Assessment of Rainfall Variability and Its Relationship to ENSO in a Sub-Andean Watershed in Central Bolivia

Amaya, Andres Gonzales; Villazón, Mauricio; Willems, Patrick

doi:10.3390/w10060701

Cited by 5 publications

(1 citation statement)

References 60 publications

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“…However, Mann-Kendall and Spearman's rho suffer from internal correlation in the time series, leading to a false conclusion of trend or no-trend (Amaya et al, 2018). Therefore, checking the time series data against autocorrelations was vital when using the Mann-Kendall method.…”

Section: Trends Analysis Of Lake Levels and Climatic Variabilitymentioning

confidence: 99%

Surface water – groundwater interactions: A case of a shallow semi-closed lake catchment in northern Tanzania

Okwir

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Conjunctive use of surface water and groundwater is rapidly growing in many developing countries as an adaptation strategy to climate variability and change. However, the interactions between the groundwater and the surface water systems are not adequately understood, especially among the East African rift valley lakes, where data paucity has limited studies and reporting on the spatial influence of catchment heterogeneity. In its humble contribution to sustainable water development, this study aimed to present a platform for understanding the influence of climatic variation and anthropogenic activities on surface water–groundwater interactions. To be relevant locally, Lake Babati, a freshwater lake in Northern Tanzania that provides the community with fish, freshwater, and a habit for hippopotamus, was studied. The study applied hydrological simulation, grey relational analysis, and stepwise regression analysis to model the hydrological behaviour of the lake. Further, it used hydrogeochemistry and environmental isotopes to identify groundwater fluxes and draw the conceptual understanding of surface water – groundwater interaction and applied topography-based indices to spatially map groundwater potentials within the catchment. The results showed that Lake Babati level is significantly declining (p-value < 0.01) at a rate of 25 mm per annum. The lake level decline could not be explained by climatic variability since the decline occurred when both evaporation and rainfall showed no significant changes either seasonally or annually. Instead, the consistent decline of the lake level in all seasons could be due to the expansion of the spillway, which effectively lowered the lake reservoir level and increased the lake outflow in rainy seasons. The hydro-geochemistry and isotopes data showed that the lake water and groundwater interact and are in hydraulic connections. Further, using Height Above Nearest Drainage based and Topography Wetness Index based methods, the study developed two groundwater potential maps to predict groundwater spatial variability and guide groundwater prospecting efforts and subsequent development. Given that Lake Babati is in a hydraulic connection with the groundwater, its consistent decline will likely impact the groundwater system. Similarly, abstracting groundwater at unsustainable rates could lower the lake levels further. Therefore, integrated water resources management is required for sustainable water resources development and management in the catchment. Mandatory and continuous monitoring of the water resources (groundwater levels, river flows, and lake levels) is recommended to generate quality in situ data for future studies.

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Section: Trends Analysis Of Lake Levels and Climatic Variabilitymentioning

confidence: 99%

Surface water – groundwater interactions: A case of a shallow semi-closed lake catchment in northern Tanzania

Okwir

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Analyzing seasonal variation in the vegetation cover using NDVI and rainfall in the dry deciduous forest region of Eastern India

Dutta

Rehman

Chatterjee

et al. 2021

Forest Resources Resilience and Conflicts

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Temporal Convolutional Networks for the Advance Prediction of ENSO

Yan

Wang

et al. 2020

Sci Rep

212

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el niño-Southern oscillation (enSo), which is one of the main drivers of earth's inter-annual climate variability, often causes a wide range of climate anomalies, and the advance prediction of enSo is always an important and challenging scientific issue. Since a unified and complete ENSO theory has yet to be established, people often use related indicators, such as the Niño 3.4 index and southern oscillation index (SOI), to predict the development trends of ENSO through appropriate numerical simulation models. However, because the ENSO phenomenon is a highly complex and dynamic model and the Niño 3.4 index and SOI mix many low-and high-frequency components, the prediction accuracy of current popular numerical prediction methods is not high. therefore, this paper proposed the ensemble empirical mode decomposition-temporal convolutional network (eeMD-TCN) hybrid approach, which decomposes the highly variable Niño 3.4 index and SOI into relatively flat subcomponents and then uses the TCN model to predict each subcomponent in advance, finally combining the sub-prediction results to obtain the final ENSO prediction results. Niño 3.4 index and SOI reanalysis data from 1871 to 1973 were used for model training, and the data for 1984-2019 were predicted 1 month, 3 months, 6 months, and 12 months in advance. The results show that the accuracy of the 1-month-lead Niño 3.4 index prediction was the highest, the 12-month-lead SOI prediction was the slowest, and the correlation coefficient between the worst SOI prediction result and the actual value reached 0.6406. Furthermore, the overall prediction accuracy on the Niño 3.4 index was better than that on the SOI, which may have occurred because the SOI contains too many high-frequency components, making prediction difficult. The results of comparative experiments with the TCN, LSTM, and eeMD-LStM methods showed that the eeMD-tcn provides the best overall prediction of both the Niño 3.4 index and SOI in the 1-, 3-, 6-, and 12-month-lead predictions among all the methods considered. this result means that the tcn approach performs well in the advance prediction of enSo and will be of great guiding significance in studying it. El Niño-Southern Oscillation (ENSO) is a sea surface temperature and air pressure shock that occurs in the equatorial Pacific Ocean 1. It is a sea-air interaction phenomenon at low latitudes, which is manifested by the El Niño-La Niña transition in the ocean and the "southern oscillation" (SO) in the atmosphere. El Niño refers to the warming phenomenon that occurs in the tropical Pacific every 2-7 years, while the cooling phenomenon is called La Niña 2. El Niño and La Niña are closely related to SO, which is the inverse-change phenomenon of the pressure field in the tropical east Pacific and tropical east Indian Ocean. The ENSO is one of the main drivers of Earth's inter-annual climate variability. It often causes a wide range of climate anomalies, triggering a variety of meteorological disasters and causing huge economic property damage in affected areas 3...

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Assessment of Rainfall Variability and Its Relationship to ENSO in a Sub-Andean Watershed in Central Bolivia

Cited by 5 publications

References 60 publications

Surface water – groundwater interactions: A case of a shallow semi-closed lake catchment in northern Tanzania

Surface water – groundwater interactions: A case of a shallow semi-closed lake catchment in northern Tanzania

Analyzing seasonal variation in the vegetation cover using NDVI and rainfall in the dry deciduous forest region of Eastern India

Temporal Convolutional Networks for the Advance Prediction of ENSO

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