Impacts and Implications of Land Use Land Cover Dynamics on Groundwater Recharge and Surface Runoff in East African Watershed

Impacts of land use/land cover and climate change on landscape sensitivity in Tunca River sub-basin: Use in spatial planning and sectoral decision processes

Managing landscape change is increasingly challenging due to rapid anthropogenic shifts. A delicate balance must be struck between the environment and change to ensure landscapes can withstand these impacts. This study conducted in the Tunca River sub-basin of Edirne province, aims to assess landscape sensitivity by examining the influence of land use/land cover (LULC) and climate change on landscape function processes. For this purpose, a methodology was developed based on ecosystem services to determine landscape sensitivity. The results revealed a LULC transformation that could lead to a 60% reduction in forest areas and a 5% and 20% increase in urban and irrigated agricultural areas, respectively. Water and erosion emerged as the most affected landscape function processes. Future scenarios from 2050 to 2070 indicate noteworthy changes in landscape sensitivity, showing an increase in sensitivity in the upper regions of the basin. The study identified high sensitivity in forested areas, moderate sensitivity in agricultural zones, and low sensitivity in micro-basins near residential areas. Protection and improvement strategies are recommended for areas with high and moderate sensitivity, while use-oriented strategies are suggested for those with low sensitivity. This study also establishes a scientific foundation for guiding the protection and management of ecologically sensitive basin areas, offering insights into the effects of landscape change processes at the micro-basin level in connection with climate change models.

An analysis of LULC changes for understanding the impact of anthropogenic activities on food security: a case study of Dudhganga watershed, India

Although the Dudhganga watershed is the primary water and food resource of the Kashmir Valley, it has undergone significant changes in food resources and strategies due to rampant urbanization in the area over the past 20 years. This urbanization has had a profound impact on the watershed and has also affected land use and land cover (LULC) patterns and environmental changes. The objective of this study is to investigate the effects of urban development on food security parameters in the Dudhganga watershed area, India, from 2000 to 2020, by evaluating LULC changes. Additionally, the study aims to examine the relationship between climate changes and LULC indices, such as the Modified Normalized Difference Water Index (MNDWI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Built-up Index (NDBI). The results indicate a 21.66% increase in barren areas, at the expense of snow-covered lands, during the 2000-2020 period. The primary land cover transition observed is towards barren areas. The predictions for LULC in 2030 highlight the need for careful management of land use and climate changes in the study area. This study can assist local government officials in reassessing food strategies by identifying areas where urban expansion should be controlled and climate impacts minimized, to prevent local hunger and ecological degradation. Therefore, the development of systematic urban planning approaches and mitigation of climate change sources are crucial. Furthermore, the adoption of advanced agricultural technology should be considered to mitigate the impact of urban expansion.

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.

Characterization of the skill of the CORDEX-Africa regional climate models to simulate regional climate setting in the East African Transboundary Omo Gibe River Basin, Ethiopia

Regional climate models (RCMs) that produce good outputs in one region or for specific variables may underperform for others. Thereby, assessing the performance of various model simulations and their corresponding mean ensemble is critical in identifying the most suitable models. In this regard, a study was conducted to evaluate the performance of ten RCMs against observations from multiple ground-based stations in the East African Transboundary Omo Gibe River Basin, Ethiopia, during the baseline period of 1986-2005. The study evaluated the models' ability to replicate various aspects of climatic variables and their corresponding statistical indicators. The results confirmed that RCMs have varying abilities to reproduce climatic conditions across the basin. The ensembles and RACMO22T (EC-EARTH) were better at replicating the average annual precipitation distribution. Meanwhile, the CCLM4-8-17 (MPI) together with the ensembles better captured the measured precipitation annually, despite the discrepancies in the actual magnitudes. All RCMs were able to simulate the seasonal precipitation patterns effectively, with RACMO22T (EC-EARTH), CCLM4-8-17 (CNRM), RCA4 (CNRM), CCLM4-8-17 (MPI), and REMO2009 (MPI) models captured superior, excluding the maximum value. Interannual and seasonal rainfall pattern variations were more significant than variations in air temperature. Additionally, a better correlation was observed between actual and simulated precipitation at multiple separate monitoring places. The RCA4 (MPI) and CCLM4-8-17 (MPI) demonstrated reasonable minimum and maximum temperatures. The RCA4 (MIROC5) model was more effective in reproducing extreme precipitation events. However, all RCMs and their ensembles tended to overestimate the return periods of these events. In general, the research highlights the importance of selecting reliable RCMs that better replicate observed climatic settings and employing the ensemble mean of top-performing models following systematic bias adjustment for a specific application.

Influence assessment of urban expansion on groundwater level fluctuations in Gandhinagar, Gujarat, India

Being the state capital of Gujarat, Gandhinagar is snowballing urban population, resulting in overexploitation of groundwater resources and consequent decline in local groundwater level. The key objective of the current research is to understand the impact of urban expansion on the groundwater level of Gandhinagar district for the last 3 decades. Long-term land use/land cover (LULC) alterations using Landsat images (1991-2021) reveal a 234% increase in overall built-up area and it is more prominent in western and southern parts than the eastern part of study area till 2021 due to urban sprawl of adjacent Ahmedabad City. Spatial distribution of groundwater levels exhibits the same pattern of groundwater level drop as that of the urban expansion and the drop of maximum depth of the groundwater level has also observed during study tenure. Rapid population growth indicates inevitable urban densification which may lead to increase in groundwater abstraction and consequent groundwater level depletion of Gandhinagar City in near future. The scenario may be worsened due to the reduction in groundwater recharge area owing to enhancement of impervious surfaces. A negative correlation is established between groundwater level and respective built-up areas from 1991 to 2011. After 2001, groundwater levels in some areas showed a rising trend and the number of those locations have increased from 2001 to 2021, indicating a sufficient supply of surface water, meeting the escalating water demand and subsequent reduction in groundwater abstraction. High fluoride content was found in many groundwater samples collected from Gandhinagar's shallow unconfined aquifer. In lieu of almost unperturbed natural groundwater recharge, built-up expansion, rising population, and over-exploitation result in groundwater level depletion in both shallow and deeper aquifers. To replenish the already depleted groundwater level and for sustainable water supply, an integration of rainwater-surface water-groundwater management plan and sustainable urban management plan is highly required. The future sustainable urban-groundwater management plan of Gandhinagar City must emphasized on the expansion of green and permeable spaces for groundwater recharge, mandatory rainwater harvesting system in every building possible, suitable area demarcation for artificial recharge, and identification of areas which are less prone to groundwater level depletion for city expansion. The outcomes of the present study will help the decision-makers to prepare inclusive and resilient urban management plan to accomplish the 6th and 11th Sustainable Development Goals of United Nations by 2030.

Impact of land-use dynamics and climate change scenarios on Groundwater recharge in the case of Anger watershed, Ethiopia

An assessment of land use dynamics and climate variability impacts on hydrological processes is vital and a prerequisite for effective water resources management. This study aimed to quantify the effect of land-use changes and long-term climate variability on the Anger watershed's annual groundwater recharge, which covers a total drainage area of 7717 km2. The WetSpass (Water and Energy Transfer between Soil, Plants, and Atmosphere under quasi-Steady State) model was used to investigate the impact of land cover and climate variability on groundwater. The Mann-Kendall (MK) test was used to analyze the spatial variations and temporal trends of the climate variables in the watershed. Input data for the model, such as land use, hydro-meteorological data, soil texture, topography, and groundwater elevation parameters, were prepared in the form of gridded maps with a 30 m resolution. The model results indicate that land-use change and climate variability considerably impact distributed groundwater recharges. Groundwater recharge decreased with land use in 2000 and 2019, respectively, as compared to baseline land usage (1985). The study also demonstrates how the anticipated future combination of less precipitation and higher temperatures has a detrimental effect on the watershed's annual average groundwater recharge. Future rising temperatures and reduced precipitation are projected to result in an average annual groundwater recharge showing significant decreases in 2050, 2080, and 2110, respectively, according to scenario-based models. The result has provided valuable information on the management and response of groundwater recharge to climate and land-use changes, particularly for the Anger watershed and for the total country as well.

Future prediction of water balance using the SWAT and CA-Markov model using INMCM5 climate projections: a case study of the Silwani watershed (Jharkhand), India

The aim of this research was to simulate the future water balance of the Silwani watershed, Jharkhand, India, under the combined effect of land use and climate change based on the Soil and Water Assessment Tool (SWAT) and Cellular Automata (CA)-Markov Chain model. The future climate prediction was done based on daily bias-corrected datasets of the INMCM5 climate model with Shared Socioeconomic Pathway 585 (SSP585), which represent the fossil fuel development of the world. After a successful model run, water balance components like surface runoff, groundwater contribution to stream flow, and ET were simulated. The anticipated change in land use/land cover (LULC) between 2020 and 2030 reflects a slight increase (3.9 mm) in groundwater contribution to stream flow while slight decrease in surface runoff (4.8 mm). The result of this research work helps the planners to plan any similar watershed for future conservation.