Page 126 - AJWEP-v22i3

Fuladipanah, et al.

global warming, urbanization, and land degradation – is trend analysis, do not require strict distributional
amplifying shifts in these variables, exacerbating risks assumptions. Both model classes have been extensively
such as floods, droughts, and disruptions to hydrological employed by researchers to conduct trend analyses in
cycles. According to the Intergovernmental Panel various fields. 16-25 A brief list of trend analysis models is
on Climate Change, anthropogenic activities have presented in Table 1.
accelerated these changes, with projections indicating This study introduces a comprehensive comparative
a global temperature rise of 1.0 – 5.7°C, a sea-level analysis of long-term trends in monthly groundwater
increase of 0.5 – 1.0 m, and an increased frequency levels across 16 provinces in the Republic of
of extreme weather events by 2100. Trend analysis, Karakalpakstan, Uzbekistan, spanning the period from
particularly through non-parametric methods, has 1990 to 2023. By integrating innovative statistical
become indispensable for evaluating temporal patterns techniques, including the innovative polygon trend
in hydroclimatic data. These methods bypass restrictive analysis (IPTA), the MK test, and Sen’s slope estimator,
assumptions (e.g., normality and independence) this research uniquely captures the temporal and spatial
and are widely applied to detect trends in variables dynamics of groundwater variations. The novelty of
such as rainfall, evapotranspiration, and wind speed. this study lies in the combined use of these advanced
Such analyses help reveal how climate change alters methodologies, providing deeper insights into regional
hydrological systems, impacting water availability, hydrometeorological patterns and their implications for
quality, and ecosystem resilience. For instance, sustainable water resource management in a climatically
precipitation anomalies – linked to both droughts and sensitive region.
floods – are increasingly destabilizing economies and It is important to note that the present study does not
ecosystems worldwide. 1-7 incorporate precipitation time series analysis, as long-
Trend analysis is a fundamental approach for term records show no significant temporal variations
detecting and forecasting future patterns in data using
objective, systematic, and quantitative methodologies. Table 1. Summary of literature review on trend
It is particularly effective for examining the impacts of analysis models
climate change on hydrometeorological variables, which
are inherently complex and stochastic, often exhibiting References Model name Trend subject
significant variability and random fluctuations around Gaddikeri et al. 26 MK Meteorological
an underlying trend. In this context, trend analysis variables
serves as a valuable tool for gaining insights into the Çelebioğlu and Tayanç 27 RM, MK Precipitation
behavior of hydroclimatic variables. However, the Qadem and Tayfur 28 MK, ITA Temperature
stochastic nature and intrinsic properties of these Alashan 29 MK Precipitation
variables necessitate specialized analytical techniques. San 30 ITA Groundwater
Traditional statistical methods commonly used for level
trend detection are often constrained by assumptions Kessabi et al. 31 MK, ITA, SSE Rainfall
such as normality, independence, and sufficient record Likinaw et al. 32 MK, ITA Extreme
length. These assumptions are frequently violated in precipitation
7-9
hydroclimatic datasets. To overcome these limitations, 33
non-parametric statistical tests – characterized by Agbo et al. MK, ITA Climatic
parameters
minimal distributional assumptions – have become
increasingly prevalent in recent years. These methods Sanogo et al. 34 MK Temperature
are better suited to accommodate the inherent variability and rainfall
and irregularities of hydrometeorological datasets. 10-15 Gul and Ren 35 ITA Precipitation
Trend analysis models are broadly categorized into Nguyen et al. 36 MK, ITA Sea level
two primary classes: Parametric and non-parametric. Seenu and Jayakumar 37 MK, ITA Extreme
Parametric models, such as regression models and time rainfall
series models, rely on specific assumptions regarding Güçlü 38 MK, ITA Rainfall
the underlying data distribution and relationships. Caloiero et al. 39 ITA Seasonal and
In contrast, non-parametric models, including the annual rainfall
Mann–Kendall (MK) test, Sen’s slope estimator, Abbreviations: ITA: Innovative trend analysis; MK: Mann–Kendall
Spearman’s rank (SR) correlation test, and innovative test; SSE: Sen’s slope estimator.

Volume 22 Issue 3 (2025) 120 doi: 10.36922/AJWEP025080052