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Hydrological research in humid tropics is particularly challenging because of highly variable hydrological conditions and high socio-economic stresses caused by rapid population increase, as is the case of Nicaragua. The objective of this research is to understand the surface and subsurface runoff generation processes in a poorly gauged coastal catchment in Nicaragua under variable humid tropical conditions. Specifically, it focuses on identifying geomorphological and hydro-climatic controls on catchment response at different spatio-temporal scales and studies the link between hydrological processes and ecosystem conditions.

The chemical composition of river water integrates a number of factors such as weathering, land use, climate, vegetation cover and human activity that individually affect its chemistry. Short term variations may also be significant. The Burdekin River, NE Australia, is an example of a class of tropical streams which experiences two to four orders of magnitude variation in discharge in response to seasonal but erratic monsoonal and cyclonic rainfall. In these systems individual discharge events last for days to weeks. Given the inherent difficulty sampling these events published data on water chemistry (and thus calculated fluxes and global budgets) may tend to be biased to low flow conditions. One such discharge event in February 1996 has been investigated for its impact on the chemistry of the water. Major cations (Na, Mg, K, Ca) all decreased in concentration as the water level rose, as did the minor elements Sr, Ba and U. Some other trace elements, notably Rb, Cr, Pb and REE were enriched in the peak flow waters. The flux of all measured elements increased substantially during the seven days of the discharge event. Such short term but significant events will have a major impact on the annual fluxes of elements delivered to the oceans from the land and global discharge budgets may need to take them into account when refining databases in the future.

Most studies to date in the humid tropics have described a similar pattern of rapid translation of rainfall to runoff via overland flow and shallow subsurface stormflow. However, study sites have been few overall, and one particular system has received very little attention so far: tropical montane cloud forests (TMCF) on volcanic substrate. While TMCFs provide critical ecosystem services, our understanding of runoff generation processes in these environments is limited. Here, we present a study aimed at identifying the dominant water sources and pathways and mean residence times of soil water and streamflow for a first‐order, TMCF catchment on volcanic substrate in central eastern Mexico. During a 6‐week wetting‐up cycle in the 2009 wet season, total rainfall was 1200 mm and storm event runoff ratios increased progressively from 11 to 54%. With the increasing antecedent wetness conditions, our isotope and chemical‐based hydrograph separation analysis showed increases of pre‐event water contributions to the storm hydrograph, from 35 to 99%. Stable isotope‐based mean residence times estimates showed that soil water aged only vertically through the soil profile from 5 weeks at 30 cm depth to 6 months at 120 cm depth. A preliminary estimate of 3 years was obtained for base flow residence time. These findings all suggest that shallow lateral pathways are not the controlling processes in this tropical forest catchment; rather, the high permeability of soils and substrate lead to vertical rainfall percolation and recharge of deeper layers, and rainfall‐runoff responses appeared to be dominated by groundwater discharge from within the hillslope. Key Points Shift from shallow to deeper runoff sources with increasing antecedent wetness Overland flow and shallow subsurface flow does not dominate runoff response Residence time analyses suggest large subsurface water storage capacity

The significant worldwide increase in observed river runoff has been tentatively attributed to the stomatal \"antitranspirant\" response of plants to rising atmospheric CO₂ [Gedney N, Cox PM, Betts RA, Boucher O, Huntingford C, Stott PA (2006) Nature 439: 835-838]. However, CO₂ also is a plant fertilizer. When allowing for the increase in foliage area that results from increasing atmospheric CO₂ levels in a global vegetation model, we find a decrease in global runoff from 1901 to 1999. This finding highlights the importance of vegetation structure feedback on the water balance of the land surface. Therefore, the elevated atmospheric CO₂ concentration does not explain the estimated increase in global runoff over the last century. In contrast, we find that changes in mean climate, as well as its variability, do contribute to the global runoff increase. Using historic land-use data, we show that land-use change plays an additional important role in controlling regional runoff values, particularly in the tropics. Land-use change has been strongest in tropical regions, and its contribution is substantially larger than that of climate change. On average, land-use change has increased global runoff by 0.08 mm/year² and accounts for [almost equal to]50% of the reconstructed global runoff trend over the last century. Therefore, we emphasize the importance of land-cover change in forecasting future freshwater availability and climate.

This study compares two nonparametric rainfall data merging methods—the mean bias correction and double-kernel smoothing—with two geostatistical methods—kriging with external drift and Bayesian combination—for optimizing the hydrometeorological performance of a satellite-based precipitation product over a mesoscale tropical Andean watershed in Peru. The analysis is conducted using 11 years of daily time series from the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) research product (also TRMM 3B42) and 173 rain gauges from the national weather station network. The results are assessed using 1) a cross-validation procedure and 2) a catchment water balance analysis and hydrological modeling. It is found that the double-kernel smoothing method delivered the most consistent improvement over the original satellite product in both the cross-validation and hydrological evaluation. The mean bias correction also improved hydrological performance scores, particularly at the subbasin scale where the rain gauge density is higher. Given the spatial heterogeneity of the climate, the size of the modeled catchment, and the sparsity of data, it is concluded that nonparametric merging methods can perform as well as or better than more complex geostatistical methods, whose assumptions may not hold under the studied conditions. Based on these results, a systematic approach to the selection of a satellite–rain gauge data merging technique is proposed that is based on data characteristics. Finally, the underperformance of an ordinary kriging interpolation of the rain gauge data, compared to TMPA and other merged products, supports the use of satellite-based products over gridded rain gauge products that utilize sparse data for hydrological modeling at large scales.

Upper-Brantas watershed in East Java, Indonesia, is a tropical watershed experiencing rapid landscape change, a phenomenon typical to developing countries. This study demonstrates the impact of Land Use Land Cover (LULC) changes on surface runoff in a tropical, urbanized, and data scarce watershed. The LULC changes were quantified between 1995 and 2015 and their impact on the hydrological processes was analyzed using the Soil and Water Assessment Tool (SWAT) model. During the study period, the watershed experienced an increase in settlement and dryland agriculture, and a decrease in the forest, rice field, and sugarcane plantation. The SWAT model results for the calibration (2003–2008) and validation (2009–2013) periods matched observed values [R2 > 0.91 and NSE (Nash-Sutcliffe Efficiency) >0.91]. In the long-term, the model predicted changes in runoff (+8%), water yield (+0.28%), groundwater (−1.8%), and evapotranspiration (−1.15%) due to changes in LULC. LULC changes showed a linear relationship with runoff generation, and the most significant factors affecting surface runoff were changes in the forest, agriculture, and settlements. Increasing urbanization, industrialization, and agricultural intensification will increase runoff which in turn will enhance the flow of nutrients and sediments into the water bodies.

Accurate surface runoff prediction is vital for water resources engineers for various applications. Advances in the artificial intelligence techniques can act as robust tools for modelling hydrological processes. The present study focuses on testing the reliability of different data sources and choosing the correct source to model the rainfall-runoff process under data scarce situations using AI techniques. In this study, an absolute homogeneity test was performed for TRMM, gridded and observed precipitation data and found that the observed precipitation dataset is homogeneous and best suitable for modelling rainfall-runoff process in Kallada river basin, Kerala. Emotional artificial neural network (EANN) is a novel hybrid neural network and it is suggested in the present study for accurate monthly surface runoff prediction. This study was also conceived to address and investigate the efficiency of EANN for forecasting monthly surface runoff and compare the performances with conventional feed forward neural network (FFNN) and multivariate adaptive regression spline (MARS) models. Suitable goodness-of-fit criteria such as Nash–Sutcliffe efficiency (NSE), root mean square error (RMSE) and coefficient of determination ( R 2 ) and graphical indicators are used for assessing the efficacy of the developed models. The results showed that the EANN model performs better with R 2  = 0.80 for the training phase and R 2  = 0.77 for validation phase compared to other models. The improvement in the performance of EANN model over FFNN model is 12% and 5.8% for coefficient of determination in the training and validation phase, respectively. Further, the Taylor diagram indicates that there is a close match between the observed and EANN model predicted values in terms of statistical parameters. Overall, this study demonstrated the effectiveness of EANN in modelling the rainfall-runoff process and also could be a useful technique in other fields of water resources engineering. Highlights The present study focuses on testing the reliability of different data sources such as gridded, observed and TRMM precipitation datasets and choosing the correct source to model the rainfall-runoff process using AI techniques. From the selected homogeneous dataset and the observed runoff data, potential predictors were identified based on correlation analysis and partial autocorrelation function (PACF). The monthly runoff prediction models were developed using three AI techniques namely FFNN, MARS and EANN in a tropical river basin (Kallada) of Kerala with scarce amount of data. The performance of the developed models were assessed using statistical indicators (NSE, RMSE, and R2) and graphical indicators (Taylor diagram, REC plots and Random walk test).

The last couple of decades have seen the outburst of a number of satellite-based precipitation products with Tropical Rainfall Measuring Mission (TRMM) as the most widely used for hydrologic applications. Transition of TRMM into the Global Precipitation Measurement (GPM) promises enhanced spatio-temporal resolution along with upgrades to sensors and rainfall estimation techniques. The dependence of systematic error components in rainfall estimates of the Integrated Multi-satellitE Retrievals for GPM (IMERG), and their variation with climatology and topography, was evaluated over 86 basins in India for year 2014 and compared with the corresponding (2014) and retrospective (1998–2013) TRMM estimates. IMERG outperformed TRMM for all rainfall intensities across a majority of Indian basins, with significant improvement in low rainfall estimates showing smaller negative biases in 75 out of 86 basins. Low rainfall estimates in TRMM showed a systematic dependence on basin climatology, with significant overprediction in semi-arid basins, which gradually improved in the higher rainfall basins. Medium and high rainfall estimates of TRMM exhibited a strong dependence on basin topography, with declining skill in higher elevation basins. The systematic dependence of error components on basin climatology and topography was reduced in IMERG, especially in terms of topography. Rainfall-runoff modeling using the Variable Infiltration Capacity (VIC) model over two flood-prone basins (Mahanadi and Wainganga) revealed that improvement in rainfall estimates in IMERG did not translate into improvement in runoff simulations. More studies are required over basins in different hydroclimatic zones to evaluate the hydrologic significance of IMERG.

Farmland expansion in the Brazilian Cerrado, considered one of the largest agricultural frontiers in the world, has the potential to alter water fluxes on different spatial scales. Despite some large-scale studies being developed, there are still few investigations in experimental sites in this region. Here, we investigate the water balance components in experimental plots and the groundwater table fluctuation in different land covers: wooded Cerrado, sugarcane, pasture and bare soil. Furthermore, we identify possible water balance trade-offs due to the different land covers. This study was developed between 2012 and 2016 in the central region of the state of São Paulo in southern Brazil. Hydrometeorological variables, groundwater table, surface runoff and other water balance components were monitored inside experimental plots containing different land covers; the datasets were analyzed using statistical parameters; and the water balance components uncertainties were computed. Replacing wooded Cerrado by pastureland and sugarcane shifts the overland flow (up to 42 mm yr−1) and the water balance residual (up to 504 mm yr−1) and may affect groundwater table behavior. This fact suggests significant changes in the water partitioning in a transient land cover and land use (LCLU) system, as the evapotranspiration is lower (up to 719 mm yr−1) in agricultural land covers than in the undisturbed Cerrado. We recommend long-term observations for continuing the evaluations initiated in this study, mainly because there are few basic studies on tropical environments at the hillslope scale and more assessments are needed for a better understanding of the real field conditions. Such efforts should be made to reduce uncertainties, validate the water balance hypothesis and catch the variability of hydrological processes.

Assessment of water availability in sub-humid regions is important due to distinct climatic and environmental conditions. In this study, Soil and Water Assessment Tool (SWAT) and Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) models have been assessed in simulating streamflows in the sub-humid tropical Kabini basin in Kerala, India, spanning 1260 km 2 . Calibration and validation utilized daily weather data from 1997 to 2015 from the Muthankera gauging station. The study investigated the impact of routing methods on runoff simulation in the ArcSWAT, exploring Muskingum and Variable Storage methods. Evaluation metrics encompassed Nash–Sutcliffe Efïciency (NSE), Coefficient of Determination ( R 2 ), Percent bias (PBIAS), RMSE-observations standard deviation ratio (RSR), and Peak Percent Threshold Statistics (PPTS) approach for high-flow values. The result indicates that HEC-HMS outperforms SWAT concerning R 2 and NSE values during daily calibration and validation. Monthly simulations showed HEC-HMS closely aligning with SWAT (Variable storage), outperforming SWAT (Muskingum). The PPTS approach proved effective in simulating high-flow values. Both models exhibited proficiency in streamflow analysis within the study area, promising predictive potential for future hydrological studies in sub-humid regions.