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Trends for monthly normalized monthly precipitation anomaly percentage (NAP) in Pakistan for the period of 2001 to 2017
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Drought is an extreme climatic event that mostly occurs as a result of low rainfall, which leads to lack of water in various agro-ecological conditions of Pakistan. The condition could be further exacerbated by the prevailing dry weather. Therefore, accurate, timely, and efficient drought monitoring is crucial to ensure that its adverse effects are...
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Citations
... The use of variables associated with soil's physical processes and vegetation's physiological processes, such as the soil moisture (Faiz et al., 2022), land cover (Poveda et al., 2011), available soil water capacity (Cooper et al., 2021;Martínez-Fernández et al., 2016;Tramblay and Quintana Seguí, 2022), remote sensing data as initial condition (Abdikan et al., 2023;Cai et al., 2023;Fathi-Taperasht et al., 2022;Wong et al., 2022); the NDVI (Abdourahamane et al., 2022;Bentchakal et al., 2022;D'Andrea et al., 2022;Li and Huimin, 2022), the CWSI (Ali et al., 2023;Chen et al., 2022;Pradawet et al., 2023;Wei et al., 2022), the LAI (Sungmin and Park, 2023;Wu et al., 2023;Zhang et al., 2023;Cai et al., 2023) and SEBAL (Aryalekshmi et al., 2021;Karishma et al., 2022;Tan et al., 2021) were important parameters to develop the SMODI index. This approach allows us to integrate and consider the primary factors and thresholds that govern the distribution and retention of soil moisture and water stress that plants have developed to withstand periods of drought, which when exceeded constitute a water risk for them. ...
... In Lahore, the majority of the population lives in urban areas, and the existing infrastructure is difficult to change quickly without proper planning, which is a challenge in a megacity. Additionally, it is hard to persuade people to leave their homes due to social and economic barriers (Ali et al., 2023;Mahmood & Hamayon, 2021). Therefore, the purpose of this study is a geo-spatial assessment of pluvial floods in the city district of Lahore. ...
This study is an effort of geo-spatial assessment of pluvial floods in District Lahore, Pakistan, caused by urban expansion and the growing frequency and intensity of high-intensity rainfall events. The use of geospatial techniques such as watershed modeling, maximum likelihood image classification, and weighted overlay analysis based on secondary data has enabled the researchers to assess the extent and severity of pluvial floods in the study area. The study’s findings highlight the high risk of pluvial floods in the central part of the study area, which is dominated by built-up land and concrete roads. The increase in the area of built-up land from 34.913 km² in 2018 to 37.442 km² in 2022 has further intensified the risk of pluvial floods. The findings of this study can assist policymakers in developing effective strategies to reduce the risks associated with pluvial floods. Alongside, it also highlights the importance of geospatial techniques to better understand and address the complex challenges of urbanization and climate change. Flood risk zone-specific strategies are recommended to reduce the risk of pluvial floods.
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Drought as a natural phenomenon has always been a serious threat to regions with hot and dry climates. One of the major effects of drought is the drop in groundwater level. This paper focused on the SPI (Standardized Precipitation Index) and SWI (Standardized Water-Level Index) to assess meteorological and hydrological drought, respectively. In the first part, we used different time frames of SPI (3, 6, 12, and 24 months) to investigate drought in Yazd, a dry province in the center of Iran for 29 years (1990–2018). Then, in the second part, the relationship between SPI and SWI was investigated in the three aquifers of Yazd by some rain gauge stations and the closest observation wells to them. In addition to using SPI and SWI, we also used different machine learning (ML) algorithms to predict drought conditions including linear model and six non-linear models of K_Nearest_Neighbors, Gradient_Boosting, Decision_Tree, XGBoost, Random_Forest, and Neural_Net. To evaluate the accuracy of the mentioned models, three statistical indicators including Score, RMSE, and MAE were used. Based on the results of the first part, Yazd province has changed from mild wet to mild drought in terms of meteorological drought (the amount of rainfall according to SPI), and this condition can worsen due to climate change. The models used in ML showed that SPI-6 (score ave = 0.977), SPI-3 (score ave = 0.936), SPI-24 (score ave = 0.571), and SPI-12 (score ave = 0.413) indices had the highest accuracy, respectively. The models of Neural_Net (score ave = 0.964–RMSE ave = 0.020–MAE ave = 0.077) and Gradient_Boosting (score ave = 0.551–RMSE ave = 0.124–MAE ave = 0.248) had the highest and lowest accuracy in prediction of the SPI in all four-time scales. Based on the results of the second part, about the SWI, Random_Forest model (score = 0.929–RMSE = 0.052–MAE = 0.150) and model of Neural_Net (score = 0.755–RMSE = 0.235–MAE = 0.456) had the highest and lowest accuracy, respectively. Also, hydrological drought (reduction of the groundwater level) of the region has been much more severe, and according to the low correlation coefficient of average SPI and SWI (R² = 0.14), we found that the uncontrolled pumping wells, as a main factor than a shortage of rainfall, have aggravated the hydrological drought, and this region is at risk of becoming a more arid region in the future.
The temperature vegetation dryness index (TVDI) is widely used for the monitoring of global or regional drought because of its strong drought-monitoring capabilities and ease of implementation. However, the temporal errors in the land surface temperature (LST) and normalized difference vegetation index (NDVI) can affect warm and cold edges, thus determining the quality of the TVDI, especially in regions affected by climate change, such as Shandong Province. This paper explores this issue in the region in 2011, using daily MODIS MOD09GA and MOD11A1 data products. For each image acquisition time, the warm and cold edges of the NDVI–LST were extracted based on the NDVI, derived from red and near-infrared reflectance data, and the LST, derived from the MOD11A1 dataset. Then, the variations in the warm and cold edges with the LST and NDVI were analyzed. Subsequently, the influence of warm and cold edges, based on the daily values of the temperature, NDVI and precipitation during the observed period, was assessed using a linear regression. The soil moisture (SM) data obtained from the Global Land Data Assimilation System (GLDAS) datasets and the crop water stress index (CWSI) obtained from the MOD16A2 products were used for the assessment. The spatial and temporal variations in drought in Shandong Province from 2011 to 2020 were measured based on Theil–Sen median trend analysis and the Mann–Kendall test. The results show that apparently random variations were evident in the temporal evolution of the slope of the warm edge, indicating that daily data were appropriate to determine the boundary of the warm edge. Daily data were also appropriate to determine the boundary of the cold edge in a similar way. Additionally, the temperature, NDVI and precipitation in this region affected by climate change had a negative correlation with the slope and a positive correlation with the intercept. The validation results show that there was a significant negative correlation between the observed TVDI and GLDAS soil moisture values (R2 > 0.62) in 12 scatter plots. Therefore, we deduced that the monthly or yearly TVDI product produced by the daily MODIS data has a higher precision than that produced by 8-day or monthly data in regions affected by climate change. The spatial and temporal variations show that the trend of slight and moderate droughts first increased and then decreased, and, in particular, some areas presented severe drought from 2011 to 2015. The results obtained in this study are important for the scheduling of irrigation and drought warnings.
Exploring the relationship between cereal yield and the remotely sensed normalized difference vegetation index (NDVI) is of great importance to decision-makers and agricultural stakeholders. In this study, an approach based on the Pearson correlation coefficient and linear regression is carried out to reveal the relationship between cereal yield and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data in the Fez-Meknes region of Morocco. The results obtained show strong correlations reaching 0.70 to 0.89 between the NDVI and grain yield. The linear regression model explains 58 to 79% of the variability in yield in regional provinces marked by the importance of cereal cultivation, and 51 to 53% in the mountainous provinces with less agricultural land devoted to major cereals. The regression slopes indicate that a 0.1 increase in the NDVI results in an expected increase in grain yield of 4.9 to 8.7 quintals (q) per ha, with an average of 6.8 q/ha throughout the Fez-Meknes region. The RMSE ranges from 2.12 to 4.96 q/ha. These results are promising in terms of early yield forecasting based on MODIS-NDVI data, and consequently, in terms of grain import planning, especially since the national grain production does not cover the demand. Such remote sensing data are therefore essential for administrations that are in charge of food security decisions.
Exploring the relationship between cereal yield and remotely sensed normalized difference vegetation index (NDVI) is of great importance to decision-makers and agricultural stakeholders. In this study, an approach based on the Pearson correlation coefficient and linear regression method is carried out to reveal the relationship between cereal yield and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data in the Fez-Meknes region of Morocco. The results obtained show strong correlations reaching 0.70 to 0.89 between NDVI and grain yield. The linear regression model explains 58 to 79% of the variability in yield in regional provinces marked by the importance of cereal cultivation, and 51 to 53% in the mountainous provinces with less agricultural land devoted to major cereals. The regression slopes indicate that a 0.1 increase in NDVI results in an expected increase in grain yield of 4.9 to 8.7 quintals (q) per ha, with an average of 6.8 q/ha throughout the Fez-Meknes region. RMSE ranges from 2.12 to 4.96 q/ha. These results are promising in terms of early yield forecasting based on MODIS NDVI data.
