time series (> 10 years) from 5338 gauges of river flow across the globe were used for model evaluation (half for calibration and half for independent validation), resulting in a median monthly KGE of 0.4. However, the World-Wide HYPE (WWH) model shows large variation in model performance, both between geographical domains and between various flow signatures. The model performs best (KGE > 0.6) in the eastern USA, Europe, South-East Asia, and Japan, as well as in parts of Russia, Canada, and South America. The model shows overall good potential to capture flow signatures of monthly high flows, spatial variability of high flows, duration of low flows, and constancy of daily flow. Nevertheless, there remains large potential for model improvements, and we suggest both redoing the parameter estimation and reconsidering parts of the model structure for the next WWH version. This first model version clearly indicates challenges in large-scale modelling, usefulness of open data, and current gaps in process understanding. However, we also found that catchment modelling techniques can contribute to advance global hydrological predictions. Setting up a global catchment model has to be a long-term commitment as it demands many iterations; this paper shows a first version, which will be subjected to continuous model refinements in the future. WWH is currently shared with regional/local modellers to appreciate local knowledge.

Weather and climatic characterization of rainfall extremes is both of scientific and societal value for hydro meteorological risk management, yet discrimination of local and large-scale forcing remains challenging in data scarce environments. Here, we present an analysis framework that separates weather regime and climate controls using data-driven process identification. The approach is based on signal-to-noise separation methods and explanatory extreme value (EV) modeling of multisite rainfall extremes. The EV models integrate the temporal component of the weather/climate driver using semi-automatic parameter identification. At weather scale, the EV models are combined with a state-based Markov model to represent the spatiotemporal structure of rainfall as weather states. At climate scale, the EV models are used to search for drivers leading to the shift of weather patterns. The drivers are brought out in a climate-to-weather signal subspace, built via dimension reduction of climate model reconstructions. We apply the framework to a complex terrain region: the Western Andean Ridge in Ecuador and Peru (0-6 degrees S) using ground data from the second half of the 20th century. Overall, we show that the framework, which does not make any prior assumption on the explanatory power of the weather and climate drivers, allows identification of well-known and new features of the regional climate in a purely data-driven fashion. Thus, the approach shows potential to identify weather controls on precipitation extremes in data-scarce and orographically complex regions in which model reconstructions are the only climate proxies.

AbstractThe seasonal predictability of daily rainfall characteristics is examined over 21 hydrologic units in the Pacific–Andean region of Ecuador and Peru (PAEP) using a nonhomogeneous hidden Markov model (NHMM) and retrospective seasonal information from general circulation models (GCMs). First, a hidden Markov model is used to diagnose four states that play distinct roles in the December–May rainy season. The estimated daily states fall into two wet states, one dry state, and one transitional dry–wet state, and show a systematic seasonal evolution together with intraseasonal and interannual variability. The first wet state represents regionwide wet conditions, while the second one represents north–south gradients. The former could be associated with the annual moisture offshore of the PAEP, thermally driven by the climatological maximum of sea surface temperatures in the Nino-1.2 region. The latter corresponds with the dynamically noisy component of the PAEP rainfall signal, associated with the annual ...

The Pacific–Andean region in western South America suffers from rainfall data scarcity, as is the case for many regions in the South. An important research question is whether the latest satellite-based and numerical weather prediction (NWP) model outputs capture well the temporal and spatial patterns of rainfall over the region, and hence have the potential to compensate for the data scarcity. Based on an interpolated gauge-based rainfall data set, the performance of the Tropical Rainfall Measuring Mission (TRMM) 3B42 V7 and its predecessor V6, and the North Western South America Retrospective Simulation (OA-NOSA30) are evaluated over 21 sub-catchments in the Pacific–Andean region of Ecuador and Peru (PAEP). In general, precipitation estimates from TRMM and OA-NOSA30 capture the seasonal features of precipitation in the study area. Quantitatively, only the southern sub-catchments of Ecuador and northern Peru (3.6–6° S) are relatively well estimated by both products. The accuracy is considerably less in the northern and central basins of Ecuador (0–3.6° S). It is shown that the probability of detection (POD) is better for light precipitation (POD decreases from 0.6 for rates less than 5 mm day−1 to 0.2 for rates higher than 20 mm day−1. Compared to its predecessor, 3B42 V7 shows modest region-wide improvements in reducing biases. The improvement is specific to the coastal and open ocean sub-catchments. In view of hydrological applications, the correlation of TRMM and OA-NOSA30 estimates with observations increases with time aggregation. The correlation is higher for the monthly time aggregation in comparison with the daily, weekly, and 15-day time scales. Furthermore, it is found that TRMM performs better than OA-NOSA30 in generating the spatial distribution of mean annual precipitation.

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Abstract. An important issue for the Pacific-Andean basin in western South-America is whether the latest satellite-based and Numerical Weather Prediction (NWP) model outputs, provide the potential to compensate data scarcity. Based on a comprehensive dataset of ground precipitation, the performance of the Tropical Rainfall Measuring Mission (TRMM) 3B42V7 and its predecessor version the 3B42V6, and the Weather Research Forecast (WRF) precipitation product (OA-NOSA30) are evaluated over 21 sub-catchments situated in the westernmost N-S axis of South America: the Pacific-Andean Basin in Ecuador and Peru (PAEP). In general, precipitation estimates from TRMM and OA-NOSA30 capture the seasonal features of precipitation in the study area. Quantitatively, only the Southern sub-catchments of Ecuador and Northern Peru (3.6–6° S) are relatively well estimated by both methods. The accuracy of both approaches is considerably less in the northern and central basins of Ecuador (0–3.6° S). It is shown that the detection probability is better for light precipitation (less than 5 mm day−1). Compared to its predecessor 3B42V7 shows modest basin-wide improvements in reducing biases. The improvement is specific to the coastal and open ocean sub-catchments. In view of hydrological applications, the correlation of TMPA's and OA-NOSA30 estimates with observations increases with time aggregation. The correlation is higher for the monthly time aggregation in comparison with the daily, weekly and 15-daily time scales. Furthermore, it is found that TMPA performs better than OA-NOSA30 in generating the spatial distribution of mean annual precipitation.

The spatiotemporal modes of seasonal rainfall variability and their relation with sea surface temperature anomalies (SSTA 1.2 indices) are examined in the transition from the coastal plain towards the western Andes cordillera in southern Ecuador/northwestern Peru using instrumental records (1970–2000) collected from the Catamayo–Chira basin. A multi-criteria data analysis is conducted within different elevation ranges. The criteria involve rotated principal components, cross correlations and temporal changes of anomalies in rainfall quantiles. The results confirm that SSTA 1.2 indices influence rainfall variability over the coastal plain (< 510 m a.s.l.) where forcing is dominant within December–May. The El Niño Southern Oscillation also plays a role inland of the coastal plain where a region of ENSO-like rainfall variability is found on the southeastern part of the basin (4°30'–5° S/79°15'–80° W) within March–May (MAM). This suggests that inland distance and elevation are only partial controls of ocean–atmospheric forcing up to ~ 1300 m a.s.l. Our analysis also provides evidence of the SSTA 1.2 indices influence in a large altitudinal range ~ 1400–2700 m a.s.l. confined to the southeastern basin. This region is found consistently perturbed by ENSO within MAM. We conclude that geo-morphological features of the southwestern Ecuadorian Andean ridges play a twofold role in the control of ocean–atmospheric forcing. They can modulate the atmospheric circulation, leading to a dissipation of the signal, or they might favor meteorological processes, leading to enhancement of orographic precipitation. This would explain the observed ENSO signals in instrumental records at locations as high as 2700 m a.s.l.