scholarly journals A stochastic model for drought risk analysis in The Netherlands

2018 ◽  
Author(s):  
Ferdinand L. M. Diermanse ◽  
Marjolein J. P. Mens ◽  
Hector Macian-Sorribes ◽  
Femke Schasfoort
Keyword(s):  
Time Series ◽  
Water Resources ◽  
The Netherlands ◽  
Stochastic Model ◽  
Drought Risk ◽  
Climate Conditions ◽  
Times Series ◽  
Historic Time ◽  
Synthetic Time Series ◽  
Hydrological Variables

Abstract. Population growth and economic developments increase the demand for water resources. Furthermore, climate change is often projected to have negative impacts on the availability of these water resources. Measures to reduce the risk of water shortages can be costly and often require long-term planning strategies. In the decision making process, a thorough understanding of these drought-related risks for the various water users is of crucial importance. Historic time series of climatologic and hydrological variables, used as input for water allocation and drought impact models, are generally too short to provide such a detailed understanding. This makes the case for using lengthy synthetic time series. The challenge is to develop synthetic time series that are realistic and representative for the current and future climate conditions. We present a stochastic model for generating realistic times series of meteorological and hydrological variables that characterise drought events. The model is applied to a case study in the Netherlands, but is generic in set-up and can thus be applied elsewhere as well. It is demonstrated that the main features of the historic time series are well reproduced. The generated synthetic times series provide valuable insights into the frequency and severity of droughts and help improve the assessment of drought risks.

scholarly journals Glacier recession and water resources in Peru’s Cordillera Blanca

2012 ◽  
Vol 58 (207) ◽  
pp. 134-150 ◽  
Author(s):  
Michel Baraer ◽  
Bryan G. Mark ◽  
Jeffrey M. McKenzie ◽  
Thomas Condom ◽  
Jeffrey Bury ◽  
...  
Keyword(s):  
Time Series ◽  
Water Resources ◽  
Hydrologic Model ◽  
Dry Season ◽  
Glacier Retreat ◽  
Cordillera Blanca ◽  
Tropical Glaciers ◽  
Daily Discharge ◽  
Synthetic Time Series ◽  
Nonparametric Statistical

AbstractThe tropical glaciers of the Cordillera Blanca, Peru, are rapidly retreating, resulting in complex impacts on the hydrology of the upper Río Santa watershed. The effect of this retreat on water resources is evaluated by analyzing historical and recent time series of daily discharge at nine measurement points. Using the Mann-Kendall nonparametric statistical test, the significance of trends in three hydrograph parameters was studied. Results are interpreted using synthetic time series generated from a hydrologic model that calculates hydrographs based on glacier retreat sequences. The results suggest that seven of the nine study watersheds have probably crossed a critical transition point, and now exhibit decreasing dry-season discharge. Our results suggest also that once the glaciers completely melt, annual discharge will be lower than present by 2-30% depending on the watershed. The retreat influence on discharge will be more pronounced during the dry season than at other periods of the year. At La Balsa, which measures discharge from the upper Río Santa, the glacier retreat could lead to a decrease in dry-season average discharge of 30%.

scholarly journals Teste de Mann-Kendall aplicado à dados hidrológicos – Desempenho dos filtros TFPW e CV2 na análise de tendências

2020 ◽  
Vol 42 ◽  
pp. e87
Author(s):  
Thais Vieira Dos Santos ◽  
Lília Dos Anjos De Freitas ◽  
Roger Dias Gonçalves ◽  
Hung Kiang Chang
Keyword(s):  
Time Series ◽  
Water Resources ◽  
Trend Analysis ◽  
Serial Correlation ◽  
Kendall Test ◽  
Precipitation Data ◽  
Accurate Analysis ◽  
Parametric Test ◽  
Trend Magnitude ◽  
Synthetic Time Series

This study brings an original comparison related to the performance of two filters on trend analysis regarding hydrological time series. We applied the Mann-Kendall test for trend analysis, a non-parametric test widely used in hydrological studies, and Sen’s slope in order to extract the trend magnitude. The presence of autocorrelation tends to impact on trend interpretation erroneously. As most of water resources data presents serial correlation, the use of filters is essential to achieve an accurate analysis regarding temporal variation of the dataset. The filters trend free pre-whitening (TFPW) and variance correction approach (CV2) were applied on monthly time series of precipitation, streamflow, storage and evapotranspiration, from 2002 to 2014, plus eighty synthetic time series. The comparison of the filters performances showed the TFPW filter as much superior, reducing the autocorrelation by at least 71.1%. While the CV2 filter, despite strongly reducing the variance, did not impact the serial correlation (in fact, reduced less than 1% in almost half of the performed simulations). The main difference was related to the precipitation data, from which CV2 suggested a negative trend, while TFPW, besides drastically reducing autocorrelation, showed that the time series does not have a statistically significant trend.

scholarly journals Stochastic Modeling of Hydroclimatic Processes Using Vine Copulas

Water ◽  
2021 ◽  
Vol 13 (16) ◽  
pp. 2156
Author(s):  
George Pouliasis ◽  
Gina Alexandra Torres-Alves ◽  
Oswaldo Morales-Napoles
Keyword(s):  
Time Series ◽  
Stochastic Modeling ◽  
Copula Models ◽  
Multiple Processes ◽  
Spatial Dependencies ◽  
Probabilistic Dependence ◽  
Temporal And Spatial ◽  
Vine Copula ◽  
Synthetic Time Series ◽  
Hydroclimatic Variables

The generation of synthetic time series is important in contemporary water sciences for their wide applicability and ability to model environmental uncertainty. Hydroclimatic variables often exhibit highly skewed distributions, intermittency (that is, alternating dry and wet intervals), and spatial and temporal dependencies that pose a particular challenge to their study. Vine copula models offer an appealing approach to generate synthetic time series because of their ability to preserve any marginal distribution while modeling a variety of probabilistic dependence structures. In this work, we focus on the stochastic modeling of hydroclimatic processes using vine copula models. We provide an approach to model intermittency by coupling Markov chains with vine copula models. Our approach preserves first-order auto- and cross-dependencies (correlation). Moreover, we present a novel framework that is able to model multiple processes simultaneously. This method is based on the coupling of temporal and spatial dependence models through repetitive sampling. The result is a parsimonious and flexible method that can adequately account for temporal and spatial dependencies. Our method is illustrated within the context of a recent reliability assessment of a historical hydraulic structure in central Mexico. Our results show that by ignoring important characteristics of probabilistic dependence that are well captured by our approach, the reliability of the structure could be severely underestimated.

scholarly journals SARIMA Approach to Generating Synthetic Monthly Rainfall in the Sinú River Watershed in Colombia

Atmosphere ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 602
Author(s):  
Luisa Martínez-Acosta ◽  
Juan Pablo Medrano-Barboza ◽  
Álvaro López-Ramos ◽  
John Freddy Remolina López ◽  
Álvaro Alberto López-Lambraño
Keyword(s):  
Time Series ◽  
Water Resources ◽  
Autocorrelation Function ◽  
Information Criterion ◽  
Monthly Rainfall ◽  
Statistical Characteristics ◽  
Rainfall Time Series ◽  
River Watershed ◽  
Statistical Measures ◽  
Sarima Models

Seasonal Auto Regressive Integrative Moving Average models (SARIMA) were developed for monthly rainfall time series. Normality of the rainfall time series was achieved by using the Box Cox transformation. The best SARIMA models were selected based on their autocorrelation function (ACF), partial autocorrelation function (PACF), and the minimum values of the Akaike Information Criterion (AIC). The result of the Ljung–Box statistical test shows the randomness and homogeneity of each model residuals. The performance and validation of the SARIMA models were evaluated based on various statistical measures, among these, the Student’s t-test. It is possible to obtain synthetic records that preserve the statistical characteristics of the historical record through the SARIMA models. Finally, the results obtained can be applied to various hydrological and water resources management studies. This will certainly assist policy and decision-makers to establish strategies, priorities, and the proper use of water resources in the Sinú river watershed.

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