Search Results (46)

Headwater streams serve as a crucial link between forest and downstream aquatic ecosystems and also act as crucial agents in carbon (C) and nutrient storage and flux. These aquatic systems play a pivotal role in regulating biogeochemical cycles. Plant litter is an important contributor of nutrients to headwater streams, having significant impacts on downstream ecosystems. However, current research predominantly focuses on the dynamics of plant litter C and nutrients such as nitrogen and phosphorus, and we know little about those of nutrients such as sodium (Na). In this study, we conducted a comprehensive evaluation of the annual dynamics of plant litter Na storage within a subtropical headwater stream. This study took place over a period of one year, from March 2021 to February 2022. Our results showed that (1) the average annual concentration and storage of litter Na was 538.6 mg/kg and 2957.6 mg/m², respectively, and litter Na storage exhibited a declining trend from stream source to mouth, while demonstrating significantly higher values during the rainy season compared to the dry season; (2) plant litter type had significant impacts on Na concentration and storage, with leaf, twig, and fine woody debris accounting for the majority of litter Na storage; and (3) hydrological (precipitation, discharge) and physicochemical (water temperature, flow velocity, pH, dissolved oxygen, alkalinity) factors jointly affected Na storage patterns. Overall, the results of this study clearly reveal the dynamic characteristics of Na storage in plant litter in a subtropical forest headwater stream, which contributes to a more comprehensive understanding of the role of headwater streams in nutrient cycling and the dynamic changes of nutrients along with hydrological processes. This research will enhance our predictive understanding of nutrient cycling at the watershed scale. Full article

Rainfall partitioning, a crucial process in shaping the local hydrological cycle, governs canopy interception and subsequent soil water recharge. While canopy structure and meteorological conditions fundamentally regulate this process, the role of plant self-organization and its interactions with meteorological drivers (non-precipitation variables in particular) remain underexplored. To address this gap, we investigated rainfall partitioning components, including the amount, intensity, efficiency, and temporal dynamics of throughfall and stemflow, in clumped and scattered Vitex negundo L. var. heterophylla (Franch.) Rehder shrubs in the Yangjuangou catchment of the Chinese Loess Plateau during the 2021–2022 rainy seasons. Despite comparable net precipitation (clumped: 83.5% vs. scattered: 84.2% of incident rains), divergent rainfall partitioning strategies emerged. Clumped V. negundo exhibited greater stemflow (8.6% vs. 5.2%), characterized by enhanced intensity, efficiency, and favorable temporal dynamics. Conversely, scattered shrubs favored throughfall generation (79.0% vs. 74.9%). Consistent with previous research, rainfall amount was recognized as the primary control on partitioning rains. Furthermore, our integrated analysis, combining machine learning with variance decomposition, highlighted the critical roles of antecedent canopy wetness (4 h pre-event leaf wetness) and wind speed thresholds (e.g., low wind vs. gust) in regulating partitioning efficiency and temporal dynamics. These findings advance the mechanistic understanding of the interplay between plant self-organization and hydrological processes, demonstrating how morphological adaptations in V. negundo optimize water harvesting in semi-arid ecosystems. This addressed the need to incorporate dynamic interplays between plant structure (specifically, self-organized patterns) and meteorological factors (particularly non-precipitation variables) into ecohydrological models, especially for improved predictions in water-limited regions. Full article

Mangrove swamp rice (MSR) production is critical for the diet of small farmers of coastal Guinea-Bissau. In mangrove swamp agroecosystems, rice is grown during the rainy season when freshwater and nutrients are abundant. However, small-scale farmers face challenges like unpredictable rainfall and rising sea levels, which increase soil salinity and acidity. This study aims to assess soil physical–chemical properties, paired with farmers’ local practices, to evaluate fertility constraints, and to support sustainable soil–plant management practices. This co-designed research contributes to filling a gap concerning the adoption of sustainable agricultural practices adapted to specific contexts in West Africa. In two regions, Oio (center) and Tombali (south), rice yields were measured in semi-controlled trials both in two agroecological settings: Tidal Mangrove (TM) and Associated Mangrove (AM) fields. 380 soil samples were collected, and rice growing parameters were assessed during the 2021 and 2022 rice sowing, transplanting, and flowering periods. Principal Component Analyses (PCA) and Multivariate Regression Analysis (MRA) were applied to understand trends and build fertility proxies in predicting yields. Significant spatial and temporal variability in the soil properties between agroecologies was found. Salinity constraints in Oio TMs limit production to an average of 110 g/m², compared to 250 g/m² in Tombali. Yield predictions account for 81% and 56.9% of the variance in TMs and AMs, respectively. Variables such as organic matter (OM), nitrogen (N), potassium (K), and precipitation positively influence yields, whereas sand content, pH, and iron oxides show a negative effect. This study advances the understanding of MSR production in Guinea-Bissau and underscores the importance of incorporating farmers’ knowledge of their diverse and complex production systems to effectively address these challenges. Full article

Seasonal precipitation forecasting (SPF) is critical for effective water resource management and risk mitigation. Large-scale climate drivers significantly influence regional climatic patterns and forecast accuracy. This study establishes relationships between key climate drivers—El Niño–Southern Oscillation (ENSO), Southern Oscillation Index (SOI), Indian Ocean Dipole (IOD), Real-time Multivariate Madden–Julian Oscillation (MJO), and Multivariate ENSO Index (MEI)—and seasonal precipitation anomalies (rainy, summer, and winter) in Eastern Thailand, utilizing Pearson’s correlation coefficient. Following the establishment of these correlations, the most influential drivers were incorporated into the forecasting models. This study proposed an advanced SPF methodology for Eastern Thailand through a Seasonal WaveNet-LSTM model, which integrates Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNNs) with Wavelet Transformation (WT). By integrating large-scale climate drivers alongside key meteorological variables, the model achieves superior predictive accuracy compared to traditional LSTM models across all seasons. During the rainy season, the WaveNet-LSTM model (SPF-3) achieved a coefficient of determination (R²) of 0.91, a normalized root mean square error (NRMSE) of 8.68%, a false alarm rate (FAR) of 0.03, and a critical success index (CSI) of 0.97, indicating minimal error and exceptional event detection capabilities. In contrast, traditional LSTM models yielded an R² of 0.85, an NRMSE of 10.28%, a FAR of 0.20, and a CSI of 0.80. For the summer season, the WaveNet-LSTM model (SPF-1) outperformed the traditional model with an R² of 0.87 (compared to 0.50 for the traditional model), an NRMSE of 12.01% (versus 25.37%), a FAR of 0.09 (versus 0.30), and a CSI of 0.83 (versus 0.60). In the winter season, the WaveNet-LSTM model demonstrated similar improvements, achieving an R² of 0.79 and an NRMSE of 13.69%, with a FAR of 0.23, compared to the traditional LSTM’s R² of 0.20 and NRMSE of 41.46%. These results highlight the superior reliability and accuracy of the WaveNet-LSTM model for operational seasonal precipitation forecasting (SPF). The integration of large-scale climate drivers and wavelet-decomposed features significantly enhances forecasting performance, underscoring the importance of selecting appropriate predictors for climatological and hydrological studies. Full article

Understanding the spatiotemporal distribution and transport of atmospheric water vapor in urban areas is crucial for improving mesoscale models and weather and climate predictions. This study employs Multi-Axis Differential Optical Absorption Spectroscopy to monitor the dynamic distribution and transport flux of water vapor in Beijing within the tropospheric layer (0–4 km) from June 2021 to May 2022. The seasonal peaks in precipitable water occur in August, reaching 39.13 mm, with noticeable declines in winter. Water vapor was primarily distributed below 2.0 km and generally decreases with increasing altitude. The largest water vapor transport flux occurs in the southeast–northwest direction, whereas the smallest occurs in the southwest–northeast direction. The maximum flux, observed at about 1.2 km in the southeast–northwest direction during summer, reaches 31.77 g/m²/s (transported towards the southeast). Before continuous rainfall events, water vapor transport, originating primarily from the southeast, concentrates below 1 km. Backward trajectory analysis indicates that during the rainy months, there was a higher proportion of southeasterly winds, especially at lower altitudes, with air masses from the southeast at 500 m accounting for 69.11%. This study shows the capabilities of MAX-DOAS for remote sensing water vapor and offers data support for enhancing weather forecasting and understanding urban climatic dynamics. Full article

The socioeconomic sector increasingly relies on accessible and cost-effective tools for predicting climatic conditions. This study employs a straightforward decision tree classifier model to identify similar monthly ENSO (El Niño Southern Oscillation) conditions from December 2000 to November 2023, using historically monthly ENSO Indices data from December 1950 to November 2000 as a reference. The latter is to construct monthly precipitation hindcasts for Mexico spanning from December 2000 to November 2023 through historically high-resolution monthly precipitation rasters. The model’s performance is evaluated at a global and local scale across seasonal periods (winter, spring, summer, and fall). Assessment using global Hansen–Kuiper Skill Score and Heidkee Skill Score metrics indicates skillful performance across all seasons (>0.3) nationwide. However, local metrics reveal a higher spatial percent of corrects (>0.40) in winter and spring, corresponding to dry seasons, while a lower percent of corrects (<0.40) are observed in more extensive areas during summer and fall, indicative of rainy seasons, due to increased variability in precipitation. The choice of averaging method influences the degree of underestimations and overestimations, impacting the model’s variability. Spearman correlations highlight regions with significant model performance, revealing potential misinterpretations of high hit rates during winter and spring. Notably, during the fall, the model demonstrates spatial skill across most of Mexico, while in the spring, it performs well in the southern and northeastern regions and, in the summer, in the northwestern areas. Integration of accurate forecasts of ENSO Indices to predict precipitation months ahead is crucial for the operational efficacy of this model, given its heavy reliance on anticipating ENSO behavior. Overall, the empirical method exhibits great promise and potential for application in other developing countries directly impacted by the El Niño phenomenon, owing to its low resource costs. Full article

Complex terrain features such as mountains and hills can obstruct the airflow and force upward motion, thereby altering local atmospheric circulation patterns. During the rainy season, these terrain characteristics are more prone to causing intense local precipitation, leading to geological hazards such as floods and debris flows. These phenomena are closely linked to the intricate influence of terrain on wind fields, highlighting the necessity for in-depth research into wind field characteristics under complex terrain conditions. To address this, we propose a neural-network-based model leveraging terrain data and horizontal wind speed data to predict atmospheric motion characteristics and terrain uplift effects in specific terrain conditions. To enhance the generalization ability of the model, we innovatively extract key physical information from the horizontal wind vector data as training parameters. By comparing with the results of the Fluent model, we validate the model’s capability in dynamic downscaling and flow field modeling. Experimental outcomes demonstrate that our model can generate terrain-adapted convective warning data with a high accuracy, even when terrain features are altered. Under unoptimized conditions, the results at a maximum resolution of 50 m require only 26 s, and the computation time can be further reduced with algorithmic improvements. This research on adaptive wind field modeling under complex terrain conditions holds significant implications for local wind field simulation and severe convective weather forecasting. Full article

This research aimed to analyze the relationship between deforestation (DFT) and climatic variables during the rainy (CHU+) and less-rainy (CHU−) seasons in the Tapajós River basin. Data were sourced from multiple institutions, including the Climatic Research Unit (CRU), Center for Weather Forecasts and Climate Studies (CPTEC), PRODES Program (Monitoring of Brazilian Amazon Deforestation Project), National Water Agency (ANA) and National Centers for Environmental Prediction/National Oceanic and Atmospheric Administration (NCEP/NOAA). The study assessed anomalies (ANOM) in maximum temperature (TMAX), minimum temperature (TMIN) and precipitation (PREC) over three years without the occurrence of the El Niño–Southern Oscillation (ENSO) atmospheric–oceanic phenomenon. It also examined areas with higher DFT density using the Kernel methodology and analyzed the correlation between DFT and climatic variables. Additionally, it assessed trends using the Mann–Kendall technique for both climatic and environmental data. The results revealed significant ANOM in TEMP and PREC. In PREC, the highest values of ANOM were negative in CHU+. Regarding temperature, the most significant values were positive ANOM in the south, southwest and northwestern regions of the basin. Concerning DFT density, data showed that the highest concentration was of medium density, primarily along the highways. The most significant correlations were found between DFT and TEMP during the CHU− season in the Middle and Lower Tapajós sub-basins, regions where the forest still exhibits more preserved characteristics. Furthermore, the study identified a positive trend in TEMP and a negative trend in PREC. Full article

Rainfall-induced soil erosion is a significant environmental issue that can lead to soil degradation and loss of vegetation. The estimated global annual loss increased by 2.5% over 11 years, from 35 billion tons in 2001 to 35.9 billion tons in 2012, mainly due to spatial changes. Indonesia is predicted to be among the largest and most intensively eroded regions among countries with higher soil erosion, regarded as hot-spots higher than 20 Mg yr⁻¹ ha⁻¹. Due to climate change, natural rainfall patterns in the tropical regions have been subject to change, with a lower number of rainy days and increased intensity of precipitation. Such changes trigger more soil erosion due to heavier rainfall kicking up dried soil particles that are exposed in the bare embankments. Unfortunately, there is no prevention available in developing countries due to the lack of availability and high prices of mitigation techniques such as terraces and covering areas with geotextiles or blankets. Erosion control blankets (ECBs) have emerged as a potential solution to mitigate soil erosion. This research article aims to evaluate the effectiveness of sugar-palm-fiber-based ECB in reducing soil erosion caused by natural rainfall. The study investigates the effectiveness of sugar-palm-based ECB in protecting against erosion at the designated embankment. During the three months of typical rainy seasons (February to April 2023), total eroded mass (kg) was collected and measured from two adjacent microplots (10 m² each), one covered with ECB and the other one left as uncovered soil (bare soil). The results indicate that eroded mass is proportional to rainfall, with coefficients of 0.4 and 0.04 for bare soil and ECB-covered embankments, respectively. The total soil loss recorded during the monitoring period was 154.6 kg and 16.7 kg for bare and ECB-covered soil, respectively. The significantly high efficiency of the up to 90% reduction in soil losses was achieved by covering the slope with sugar-palm-fiber-based ECB. The reason for this may be attributed to the intrinsic surface properties of sugar palm fiber ropes and the soil characteristics of the plot area. Sugar palm (Arenga pinnata) fiber has higher lignocellulosic contents that produce a perfect combination of strong mechanical properties (higher tensile strength and young modulus) and a higher resistance to weathering processes. Although the cost of production of handmade sugar-palm-fiber-based ECB is now as high as 4 EUR, further reductions in cost production can be achieved by introducing machinery. Compared to typical ECBs which have smaller openings, sugar-palm-based ECB has larger openings that allow for vegetation to grow and provide it with a lower density. As such, we recommend improvements in the quality of palm-fiber-based ECB via the introduction of further automation in the production process, so that the price can be reduced in line with other commercially available natural fibers such as jute and coir. Full article

Warm zone rainstorms and frontal rainstorms are two types of rainstorms that often occur in the rainy season in Jiangxi (located in the eastern part of China). The ability to correctly identify the type of rainstorms is important for accurate forecasting of rainstorms. Two heavy rainstorms took place in Jiangxi province. The first heavy rainstorm occurred from 20:00 BJT (Beijing Time) on 6 June to 20:00 BJT on 9 June (referred to as the “6.9” process) and another heavy rainstorm occurred from 20:00 BJT on 21 June to 20:00 BJT on 22 June (referred to as the “6.9” process), 2019. We analyzed the two rainstorms’ processes by using ground-based observation data, NCEP/FNL reanalysis data, ECMWF and CMA-SH9 numerical forecasting products. The results show that: “6.9” process is a warm area rainstorm, and a strong northeast cold vortex exists at 500 hPa geopotential height. The northwesterly flow behind the northeast cold vortex trough is stronger. The position of the northern edge of the subtropical high pressure is more south than that at “6.22” process. The rainstorm is in the precipitation zone of the warm temperature ridge over 925 hPa geopotential height, and with more convective character than “6.22” process. The process of “6.22” is a frontal rainstorm. The convective character of precipitation is weaker. The rainstorm precipitation zones are in a strong temperature front area at 925 hPa geopotential height and there is a tendency for vertical convection to develop into oblique upward convection in the late stage of the rainstorm. The precipitation location and intensity forecast by CMA-SH9 at the “6.9” process is better than that of ECMWF, while ECMWF’s prediction of the precipitation zone and weather condition of the “6.22” process is better. Full article

Water conflicts have been a significant issue in Brazil, especially in the Sao Francisco River basin. Subseasonal forecasts, up to a 60-day forecast range, can provide information to support decision-makers in managing water resources in the river basin, especially before drought events. This report aims to evaluate 5-year mean subseasonal simulations generated by the Eta regional model for the period from 2011 to 2016 and assess the usefulness of this information to support decision-making in water resource conflicts in the Sao Francisco River basin. The capability of the Eta model to reproduce the drought events that occurred between the years 2011 and 2016 was compared against the Climate Prediction Center Morphing (CMORPH) precipitation data. Two sets of 60-day simulations were produced: one started in September (SO) and the other in January (JF) of each year. These months were chosen to evaluate the model’s capability to reproduce the onset and the middle of the rainy seasons in central Brazil, where the upper Sao Francisco River is located. The SO simulations reproduced the observed spatial distribution of precipitation but underestimated the amounts. Precipitation errors exhibited large variability across the subbasins. The JF simulations also reproduced the observed precipitation distribution but overestimated it in the upper and lower subbasins. The JF simulations better captured the interannual variability in precipitation. The 60-day simulations were discretized into six 10-day accumulations to assess the intramonthly variability. They showed that the simulations captured the onset of the rainy season and the small periods of rainy months that occurred in these severe drought years. This research is a critical step to indicate subbasins where the model simulation needs to be improved and provide initial information to support water allocation in the region. Full article

In light of the 2020–2021 flood season in Guangdong, we conducted a comprehensive assessment of short-term precipitation forecasts generated by the ensemble prediction system (EPS) based on the China Meteorological Administration Tropical Regional Atmosphere Model for the South China Sea (CMA-TRAMS). Furthermore, we applied four distinct strategies to cluster the ensemble forecast data produced by the model for precipitation, aiming to enhance our understanding of their applicability in short-term precipitation forecasting for Guangdong. Our key findings were as follows.: Precipitation during the 2020–2021 flood season in Guangdong exhibited distinct characteristics. The impacting areas of frontal and subtropical high-edge rainfall were relatively scattered, predominantly occurring in the evening and nighttime. In contrast, monsoon precipitation and return-flow precipitation were concentrated, with their impacts lasting from early morning to evening. Notably, the errors using the ensemble maximum and minimum values were large, while the errors for the ensemble mean values and medians were small. This indicated that the model’s short-term precipitation forecasts possessed a high degree of stability. The vertical shear of different types of precipitation exerted a noticeable influence on the model’s performance. The model consistently displayed a tendency to underestimate short-term precipitation in Guangdong; however, this bias decreased with longer lead times. Simultaneously, the model’s dispersion increased with longer lead times. In terms of mean absolute error (MAE) test results, there was little difference in the performance of ensemble primary forecasts under various strategies, while the “ward” strategy performed well in sub-primary cluster forecasts. This was particularly true for areas and types of precipitation where the model’s performance was poor. While the clustering approach lagged behind ensemble mean forecasts in predicting rainy conditions, it exhibited improvement in forecasting short-term heavy rainfall events. The “complete” and “single” strategies consistently delivered the most accurate forecasts for such events. Our study sheds light on the effectiveness of clustering methods in improving short-term precipitation forecasts for Guangdong, particularly in regions and conditions where the model initially struggled. These findings contribute to our understanding of precipitation forecasting during flood seasons and can inform strategies for enhancing forecast accuracy in similar contexts. Full article

The Meiyu season is a typical rainy season in East Asia that is controlled by summer monsoon. Despite extensive research on its impact, it is unclear how urbanization modifies precipitation during the Meiyu season in the background of the monsoon influence. To address this gap, this study investigated the effects of urbanization and monsoon on the modification of precipitation during the Meiyu season (PDM) in the megacity of Shanghai, China. Through homogenization analysis of the original observational data, we assessed the temporal and spatial variation in PDM in Shanghai during two stages of urbanization. Our findings revealed that both total precipitation and extreme daily precipitation during the Meiyu season in Shanghai have significantly increased since 1961. The spatial heterogeneity of PDM has also enhanced during the rapid urban process that has occurred since 1986. The long-term trend of increasing precipitation in Shanghai showed a synchronous variation with the East Asian subtropical summer monsoon (EASM) in 1961–2021. Over the interannual time scale, the significant positive correlation between PDM and EASM during the slow urbanization period (Stage 1: 1961–1985) changed to a non-significant correlation during the rapid urbanization period (Stage 2: 1986–2021), which was associated with the enhanced convective precipitation in Shanghai during the Meiyu season. Urbanization induced more convective precipitation and further weakened the association between PDM and EASM over the central city and nearby areas during Stage 2. The rapid urbanization process also resulted in increased differences in near-surface wind between urban and non-urban areas, which facilitated more PDM over the central city due to the urban friction effect and wind shear in Stage 2. Furthermore, our analysis suggests that the increase in precipitation may be associated with the enhanced coupling of cold air intrusion with the warmer climate background due to the urban heat effect occurring in Stage 2. These findings contribute to a better understanding of how urbanization and monsoons affect PDM in East Asian megacities and serve as a unique reference for climate prediction in this region. Full article

A mechanistic understanding of phenology, the seasonal timing of life history events, is important for understanding species’ interactions and the potential responses of ecological communities to a rapidly changing climate. We present analysis of a seven-year dataset on the breeding phenology of wood frogs (Rana sylvatica), tiger salamanders (Ambystoma tigrinum), blue-spotted salamanders (Ambystoma laterale), and associated unisexual Ambystoma salamanders from six wetlands in Southeast Michigan, USA. We assess whether the ordinal date of breeding migrations varies among species, sexes, and individual wetlands, and we describe the specific environmental conditions associated with breeding migrations for each species/sex. Breeding date was significantly affected by species/sex identity, year, wetland, and the interactions between species/sex and year as well as wetland and year. There was a great deal of variation among years, with breeding occurring nearly synchronously among groups in some years but widely spaced between groups in other years. Specific environmental triggers for movement varied for each species and sex and changed as the breeding season progressed. In general, salamanders responded to longer temperature lags (more warmer days in a row) than wood frogs, whereas wood frogs required longer precipitation lags (more rainy days in a row) than salamanders. Wood frogs were more likely to migrate around the time of a new moon, whereas in contrast, Ambystoma salamander migration was not associated with a moon phase. Ordinal day was an important factor in all models, suggesting that these amphibians require a latency period or similar mechanism to avoid breeding too early in the year, even when weather conditions appear favorable. Male wood frogs migrated earlier than female wood frogs, and male blue-spotted salamanders migrated earlier than female A. laterale and associated unisexual females. Larger unisexual salamanders migrated earlier than smaller individuals. Differences in species’ responses to environmental cues led to wood frogs and A. laterale breeding later than tiger salamanders in colder years but not in warmer years. This suggests that, as the climate warms, wood frog and A. laterale larvae may experience less predation from tiger salamander larvae due to reduced size differences when they breed simultaneously. Our study is one of few to describe the proximate drivers of amphibian breeding migrations across multiple species, wetlands, and years, and it can inform models predicting how climate change may shift ecological interactions among pond-breeding amphibian species. Full article

The strategy for assessing simulations produced by climate models established as part of the Atmospheric Model Intercomparison Project (AMIP) delivers an outline for model analysis, verification/validation, and intercomparison. Numerical models are continuously being developed to find the best representation for the amount and distribution of precipitation in Brazil to improve the country’s precipitation forecast. This article describes the key features of the Brazilian Global Atmospheric Model (BAM) (developed by the Center for Weather Forecasting and Climate Studies of the National Institute for Space Research (CPTEC/INPE)) and analyses of its performance for annual rainfall climate simulations. This study considered the representation of the annual precipitation in Brazil mainly during the rainy season in the central part of Brazil by the BAM. The model was run over the 1990 to 2015 period using spectral Eulerian model dynamics with a 70-horizontal resolution of approximately 1.0${}^{\circ }$× 1.0${}^{\circ }$ and 42 vertical sigma levels. The analysis was divided into two stages: the annual precipitation and the rainy season precipitation. Model precipitation analyses were performed using statistical methods, such as the mean and standard deviation, comparing modeled data with observed data from two datasets, data from the XAV (observed data from INMET, ANA, and DAEE), and the Climate Prediction Center (CPC). In general, the BAM model simulations reasonably replicated the configuration of the spatial distribution of precipitation in the Brazilian territory almost entirely, especially compared with the XAV. The accumulated precipitation in the southern region presented great variation, accumulating from 750 mm year${}^{-1}$ in the extreme south to 1750 mm year${}^{-1}$ in the north of this region. Average values of the BAM accumulated precipitation ranged from 1000 to 2000 mm year${}^{-1}$, within the expected average, compared to observed values of 750–1500 mm year${}^{-1}$ (CPC and XAV, correspondingly). Although there was an underestimation of the accumulated precipitation by the model, the model reasonably reproduced the precipitation during the rainy season. The performed assessment identified model aspects that need to be improved. Full article