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Trends in Floods and Low Flows in the United States: Impact of Spatial Correlation
Abstract
Trends in flood and low flows in the US were evaluated using a regional average Kendall's S trend test at two spatial scales and over two timeframes. Field significance was assessed using a bootstrap methodology to account for the observed regional cross-correlation of streamflows. Using a 5% significance level, we found no evidence of trends in flood flows but did find evidence of upward trends in low flows at the larger scale in the Midwest and at the smaller scale in the Ohio, the north central and the upper Midwest regions. A dramatically different interpretation would have been achieved if regional cross-correlation had been ignored. In that case, statistically significant trends would have been found in all but two of the low flow analyses and in two-thirds of the flood flow analyses. We show that the cross-correlation of flow records dramatically reduces the effective number of samples available for trend assessment. We also found that low flow time series exhibit significant temporal persistence. Even when the serial correlation was removed from the time series, significant trends in low flow series were apparent, though the number of significant trends decreased.
... In Fig. 6, the US is broken into nine "superregions," which are based on major drainage divides and have been employed in other regional hydrologic trend analyses (e.g. Douglas et al. 2000). (SW), Columbia Basin (CB), and California-Great Basin (CA). ...
... Unlike for Q01, we see many significant changes in the , and Columbia Basin (CB)) having more than 40% of sites with significant changes in the average Q99. These results are similar to those found by Douglas et al. (2000), who examined regional significance of trends in high and low flows and found the upper midwestern US to have the most significant trends in low flows. They are also similar to results found by McCabe and Wolock (2002), who identified significant changes in low streamflows in the northeastern US using a 1970 break point. ...
... These results are consistent with those found by McCabe and Wolock (2002), who identified step changes in precipitation patterns in the eastern US that drove changes in low flows before and after 1970. Douglas et al. (2000) also identified regionally significant trends in low flows in the upper midwestern US, which is consistent with our results. We recommend the application of the MMMT considering other break points to further explore and illustrate the impacts of drivers of changes in hydrologic patterns across the US. ...
Utilizing annual flow duration curves (FDCs), the modified Mood’s median test (MMMT) was applied to quantiles of the median annual FDCs at 122 disturbed sites in the conterminous US. A simple graphical tool based on the MMMT was combined with a false discovery rate multiple comparison procedure to illustrate the field significance of hydrologic alteration over a wide range of streamflow conditions. Hydrologic disturbance was mostly caused by dams, although other forms of alteration were explored. In addition, 249 minimally regulated Hydro-Climatic Data Network (HCDN) sites were investigated to assess regional climatic drivers of hydrologic alteration. Results indicate that dams can have varying effects on the hydrologic regime, but generally impact low flows more often than high flows in the US. At HCDN sites, we observe significant changes to hydrologic regimes in many regions, especially to low and median flows in the upper midwestern and eastern US.
... The possible serial correlation of the data affects the variance of the S statistic and can therefore influence the MK test result. The prewhitening procedure can be applied to time series to remove the effects of serial correlation [66,67], but it can also remove the effects of trend in the time series [68][69][70][71]. To eliminate the effect of autocorrelation, Yue and Wang [72] proposed a Modified Mann-Kendall (MMK) test by inserting a correction to the variance in the Equation (13). ...
... Despite the effectiveness of the MMK test in detecting trends in a time series, it does not provide information on its magnitude. To determine the rate of change, Sen's slope method [78] is used, which is a robust method for estimating trend magnitude [70]. To perform the test, one calculates the slope ∆, given by ...
Drought is the most complex natural hazard that can occur over large spatial scales and during long time periods. It affects more people than any other natural hazard, particularly in areas with a dry climate, such as the semiarid region of the Brazilian Northeast (NEB), which is the world’s most populated dry area. In this work, we analyzed trends and the spatial distribution of drought characteristics (frequency, affected area, and intensity) based on the Standardized Precipitation Index (SPI) on annual (SPI-12) and seasonal (SPI-3) scales. The study used monthly precipitation data recorded between 1962 and 2012 at 133 meteorological stations in Pernambuco State, Brazil, which is located in the eastern part of the NEB and has more than 80% of its territory characterized by a semiarid climate. The regions of Sertão, Agreste, and Zona da Mata of Pernambuco were considered for comparison. The Mann–Kendall and Sen’s slope tests were used to detect the trend and determine its magnitude, respectively. The results indicated that annual droughts in the state of Pernambuco became more frequent from the 1990s onwards, with summer having the greatest spatial coverage, followed by winter, autumn, and spring. Sertão presented a greater number of stations with a significant positive trend in drought frequency. Regarding the drought-affected area, global events occurred in a greater number of years on an annual scale and during the summer. Trend analysis pointed to an increase in areas with drought events on both scales. As for the drought intensity, the entire state of Pernambuco experienced drought events with high intensity during the autumn. The relationship between drought characteristics indicated an increase in the affected area as the result of an increase in drought intensity.
... The process of ensuring data integrity involves three key tests: (1) the test for homogeneity ensures consistent data sources over time which is determined by the homogeneity test of Man Withney (Wilcoxon) who was originally introduced by Wilcoxon for comparing two groups of equal size [14]; Later, Mann and Whitney expanded it, making it synonymous with the Mann-Whitney U test [15]; (2) the stationarity test checks that the statistical properties of the series do not change over time. In this research, the Mann-Kendall (MK) statistical test is employed to evaluate the stationarity of the sampled data [16, 17,18]. This test has an advantage over others thanks to their independence from assumptions about the data's distribution, making them particularly suitable for analyzing hydrometeorological time series. ...
... The resolution of this system is described in [12][13][14][15][16][17][18][19][20][21][22][23][24][25]. ...
This work aims to analyze the yearly most extreme release of the Nekor River monitoring station Tamellaht between 1973 and 2011 and to predict possible future events using the Flood Frequency Analysis Method (FFA). We use the four most estimated distributions that are accessible for prediction of hydrological risk: the three Log Normal, LogPerson Type III, Weibull and GAMMA distributions, and conclude that the Weibull distribution is the suitable statistical model that describe well into our data series, even though the other distributions show data adjustment. Given the Weibull dispersion, the upsides of 580.3 m3/s, 1339 m3/s and 2146.7 m3/s are for the time of return of 10, 50 and 100 years, individually, still high relying upon the semi-dry environment that wins around this region. In fact, the period of extreme returns of the 10th period which can cause dangerous flooding especially considering the mountainous characteristics of the region. The magnitude of the floods is greater because the return period is greater, which explains the semi-arid climate of this region. In addition, a simple statistical description shows that the maximum flow trend has declined over the years, reflecting a possible impact of climate change phenomena.
... For example, The Mann-Kendall (MK) trend test procedure is supplemented by calculating the trend slope as the median of all the possible slopes in the record series (Mann 1945;Sen 1968;Kendall 1970). The same trend test has been applied by many authors in a number of atmospheric, environmental and hydrology researchers on hydro-meteoro-climatological time series records (Hirsh et al. 1982;van Belle and Hughes Z. Şen 1984;Hirsh and Slack 1984;Demaree and Nicolis 1990;Mclead et al. 1991;Yue et al. 1993;Gan 1998;Taylor and Lotfis 1989;Lins and Slack 1999;Douglas et al. 2000;Hamilton et al. 2001;Hamed 2008;von Storch 1995;Şen 2012). Other trend procedures are the Spearman's tau (Spearman 1904) and traditional regression analysis. ...
... Yue and Wang (2002) and then Bayazıt and Önöz (2007) stated that the dependency structure can be reduced by pre-whitening and thus the serial correlation coefficients can be brought close to zero for reliable MK trend testing. Douglas et al. (2000) noted that after the pre-whitening some flows in the United States, the trend was less pronounced than before prewhitening. The pre-whitening procedure has been applied to temperature trend analyses prior to MK test trend identification without any evidence of its ability to fulfill independence structure (Zhang et al. 2001;Hamilton et al. 2001;Burn and Hag Elnur 2002). ...
In this paper, trends are determined by population characteristics through a set of the risk levels (exceedence probabilities) from CDFs. This is a new method in which unique trend identification is possible at any risk level, not just the average. The entire approach is based on the cumulative distribution functions (CDF) risk levels for time series records and their two equal halves. At a given risk level the corresponding data amounts represent weights at two ends of a seesaw and the whole data amount the support. In this way, also extreme values such as low (dry, drought) and high (wet, flood) trend possibilities can be defined without any restrictive assumptions. The application of the methodology is given for annual discharge measurements of Danube River, in addition to the annual precipitation records from Istanbul meteorology station.
... This observation is particularly pronounced for Q max , with 93.7% of NAT catchments displaying stationary behavior (Figure 3c). This finding aligns with previous assessments of trends in annual flood flows (Douglas et al., 2000;Lins & Slack, 1999;S. Zhang et al., 2022), collectively indicating a lack of sufficient evidence of a robust climate change effect on global high flow (or flood flow) trends. ...
... This means that deviations in annual Q min from its natural variability, signifying non-stationarity, could be more pronounced than those in Q max . Furthermore, baseflow fluctuations reflect variations in basin storage, which is key a state variable in the catchment water balance that is closely intertwined with climate changes (Douglas et al., 2000;Taylor et al., 2013;Wu et al., 2020). For example, instances of non-stationary declines in Q min within NAT catchments are evident in the western United States, eastern Brazil, Chile, western Europe and southern Australia (Figure 4c). ...
Plain Language Summary
Understanding stationarity in streamflow series is crucial for predicting future hydrological changes based on historical data. This study provides the first global assessment of long‐term stationarity of annual streamflow extremes, specifically the highest (Qmax) and lowest (Qmin) monthly streamflow. By analyzing streamflow observations from 11,069 catchments worldwide, we find that the stationarity of historical Qmax and Qmin series remain in a majority of catchments predominantly influenced by climate change, indicating that historical climate change alone has not disrupted the predictability of these extreme streamflows. However, in catchments where human activities like irrigation, dam construction, or urban development have directly intervened, the prevalence of non‐stationary annual Qmax and Qmin series is 3.9 and 1.7 times greater than in natural catchments, respectively. This underscores the substantial impact of human modifications on the terrestrial water cycle, highlighting the need for comprehensive measures to manage these alterations effectively.
... Few other popular models used in nonstationary FFA are discussed in this section. The r-largest and peaks-over-threshold (POT) approaches have been useful for modeling nonstationary extreme events, using annual maxima as well as POT data (Douglas et al. 2000;Mudelsee et al. 2003). The r-largest approach is a method for modeling extreme events by fitting a distribution to the r largest order statistics within a specified period (Douglas et al. 2000). ...
... The r-largest and peaks-over-threshold (POT) approaches have been useful for modeling nonstationary extreme events, using annual maxima as well as POT data (Douglas et al. 2000;Mudelsee et al. 2003). The r-largest approach is a method for modeling extreme events by fitting a distribution to the r largest order statistics within a specified period (Douglas et al. 2000). This technique focuses on modeling the most extreme floods and provides a means of estimating the return levels associated with rare discharge levels. ...
Recent changes in the climate, land use/land cover, and field-scale water resources allocation at the catchment scale have rendered the conventional hypothesis of the stationarity of hydrologic extremes unreliable. The current understanding of evolving patterns of hydrological variables has led to the development of nonstationary approaches, particularly in extreme event frequency analysis. A comprehensive review of the different approaches for nonstationary flood frequency analysis is presented in this chapter. The popular methods including generalized additive models for location, scale, and shape (GAMLSS) framework; probability-based approaches using Gumbel distribution and Log Pearson distribution III (LP 3), Bayesian approaches, r-largest, peaks-over-threshold, time-varying moments; among others are discussed. Additionally, the challenges associated with nonstationary hydrological frequency analysis and future research directions in the analysis of flood extremes are briefly addressed. It is evident that nonstationarity needs to be incorporated in flood risk assessment framework for addressing the likely impacts of potential future climate change in water resources management.
... These traditional classical methods used some assumptions (e.g., time series are typically distributed, independent serial formation/structure), and their validity is hardly possible in hydro-climatic variables time series. Therefore, pre-whitening (Douglas et al. 2000;Matalas and Sankarasubramanian 2003;Von Storch 1999;Yue et al. 2002a;Zhang et al. 2001) and recently introduced overwhitening approach (Şen 2017a) techniques were used before analyzing trends and climate change analysis. Consequently, Sen (2012) introduced the " Innovative Trend Analysis (ITA)" for the detection of the trend in the hydro-climatic time series. ...
The Mann–Kendall (MK) trend test, Innovative Trend Analysis (ITA), double-ITA (D-ITA), triple-ITA (T-ITA), and Innovative Triangular Trend Analysis (ITTA) were used to analyze long-term trends in the annual and seasonal streamflow of the Tuotuohe and Zhimenda hydrological gauging stations in the Source Region of the Yangtze River (SRYR). The traditional MK test provides the average trends, while the other methods used in this study provide the graphical illustrations and trend stability (monotonic/non-monotonic). For example, the Tuotuohe station during summer using MK showed (0.05 m³/sec/year), and the ITA showed the monotonic increasing trend (1.12 m³/sec/year). In contrast, the ITTA showed unstable (monotonic and non-monotonic) trends while the highest trend magnitude was found from the 2nd to 5th sub-time series (6.6 m³/sec/year) followed by 1st to 5th sub-time series (5.51 m³/sec/year). The ITTA also showed the non-monotonic decreasing trend (-1.09 m³/sec/year) from 1st to 2nd sub-time series. The D-ITA showed the monotonic increasing trend from 1st to 2nd sub-time series (0.83 m³/sec/year). The non-monotonic increasing trend from 2nd to 3rd sub-time series (0.4 m³/sec/year) and T-ITA showed the non-monotonic trend for 1st to 2nd and 2nd to 3rd sub-time series (0.62 and 1.45 m³/sec/year, respectively), whereas the monotonic trend for 3rd to 4th sub-time series (14.94 m³/sec/year). Similarly, there are more instabilities and fluctuations in trend magnitudes found in the ITTA compared to D-ITA, T-ITA, and ITA. At the same time, the MK only provides the average trend values for a given time series. This showed that the ITTA method is better for understanding the trends and fluctuations in any basin, and the traditional MK test cannot detect these fluctuations.
... One of the major problems in the Mann and Kendall (MK) trend identification methodology is the assumption that time series must be serially independent, which cannot be met with hydro-meteorological data and hence pre-whitening (Yue and Wang 2002) or over-whitening (Şen 2017 procedures are offered to alleviate this requirement. In different water related disciplines many researchers applied the MK methodology holistically (overall) on a given time series records (Hirsch et al. 1982;van Belle and Hughes 1984;Hirsch and Slack 1984;Cailas et al. 1986;Hipel et al. 1988;Demaree and Nicolis 1990;Yu et al. 1993;Gan 1998;Taylor and Loftis 1989;Lins and Slack 1999;Douglas et al. 2000;Hamilton et al. 2001;Kalra et al. 2008;O'Brien et al. 2021;Mateus and Pitoto 2022). ...
In the last 30 years, there are many publications in the literature due to global warming and climate change impacts exhibiting non-stationary behaviors in hydro-meteorology time series records especially in the forms of increasing or decreasing trends. The conventional trend analyzes cover the entire recording time with a single straight-line trend and slope. These methods do not provide information about up and down partial moving trends evolution at shorter durations along the entire record length. This paper proposes a dynamic methodology for identifying such evolutionary finite duration moving trend method (MTM) identifications and interpretations. The purpose of choosing MTM was to investigate the dynamic partial trend evolution over the recording period so that dry (decreasing trend) and wet (increasing trend) segments could be objectively identified and these trends could assist in water resources management in the study area. The moving trend analysis is like the classical moving average methodology with one important digression that instead of arithmetic averages and their horizontal line representations, a series of finite duration successive increasing and decreasing trends are identified over a given hydro-meteorology time series record. In general, partial moving trends of 10-year, 20-year, 30-year and 40-year occur above or below the overall trend and thus provide practical insight into the dynamic trend pattern with important implications. The moving trend methodology is applied to annual records of Danube River discharges, New Jersey state wise temperatures and precipitation time series from the City of Istanbul.
... The statistical importance of the trend in seasonal series from 1901 to 2002 was analyzed using the non-parametric Mann-Kendall (MK) test for RF, T, PEV, and GWL (1970-2020) and discussed. The MK test was employed by several researchers (e.g., Yu et al. 1993;Douglas et al. 2000;Burn et al. 2004;Singh et al. 2008;Coen et al. 2020) to ascertain the presence of a statistically significant trend in hydrological climatic variables, such as T, RF, and streamflow, concerning climate change. The MK test checks the null hypothesis of no trend versus the alternative hypothesis of the existence of an increasing or decreasing trend. ...
Hydro-meteorological parameters significantly influence groundwater storage in unconfined aquifer. Although the unconfined aquifer is closer to the surface, hence, the dry conditions have an impact on the water level. The relationship between the hydro-meteorological parameters and the unconfined groundwater level (GWL) has not received much attention or study. Hence, the impact of hydro-meteorological parameters [e.g. rainfall (RF), air temperature (T), and potential-evapotranspiration (PEV)] on the GWL is studied over West Bengal, India. The region is categorized into four zones (zone-1, zone-2, zone-3, and zone-4), considering the geo-hydrological scenario. GWL raised by ≈1.63m, 3.51m, 3m, and 2.24 m owning to the RF of ≈ 880 mm, 953 mm, 1083 mm, and 1593 mm during monsoon in zone-1, zone-2, zone-3, and zone-4, respectively. The groundwater table is 6.9 m, 95.2 m, 22.3 m, and 80.2 m in the respective zones during winter. The groundwater flows seasonally from zone-2 to zone-1 and zone-3, and similarly from zone-4 to zone-3 and zone-1. In zone-1, as RF increased in monsoon, the shallow GWL occurred and PEV caused water loss. PEV and T have the dependency on deeper GWL in other seasons. PEV and T are critical factors in Zone-2's GWL depletion in all seasons. Winter, pre-monsoon, and post-monsoon showed a correlation between the deeper GWL and PEV and T in Zone-3 and Zone-4. When sufficient RF occurred, the deeper GWL enhanced to shallower GWL. In monsoon, recharge of the unconfined aquifer occurred owing to RF. The deeper GWL developed in post-monsoon because of lower RF and increased PEV and T.
... The Mann-Kendall test is frequently used for trend analysis (Farooq et al. 2021;Hamadalnel et al. 2021;Imam et al. 2022); nevertheless, its efficacy is constrained in cases involving serial correlation and nonmonotonic trends. The presence of serial correlation has a significant impact on the probability of rejecting the null hypothesis, which asserts that there is no trend in the time series (Douglas et al. 2000;Yue et al. 2003). To tackle this issue, pre-whitening techniques are employed to eliminate autocorrelation before performing the Mann-Kendall test (Bayazit and Önöz, 2007;Gupta et al. 2021;Yue and Wang 2002). ...
The 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.
... This leads to the same results as that based on the Bayesian method. It is important to note that no spatial autocorrelation was observed between stations (Douglas et al. 2000). To avoid making the text unnecessarily heavy, the mathematical equations of these dif- ...
The objective of this study is to compare the spatiotemporal variability of seasonal daily mean flows measured in 17 watersheds , grouped into three homogeneous hydroclimatic regions, during the period 1930-2023 in southern Quebec. With regard to spatial variability, unlike extreme daily flows, seasonal daily mean flows are very poorly correlated with physiographic factors and land use and land cover. In fall, they are not correlated with any physiographic or climatic factor. In winter, they are positively correlated with the rainfall and winter daily mean maximum temperatures. In spring, they are strongly correlated positively with the snowfall but negatively with the spring daily mean maximum temperatures. However, in summer, they are better correlated with forest area and, to a lesser extent, with the rainfall. As for their temporal variability, the application of six different statistical tests revealed a general increase in daily mean flows in winter due to early snowmelt and increased rainfall in fall. In summer, flows decreased significantly in the snowiest hydroclimatic region on the south shore due to the decrease in the snowfall. In spring, no significant change in flows was globally observed in the three hydroclimatic regions despite the decrease in the snowfall due to the increase in the rainfall. In fall, flows increased significantly south of 47°N on both shores due to the increase in the rainfall. This study demonstrates that, unlike extreme flows, the temporal variability of seasonal daily average flows is exclusively influenced by climatic variables in southern Quebec. Due to this influence, seasonal daily mean flows thus appear to be the best indicator for monitoring the impacts of changes in precipitation regimes and seasonal temperatures on river flows in southern Quebec.
... Environmental change primarily impacts hydrological process by changing the hydrological driving factors and underlying surface conditions. Human activities exert the most direct influence on the hydrological characteristics of the basin [1,2]. Luanhe River is rich in water resources [3], which is one of the important water systems in the Haihe River Basin and one of the main water supply sources in North China. ...
Over the past 50 years, there have been significant changes in the runoff process in the Luanhe River basin, exacerbating the scarcity of water resources and their spatiotemporal variability. Therefore, conducting research on the characteristics, trends, and cycles of runoff changes in the Luanhe River basin is of great theoretical and practical significance. This study selected rainfall data from the hydrological stations in Weichang, Chengde, and Qinhuangdao in the Luanhe River basin, covering the period from 1985 to 2008, as well as runoff data from the Hanjiaying, Sandaohezi, and Chengde stations. Based on linear trend regression analysis, the Mann–Kendall rank correlation test, Spearman correlation test, Mann–Kendall method, and Mann–Whitney–Pettitt change point analysis method, this study analyzed the trends in water quantity changes and their change points in the Luanhe River basin. The results of the precipitation at the Weichang and Chengde stations show a non-significant rising trend, remaining relatively stable with slightly increases. Conversely, the precipitation of Qinhuangdao Station shows a decreasing trend over time, albeit non-significant. Considering the detailed diagnostic results from both the Mann–Kendall (M-K) and MWP methods, the change point for Weichang precipitation is identified as 2007, while for Chengde, it spans from 1999 to 2002, and for Qinhuangdao, it is around 1997. The trend of the runoff series of three stations shows a significant decreasing trend and strong significance, and the change point for the annual runoff at the Hanjiaying station and the Sandaohezi station is identified as 2006, and for the Chengde station, the primary change point is 2006, with a secondary change point around 2002. The findings of this research can provide scientific references for the rational development and utilization of regional water resources.
... When |Z| > Z 1 − α/2, the null hypothesis is rejected and a signifcant Advances in Meteorology trend exists in the time series. Z 1 − α/2 is obtained from the standard normal distribution table [72]. In this study, the signifcance levels chosen are as follows: α � 0.01 (or 99% confdence interval), α � 0.05 (or 95% confdence interval), and α � 0.1 (or 90% confdence interval). ...
This study examines the spatiotemporal variability of mean monthly temperature in the Moulouya watershed of northeastern Morocco, highlighting associated trends. To this end, statistical methods widely recommended by climate researchers were adopted. We used monthly mean temperature data for the period 1980–2020 from 9 measuring stations belonging to the Moulouya Watershed Agency (ABHM). These stations were rigorously selected, taking into account their reliability, the length of their records, and their geographical position in the basin. In addition, a quality test and homogenization of the temperature series were carried out using the Climatol tool. The results obtained show a significant upward trend in mean monthly temperature, mainly pronounced during the summer months, in the Moulouya watershed. In fact, Z values generally exceeded the 0.05 significance level at all stations during April, May, June, July, August, and October. According to the results of Sen’s slope test, mean monthly temperatures show an annual increase ranging from 0 to 0.13°C. The maximum magnitude of warming is recorded in July, specifically at Oujda Station. On an overall watershed scale, May, August, and July show a rapid warming trend, with average rates of 0.093, 0.086, and 0.08°C per year, respectively. By contrast, the series for the other months show no significant trend. Significant trend change points were also identified at watershed and station scales, mainly around 2000, primarily for accelerated warming of the summer months.
... The calculated β is the estimated magnitude of the trend slope in the time series of the data. Mann-Kendall mutation detection is a nonparametric statistical test with the advantage of ease of computation and has been used in recent years in combination with Sen slope estimation to specify the starting time point of mutation occurrence (Douglas et al., 2000;Partal & Kahya, 2006). ...
Exploring the effect of climate change and human activities on vegetation is a key requisite for the reconstruction of regional ecological environments. Therefore, based on long‐term vegetation GIMMS Normalized Difference Vegetation Index (NDVI) data, climate data, and statistical data, the present study applied the Hasse diagram technique and combined the multivariate regression residual analysis to quantitatively analyze the impact of human activities and climate change on vegetation in Inner Mongolia from detailed human activities with some innovations. The results show that (1) NDVI shows an overall increasing trend over the last 39 years, with an abrupt change in 2000; moreover, vegetation growth was better before the abrupt change (PI: 1982–2000) than after it (PII: 2001–2020), with significant downward trends in Xilin Gol and Hulunbuir. (2) Human activities can promote as well as inhibit vegetation, and the promotion effect was larger during 1982–2000 than during 2001–2020, whereas the inhibition effect was larger during 2001–2020. In addition, during PI, vegetation in Inner Mongolia generally experienced promotion by human activities and climate change, while during PII, climate‐driven promotion had the strongest effect, followed by human‐driven inhibition mainly distributed in Xilin Gol. (3) The result of the Hasse diagram analysis shows that the dominant pathways of human activities affecting most of the cities were economic factors and urbanization during PI and economization during PII.
... A positive b indicates an upward trend, and vice versa. A resampling-based bootstrap procedure was applied to determine the field significance of the MK test results for all indices over the study area (Douglas et al., 2000;Renard et al., 2008). Details of the field significance test can be found in Wang et al. (2022). ...
The Pearl River is the second-largest river in China in terms of discharge and has experienced significant changes due to human activities and climate change. The aim of the current study was to detect spatiotemporal variations in runoff and sediment discharge in the Pearl River basin (PRB) over the past 60 years and to reveal the driving factors based on the collection of hydrological and meteorological data and land use data. The results showed that the average sediment load in the PRB was 64.7 Mt/y, with a significant decreasing rate of À7.6 Mt/10 y. The increase in vegetation coverage (by 0.4%/10 y) and the presence of large reservoirs were the main factors leading to the decreasing trend in the sediment load. However, in some subbasins with limited reservoir construction, increased rainfall erosivity during the dry season, along with land use conversion leading to a rapid increase in bare land and construction sites, contributed to an upward trend in the sediment load. The runoff discharge in the PRB remained relatively stable, with a change rate of À2.3 km 3 /10 y, and its variations were closely related to annual and seasonal rainfall changes. Human water consumption resulted in a lower measured runoff than natural runoff levels. A significant linear relation between the two confirmed the impact of human activities. The current study emphasizes the importance of considering both natural and anthropogenic factors in understanding runoff and sediment dynamics in the PRB and contributes to the knowledge of basin hydrology for guiding the formulation of effective water management strategies for sustainable regional development.
... However, it assumes that the data is randomly ordered, which is often not the case in natural time series (Wasserstein et al. 2019). To address this issue, we employed a modified 'pre-whitening' method within the MK test to remove serial correlation from the hydrometeorological time series (von Storch and Navarra 1995;Douglas et al. 2000). This modified MK test, known as PWMK, provides a more reliable trend analysis. ...
Climate change has remarkable global impacts on hydrological systems, prompting the need to attribute past changes for better future risk estimation and adaptation planning. This study evaluates the differences in simulated discharge from hydrological models when driven by a set of factual and counterfactual climate data, obtained using the Inter-Sectoral Impact Model Intercomparison Project's recommended data and detrending method, for quantification of climate change impact attribution. The results reveal that climate change has substantially amplified streamflow trends in the Upper Yangtze and Upper Yellow basins from 1961 to 2019, aligning with precipitation patterns. Notably, decreasing trends of river flows under counterfactual climate have been reversed, resulting in significant increases. Climate change contributes to 13%, 15% and 8% increases of long-term mean annual discharge, Q10, and Q90 in the Upper Yangtze at Pingshan, and 11%, 10%, 10% in the Upper Yellow at Tangnaihai. The impact are more pronounced at headwater stations, particularly in the Upper Yangtze, where they are twice as high as at the Pingshan outlet. Climate change has a greater impact on Q10 than on Q90 in the Upper Yangtze, while the difference is smaller in the Upper Yellow. The impact of climate change on these flows has accelerated in the recent 30 years compared to the previous 29 years. The attribution of detected differences to climate change is more obvious for the Upper Yangtze than for the Upper Yellow.
... Where the sample size (n) is greater than 10, the standard normal variate (z) is computed by using Eq. (4) (Douglas et al. 2000): ...
Crime and criminality are global concerns that pose significant risks to societal well-being and quality of life. The urbanization process and the land use of cities profoundly influence the occurrence, types, and intensity of crimes. The design and management of cities play a pivotal role in ensuring overall safety and security. Although Kolkata is considered a safe city, recent reports from the National Crime Records Bureau highlight an increase in crimes against women, placing Kolkata as the second-highest in West Bengal. The crime rate in the city has witnessed a substantial 63.46% increase over the past three years, categorized as “high” on the rating scale. Little is known about the spatial and temporal patterns of crimes in Kolkata. This study aims to analyze the spatiotemporal patterns of murder, theft, snatching, drugging, pickpocketing, and crimes against women, using regression models. Using geoinformatics and geostatistics, the research also seeks to identify the spatial clustering, significant crime hotspots, and their shifting between 2015 and 2020 and also attempts to analyze the impact of urban land use on crime rates. The study reveals a significant temporal change in crime patterns over 30 years, with bare land consistently impacting crime across all categories. While thefts, snatching, pickpocketing, drugging, and crimes against women exhibit relatively stable hotspots, murder hotspots have increased and become more dispersed. The unique land use patterns of port areas influence murder, drugging, and crimes against women, while dense built-up spaces contribute to higher rates of pickpocketing.
... The Mann-Kendall statistical test (MK) is a non-distribution (also known as non-parametric statistical) test (Douglas et al., 2000;Partal and Kahya, 2006), in which the dataset does not need to be in a particular order and is not affected by outliers. The MK test of the time series was computed using Equations 7-10: ...
Climate change is manifesting rapidly in the form of fires, droughts, floods, resource scarcity, and species loss, and remains a global risk. Owing to the disaster risk management, there is a need to determine the Dead Fuel Index (DFI) threshold of the fire occurrence area and analyze the spatio-temporal variation of DFI to apply prevention measures efficiently and facilitate sustainable fire risk management. This study used the MODIS Burned Area Monthly L3 (MCD64A1), Landsat Global Burned Area (BA) products, and MODIS Surface Reflectance 8-Day L3 (MOD09A1) data from 2001 to 2020 to calculate the values of the DFI in the study area before the occurrence of fire. The results showed that: (1) The inversion of the meadow steppe DFI values in the fire area was distributed in the range of 14-26, and the fire rate was the highest in the range of 20-22. The inversion of the typical steppe DFI values in the fire area was distributed in the range of 12-26, and the fire rate was the highest in the range of 16-22. (2) Areas with high fire DFI values included Khalkhgol, Matad, Erdenetsagaan, Bayandun, Gurvanzagal, Dashbalbar in Mongolia, and scattered areas of the Greater Khin-gan Mountains (forest edge meadow steppe area), East and West Ujumqin Banner, and Xin Barag Right Banner. The highest fire probability of fire occurred during October and April. (3) The DFI values were sensitive to changes in altitude. The results of this study may provide useful information on surface energy balance, grassland carbon storage, soil moisture, grassland health, land desertification, and grazing in the study area, especially for fire risk management.
... Therefore, (von Storch, 1999) suggested pre whitening method, which transforms the time series data to remove autocorrelation. This method, which employs the lag-1 serial correlation coefficient (r 1 ) of the sample data X i , has been widely used by researchers since its introduction (Douglas et al., 2000;Kulkarni & von Storch, 1992; "Evaluation of Snow Cover Changes Trend Using GEE and TFPW-MK Test (Case Study: Marber Basin-Isfahan)", 2021; Zhang et al., 2000): ...
Water scarcity is a significant issue in Iran, especially on its central plateau. Although climate change contributes to this problem, mismanagement and over-exploitation of available water resources worsened the situation. This study investigated water conservation opportunities in the Eskandari watershed, a crucial agricultural region in the Zayandeh-Rud River basin. We examined the trend of changes in all the factors affecting water resources in this watershed, including precipitation, discharge, leaf area index (LAI), inter-basin water transfer, and groundwaters from 2004 to 2019 at an annual scale. The classic Mann-Kendall (MK) and the non-parametric Trend-Free pre-whitening Mann-Kendall (TFPW-MK) statistical tests were employed to analyze the changing trends of these parameters over time. The results indicated that precipitation, discharge, and cultivated area have not shown any significant trend over 16 years. While in this period, the inter-basin water transfer tunnel entered into the basin with an upward trend, the water volume of all three aquifers experienced a drastic negative trend, suggesting an imbalance between the inflow and outflow of the watershed. Based on the groundwater depletion and the inter-basin water transfer inflow, an estimated 336.14 million cubic meters of water were consumed over the study period. This loss aligned with estimated water wastage in the form of wind drift and evaporation losses (WDEL) caused by the development of sprinkler irrigation systems in the study area. To address water scarcity and conserve water resources in the Eskandari watershed, it is essential to adopt sustainable irrigation practices that consider reducing the pressure on aquifers.
... where t is the extent of any given tie and denotes the summation over all ties. For the cases that n is larger than 10, the standard normal variate z is computed by using the following equation (Douglas et al., 2000). ...
This research work is aimed to find the extent of participation of women entrepreneurs in microenterprise like mushroom cultivation and marketing by exploring their work and time management with decision-making strategies. A survey of 60 women entrepreneurs engaged in mushroom cultivation was conducted to assess women entrepreneurs' time and decision-making issues and employee attitudes. A snowball sampling was used to compare the work-life balance of startups and established women entrepreneurs who worked alone, with spouses, or with partners. The study findings revealed that majority of women are actively engaged in mushroom cultivation activities and have given equal time to households and entrepreneurial activities. But still, their role in decision-making in running a microenterprise like mushroom cultivation and marketing is very low. They are working as subordinates or work jointly, and this may due to their socioeconomic factors status which keeps them away from decision-making process. This research work would be helpful for academicians, enterprises, human resource and management consultants, policymakers and professionals to understand management practices of women engaged in relation to decision-making, work distribution and time management in the agricultural microenterprise and also in their household. HIGHLIGHTS m The majority of women have actively engaged in mushroom farming, namely in the tasks of packing and spinning. However, they have limited opportunities to participate in marketing activities. m Despite playing a crucial role in mushroom cultivation, their standing in decision-making processes regarding raw material selection, purchasing frequency, location, source, payment method, transportation, purchasing delegation, and marketing is minimal and questionable.
... The MK test has been employed by several researchers (e.g. Yu et al., 1993;Douglas et al., 2000;Burn et al., 2004;Singh et al., 2008a, b) to ascertain the presence of statistically significant trend in hydrological climatic variables, such as temperature, precipitation, potential evapotranspiration, vapor pressure, and groundwater, concerning climate change. The MK test checks the null hypothesis of no trend versus the alternative hypothesis of the existence of an increasing or decreasing trend. ...
Foreword Climate change has an inextricable effect on all aspects of nature, including water, energy, farming, vegetation, landscapes, sea level, biodiversity, and so on. The Fourth Assessment Report of the Intergovernmental Panel on Climate Change reported that climate change is caused by widespread human activities. Rainfall patterns in the sense of climate change have important consequences for a predominantly reliant rainfall of southeast monsoon and almost 70% of the country's total annual rainfall. In India, Monsoon rainfall is critical not only for cultivation during the summer season but also for recharging groundwater for irrigation during the dry season. The "Indian Summer monsoon" is a component of the "Asian Monsoon", with a "broad spectrum of changes in daily, sub-seasonal, inter-annual, decadal and centennial time scales". Further, in India, the inter-annual monsoon rainfall variability causes large-scale droughts and floods, having a significant impact on Indian agriculture and the economy. As a result, the fate of Indian farmers is directly associated with the frequency and intensity of monsoon rainfall. Climate variability has an impact on the environment, and its negative consequences in the dynamic shift in surface and groundwater resources. According to a recent NASA assessment, India lost about 109 Km 3 of water between the years 2002-2008, resulting in a 0.33 m/year decline in groundwater level. The quantity and quality of groundwater in the coastal area has degraded as a result of multiple factors such as urbanization, industrialization, unscientific land use, lack of public knowledge, and saline intrusion. Groundwater is the most important source of freshwater for our ecosystem's hydrological cycle. This report consists of six chapters, which deal with the long-term variation of hydro-meteorological parameters and their influence on the groundwater regime of the State of West Bengal. Various methodologies (Linear-regression, Mann-Kendall trend analysis, and Sen-Slope) and data-set (Rainfall, Air-temperature, potential evapotranspiration, vapor-pressure, and groundwater level) are utilized to complete the report. Head of the Office CGWB, ER, Kolkata Preface In this investigation, we have focused on exploring the long-term variation of hydro-meteorological parameters and their impact on groundwater levels over West Bengal India. The study specifically emphasizes the impact of hydro-meteorological parameters on the unconfined aquifer over the region. Rapid socioeconomic development during the last few years over the region could speed up concretization and deforestation over the region. So, this is an attempt at trend analysis of the groundwater level of the unconfined aquifer in the different zones over the region. The specific objectives were to contextualize the observed seasonal hydro-meteorological parameters and their influence on the seasonal groundwater level. In this study, linear regression, Mann-Kendall test analysis, and Sen Slope have been utilized in the analysis of the climatic data of rainfall, air temperature, potential-evapotranspiration, vapour pressure, and groundwater level over the West Bengal regime. For proper interpretation of the climatic data, here different techniques have been utilized for data processing. After the pre-whitening process, for the non-significant auto-correlation coefficient, the Sen-Slope is improved, while for the significant value, the slope is degraded. In some cases, there is no trend without pre-whitening; however, after pre-whitening negative trend is manifested. In terms of rainfall in different seasons, no trend (Mann-Kendall test) is observed over the study region.
... The MK trend test has been applied to measure the occurring trend (variations) in terms of rainfall nonparametrically for met data (Douglas et al., 2000;Modarres & da Silva, 2007). The MK test is statistically derived as: ...
... However, these methods have restrictive assumptions [43], and the presence of positive serial correlation in time series data can increase the probability of identifying a trend even when there is none [44]. To address this issue, the pre-whitening technique was proposed, but it can remove a significant portion of data trend [45,46]. To solve this problem, the innovative trend analysis (ITA) method was proposed by Ref. [47], which does not impose any restrictive assumptions and has shown greater effectiveness than traditional methods in identifying drought trends (e.g., such as serial correlation or seasonal cycles) [48]. ...
Drought is a hazardous natural disaster that can negatively affect the environment, water resources, agriculture, and the economy. Precise drought forecasting and trend assessment are essential for water management to reduce the detrimental effects of drought. However, some existing drought modeling techniques have limitations that hinder precise forecasting, necessitating the exploration of suitable approaches. This study examines two forecasting models, Long Short-Term Memory (LSTM) and a hybrid model integrating regularized extreme learning machine and Snake algorithm, to forecast hydrological droughts for one to six months in advance. Using the Multivariate Standardized Streamflow Index (MSSI) computed from 58 years of streamflow data for two drier Malaysian stations, the models forecast droughts and were compared to classical models such as gradient boosting regression and K-nearest model for validation purposes. The RELM-SO model outperformed other models for forecasting one month ahead at station S1, with lower root mean square error (RMSE = 0.1453), mean absolute error (MAE = 0.1164), and a higher Nash-Sutcliffe efficiency index (NSE = 0.9012) and Willmott index (WI = 0.9966). Similarly, at station S2, the hybrid model had lower (RMSE = 0.1211 and MAE = 0.0909), and higher (NSE = 0.8941 and WI = 0.9960), indicating improved accuracy compared to comparable models. Due to significant autocorrelation in the drought data, traditional statistical metrics may be inadequate for selecting the optimal model. Therefore, this study introduced a novel parameter to evaluate the model's effectiveness in accurately capturing the turning points in the data. Accordingly, the hybrid model significantly improved forecast accuracy from 19.32 % to 21.52 % when compared with LSTM. Besides, the reliability analysis showed that the hybrid model was the most accurate for providing long-term forecasts. Additionally, innovative trend analysis, an effective method, was used to analyze hydrological drought trends. The study revealed that October, November, and December experienced higher occurrences of drought than other months. This research advances accurate drought forecasting and trend assessment, providing valuable insights for water management and decision-making in drought-prone regions.
... For example, most hydrometeorology time series records lack particularly small sample sizes, the requirement for a normal probability distribution function (PDF) and particularly serial independence. The Mann-Kendall (MK) trend detection test (Mann 1945;Kendall 1975) has been applied in numerous research works (Douglas et al. 2000;Yue and Pilon 2004;Bouza-Deaño et al. 2008;Şen 2013;Wang et al.2021;Şişman et al. 2022;Quenn et al. 2023). Before applying this method, pre-whitening (Yue and Wang 2002) or over-whitening (Şen 2017a, b a) processes are applied and serially dependent series are converted into independent series (Hamed 2008). ...
The effects of global warming and climate change appear as increasing or decreasing trends in hydrometeorological records in different locations. Identifying trends in long-term (more than 30 year) data is possible through a variety of methodologies as well as classical Mann–Kendall (MK), Regression (R), Spearman’s Rho (SR) methods in addition to the innovative trend analysis (ITA) approaches. The most used method in the literature is the MK trend determination test, but it has limited assumptions, and the trend slope is calculated according to Sen’s median procedure. However, Sen’s approach is an empirical methodology that considers a single median slope from all possible consecutive lag slopes. This paper provides the theoretical probability distribution function (PDF) that matches Sen’s slopes, and an innovative probabilistic trend slope methodology is proposed with a set of slope risk levels, rather than statistical slope calculation. The application of the proposed methodology is presented for long-term Danube River annual discharge data in Romania and precipitation records at Antalya resort center in the south along the Mediterranean coast of Turkey. A set of trend lines, objectively different risk levels, are obtained. It has been determined that there are decreasing and increasing monotonic trends in each historical time series record. Therefore, for extreme events, it is possible to consider a particular risk level trend characteristic, i.e., floods and droughts, rather than the classical mean or median level trend definition.
... Various studies showed that MK test is a powerful tool in analysing the seasonal and annual trends in climate data (such as rainfall and temperature). Companied with Sen's estimator (also non-parametric), both trends and slope magnitudes of rainfall, temperature, runoff, evapotranspiration, and other hydrological data were widely documented in literature (Douglas et al., 2000;Fan et al., 2016;Peng et al., 2017;. In fact, MK test is highly recommended by the World Meteorological Organization to identify trends in hydro-meteorological time series. ...
Study region: New South Wales, southeast Australia Study focus: Estimating potential evapotranspiration (ETp) rates, detecting its temporal trends and analysing its interannual oscillation are critical for long-term assessment of water availability and regional drought. This study aimed to evaluate the comprehensive performance of 12 simplified models in characterising ETp against the benchmark Penman model across different climate sites in southeast Australia. This study used Taylor skill score (S), normalised root mean square error (nRMSE) and relative mean bias error (rMBE) to estimate models’ capability in estimating ETp rates. Then, this study adopted Mann-Kendall test and continuous wavelet transform (CWT) to test temporal trends and periodicity of ETp estimated by all models. New hydrology insights for the region: Jensen-Haise model was capable to produce fair (nRMSE ≤ 30%) estimates of daily ETp across all stations. Models except Mak1 were generally able to produce reasonable estimates of ETp at larger time scale. In addition, we found that the 12 alternative ETp models generally agreed with the Penman model on the primary (9.6–12.4-year) and quasi (2.6–3.9-year) periods of ETp, but they did not show matchable ability in detecting ETp temporal trends. The comprehensive investigation on models’ performance will shed light on models’ selection in estimation of drought and hydrological cycle.
... However, many studies suggested an increase in the nonstationary nature of climate patterns. Keeping this in mind, various studies have attempted to investigate the validity of this concept in flood regimes in many different parts of the world, taking into account the influence of natural climatic variability [7][8][9][10][11][12][13] or land use changes [14][15][16]. Therefore, there is convincing evidence that the stationarity assumptions must be revised under the changing climate [17], and the development and planning of water resources and the design of hydraulic structures need to be evaluated by considering the nonstationarity approach. ...
Recent improvements in time series studies of hydro-climatological variables have led to the belief that the effects of nonstationarity are substantial enough to call the idea of traditional stationary approaches into doubt. The mean and variability of annual and seasonal rainfall in Pakistan are changing due to anthropogenic climate change. With the use of stationary and nonstationary frequency analysis techniques, this study set out to assess the impacts of nonstationarity in Southern Punjab, Pakistan, over the historical period of 1970-2015 and the future periods of 2020-2060 and 2060-2100. Four frequency distributions, namely Generalized Extreme Value (GEV), Gumbel, normal, and lognormal, were used. The findings of the nonstationarity impact across Southern Punjab showed different kinds of impacts, such as an increase or reduction in the return level of extreme precipitation. In comparison to other distributions, GEV provided the finest fit. the annual nonstationarity impacts for the 100-year return level were increased up to 15.2%, 8.7%, 58.3%, 18.7%, and 20%, respectively. Moreover, extreme precipitation was found to be increasing during the historical and projected periods, which may increase floods, while less water availability appeared at a seasonal scale (summer) during 2061-2100. The increased nonstationarity effects emphasized adapting these nonstationarities induced by climate change into the design of water resource structures.
... When the sample size, n > 10, the following equation was used to determine the standard normal test statistic Z as suggested by Douglas et al. (2000): ...
This study was conducted in the Siraro district, Ethiopia's central rift valley, to assess drought frequency, and extremes from 1986 to 2016 and farmers' perceptions of these parameters. The Mann–Kendall test was used for trend analysis. A household survey was conducted to find out how farmers perceive the drought trend. The results revealed that the rainfall anomaly index values ranged from – 6. in 2009 to 4.52 in 2010, with sixteen years of positive rainfall anomalies and fifteen years of negative rainfall anomalies. The standard precipitation–evapotranspiration index (SPEI) ranged from – 2.2 to 1.69. The study also noted temporal variation and irregular distribution of precipitation across seasons and years. Seasonal and annual minimum and maximum temperatures showed significant increasing trends. The findings match up with the perception tendencies of most farmers in terms of average minimum and maximum temperatures and frequency of droughts. Potential evapotranspiration of the area was from 113.8 to 199.1 mm, with a mean of 151.2 mm. Moreover, most months of the observed periods showed negative climate water balances. Drought frequency and severity appear to have had a negative impact on the agricultural sector in the studied area.. As a result, there is a need for adaptation strategies to combat the impacts of recurring droughts.
... It effectively eliminates the possibility of a significant trend being found in the MK test (Bayazit & Önöz, 2004). Thus, in the current study, the MK test is performed after pre-whitening for selected stations with serial autocorrelation of lag-1 in order to eliminate the effect of autocorrelation in the time series (Douglas et al., 2000;Hamed & Rao, 1998;Yip et al., 2013). The non-parametric Sen's slope estimator is used in the present study to find the intensity of the trend. ...
Initial reports signify some specific isolated locations in different latitudes, revealing a paradoxical increase in both heavy and very heavy rainfall events and also an increment in total, i.e., in both rainfall and temperature, over ecologically sensitive areas along the Western Ghats (WG). This paper presents a coherent study of the full-scale of daily rainfall and temperature over 27 well-spaced stations in the study area to determine its extent and investigate whether or not this contradictory behaviour is real. Also, an attempt has been made to assess the differential behaviour of rainfall, temperature, and heavy rainfall events in association with land use and land cover change (LULC). The analysis revealed that rainfall and temperature over the study area are increasing, whereas heavy rainfall events have increased during 1981–2020 with strong peaks after 2000 around 18–19°N (Mumbai metropolitan region), 14–16°N (mining and quarrying regions in Goa), and 9–12°N (a narrow strip of land spanning across the coastal towns of Karnataka and Kerala) latitudes. The majority of the rainfall excess years coincided with El Nino years, indicating that El Nino does not affect rainfall negatively. However, rainfall over the WG is influenced by local relief and cascading topography. The spatial pattern of average annual rainfall shows a decreasing trend from south to north because the elevation and span of rainfall occurrence are higher in the southern part of WG. The findings of the current research will help in building a strategy to address trends and patterns of climatic variables in association with LULC.
... where t is the level of any particular tie and ∑t indicates the summation of all ties. When the magnitude of n exceeds 10, Uncorrected Proof Equation (4) will be applied to calculate the standard normal variate Z (Douglas et al. 2000). ...
Lack of water reserves in artificial reservoirs poses serious challenges in meeting various human requirements, especially during periods of water scarcity. In the current research, the Total Outflow (TO) of the Mahabad Dam reservoir has been estimated under six scenarios including the Monthly Cumulative Rainfall (MCR), Snow Water Equivalent (SWE), Stream Flow (SF), Mean Temperature (T), Pan Evaporation (Ep), Sediment Flushing Gate Outlet (SFGO), Penstock Outflow (PO), Evaporation Losses (EL), Cumulative Non-Scheduled Discharge (CNSD), Live Storage Volume (LSV), Water Surface Area (WSA), Monthly Water Level (MWL), Total Allocated Water (TAW), and Generated Power (GP) variables for the 2001–2021 period. Estimation of TO is accomplished via individual and Wavelet-developed (W-developed) data-mining approaches, including Artificial Neural Networks (ANNs), wavelet-ANNs (WANNs), adaptive neuro-fuzzy inference system (ANFIS), wavelet-ANFIS (WANFIS), Gene Expression Programming (GEP), and wavelet-GEP (WGEP). The obtained values of RMSE for WGEP1–WGEP6 models account for 5.917, 2.319, 4.289, 8.329, 10.713, and 9.789 million cubic meters (MCM), respectively, which is based on the following scenarios: reservoir inlet elements, reservoir outlet elements, consumption, storage characteristic, climate, and energy. This research revealed that combining the wavelet theory (WT) with individual models can be a powerful method to improve the modeling performance in the TO estimation.
... von Storch (1999) proposed the prewhitening technique to remove serial correlation from the data before applying the Mann-Kendall (MK) test to identify trends in hydrometeorological data. Nevertheless, Douglas et al. (2000) and Yue et al. (2002) stated that the prewhitening technique may compromise data quality and adversely affect the trend. To deal with this issue, Şen (2012) proposed a trend detection technique named innovative trend analysis (ITA) to overcome these restrictive measures with universal applicability. ...
Precipitation is a meteorological variable that plays a vital role in determining water resources and managing water risks. The intensity and frequency of precipitation extremes have increased in recent years; however, the changes in precipitation regimes across Pakistan remain unclear. This study analysed spatial and temporal changes in the precipitation concentration index (PCI) and concentration index (CI) from 100 meteorological stations across Pakistan from 1981 to 2017. Using innovative trend analysis (ITA) and Pettitt's test, we detected trends and change points in the annual and wet season PCI and CI. Additionally, we examined the relationship between PCI/CI values and latitude and altitude. The results revealed that the CI and PCI ranged between 0.48–0.78 and 10–55, respectively, with higher values being more prevalent in the dry and hot zones of the country. The study established a linear relationship between PCI/CI values, latitude and altitude, with higher values at lower altitudes and a decrease in values from south to north. Most stations presented a significant positive ITA trend for PCI and CI from 1981 to 2017, with a change between 1985 and 1995, indicating an overall increase in heavy precipitation events in the area. These findings are crucial for disaster preparedness, water resource planning, the mitigation of drought and flood risks, and conservation efforts.
... МК тест примењен је за утврђивање постојања тренда током одређеног временског периода. Изабрали смо овај тест јер су многе тренд анализе засноване на његовој примени (Douglas et al., 2000). MК је непараметријски тест и не захтева претпоставку о постојању било које функције расподеле података. ...
Global research has indicated that there are differences in mathematics achievement among students relative to their gender. The differences have decreased in the last couple of decades, but they have not completely disappeared. Mathematics competitions play an important role in increasing students' motivation, interest, and self-confidence, as well as in identifying and supporting mathematically gifted students. For this reason, the question is whether and to what extent there are differences in achievement at mathematics competitions relative to gender. The aim of this paper is to analyse the continuity of interest and students' achievement, and to investigate the trends at regional mathematics competitions relative to gender in the 2014-2023 period. The research sample consists of 53490 primary school students, from fourth to eighth grade. Quantitative and qualitative methods were applied. The results indicate that there are differences in participation and achievement relative to students' gender. In the observed period, there was a trend of an increased male representation. A slight decrease in the representation of boys was identified during the transition from earlier grades to subject teaching, and this difference becomes greater in the eighth grade. When it comes to achievement, there is a statistically significant difference, usually to the benefit of the boys, in 60% of cases. There are more boys among the 5% of the students with the highest achievement. The findings indicate that more attention should be paid to this issue and that the cause of the gender differences regarding participation and achievement at competitions should be identified and ways should be found for overcoming it.
... Many authors applied prewhitening to hydro-meteorological time series in preparation for the Mann-Kendall test. Douglas et al. (2000) applied it in the analysis of floods and low flow; Admassu and Seid (2006) in the analysis of rainfall trend; Shadmani et al. (2012) in the analysis of reference evapotranspiration, Mishra et al. (2013) in a study of temperature variation, Akhtar et al. (2015) in climate change analysis; and Verma et al. (2016) in precipitation analysis. Despite the argument put forward on the need to apply pre-whitening before the Mann-Kendall test, some authors still find it unnecessary and observed that pre-whitening is not suitable for eliminating the effect of serial correlation on the Mann-Kendall test when a trend exists. ...
Climate and water resources are interconnected in a complex way such that a change in any one induces a change in another. Trend analysis is usually employed to assess and understand the long term pattern of climatic and hydrologic (hydro-meteorological) time series data in order to assess its impact on the environment, particularly water resources. Parametric and non-parametric statistical methods were employed at various times for trend tests depending on the nature of the data at hand. Even though the parametric method was observed to be more robust in making a decisive conclusion, there are certain conditions that need to be met by the data and it was observed that Hydro-meteorological data does not meet most of the conditions. As such, non-parametric procedures for detecting trends were found to be suitable for hydro-meteorological time series. This paper found the rank based Mann-Kendall as one of the most commonly method employed in detecting the trend of hydro-meteorological and Seasonal Kendall Slope (Sen's slope) for detecting the magnitude of the trend. It was observed that hydro-meteorological data are sometimes serially dependent therefore the problem of serial correlation and seasonality will make the application of Mann-Kendall test to have limited applicability, hence, application of pre-whitening procedure is recommended before subjecting the data to the test. But Monte Carlo Simulation Investigation reveals that effect of serial correlation is dependent upon sample size and trend magnitude, when the sample size and trend magnitude are large, the serial correlation will no longer affect Mann-Kendall test. It is concluded that Mann-Kendall and Sen's Slope Estimate method to be a suitable method of assessing trends within hydro-meteorological time series and authors were advised to make the sample size hydro-meteorological time series to be analyzed to be large enough to take care of the effect of serial correlation.
... The groundwater level analyses included the assessments of trends at the regional level, which aimed to elucidate whether something can be concluded on the regional scale based on local groundwater level trend tests. Two approaches were employed, namely, the empirical method by Douglas et al. (2000) and the regional Kendall test (Helsel & Frans 2006), which are suitable when the correlation among groundwater observation sites is consequential and absent, respectively. These analyses showed exclusively significant results through the regional Kendall test. ...
Managed Aquifer Recharge (MAR) is a promising technique for water management. It comprises a group of technologies that enhance the infiltration of various water sources into aquifers. The water stored underground can serve different uses, such as irrigation, industrial and drinking water supply, and the recovery or preservation of environmental assets. The uptake of MAR is rapidly increasing worldwide under the threat of multiple pressures, including climate change, the decline in aquifer storage and environmental degradation. The present report is part of the Horizon 2020 MSCA "Managed Aquifer Recharge Solutions Training Network" (MARSoluT ITN, 2019-2023), which aimed at training experts in MAR (https://www.marsolut-itn.eu/). Report D4.4 deals with the objectives of work package 4 (WP4) and seeks to evaluate the performance of MAR sites across the Mediterranean using monitoring data. D4.4 continues a line of research started in the FP7 project "Demonstrating Managed Aquifer Recharge as a Solution to Water Scarcity and Drought" (MARSOL, 2013-2016) through MARSOL work package 13 (WP13) and its Deliverables D13.1 and D13.3, which provided technical solutions for MAR. The performance of six MAR sites across the Mediterranean was evaluated, namely, The Algarve, Portugal (UAlg); The Los Arenales MAR sites, Spain (TRAGSA); the Suvereto MAR site, Italy (SSSA); the Pwales MAR site, Malta, (EWA); the Argolis Field, Greece (NTUA); and the Menashe streams MAR site, Israel (ARO). The performance was evaluated in terms of seven categories: yearly recharge volumes, impacts on groundwater levels, impacts on water quality, infiltration rates and clogging, site upgrade, financial aspects, and other aspects. The site performance evaluation involved research conducted primarily within the framework of the MARSoluT project. In general, the sites show satisfactory performance after several years of operations. In the Algarve, MAR could help to palliate some of the current issues, but other measures are also required.
In addition, a calculation for the unintentional recharge of groundwater caused by transversal structures (dykes and dams) has been conducted as a starting point for a future more accurate estimation. The volume infiltrated from the about 27,600 in-river structures ranges between 800 and 1,200 Mm3/year for the Spanish territory, representing a starting point for this new line of action about (un)managed aquifer recharge at a large scale. The obtained figures will be fine-tuned in the future of this initial figure. The site performance evaluation research involves multiple tools and diverse approaches, including numerical groundwater modelling, analytical hydrochemical characterisation, field and laboratory experiments, and geospatial analysis. A total of 20 technical solutions were added to the list that started in MARSOL with Deliverable D13.1. These technological solutions are related to multiple aspects of MAR, such as operation, planning, maintenance, and site upgrade. The advances in MAR sciences and engineering reflected in this report showcase successful MAR experiences and provide technical solutions that can support the market penetration of MAR in the Mediterranean region and beyond.
... Parameter Units Data range years (months) Mean Standard deviation Minimum value Median value Maximum valueof all parameters across all vegetation zones. We avoided assumptions such the Mann-Kendall (MK) pre-whitening procedure. This was done to preserve the series' authenticity; we used the originally recorded and calculated data for the analysis.(Douglas et al. 2000;Yue et al. 2002;Piyoosh and Ghosh 2017;Almazroui and Şen 2020).The results for the MK test is presented inTable 4. A significance level of 5% (0.05) was used as well as test results, such as the Kendall Z-value, test statistic (S), and the results of the Ho hypothesis to show the significance of ...
The vegetation in Nigeria is split into seven distinct zones which are sandwiched between the Sahara Desert in Northern Africa and the Atlantic Ocean to the south of Nigeria. The activities that promote climate change such as deforestation and greenhouse gas emission has led to land area encroachment. This study employed Sen’s innovative trend analysis (ITA) and Mann–Kendall (MK) test to investigate trends in surface temperture, photosynthethically active radiation (PAR), relative humidity, and precipitation. The data spans a period of 40 years from 1981 to 2020. Sen’s ITA evaluates the data by showing ‘low’ ‘medium’, and ‘high’ record groups. The results from MK test shows that some parameters indicated an increasing trend, while others indicated a decreasing, save for PAR which showed an insignificant trend. Sen’s ITA also identified increasing, decreasing, or no trend in parameters across the vegetation zones. It is concluded that there is an increase in parameters that may translate to a greener vegetaion across the vegetation zones. This study is very important for the identification of trend changes for vegetation parameters, indicating the possibility of desert encroachment or a change in vegetation cover. This will buttress on the importance and possibilities of regreening in Nigeria and Africa as a whole.
... If |ZS|>1.96 (at the 5% significance level) or >2.576 (at the 1% significance level), then the null hypothesis of no trend is denied.The Mann-Kendall test has been utilized frequently to evaluate the importance of trends throughout hydro-meteorological time-series data[26][27][28][29][30][31]. ...
This study presents a comprehensive assessment of the historical climatology of Dhaka city over a period of fifty years (1971-2021), utilizing five distinct climate parameters: Skin Temperature, Air Temperature, Rainfall, Wind Speed, and Humidity. The study employed various statistical methods, such as Linear regression, the Mann-Kendall test model, and Sen’s slope estimator method, to examine the significant patterns in climate data and quantify the degree of fluctuations in the variables. The results of the Mann-Kendall Test provided evidence that each parameter's values displayed a statistically significant trend. The Sen's Slope estimator revealed a declining trend in the monthly mean value of all climate parameters except for wind speed and humidity. The annual average Skin and Air Temperature in Dhaka City increased at a rate of 0.033°C and 0.065°C respectively over the study period; as a result, the average annual Rainfall and Humidity showed an increasing tendency, which was 1.225 mm and 0.086% respectively. The Monsoon period demonstrated the highest rainfall and humidity levels, while the winter season demonstrated the lowest levels of these parameters. The trend of Wind Speed has exhibited a decrease at a rate of 0.032 m/s over the past five decades. A critical analysis evaluates the trends and patterns observed in the data. This research attempts to improve understanding of the components that have influenced the climate of Dhaka City by evaluating historical data.
... In addition, we quantified if significant trends were present in the dataset, specifically for temperature, TN, TP, TN : TP, Chl a, and cyanobacterial abundances and abundances of other algal groups over the years, and during specific months of the year over the years with a Mann-Kendall test and a seasonally corrected Mann-Kendall test (all months over the years), respectively using the R package "Kendall" (McLeod 2022). A non-parametric Mann-Kendall test is widely used for assessing climatic time series data, as it can cope with outliers (Douglas et al. 2000; Atta-ur-Rahman and Dawood 2017; Nashwan and Shahid 2019). In addition, it is also used for analyzing phytoplankton data over time (Marshall et al. 2009;Lynam et al. 2010;Winter et al. 2011). ...
Nutrient loading of freshwater and marine habitats has increased during the last century as a result of anthro-pogenic activities. From the 1980s onwards, following implementation of new policy targeting eutrophication, total phosphorus (TP) and total nitrogen (TN) loads were reduced in many European waters. Often, however, decreases in TP were stronger as compared to TN, leading to increased TN : TP ratios. Our analysis shows that the large and shallow lake IJsselmeer (the Netherlands) experienced a similar trend, whereas TN was reduced by 50%, TP was reduced by 89% between 1975 and 2018. Most of this nutrient load reduction was achieved before the year 2000, changes in nutrient concentrations in the lake became smaller afterwards, especially for TN, leading to a further increase in stoichiometric imbalance up to a yearly averaged TN : TP (molar) of 296 in 2018. The observed changes in nutrients were accompanied by a decline in total phytoplankton biomass, and slight declines in phytoplankton genus evenness and diversity. Although biomass decreases likely resulted from the overall decrease in nutrient availabilities, the reduced diversity may have resulted from the shift toward very high TN : TP ratios that indicate relatively low TP levels and enhanced competition for phosphorus. Overall, our findings demonstrate long-term trends with decreased phytoplankton biomass and diversity following reduced nutrient concentrations and enhanced stoichiometric imbalance. Ultimately, such changes at the food web base may alter the structure and functioning of the entire aquatic food-web in lake IJsselmeer.
... The previous studies on temperature extremes include analysis of Australian heatwaves (Perkins and Alexander 2013), changes in the frequency of warm and cold exceedances in India (Dash and Mamgain 2011), trends in heatwave indices of India (Kothawale et al. 2010;Rohini et al. 2016) and Pakistan (Khan et al. 2019), and changes in maximum, minimum, and mean temperatures of Iran (Ghasemi 2015). In the last few decades, some studies have revealed that climatic and meteorological records exhibit some type of non-stationarity, such as trends and shifts (Douglas et al. 2000;Yan et al. 2002;Tank and Können 2003). ...
Extreme temperature events are one of the most serious threats resulting from climatic change across the globe. Quantifying the intensity, duration, and frequency of temperature extremes is of huge societal and scientific interest. Recent evidence in climate change challenges the stationarity assumption conventionally followed while performing temperature-duration-frequency (TDF) analysis. India has distinct climate zones and topography, distributing climate threats unevenly. In this study, stationary (S) and non-stationary (NS) TDF analysis for India and its seven temperature homogenous areas is performed using a gridded (1° × 1°) daily maximum temperature dataset from 1951 to 2019. Time is employed as a covariate to incorporate linear, quadratic, and exponential trends in the location and/or scale parameters of the generalized extreme value distribution to demonstrate the impact of non-stationarity in developing TDF curves. According to the findings, NS TDF models provide a better fit to the dataset when compared to the S TDF model. More than 55% of the grid points have NS Model-1, viz., location parameter linearly varying with time, as the best-fit model. In contrast to their stationary counterparts, NS temperature return levels were consistently higher across all return periods. Furthermore, temperature homogenous zones in the North-West, North Central, and Interior Peninsula are more susceptible to temperature rises beyond 45°C. While envisioning long-term solutions in a changing climate scenario, considering non-stationarity significantly improved the accuracy of TDF curves. This will indeed support more robust predictions, which will ultimately aid in the mitigation of future extreme temperature events.
... Trend analysis is the best technique to recognize future fluctuations in hydrometeorological elements for risk management, flood and drought monitoring, and water resource planning, design, and management [33,34]. Numerous investigators in the world and India have reported either positive or negative tendencies in hydrological and meteorological elements using the widely accepted techniques of trend analysis such as Mann-Kendall (MK), Sen's slope (SS), and innovative trend analysis (ITA) [35][36][37][38][39][40][41][42][43][44][45][46][47][48]. However, the major disadvantage of the MK test is that it is not appropriate for time-series data in which autocorrelation or periodicities exist. ...
Climate change can have an influence on rainfall that significantly affects the magnitude frequency of floods and droughts. Therefore, the analysis of the spatiotemporal distribution, variability , and trends of rainfall over the Mahi Basin in India is an important objective of the present work. Accordingly, a serial autocorrelation, coefficient of variation, Mann-Kendall (MK) and Sen's slope test, innovative trend analysis (ITA), and Pettitt's test were used in the rainfall analysis. The outcomes were derived from the monthly precipitation data (1901-2012) of 14 meteorology stations in the Mahi Basin. The serial autocorrelation results showed that there is no autocorrelation in the data series. The rainfall statistics denoted that the Mahi Basin receives 94.8% of its rainfall (821 mm) in the monsoon period (June-September). The normalized accumulated departure from the mean reveals that the annual and monsoon rainfall of the Mahi Basin were below average from 1901 to 1930 and above average from 1930 to 1990, followed by a period of fluctuating conditions. Annual and monsoon rainfall variations increase in the lower catchment of the basin. The annual and monsoon rainfall trend analysis specified a significant declining tendency for four stations and an increasing tendency for 3 stations, respectively. A significant declining trend in winter rainfall was observed for 9 stations under review. Likewise, out of 14 stations, 9 stations denote a significant decrease in pre-monsoon rainfall. Nevertheless, there is no significant increasing or decreasing tendency in annual, monsoon, and post-monsoon rainfall in the Mahi Basin. The Mann-Kendall test and innovative trend analysis indicate identical tendencies of annual and seasonal rainfall on the basin scale. The annual and monsoon rainfall of the basin showed a positive shift in rainfall after 1926. The rainfall analysis confirms that despite spatiotemporal variations in rainfall, there are no significant positive or negative trends of annual and monsoon rainfall on the basin scale. It suggests that the Mahi Basin received average rainfall (867 mm) annually and in the monsoon season (821 mm) from 1901 to 2012, except for a few years of high and low rainfall. Therefore, this study is important for flood and drought management, agriculture, and water management in the Mahi Basin. Keywords: innovative trend analysis; Mahi Basin; Mann-Kendall test; normalized accumulated departure from the mean; pre-monsoon and post-monsoon; seasonal and annual analysis
... Renard et al. [37] suggested the resampling-based bootstrap and the FDR (False Discovery Rate) methods, as these two methods are both adequate and robust in detecting field significance. The resampling-based bootstrap procedure was applied to determine the field significance of the MK test for all indices [37,38]. The procedure can be found in Wang et al. [20]. ...
Evaluation of changes in dry-wet climate is crucial in the context of global climate change to ensure regional water resources, ecosystem stability, and socioeconomic development. Long-term daily meteorological data, including temperature, precipitation, relative humidity, wind speed, sunshine duration, and air pressure data from 1680 stations across mainland China from 1971 to 2019, were collected to investigate the temporal and spatial variations in aridity index (AI), precipitation (P), reference evapotranspiration (ET 0), and the underlying driving climatic factors. Results indicated that the Northwest, Northeast, and Huang-Huai regions were undergoing significant wetting processes, while the Southwest and Southeast China were undergoing significant drying processes. The changing AI was mainly decided by the changing trends of ET 0. For most regions, ET 0 has undergone significant increases. The average increasing rate over mainland China was 3.76 mm/10a. Stations with decreasing trends were mainly located in the Tibet Plateau, Huang-Huai, and northern Northeast China. Trends in ET 0 were negatively affected by the increasing changes in relative humidity and positively affected by the decreasing changes in wind speed and sunshine duration and the increasing changes in air temperature. Wind speed and relative humidity were found to be the main dominant factors driving the changes in ET 0 , and their contribution varied with regions. Huang-Huai and northern Northeast China showed a significant downward trend in ET 0 , mainly driven by the decrease in wind speed, while the increase in relative humidity was the primary contributor to the significant upward trends in ET 0 across all other regions in China.
... The Mann-Kendall trend test was used alongside the TSSE method to estimate linear trends for comparison with the non-linear EEMD trends. The Mann-Kendall trend test detects the presence of a significant trend in a time series using rank (Mann, 1945;Kendall, 1975) and has been used in numerous environmental studies (Atta-ur-Rahman and Dawood, 2017;Praveen et al., 2020;Douglas et al., 2000). The Mann-Kendall test requires independent data, although in reality most time series are autocorrelated (Hamed and Rao, 1998). ...
Sea surface temperature observations have shown that western boundary currents, such as the East Australian Current (EAC), are warming faster than the global average. However, we know little about coastal temperature trends inshore of these rapidly warming regions, particularly below the surface. In addition to this, warming rates are typically estimated linearly, making it difficult to know how these rates have changed over time. Here we use long-term in situ temperature observations through the water column at five coastal sites between approximately 27.3–42.6∘ S to estimate warming trends between the ocean surface and the bottom. Using an advanced trend detection method, we find accelerating warming trends at multiple depths in the EAC extension region at 34.1 and 42.6∘ S. We see accelerating trends at the surface and bottom at 34.1∘ S but similar trends in the top 20 m at 42.6∘ S. We compare several methods, estimate uncertainty, and place our results in the context of previously reported trends, highlighting that magnitudes are depth-dependent, vary across latitude, and are sensitive to the data time period chosen. The spatial and temporal variability in the long-term temperature trends highlight the important role of regional dynamics against a background of broad-scale ocean warming. Moreover, considering that recent studies of ocean warming typically focus on surface data only, our results show the necessity of subsurface data for the improved understanding of regional climate change impacts.
The monthly and annual trends and variance of rainfall have been studied for five stations in an economically important Bangladeshi district named Sirajganj since 1965 to 2021. Natural disasters have prevalent in Sirajganj which is indispensable to assess. But, several researchers have been normally focused on river bank management and flood risk assessment. However, no extensive research has been conducted on Sirajganj based on non-normally distributed time series meteorological data such as rainfall time series so the current study is very important. In this study, the non-parametric Mann-Kendall and Sen's methods have been used to determine the statistical significance of a positive or negative trend in rainfall data. Also, cumulative sum charts and bootstrapping, one-way ANOVA, Tukey's range tests, and linear regression have been used to discover the incidence of abrupt changes, compare the significant difference in monthly and annual rainfall data, multiple comparisons amidst mentioned stations to find changes, and to investigate the changeover on dry and rainy days, respectively. The analysis showed a statistically significant decreasing trends in monthly and annual rainfall series. As well, changes from positive to negative direction have been recognized in the February, May, July, September, and annual rainfall time sequence. Besides, ANOVA and Tukey's range tests revealed a statistically substantial difference in all monthly and annual rainfall volume excluding January, March, and June. Additionally, these two tests demonstrated momentous differences in all monthly and annual frequency of rainfall categories excepting January and April. However, Linear regression analysis revealed that the number of dry days gradually reduced at the end of the dry winter, though the number of rainy days decreased during the rainy season. As in, the number of rainy days replaces the number of dry days during the dry season and vice versa during the rainy season. Even though, with very few exceptions, the volume of rainfall decreases throughout the year. The outcomes of this research might helpful for implementing the planning and evaluating hydrological projects on Sirajganj district.
Temporal hydro-meteorological time series have different components, such as the deterministic (periodicity, trend, jump) and stochastic (uncertainty, statistical, probabilistic) parts that are important for practical applications and prediction in water resources management studies. For many years, stochastic components were assumed to be stationary in order to reliably implement stochastic modelling procedures. In the last 30 years, there are many publications in the literature due to global warming and accordingly, climate change, which exhibits non-stationary behaviors in hydro-meteorology time series records. Oftentimes, classical trend analyzes cover the entire recording time with a single holistic straight-line trend and slope. Such an approach does not provide information on trend evolutionary development at shorter times over the entire record length. This paper proposes a methodology for identifying local finite length trends in a systematic way that moves dynamically over a series of short time frames for internal trend evolution developments and interpretations. In general, partial moving trends of 10-year, 20-year, 30-year and 40-year occur above or below the overall trend and thus provide practical insight into the dynamic trend pattern with important computational results and time series internal structural development with key comments. The moving trend method is similar to the classical moving average methodology with one important difference that instead of arithmetic averages and their horizontal lines, a series of local trend are given over the recording period with increasing or decreasing partial trends. The moving trend methodology is applied to annual records of Danube River discharges, New Jersey state wise temperatures and precipitation time series from the City of Istanbul.
This study analyzed the annual and low-flow trends of rivers located in Serbia, a part of southeast Europe. To achieve this, a comprehensive framework for flow trend estimation was established using both the conventional Mann–Kendall trend test and its modified version, which considers the serial correlation of flows, alongside the Theil-Sen trend estimator for modeling linear trends. The investigation was performed at a large spatial scale for sites of 80 hydrological stations with
negligible human influence. The most important rivers are given as follows: the Velika Morava, Južna Morava, Kolubara, Nišava, Timok, and Toplica rivers. Selected hydrological stations at these rivers drain the watershed areas from 79 km2 to 995 km2 with the respective recording periods (average length of the recorded data is equal to 60 years). The results implied that decreasing annual and low-flow trends are occurring for southern and eastern Serbian river streams while increasing trends were estimated at only a few hydrological sites. Hence, the results assist in revealing the most vulnerable river basins with statistically significant decreasing trends and provide a solid basis for the estimation of the design annual and low-flow trends in Serbia.
Droughts are one of the most frequent and destructive natural disasters worldwide. In the past decades, drought events in China are frequent and caused severe socio‐economic losses. To better predict and manage droughts, the spatiotemporal characteristics of the three types of droughts and propagation time (PT) from meteorological to agricultural and hydrological droughts in China during 1982–2014 were analyzed based on drought indices, while the causes of drought propagation were discussed. The results showed that meteorological droughts exhibited an insignificant trend. Agricultural droughts mainly aggravated in the northeastern and central regions. And the hydrological droughts were long‐lasting and exacerbated in most areas. The propagation speed from meteorological to agricultural and hydrological droughts was extremely rapid (1–2 months) in southeast China, and the relationships among droughts were close (correlation coefficient/R > 0.6). The propagation from meteorological to hydrological droughts was slower (6–8 months) in central China. In northwest China, the association between meteorological and hydrological droughts was weak (R < 0.4). Climatic conditions (especially temperature) played a dominant role in the propagation from meteorological to agricultural and hydrological droughts, explaining 63.3% and 52.6% of the variations in the PT, respectively. Urbanization, agricultural activities, elevation, and vegetation contributed to the propagation from meteorological to agricultural droughts. Reservoirs, agricultural activities, and vegetation also affected the propagation from meteorological to hydrological droughts by regulating hydrological processes. These findings are of vital significance to the prediction, warning, and management of different droughts.
This study investigates the impact of climate change on river systems within the Eastern Mediterranean Basin (EMB), utilizing the Mann-Kendall (MK) test, enhanced by Sen’s slope estimator and Şen’s Innovative Trend Analysis (ITA) methods. The research focuses on hydrological changes in river systems, particularly streamflow trends, and their implications under climate change and anthropogenic activities. The methodology includes a detailed analysis of hydro-meteorological series, including streamflow data from different observation stations. To satisfy the serial independence requirement assumption of MK test pre-whitening and over-whitening methodologies are applied effectively. The study area comprises 10 sub-basins in the EMB, with particular attention to the Göksu River and its tributaries. The results clearly show significantly decreasing trends in the annual streamflow values at several stations, demonstrating the considerable influence of climate and environmental changes on the basin's hydrology. These trends are also critically analyzed with the help of ITA graphs, which provide insights into the spatial and temporal variability of streamflow patterns. This research contributes to a better understanding of hydrological responses to climatic variability, providing vital information for water resource management and policies in regions undergoing significant environmental changes.
This paper is an effort of geo-statistical analysis of rainfall variability and trend detection in the eastern Hindu Kush region located in the northwest of Pakistan. The eastern section of the HK region lies in the western part of Pakistan. Exploring rainfall variability and quantifying its trend and magnitude is one of the key indicators among all climatic parameters. In the study area, Pakistan Meteorology Department (PMD) has established seven meteorological stations: Drosh, Chitral, Dir, Timergara, Saidu Sharif, Malam Jabba, and Kalam. Daily, mean monthly, and mean annual rainfall time series data for all the met stations were geo-statistically analyzed in the GIS environment for detecting monthly and annual variability in rainfall, variability, and trend detection. Mann-Kendall (MK) and Theil-Sen's slope (TSS) statistical tests were applied to rainfall data. Initially, the MK test was applied for detection of trends and TSS test was used to quantify the change in magnitude. The results indicate that the rainfall variability in intensity and trend pattern detection. The analysis confirms that an extremely significant rainfall trend in the case of mean annual rainfall was predicted at Dir and Malam Jabba meteorological stations. Opposite to this, at Kalam and Chitral stations, a less significant rainfall trend was noted. In a similar context, no prominent rainfall trend has been found at Drosh, Timergara, and Saidu Sharif meteorological stations. Likewise, using TSS, an extremely negative variation in the magnitude of rainfall was verified at Kalam and Malam Jabba. However, a noteworthy positive change in rainfall magnitude has been noted at Dir and Saidu Sharif meteorological stations. The findings of this research have the potential to assist the decision and policy makers and academicians to think truly and conduct more scientific research studies to mitigate climate change.
Bu çalışmada, hidroljik verilerin değişkenlik analizinde
kullanılan trend tesleri ele alınmıştır. Testlerin uygulamasında Türkiye
geneline yayılmış akarsulara ait yıllık pik akım serileri değerleri
kullanılmıştır.
Despite increasing magnitude and frequency of wildfire, understanding hydrological processes contributing to changes in streamflow is not well examined for the entire western contiguous United States (WCONUS). This study provides insight into whether considering spatially varying watershed characteristics, including burn severity patterns, can better explain streamflow trends at broad spatial and temporal scales. Standard geographically weighted regression (GWR) and multi‐scalar (MS) GWR were benchmarked against ordinary least squares (OLS) regression to determine if spatially varying coefficients could better explain spatially varying patterns and processes of peak flow, low flow, center timing of flow, and flashiness trends in burned watersheds. In most cases, GWR reduced spatial autocorrelation and provided insight into regional factors explaining streamflow trends. Both OLS and GWR models did not find burn severity‐based metrics significant in explaining streamflow trends, except the peak flow trends model that included the spatial arrangement of high severity patches quantified using landscape shape index (LSI). Positive peak flow trends coincided with increasing LSI values in Northern California and negative peak flow trends with increasing LSI in the southern WCONUS. This research could guide regionally specific post‐fire response and inform the development of new spatial metrics that could better represent post‐fire hydrological processes.
Precipitation responses over Canada associated with the two extreme phases of the Southern Oscillation (SO), namely El Nino and La Nina are identified. Using the best available precipitation data from 1911 to 1994, both the spatial and temporal behavior of the responses are analyzed from the El Nino/La Nino onset to several seasons afterward. Composite and correlation analyses indicate that precipitation over a large region of southern Canada extending from British Columbia, through the prairies, and into the Great Lakes region is significantly influenced by the SO phenomenon. The results show a distinct pattern of negative (positive) precipitation anomalies in this region during the first winter following the onset of El Nino (La Nina) events. During this same period, significant positive precipitation anomalies occur over the northern prairies and southeastern Northwest Territories in association with El Nino events. Statistical significance of the responses is tested by the Student`s t-test and the Wilcoxon rank-sum test, while field significance is established through the Monte Carlo procedure. All of the significant precipitation anomalies can be explained by the associated upper-atmospheric flow patterns, which during the first winter following the onset of El Nino events resemble the positive (negative) phase of the Pacific-North American (PNA) pattern. Significant correlations between Southern Oscillation index (SOI) values and the observed precipitation anomalies over southern Canada suggest the possibility of developing a long-range forecasting technique for Canadian precipitation based on the occurrence and evolution of the various phases of the SO. 31 refs., 10 figs., 3 tabs.
Twentieth century trends of precipitation are examined by a variety of methods to more fully describe how precipitation has changed or varied. Since 1910, precipitation has increased by about 10% across the contiguous United States. The increase in precipitation is reflected primarily in the heavy and extreme daily precipitation events. For example, over half (53%) of the total increase of precipitation is due to positive trends in the upper 10 percentiles of the precipitation distribution. These trends are highly significant, both practically and statistically. The increase has arisen for two reasons. First, an increase in the frequency of days with precipitation ]6 days (100 yr)1[ has occurred for all categories of precipitation amount. Second, for the extremely heavy precipitation events, an increase in the intensity of the events is also significantly contributing (about half) to the precipitation increase. As a result, there is a significant trend in much of the United States of the highest daily year-month precipitation amount, but with no systematic national trend of the median precipitation amount.These data suggest that the precipitation regimes in the United States are changing disproportionately across the precipitation distribution. The proportion of total precipitation derived from extreme and heavy events is increasing relative to more moderate events. These changes have an impact on the area of the United States affected by a much above-normal (upper 10 percentile) proportion of precipitation derived from very heavy precipitation events, for example, daily precipitation events exceeding 50.8 mm (2 in.).
The biases and large-scale inhomogeneities in the time series of measured precipitation and snowfall over the United States and Canada are discussed and analyzed. The spatial statistical characteristics of monthly and annual snowfall and total precipitation are investigated and parameterized. After adjustments and selection of the best' network, reliable first guess' estimates of North American snowfall and precipitation are obtained. Century-long time series of unbiased annual precipitation over the regions to the south of 55[degrees]N and 40-year time series of unbiased area-averaged annual precipitation and snowfall for all of North America are developed. The analysis of their trends shows the following. (1) During the last 100 years, annual precipitation has increased in southern Canada (south of 55[degrees]N) by 13% and in the contiguous United States by 4%; however, the main domain of this century-scale precipitation increase is eastern Canada and adjacent to it northern regions of the United States. (2) Up to a 20% increase has occurred in annual snowfall and rainfall during the last four decades in Canada north of 55[degrees]N. The relationships between century-long precipitation time series over North America with Northern Hemisphere surface air temperature and the South Oscillation index (SOI) are investigated. It is shown that ENSO (negative anomaly of SOI) is usually accompanied by an increase of precipitation whenever it affects the United States (especially in the southwestern region of the country).
This collection of lectures, mostly literature reviews, emphasizes the application of statistical tools which are in widespread use in various aspects of climatology. Section topics include the following: necessity of statistics and climate studies and misuses of statistical analysis in climate research; analyzing the observed climate; simulating and predicting climate; (climate) pattern analysis.
Erosion et transport particulate par le Niger: du bassin supérieur à l'exutoire du delta intérieur (bilan de cinq années d'observation) Résumé Les chroniques hydrologiques du Niger supérieur et de sa cuvette lacustre montrent un appauvrissement de la ressource en eau depuis deux décennies lié aux déficits pluviométriques et à l'amenuisement des ressources souterraines. La cuvette lacustre—delta central du Niger—constitue un hydrosystème particulier de lacs et de plaines d'inondation où les pertes annuelles, dues essentiellement à l'évaporation, sont extrêmement impor-tantes. Les mesures de flux fluviaux de matières particulaires, entreprises depuis 1990 sur le Niger amont et son delta, ont montré une perte globale de matières en suspension à l'intérieur du delta central. Les variations saisonnières de la charge solide, en amont et en aval du delta central, permettent une première approche du fonctionnement de cet hydrosystème. La forte hydraulicité de l'année 1994/1995 a mis en évidence la complexité du phénomène de transport fluvial de matière à l'intérieur du delta.
New data are presented on the changes of mean global surface air
temperature and annual precipitation over extratropical continents of
the Northern Hemisphere. Global warming occurred during the last century
with a mean trend of 0.5°C/100 years. It is shown that for the same
period the annual precipitation over the land in the 35°-70°N
zone increased by 6%. The observed variations of precipitation coincide
with the results of general circulation modeling of doubled
CO2 equilibrium climate change by sign but contradict by
scale.
This paper considers the distribution of flood flows in the Upper Mississippi, Lower Missouri, and Illinois Rivers and their relationship to climatic indices. Global climate patterns including El Niño/Southern Oscillation, the Pacific Decadal Oscillation, and the North Atlantic Oscillation explained very little of the variations in flow peaks. However, large and statistically significant upward trends were found in many gauge records along the Upper Mississippi and Missouri Rivers: at Hermann on the Missouri River above the confluence with the Mississippi (p = 2 percent), at Hannibal on the Mississippi River (p < 0.1 percent), at Meredosia on the Illinois River (p = 0.7 percent), and at St. Louis on the Mississippi below the confluence of all three rivers (p = 1 percent). This challenges the traditional assumption that flood series are independent and identically distributed random variables and suggests that flood risk changes over time.
Two contrasting 18 yr periods (1950–1967 and 1968–1985) were compared to illustrate the hydrologic and water resources effects of a change to a wetter climatic regime over Illinois. For the nine State Climate Divisions, precipitation increases and fluctuations in wetness measured by Palmer Drought Indices revealed a marked shift between the periods. The seasonal variability and spatial coherence of this precipitation climate fluctuation and its impacts are examined in detail and quantitative relationships are derived between Drought Indices and measured soil moisture and streamflow at several sites. Riverflow and well level changes are consistent with this climate change on the 20–40 yr time-scale which has had some significance for water management in the area.
A simple approach to long-range forecasting of monthly or seasonal quantities is as the average of observations over some number of the most recent years. Finding this `optimal climate normal' (OCN) involves examining the relationships between the observed variable and averages of its values over the previous one to 30 years and selecting the averaging period yielding the best results. This procedure involves a multiplicity of comparisons, which will lead to misleadingly positive results for developments data. The statistical significance of these OCNs are assessed here using a resampling procedure, in which time series of U.S. Climate Division data are repeatedly shuffled to produce statistical distributions of forecast performance measures, under the null hypothesis that the OCNs exhibit no predictive skill. Substantial areas in the United States are found for which forecast performance appears to be significantly better than would occur by chance.Another complication in the assessment of the statistical significance of the OCNs derives from the spatial correlation exhibited by the data. Because of this correlation, instances of Type I errors (false rejections of local null hypotheses) will tend to occur with spatial coherency and accordingly have the potential to be confused with regions for which there may be real predictability. The `field significance' of the collections of local tests is also assessed here by simultaneously and coherently shuffling the time series for the Climate Divisions. Areas exhibiting significant local tests are large enough to conclude that seasonal OCN temperature forecasts exhibit significant skill over parts of the United States for all seasons except SON, OND, and NDJ, and that seasonal OCN precipitation forecasts are significantly skillful only in the fall. Statistical significance is weaker for monthly than for seasonal OCN temperature forecasts, and the monthly OCN precipitation forecasts do not exhibit significant predictive skill.
Recent climate change in tropical convection in the western Pacific and Indian Ocean regions is inferred from the outgoing longwave radiation (OLR) records. The systematic bias in the OLR series is first corrected and results of the rotated empirical orthogonal function analysis indicate that the bias, to a first approximation, has been corrected.Linear regression analysis and nonparametric Mann-Kendall rank statistics are employed to detect trends. From 1974 to 1992, trend analyses based on the entire consecutive monthly records suggest a significant decrease in OLR over the tropical central-western Pacific and a large portion of the Indian Ocean. In contrast, northern Australia shows the largest increase in OLR over time. The significance of the local linear trend pattern has been determined via a Monte Carlo simulation technique that scrambles OLR time series at each grid point `simultaneously' and results show the field significance.An increase in convection shows a preference to occur in the summer hemisphere. During the boreal summer half-year, this is seen in a region extending from the Arabian Sea across southeast Asia eastward to the northwest Pacific, with the largest value over the Bay of Bengal. More summer monsoon rainfall is likely to have occurred in these regions. For the austral summer half-year, enhanced convection is found over the equatorial south-central Pacific and the south-central Indian Ocean. Time series of tropical cyclone counts in the northwest Pacific, the Bay of Bengal, and the south-central Indian Ocean also reveal a general level of increase. Regardless of seasonality, a positive trend in OLR is always observed over a large portion of tropical Australia.A sensitivity test is conducted to investigate the change in linear trend patterns by removing the years during which the El Niño-Southern Oscillation phenomenon occurred. Although the enhanced convection over the Bay of Bengal, the south Indian Ocean, and the northwest Pacific are still noticeable, it is much weaker over the equatorial south-central Pacific than when the complete duration series were used. Other sensitivity tests are also conducted to examine the change in linear trend patterns by varying data lengths and by skipping the missing 10-month observation in the OLR time series; results are basically similar to those when complete data are used. The authors speculate that monsoon convection over the tropical western Pacific and the Indian Ocean has undergone a change in the climate mean state, probably on a decadal timescale.
Historical records of warm- and cold-season floods and associated heavy-precipitation events during 1921–1985 in the Midwest were examined for temporal fluctuations and trends. Floods in basins in the northern Midwest exhibited upward trends in both seasons but no statistically significant temporal changes existed elsewhere. The incidence of heavy-precipitation events also increased in this same area, a region where thunderstorm incidences and cyclone frequencies have also been on the increase since 1920. Pentads of high flood incidences all occurred in major wet periods (generally the 1970s and 1980s), and pentads of lowest flood incidences occurred in notable droughts (1930s and 1951–1965). The times of these droughts (early) and wet periods (late) further help explain the tendency for floods to increase during the 1921–1985 study period. When precipitation over 5-year periods decreased 8% or more below average, or increased by 7% above average, the number of floods was greatly reduced or increased. This suggests that future significantly drier climate conditions in the Midwest could have few floods, and significantly wetter conditions could have increased flood activity.
Discharge records where flows have not been subject to overt anthropogenic controls have been identified for over 1500 streamflow gauging stations throughout the United States in the US Geological Survey Hydro-Climatic Data Network. These stations fall within all 21 water resources regions of the United States. Analysis of runoff in 20 regions, where long-term daily records are available, shows an increasing trend in 16 regions. Further analysis using a stratified subset of 65 sites shows an increase in baseflow at approximately 90% of the sites during the past 50 years, regardless of the size of the drainage area. Because anthropogenic alterations of watershed characteristics cannot explain these hydrologie changes, then meteorological or climatic forces are implicated.
Seasonal averages of 700 mb height data are used to illustrate the problem and to demonstrate how the data set properties are taken into account. Papers by Hancock and Yarger (1979), Nastrom and Belmont (1980) and Williams (1980) are critically examined in light of these considerations and Monte Carlo strategies for clarification of ambiguities suggested. -from Authors
Spatial patterns in trends of four monthly variables: average temperature, precipitation, streamflow, and average of the daily temperature range were examined for the continental United States for the period 1948-88. The data used are a subset of the Historical Climatology Network (1036 stations) and a stream gage network of 1009 stations. Trend significance was determined using the nonparametric seasonal Kendall's test on a monthly and annual basis, and a robust slope estimator was used for determination of trend magnitudes. A bivariate test was used for evaluation of relative changes in the variables, specifically, streamflow relative to precipitation, streamflow relative to temperature, and precipitation relative to temperature.Strong trends were found in all of the variables at many more stations than would be expected due to chance. There is a strong spatial and seasonal structure in the trend results. For instance, although annual temperature increases were found at many stations, mostly in the North and West, there were almost as many downtrends, especially in the South and East. Among the most important trend patterns are (a) increases in March temperature at almost half of the stations; (b) increases in precipitation from September through December at as many as 25 percent of the stations, mostly in the central part of the country; (c) strong increases in streamflow in the period November-April at a maximum of almost half of the stations, with the largest trend magnitudes in the north-central states; (d) changes in the temperature range (mostly downward) at a large number of stations beginning in late spring and continuing through winter, affecting as many as over half of the stations. The observed trends in streamflow are not entirely consistent with the changes in the climatic variables and may be due to a combination of climatic and water management effects.
Secular trends in streamflow are evaluated for 395 climate-sensitive streamgaging stations in the conterminous United States using the non-parametric Mann-Kendall test. Trends are calculated for selected quantiles of discharge, from the 0th to the 100th percentile, to evaluate differences between low-, medium-, and high-flow regimes during the twentieth century. Two general patterns emerge; trends are most prevalent in the annual minimum (Q0) to median (Q50) flow categories and least prevalent in the annual maximum (Q100) category; and, at all but the highest quantiles, streamflow has increased across broad sections of the United States. Decreases appear only in parts of the Pacific Northwest and the Southeast. Systematic patterns are less apparent in the Q100 flow. Hydrologically, these results indicate that the conterminous U.S. is getting wetter, but less extreme.
Spatial patterns in trends of four monthly variables: average temperature, precipitation, streamflow, and average of the daily temperature range were examined for the continental United States for the period 1948-88. The data used are a subset of the Historical Climatology Network (1036 stations) and a stream gage network of 1009 stations. Trend significance was determined using the nonparametric seasonal Kendall's test on a monthly and annual basis, and a robust slope estimator was used for determination of trend magnitudes. A bivariate test was used for evaluation of relative changes in the variables, specifically, streamflow relative to precipitation, streamflow relative to temperature, and precipitation relative to temperature. Strong trends were found in all of the variables at many more stations than would be expected due to chance. There is a strong spatial and seasonal structure in the trend results. For instance, although annual temperature increases were found at many stations, mostly in the North and West, there were almost as many downtrends, especially in the South and East. Among the most important trend patterns are (a) increases in March temperature at almost half of the stations; (b) increases in precipitation from September through December at as many as 25 percent of the stations; (c) strong increase in streamflow in the period November-April at a maximum of almost half of the stations, with the largest trend magnitudes in the north-central states; (d) changes in the temperature range (mostly downward) at a large number of stations beginning in late spring and continuing through winter, affecting as many as over half of the stations. The observed trends in streamflow are not entirely consistent with the changes in the climatic variables and may be due to combination of climatic and water management effects. 34 refs., 16 figs, 7 tabs.
Although a vast amount of literature exists on the selection of an appropriate probability distribution for annual maximum floodflows, few studies have examined which probability distributions are most suitable to fit to sequences of annual minimum streamflows. Probability plots have been used widely in hydrology as a graphical aid to assess the goodness of fit of alternative distributions. Recently, probability-plot correlation-coefficient (PPCC) tests were introduced to test the normal, two-parameter lognormal and Gumbel hypotheses. Those procedures are extended here to include both regional and at-site tests for the two-parameter Weibull and lognormal distributional hypotheses. In theory, PPCC-hypothesis tests can only be developed for two-parameter distributions that exhibit a fixed shape. Nevertheless, the PPCC is a useful goodness-of-fit statistic for comparing three-parameter distributions. The PPCC derived from fitting the two- and three-parameter lognormal, two- and three-parameter Weibull, and log-Pearson type III distributions to sequences of annual minimum seven-day low flows at 23 sites in Massachusetts are compared. How the PPCC can be used to discriminate among both competing distributional hypotheses for the distributions of fixed shape and competing parameter-estimation procedures for the distributions with variable shape is described. An approximate regional PPCC test is developed and used to show that there is almost no evidence to contradict the hypothesis that annual minimum seven-day low flows in Massachusetts are two-parameter lognormal.
L-moment diagrams are constructed for annual minimum, average, and maximum streamflows at more than 1,455 river basins in the United States. Goodness-of-fit comparisons reveal that the generalized extreme value (GEV), three-parameter lognormal (LN3) and the log Pearson Type III (LP3) distributions provide good approximations to the distribution of annual maximum flood flows. These results are consistent with other L-moment studies. A World Meteorological Organization survey of 54 agencies in 28 countries reveals that the LN3 distribution is not a standard in any country, GEV is a standard in one country, and LP3 is a standard in seven countries. The time is ripe for agencies and countries to reevaluate their standards with respect to the choice of a suitable model for flood frequency analysis. L-moment diagrams also reveal that among numerous alternatives, the Pearson Type III (P3) distribution provides the best fit to both annual minimum and annual average streamflows.
Historical records of streamflow for an eastward- and a westward-draining stream in the northern Sierra Nevada have been analyzed for evidence of changes in runoff characteristics and patterns of variability. A trend of increasing and more variable winter streamflow began in the mid-1960s. Mean monthly streaniflow during December through March was substantially greater for water years 1965–1990 compared to water years 1939–1964. Increased winter and early-spring streamflow during the later period is attributed to small increases in temperature, which increase the rain-to-snow ratio at lower altitudes and cause the snowpack to melt earlier in the season at higher altitudes. The timing of snowmelt runoff on the western slope of the Sierra Nevada is more sensitive than it is on the eastern slope to changes in temperature, owing to predominantly lower altitudes on the west side. This difference in sensitivity suggests that basins on the east side of the Sierra Nevada have a more reliable water supply (as snow storage) than western-slope basins during warming trends.
Trends in heavy rainfall, total rainfall and number of dry days in Australia have been analysed using daily rainfall records at 125 stations. Summer and winter halves of the year were considered separately for the period 1910–1990. The summer half-year is defined as November–April, while the winter-half is May–October. Heavy rainfall is defined as the 90th and 95th percentiles of daily rainfall in each half-year. The magnitude of trends was derived from linear regression while statistical significance was determined by Kendall-Tau and field significance tests.Increasing trends in heavy rainfall and total rainfall have occurred during the summer half-year, but only 10–20% of stations have statistically significant trends. During the winter half-year, heavy rainfall and total rainfall have also increased, except in far southwest Western Australia and inland Queensland. There has been a reduction in the number of dry days in both halves of the year, except in far southwest Western Australia and at a few stations in eastern Australia where there has been an increase in the number of dry days in the winter half-year. Changes in the number of dry days were statistically significant at over 50% of stations.Hence there are regions showing coherent increases and decreases in rainfall which may be due to systematic changes in climate during the last century. Trends were averaged over three broad regions with adequate station coverage. There has been a general decrease in dry days with an increase in total and heavy rainfall intensity in the northeast and southeast, and a decrease in total and heavy rainfall in the southwest. These rainfall changes are related to changes in other climate variables such as temperature and cloud cover in Australia. © 1998 Royal Meteorological Society
Precipitation elasticity of streamflow, P , provides a measure of the sensitivity of streamflow to changes in rainfall. Watershed model– based estimates of P are shown to be highly sensitive to model structure and calibration error. A Monte Carlo experiment compares a nonparametric estimator of P with various watershed model– based approaches. The nonparametric estimator is found to have low bias and is as robust as or more robust than alternate model-based approaches. The nonparametric estimator is used to construct a map of P for the United States. Comparisons with 10 detailed climate change studies reveal that the contour map of P introduced here provides a validation metric for past and future climate change investigations in the United States. Further investigations reveal that P tends to be low for basins with significant snow accumulation and for basins whose moisture and energy inputs are seasonally in phase with one another. The Budyko hypothesis can only explain variations in P for very humid basins.
The concept of the return period is widely used in the analysis of the risk of extreme events and in engineering design. For example, a levee can be designed to protect against the 100-year flood, the flood which on average occurs once in 100 years. Use of the return period typically assumes that the probability of occurrence of an extreme event in the current or any future year is the same. However, there is evidence that potential climate change may affect the probabilities of some extreme events such as floods and droughts. In turn, this would affect the level of protection provided by the current infrastructure. For an engineering project, the risk of an extreme event in a future year could greatly exceed the average annual risk over the design life of the project. An equivalent definition of the return period under stationary conditions is the expected waiting time before failure. This paper examines how this definition can be adapted to nonstationary conditions. Designers of flood control projects should be aware that alternative definitions of the return period imply different risk under nonstationary conditions. The statistics of extremes and extreme value distributions are useful to examine extreme event risk. This paper uses a Gumbel Type I distribution to model the probability of failure under nonstationary conditions. The probability of an extreme event under nonstationary conditions depends on the rate of change of the parameters of the underlying distribution.
Statistical models consisting of a trend plus serially correlated noise may be fitted to observed climate data such as global surface temperature, the trend and noise representing systematic change and other variations, respectively. When such a model is fitted, the estimated character of the noise determines the precision of the estimated trend, and hence the precision of the estimate of the magnitude of the systematic change in the variable considered. The results of fitting such models to global temperature imply that there is uncertainty in the amount of temperature change over the past century of up to ± 0.2 °C, but that the change of around one half of a degree Celsius is significantly different from zero.
The statistical models for climate variability also imply that the observed temperature data provide only imprecise information about the climate sensitivity. This is defined here as the equilibrium response of global temperature to a doubling of the atmospheric concentration of carbon dioxide. The temperature changes observed to date are compatible with a wide range of climate sensitivities, from 0.7 °C to 2.2 °C. When data uncertainties are taken into account, the interval widens even further.
We discuss the following problem given a random sample X = (X
1, X
2,…, X
n) from an unknown probability distribution F, estimate the sampling distribution of some prespecified random variable R(X, F), on the basis of the observed data x. (Standard jackknife theory gives an approximate mean and variance in the case R(X, F) = \(\theta \left( {\hat F} \right) - \theta \left( F \right)\), θ some parameter of interest.) A general method, called the “bootstrap”, is introduced, and shown to work satisfactorily on a variety of estimation problems. The jackknife is shown to be a linear approximation method for the bootstrap. The exposition proceeds by a series of examples: variance of the sample median, error rates in a linear discriminant analysis, ratio estimation, estimating regression parameters, etc.
Regional temporal trends of precipitation quantiles in the US
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Information content of the regional mean
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Streamflow trends in the United States
- Lins
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Long term variability in flood arrival rates: the presence of annual flood event clustering
- F R Walker
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Cornell University, Ithaca, NY.
FRIEND'97 Ð Regional Hydrology: Concepts and Models for Sustainable water Resource Management
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Statistical ®eld signi®cance and its determination by Monte Carlo techniques
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Information content of the regional mean
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mean. Proceedings of the International Symposium on Uncertainties in Hydrologic and Water Resource Systems, vol. 2,
University of Arizona, Tucson, December 11±14.