The role of anthropogenic activities in karst spring discharge volatility

Wu, Jing Cynthia; Yin, Jifu; Hao, Yonghong; Liu, Yan; Fan, Ye; Huo, Xueli; Liu, Youcun; Yeh, Tian Chyi Jim

doi:10.1002/hyp.10407

Cited by 3 publications

(1 citation statement)

References 29 publications

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“…Human activities notably influence the variability of hydrological time series. Wu et al [86] presented a SARIMA-GARCH adaptation, which considers the impact of human activities and natural stressors in the volatility of a karst spring discharge in China, aiming to improve water resource management in regions influenced by anthropogenic activities.…”

Section: Discussionmentioning

confidence: 99%

Assessment of Time Series Models for Mean Discharge Modeling and Forecasting in a Sub-Basin of the Paranaíba River, Brazil

Costa,

Menezes Filho,

Canales

et al. 2023

Hydrology

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Stochastic modeling to forecast hydrological variables under changing climatic conditions is essential for water resource management and adaptation planning. This study explores the applicability of stochastic models, specifically SARIMA and SARIMAX, to forecast monthly average river discharge in a sub-basin of the Paranaíba River near Patos de Minas, MG, Brazil. The Paranaíba River is a vital water source for the Alto Paranaíba region, serving industrial supply, drinking water effluent dilution for urban communities, agriculture, fishing, and tourism. The study evaluates the performance of SARIMA and SARIMAX models in long-term discharge modeling and forecasting, demonstrating the SARIMAX model’s superior performance in various metrics, including the Nash–Sutcliffe coefficient (NSE), the root mean square error (RMSE), and the mean absolute percentage error (MAPE). The inclusion of precipitation as a regressor variable considerably improves the forecasting accuracy, and can be attributed to the multivariate structure of the SARIMAX model. While stochastic models like SARIMAX offer valuable decision-making tools for water resource management, the study underscores the significance of employing long-term time series encompassing flood and drought periods and including model uncertainty analysis to enhance the robustness of forecasts. In this study, the SARIMAX model provides a better fit for extreme values, overestimating peaks by around 11.6% and troughs by about 5.0%, compared with the SARIMA model, which tends to underestimate peaks by an average of 6.5% and overestimate troughs by approximately 76.0%. The findings contribute to the literature on water management strategies and mitigating risks associated with extreme hydrological events.

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Section: Discussionmentioning

confidence: 99%

Assessment of Time Series Models for Mean Discharge Modeling and Forecasting in a Sub-Basin of the Paranaíba River, Brazil

Costa,

Menezes Filho,

Canales

et al. 2023

Hydrology

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Features and Hotspots in Karst Groundwater

袁¹

2018

JWRR

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Karst covers more than 20% of lithosphere area. Karst groundwater resource is a vital water resource. However, a karst water-bearing system is a kind of dual media with distinct hydraulic properties, of which heterogeneity is significant and two mechanisms of surface water infiltration are of the same importance. The connection across precipitation, surface water and karst groundwater is very close. And the conversion among them is nonlinear with considerable complexity. Hydrologic processes, flow rates and the discharging area are readily impacted by the changing environment in a karst groundwater flow system. Discharges of karst springs are of scale effect and multi-fractal characteristics. And the composition of discharge is complicated and changeable. Furthermore, hydrogeochemical characteristics of karst groundwater are sensitive to the changing environment determined by variable hydrologic processes of karst and biogeochemical reactions. Therefore, traditional geohydrologic methods can't be applied directly in a karst basin and the common difficulties in hydrologic researches tend to be amplified. At present, researches in karst hydrology are relatively laggard. The karst in China is type diversity and representative all over the world. Studies in regard to karst hydrologic processes and the karst CO2-H2O-CaCO3 system have made progress in China. In future, responses and feedbacks to the global change, water resource and environment, and hydrologic processes modeling of a karst groundwater flow system are the potential researching hotspots.

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Artificial neural networks applied for flood forecasting in ungauged basin – the Paranaíba river study case

Brandão,

de Menezes Filho,

Oliveira

et al. 2024

Proc. IAHS

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Abstract. Flow simulation using artificial neural networks (ANNs) in the modelling has been widely applied and has gained prominence in regions lacking data. The hydrological variables are subject to the influence of morphological characteristics and urbanization in the watershed. Statistical models, such as ANNs, need to be able to identify the relationship between the hydrological inputs and outputs of the model, without explicitly considering the other relationships involved in physical processes. This work aimed to apply a Multilayer Perceptron (MLP) neural network for predicting flows in an urban basin subject to recurrent floods, using precipitation and flow data from previous periods as inputs. After model calibration and validation for the current state of the basin (2018–2019), its responses were analysed using input data before the basin urbanization (1985–1986) to identify the error behaviour at the output as a proxy for the basin changes effect. Its efficiency was evaluated using hydrographs, showing satisfactory results in both periods. In the urbanization period, there is more dispersion for maximum flows. For the day 4 steps back in the current forecast, NSE = 0.59 was observed, whereas in the other period, NSE = 0.70. The evaluation of the models for the current period of basin urbanization showed that the model could capture the basin's physical dynamics within the established static relationship. Also, the result found in the statistical relationships for the inputs showed once again the impact of urbanization on the basin.

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The role of anthropogenic activities in karst spring discharge volatility

Cited by 3 publications

References 29 publications

Assessment of Time Series Models for Mean Discharge Modeling and Forecasting in a Sub-Basin of the Paranaíba River, Brazil

Assessment of Time Series Models for Mean Discharge Modeling and Forecasting in a Sub-Basin of the Paranaíba River, Brazil

Features and Hotspots in Karst Groundwater

Artificial neural networks applied for flood forecasting in ungauged basin – the Paranaíba river study case

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