FIGURE 5 - available via license: Creative Commons Attribution 4.0 International
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Density-colored scatter plot between different satellite products and rain gauge measurement of SPI from 2001-2016: a) SPI3J_CHIRPS vs. SPI3J_Station data; b) SPI3A_CHIRPS vs. SPI3A_Station data; c) SPI6A_CHIRPS vs. SPI6A_Station data; d) SPI3J_TRMM vs. SPI3J_Station data e) SPI3A_TRMM vs. SPI3A_Station data; f) SPI6A_TRMM vs. SPI6A_Station data; g) SPI3J_PERSIANN-CDR vs. SPI3J_Station data, h) SPI3A_PERSIANN-CDR vs. SPI3A_Station data; i) SPI6A_PERSIANN-CDR vs. SPI6A_Station data (The colored bars indicate the SPI value)
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Drought is a very complex natural hazard and has a negative impact on the global ecosystem as a whole. Recently Bangladesh has been experiencing by different degree of dryness as a consequence of high climate variability, affecting the crop production to a great extent in the last couple of decades. In this context, the present study was made an ef...
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... compared the SPI (SPI3J, SPI3A, and SPI6A) derived from TRMM, CHIRPS, and PERSIANN-CDR with in-situ observations. The performance of satellite based SPI are evaluated based on R 2 , R, and RMSE value against insitu observation. The colored scatter plot are presented in Fig. 5 demonstrating comparable performance of satellite data with in-situ measurement for SPI values over 33 station in Bangladesh. The three satellites i.e. TRMM, CHIRPS, and PERSIANN-CDR give SPI estimates are in good agreement with those based on gauge observation with high R 2 value and low RMSE. This analysis suggested that all these ...
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Drought is a very complex natural hazard and has a negative impact on the global ecosystem as a whole. Recently Bangladesh has been experiencing by different degree of dryness as a consequence of high climate variability, affecting the crop production to a great extent in the last couple of decades. In this context, the present study was made an ef...
Citations
... For example, assessing the agricultural livelihood vulnerability in coastal regions under climate change, Hoque et al. [31] showed that southeastern coastal regions, including our study region (Hatiya and Noakhali Sadar sub-districts), are most vulnerable to climate change. Droughts in the paddy growing season in Bangladesh were reported by Prodhan et al. [55], who showed that severe to extreme drought was more frequent during the paddy (Boro rice) growing season. These increasingly intense droughts affect crop production in many coastal districts and make the community's livelihood vulnerable [56]. ...
Climate change impacts crop production worldwide, and coastal regions are particularly vulnerable to its adverse effects. Given the projected rise in temperature and shifting precipitation patterns, it is crucial to examine the current challenges faced by farmers in coastal Bangladesh. Using Focus Group Discussions (FGDs) and Key Informant Interviews (KIIs), we assessed the perceptions and experiences of farmers and stakeholders regarding the existing agricultural practices, the challenges they face in crop cultivation, and the adoption of climate-adaptive practices in 2 sub-districts in the southeastern coastal region of Bangladesh. Moreover, using the Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Terrestrial Water Storage Index (STI), we assessed the frequency and intensity of different climatic conditions in these two sub-districts. Results show that 100% of the respondents reported an increase in dry climatic conditions, the occurrence of untimely precipitation, and a decline in irrigation water during the cropping season. All the respondents in the FGDs expressed a loss of crop production because of these climate-induced disturbances. Despite these challenges, farmers have been implementing several climate-adaptive practices. Among the 9 mentioned climate-adaptive practices, 50% of FGD respondents utilize organic fertilizers, 42% cultivate heat- and drought-resilient crop varieties, use improved irrigation and harvest rainwater, and 25% cultivate integrated crops. The results of quantitative analysis of 3- and 6-month SPEI and STI values show that this region experienced frequent and intense dry climatic conditions during the growing-season, which supports the farmers’ and stakeholders’ concern about the increasing occurrence of droughts during crop growing periods. The results suggest that despite adopting climate-resilient practices under increasing growing-season droughts, farmers require support from the government and NGOs in capacity-building training and input support (e.g., stress-resilient seeds). This study holds practical implications for government, NGOs, and policymakers for ensuring sustainable agricultural productivity in the coastal region of Bangladesh.
... The traditional cultivation of paddy has been significantly affected by the untimely precipitation and water shortages during the growing season, decreasing crop production. Several studies conducted in coastal regions of Bangladesh, including Hoque et al. and Aziz et al., have reported similar findings, emphasizing the detrimental effects of soil and water salinity [47,48] . These previous studies demonstrate that the coastal region, including the southwestern coastal belt, is highly vulnerable to sea level rise, salinity, climate-induced shocks (e.g., drought and heatwave), and outbreaks of pests. ...
The coastal region of Bangladesh is significantly influenced by soil and water salinity, which is further exacerbated by the increasing frequency of tropical cyclones and rising sea levels. Understanding the extent of salinity and its challenges is crucial for promoting sustainable agriculture and ensuring access to safe drinking water. Using quantitative (soil and water parameters) and qualitative (focus group discussion and key informant interview) data, we investigated (i) soil and water salinity and soil nutrient contents; and (ii) adaptive practices in agriculture and drinking water management in three sub-districts (Assasuni, Dacope and Morrelganj) in the southwestern coastal region of Bangladesh. Results show that soil salinity levels did not significantly differ among the sub-districts, with Assasuni having slightly higher soil salinity (8.24 dS m-1) compared to Dacope (8.08 dS m-1) and Morrelganj (7.96 dS m-1). Significant differences were observed in the salinity level of pond and canal water among the sub-districts, with Assasuni having the highest levels of salinity in both pond (13.98 dS m-1) and canal water, compared to other sub-districts. Soil and water salinity were the major challenges reported by the respondents; however, climate-induced stresses (e.g., untimely precipitation) and outbreaks of pests during droughts have been identified as prominent issues in sustainable agriculture. Rainwater harvesting has been identified as a viable adaptive technique in drinking water management, offering a feasible solution to address water and soil salinity. The study underscores the importance of implementing adaptive practices (e.g., rainwater harvesting) to address water scarcity and salinity issues in the coastal region and promote resilient agricultural systems.
... Though conventional field survey methods can relatively accurately assess the degree of tree decline, they are labor intensive, time consuming, and inefficient. Remote sensing has high accuracy, good efficiency, and strong adaptability [7,8]. In recent years, technological advancements have reduced the cost of acquiring remotesensing data, thus facilitating the application of novel remote-sensing techniques in forestry investigation and research [9,10]. ...
The deterioration of farmland shelterbelts in the Ulan Buh desert oases could weaken their protective functions. Therefore, an accurate method is essential to assess tree decline degree in order to guide the rejuvenation and transformation of these shelterbelts. This study selected three typical farmland shelterbelts in the Ulan Buh desert oases as the objects. Terrestrial laser scanning (TLS) and airborne hyperspectral imagery (AHI) were used to acquire point cloud data and detailed spectral information of trees. Point cloud and spectral characteristics of trees with varying decline levels were analyzed. Six models were constructed to identify decline degree of shelterbelts, and model accuracy was evaluated. The coefficient of determination between the structural parameters of trees extracted by TLS and field measurements ranged from 0.76 to 0.94. Healthy trees outperformed declining trees in structural parameters, particularly in tridimensional green biomass and crown projection area. Spectral reflectance changes in the 740–950 nm band were evident among the three tree types with different decline levels, decreasing significantly with increased decline level. Among the TLS-derived feature parameters, the canopy relief ratio of tree points and point cloud density strongly correlated with the degree of tree decline. The plant senescence reflectance index and normalized difference vegetation index exhibited the closest correlation with tree decline in AHI data. The average accuracy of the models constructed based on the feature parameters of LiDAR, AHI, and the combination of both of them were 0.77, 0.61, and 0.81, respectively. The light gradient-boosting machine model utilizing TLS–AHI comprehensive feature parameters accurately determined tree decline. This study highlights the efficacy of employing feature parameters derived from TLS alone to accurately identify tree decline. Combining feature parameters from the TLS and AHI enhances the precision of tree decline identification. This approach offers guidance for decisions regarding the renewal and transformation of declining farmland shelterbelts.
... Bangladesh is one of the nations across the globe that faces the greatest threat from climate change. Bangladesh has a lot of different natural disasters like droughts, floods, flash floods, river bank erosion, and cyclones (Prodhan et al., 2020(Prodhan et al., , 2022b. In addition, the nation is undergoing climate change, which can be observed in the form of an increase in temperature, an increase in the level of the sea, and variations in the seasons, such as the way rainfalls is changing, which affects plants and crops (Cao et al., 2022;Chowdhury et al., 2012;Hoque et al., 2019Hoque et al., , 2022Nasim et al., 2019). ...
... This shift in weather would have severe consequences for agriculture and the environment (Prodhan et al., 2022a). Rising temperatures are the principal climate change impact on Bangladeshi agriculture, which lead to more evaporation and droughts, which make it hard to get enough water for irrigation and other uses in northwest Bangladesh (Hoque et al., 2010;Mandal, 2008;Nasim et al., 2019;Prodhan et al., 2020). Many issues with soil health that reduce agricultural output are the result of a combination of climate change and unreasonable human activity. ...
Mankind has developed a technique to generate synthetic reactive nitrogen, which serves as a fertilizer to enhance food production. However, once reactive nitrogen molecules are formed, they become highly mobile and can persist in the environment for extended periods, leading to various negative consequences. One such consequence is the increased emission of nitrous oxide (N2O), a long-lived radiatively active greenhouse gas (GHG) with a molecular heat trapping effect approximately 310 times stronger than other gases, posing a significant environmental challenge. To address this issue, optimizing nitrogen (N) fertilization becomes crucial. The goal is to match the supply of nitrogen from fertilizers with the crop’s demand, thereby reducing excess soil nitrogen. By achieving this balance, the production of soil N2O can also be minimized. Several strategies can be employed to achieve this, such as using slow-release fertilizers that gradually release nutrients into the soil. Additionally, the use of chemical urease inhibitors (UI) and nitrification inhibitors (NI) can slow down the conversion of urea to NH4+, further reducing nitrogen loss. Another effective approach is to adopt a split application method, where fertilizer is applied multiple times throughout the crop cycle. This strategy aims to synchronize fertilizer application with the rapid nitrogen demand of the plants, thereby minimizing N2O emissions. Furthermore, foliar nitrogen fertilizer application can be employed, allowing the active absorption of nitrogen into the interior of the leaf blade through plasmodesmata, hydrophilic pores in the waxy cuticle of the leaf surface, and stomata distributed on the leaf surface. These mechanisms enable the efficient absorption of available nutrients. Moreover, the application of nanotechnology offers a promising solution by reducing the reactivity of nutrient inputs into the agricultural system without compromising productivity, holding a great potential for sustainable agriculture.KeywordsFertilizer applicationN2O emissionAgriculture and nutrient
... Drought phenomena are frequently identified by more than a hundred indices developed by many global research scholars (Niemeyer 2008;Miah et al. 2017). These indices have been successfully utilized in hydrometeorology, climatology, and agriculture (Zargar et al. 2011;Prodhan et al. 2020). The SPI has become the most widespread drought index, measured from only the rainfall data set (McKee et al.1993). ...
... They require only one or two parameters, easily available at various timescales. Current literature associated with droughtrelated studies (Shahid and Behrawan 2008;Alamgir et al. 2015;Rahman and Lateh 2016;Miah et al. 2017;Islam et al. 2017;Uddin et al., 2020;Prodhan et al. 2020;Zinat et al. 2020;Mondol et al. 2021;Kamruzzaman et al. 2022) commonly concentrated on SPI-based techniques in the country. However, a few papers also utilized remote-sensing tools for drought evaluation in Bangladesh (Alamgir et al. 2015;Rahman and Lateh 2016;Prodhan et al. 2020). ...
... Current literature associated with droughtrelated studies (Shahid and Behrawan 2008;Alamgir et al. 2015;Rahman and Lateh 2016;Miah et al. 2017;Islam et al. 2017;Uddin et al., 2020;Prodhan et al. 2020;Zinat et al. 2020;Mondol et al. 2021;Kamruzzaman et al. 2022) commonly concentrated on SPI-based techniques in the country. However, a few papers also utilized remote-sensing tools for drought evaluation in Bangladesh (Alamgir et al. 2015;Rahman and Lateh 2016;Prodhan et al. 2020). Nevertheless, due to the lack of proper scientific investigation, a single index cannot manage the drought hazard. ...
Drought is one of the most geoenvironmental disasters that has notably influenced socioeconomic
progress in recent eras. There has been considerable attention to drought appraisal at various spa�tiotemporal scales varying from regional to continental levels (Sheffield et al. 2012; Peña-Gallardo
et al. 2019). In the context of climate change, an increase in global temperature may change the
water cycle via elevated evaporation demand (Vicente-Serrano et al. 2020). As a result, precipitation
decreases integrated with high evaporation demand can substantially enhance drought severity and
frequency worldwide (Islam et al. 2017; Vicente-Serrano et al. 2020). The effects of drought may
differ significantly from one area to another because of the existing climatic conditions, adaptation
plans, and prevailing infrastructure (Dai et al. 2018)
... The farmers who experienced drought risk in the dry winter season are less likely to irrigate their plots with surface water than their counterparts who did not face the risk. This is because average rainfall is low in the drought-affected region, especially in winter season, which depletes the amount and source of surface water (Prodhan et al., 2020). ...
... located in the southern region are more efficient than those in the northern region. Drought is prominent in the northern part of the country, which results in water and nutrient depletion in the soil (Prodhan et al., 2020). The declining trend in groundwater level, caused by groundwater irrigation and other purposes, is a major concern for farming in the north western region (Alauddin and Sharma, 2013;Dey et al., 2017;Rahman et al., 2021). ...
The choice of irrigation water sources is crucial in rice farming as water availability and cost can vary across water sources. Groundwater caters three-quarters of the total irrigated land in Bangladesh, where rice area alone occupies 80% of the total irrigated land. The present study compares productivity and efficiency differences and determinants of surface and groundwater irrigation users based on a sample of 6947 dry-winter rice growing plots from the nationally representative Bangladesh Integrated Household Survey-2018 database. A range of methods was adopted to correct for heterogeneity in irrigation water source choice decision, self-selection and observable biases. This involved an estimation of a Stochastic Production Frontier (SPF) model with the pooled sample first, then an application of Propensity Score Matching (PSM) to remove self-selection and observable biases, then a test of heterogeneity in irrigation source choices was conducted, and finally estimated two SPF separately for matched samples of groundwater and surface water irrigation users. Results revealed a robust effect of groundwater irrigation in enhancing rice productivity and efficiency. Seed and its quality, fertilizer and soil type are also significant drivers of rice productivity. The significant drivers of efficiency are plot ownership, irrigation frequency, subsidy and family size. Large farms with groundwater-irrigated plots are relatively more efficient. Significantly lower efficiency exists in areas vulnerable to drought. These results raise sustainability concerns owing to the high level of groundwater extraction and falling water table. Policymakers need to devise innovative strategies to increase use of surface water irrigation without sacrificing productivity and efficiency, which has been a priority policy drive in Bangladesh.
... The crops are grown in those areas generally experienced meteorological drought, especially during the summer and winter seasons (Habiba et al. 2012(Habiba et al. , 2013. But there are some non-meteorological drought-prone areas exist in the country where crops also face agricultural drought (Prodhan et al. 2020;Shahid and Behrawan 2008). Therefore, the study aimed to identify the agricultural drought-prone areas of the country by agricultural drought map and also to figure out the vulnerable area(s) for crop production in kharif period (summer and rainy seasons). ...
Bangladesh is an agriculture-dependent country and very often this sector struggles a lot due to various natural hazards including drought and flood. Almost every year in kharif crop season, Bangladesh undergoes through drought which causes a lot of yield loss. So, this has become important to identify the drought-prone areas to reduce the risk of crops yield loss and for policymaking to suggest alternative drought-tolerant crops. The agricultural drought is related to soil properties because of having spatially dynamic in nature. In the present study, agricultural drought has been assessed comprising meteorological drought of kharif season and water holding capacity (WHC) in Geographic Information System (GIS) platform, as GIS is a widely used as a powerful tool to manage and model the spatial data. The metrological drought map has prepared by rainfall data with the calculation of Standardized Precipitation Index (SPI) using GIS and WHC of soil map has converted from soil texture map. Finally, the agricultural drought map has been derived by overlying metrological drought map and WHC of soil map. The study has found that the north-west and south-west region along with Barisal division of Bangladesh are more prone to agricultural drought area than the other parts. Some districts of the middle part of the country (Dhaka, Manikganj and Faridpur: 492 km2) also suffered in extreme agricultural drought during kharif season.
... Besides these indexes, SPEI [58] and SPI [64] were calculated on a 3-month, 6-month, and 12-month time scale, which are able to reflect the dry and wet condition of a particular area. SPI and SPEI were confirmed as preferable meteorological drought indices by several researchers in their studies [65][66][67] due to their simplicity and flexibility for calculation at different time scales. Furthermore, crop growth is hindered by high surface temperature, which was measured in this study using the temperature condition index (TCI) [68]. ...
Drought, a climate-related disaster impacting a variety of sectors, poses challenges for millions of people in South Asia. Accurate and complete drought information with a proper monitoring system is very important in revealing the complex nature of drought and its associated factors. In this regard, deep learning is a very promising approach for delineating the non-linear characteristics of drought factors. Therefore, this study aims to monitor drought by employing a deep learning approach with remote sensing data over South Asia from 2001–2016. We considered the precipitation, vegetation, and soil factors for the deep forwarded neural network (DFNN) as model input parameters. The study evaluated agricultural drought using the soil moisture deficit index (SMDI) as a response variable during three crop phenology stages. For a better comparison of deep learning model performance, we adopted two machine learning models, distributed random forest (DRF) and gradient boosting machine (GBM). Results show that the DFNN model outperformed the other two models for SMDI prediction. Furthermore, the results indicated that DFNN captured the drought pattern with high spatial variability across three penology stages. Additionally, the DFNN model showed good stability with its cross-validated data in the training phase, and the estimated SMDI had high correlation coefficient R2 ranges from 0.57~0.90, 0.52~0.94, and 0.49~0.82 during the start of the season (SOS), length of the season (LOS), and end of the season (EOS) respectively. The comparison between inter-annual variability of estimated SMDI and in-situ SPEI (standardized precipitation evapotranspiration index) showed that the estimated SMDI was almost similar to in-situ SPEI. The DFNN model provides comprehensive drought information by producing a consistent spatial distribution of SMDI which establishes the applicability of the DFNN model for drought monitoring.
... FFt is used to decide which datasets are best to capture CDDs. FFt is already applied in previous study for risk assessment of droughts based on gridded and remote sensing data (Prodhan et al., 2020). Martignon et al. (2008) described a supervised machine learning algorithm (FFt) to make decisions and solve tasks based on binary classification. ...
The reliability of long‐term precipitation estimates is vital for climatology and hydrometeorology applications. Different climatic zones and high rain gauge network (more than 800) of China are a suitable topography for performance evaluation of different long‐term precipitation datasets. In this study, seven long‐term precipitation datasets are tested against in situ observations at different time scales (1981–2016) at 813 grid points. Well‐known statistical indicators and Fast‐frugal tree (FFt) decision model are employed to identify the best long‐term datasets. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Climate Data Record is the only datasets that did not perform well in the study area. Asian Precipitation‐Highly‐Resolved Observational Data Integration Towards Evaluation (APHRODITE), Global Precipitation Climatology Centre (GPCC), and Climate Prediction Center (CPC‐Global) estimates are comparable with in situ observations. Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), National Centers for Environmental Prediction (NCEP2) overestimate the precipitation extremes in the region. There exists a difference of 100–250 mm among precipitation datasets at an annual scale. All of the seven long‐term datasets underestimate the rxdays across China. The minimum range of rxdays (maximum precipitation amount in under defined days: 1 day or 5 days) captured by datasets is comparable except PERSIANN‐CDR. The maximum range calculated with PERSIANN‐CDR is 55.01 (rx1day), and 129.67 (rx5day), much less than the in situ rxdays. This analysis shows that datasets failed to capture maximum precipitation intensity in the region as well. FFt decision model results show that APHRODITE ranked first based on calculated consecutive dry days among all six other datasets in the most climatic zones. Overall, results indicate that data assimilation, the spatial coverage of ground stations, and interpolation techniques used to develop the datasets may limit the reliability of precipitation datasets in the study area.
... They also showed that a drought event occurs in our country in every 2.5 years. Similarly, Prodhan et al., (2020) used SPI and VCI to model agricultural drought hazard risk from 2001 to 2016, where they found that 6-month SPIidentified drought are more frequent and concluded that the Boro rice-growing season (November to May of next year) is more vulnerable to drought. ...
Drought is an affliction for a region that primarily depends on agriculture as economic activity. Commonly monitoring and characterizing of drought is performed by only analyzing the meteorological aspect, assuming precipitation as the primary source of water. However, in riverine Bangladesh, this can lead to an erroneous conclusion, as there is a multitude of available water sources. Consequently, in this study, vegetation condition (Standard Vegetation Index), soil moisture (Soil Moisture Index), and precipitation (Standard Precipitation Index) are separately investigated from 2003 to 2019, in the Northwestern Teesta floodplain. Subsequently, statistical regression analysis is performed to determine the relationships between different aspects of drought. In addition, information obtained from field visits and expert opinions has also been assimilated. Analysis of vegetation and soil moisture condition presents a progressively improving scenario. However, SPI shows an incessant decline in meteorological drought conditions, especially after 2007. Evidently, regression analysis does not provide any indication of an interrelationship between the studied agricultural and meteorological parameters. Presumably, this absence is instigated because the study area is highly irrigated as the groundwater table is suitably near the surface and the existence of nearby Teesta river allows for the utilization of surface water. Moreover, the cropping pattern is shifting toward crops that require much less water and to places where soil moisture is scarce. Thus, this study addresses the gap in knowledge regarding the nature of agricultural drought and the dynamics of different aspects of drought which will be invaluable for the water management and agricultural policy in the study area as well as other regions with a similar backdrop.
