Analysis of hydrological data with correlation matrices: technical implementation and possible applications

Abstract

Changing political frameworks in addition to novel and more cost-effective means to investigate the subsurface have led to an increase in the availability of hydrological data. This wealth of data, however, poses new challenges in effectively making use of it. Traditional tools such as spreadsheets or proprietary datalogger software often do not scale easily with a larger amount of available datasets, requiring considerable user interaction. Also, comparing different locations and types of data can be difficult and tedious. Thus, a python script is presented that enables the user to quickly visualize and compare different types of data such as for example groundwater levels or precipitation amounts. This is done by first standardizing the data using different drought indices and, subsequently, visualization of correlation matrices or plots of data on maps. This approach can be used for data quality control (identifying erroneous data, classifying data into different types), data comparison (comparing different types of data, such as groundwater and precipitation; comparing different locations) and to visualize and analyze the development of hydrological data and their correlation patterns over time. Prospects and limitations of the approach are illustrated and discussed using various example applications.

Drought monitoring and prediction in climate vulnerable Pakistan: Integrating hydrologic and meteorologic perspectives

“…hydrological and meteorological droughts (Barker et al, 2016;Haas et al, 2018;Hannaford et al, 2011;Wong et al, 2013).…”

Stochastic modeling of artificial neural networks for real-time hydrological forecasts based on uncertainties in transfer functions and ANN weights

“…It is well-known that in training the ANN model, the training hydrological-dataset size, such as the precipitation, river flow and water level, might impact the performance of optimizing the associated parameters (Foody & McCulloch 1995). Furthermore, there are frequently high correlations and resolution among hydrological variables in time and space (Wu et al 2006;Haas et al 2018). Accordingly, applying a big dataset in successfully training ANN model is a progressive and significant task in order to avoid the model overfitting or underfitting of the model associated with numerous parameters (Chiroma et al 2018;Chalumuri et al 2020).…”

Overview of software for analysis and visualization of hydrological data

“…Сучасні дослідження та розвиток ГІС інструментарію для розв'язання гідрологічних задач можна побачити в роботах [7][8][9][10][11][12][13][14][15][16][17].…”