Soil water influences vegetation patterns and stands in landscapes and these important determinant factors of rainfall-runoff generation in a river basin. This challenge in the hydrological sciences was appreciated in the International Association of Hydrological Sciences (IAHS) initiative aimed at achieving advances in PUB. The global meteorology, surface solar energy, and climatology data are important parameters that are usually overestimated due to their change dynamics broadly being at a large landscape scale. The commonly used regionalization approach can be erroneous and should be attempted only with great care, and it is important to use reliable online proven site-specific datasets. River flow data is one of the major challenges in river basins hydrology studies and Predictions in Ungauged Basins (PUB) should carefully limit uncertainty in assessments. Impacts of climate variability and land use/cover change on landscape hydrology are difficult to determine in ungauged river basins because of the difficulty to estimate meteorological parameters and their surface rainfall-runoff effects. Modelling landscape hydrology with distributed models is important to understand river flow changes at spatial and temporal scales. The quantity and characteristics of rainfall-runoff in a landscape are affected by a combination of LULC as well as slope and soil characteristics which are unique for different landscapes. Knowledge of land use/cover (LULC) variations and changes are important in rainfall-runoff studies to determine factors affecting overland flow and water losses. Modelling of landscape rainfall-runoff to determine amounts and contributing areas is important for land to use/cover planning, and environmental management as this offers information on river water source areas. Rainfall-runoff relationship with field-collected data could be because of landscapeĬharacteristics or topsoil layer not catered for in the FAO soil data. NASA-POWER data has the potential for use for modelling the rainfall-runoff in the basin. The river basin is ungauged for hydrologic data. Was used because there w as no historical rainfall and river flow data since Timing of peaks and lows in rainfall and riverįlow observed in the field and modelled were confirmed by residents as the trend in the area. Were uniform trends for the rainfall, temperature, and relative humidity in Rainfall-runoff relationship for gauged data (R 2 = 0.8131). Rainfall-runoff relationship usin g NASA-POWER data for the area (R 2 = 0.7749) for the studied period (2019-2021). Region were used for comparison with soil data collected during fieldwork.įield collected data showed that soil in the area is predominantly sandy loamĪnd only sand content and bulk density were uniformly distributed across the FAO soilĪnd land use/cover datasets which are globally available and widely used in the SWAT (Soil and WaterĪssessment Tool) model was used to assess rainfall - runoff data generated using the NASA-POWER datasetĪnd gauged rainfall and river flow data collected during fieldwork. River basin FAO digital soil and land use/cover maps and field - collected data were used. Of 30 m × 30 m downloaded from USGS ( the United States Geological Survey) website clipped DEM (Digital Elevation Model) of 1:250,000 scale and a grid resolution Nations) soil and land use/cover data for modelling rainfall-runoff in Incalaue Sensed rainfall data and FAO (Food and Agriculture Organization of the United Space Administration and Prediction of Worldwide Energy Resources) remotely This studyĪssessed the potential of a combination of NASA-POWER (National Aeronautics and Scientific methods are needed for this wide expanse of land. NSR is a data-poor remote area and there is a needįor rainfall-runoff data to inform decisions on water resources management, and Incalaue is a tributary of Lugenda River in NSR (Niassa Special Reserve) in
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