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

Haas, Johannes Christoph; Switanek, Matthew B.; Birk, Steffen

doi:10.1007/s12665-018-7469-4

Cited by 9 publications

(4 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

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

Section: Integrating Meteorologic and Hydrologic Drought Indicesmentioning

confidence: 99%

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

Akhtar

Mushtaq²,

Hashmi³

2020

Preprint

View full text Add to dashboard Cite

Abstract. Effective drought monitoring, prediction and early warning systems are crucial for management of human activities associated with water use in a climate affected world. In Pakistan, surface water flows predominantly originate from the transboundary Upper Indus sub-catchments of Chenab, Jhelum, Indus and Kabul rivers. Hence, impact of droughts manifested through water deficits in these catchments are strongly felt by downstream users. Use of different drought indicators is limited in Pakistan's operational drought monitoring system. Moreover, there is very limited prior literature that explores the use of multiple indicators for unearthing relationships between different drought types. This study aims to explore the relationship between meteorological and hydrological droughts in the Upper Indus catchments of Pakistan using the Standard Precipitation and Evaporation Index (SPEI) and the Standard Streamflow Index (SSI). Since there are no previous studies for the Indus that compare different distributions for SSI computation, we compare five distributions to adequately compute SSI values at catchment outlets. Our most crucial contribution in this study is analysis of seasonal cross-correlations and lagged cross-correlations between SSI and SPEI for the above-mentioned four catchments. The cross-correlation analysis shows strong lagged (with up to 2 lag months) cross-correlations between SPEI and SSI for Chenab, Jhelum and Kabul catchments in early Kharif months. These correlations may be used in operational drought monitoring and forecasting systems, and also in reservoir planning and operations (for Mangla resrvoir in Jhelum) in drought conditions. We strongly believe that the findings of this study can be used in future to collectively explore hydrological and meteorological drought perspectives in Pakistan and to successfully incorporate multiple indicators into operational drought management.

show abstract

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

Section: Integrating Meteorologic and Hydrologic Drought Indicesmentioning

confidence: 99%

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

Akhtar

Mushtaq²,

Hashmi³

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…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).…”

mentioning

confidence: 99%

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

Hsu

Chang

2021

Hydrology Research

View full text Add to dashboard Cite

This study proposes a stochastic artificial neural network (named ANN_GA-SA_MTF), in which the parameters of the multiple transfer functions considered are calibrated by the modified genetic algorithm (GA-SA), to effectively provide the real-time forecasts of hydrological variates and the associated reliabilities under the observation and predictions given (model inputs); also, the resulting forecasts can be adjusted through the real-time forecast-error correction method (RTEC_TS&KF) based on difference between real-time observations and forecasts. The observed 10-days rainfall depths and water levels (i.e., hydrological estimates) from 2008 to 2018 recorded within the Shangping sub-basin in northern Taiwan are adopted as the study data and their stochastic properties are quantified for simulating 1,000 sets of rainfall and water levels at 36 10-days periods as the training datasets. The results from the model verification indicate that the observed 10-days rainfall depths and water levels are obviously located at the prediction interval (i.e., 95% confidence interval), revealing that the proposed ANN_GA-SA_MTF model can capture the temporal behavior of 10-days rainfall depths and water levels within the study area. In spite of the resulting forecasts with an acceptable difference from the observation, their real-time corrections have evident agreement with the observations, namely, the resulting adjusted forecasts with high accuracy.

show abstract

“…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].…”

unclassified

Overview of software for analysis and visualization of hydrological data

Baturinets¹,

Антоненко²

2019

APAIT

View full text Add to dashboard Cite

Розглянуто програмні засоби для аналізу, моделювання та візуалізації гідрологічних даних. Представлено огляд основних можливостей комерційних програмних продуктів та програмних засобів з відкритим вихідним кодом. Визначено особливості використання проектів з відкритим вихідним кодом при створенні програмних засобів.Ключові слова: візуалізація даних, аналіз гідрологічних даних, ГІС, GRASS, QGIS, SAGA, ESRI.Рассмотрены программные средства для анализа, моделирования и визуализации гидрологических данных. Представлен обзор основных возможностей коммерческих программных продуктов и программных продуктов с открытым исходным кодом. Определены возможности использования проектов с открытым исходным кодом при создании программных средств.Ключевые слова: визуализация данных, анализ гидрологических данных, ГИС, GRASS, QGIS, SAGA, ESRI.The use of various software tools, including geoinformation technologies, in modern hydrological research is due to the large volumes of spatial information used, the complexity, specifics of its processing, analysis and visualization. The main goal of the work is to review the software tools and their capabilities in conducting hydrological studies. Not only commercial software tools are considered in this paper, but also so-called OpenSource projects, which in one way or another provide solutions to hydrological tasks. In the analysis of literature, the authors of the article are considering works on application of existing software tools in hydrological research, as well as new, modern developments in this direction. The following commercial software products were considered: ESRI's product line (ArcMap, ArcCatalog, ArcToolbox), ESRI and Aquaveо's joint campaign development software package (Arc

show abstract

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

Cited by 9 publications

References 27 publications

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

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

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

Overview of software for analysis and visualization of hydrological data

Contact Info

Product

Resources

About