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Abstract Water plays a critical role in adapting food and energy systems to climate change in the Andes. However, robust climate solutions that work across sectors have not been explored for these working landscapes. Here we develop a framework consisting of high-fidelity catchment and hydropower models while considering climate and other uncertainties in a robust, bottom-up manner for the upper Huallaga River in Peru. We then explore adaptation strategies across sectors. Our results highlight that irrigation will be critical for maintaining food production under climate change. Yet, an unplanned expansion of irrigation could lead to a near total loss of firm hydropower downstream. Exploring 10,080 possible landuse/management adaptation pathways, we find limited opportunities for win-win solutions under climate change. In the light of such challenges, our results highlight the need for navigating structural trade-offs between upstream land and water users and downstream interests in the Andes in a strategic and equitable manner.

La frecuencia y magnitud de los eventos climáticos extremos de precipitación han aumentado de forma significativa en varios países del mundo, incluido Perú. Estos hechos causan pérdidas económicas y humanas, especialmente en países en vías de desarrollo. La información y metodologías que permitan prevenir o diseñar estrategias para afrontarlas son escasas o inexistentes. El objetivo de esta investigación fue analizar la capacidad del producto satelital IMERG (Integrated Multi-satellitE Retrievals) del satélite GPM (Global Precipitation Measurement) y datos observados a partir de estaciones meteorológicas mediante un enfoque mixto para estimar la distribución de lluvias extremas en la región del Cusco, ubicado al sur del Perú. Este enfoque mixto aprovechó las ventajas que ofrecen ambas fuentes de información, como es la solidez de los datos observados a lo largo de varios años y la resolución temporal horaria del producto satelital. La metodología se basó en una curva de crecimiento de cada región homogénea, factor de corrección y parámetros que estiman la función intensidad y duración para toda la región Cusco. Los resultados se evaluaron mediante validación cruzada entre los valores de precipitación diaria obtenidos del producto IMERG, enfoque mixto y precipitación observada para periodos de retorno de 2, 5, 10, 20, 50, 100, 200, 500 y 1 000 años. Los resultados sugieren que la combinación de datos observados de lluvia y del satélite IMERG puede ser una alternativa para estimar lluvias extremas en la región Cusco.

The 2022-23 hydrological year in the Lake Titicaca, Desaguadero River, and Lake Poopó hydrological system (TDPS) over the South American Altiplano constituted a historically dry period. This drought was particularly severe during the pre-wet season (October–December), when the TDPS and the adjacent Andean-Amazon region experienced as much as 60% reductions in rainfall. Consequently, Titicaca Lake water levels decreased by 0.05 m from December to January, which is part of the rising lake level period of normal conditions. Such conditions have not been seen since the El Niño-related drought of 1982-83. Using a set of hydroclimatic, Sea Surface Temperature (SST) and atmospheric reanalysis datasets, we find that this new historical drought was associated with enhanced southerly moisture flux anomalies, reducing the inflow of moisture-laden winds from the Amazon basin to the TDPS. Such anomalies in moisture transport were not seen since at least the 1950s. The atmospheric dynamics associated with this drought are related to La Niña SST anomalies via subtropical teleconnections associated with Rossby wave trains towards South America, further extended by subtropical Atlantic Ocean SST anomalies. This feature reduced the atmospheric moisture inflow from the Amazon and weakened the development of the Bolivian High in the upper troposphere. These results document a new atmospheric mechanism related to extreme droughts in the TDPS associated with La Niña SST anomalies during the pre-wet season. This goes beyond the traditional understanding of El Niño events, especially the strongest ones, being associated with dry conditions in the TDPS during the wet season (December–March).

Abstract In 2023 Amazonia experienced both historical drought and warm conditions. On October 26th 2023 the water levels at the port of Manaus reached its lowest record since 1902 (12.70 m). In this region, October monthly maximum and minimum temperature anomalies also surpassed previous record values registered in 2015 (+ 3 °C above the normal considering the 1981–2020 average). Here we show that this historical dry and warm situation in Amazonia is associated with two main atmospheric mechanisms: (i) the November 2022–February 2023 southern anomaly of vertical integrated moisture flux (VIMF), related to VIMF divergence and extreme rainfall deficit over southwestern Amazonia, and (ii) the June–August 2023 downward motion over northern Amazonia related to extreme rainfall deficit and warm conditions over this region. Anomalies of both atmospheric mechanisms reached record values during this event. The first mechanism is significantly correlated to negative sea surface temperature (SST) anomalies in the equatorial Pacific (November–February La Niña events). The second mechanism is significantly correlated to positive SST anomalies in the equatorial Pacific, related to the impacts of June–September El Niño on the Walker Circulation. While previous extreme droughts were linked to El Niño (warmer North Tropical Atlantic SST) during the austral summer (winter and spring), the transition from La Niña 2022–23 to El Niño 2023 appears to be a key climatic driver in this record-breaking dry and warm situation, combined to a widespread anomalous warming over the worldwide ocean.

Abstract Gridded high-resolution climate datasets are increasingly important for a wide range of modelling applications. Here we present PISCOt (v1.2), a novel high spatial resolution (0.01°) dataset of daily air temperature for entire Peru (1981–2020). The dataset development involves four main steps: (i) quality control; (ii) gap-filling; (iii) homogenisation of weather stations, and (iv) spatial interpolation using additional data, a revised calculation sequence and an enhanced version control. This improved methodological framework enables capturing complex spatial variability of maximum and minimum air temperature at a more accurate scale compared to other existing datasets (e.g. PISCOt v1.1, ERA5-Land, TerraClimate, CHIRTS). PISCOt performs well with mean absolute errors of 1.4 °C and 1.2 °C for maximum and minimum air temperature, respectively. For the first time, PISCOt v1.2 adequately captures complex climatology at high spatiotemporal resolution and therefore provides a substantial improvement for numerous applications at local-regional level. This is particularly useful in view of data scarcity and urgently needed model-based decision making for climate change, water balance and ecosystem assessment studies in Peru.

This study investigates the hydrological dynamics of the Pativilca Basin in the Southern Hemisphere using the SWAT (Soil and Water Assessment Tool) model. Seventy-seven watersheds across a mountainous region were analyzed using elevation data, land cover, soil type, and gridded meteorological products (RAIN4PE and PISCO) for hydrological simulations. Watershed delineation, aided by a Digital Elevation Model, enabled the identification of critical drainage points and the definition of Hydrological Response Units (HRUs). The model calibration and validation, performed using the SWAT-CUP with the SUFI-2 algorithm, achieved Nash–Sutcliffe Efficiency (NSE) values of 0.69 and 0.72, respectively. Cluster analysis categorized the watersheds into six distinct groups with unique hydrological and climatic characteristics. The results showed significant spatial variability in the precipitation and temperature, with pronounced seasonality influencing the daily flow patterns. The higher-altitude watersheds exhibited greater soil water storage and more effective aquifer recharge, whereas the lower-altitude watersheds, despite receiving less precipitation, displayed higher flows due to runoff from the upstream areas. These findings emphasize the importance of incorporating seasonality and spatial variability into water resource planning in mountainous regions and demonstrate the SWAT model’s effectiveness in predicting hydrological responses in the Pativilca Basin, laying the groundwork for future research in mountain hydrology.

Study region: A total of 11,913 sub-catchments in Peru and transboundary catchments with neighboring countries in South America. Study focus: This paper aims to develop a national hydrological model using physiographic and climatic characteristics to identify donor and receptor sub-catchments (sub-zones). Therefore, we use the hydrometeorological PISCO dataset (0.1º x 0.1º) to drive a sub-catchment conceptual rainfall-runoff (ARNO/VIC) model and a river-routing (RAPID) model in thousands of river reaches. We identify 43 hydrological zones (with 122 sub-zones) to run the hybrid hydrological modeling framework (ARNO/VIC+RAPID) with previously calibrated and validated parameters with 43 fluviometric stations for 1981–2020. Simulated flow series show a higher performance at daily scale (KGE ≥ 0.75, NSEsqrt ≥ 0.65, MARE ≤ 1, and −25% ≤ PBIAS ≤ 25%) for catchments located at the Pacific coast and the Andes-Amazon transition, and good representation (R≥0.75) of seasonal and interannual variability. New hydrological insights for the region: Increasing hydrological hazards such as floods highlight the importance of a systematic hydrological analysis and modeling at national level in gauged and ungauged catchments in Peru. This study introduces a new hydrological dataset of simulated daily flow series. The results are helpful for short-term flood risk scenario simulations and long-term water resource planning as the outcomes can provide valuable information for hydrologists and water resource managers in Peruvian regions with limited or no access to in-situ networks.

The watershed hydrologic conditions in the Madre de Dios (MDD) Basin in the Peruvian Amazon have been irreversibly impacted by deforestation and changes in land cover. These changes have also had detrimental effects on the geomorphology, water quality, and aquatic habitat within the basin. However, there is a scarcity of hydrological modeling studies in this area, primarily due to the limited availability of hydrometeorological data. The primary objective of this study was to examine how deforestation impacts the hydrological conditions in the MDD Basin. By implementing the Soil and Water Assessment Tool (SWAT) model, this study determined that replacing 12% of the evergreen broadleaf forest area with bare land resulted in a significant increase in surface runoff, by 38% monthly, a 1% annual reduction of evapotranspiration, and an average monthly streamflow increase of 12%. Changes in spatial patterns reveal that the primary impacted watershed is the Inambari River subbasin, a significant tributary of the Madre de Dios River. This area experiences an annual average surge of 187% in surface runoff generation while witnessing an annual average reduction of 8% in evapotranspiration. These findings have important implications, as they can contribute to instances of flooding and extreme inundation events, which have already occurred in the MDD region.

In regions with limited precipitation information, like Peru, many studies rely on precipitation data derived from satellite products (SPP) and model reanalysis. These products provide near-real-time information and offer global spatial coverage, making them attractive for various applications. However, it is essential to consider their uncertainties when conducting hydrological simulations, especially in a key region like the Pacific drainage (Pd), where 56% of the Peruvian population resides (including the capital, Lima). This study, for the first time, assessed the performance of two bottom-up Satellite-based Precipitation Products (SPP), GPM + SM2RAIN and SM2RAIN-ASCAT, and one top-down approach SPP, ERA5-Land, for runoff simulation in the Pacific drainage of Peru. Hydrological modeling was conducted on 30 basins distributed across the Pd, which were grouped into 5 regions (I–V, ordered from south to north). The results showed that SM2RAIN-ASCAT performed well in regions I-III-IV, ERA5-Land in region II, and GPM + SM2RAIN in region V. The hydrological model GR4J was tested, and better efficiency criteria were obtained with SM2RAIN-ASCAT and GPM + SM2RAIN when comparing the simulated versus observed streamflows. The hydrological modeling using SM2RAIN-ASCAT and GPM + SM2RAIN demonstrated satisfactory efficiency metrics (KGE > 0.75; NSE > 0.65). Additionally, ten hydrological signatures were quantified to assess the variability of the simulated streamflows in each basin, with metrics such as Mean Flow (Q mean), 5th Quantile Flow (Q5), and 95th Quantile Flow (Q95) showing an overall better performance. Finally, the results of this study demonstrate the reliability of using bottom-up satellite products in Pd basins.

Measurement(s) reference evapotranspiration • air temperature • sunshine duration • dew point temperature • wind speed Technology Type(s) weather station Factor Type(s) reference evapotranspiration Sample Characteristic - Environment Climatology • Hydrology • Meteorology • Ecology • Earth Science Sample Characteristic - Location Peru • Andes

In soil erosion estimation models, the variables with the greatest impact are rainfall erosivity (RE), which is the measurement of precipitation energy and its potential capacity to cause erosion, and erosivity density (ED), which relates RE to precipitation. The RE requires high temporal resolution records for its estimation. However, due to the limited observed information and the increasing availability of rainfall estimates based on remote sensing, recent research has shown the usefulness of using observed-corrected satellite data for RE estimation. This study evaluates the performance of a new gridded dataset of RE and ED in Peru (PISCO_reed) by merging data from the IMERG v06 product, through a new calibration approach with hourly records of automatic weather stations, during the period of 2000–2020. By using this method, a correlation of 0.94 was found between PISCO_reed and RE obtained by the observed data. An average annual RE for Peru of 7840 MJ · mm · ha−1· h−1 was estimated with a general increase towards the lowland Amazon regions, and high values were found on the North Pacific Coast area of Peru. The spatial identification of the most at risk areas of erosion was evaluated through a relationship between the ED and rainfall. Both erosivity datasets will allow us to expand our fundamental understanding and quantify soil erosion with greater precision.

This article introduces a high-resolution (0.1°) gridded dataset of hourly precipitation across Peru for the period 2015–2020, called PISCOp_h. The product was developed using a temporal disaggregation technique based on the gridded daily precipitation dataset PISCOp and additional data from 309 automatic weather stations and three satellite precipitation products (IMERG-Early, PERSIANN-CCS, and GSMaP_NRT). The workflow of PISCOp_h involved the spatial interpolation of hourly precipitation and a bias correction of the diurnal rainfall cycle. Based on a technical validation, we demonstrated that PISCOp_h provides moderate to high efficiency in characterizing the frequency, intensity, and temporal coherence of hourly precipitation, particularly in central and southern Peru. PISCOp_h represents an important advance to construct gridded hourly precipitation products under challenging environmental conditions in, e.g., mountain regions with complex terrain. This new dataset provides a useful baseline for future studies in hydrology, climatology, and meteorology. The data collection described is available on figshare: https://doi.org/10.6084/m9.figshare.c.5743166.

Abstract The present research has assessed the spatial distribution of drought risk in the Titicaca Lake Basin located in Peruvian territory for a district scale, based on hazard and vulnerability. Drought hazard has been quantified with the deficit of precipitation using the Standardized Precipitation Index (SPI) for a time scale of 3-months, and the vulnerability has been obtained according to the socio-economic and physical indicators of the Basin. The results show that the drought is strongly modulated by anomalous SST conditions of the surrounding Oceans, mainly by the Pacific region. Over the Titicaca Lake Basin was identified that about 50% of districts present a high to very high risk of drought mainly, in the northwestern, central-east, and central-south of the Basin. These districts have a larger deficit of precipitation and showed indicators that are more vulnerable to the drought hazard due to that economically depends on precarious rainfed agriculture and an extensive and mixed livestock system. Within this area are the most important provinces of the high-Andean region, such as Puno, San Roman, Azángaro, Melgar, and Carabaya, and outstanding districts as Puno and Juliaca, considered the economic capital of the department of Puno.

The Andes is the most biodiverse region across the globe. In addition, some of the largest urban areas in South America are located within this region. Therefore, ecosystems and human population are affected by hydroclimate changes reported at global, regional and local scales. This paper summarizes progress of knowledge about long-term trends observed during the last two millennia over the entire Andes, with more detail for the period since the second half of the 20th century, and presents a synthesis of climate change projections by the end of the 21st century. In particular, this paper focuses on temperature, precipitation and surface runoff in the Andes. Changes in the Andean cryosphere are not included here since this particular topic is discussed in other paper in this Frontiers special issue, and elsewhere (e.g. IPCC,2019b). While previous works have reviewed the hydroclimate of South America and particular sectors (i.e., Amazon and La Plata basins, the Altiplano, Northern South America, etc.) this review includes for the first time the entire Andes region, considering all latitudinal ranges: tropical (North of 27°S), subtropical (27°S−37°S) and extratropical (South of 37°S). This paper provides a comprehensive view of past and recent changes, as well as available climate change projections, over the entire Andean range. From this review, the main knowledge gaps are highlighted and urgent research necessities in order to provide more mechanistic understanding of hydroclimate changes in the Andes and more confident projections of its possible changes in association with global climate change.

Study region: The Chancay-Huaral (CH) coastal river basin in the Lima Region, Peru, between the Pacific Ocean and the Andean Cordillera. Study focus: Climate change impacts on annual and monthly discharges in the CH Basin are assessed for the future period 2051–2080. Hydrological modeling is sensitive to biases in input variables. Therefore, bias-corrected time series of temperature and precipitation from 31 General Circulation Models (GCMs) with the emission scenarios RCP4.5 and RCP8.5 (Representative Concentration Pathways) were used as inputs for the Water Evaluation and Planning System model (WEAP). Bias correction and downscaling of the GCMs were implemented using a quantile mapping method. New hydrological insights for the region: On average, GCMs indicate increased annual mean temperatures by 3.1 °C (RCP4.5) and by 4.3 °C (RCP8.5) and precipitation sum by 20% (RCP4.5) and by 28% (RCP8.5). With increasing total precipitation, river discharges are also found to increase, but the variability among the GCMs is considerable. The largest increases in monthly discharge are projected to occur in the wet season (November − April) − with up to 31% increase of December multi-model mean. Despite the larger annual discharge for the mean multi-model result, discharges in the dry season may decrease according to some GCMs, showing the need for an adapted future water management.

La agresividad de la lluvia contribuye a la erosividad del suelo en regiones de alta montaña, y por tal a la sedimentación en la parte baja de la cuenca. La reducción de incertidumbre acerca de la agresividad de la lluvia en regiones costeras y andinas contribuye a la formulación de medidas de mitigación que contribuyan a la reducción de erosión y pérdida de nutrientes. Este estudio presenta un análisis espacial y temporal de la agresividad climática en la cuenca del río Guayas ubicada en la costa y Andes ecuatoriales. Se seleccionó datos mensuales registrados de 30 estaciones pluviométricas para el periodo de 1968-2014. Se determinó zonas homogéneas de precipitación mediante el método k-means. Los resultados indicaron dos regiones homogeneas predominantes, la primera ubicada al oeste en la zona costera y andina (85.2% del área de la Cuenca), con un índice de agresividad alto y muy alto, mientras que la distribución de la precipitación en la segunda región (Alta montaña) resultó de muy baja a baja agresividad. La mayor agresividad potencial de la lluvia le corresponde una mayor acumulación de precipitación promedio anual, lo que indica una alta influencia estacional de las lluvias, i.e, una mayor cantidad de lluvia puede precipitar en un número reducido de meses consecutivos. Los valores de concentración revelan una gradiente regional en dirección este-oeste, que va de moderadamente a fuertemente estacional. El análisis de tendencias de la concentración de lluvia mensual no muestra cambios significativos en el periodo de estudio. No obstante, nuestros hallazgos explican el porqué la región oeste y sur de la cuenca del río Guayas está expuesta a problemas de sedimentación en la parte baja, producto de la capacidad erosiva de la lluvia en la parte alta y media de la cuenca.

This study investigates the applicability of Satellite Precipitation Products (SPPs) in near real-time for the simulation of sub-daily runoff in the Vilcanota River basin, located in the southeastern Andes of Peru. The data from rain gauge stations are used to evaluate the quality of Integrated Multi-satellite Retrievals for GPM–Early (IMERG-E), Global Satellite Mapping of Precipitation–Near Real-Time (GSMaP-NRT), Climate Prediction Center Morphing Method (CMORPH), and HydroEstimator (HE) at the pixel-station level; and these SPPs are used as meteorological inputs for the hourly hydrological modeling. The GR4H model is calibrated with the hydrometric station of the longest record, and model simulations are also verified at one station upstream and two stations downstream of the calibration point. Comparing the sub-daily precipitation data observed, the results show that the IMERG-E product generally presents higher quality, followed by GSMaP-NRT, CMORPH, and HE. Although the SPPs present positive and negative biases, ranging from mild to moderate, they do represent the diurnal and seasonal variability of the hourly precipitation in the study area. In terms of the average of Kling-Gupta metric (KGE), the GR4H_GSMaP-NRT’ yielded the best representation of hourly discharges (0.686), followed by GR4H_IMERG-E’ (0.623), GR4H_Ensemble-Mean (0.617) and GR4H_CMORPH’ (0.606), and GR4H_HE’ (0.516). Finally, the SPPs showed a high potential for monitoring floods in the Vilcanota basin in near real-time at the operational level. The results obtained in this research are very useful for implementing flood early warning systems in the Vilcanota basin and will allow the monitoring and short-term hydrological forecasting of floods by the Peruvian National Weather and Hydrological Service.

Los impactos de El Niño (EN) y La Niña (LN) en las lluvias del Perú son evaluados utilizando datos de lluvias mensuales (1965-2007) de 155 estaciones distribuidos sobre las tres vertientes hidrográficas del Perú: 85 en la del Pacífico (VP), 21 en la del Lago Titicaca (VT) y 49 en la del Amazonas (VA). En una primera etapa, clasificamos los eventos El Niño y La Niña utilizando el Índice Troup de Oscilación del Sur (IOS) sobre la base de años hidrológicos (septiembre a agosto). Con esta información, los años se clasificaron en El Niño fuerte (ENF), El Niño moderado (ENM), La Niña moderada (LNM) y La Niña fuerte (LNF). Los resultados muestran que sólo durante los eventos ENF y LNF se observa un alto porcentaje de estaciones con anomalías significativas de precipitación y sobre todo localizadas en la VP y VT durante el periodo de diciembre a mayo. Nuestro análisis confirma que la parte norte de la VP presenta un aumento de las lluvias durante ENF, mientras que la región sur andina de la VP presenta disminución (aumento) de lluvias durante ENF (LNF). La VT, por su parte, presenta un importante déficit de lluvias durante ENF. La variación de la precipitación en la VA en cambio, es más sensible a los eventos de LNF, durante los cuales se observan lluvias más importantes de lo normal. En una segunda etapa, se analizan los principales modos de variabilidad interanual de las lluvias en el Perú utilizando la técnica de funciones ortogonales empíricas (EOF). Los resultados son relacionados con la variabilidad de la temperatura superficial del mar y los índices IOS, E y C del Pacífico ecuatorial. Se encontró que el principal modo de variabilidad de las lluvias (CP1, 37% de la varianza total) está asociado a los dos eventos EN extraordinarios (1983 y 1998), lo cual generó abundantes lluvias en el norte de la VP y sequías en la VT y la VA. Por su parte, el segundo modo de variabilidad de las lluvias (CP2, 25%) se correlaciona con las anomalías de la temperatura superficial del mar en el Pacífico ecuatorial central, condiciones frías en esta región (LN) causan más lluvias de lo normal en la región Andina de la VP, la VT y en la VA. En conclusión, se observa que la variabilidad del océano Pacífico no permite explicar la totalidad de la variabilidad pluviométrica en el Perú, por lo cual se describe como perspectiva considerar otras regiones como el Océano Atlántico Tropical.

The impact of a changing climate on the Amazon basin is a subject of intensive research because of its rich biodiversity and the significant role of rainforests in carbon cycling. Climate change has also a direct hydrological impact, and increasing efforts have focused on understanding the hydrological dynamics at continental and subregional scales, such as the Western Amazon. New projections from the Coupled Model Inter-comparison Project Phase 5 ensemble indicate consistent climatic warming and increasing seasonality of precipitation in the Peruvian Amazon basin. Here we use a distributed land surface model to quantify the potential impact of this change in the climate on the hydrological regime of the upper Amazon river. Using extreme value analysis, historical and future projections of the annual minimum, mean, and maximum river flows are produced for a range of return periods between 1 and 100 yr. We show that the RCP 4.5 and 8.5 scenarios of climate change project an increased severity of the wet season flood pulse (7.5% and 12% increases respectively for the 100 yr return floods). These findings agree with previously projected increases in high extremes under the Special Report on Emissions Scenarios climate projections, and are important to highlight due to the potential consequences on reproductive processes of in-stream species, swamp forest ecology, and socio-economy in the floodplain, amidst a growing literature that more strongly emphasises future droughts and their impact on the viability of the rainforest system over greater Amazonia.

In the last two decades, rainfall estimates provided by the Tropical Rainfall Measurement Mission (TRMM) have proven applicable in hydrological studies. The Global Precipitation Measurement (GPM) mission, which provides the new generation of rainfall estimates, is now considered a global successor to TRMM. The usefulness of GPM data in hydrological applications, however, has not yet been evaluated over the Andean and Amazonian regions. This study uses GPM data provided by the Integrated Multi-satellite Retrievals (IMERG) (product/final run) as input to a distributed hydrological model for the Amazon Basin of Peru and Ecuador for a 16-month period (from March 2014 to June 2015) when all datasets are available. TRMM products (TMPA V7, TMPA RT datasets) and a gridded precipitation dataset processed from observed rainfall are used for comparison. The results indicate that precipitation data derived from GPM-IMERG correspond more closely to TMPA V7 than TMPA RT datasets, but both GPM-IMERG and TMPA V7 precipitation data tend to overestimate, compared to observed rainfall (by 11.1% and 15.7 %, respectively). In general, GPM-IMERG, TMPA V7 and TMPA RT correlate with observed rainfall, with a similar number of rain events correctly detected (~20%). Statistical analysis of modeled streamflows indicates that GPM-IMERG is as useful as TMPA V7 or TMPA RT datasets in southern regions (Ucayali basin). GPM-IMERG, TMPA V7 and TMPA RT do not properly simulate streamflows in northern regions (Marañón and Napo basins), probably because of the lack of adequate rainfall estimates in northern Peru and the Ecuadorian Amazon.