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Heat map of cumulative monthly precipitation data, where rainfall monthly values (mm) are represented by colors
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Hydrological Mediterranean situation has undergone rapid changes over the last years. This fact causes heavy consequences for both regional water authorities and water utilities to properly plan the future groundwater management. This work analyzes the presence of hydrological changes at local scales, through the reconstruction and time series anal...
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... phenomenon was also visible on the heatmap ("hydroTSM" package of R software, Zambrano-Bigiarini 2017), where a color variation begins around the 80 s ending, in accordance with change point analysis results (Fig. 4). (Cleveland 1988) In particular, it is possible to see a clear red-orange case rise from 1990 that indicates a decrease in rainfall quantity between 0 and 50 ...
Citations
... The rate of groundwater extraction is significantly higher than the rate of recharge, and this imbalance has been occurring for several years, leading to a decline in groundwater levels. This phenomenon is also exacerbated by climate change, which impacts not only the quantity and continuity of groundwater but also its quality (Salvati et al., 2015;Zhi-Jie et al., 2015;Zirulia et al., 2021). ...
Uncontrolled and excessive use of groundwater leads to economic, social, and environmental impacts. This study investigated factors influencing the low willingness to utilize piped water. The research was conducted in Duren Sawit Village, East Jakarta. The study focused on the sources of household clean water and the factors affecting the low utilization of piped water. A qualitative research approach was employed through in-depth interviews with key informants selected using purposive sampling, and the problem was analyzed using a fishbone diagram. The study found 81% of households use groundwater, only 9% use piped water, and 10% use both sources. The findings reveal that 25% of the households using piped water still rely entirely on groundwater. The low utilization of piped water was influenced by several factors, including socio-economic conditions, environmental behavior, groundwater quality, performance of PAM Jaya, and groundwater regulations for household usage. Based on the analysis, strategies for accelerating piped water utilization were proposed, including socialization and education on groundwater conservation, quality of groundwater, improvement of PAM Jaya performance, and changes in regulations regarding groundwater use for household. Environmental behavior is identified as the primary factor influencing the high use of groundwater. Several strategies involving stakeholders are necessary to promote the use of piped water among households. Keywords: Environmental behaviour, Ground water, Ground water regulation, Piped water, Socio economic.
... On the other hand, Unnikrishnan and Jothiprakash (2020) proposed the integration of singular spectrum analysis, ARIMA and artificial neural networks (ANNs) in a hybrid model to predict daily rainfall in a catchment with reliable accuracy. At the same time, decadal forecasting of hydrological data is in its early stages due to lack of knowledge of hydrological predictability and forecasting skill at interannual to decadal scales (Zhu et al 2019), particularly in Italy, where little has been published (Diodato et al 2017, Zirulia et al 2021. However, with the lack of knowledge of complex atmospheric processes, the uncertainty of forecasts increases with the lead time (Diodato et al 2019, Ashok andPekkat 2022). ...
In the Mediterranean basin, climate change signals are often representative of atmospheric transients in precipitation patterns. Remote mountaintop rainfall stations are far from human influence and can more easily unveil climate signals to improve the accuracy of long-term forecasts. In this study, the world’s longest annual precipitation time-series (1884-2021) from a remote station, the Montevergine site (1284 m a.s.l.) in southern Italy, was investigated to explain its forecast performance in the coming decades, offering a representative case study for the central Mediterranean. For this purpose, a Seasonal AutoRegressive-exogenous Time Varying process with Exponential Generalised Autoregressive Conditional Heteroscedasticity (SARX(TVAR)-EGARCH) model was developed for the training period 1884-1991, validated for the interval 1992-2021, and used to make forecasts for the time-horizon 2022-2051, with the support of an exogenous variable (Dipole Mode Index). Throughout this forecast period, the dominant feature is the emergence of an incipient and strong upward drought trend in precipitation until 2035. After this change-point, rainfall increases again, more slightly, but with considerable values towards the end of the forecast period. Although uncertainties remain, the results are promising and encourage the use of SARX(TVAR)-EGARCH in climate studies and forecasts in mountain sites.
... Lee et al. (2020) recently employed the LSTM model to simulate hydroclimatological variables and proved that the LSTM model can be a good alternative to reproduce the long-term dependence structure of a historical time series since the LSTM model was originally invented to reproduce the long-term memory as presented in the name itself (i.e., LSTM). Likewise, there are a number of alternative models to illustrate the nonlinearities and nonstationarity embedded in hydroclimatological series (Khaliq et al., 2009;Myronidis et al., 2018;Sakiur Rahman et al., 2018;Zirulia et al., 2021). ...
Hydrological time series often present nonstationarities such as trends, shifts, or oscillations due to anthropogenic effects and hydroclimatological variations, including global climate change. For water managers, it is crucial to recognize and define the nonstationarities in hydrological records. The nonstationarities must be appropriately modeled and stochastically simulated according to the characteristics of observed records to evaluate the adequacy of flood risk mitigation measures and future water resources management strategies. Therefore, in the current study, three approaches were suggested to address stochastically nonstationary behaviors, especially in the long‐term variability of hydrological variables: as an overall trend, shifting mean, or as a long‐term oscillation. To represent these options for hydrological variables, the autoregressive model with an overall trend, shifting mean level (SML), and empirical mode decomposition with nonstationary oscillation resampling (EMD‐NSOR) were employed in the hydrological series of the net basin supply in the Lake Champlain‐River Richelieu basin, where the International Joint Committee recently managed and significant flood damage from long consistent high flows occurred. The detailed results indicate that the EMD‐NSOR model can be an appropriate option by reproducing long‐term dependence statistics and generating manageable scenarios, while the SML model does not properly reproduce the observed long‐term dependence, that are critical to simulate sustainable flood events. The trend model produces too many risks for floods in the future but no risk for droughts. The overall results conclude that the nonstationarities in hydrological series should be carefully handled in stochastic simulation models to appropriately manage future water‐related risks.
... SARIMA was also used in the precipitation and temperature predictions at the Tehri and Uttarkashi stations in the Bhagirathi river basin in the state of Uttarakhand, India [39]. SARIMA was applied to a coastal Tuscany watershed to study hydrological cycle changes at the local scales (area) [40]. Furthermore, SARIMA was applied for the spatiotemporal analysis in the Highlands region, Algeria, to predict the local scale of precipitation [41]. ...
Many machine-learning applications and methods are emerging to solve problems associated with spatiotemporal climate forecasting; however, a prediction algorithm that considers only short-range sequential information may not be adequate to deal with periodic patterns such as seasonality. In this paper, we adopt a Periodic Convolutional Recurrent Network (Periodic-CRN) model to employ the periodicity component in our proposals of the periodic representation dictionary (PRD). Phase shifts and non-stationarity of periodicity are the key components in the model to support. Specifically, we propose a Soft Periodic-CRN (SP-CRN) with three proposals of utilizing periodicity components: nearby-time (PRD-1), periodic-depth (PRD-2), and periodic-depth differencing (PRD-3) representation to improve climate forecasting accuracy. We experimented on geopotential height at 300 hPa (ZH300) and sea surface temperature (SST) datasets of ERA-Interim. The results showed the superiority of PRD-1 plus or minus one month of a prior cycle to capture the phase shift. In
addition, PRD-3 considered only the depth of one differencing periodic cycle (i.e., the previous year) can significantly improve the prediction accuracy of ZH300 and SST. The mixed method of PRD-1, and PRD-3 (SP-CRN-1+3) showed a competitive or slight improvement over their base models. By adding the metadata component to indicate the month with one-hot encoding to SP-CRN-1+3, the prediction result was a drastic improvement. The results showed that the proposed method could learn four years of periodicity from the data, which may relate to the El Niño–Southern Oscillation (ENSO) cycle.
... As reported by [2,15], the Cecina Valley area will likely experience a considerable reduction in total precipitations as well as an increase in temperatures, shifting from a semi-arid climate to a more arid one [16]. This change could also have a negative influence over the hydrodynamics and hydrogeochemistry of this SSWB, since acknowledge infiltration of rainfall is the main recharge source for this aquifer [14]. ...
... Furthermore, the data were standardized to have a mean equal to 0 and a standard deviation equal to 1 in order to guarantee that all variables would be considered equally weighted [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23], in the following statistical analysis. Figure 2 shows the distribution of all the ions considered before and after the standardization. ...
... This situation could be reflecting two phenomena: (i) diminution of the amount of recharge waters received and (ii) increased extraction of water that leads to a lowering of the piezometric level. Both phenomena are not mutually exclusive and could also be taking place simultaneously according to [2,16] for the Tuscany area in a climate change scenario a decrease in the amount and frequency of precipitation should be expected. The shift into a more arid climate would also lead to an increase in the influence of evapotranspiration over the groundwater level [30] increasing the concentrations of the ions in the waters that reach and recharge the groundwater. ...
The hydrogeochemical characteristics of the significant subterranean water body between “Cecina River and San Vincenzo” (Italy) was evaluated using multivariate statistical analysis methods, like principal component analysis and self-organizing maps (SOMs), with the objective to study the spatiotemporal relationships of the aquifer. The dataset used consisted of the chemical composition of groundwater samples collected between 2010 and 2018 at 16 wells distributed across the whole aquifer. For these wells, all major ions were determined. A self-organizing map of 4 × 8 was constructed to evaluate spatiotemporal changes in the water body. After SOM clustering, we obtained three clusters that successfully grouped all data with similar chemical characteristics. These clusters can be viewed to reflect the presence of three water types: (i) Cluster 1: low salinity/mixed waters; (ii) Cluster 2: high salinity waters; and (iii) Cluster 3: low salinity/fresh waters. Results showed that the major ions had the greater influence over the groundwater chemistry, and the difference in their concentrations allowed the definition of three clusters among the obtained SOM. Temporal changes in cluster assignment were only observed in two wells, located in areas more susceptible to changes in the water table levels, and therefore, hydrodynamic conditions. The result of the SOM clustering was also displayed using the classical hydrochemical approach of the Piper plot. It was observed that these changes were not as easily identified when the raw data were used. The spatial display of the clustering results, allowed the evaluation in a hydrogeological context in a quick and cost-effective way. Thus, our approach can be used to quickly analyze large datasets, suggest recharge areas, and recognize spatiotemporal patterns.
Büyük su kütleleri arazi kullanımını değiştirir ve bölgesel iklimi etkiler. Bölgesel iklimdeki değişimler meteorolojik verilerin analizi ile belirlenir. Bu çalışmada, Suğla Gölü'nün bölge iklimine olan etkilerinin 60 yıllık dönemde (1960 - 2020) trend analizleri, Otoregresif Entegre Hareketli Ortalamalar (ARIMA) modelleri ve uzaktan algılama teknikleri kullanılarak araştırılması amaçlanmıştır. Bu amaçlar doğrultusunda, 4 meteoroloji istasyonundan elde edilen nem, sıcaklık, rüzgâr ve yağış zaman serilerine uygulanan trend analizleri meteorolojik verilerdeki herhangi bir pozitif veya negatif trendin varlığını tespit etmek için kullanılmıştır. Trend analizlerinin ardından, zaman serisi kestirimi ve tahmin yöntemi olan ARIMA modelleri ile meteorolojik veriler için 10 yıllık dönemde (2020 - 2030) tahminlerde bulunulmuştur. Uzaktan algılama teknikleriyle, 1984, 1990, 2000, 2006, 2010, 2020 ve 2022 yıllarına ait Landsat uydu görüntüleri kullanılarak arazi kullanımındaki değişimler belirlenmiştir. Çalışma sonucunda, tüm istasyonlarda trend analiz ve ARIMA tahminlerinde artan ve azalan trendler tespit edilmiştir.
