Understanding land use and land cover changes has become a necessity in managing and monitoring natural resources and development especially urban planning. Remote sensing and geographical information systems are proven tools for assessing land use and land cover changes that help planners to advance sustainability. Our study used remote sensing and geographical information system to detect and predict land use and land cover changes in one of the world’s most vulnerable and rapidly growing city of Kathmandu in Nepal. We found that over a period of 20 years (from 1990 to 2010), the Kathmandu district has lost 9.28% of its forests, 9.80% of its agricultural land and 77% of its water bodies. Significant amounts of these losses have been absorbed by the expanding urbanized areas, which has gained 52.47% of land. Predictions of land use and land cover change trends for 2030 show worsening trends with forest, agriculture and water bodies to decrease by an additional 14.43%, 16.67% and 25.83%, respectively. The highest gain in 2030 is predicted for urbanized areas at 18.55%. Rapid urbanization—coupled with lack of proper planning and high rural-urban migration—is the key driver of these changes. These changes are associated with loss of ecosystem services which will negatively impact human wellbeing in the city. We recommend city planners to mainstream ecosystem-based adaptation and mitigation into urban plans supported by strong policy and funds.

This study was carried out in the southern zone of Tigray to identify and characterize traditional common agroforestry practices and understand the existing knowledge of farm households on the management of trees under different agroforestry in different agroecologies. We conducted reconnaissance and diagnostic surveys by systematically and randomly selecting 147 farming households in the three agroecologies of the study area. A logit regression model was employed to determine how these factors influence farmers’ adoption decision. The findings indicate that a majority of the households (46.3%) were engaged in homestead agroforestry practices (AFP), followed by live fence (25.9%) and farmland or parkland (15%) agroforestry practices. The study identified Carica papaya, Malus domestic, Persea americana, Mangifera indica, Ziziphus spina-christi, and Balanites aegyptiaca as the most dominant fruit tree species found in the home garden agroforestry. In total, 68% of the households had some of these fruit trees around their home gardens. We also established the three most dominant agricultural production systems as: i) Agricultural production system, composed of fruit tree + cereal crops + Ziziphus spina-christi + Balanites aegyptiaca and/or acacia species; ii) agricultural production system, consisting of cash crops, like Coffee arabica and Catha edulies + fruit trees + Cordia africana + Balanites aegyptiaca and/or acacia species; and iii) agricultural production, composed of fruit trees + vegetables within a boundary of Sesbania sesban and other acacia species in the modern irrigated land. Furthermore, 90.16% of the households in the highlands reported a shortage of farmland for planting trees as the main constraint. About 34.44% farmers reported using leaves of Cordia africana, Balanites aegyptiaca, pods of acacia species, and crop residue as the main source of animal fodder. In total, 86.4% of the households also recognized the importance of multipurpose trees for soil fertility enhancement, control of runoff, microclimate amelioration, environmental protection, and dry season animal fodder. According to the logit model analysis, sex, family size, educational level, and landholding significantly (p < 0.05) influence the household’s role in the adoption of agroforestry practices. Based on these findings, farmers used different adaptation strategies, such as planting of multi-purpose trees (34.7%), conservation tillage to minimize both erosion and runoff potentials as soil conservation strategies (27.2%), varying planting dates, use of drought tolerant crop varieties (16.3%), and others based on farmers’ indigenous knowledge passed down from generation to generation. We conclude that agroforestry practices are important components of farming systems in Tigray, resulting in diversified products and ecological benefits that improve socio-ecological resilience. Therefore, we recommend that agroforestry practices are mainstreamed into development plans, especially in agriculture.

This study analyzes the impact of land use/land cover (LULC) changes on the hydrology of the dry Afromontane forest landscape in northern Ethiopia. Landsat satellite images of thematic mapper (TM) (1986), TM (2001), and Operational Land Imager (OLI) (2018) were employed to assess LULC. All of the images were classified while using the maximum likelihood image classification technique, and the changes were assessed by post-classification comparison. Seven LULC classes were defined with an overall accuracy 83–90% and a Kappa coefficient of 0.82–0.92. The classification result for 1986 revealed dominance of shrublands (48.5%), followed by cultivated land (42%). Between 1986 and 2018, cultivated land became the dominant (39.6%) LULC type, accompanied by a decrease in shrubland to 32.2%, as well as increases in forestland (from 4.8% to 21.4%) and bare land (from 0% to 0.96%). The soil conservation systems curve number model (SCS-CN) was consequently employed to simulate forest hydrological response to climatic variations and land-cover changes during three selected years. The observed changes in direct surface runoff, the runoff coefficient, and storage capacity of the soil were partially linked to the changes in LULC that were associated with expanding bare land and built-up areas. This change in land use aggravates the runoff potential of the study area by 31.6 mm per year on average. Runoff coefficients ranged from 25.3% to 47.2% with varied storm rainfall intensities of 26.1–45.4 mm/ha. The temporal variability of climate change and potential evapotranspiration increased by 1% during 1981–2018. The observed rainfall and modelled runoff showed a strong positive correlation (R² = 0.78; p < 0.001). Regression analysis between runoff and rainfall intensity indicates their high and significant correlation (R² = 0.89; p < 0.0001). Changes were also common along the slope gradient and agro-ecological zones at varying proportions. The observed changes in land degradation and surface runoff are highly linked to the change in LULC. Further study is suggested on climate scenario-based modeling of hydrological processes that are related to land use changes to understand the hydrological variability of the dry Afromontane forest ecosystems.

Hydrologic models play an indispensable role in managing the scarce water resources of a region, and in developing countries, the availability and distribution of data are challenging. This research aimed to integrate and compare the satellite rainfall products, namely, Tropical Rainfall Measuring Mission (TRMM 3B43v7) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), with a GR2M hydrological water balance model over a diversified terrain of the Awash River Basin in Ethiopia. Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS), coefficient of determination (R²), and root mean square error (RMSE) and Pearson correlation coefficient (PCC) were used to evaluate the satellite rainfall products and hydrologic model performances of the basin. The satellite rainfall estimations of both products showed a higher PCC (above 0.86) with areal observed rainfall in the Uplands, the Western highlands, and the Lower sub-basins. However, it was weakly associated in the Upper valley and the Eastern catchments of the basin ranging from 0.45 to 0.65. The findings of the assimilated satellite rainfall products with the GR2M model exhibited that 80% of the calibrated and 60% of the validated watersheds in a basin had lower magnitude of PBIAS (<±10), which resulted in better accuracy in flow simulation. The poor performance with higher PBIAS (≥±25) of the GR2M model was observed only in the Melka Kuntire (TRMM 3B43v7 and PERSIANN-CDR), Mojo (PERSIANN-CDR), Metehara (in all rainfall data sets), and Kessem (TRMM 3B43v7) watersheds. Therefore, integrating these satellite rainfall data, particularly in the data-scarce basin, with hydrological data, generally appeared to be useful. However, validation with the ground observed data is required for effective water resources planning and management in a basin. Furthermore, it is recommended to make bias corrections for watersheds with poorlyww performing satellite rainfall products of higher PBIAS before assimilating with the hydrologic model.