Hydroclimatic Time Series Research Articles

Multi-Method Comparative Analysis of Hydroclimatic Trends and Variability in Dry Creek Catchment, South Australia

AbstractThe trend and variability of hydroclimatic variables over time are apparent in seasonal creeks, especially those located in urbanized areas. Understanding hydro-climatic trends in urban areas is crucial for the sustainable management of water resources and the environment. This study aimed to explore the spatiotemporal variability and trends of hydroclimate variables as well as the potential connection between rainfall and streamflow in Dry Creek catchment, South Australia. The trend-free pre-whitening Mann–Kendall (TFPW-MK) test and Innovative Trend Analysis (ITA) were utilized to examine the monotonic and nonmonotonic trends, respectively, and multiple statistical tests were employed to examine the change points in the hydroclimatic time series. Sen’s slope, Simple Linear Regression (SLR), and ITA were used as alternative approaches to assess the magnitudes of change and overcome the limitations in the underlying assumptions of the various methodologies. The variability in the hydroclimate time series was estimated using several indices, such as the coefficient of variation, seasonality indices, flashiness index, and mean zero flow index. The analyses revealed important findings, notably the high variability of rainfall and streamflow during dry periods. Streamflow displayed greater variability compared to rainfall, with high CV values recorded both seasonally and annually. Furthermore, there was a significant upward trend in seasonal rainfall during winter. Additionally, the maximum and mean temperatures demonstrated a statistically significant increase, which can be attributed to global warming and significant urbanization in the catchment area. Comparative analysis has confirmed that the ITA has superior detection capabilities for nonmonotonic trends, outperforming other methods. It excels at presenting graphical representations that accurately depict trends, effectively differentiating between low, medium, and high values. The strong relationship between rainfall and streamflow demonstrated by the Tanh curve suggests that rainfall is the most reliable predictor of streamflow. The outcomes of this investigation are expected to support local governmental organizations and decision-makers in comprehending the spatial and temporal features of rainfall, as well as its correlation with streamflow. This information will further assist in developing flood and drought mitigation strategies backed by empirical evidence. Graphical Abstract

Quantification of Long-Range Dependence in Hydroclimatic Time Series: A Method-Comparison Study

Abstract Accurate evaluation of the long-range dependence in hydroclimatic time series is important for understanding its inherent characteristics. However, the reliability of its evaluation may be questioned, since different methods may yield various outcomes. In this study, we evaluate the performances of seven widely used methods for estimating long-range dependence: absolute moment estimation, difference variance estimation, residuals variance estimation, rescaled range estimation, periodogram estimation, wavelet estimation (WLE), and discrete second derivative estimation (DSDE). We examine the influences of six major factors: data length, mean value, three nonstationary components (trend, jump, and periodicity), and one stationary component (short-range dependence). Results from the Monte Carlo experiments show that WLE and DSDE have greater credibility than the other five methods. They also reveal that data length, as well as stationary and nonstationary components, have notable influences on the evaluation of long-range dependence. Following it, we use the WLE and DSDE methods to evaluate the long-range dependence of precipitation during 1961–2015 on the Tibetan Plateau. The results indicate that the precipitation variability mirrors the long-range dependence of the Indian summer monsoon but with obvious spatial difference. This result is consistent with the observations made by previous studies, further confirming the superiority of the WLE and DSDE methods. The outcomes from this study have important implications for modeling and prediction of hydroclimatic time series.

Uncertainty Quantification of Deep Learning–Based Statistical Downscaling of Climatic Parameters

Abstract This study investigated the uncertainty involved in statistical downscaling of hydroclimatic time series obtained by artificial neural networks (ANNs). Phase 6 of the Coupled Model Intercomparison Project (CMIP6) general circulation model (GCM) Canadian Earth System Model, version 5 (CanESM5), was used as large-scale predictor data for downscaling temperature and precipitation parameters. Two ANNs, feed forward and long short-term memory (LSTM), were utilized for statistical downscaling. To quantify the uncertainty of downscaling, prediction intervals (PIs) were estimated via the lower upper bound estimation (LUBE) method. To assess performance of proposed models in different climate regimes, data from the Tabriz and Rasht stations in Iran were employed. The calibrated models via historical GCM data were used for future projections via the high-forcing and fossil fuel–driven development scenario shared socioeconomic pathway (SSP) 5-8.5. Projections were compared with the Canadian Regional Climate Model 4 (Can-RCM4) projections via the same scenario. Results indicated that both LSTM-based point predictions and PIs are more accurate than the feedforward neural network (FFNN)-based predictions, with an average of 55% higher Nash–Sutcliffe efficiency (NSE) for point predictions and 25% lower coverage width criterion (CWC) for PIs. Projections suggested that Tabriz is going to experience a warmer climate with an increase in average temperature of 2° and 5°C for near and far futures, respectively, and a drier climate with a 20% decrease in precipitation until 2100. Future projections for the Rasht station, however, suggested a more uniform climate with less seasonal variability. Average precipitation will increase by up to 25% and 70% until near and far future periods, respectively. Ultimately, point predictions show that the average temperature in Rasht will increase by 1°C until the near future and then be a constant average temperature until the far future. Significance Statement The downscaling of hydroclimatic parameters is subject to uncertainty. The best way is to provide an area with the highest contingency of them as a prediction interval. The reduction width of such an interval leads to increased confidence in explaining and predicting these processes. We proposed and applied a deep learning–based machine learning method for both point prediction and prediction interval estimation of temperature and precipitation parameters for the future over two different climatic regions. The results show the superiority of such a machine learning–based prediction interval estimation for quantification of the downscaling uncertainty.

Hydroclimatic time series features at multiple time scales

A comprehensive understanding of the behaviours of the various geophysical processes and an effective evaluation of time series (else referred to as “stochastic”) simulation models require, among others, detailed investigations across temporal scales. In this work, we propose a novel and detailed methodological framework for advancing and enriching such investigations in a hydroclimatic context. This specific framework is primarily based on a new feature compilation for multi-scale hydroclimatic analyses, and can facilitate largely interpretable feature investigations and comparisons in terms of temporal dependence, temporal variation, “forecastability”, lumpiness, stability, nonlinearity (and linearity), trends, spikiness, curvature and seasonality. Multifaceted characterizations are herein obtained by computing the values of the proposed feature compilation across nine temporal resolutions (i.e., the 1-day, 2-day, 3-day, 7-day, 0.5-month, 1-month, 2-month, 3-month and 6-month ones) and three hydroclimatic time series types (i.e., temperature, precipitation and streamflow) for 34-year-long time series records originating from 511 geographical locations across the contiguous United States. Based on the acquired information and knowledge, similarities and differences between the examined time series types with respect to the evolution patterns characterizing their feature values with increasing (or decreasing) temporal resolution are identified. Moreover, the computed features are used as inputs to unsupervised random forests for detecting any meaningful clusters between the examined hydroclimatic time series. This clustering plays an illustrative role within this research, as it facilitates the identification of spatial patterns (with them consisting an important scientific target in hydroclimatic research) and their cross-scale comparison. We find that these specific patterns are largely analogous across temporal resolutions for the examined continental-scale region. We also apply explainable machine learning to compare the features with respect to their usefulness in clustering the time series at the various temporal resolutions. These latter investigations play a vital role within the proposed methodological framework, as they allow interpretation of hydroclimatic similarity at the various temporal resolutions. For most of the features, this usefulness can vary to a notable degree across temporal resolutions and time series types, thereby implying the need for conducting multifaceted time series characterizations for the study of hydroclimatic similarity.

Assessment of Future Land Use/Land Cover Scenarios on the Hydrology of a Coastal Basin in South-Central Chile

Land use and land cover (LULC) change is one of the clearest representations of the global environmental change phenomenon at various spatial and temporal scales. Chile is worldwide recognized to have areas dedicated to non-native forest plantations that specifically in coastal range show high environmental and economic deterioration, questioning the sustainability of the forestry industry. Currently, there are no studies in Chile that reveal the real effects of the LULC change on the water balance at basin or sub-basin scales associated with future scenarios, which might contribute to territorial decision-making and reveal the real magnitude of the effects of these dynamics. In this study, in order to study LULC dynamics in a coastal basin in South-Central Chile, we assessed and analyzed the effects of future LULC change scenarios on the hydrological processes by generating future synthetic land cover maps from Landsat (Landsat 5 TM and Landsat 8 OLI) image datasets. The hydrological model Soil Water Assessment Tool (SWAT) was calibrated and validated, using hydroclimatic time series, to simulate discharges and other hydrological components over those future LULC scenarios. The LULC future scenarios were projected using combined Markov chain analysis (CA–Markov) and cellular automata algorithms for the near (2025), middle (2035) and far (2045) future. The results revealed that the effects on the different components of the water balance of the basin are not as significant except in the soil water transfer in percolation (increase 72.4%) and groundwater flow (increase 72.5%). This trend was especially observed in sub-basins with non-native forest plantations that dominated land cover in the year 2035, in which an increase of 43.6% in percolation and groundwater flows resulted in increased aquifer recharge and water storage, mainly offset by a decrease of 27% in the evapotranspiration. This work demonstrates the importance of evaluating the impacts of the dynamics of LULC on the hydrological response of a coastal basin, and also on how the land use governance and policy are closely linked to that of water resources.

Wavelet analysis of hydro-climatic time-series and vegetation trends of the Upper Aragón catchment (Central Spanish Pyrenees)

Water managers and researchers noted with concern a nearly generalized decline in Mediterranean rivers discharge over the last decades. Changes in climatic forces (precipitation and air temperature) and land use and land cover (LULC) changes characterized by re-vegetation and greenness are the two most possible explanations for this discharge decline. The direct impact on river discharge stemming from these changes is difficult to assess and their role is generally studied separately. Here, we use the method of wavelet transformation to interpret the time-scale dependency of catchment discharge concerning the uneven temporal climatic fluctuations and re-vegetation processes. We analyzed the temporal variation of concurrent air temperature, precipitation and river discharge time-series for the Upper Aragón catchment, located in the Central Spanish Pyrenees. A long-term database collected over 60 years (1956–2020) was used. Land cover maps corresponding to different decades were used and the results indicated that the catchment experienced a significant increase in the area covered by mixed and broadleaf forests, mostly as a consequence of land abandonment. We show how temperature slightly increased and precipitation moderately decreased. However, catchment discharge experienced a sharp decline in its magnitude and also changes in its temporal variability dynamics. The relevance of the seasonal time-scales with regard to the available discharge is reduced, which strengthens the importance of the inter-annual time-scales for the catchment discharge dynamics. Furthermore, the catchment storage-discharge cycle at inter-annual time-scales is also reduced. Such changes can mostly be attributed to the changes in plant coverage, with an increasing weight in shaping hydrological processes at catchment scale due to the greenness effect. As such, we conclude that LULC changes have played a dominant role on the river discharge dynamics. Climatic trends, on the contrary, have been small, and they have played a secondary role in the decline of river discharge. Future research can use these observations to constrain the pace of upcoming water demands based on the available water resources at Mediterranean catchment scale.

Meaningful Trend in Climate Time Series: A Discussion Based On Linear and Smoothing Techniques for Drought Analysis in Taiwan

Finding significant trends in hydroclimate time series has been deemed an essential task in numerous studies. Despite the existence of various trend detection methods, statistical significance is mostly examined for linear trends and related to the meaningfulness of the found trends. We wish to draw attention to a more general definition of meaningful trends by cross-referencing not only linear but also smoothing techniques. We apply linear regression (LR) and two smoothing techniques based on regularized minimal-energy tensor-product B-splines (RMTB) to the trend detection of standardized precipitation index (SPI) series over Taiwan. LR and both RMTB-based methods identify an overall upward (wetting) trend in the SPI series across the time scales in Taiwan from 1960 to 2019. However, if dividing the entire time series into the earlier (1960–1989) and later (1990–2019) sub-series, we find that some downward (drying) trends at varied time scales migrate from southcentral–southwestern to eastern regions. Among these significant trends, we have more confidence in the recent drying trend over eastern Taiwan since all the methods show trend patterns in highest similarity. We also argue that LR should be used with great caution, unless linearity in data series and independence and normality in residuals can be assured.

Quantile Trend Regression and Its Application to Central England Temperature

The identification and estimation of trends in hydroclimatic time series remains an important task in applied climate research. The statistical challenge arises from the inherent nonlinearity, complex dependence structure, heterogeneity and resulting non-standard distributions of the underlying time series. Quantile regressions are considered an important modeling technique for such analyses because of their rich interpretation and their broad insensitivity to extreme distributions. This paper provides an asymptotic justification of quantile trend regression in terms of unknown heterogeneity and dependence structure and the corresponding interpretation. An empirical application sheds light on the relevance of quantile regression modeling for analyzing monthly Central England temperature anomalies and illustrates their various heterogenous trends. Our results suggest the presence of heterogeneities across the considered seasonal cycle and an increase in the relative frequency of observing unusually high temperatures.

Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale

Statistical analyses and descriptive characterizations are sometimes assumed to be offering information on time series forecastability. Despite the scientific interest suggested by such assumptions, the relationships between descriptive time series features (e.g., temporal dependence, entropy, seasonality, trend and linearity features) and actual time series forecastability (quantified by issuing and assessing forecasts for the past) are scarcely studied and quantified in the literature. In this work, we aim to fill in this gap by investigating such relationships, and the way that they can be exploited for understanding hydroclimatic forecastability and its patterns. To this end, we follow a systematic framework bringing together a variety of –mostly new for hydrology– concepts and methods, including 57 descriptive features and nine seasonal time series forecasting methods (i.e., one simple, five exponential smoothing, two state space and one automated autoregressive fractionally integrated moving average methods). We apply this framework to three global datasets originating from the larger Global Historical Climatology Network (GHCN) and Global Streamflow Indices and Metadata (GSIM) archives. As these datasets comprise over 13,000 monthly temperature, precipitation and river flow time series from several continents and hydroclimatic regimes, they allow us to provide trustable characterizations and interpretations of 12-month ahead hydroclimatic forecastability at the global scale. We first find that the exponential smoothing and state space methods for time series forecasting are rather equally efficient in identifying an upper limit of this forecastability in terms of Nash-Sutcliffe efficiency, while the simple method is shown to be mostly useful in identifying its lower limit. We then demonstrate that the assessed forecastability is strongly related to several descriptive features, including seasonality, entropy, (partial) autocorrelation, stability, (non)linearity, spikiness and heterogeneity features, among others. We further (i) show that, if such descriptive information is available for a monthly hydroclimatic time series, we can even foretell the quality of its future forecasts with a considerable degree of confidence, and (ii) rank the features according to their efficiency in explaining and foretelling forecastability. We believe that the obtained rankings are of key importance for understanding forecastability. Spatial forecastability patterns are also revealed through our experiments, with East Asia (Europe) being characterized by larger (smaller) monthly temperature time series forecastability and the Indian subcontinent (Australia) being characterized by larger (smaller) monthly precipitation time series forecastability, compared to other continental-scale regions, and less notable differences characterizing monthly river flow from continent to continent. A comprehensive interpretation of such patters through massive feature extraction and feature-based time series clustering is shown to be possible. Indeed, continental-scale regions characterized by different degrees of forecastability are also attributed to different clusters or mixtures of clusters (because of their essential differences in terms of descriptive features).

Multi-station runoff-sediment modeling using seasonal LSTM models

In this study two types of seasonal long-short-term memory (LSTM) artificial neural networks, named sequenced-LSTM (SLSTM) and wavelet-LSTM (WLSTM), were presented to model runoff-sediment process of three gauging stations, located in Missouri and Upper Mississippi regions in both daily and monthly scales. For this purpose, twenty-year observed streamflow and suspended sediment load (SSL) data were employed in both daily and monthly scales. The proposed seasonal models have full profits of classic LSTM model in time series processing and handle sole LSTM model’s weaknesses in failing to capture seasonal information of the process which usually exist in hydro-climate time series. The proposed models enhanced the long-short autoregressive dependency of runoff-sediment data by taking into consideration of very long seasonal dependency of data. The obtained outputs indicate the outperformance of proposed seasonal LSTM models to the classic LSTM and feed forward neural network models in test step up to about 25% and 28% in daily and monthly scales, respectively.

Climate teleconnections, interannual variability, and evolution of the rainfall regime in a tropical Caribbean island: case study of Barbados

A limited number of studies have focused on the hydroclimate dynamics of tropical Caribbean islands. The present study aims to analyze the rainfall regime in Barbados. CHIRPS gridded dataset, at a resolution of 0.05°×0.05°, providing daily rainfall data from January 1981 until 2018 was used. The variables analyzed were the annual and seasonal maximum rainfall, the total annual and seasonal rainfall, and the number of rainy days per year and per season. Potential change points in rainfall time-series were detected with a Bayesian multiple change point detection procedure. Time series were then analyzed for detection of trends using the modified Mann-Kendall test. The true temporal slopes of the rainfall time series were obtained with the Theil-Sen’s statistic. The links between rainfall and various global climate oscillation indices were also investigated. Results indicate that no change points or significant trends were observed in the annual rainfall time series. However, it was found that some climate indices have a strong correlation with precipitation on the island, especially for the total rainfall and the number of rainy days. A stationary and non-stationary frequency analysis is carried out on the rainfall annual variables using climate oscillation indices as covariates, and uncertainties on quantile estimates are identified. It is shown that non-stationary models lead to a better fit to rainfall data. Empirical mode decomposition (EMD) is used for the long-term prediction of hydro-climatic time series. Rainfall annual time series were extended with this method for a period of 20 years. Results indicate that, within that period, annual maximum rainfall will increase by about 12 mm (or 0.6 mm/year), total annual rainfall will increase by about 200 mm (or 10 mm/year) and the number of rainy days per year will see a slight decrease by about 3 days (or 0.15 day/year).

Nonoverlapping Block Stratified Random Sampling Approach for Assessment of Stationarity

Assessment of stationarity in any hydroclimatic time series is a vital task for hydrologic design and climate change assessments. Methods that rely on the presence of statistically significant trends or change points to derive inferences about stationarity may fail to check for all the time-invariant characteristics of time series. An approach that uses nonoverlapping stratified random sampling blocks of time series and nonparametric tests is proposed and evaluated in this study to assess stationarity. Chronologically continuous data from contiguous blocks are evaluated using two-sample and multisample nonparametric tests for the assessment of distributional, median and variance similarity, invariance of statistical moments, and autocorrelation at several lags. Explicit methods for the evaluation of different characteristics of time series are also developed to assess the two forms (viz., weak and strict) of stationarity. The multiple test evaluations are weighted using the analytical hierarchy process (AHP) to draw conclusions about stationarity. The proposed approach is tested using several real-world and synthetically generated hydroclimatic data sets through extensive simulations. Stationarity assessments derived from nonparametric trend and unit root tests have been compared to those from the approach developed. Results from this study point to the correct identification of weak and strict stationarity in real-world hydroclimatic time series compared to similar evaluations from unit root and trend tests. The robustness of the approach is also confirmed by an accurate assessment of the stationarity of several synthetic time series representing various hydroclimatic processes at different time scales. The proposed approach is a conceptually simple and superior alternative to trend and unit root tests since it provides a comprehensive assessment of stationarity using multiple nonparametric tests.

A modelling framework and R-package for evaluating system performance under hydroclimate variability and change

Effective implementation of a scenario-neutral climate impact assessment relies on the integration of many modelling components at multiple stages: from the generation of appropriate climate boundary conditions that can be used to rigorously ‘stress-test’ a system, to the simulation, visualisation and interpretation of resulting system performance. For systems with complex temporal dynamics, the generation of climate forcing time series combined with the significant simulation and visualisation demands represent a barrier to the wide-scale adoption of scenario-neutral approaches. A unified five-step framework and supporting R-package, foreSIGHT (Systems Insights from the Generation of Hydroclimate Time series), are introduced that enable the application of a scenario-neutral climate impact assessment from appropriate time series generation to the analysis of system performance. The software can be applied to compare both current system performance and selected alternative system management or design options and its application is demonstrated for a case study.

Comparative Analysis of Climate Change Impacts on Meteorological, Hydrological, and Agricultural Droughts in the Lake Titicaca Basin

The impact of climate change on droughts in the Lake Titicaca, Desaguadero River, and Lake Poopo basins (TDPS system) within the Altiplano region was evaluated by comparing projected 2034–2064 and observed 1984–2014 hydroclimate time series. The study used bias-corrected monthly climate projections from the fifth phase of the Coupled Model Intercomparison Project (CMIP5), under the Representative Concentration Pathway 8.5 (RCP8.5) emission scenarios. Meteorological, agricultural, and hydrological droughts were analyzed from the standardized precipitation, standardized soil moisture, and standardized runoff indices, respectively, the latter two estimated from a hydrological model. Under scenarios of mean temperature increases up to 3 °C and spatially diverse precipitation changes, our results indicate that meteorological, agricultural, and hydrological droughts will become more intense, frequent, and prolonged in most of the TDPS. A significant increase in the frequency of short-term agricultural and hydrological droughts (duration of 1–2 months) is also projected. The expected decline in annual rainfall and the larger evapotranspiration increase in the southern TDPS combine to yield larger projected rises in the frequency and intensity of agricultural and hydrological droughts in this region.

Global-scale massive feature extraction from monthly hydroclimatic time series: Statistical characterizations, spatial patterns and hydrological similarity

Hydroclimatic time series analysis focuses on a few feature types (e.g., autocorrelations, trends, extremes), which describe a small portion of the entire information content of the observations. Aiming to exploit a larger part of the available information and, thus, to deliver more reliable results (e.g., in hydroclimatic time series clustering contexts), here we approach hydroclimatic time series analysis differently, i.e., by performing massive feature extraction. In this respect, we develop a big data framework for hydroclimatic variable behaviour characterization. This framework relies on approximately 60 diverse features and is completely automatic (in the sense that it does not depend on the hydroclimatic process at hand). We apply the new framework to characterize mean monthly temperature, total monthly precipitation and mean monthly river flow. The applications are conducted at the global scale by exploiting 40-year-long time series originating from over 13 000 stations. We extract interpretable knowledge on seasonality, trends, autocorrelation, long-range dependence and entropy, and on feature types that are met less frequently. We further compare the examined hydroclimatic variable types in terms of this knowledge and, identify patterns related to the spatial variability of the features. For this latter purpose, we also propose and exploit a hydroclimatic time series clustering methodology. This new methodology is based on Breiman's random forests. The descriptive and exploratory insights gained by the global-scale applications prove the usefulness of the adopted feature compilation in hydroclimatic contexts. Moreover, the spatially coherent patterns characterizing the clusters delivered by the new methodology build confidence in its future exploitation. Given this spatial coherence and the scale-independent nature of the delivered feature values (which makes them particularly useful in forecasting and simulation contexts), we believe that this methodology could also be beneficial within regionalization frameworks, in which knowledge on hydrological similarity is exploited in technical and operative terms.

Decadal-Scale Changes in the Seasonal Surface Water Balance of the Central United States from 1984 to 2007

AbstractVariations in climate have important influences on the hydrologic cycle. Observations over the continental United States in recent decades show substantial changes in hydrologically significant variables, such as decreases in cloud cover and increases in solar radiation (i.e., solar brightening), as well as increases in air temperature, changes in wind speed, and seasonal shifts in precipitation rate and rain/snow ratio. Impacts of these changes on the regional water cycle from 1984 to 2007 are evaluated using a terrestrial ecosystem/land surface hydrologic model (Agro-IBIS). Results show an acceleration of various components of the surface water balance in the Upper Mississippi, Missouri, Ohio, and Great Lakes basins over the 24-yr period, but with significant seasonal and spatial complexity. Evapotranspiration (ET) has increased across most of our study domain and seasons. The largest increase is found in fall, when solar brightening trends are also particularly significant. Changes in runoff are characterized by distinct spatial and seasonal variations, with the impact of precipitation often being muted by changes in ET and soil-water storage rate. In snow-dominated regions, such as the northern Great Lakes basin, spring runoff has declined significantly due to warmer air temperatures and an associated decreasing ratio of snow in total precipitation during the cold season. In the northern Missouri basin, runoff shows large increases in all seasons, primarily due to increases in precipitation. The responses to these changes in the regional hydrologic cycle depend on the underlying land cover type—maize, soybean, and natural vegetation. Comparisons are also made with other hydroclimatic time series to place the decadal-scale variability in a longer-term context.

Influence of output size of stochastic weather generators on common climate and hydrological statistical indices

While Stochastic Weather Generators (SWGs) are used intensively in climate and hydrological applications to simulate hydroclimatic time series and estimate risks and performance measures linked to climate variability, there have been few investigations into how many realizations are required for a robust estimation of these measures. Given the computational cost and time necessary to force climate-sensitive systems with multiple realizations, the estimation of the optimal number of synthetic time series to generate with a particular SWG for a predefined accuracy when estimating a particular risk or performance measure is particularly important. In this paper, the required number of realizations of five SWGs coupled with a SWAT model (the Soil and Water Assessment Tool) needed in order to achieve a predefined Relative Root Mean Square Error is investigated. The statistical indices used are the mean, standard deviation, skewness, and kurtosis of four hydroclimatic variables: precipitation, maximum and minimum temperature, and annual streamflow obtained for each observed and model-generated time series. While the results vary somewhat across SWGs, variables and indicators, they overall show that the marginal improvement decreases dramatically after 25 realizations. The results also indicate that the benefit of generating more than 100 realizations of climate and streamflow data is very minimal. The methodology presented herein can be applied in further investigations of other set of risk indicators, SWGs, hydrological models, and watersheds to minimize the required workload.

Moving correlation coefficient-based method for jump points detection in hydroclimate time series

The jump points detection is critical to the understanding of hydrologic variability, especially in investigating the anthropogenic effects. Conventional methods are mainly statistical and cannot directly reflect the jump change degrees. This article proposes a moving correlation coefficient-based detection (MCCD) method for the detection of jump points (JPs) in hydroclimate data. The correlation coefficient (CC) between the potential jump component and the original data is calculated, and the CC series is realized by moving from the starting to the ending points of the original time series. Bigger CC value reflects higher jump degree; the position with the biggest absolute CC value is the JP that is the most expected. Its significance is evaluated by comparing its value with the CC threshold value at the relevant significance level. Monte-Carlo experimental results verify the MCCD method’s higher efficiency compared with four commonly used conventional methods. It is especially noteworthy that the results indicate its stable efficiency, even when encountering the influences of some unfavorable factors. By applying the MCCD method to the Lancang River Basin, the JP of runoff in 2004 is detected at the Yunjinghong station in the lower reach. It is mainly attributed to the construction and operation of some major water hydropower projects, while the stable variations of areal precipitation and actual evapotranspiration, as well as the stable land-cover conditions, contribute little to the abrupt decrease in runoff. The MCCD method can be an effective alternative for the detection of JPs in hydroclimate data.

Comparison of different methods for detecting change points in hydroclimatic time series

An abrupt change is an important manifestation of hydroclimatic variability. Accurate detection of change points is a critical issue in hydroclimatic and climate change studies. In the article, we evaluated the performances of 12 methods (including both parametric and non-parametric) for detecting change points in hydroclimatic time series by considering the influences of eight major factors. Different methods exhibited different efficiencies and eight of the methods performed better which are recommended for application. Furthermore, the mean values of series and locations of change points were found to have little influence on the detection of change points. However, for a time series with smaller variance but bigger skewness and larger difference in the mean values before and after the change point, the abrupt changes can be more easily and accurately detected. Detection of change points in shorter series would have larger uncertainty. Based on the Monte-Carlo experiments, the efficiency of each method was quantified and its capability was quantitatively clarified. Detection of abrupt changes in precipitation over Southwest China showed that the Indian monsoon had a dominant influence on precipitation in the regions south of 30°N and west of 110°E. Since 2007 the Indian monsoon has maintained a weakening pattern, causing a decrease in precipitation on the Yunnan-Guizhou Plateau, which is one of the main causes of frequently occurring droughts. Results of this study can be a useful reference for choosing a method to detect change points in hydroclimate time series, and be an important complement for the detection and attribution of hydroclimatic variability.

Spatial distribution of secular trends in rainfall indices of Peninsular Malaysia in the presence of long‐term persistence

AbstractThe presence of long‐term persistence (LTP) in hydroclimatic time series can lead to considerable change in the significance of trend. Therefore, past findings of climatic trend analysis without considering LTP in time series has become a disputable issue. The objective of this study was to assess the spatial patterns in the trends of annual and seasonal rainfall amounts and extremes in Peninsular Malaysia considering LTP. Daily rainfall data of APHRODITE (Asian Precipitation – Highly‐Resolved Observational Data Integration Towards Evaluation) for the period 1951–2007 was used to assess the trends using the classical Mann–Kendall (MK) test and the modified version of the MK test, which can remove the influence of LTP in the significance of trends. The results indicate that significant trends in different rainfall indices of Peninsular Malaysia obtained using the MK test reduced drastically when LTP was taken into consideration. There was almost no change in annual and seasonal rainfall amounts, which contradicts the findings of previous studies. The field significance of regional trends revealed an increase in wet spells at an average rate of 4.8 and 4.9 days/decade in the southeast and the southwest respectively during the northeast monsoon, and a decrease in rainy days by −1.4 days/decade in the north and an increase both in dry spells by 1.0 day/decade in the southeast and in the maximum 1 day rainfall by 1.7 mm/decade on the west coast during the southwest monsoon. The results indicate that the trends in rainfall indices reported in the maritime continent in previous studies should be re‐evaluated as most of them are due to LTP.