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1

Modelling causality between agricultural and meteorological drought indices in the Corong River basin, East Java Indonesia

Affandy Nur A., Iranata Data, Anwar Nadjadji, Maulana Mahendra A., Prastyo Dedy D., Jaelani Lalu M., Suryadi F. X.

Journal of Water and Land Development

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2023

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no. 58

178--188

EN

The Lamongan Regency is an area in East Java, Indonesia, which often experiences drought, especially in the south. The Corong River basin is located in the southern part of Lamongan, which supplies the irrigation area of the Gondang Reservoir. Drought monitoring in the Corong River basin is very important to ensure the sustainability of the agricultural regions. This study aims to analyse the causal relationship between meteorological and agricultural drought indices represented by standardised precipitation evapotranspiration index (SPEI) and standard normalisation difference vegetation index (NDVI), using time series regression. The correlation between NDVI and SPEI lag 4 has the largest correlation test results between NDVI and SPEI lag, which is 0.41. This suggests that the previous four months of meteorological drought impacted the current agricultural drought. A time series regression model strengthens the results, which show a causal relationship between NDVI and SPEI lag. According to the NDVI-SPEI-1 lag 4 time series model, NDVI was influenced by NDVI in the previous 12 periods, and SPEI-1 in the last four periods had a determinant coefficient value of 0.4. This shows that the causal model between SPEI-1 and NDVI shows a fairly strong relationship for drought management in agricultural areas (irrigated areas) and is considered a reliable and effective tool in determining the severity and duration of drought in the study area.

2

Spatiotemporal analysis of droughts in Hirfanli Dam basin, Turkey by the Standardised Precipitation Evapotranspiration Index (SPEI)

Zeybekoglu Utku

Acta Geophysica

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2022

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Vol. 70, no 1

361--371

EN

Drought is generally defined as a disaster affecting vital activities negatively because of water scarcity as a result of precipitation falling below the recorded normal levels. In the present study, the Standardized Precipitation Evapotranspiration Index was applied for the first time in Hirfanli Dam basin, which has the characteristics of a semiarid climate in Turkey. The annual drought events in the basin between 1968 and 2017 were investigated by using the precipitation and temperature data obtained from Gemerek, Kayseri, Kirsehir, Nevsehir, Sivas, and Zara meteorology observation stations located in Hirfanli Dam basin. The dry and wet years were determined in the basin, and evaluations were made in this respect. The years when the most severe droughts happened in the basin were determined, and drought maps, which showed the spatial distribution of drought, were prepared. In the light of the analyses and maps made, it was found that the most severe drought happened in 2001 in Hirfanlı Dam basin.

3

Multitemporal meteorological drought forecasting using Bat-ELM

Gholizadeh Reza, Yılmaz Hasan, Danandeh Mehr Ali

Acta Geophysica

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2022

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Vol. 70, no 2

917--927

EN

The advancement of the machine learning (ML) models has demonstrated notable progress in geosciences. They can identify the underlying process or causality of natural hazards. This article introduces the development and verification procedures of a new hybrid ML model, namely Bat-ELM for predictive drought modelling. The multi-temporal standardized precipitation evapotranspiration index (SPEI-3 and SPEI-6) is computed as the meteorological drought index at two study regions (Beypazari and Nallihan), located in Ankara province, Turkey. The proposed hybrid model is obtained by integrating the Bat optimization algorithm as the parameter optimizer with an extreme learning machine (ELM) as the regressor engine. The efficiency of the intended model was evaluated against the classic artificial neural network (ANN) and standalone ELM models. The evaluation and assessment are conducted using statistical metrics and graphical diagrams. The forecasting results showed that the accuracy of the proposed model outperformed the benchmark models. In a quantitative assessment, the Bat-ELM model attained minimal root mean square error for the SPEI-3 and SPEI-6 (RMSE=0.58 and 0.43 at Beypazari station and RMSE=0.53 and 0.37 at Nallihan station) over the testing phase. This indicates the new model approximately 20 and 15% improves the forecasting accuracy of traditional ANN and classic ELM techniques, respectively.

4

Weather drought index prediction using the support vector regression in the Ansegmir Watershed, Upper Moulouya, Morocco

Bekri My Hachem, El Hmaidi Abdellah, Ousmana Habiba, El Faleh El Mati, Berrada Mohamed, El Aissaoui Kamal, Essahlaoui Ali, El Ouali Abdelhadi

Journal of Water and Land Development

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2021

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no. 50

187--194

EN

The purpose of this study is to develop mathematical models based on artificial intelligence: Models based on the support vectors regression (SVR) for drought forecast in the Ansegmir watershed (Upper Moulouya, Morocco). This study focuses on the prediction of the temporal aspect of the two drought indices (standardized precipitation index – SPI and standardized precipitation-evapotranspiration index – SPEI) using six hydro-climatic variables relating to the period 1979–2013. The model SVR3-SPI: RBF, ε = 0.004, C = 20 and γ = 1.7 for the index SPI, and the model SVR3-SPEI: RBF ε = 0.004, C = 40 and γ = 0.167 for the SPEI index are significantly better in comparison to other models SVR1, SVR2 and SVR4. The SVR model for the SPI index gave a correlation coefficient of R = 0.92, MSE = 0.17 and MAE = 0.329 for the learning phase and R = 0.90, MSE = 0.18 and MAE = 0.313 for the testing phase. As for the SPEI index, the overlay is slightly poorer only in the case of the SPI index between the observed values and the predicted ones by the SVR model. It shows a very small gap between the observed and predicted values. The correlation coefficients R = 0.88 for the learning, R = 0.86 for testing remain higher and corresponding to a quadratic error average MSE = 0.21 and MAE = 0.351 for the learning and MSE = 0.21 and MAE = 0.350 for the testing phase. The prediction of drought by SVR model remain useful and would be extremely important for drought risk management.

5

Drought classification using gradient boosting decision tree

Danandeh Mehr Ali

Acta Geophysica

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2021

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Vol. 69, no. 3

909--918

EN

This paper compares the classification and prediction capabilities of decision tree (DT), genetic programming (GP), and gradient boosting decision tree (GBT) techniques for one-month ahead prediction of standardized precipitation index in Ankara province and standardized precipitation evaporation index in central Antalya region. The evolved models were developed based on multi-station prediction scenarios in which observed (reanalyzed) data from nearby stations (grid points) were used to predict drought conditions in a target location. To tackle the rare occurrence of extreme dry/wet conditions, the drought series at the target location was categorized into three classes of wet, normal, and dry events. The new models were trained and validated using the frst 70% and last 30% of the datasets, respectively. The results demonstrated the promising performance of GBT for meteorological drought classification. It provides better performance than DT and GP in Ankara; however, GP predictions for Antalya were more accurate in the testing period. The results also exhibited that the proposed GP model with a scaled sigmoid function at root can efortlessly classify and predict the number of dry, normal, and wet events in both case studies.

6

High-resolution spatio-temporal analyses of drought episodes in the western Mediterranean basin (Spanish mainland, Iberian Peninsula)

González-Hidalgo J. C., Vicente-Serrano S. M., Peña-Angulo D., Salinas C., Tomas-Burguera M., Beguería S.

Acta Geophysica

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2018

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Vol. 66, no. 3

381--392

EN

The purpose of this research was to identify major drought events on the Spanish mainland between 1961 and 2014 by means of two drought indices, and analyze the spatial propagation of drought conditions. The indices applied were the standardized precipitation index (SPI) and the standardized evaporation precipitation index (SPEI). The first was calculated as standardized anomalies of precipitation at various temporal intervals, while the second examined the climatic balance normalized at monthly scale, incorporating the relationship between precipitation and the atmospheric water demand. The daily meteorological data from Spanish Meteorological Archives (AEMet) were used in performing the analyses. Within the framework of the DESEMON project, original data were converted into a high spatial resolution grid (1.1 km2) following exhaustive quality control. Values of both indices were calculated on a weekly scale and different timescales (12, 24 and 36 months). The results show that during the first half of the study period, the SPI usually returned a higher identification of drought areas, while the reverse was true from the 1990s, suggesting that the effect from atmospheric evaporative demand could have increased. The temporal propagation from 12- to 24-month and 36-month timescales analyzed in the paper seems to be a far from straightforward phenomenon that does not follow a simple rule of time lag, because events at different temporal scales can overlap in time and space. Spatially, the propagation of drought events affecting more than 25% of the total land indicates the existence of various spatial gradients of drought propagation, mostly east–west or west–east, but also north–south have been found. No generalized episodes were found with a radial pattern, i.e., from inland to the coast.

7

Changes in Drought Conditions in Poland over the Past 60 Years Evaluated by the Standardized Precipitation-Evapotranspiration Index

Somorowska U.

Acta Geophysica

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2016

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Vol. 64, no. 6

2530--2549

EN

This paper investigates the variability of drought conditions in Poland in the years 1956-2015 with the use of the Standardized Precipitation-Evapotranspiration Index (SPEI). The study provides a new insight into the phenomenon of the past expansion of the drought-affected area as well as evidence of drying trends in a spatiotemporal context. 3-month, 6-month, and 12-month SPEI were considered, representing drought conditions relevant to agriculture and hydrology. The analysis demonstrates that the spatial extent of droughts shows a broad variability. The annual mean of the percentage of the area under drought has witnessed an increase for all three SPEI timescales. This also pertains to the mean area affected by drought over the growing season (April-September). A decreasing trend in the SPEI values indicates an increase in the severity of droughts over the 60-year period in question in an area extending from the south-west to the central part of Poland.