Browsing by Author "Keesara, Venkata Reddy"

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    Analysis of Land Use and Land Cover Using Machine Learning Algorithms on Google Earth Engine for Munneru River Basin, India
    Loukika, Kotapati Narayana; Keesara, Venkata Reddy; Sridhar, Venkataramana (MDPI, 2021-12-13)
    The growing human population accelerates alterations in land use and land cover (LULC) over time, putting tremendous strain on natural resources. Monitoring and assessing LULC change over large areas is critical in a variety of fields, including natural resource management and climate change research. LULC change has emerged as a critical concern for policymakers and environmentalists. As the need for the reliable estimation of LULC maps from remote sensing data grows, it is critical to comprehend how different machine learning classifiers perform. The primary goal of the present study was to classify LULC on the Google Earth Engine platform using three different machine learning algorithms—namely, support vector machine (SVM), random forest (RF), and classification and regression trees (CART)—and to compare their performance using accuracy assessments. The LULC of the study area was classified via supervised classification. For improved classification accuracy, NDVI (normalized difference vegetation index) and NDWI (normalized difference water index) indices were also derived and included. For the years 2016, 2018, and 2020, multitemporal Sentinel-2 and Landsat-8 data with spatial resolutions of 10 m and 30 m were used for the LULC classification. ‘Water bodies’, ‘forest’, ‘barren land’, ‘vegetation’, and ‘built-up’ were the major land use classes. The average overall accuracy of SVM, RF, and CART classifiers for Landsat-8 images was 90.88%, 94.85%, and 82.88%, respectively, and 93.8%, 95.8%, and 86.4% for Sentinel-2 images. These results indicate that RF classifiers outperform both SVM and CART classifiers in terms of accuracy.
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    Analysis of the causes of extreme precipitation in major cities of Peninsular India using remotely sensed data
    Kotrike, Tharani; Keesara, Venkata Reddy; Sridhar, Venkataramana (Elsevier, 2024-01)
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    Assessment of the Impact of Climate Change on Streamflow and Sediment in the Nagavali and Vamsadhara Watersheds in India
    Nagireddy, Nageswara Reddy; Keesara, Venkata Reddy; Venkata Rao, Gundapuneni; Sridhar, Venkataramana; Srinivasan, Raghavan (MDPI, 2023-06-26)
    Climate-induced changes in precipitation and temperature can have a profound impact on watershed hydrological regimes, ultimately affecting agricultural yields and the quantity and quality of surface water systems. In India, the majority of the watersheds are facing water quality and quantity issues due to changes in the precipitation and temperature, which requires assessment and adaptive measures. This study seeks to evaluate the effects of climate change on the water quality and quantity at a regional scale in the Nagavali and Vamsadhara watersheds of eastern India. The impact rainfall variations in the study watersheds were modeled using the Soil and Water Assessment Tool (SWAT) with bias-corrected, statistically downscaled models from Coupled Model Intercomparison Project-6 (CMIP-6) data for historical (1975–2014), near future (2022–2060), and far future (2061–2100) timeframes using three Shared Socioeconomic Pathways (SSP) scenarios. The range of projected changes in percentage of mean annual precipitation and mean temperature varies from 0 to 41.7% and 0.7 °C to 2.7 °C in the future climate, which indicates a warmer and wetter climate in the Nagavali and Vamsadhara watersheds. Under SSP245, the average monthly changes in precipitation range from a decrease of 4.6% to an increase of 25.5%, while the corresponding changes in streamflow and sediment yield range from −11.2% to 41.2% and −15.6% to 44.9%, respectively. Similarly, under SSP370, the average monthly change in precipitation ranges from −3.6% to 36.4%, while the corresponding changes in streamflow and sediment yield range from −21.53% to 77.71% and −28.6% to 129.8%. Under SSP585, the average monthly change in precipitation ranges from −2.5% to 60.5%, while the corresponding changes in streamflow and sediment yield range from −15.8% to 134.4% and −21% to 166.5%. In the Nagavali and Vamsadhara watersheds, historical simulations indicate that 2438 and 5120 sq. km of basin areas, respectively, were subjected to high soil erosion. In contrast, under the far future Cold-Wet SSP585 scenario, 7468 and 9426 sq. km of basin areas in the Nagavali and Vamsadhara watersheds, respectively, are projected to experience high soil erosion. These results indicate that increased rainfall in the future (compared to the present) will lead to higher streamflow and sediment yield in both watersheds. This could have negative impacts on soil properties, agricultural lands, and reservoir capacity. Therefore, it is important to implement soil and water management practices in these river basins to reduce sediment loadings and mitigate these negative impacts.
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    Climate Change Impact on Water Resources of Tank Cascade Systems in the Godavari Sub-Basin, India
    Ramabrahmam, Koppuravuri; Keesara, Venkata Reddy; Srinivasan, Raghavan; Pratap, Deva; Sridhar, Venkataramana (Springer, 2023-05-01)
    The availability of water at the regional and river basin scales in the future will be significantly impacted by climate change. Effective water management in the sub-basin is essential for ensuring long-term sustainability in the face of changing climatic conditions. The Maner River basin is a significant contributor to the Godavari River, and agriculture serves as the primary source of income for the majority of individuals residing in the subbasin. Nearly 50–65% of irrigational fields in the Maner basin are cultivated using local Tank Cascade Systems (TCS) and reservoirs that are managed by monsoon precipitation. The regional level climate change impact on the water resources of these tank cascade systems is important for sustainable management of water resources. In this study, The NEX-GDDP RCM models of CCSM4, MPI-ESM-LR and MIROC-ESM-CHEM were utilized to examine climate patterns during historical and future periods under RCP 4.5 and RCP 8.5 scenarios. The Maner sub-basin and KTCS (Katakshapur Tank Cascade System) were modeled using the SWAT hydrological model to simulate runoff and water availability. The average monsoon (July-October) streamflow increase in the Maner basin during the near, mid, and far futures is projected to be 47%, 66%, and 114% under the RCP 4.5 scenario, and 53%, 72%, and 69% under the RCP 8.5 scenario, respectively. Excess flow may overflow from Ramchandrapur, Mallampalli, and Dharmaraopalli tanks to the downstream Katakshapur tank since it can accommodate the up to 18.91 Mm3. To enhance water management in response to climate change, one potential adaptation strategy is to utilize the surplus inflow to refill downstream artificial ponds, which can aid in the replenishment of groundwater and the provision of water supply to tail end tanks.
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    Climate Change Impacts on Streamflow in the Krishna River Basin, India: Uncertainty and Multi-Site Analysis
    Naga Sowjanya, Ponguru; Keesara, Venkata Reddy; Mesapam, Shashi; Das, Jew; Sridhar, Venkataramana (MDPI, 2022-12-01)
    In Peninsular India, the Krishna River basin is the second largest river basin that is overutilized and more vulnerable to climate change. The main aim of this study is to determine the future projection of monthly streamflows in the Krishna River basin for Historic (1980–2004) and Future (2020–2044, 2045–2069, 2070–2094) climate scenarios (RCP 4.5 and 8.5, respectively), with the help of the Soil Water and Assessment Tool (SWAT). SWAT model parameters are optimized using SWAT-CUP during calibration (1975 to 1990) and validation (1991–2003) periods using observed discharge data at 5 gauging stations. The Cordinated Regional Downscaling EXperiment (CORDEX) provides the future projections for meteorological variables with different high-resolution Global Climate Models (GCM). Reliability Ensemble Averaging (REA) is used to analyze the uncertainty of meteorological variables associated within the multiple GCMs for simulating streamflow. REA-projected climate parameters are validated with IMD-simulated data. The results indicate that REA performs well throughout the basin, with the exception of the area near the Krishna River’s headwaters. For the RCP 4.5 scenario, the simulated monsoon streamflow values at Mantralayam gauge station are 716.3 m3/s per month for the historic period (1980–2004), 615.6 m3/s per month for the future1 period (2020–2044), 658.4 m3/s per month for the future2 period (2045–2069), and 748.9 m3/s per month for the future3 period (2070–2094). Under the RCP 4.5 scenario, lower values of about 50% are simulated during the winter. Future streamflow projections at Mantralayam and Pondhugala gauge stations are lower by 30 to 50% when compared to historic streamflow under RCP 4.5. When compared to the other two future periods, trends in streamflow throughout the basin show a decreasing trend in the first future period. Water managers in developing water management can use the recommendations made in this study as preliminary information and adaptation practices for the Krishna River basin.
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    Flow Simulation and Storage Assessment in an Ungauged Irrigation Tank Cascade System Using the SWAT Model
    Ramabrahmam, Koppuravuri; Keesara, Venkata Reddy; Srinivasan, Raghavan; Pratap, Deva; Sridhar, Venkataramana (MDPI, 2021-11-27)
    In the semi-arid regions of South Asia, tank systems are the major source of irrigation. In India, the Telangana state government has initiated the Mission Kakatiya program to rejuvenate irrigation tank systems. Understanding the hydrological processes that supply water to these systems is critical to the success of these types of programs in India. The current study attempted to comprehend the hydrological processes and flow routing in the Salivagu watershed tank cascade system in Telangana. There are a lot of ungauged tank cascade systems in this region. Soil Water Assessment Tool (SWAT), a physically-based model, was used to simulate flow patterns in the Salivagu watershed with and without tank systems. The geospatially extracted area and volume were used for this study provided by WBIS-Bhuvan-NRSC. Additionally, the Katakshapur Tank Cascade System (KTCS) was chosen to analyze the water availability in each tank using the water balance approach. The Salivagu watershed flow simulation without tanks overestimated streamflow. The volume difference in flow between with and without tank was 606 Mm3, 615.9 Mm3, and 1011 Mm3 in 2017, 2018, and 2019, respectively. The SWAT simulated volumes of the Ramchandrapur and Dharmaraopalle tanks in KTCS were merely satisfied because the tank size was less than 0.7 km2 and the storage capacity was up to 1 Mm3. Due to tank sizes more than 0.8 km2 and capacities greater than 2 Mm3, the Mallampalli and Katakshapur tank simulation findings were in good agreement with WBIS-Bhuvan-NRSC. This research advances our understanding of the hydrological processes in ungauged cascading tank systems in tropical semi-arid regions.
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    Future prediction of scenario based land use land cover (LU&LC) using DynaCLUE model for a river basin
    Loukika, Kotapati Narayana; Keesara, Venkata Reddy; Buri, Eswar Sai; Sridhar, Venkataramana (Elsevier, 2023-11)
    Human activities that cause changes to the surface of the Earth lead to alterations in Land Use and Land Cover (LU&LC) which have an impact on biodiversity, ecosystem functioning, and the well-being of humans. In order to comprehend and manage the effects of human activities on the environment, prediction of scenario-based LU&LC in future periods are crucial. Scenario-based predictions of LU&LC provide valuable insights for decision-makers in the sustainable governance of land and water resources. In the present study, the Dynamic Conversion of Land Use and its Effects (DynaCLUE) modelling platform was used to predict future LU&LC for Munneru river basin, India. Using six different user defined scenarios LU&LC change patterns were analyzed in 2030, 2050 and 2080 so as to understand the pressure on the natural resources and to plan sustainable Land Use Planning by preserving the important land use classes. The connection between LU&LC classes and input driving factors was quantified using Binary Logistic Regression (BLR) analysis. The β-coefficient was estimated using LU&LC type as a dependent variable and driving factors as independent variables. The demands of each LU&LC type, spatial policies and constraints, characteristics of each location and land use conversions are used as inputs for prediction of future LU&LC maps. Major conversions in LU&LC observed in this basin from last two decades are the rapid increase in built-up area due to urbanization in the outskirts of cities and towns. The major LU&LC changes projected for the period of 2019–2080 are expansion of built-up area ranging from 42.5% to 88.5%, and a reduction in barren land ranging from 57.3% to 74.5% across all six scenarios in the entire basin. The projected LU&LC maps under different scenarios provide valuable insights that could aid local communities, government agencies, and stakeholders in systematically allocating resources at the local level and in preparing the policies for long-term benefits.
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    Impacts of climate change on water provisional services in Tungabhadra basin using InVEST Model
    Bejagam, Vijaykumar; Keesara, Venkata Reddy; Sridhar, Venkataramana (Wiley, 2021-11-08)
    Water is one of the most important ecosystem services because it is essential for food and energy production. The Tungabhadra basin, located in peninsular India, has a variety of challenges, including inter-basin water-sharing issues, low agricultural productivity and value, and rising need for renewable energy production. The Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) water yield model is used to analyze the consequences of climate change on water related services such as water yield and hydropower generation in the densely populated Tungabhadra basin. The impact of climate change on water supply services is studied for the period 1971–2000 as well as the future period 2021–2040. The model is calibrated using streamflow data collected at the Bawapuram gauge station in Telangana and there is a strong correlation between observed and simulated flow. The water yield for the entire basin declined by 33 and 50% under the Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios, respectively. The RCP 4.5 scenario reduces hydropower production and its Net Present Value (NPV) by 41 to 59%, whereas the RCP 8.5 scenario reduces production and NPV by 56 to 67%. The assessment of ecosystem services at the catchment scale revealed that the basin could be vulnerable to climate change due to a dramatic drop in ecosystem services. The methodology developed in this study can be applied to other river basins where quantifying ecosystem services is critical.
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    Predicting the Effects of Land Use Land Cover and Climate Change on Munneru River Basin Using CA-Markov and Soil and Water Assessment Tool
    Loukika, Kotapati Narayana; Keesara, Venkata Reddy; Buri, Eswar Sai; Sridhar, Venkataramana (MDPI, 2022-04-21)
    It is important to understand how changing climate and Land Use Land Cover (LULC) will impact future spatio-temporal water availability across the Munneru river basin as it aids in effective water management and adaptation strategies. The Munneru river basin is one of the important sub-basins of the Krishna River in India. In this paper, the combined impact of LULC and Climate Change (CC) on Munneru water resources using the Soil and Water Assessment Tool (SWAT) is presented. The SWAT model is calibrated and validated for the period 1983–2017 in SWAT-CUP using the SUFI2 algorithm. The correlation coefficient between observed and simulated streamflow is calculated to be 0.92. The top five ranked Regional Climate Models (RCMs) are ensembled at each grid using the Reliable Ensemble Averaging (REA) approach. Predicted LULC maps for the years 2030, 2050 and 2080 using the CA-Markov model revealed increases in built-up and kharif crop areas and decreases in barren lands. The average monthly streamflows are simulated for the baseline period (1983–2005) and for three future periods, namely the near future (2021–2039), mid future (2040–2069) and far future (2070–2099) under Representation Concentration Pathway (RCP) 4.5 and 8.5 climate change scenarios. Streamflows increase in three future periods when only CC and the combined effect of CC and LULC are considered under RCP 4.5 and RCP 8.5 scenarios. When compared to the CC impact in the RCP 4.5 scenario, the percentage increase in average monthly mean streamflow (July–November) with the combined impact of CC and LULC is 33.9% (near future), 35.8% (mid future), and 45.3% (far future). Similarly, RCP 8.5 increases streamflow by 33.8% (near future), 36.5% (mid future), and 38.8% (far future) when compared to the combined impact of CC and LULC with only CC. When the combined impact of CC and LULC is considered, water balance components such as surface runoff and evapotranspiration increase while aquifer recharge decreases in both scenarios over the three future periods. The findings of this study can be used to plan and develop integrated water management strategies for the basin with projected LULC under climate change scenarios. This methodology can be applied to other basins in similar physiographic regions.
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    Prediction of Future Lake Water Availability Using SWAT and Support Vector Regression (SVR)
    Jayanthi, Sri Lakshmi Sesha Vani; Keesara, Venkata Reddy; Sridhar, Venkataramana (MDPI, 2022-06-07)
    Lakes are major surface water resource in semi-arid regions, providing water for agriculture and domestic use. Prediction of future water availability in lakes of semi-arid regions is important as they are highly sensitive to climate variability. This study is to examine the water level fluctuations in Pakhal Lake, Telangana, India using a combination of a process-based hydrological model and machine learning technique under climate change scenarios. Pakhal is an artificial lake built to meet the irrigation requirements of the region. Predictions of lake level can help with effective planning and management of water resources. In this study, an integrated approach is adopted to predict future water level fluctuations in Pakhal Lake in response to potential climate change. This study makes use of the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset which contains 21 Global Climate Models (GCMs) at a resolution of 0.25 × 0.25° is used for the study. The Reliability Ensemble Averaging (REA) method is applied to the 21 models to create an ensemble model. The hydrological model outputs from Soil and Water Assessment Tool (SWAT) are used to develop the machine-learning based Support Vector Regression (ν-SVR) model for predicting future water levels in Pakhal Lake. The scores of the three metrics, correlation coefficient (R2), RMSE and MEA are 0.79, 0.018 m, and 0.13 m, respectively for the training period. The values for the validation periods are 0.72, 0.6, and 0.25 m, indicating that the model captures the observed lake water level trends satisfactorily. The SWAT simulation results showed a decrease in surface runoff in the Representative Concentration Pathways (RCP) 4.5 scenario and an increase in the RCP 8.5 scenario. Further, the results from ν-SVR model for the future time period indicate a decrease in future lake levels during crop growth seasons. This study aids in planning of necessary water management options for Pakhal Lake under climate change scenarios. With limited observed datasets, this study can be easily extended to the other lake systems.
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    Sensitivity of Microphysical Schemes on the Simulation of Post-Monsoon Tropical Cyclones over the North Indian Ocean
    Venkata Rao, Gundapuneni; Keesara, Venkata Reddy; Sridhar, Venkataramana (MDPI, 2020-11-30)
    Tropical Cyclones (TCs) are the most disastrous natural weather phenomenon, that have a significant impact on the socioeconomic development of the country. In the past two decades, Numerical Weather Prediction (NWP) models (e.g., Advanced Research WRF (ARW)) have been used for the prediction of TCs. Extensive studies were carried out on the prediction of TCs using the ARW model. However, these studies are limited to a single cyclone with varying physics schemes, or single physics schemes to more than one cyclone. Hence, there is a need to compare different physics schemes on multiple TCs to understand their effectiveness. In the present study, a total of 56 sensitivity experiments are conducted to investigate the impact of seven microphysical parameterization schemes on eight post-monsoon TCs formed over the North Indian Ocean (NIO) using the ARW model. The performance of the Ferrier, Lin, Morrison, Thompson, WSM3, WSM5, and WSM6 are evaluated using error metrics, namely Mean Absolute Error (MAE), Mean Square Error (MSE), Skill Score (SS), and average track error. The results are compared with Indian Meteorological Department (IMD) observations. From the sensitivity experiments, it is observed that the WSM3 scheme simulated the cyclones Nilofar, Kyant, Daye, and Phethai well, whereas the cyclones Hudhud, Titli, and Ockhi are best simulated by WSM6. The present study suggests that the WSM3 scheme can be used as the first best scheme for the prediction of post-monsoon tropical cyclones over the NIO.
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    Spatio-Temporal Analysis of Climatic Variables in the Munneru River Basin, India, Using NEX-GDDP Data and the REA Approach
    Buri, Eswar Sai; Keesara, Venkata Reddy; Loukika, Kotapati Narayana; Sridhar, Venkataramana (MDPI, 2022-02-02)
    For effective management practices and decision-making, the uncertainty associated with Regional Climate Models (RCMs) and their scenarios need to be assessed in the context of climate change. The present study analyzes the various uncertainties in the precipitation and temperature datasets of NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) under Representative Concentrative Pathways (RCPs) 4.5 and 8.5 over the Munneru river basin, in India, using the Reliable Ensemble Averaging (REA) method. From the available 21 RCMs, the top five ranked are ensembled and bias-corrected at each grid using the non-parametric quantile mapping method for the precipitation and temperature datasets. The spatio-temporal variations in precipitation and temperature data for the future periods, i.e., 2021–2039 (near future), 2040–2069 (mid future) and 2070–2099 (far future) are analyzed. For the period 2021–2099, annual average precipitation increases by 233 mm and 287 mm, respectively, the in RCP 4.5 and RCP 8.5 scenarios when compared to the observed period (1951–2005). In both the RCP 4.5 and RCP 8.5 scenarios, the annual average maximum temperature rises by 1.8 °C and 1.9 °C, respectively. Similarly, the annual average minimum temperature rises by 1.8 °C and 2.5 °C for the RCP 4.5 and RCP 8.5 scenarios, respectively. The spatio-temporal climatic variations for future periods obtained from high-resolution climate model data aid in the preparation of water resource planning and management options in the study basin under the changing climate. The methodology developed in this study can be applied to any other basin to analyze the climatic variables suitable for climate change impact studies that require a finer scale, but the biases present in the historical simulations can be attributed to uncertainties in the estimation of climatic variable projections. The findings of the study indicate that NEX-GDDP datasets are in good agreement with IMD datasets on monthly scales but not on daily scales over the observed period, implying that these data should be scrutinized more closely on daily scales, especially when utilized in impact studies.
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    Streamflow and Sediment Yield Analysis of Two Medium-Sized East-Flowing River Basins of India
    Nagireddy, Nageswara Reddy; Keesara, Venkata Reddy; Sridhar, Venkataramana; Srinivasan, Raghavan (MDPI, 2022-09-21)
    With increased demand for water and soil in this Anthropocene era, it is necessary to understand the water balance components and critical source areas of land degradation that lead to soil erosion in agricultural dominant river basins. Two medium-sized east-flowing rivers in India, namely Nagavali and Vamsadhara, play a significant role in supporting water supply and agriculture demands in parts of the Odisha districts of Kalahandi, Koraput and Rayagada, as well as the Andhra Pradesh districts of Srikakulam and Vizianagaram. Floods are more likely in these basins as a result of cyclones and low-pressure depressions in the Bay of Bengal. The water balance components and sediment yield of the Nagavali and Vamsadhara river basins were assessed using a semi-distributed soil and water assessment tool (SWAT) model in this study. The calibrated model performance revealed a high degree of consistency between observed and predicted monthly streamflow and sediment load. The water balance analysis of Nagavali and Vamsadhara river basins showed the evapotranspiration accounted for 63% of the average annual rainfall. SWAT simulated evapotranspiration showed a correlation of 0.78 with FLDAS data. The calibrated SWAT model showed that 26.5% and 49% of watershed area falling under high soil erosion class over Nagavali and Vamsadhara river basins, respectively. These sub watersheds require immediate attention to management practices to improve the soil and water conservation measures.
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    Studying the Relationship between Satellite-Derived Evapotranspiration and Crop Yield: A Case Study of the Cauvery River Basin
    Anand, Anish; Keesara, Venkata Reddy; Sridhar, Venkataramana (MDPI, 2024-08-05)
    Satellite-derived evapotranspiration (ETa) products serve global applications, including drought monitoring and food security assessment. This study examines the applicability of ETa data from two distinct sources, aiming to analyze its correlation with crop yield (rice, maize, barley, soybean). Given the critical role of crop yield in economic and food security contexts, monthly and yearly satellite-derived ETa data were assessed for decision-makers, particularly in drought-prone and food-insecure regions. Utilizing QGIS, zonal statistics operations and time series graphs were employed to compare ETa with crop yield and ET anomaly. Data processing involved converting NRSC daily data to monthly and extracting single-pixel ET data using R Studio. Results reveal USGSFEWS as a more reliable ETa source, offering better accuracy and data continuity, especially during monsoon seasons. However, the correlation between crop yield and ETa ranged from 12% to 35%, while with ET anomaly, it ranged from 35% to 55%. Enhanced collection of satellite-based ETa and crop-yield data is imperative for informed decision-making in these regions. Despite limitations, ETa can moderately guide decisions regarding crop-yield management.