Cellular Automata Markov Chain Model Research Articles

Assessing and predicting the dynamics of land use/land cover in northern Bangladesh using cellular Automata-Markov chain model

ABSTRACT Rapid urbanization and land use changes significantly impact environmental sustainability and resource management, particularly in developing regions. Therefore, this study examines the spatiotemporal dynamics of land use and land cover (LULC) in Rangpur, Bangladesh, from 1991 to 2021 and projects future trends to 2041. Using supervised and unsupervised classification techniques, along with cellular automata and Markov-chain models, we assessed historical LULC changes and predicted future scenarios. Results show a 38.86% increase in built-up areas (BAs) and a 49.86% decrease in vegetation land (VL) during the study period, with classification accuracy above 87%. Projections indicate a further loss of over 210 km² of VL and an increase of more than 123 km² in urban areas by 2041. Notably, urban expansion is linked to the development of road networks, with significant growth from 115.06 km2 in 2021 to 124.33 km2 by 2041 within a 15-kilometer radius around the city center. These findings offer crucial insights for urban planning, emphasizing the need for sustainable strategies to manage urban expansion and protect environmental and socio-economic resilience in Rangpur.

Land-use/cover change and future prediction by integrating the ML techniques of random forest and CA-Markov chain model of the Ganges alluvial tract of Eastern India

Land-use/cover change and future prediction by integrating the ML techniques of random forest and CA-Markov chain model of the Ganges alluvial tract of Eastern India

Exploring the dynamics and future projections of land use land cover changes by exploiting geospatial techniques; A case study of the Kabul River Basin

Global land cover change has caused significant environmental degradation and biodiversity loss. It affects ecosystem functions, livelihoods, and climate variation and has drawn substantial attention in recent decades. In the Kabul River Basin (KRB), there are limited studies on the historical Land Use/Land Cover (LULC) pattern, transition, intensity and future perspective. Therefore, this study aims to investigate long-term LULC changes and major drivers of LULC in the KRB over the past thirty years (1990–2020) and then to project the future LULC pattern for the years 2030, 2040 and 2050. Landsat Imageries of (1990–2020) were used as input data by utilizing the Random Forest Classifier algorithm (RF) in the Google Earth Engine (GEE) to classify the LULC. The LULC was then projected for the future, using the Cellular Automata Markov Chain Model (CA-MCM). The results demonstrated drastic LULC changes, controlled primarily by urbanization and agriculture expansion, which expanded from 467 Km2 (0.7 %) to 2312 km2 (3.4 %) and 6528 km2 (9.6 %) to 10812 (15.9 %), between 1990 and 2020. In contrast, bare land decreased from 70606 km2 (82.1 %) to 48212 km2 (70.9 %) between 1990 and 2020. In addition, the study depicts that the expansion in built-up and vegetation areas in the KRB during the study period were at the utilization of bare land. Future LULC predictions indicated that between 2020 and 2050, bare land would trend downward from 48212 km2 (70.9 %) to 46172 km2 (67.9 %), while vegetation and built-up areas would trend upward from 2312 km2 (3.4 %) to 3640 km2 (5.3 %), 10812 km2 (15.9 %) to 11622 km2 (17.1 %), and water bodies and snowcover would slightly vary from 1.2 % to 0.9 % and 7.9 %–9.0 %. In addition, the results of LULC dynamics reveal a significant strong positive correlation between population and built, as well as population and vegetation. Conversely, there is a strong negative correlation between population and bare land. Our results provide precise insights on LULC patterns and trends in the KRB, which could be employed to design a sustainable framework for land use and ecosystem protection.

Quantitative prediction of water quality in Dongjiang Lake watershed based on LUCC

Land Use/ Cover Change (LUCC) plays a crucial role in influencing hydrological processes, nutrient cycling, and sediment transport in watersheds, ultimately impacting water quality on both spatial and temporal scales. Accurately predicting changes in watershed water quality is beneficial for the sustainable management of water resources. Current models often lack the ability to effectively predict water quality changes in a dynamic spatio-temporal context, particularly in complex watershed environments. The overall purpose of the study is to establish a comprehensive and dynamic modeling framework that links LUCC with water quality, allowing for accurate predictions of future water quality under varying land use scenarios. The model, which uses water quality as the dependent variable and LUCC as the independent variable, was developed to quantitatively predict changes in watershed water quality. To achieve this, annual multi-period remote sensing images from Landsat-5, Landsat-8 or Sentinel-2 satellites spanning from 1992 to 2022 were analyzed. Random Forest (achieving a Kappa coefficient of 0.9468) were employed to classify land use within the watershed. Based on classification results, a Cellular Automata-Markov chain model (CA-Markov) was constructed to simulate and predict the spatio-temporal patterns of land use, incorporating driving factors such as proximity to water systems, roads, elevation, and slope. Validation of the model using LUCC data from 2020 yielded a high prediction accuracy with a Kappa coefficient of 0.9505. The CA-Markov model was further utilized to project LUCC under three different scenarios—natural development, ecological protection, and arable land protection—between 2023 and 2033. Based on these projections, the coupled water quality and LUCC model was employed to predict water quality changes in the watershed over the same period. Key findings indicate that water quality is likely to improve under ecological protection scenario, while deterioration is expected under natural development scenario and cropland protection scenario due to urban expansion, agricultural practices, and water diversion for irrigation. This study provides a robust framework for watershed management, offering scientific guidance for source management and water purification efforts, thereby contributing significantly to the sustainable development of water resources.

Approach maximum likelihood classification and cellular automata markov chain model for land use/land cover change prediction in Nagan Raya Country, Indonesia

Approach maximum likelihood classification and cellular automata markov chain model for land use/land cover change prediction in Nagan Raya Country, Indonesia

Integrated use of the CA–Markov model and the Trends.Earth module to enhance the assessment of land cover degradation

This study aims to demonstrate the potential of assessing future land cover degradation status by combining the forecasting capabilities of the Cellular-Automata and Markov chain (CA-Markov) models in Idris Selva with the land cover degradation (LCD) model in the Trends.Earth module. The study focuses on the upper Zambezi Basin (UZB) in southern Africa, which is one of the regions with high rates of land degradation globally. Landsat satellite imagery is utilised to generate historical (1993–2023) land cover and land use (LCLU) maps for the UZB, while the global European Space Agency Climate Change Initiative (ESA CCI) LCLU maps are obtained from the Trends.Earth module. The CA-Markov chain model is employed to predict future LCLU changes between 2023 and 2043. The LCD model in the Trends.Earth module in QGIS 3.32.3 is then used to assess the historical and forecasted land cover degradation status. The findings reveal that land cover degradation maps produced from local LCLU classifications provide more detailed information compared to those produced from the global ESA CCI LCLU product. Between 2023 and 2043, the UZB is predicted to experience a net reduction of approximately 3.2 million hectares of forest cover, with an average annual reduction rate of − 0.13%. In terms of land cover degradation, the UZB is forecasted to remain generally stable, with 87% and 96% of the total land cover area expected to be stable during the periods 2023–2033 and 2033–2043, respectively, relative to the base years 2023 and 2033. Reduction in forest cover due to the expansion of grassland, human settlements, and cropland is projected to drive land cover degradation, while improvements in forest cover are anticipated through the conversion of grassland and cropland into forested areas. It appears that using locally produced LCLU with high-resolution images provides better assessments of land degradation in the Trends.Earth module than using global LCLU products. By leveraging the opportunities offered by models with capacity to predict LCLU such as the CA–Markov model and the capabilities of the LCD model, as evidenced in this study, valuable forecasted information can be effectively obtained for monitoring land cover degradation. This information can then be used to implement targeted interventions that align with the objective of realising the United Nations' land degradation neutral world target by 2030.

Monitoring and predicting development of built-up area in sub-urban areas: A case study of Sleman, Yogyakarta, Indonesia

Monitoring built-up areas in the previous year and possible predictions for the following year are important in planning regional development and controlling the expansion of built-up areas. This study detects changes in the built-up area (2018–2022). It predicts the future (2026) using Landsat satellite imagery in the Sleman Regency, Yogyakarta Special Region, Indonesia study area. Mapping built-up areas is identified using the Normalized Difference Built-Up Index (NDBI). Vegetation conditions were analyzed using the Normalized Difference Vegetation Index (NDVI). Changes in the built-up area are predicted using the CA-Markov chain model for 2026. The prediction is calibrated by comparing the simulated map with the results of the classification of built-up areas in 2022. The research findings show that the built-up area has increased by 12.84 % from 2018 to 2022 and is predicted to increase by 15.48 % in 2026. The existence of built-up areas has an influence on land surface temperatures where the analysis results show a moderate correlation between NDBI and LST, namely 2018 (R2 = 0.401), 2019 (R2 = 0.323), 2020 (R2 = 0.401), 2021 (R2 = 0.415), and 2022 (R2 = 0.384). The higher the NDBI value, the higher the LST value, and vice versa. Therefore, regional development planning, mainly built-up areas, is an important recommendation for decision-makers in the study area.

Prediction of land cover change in the Belawan watershed using the cellular automata-markov chain model

Belawan Watershed is one of the watersheds that cross Medan City, the capital of North Sumatra province. The spatial distribution of land cover affects the hydrological conditions in this watershed. The purpose of this study is to predict land cover in 2032 so that it can be used as information to mitigate changes in forest land cover. The method used is Cellular Automata-Markov Chain modeling with five change parameters (driving factors): distance from the river, distance from the road, altitude, population density, and slope. Land cover changes in the Belawan watershed used for this modeling include land cover in 2012, 2017, and 2022 and predicted land cover in 2032. The results showed that the predicted land cover is the water body 998.409 ha (1.36%), dryland forest 970.399 ha (1.32%), mangrove forest 3,351.496 ha (4.58%), settlement 11,600.105 ha (15.85%), plantation 38,787.924 ha (53.01%), agriculture 8,674.632 ha (11.85%), rice fields 5,568.183 ha (7.6%) and ponds 3,218.175 ha (4.3%). The area of forests and mangroves tends to continue to decline, so mitigation efforts are needed to reduce the reduction rate in forest and mangrove land cover.

Simulating urban expansion scenario by Logistic regression (LR) with CA-Markov chain Model for Bhanjanagar town (Odisha, India)

Simulating urban expansion scenario by Logistic regression (LR) with CA-Markov chain Model for Bhanjanagar town (Odisha, India)

Spatio-temporal analysis of land use land cover change and its impact on land surface temperature of Sialkot City, Pakistan

The dynamic interplay between urbanization and its impacts on climate is a subject of recent concern, particularly in rapidly urbanizing cities of Pakistan. This research investigated the spatio-temporal effects of urban growth in terms of Land Use Land Cover changes on the thermal environment (Land Surface Temperature) of the Sialkot city, Pakistan using satellite data spanning four distinct time periods (1989, 2000, 2009 and 2020) and predicted changes for year 2030 by employing Cellular Automata Markov Chain Model. Satellite imagery (Landsat 5, 7 and 8) was processed, and maximum likelihood supervised classification was done to generate LULC maps for each of the aforementioned years. In addition to LULC classification, thermal bands of satellite data (for summer and winter) were processed to compute Land Surface Temperature (LST) of the city. The prediction of LULC changes and LST was done for year 2030 using Cellular Automata Markov Chain Model. The accuracy of classified and prediction maps was checked using Kappa Index. The LULC analysis revealed 4.14% increase in the built-up area and 3.43% decrease in vegetation cover of the city during 1989 to 2020. Both land covers are expected to change in the future (year 2030) by + 1.31% (built-up) and − 1.1% (vegetation). Furthermore, a declining trend in the barren land and water bodies was also observed over time. These LULC changes were found affecting the LST of study area. The transformation of vegetation cover into built-up area resulted in an increase in LST over time. A notable rise of 4.5 °C (summer) and 5.7 °C (winter) in the mean LST of Sialkot was observed during 1989 to 2020 and further increases are anticipated in year 2030. This study calls for attention of the policy makers to reduce human impact on the local climate of the city. The study will also help city developers in analyzing the urban population growth trend, finding suitable location to built new infrastructure by governmental authorities and how the rising temperature can affect energy demand and agriculture production of the city in future.

Spatio-temporal prediction of land use and land cover change in Bahi (Manyoni) Catchment, Tanzania, using multilayer perceptron neural network and cellular automata-Markov chain model.

Evaluation of land use and land cover (LULC) change is among vital tools used for tracking environmental health and proper resource management. Remote sensing data was used to determine LULC change in Bahi (Manyoni) Catchment (BMC) in central Tanzania. Landsat satellite images from Landsat 5 TM and Landsat 8 OLI/TIRS were used, and support vector machine (SVM) algorithm was applied to classify the features of BMC. The obtained kappa values were 0.74, 0.83 and 0.84 for LULC maps of 1985, 2005 and 2021, respectively, which indicates the degree of accuracy from produced being substantial to almost perfect. Classified maps along with geospatial, socio-economic and climatic drivers with sufficient explanatory power were incorporated into MLP-NN to produce transition potential maps. Transition maps were subsequently used in cellular automata (CA)-Markov chain model to predict future LULC for BMC in immediate-future (2035), mid-future (2055) and far-future (2085). The findings indicate BMC is expected to experience significant expansion of agricultural lands and built land from 31.89 to 50.16% and 1.48 to 9.1% from 2021 to 2085 at the expense of open woodland, shrubland and savanna grassland. Low-yield crop production, water scarcity and population growth were major driving forces for rapid expansion of agricultural lands and overall LULC in BMC. The findings are essential for understanding the impact of LULC on hydrological processes and offer insights for the internal drainage basin (IDB) board to make necessary measures to lessen the expected dramatic changes in LULC in the future while sustaining harmonious balance with livelihood activities.

Assessment of future prediction of urban growth and climate change in district Multan, Pakistan using CA-Markov method

This research utilized remote sensing (RS) technology to analyze and monitor land use land cover (LULC) and climate change. This work mainly involves three components: (i) investigating the historical trends of LULC and land surface temperature (LST) using Landsat data for 1990, 2005, and 2020. (ii)Evaluate the impact of climate change on urban temperature for increased heat stress in the Multan region and (iii) Future predictions of LULC and LST for 2035 and 2050 by the CA-Markov Chain model. Supervised classification techniques were employed to classify the images into various LULC classes. Historical trends of LULC revealed that over the study period, the built-up area in Multan has increased by almost 30,435 Ha (8.34%), which would continue to reach40003 ha (10.96%) from 1990 to 2020 at the cost of vegetation loss, which is countlessly decreasing and will get from 283,145 Ha (−77.57%) in 2035 to264443 Ha (−71.4%) till 2050. Further, the findings about LST revealed that over the region, the average LST values have increased from 1.2 °C over the last two decades and will additionally include 1.8 °C until 2050.This study underscores the importance of RS technology in understanding past LULC changes and predicting future land transformations and temperature variations. The results contribute valuable insights for policymakers, land managers, and urban planners to make informed decisions for sustainable LULC and climate adaptation strategies in the face of growing urbanization and climate change challenges.

An Assessment of Landscape and Land Use/Cover Change and Its Implications for Sustainable Landscape Management in the Chittagong Hill Tracts, Bangladesh

Human-caused environmental change has profoundly impacted resource management and land use patterns in Bangladesh’s Chittagong Hill Tracts. This study used multi-temporal Landsat images from 1998, 2008, and 2018 to analyze land use and land cover changes, particularly those associated with forest cover changes, in Bangladesh’s Chittagong Hill Tracts. Using object-based image classification, Landsat images from 1998, 2008, and 2018 were separated into four categories based on their dominant land use and land cover features: forest, grassland, water bodies, and bare land. Post-classification comparison was used to assess the degree and frequency of change, and this method was further developed to evaluate the balance, fluctuation, and adaptation of forests. In addition, the spatial structure of land cover and temporal trajectories related to changes in forest cover were studied. The CA–Markov chain model was also used to anticipate the 2048 LULC map. The image classification of the years 1998, 2008, and 2018 showed that the overall accuracy was 89.65%, 84.44%, and 86.26%; producer accuracy was 90.00%, 68.75%, and 72.22%; and the Kappa coefficient was 85.68, 82.84, and 76.36, respectively. The results showed that between 1998 and 2018, forest cover increased by 58.03%, transforming grassland to forest; grassland increased by 29.50%, converting bare land to grassland; and forest conversion to grassland was 13.34%. In addition, the result of the landscape metric revealed that during the whole study period, class level indicated a fragmentation of forest, bare land, grassland, and water in the CHT, and landscape level indicated by Shannon’s Diversity Index and Shannon’s Evenness Index showed a slight decrease in the land. Based on the CA–Markov model, forest area is predicted to expand to 9129 Km2 in 2048; however, other land uses (bare land and grassland) continue to decrease. This substantial increase in forest cover results from effective forest management based on community forestry practices and the successful execution of Bangladesh’s national forest strategy. However, as Bangladesh’s population rises, so does the country’s need for lumber/timber. Bangladesh’s government should revise its forest policy to meet the local community’s needs without endangering the forest, and policymakers must take climate change seriously. Our strategy for evaluating the critical indicators of changes in forest cover and pathways of change will aid in connecting these patterns to the dynamics of change, such as deforestation and reforestation. It would therefore serve as a framework for developing effective conservation and management plans for the Chittagong Hill regions in Bangladesh.

Application of geospatial technology for the land use/land cover change assessment and future change predictions using CA Markov chain model

Application of geospatial technology for the land use/land cover change assessment and future change predictions using CA Markov chain model

Projection of urban land surface temperature: An inter- and intra-annual modeling approach

An easily applied and efficiently exploited approach was proposed for the prediction of inter- and intra-annual land surface temperature (LST) in a city's central zone. Chengdu is chosen as the study area to demonstrate the applicability of the novel approach. Built-up areas were extracted using Landsat images in 2008, 2013, and 2019 and projected for 2030 using the cellular automata-Markov chain model, indicating that built-up areas grew nearly 109% during 2008–2019 in Chengdu and will continue to grow up to 2030. Employing the multiple linear regression, the LST of built-up areas in 2019 was predicted using the normalized difference built-up index (NDBI) and the mean LST of its surrounding pixels (LST-R3) in 2013 (R2 = 0.981). The model's intra-annual application for 2019 showed a mean LST deviation of 0.93 °C. According to the model's inter-annual application, the projected LST of the built-up areas in 2030 will increase to 36.5 ± 2.0 °C. The results can contribute to achieving a thermally comfortable urban environment by providing insights into the impact of urban expansion on the urban heat island (UHI) phenomenon under the limited availability of continuous LST data.

Model Dinamika Spasial Perubahan Tutupan Lahan dan Daya Dukung Lahan Permukiman Kota Ambon Tahun 2031

The rate of population growth that continues to increase in Ambon city with the availability of suitable land is relatively constant, resulting in inconsistencies between land requirements and available land. This study aims to analyze land cover changes in Ambon City in 2013, 2018, 2023 and predict land cover in 2031 using the Cellular Automata Markov Chain (CAMC) and the carrying capacity index of residential areas. The driving factors used in this study were elevation, slope, distance from the coastline, distance from the main road, distance from the river, distance from the center of economic activity and distance from protected areas. Based on the predictions of the Cellular Automata Markov Chain model in 2031, the built-up land cover has increased in area to 4,958.33 ha or 49,583,268 m2, with a predicted population of Ambon City in 2031 of 2,445,961 people. The calculation of the carrying capacity of residential land in 2031 in Ambon City results in the carrying capacity index of residential land of 1.27 m²/capita, meaning that if DDPm > 1 indicates the carrying capacity of residential land is high and still able to accommodate residents to live and build houses in the area. In other words, Ambon City can still accommodate an increase in settlements of 1.27 m²/capita.

Impact of urban and peri-urban growth on arable land (1976–2029) in a medium sized city of Shire Indaselassie, North Western Tigray, Ethiopia

Urban and peri-urban areas have been growing rapidly globally due to population increase and other factors. However, poor urban land use planning caused remarkable impacts on the loss of potentially arable land. This study aimed to analyze the spatio-temporal patterns of urban and peri-urban expansion and its impact on arable land in the Shire Indaselassie city, North Western Tigray. Multi-temporal and spectral Landsat images were used as an input and processed using TerrSet, ERDAS imagine 2015, and ArcGIS 10.8.1 softwares. The Cellular Automata-Markov Chain model was used to predict the future urban and peri-urban areas. Pearson correlation coefficient was applied to examine the association among the population, urban, peri-urban growth, and its impact on arable land in Stata/SE v14. The findings revealed that urban and peri-urban lands increased by 7.5 km2 (22.1%) and 1.0 km2 (2.9%), while arable land decreased by − 0.1 km2 (− 0.2%) from 1976 to 2019. It is also predicted to increase by 1.0 km2 (2.80%) and 0.9 km2 (2.70%) until 2029, while arable land is anticipated to shrink by − 1.0 km2 (− 3.0%). The relationship among population growth, urban, and peri-urban land shows positive (r = 0.942, p = 0.058; r = 0.985, p = 0.016). However, arable land has been negatively correlated with population growth, urban, and peri-urban lands (r = − 0.610, p = 0.392). The result of this research is indispensable for urban planners and decision makers to optimize urban and peri-urban expansion related information without jeopardizing the size of arable lands.

Multi-scenario simulation of land use and land cover based on shared socioeconomic pathways: The case of coastal special economic zones in China

Urban land-use change simulations without considering the sustainable planning policies, especially in special economic park highly concerned by planners, might lack the reliability and availability. Thus, this study proposes a novel planning support systems integrating the Cellular Automata Markov chain model and Shared Socioeconomic Pathways (CA-Markov-SSPs) for predicting the changing of land use and land cover (LULC) at the local and system level by using a novel machine learning-driven, multi-source spatial data modelling framework. Using multi-source satellite data of coastal special economic zones from 2000 to 2020 as a sample, calibration validation based on the kappa indicates a highest average reliability above 0.96 from 2015 to 2020, and the cultivated land and built-up land classes of LULC is the most significant changes in 2030 by using the transition matrix of probabilities, the other classes except water bodies continue to increase. And the non-sustainable development scenario can be prevented by the multiple level collaboration of socio-economic factors. This research intended to help decision makers to confine irrational urban expansion and achieve the sustainable development.

Sustainable agricultural lands management by analyzing and predicting urban growth—a case study of Mitidja Plain, Algeria

The suitability of agricultural lands is basic data for both sustainable agricultural planning and optimal orientation of future urbanization projects. Indeed, unplanned urban expansion leads to many social and ecological problems. The prediction of urban expansion will strongly help the decision makers and local authorities to create a suitable future urban planning while preserving the regional ecosystem and especially the agricultural lands. Using multitemporal satellite images acquired in 1997, 2001, and 2013 over the Mitidja plain in Algeria, the aim of this study is the analysis of the land use and land cover change (LULCC) spatio-temporal distributions from 1997 to 2013 to model and then predict the future LULCC in 2025. The cellular automata Markov chain model (CA-Markov) that we developed is used for LULCC prediction with a focus on urban sprawl and agricultural lands degradation over the Mitidja plain. Once calibrated and validated, the CA-Markov model generates land suitability maps that indicate the best locations for urban expansion while safekeeping the agricultural lands. The results are interesting and present valuable tools for urban planners and policymakers to ensure the sustainability management of agricultural areas over the study region.

Estimation of Water Balance for Anticipated Land Use in the Potohar Plateau of the Indus Basin Using SWAT

Land Use/Land Cover (LU/LC) change is among the dominant driving factors that directly influence water balance by transforming hydrological responses. Consequently, a thorough comprehension of its impacts is imperative for sustainable water resource planning and development, notably in developing worlds such as Pakistan, where agriculture is a major livelihood. This research intends to assess the continuing changes in LU/LC and evaluate their probable repercussions on the hydrological regime of the Potohar Plateau. The maximum likelihood classification (MLC) algorithm for land use classification of the high-resolution satellite imageries, the Cellular-Automata Markov Chain Model (CA-MCM) for the projection of LU/LC maps, and the Soil and Water Assessment Tool (SWAT) in tandem with SWAT-CUP for hydrological modeling were employed in this research. The high-resolution climatic dataset (10 × 10 km) was used in SWAT. The LU/LC analysis revealed a continual propagation of agricultural and built-up lands at the detriment of forest and barren land during the last three decades, which is anticipated to continue in the future, too. Hydrological model calibrations and validations were performed on multi-basins, and the performance was evaluated using different statistical coefficients, e.g., the coefficient of determination (R2), Nash–Sutcliffe Efficiency (NSE), Kling–Gupta Efficiency (KGE), and Percent Bias (PBIAS). The results yielded that the model performed very well and demonstrated the model’s robustness in reproducing the flow regime. The water balance study revealed that the anticipated LU/LC changes are projected to decrease the mean annual surface runoff, water yield, and streamflow due to an increase in percolation, lateral flow, sub-surface flow, and evapotranspiration. More significant variations of the water balance components were observed at the sub-basin level, owing to the heterogeneous spatial distribution of LU/LC, than at the basin level. The outcome of this study will provide pragmatic details to legislative bodies to develop land and water management ameliorative strategies to harness hydrological changes sustainably.