Search Results (18)

During gravity field modeling, the conventional acceleration approach rarely incorporates KBR inter-satellite range rate data from the GRACE mission. To propose an improved acceleration method, this study introduces initial orbital position and velocity vectors to be estimated along with a combination of Cowell, KSG, and Adams integrators. In addition to achieving a full-rank design matrix regarding orbit corrections when constructing observation equations, the proposed method is capable of utilizing range rate observations for gravity field estimation. To verify the reliability of this approach, GRACE data from April 2002 to December 2016 was used to calculate a time series of monthly gravity solutions up to a degree and order of 96, referred to as Tongji-Acc RL06 in this paper. The computed time series are compared with the official models (i.e., CSR RL06, GFZ RL06, and JPL RL06) in terms of geoid degree variances, signal contents over distinct areas, and noise levels in desert regions. The investigations lead to the following conclusions: (a) the geoid degree variances indicate that Tongji-Acc RL06 exhibits comparable signal levels (approximately below 20 degrees) to the other three models while demonstrating lower noise at higher degrees (above 40 degrees); (b) the analysis over the globe, typical river basins, and land–ice regions illustrates that the solutions derived using the proposed acceleration method agree well with the official models based on the dynamic approach; (c) especially over the two large-scale river basins (i.e., Amazon and Zambezi) and another two small-scale river basins (i.e., Tennessee and Irrawaddy), Tongji-Acc RL06 significantly improves the SNR values; and (d) in the cases of the Sahara and Karakum deserts, Tongji-Acc RL06 achieves noise reductions of over 55.8% and 61.5% relative to CSR RL06, respectively. In general, the signal and noise analyses demonstrate that the proposed acceleration-based approach can effectively extract gravity field signals from KBR inter-satellite range rate observations with improved SNR, while significantly reducing the high-frequency noise. Full article

Hydrological modeling of large river basins and flood plains continues to be challenged by the low availability and quality of observed data for modeling input and model calibration. Global datasets are often used to bridge this gap, but are often difficult and time consuming to acquire, particularly in low resource regions of the world. Numerous calls have been made to standardize and share data to increase local basin modeling capacities and reduce redundancy in efforts, but barriers still exist. We discuss the challenges of hydrological modeling in data-scarce regions and describe a freely available online tool site developed to enable users to extract input data for any basin of any size. The site will allow users to visualize, map, interpolate, and reformat the data as needed for the intended application. We used our hydrological model of the Upper Zambezi basin and the Chobe-Zambezi floodplains to illustrate the use of this online toolset. Increasing access and dissemination of hydrological modeling data is a critical need, particularly among users where data requirements and access continue to impede locally driven management of hydrological systems. Full article

Soil maps can usefully serve in data scarce regions, for example for yield (gap) assessments using a crop simulation model. The soil property estimates’ contribution to inaccuracy and uncertainty can be functionally evaluated by comparing model results using the estimates as input against independent observations. We conducted a functional evaluation of digital maps of soil hydraulic properties of the Zambezi River Basin using a crop growth model AquaCrop. AquaCrop was run, alimented with local meteorological data, and with soil hydraulic properties derived from the digital maps of digital soil mapping (DSM) techniques, as opposed to estimations from the widely used Saxton and Rawls pedotransfer functions. The two simulated time series of canopy cover (CC) (AquaCrop-CC-DSM and AquaCrop-CC-Saxton), which were compared against canopy cover data derived from the remotely sensed Leaf Area Index (LAI) from the MODIS archive (MODIS-CC). A pairwise comparison of the time series resulted in a root mean squared error (RMSE) of 0.07 and a co-efficient of determination (R²) of 0.93 for AquaCrop-CC-DSM versus MODIS-CC, and an RMSE of 0.08 and R² of 0.88 for AquaCrop-CC-Saxton versus MODIS-CC. In dry years, the AquaCrop-CC-DSM deviated less from the MODIS-CC than the AquaCrop-CC-Saxton (p < 0.001), although this difference was not significant in wet years. The functional evaluation showed that soil hydraulic property estimates based on digital soil mapping outperformed those based on Saxton and Rawls when used for simulating crop growth in dry years in the Zambezi River Basin. This study also shows the value of conducting a functional evaluation of estimated (static) soil hydraulic properties in terms of dynamic model output. Full article

It is critical to produce more crop per drop in an environment where water availability is decreasing and competition for water is increasing. In order to build such agricultural production systems, well parameterized crop growth models are essential. While in most crop growth modeling research, focus is on gathering model inputs such as climate data, less emphasis is paid to collecting the critical soil hydraulic properties (SHPs) data needed to operate crop growth models. Collection of SHPs data for the Zambezi River Basin (ZRB) is extremely labor-intensive and expensive, thus alternate technologies such as digital soil mapping (DSM) must be explored. We evaluated five types of DSM models to establish the best spatially explicit estimates of the soil water content at pF0.0 (saturation), pF2.0 (field capacity), and pF4.2 (wilting point), and of the saturated hydraulic conductivity (Ksat) across the ZRB by using estimates of locally calibrated pedotransfer functions of 1481 locations for training and testing the DSM models, as well as a reference dataset of measurements from 174 locations for validating the DSM models. We produced coverages of environmental covariates from various source datasets, including climate variables, soil and land use maps, parent materials and lithologic units, derivatives of a digital elevation model (DEM), and Landsat imagery with a spatial resolution of 90 m. The five types of models included multiple linear regression and four machine learning techniques: artificial neural network, gradient boosted regression trees, random forest, and support vector machine. Where the residuals of the initial DSM models were spatially autocorrelated, the models were extended/complemented with residual kriging (RK). Spatial autocorrelation in the model residuals was observed for all five models of each of the three water contents, but not for Ksat. On average for the water content, the R² ranged from 0.40 to 0.80 in training and test datasets before adding kriged model residuals and ranged from 0.80 to 0.95 after adding model residuals. Overall, the best prediction method consisted of random forest as the deterministic model, complemented with RK, whereby soil texture followed by climate and topographic elevation variables were the most important covariates. The resulting maps are a ready-to-use resource for hydrologists and crop modelers to aliment and calibrate their hydrological and crop growth models. Full article

The Zambezi River Basin is considered to be highly vulnerable to the impacts of climate change and adverse weather events, which might cause serious environmental, economic, and social consequences for millions of people. Therefore, it is crucial to understand how natural resource-dependent people perceive climate change, and how they adapt to the changes, as it is very important for climate change adaptation policy formulation and its implementation. Therefore, this study seeks to assess fishers perceptions of climate change, its impacts on fishery resources and livelihoods, and their adaptation strategies. Data were collected from 120 fishers in two basins (Binga and Kariba) along the shores of Lake Kariba using a mixed-method research approach. Meteorological data were obtained from the Meteorological Department Services of Zimbabwe (MSDZ). The findings show that fishers of Lake Kariba have observed changes in temperature and rainfall trends. Fishers believe that the perceived changes of the climatic variables have led to a decline in fish productivity and fish catches. To cope with declining fish stocks and catches, fishers have adopted several adaptation strategies, including changing fishing gear, targeting new fish species, and increasing fishing efforts. These study findings help to set a path towards local specific climate change adaptation strategies for small-scale fishers. This study provided relevant information for policy makers and fisheries stewards to formulate appropriate policies and programmes aimed at enhancing fishers adaptation to climate change and promote sustainable fisheries. Full article

The combination of Hydrological Models and high-resolution Satellite Precipitation Products (SPPs) or regional Climatological Models (RCMs), has provided the means to establish baselines for the quantification, propagation, and reduction in hydrological uncertainty when generating streamflow forecasts. This study aimed to improve operational real-time streamflow forecasts for the Upper Zambezi River Basin (UZRB), in Africa, utilizing the novel Variational Ensemble Forecasting (VEF) approach. In this regard, we describe and discuss the main steps required to implement, calibrate, and validate an operational hydrologic forecasting system (HFS) using VEF and Hydrologic Processing Strategies (HPS). The operational HFS was constructed to monitor daily streamflow and forecast them up to eight days in the future. The forecasting process called short- to medium-range (SR2MR) streamflow forecasting was implemented using real-time rainfall data from three Satellite Precipitation Products or SPPs (The real-time TRMM Multisatellite Precipitation Analysis TMPA-RT, the NOAA CPC Morphing Technique CMORPH, and the Precipitation Estimation from Remotely Sensed data using Artificial Neural Networks, PERSIANN) and rainfall forecasts from the Global Forecasting System (GFS). The hydrologic preprocessing (HPR) strategy considered using all raw and bias corrected rainfall estimates to calibrate three distributed hydrological models (HYMOD_DS, HBV_DS, and VIC 4.2.b). The hydrologic processing (HP) strategy considered using all optimal parameter sets estimated during the calibration process to increase the number of ensembles available for operational forecasting. Finally, inference-based approaches were evaluated during the application of a hydrological postprocessing (HPP) strategy. The final evaluation and reduction in uncertainty from multiple sources, i.e., multiple precipitation products, hydrologic models, and optimal parameter sets, was significantly achieved through a fully operational implementation of VEF combined with several HPS. Finally, the main challenges and opportunities associated with operational SR2MR streamflow forecasting using VEF are evaluated and discussed. Full article

The Zambezi River basin is the fourth largest basin in Africa and the largest in southern Africa, comprising 5% of the total area of the continent. The basin is extremely vulnerable to climate change effects due to its highly variable climate. The purpose of this study was to evaluate the impact of climate change on streamflow in one of the sub-basins, the Kabombo basin. The multi- global climate model projections were used as input to the Soil Water Assessment Tool (SWAT) hydrological model for simulation of streamflow under RCP 4.5 and RCP 8.5 climate scenarios. The model predicted an annual streamflow increase of 85% and 6% for high uncertainty and strong consensus, respectively, under RCP 8.5. The model predicted a slightly reduced annual streamflow of less than 3% under RCP 4.5. The majority of simulations indicated that intra-annual and inter-annual streamflow variability will increase in the future for RCP 8.5 while it will reduce for the RCP 4.5 scenario. The predicted high and moderate rise in streamflow for RCP 8.5 suggests the need for adaptation plans and mitigation strategies. In contrast, the streamflow predicted for RCP 4.5 indicates that there may be a need to review the current management strategies of the water resources in the basin. Full article

Groundwater resources are largely used in rural communities of river basins due to their acceptable water quality and reliability for domestic purposes where little or no treatment is required. However, groundwater resources have been affected by changes in land use, mining activities, agricultural practices, industrial effluent, and urbanisation among anthropogenic influences while climate change impacts and volcanic eruptions have affected its involvement among the natural phenomena. The purpose of the study was to assess groundwater potential in the basin with the use of Analytical Hierarchy Process (AHP), remote sensing, GIS techniques, and groundwater occurrence and movement influencing factors. These factors were used to produce seven thematic maps, which were then assigned weights and scale using an AHP tool, based on their degree of influence on groundwater occurrence and movement. A weighted groundwater potential map was produced with four zones denoted as 0.4% (317 km²) for very good potential; 27% (19,170 km²) for good potential; 61% (43,961 km²) for moderate potential and 12% (8639 km²) for poor potential. Validation, using existing boreholes, showed that 89% were overlain on moderate to very good potential zones and henceforth considered to be a novel approach which is useful for groundwater resources assessment and integrated water management in the basin. Full article

Hydro-climatic resilience is an essential element of food security. The miombo ecosystem in Southern Africa supports varied land uses for a growing population. Albedo, Leaf Area Index (LAI), Fractional Vegetation Cover (FVC), Solar-Induced chlorophyll Fluorescence (SIF), and precipitation remote-sensing data for current climate were jointly analyzed to explore vegetation dynamics and water availability feedbacks. Changes in the surface energy balance tied to vegetation status were examined in the light of an hourly albedo product with improved atmospheric correction derived for this study. Phase-space analysis shows that the albedo’s seasonality tracks the landscape-scale functional stability of miombo and woody savanna with respect to precipitation variations. Miombo exhibits the best adaptive traits to water stress which highlights synergies among root-system water uptake capacity, vegetation architecture, and landscape hydro-geomorphology. This explains why efforts to conserve the spatial structure of the miombo forest in sustainable farming of seasonal wetlands have led to significant crop yield increases. Grass savanna’s high vulnerability to water stress is illustrative of potential run-away impacts of miombo deforestation. This study suggests that phase-space analysis of albedo, SIF, and FVC can be used as operational diagnostics of ecosystem health. Full article

In large freshwater river basins across the globe, the composite influences of large-scale climatic processes and human activities (e.g., deforestation) on hydrological processes have been studied. However, the knowledge of these processes in this era of the Anthropocene in the understudied hydrologically pristine South Central African (SCA) region is limited. This study employs satellite observations of evapotranspiration (ET), precipitation and freshwater between 2002 and 2017 to explore the hydrological patterns of this region, which play a crucial role in global climatology. Multivariate methods, including the rotated principal component analysis (rPCA) were used to assess the relationship of terrestrial water storage (TWS) in response to climatic units (precipitation and ET). The use of the rPCA technique in assessing changes in TWS is warranted to provide more information on hydrological changes that are usually obscured by other dominant naturally-driven fluxes. Results show a low trend in vegetation transpiration due to deforestation around the Congo basin. Overall, the Congo (r2 = 76%) and Orange (r2 = 72%) River basins maintained an above-average consistency between precipitation and TWS throughout the study region and period. Consistent loss in freshwater is observed in the Zambezi (−9.9 ± 2.6 mm/year) and Okavango (−9.1 ± 2.5 mm/year) basins from 2002 to 2008. The Limpopo River basin is observed to have a 6% below average reduction in rainfall rates which contributed to its consistent loss in freshwater (−4.6 ± 3.2 mm/year) from 2006 to 2012.Using multi-linear regression and correlation analysis we show that ET contributes to the variability and distribution of TWS in the region. The relationship of ET with TWS (r = 0.5) and rainfall (r = 0.8) over SCA provides insight into the role of ET in regulating fluxes and the mechanisms that drive precipitation in the region. The moderate ET–TWS relationship also shows the effect of climate and anthropogenic influence in their interactions. Full article

Having updated knowledge of cropland extent is essential for crop monitoring and food security early warning. Previous research has proposed different methods and adopted various datasets for mapping cropland areas at regional to global scales. However, most approaches did not consider the characteristics of farming systems and apply the same classification method in different agroecological zones (AEZs). Furthermore, the acquisition of in situ samples for classification training remains challenging. To address these knowledge gaps and challenges, this study applied a zone-specific classification by comparing four classifiers (random forest, the support vector machine (SVM), the classification and regression tree (CART) and minimum distance) for cropland mapping over four different AEZs in the Zambezi River basin (ZRB). Landsat-8 and Sentinel-2 data and derived indices were used and synthesized to generate thirty-five layers for classification on the Google Earth Engine platform. Training samples were derived from three existing landcover datasets to minimize the cost of sample acquisitions over the large area. The final cropland map was generated at a 10 m resolution. The performance of the four classifiers and the viability of training samples were analysed. All classifiers presented higher accuracy in cool AEZs than in warm AEZs, which may be attributed to field size and lower confusion between cropland and grassland classes. This indicates that agricultural landscape may impact classification results regardless of the classifiers. Random forest was found to be the most stable and accurate classifier across different agricultural systems, with an overall accuracy of 84% and a kappa coefficient of 0.67. Samples extracted over the full agreement areas among existing datasets reduced uncertainty and provided reliable calibration sets as a replacement of costly in situ measurements. The methodology proposed by this study can be used to generate periodical high-resolution cropland maps in ZRB, which is helpful for the analysis of cropland extension and abandonment as well as intensity changes in response to the escalating population and food insecurity. Full article

Precipitation plays an important role in the food production of Southern Africa. Understanding the spatial and temporal variations of precipitation is helpful for improving agricultural management and flood and drought risk assessment. However, a comprehensive precipitation pattern analysis is challenging in sparsely gauged and underdeveloped regions. To solve this problem, Version 7 Tropical Rainfall Measuring Mission (TRMM) precipitation products and Google Earth Engine (GEE) were adopted in this study for the analysis of spatiotemporal patterns of precipitation in the Zambezi River Basin. The Kendall’s correlation and sen’s Slop reducers in GEE were used to examine precipitation trends and magnitude, respectively, at annual, seasonal and monthly scales from 1998 to 2017. The results reveal that 10% of the Zambezi River basin showed a significant decreasing trend of annual precipitation, while only 1% showed a significant increasing trend. The rainy-season precipitation appeared to have a dominant impact on the annual precipitation pattern. The rainy-season precipitation was found to have larger spatial, temporal and magnitude variation than the dry-season precipitation. In terms of monthly precipitation, June to September during the dry season were dominated by a significant decreasing trend. However, areas presenting a significant decreasing trend were rare (<12% of study area) and scattered during the rainy-season months (November to April of the subsequent year). Spatially, the highest and lowest rainfall regions were shifted by year, with extreme precipitation events (highest and lowest rainfall) occurring preferentially over the northwest side rather than the northeast area of the Zambezi River Basin. A “dry gets dryer, wet gets wetter” (DGDWGW) pattern was also observed over the study area, and a suggestion on agriculture management according to precipitation patterns is provided in this study for the region. This is the first study to use long-term remote sensing data and GEE for precipitation analysis at various temporal scales in the Zambezi River Basin. The methodology proposed in this study is helpful for the spatiotemporal analysis of precipitation in developing countries with scarce gauge stations, limited analytic skills and insufficient computation resources. The approaches of this study can also be operationally applied to the analysis of other climate variables, such as temperature and solar radiation. Full article

Chongwe River Catchment, a sub-catchment of the Zambezi River Basin, has been experiencing changes in land use/land cover (LULC) and in its hydrology. This study aims to assess the impact of LULC changes on the catchment’s hydrological components such as streamflow, evapotranspiration and water abstractions. LULC change data, detected from the 1984, 1994, 2014 and 2017 USGS Landsat imagery using a maximum likelihood supervised classifier, were integrated into the WEAP Model along with soil, slope and hydro–climate data. The results showed that between 1984 and 2017 built-up area increased by 382.77% at 6.97 km²/year, irrigated agriculture increased by 745.62% at 1.70 km²/year, rainfed farms/ranch/grassland increased by 14.67% at 14.53 km²/year, forest land decreased by 41.11% at 22.33 km²/year and waterbodies decreased by 73.95% at 0.87 km²/year. Streamflow increased at a rate of 0.13 Mm³ per annum in the wet seasons and showed a high variation with flow volume of 79.68 Mm³ in February and 1.01 Mm³ in September. Annual actual evapotranspiration decreased from 840.6 mm to 796.3 mm while annual water abstraction increased from 8.94 mm to 23.2 mm from the year 1984 to 2017. The pattern of LULC change between 1984 and 2017 has negatively impacted the hydrology of the Chongwe River Catchment. From these findings, an integrated catchment management and protection approach is proposed to mitigate the negative impacts of LULC dynamics on hydrological components in the Chongwe River Catchment. Full article

The Upper Zambezi River Basin (UZRB) delineates a complex region of topographic, soil and rainfall gradients between the Congo rainforest and the Kalahari Desert. Satellite imagery shows permanent wetlands in low-lying convergence zones where surface–groundwater interactions are vigorous. A dynamic wetland classification based on MODIS Nadir BRDF-Adjusted Reflectance is developed to capture the inter-annual and seasonal changes in areal extent due to groundwater redistribution and rainfall variability. Simulations of the coupled water–carbon cycles of seasonal wetlands show nearly double rates of carbon uptake as compared to dry areas, at increasingly lower water-use efficiencies as the dry season progresses. Thus, wetland extent and persistence into the dry season is key to the UZRB’s carbon sink and water budget. Whereas groundwater recharge governs the expansion of wetlands in the rainy season under large-scale forcing, wetland persistence in April–June (wet–dry transition months) is tied to daily morning fog and clouds, and by afternoon land–atmosphere interactions (isolated convection). Rainfall suppression in July–September results from colder temperatures, weaker regional circulations, and reduced instability in the lower troposphere, shutting off moisture recycling in the dry season despite high evapotranspiration rates. The co-organization of precipitation and wetlands reflects land–atmosphere interactions that determine wetland seasonal persistence, and the coupled water and carbon cycles. Full article

River discharge, which represents the accumulation of surface water flowing into rivers and ultimately into the ocean or other water bodies, may have great impacts on water quality and the living organisms in rivers. However, the global knowledge of river discharge is still poor and worth exploring. This study proposes an efficient method for mapping high-resolution global river discharge based on the algorithms of drainage network extraction. Using the existing global runoff map and digital elevation model (DEM) data as inputs, this method consists of three steps. First, the pixels of the runoff map and the DEM data are resampled into the same resolution (i.e., 0.01-degree). Second, the flow direction of each pixel of the DEM data (identified by the optimal flow path method used in drainage network extraction) is determined and then applied to the corresponding pixel of the runoff map. Third, the river discharge of each pixel of the runoff map is calculated by summing the runoffs of all the pixels in the upstream of this pixel, similar to the upslope area accumulation step in drainage network extraction. Finally, a 0.01-degree global map of the mean annual river discharge is obtained. Moreover, a 0.5-degree global map of the mean annual river discharge is produced to display the results with a more intuitive perception. Compared against the existing global river discharge databases, the 0.01-degree map is of a generally high accuracy for the selected river basins, especially for the Amazon River basin with the lowest relative error (RE) of 0.3% and the Yangtze River basin within the RE range of ±6.0%. However, it is noted that the results of the Congo and Zambezi River basins are not satisfactory, with RE values over 90%, and it is inferred that there may be some accuracy problems with the runoff map in these river basins. Full article