Your search found 18 records
1 Dossou-Yovo, E. R.; Zwart, Sander J.; Kouyate, A.; Ouedraogo, I.; Bakare, O. 2019. Predictors of drought in inland valley landscapes and enabling factors for rice farmers’ mitigation measures in the Sudan-Sahel Zone. Sustainability, 11(1): 1-17. [doi: https://doi.org/10.3390/su11010079]
Drought ; Valleys ; Landscape ; Agricultural production ; Rice fields ; Farmers ; Crop production ; Soil properties ; Precipitation ; Evapotranspiration ; Groundwater ; Water availability ; Water balance ; Land ownership ; Socioeconomic environment ; Remote sensing ; Gender ; Women's participation / Sahel Region / Sudan / West Africa / Sudano Sahelian Region / Burkina Faso / Mali / Nigeria
(Location: IWMI HQ Call no: e-copy only Record No: H049050)
(2.55 MB)
Drought is a noteworthy cause of low agricultural profitability and of crop production vulnerability, yet in numerous countries of Africa little to no consideration has been paid to readiness for drought calamity, particularly to spatial evaluation and indicators of drought occurrence. In this study, biophysical and socio-economic data, farmers’ community surveys and secondary data from remote sensing on soil characteristics and water demand were used to evaluate the predictors of drought in inland valley rice-based production systems and the factors affecting farmers’ mitigation measures. The study intervened in three West African countries located in the Sudan-Sahel zone, viz. Burkina Faso, Mali and Nigeria. Significant drying trends occurred at latitudes below 11°30' whilst significant wetting trends were discerned at latitude above 11°30'. Droughts were more frequent and had their longest duration in the states of Niger and Kaduna located in Nigeria and in western Burkina Faso during the period 1995–2014. Among 21 candidate predictors, average annual standardized precipitation evapotranspiration index and duration of groundwater availability were the most important predictors of drought occurrence in inland valleys rice based-production systems. Land ownership and gender affected the commitment of rice farmers to use any mitigation measure against drought. Drought studies in inland valleys should include climatic water balance and groundwater data. Securing property rights and focusing on women’s association would improve farmers’ resilience and advance drought mitigation measures.
2 Akpoti, K.; Kabo-bah, A. T.; Zwart, Sander J.. 2019. Agricultural land suitability analysis: state-of-the-art and outlooks for integration of climate change analysis. Agricultural Systems, 173:172-208. [doi: https://doi.org/10.1016/j.agsy.2019.02.013]
Agricultural land ; Sustainable agriculture ; Sustainable Development Goals ; Land suitability ; Land use ; Integration ; Climate change ; Machine learning ; Crop production ; Crop yield ; Crop modelling ; Food security ; Environmental impact ; Planning ; Water availability ; Socioeconomic environment ; Ecosystems
(Location: IWMI HQ Call no: e-copy only Record No: H049142)
Agricultural land suitability analysis (ALSA) for crop production is one of the key tools for ensuring sustainable agriculture and for attaining the current global food security goal in line with the Sustainability Development Goals (SDGs) of United Nations. Although some review studies addressed land suitability, few of them specifically focused on land suitability analysis for agriculture. Furthermore, previous reviews have not reflected on the impact of climate change on future land suitability and how this can be addressed or integrated into ALSA methods. In the context of global environmental changes and sustainable agriculture debate, we showed from the current review that ALSA is a worldwide land use planning approach. We reported from the reviewed articles 69 frequently used factors in ALSA. These factors were further categorized in climatic conditions (16), nutrients and favorable soils (34 of soil and landscape), water availability in the root zone (8 for hydrology and irrigation) and socio-economic and technical requirements (11). Also, in getting a complete view of crop’s ecosystems and factors that can explain and improve yield, inherent local socio-economic factors should be considered. We showed that this aspect has been often omitted in most of the ALSA modeling with only 38% of the total reviewed article using socio-economic factors. Also, only 30% of the studies included uncertainty and sensitivity analysis in their modeling process. We found limited inclusions of climate change in the application of the ALSA. We emphasize that incorporating current and future climate change projections in ALSA is the way forward for sustainable or optimum agriculture and food security. To this end, qualitative and quantitative approaches must be integrated into a unique ALSA system (Hybrid Land Evaluation System - HLES) to improve the land evaluation approach.
3 Djagba, J. F.; Kouyate, A. M.; Baggie, I.; Zwart, Sander J.. 2019. A geospatial dataset of inland valleys in four zones in Benin, Sierra Leone and Mali. Data in Brief, 23:103699. [doi: https://doi.org/10.1016/j.dib.2019.103699]
Spatial data ; Datasets ; Agricultural development ; Farmers ; Socioeconomic environment ; Geographical distribution ; Valleys / West Africa / Benin / Sierra Leone / Mali
(Location: IWMI HQ Call no: e-copy only Record No: H049424)
https://www.sciencedirect.com/science/article/pii/S2352340919300484/pdfft?md5=a512268a8fc2fb761b9bc45e16f7abe3&pid=1-s2.0-S2352340919300484-main.pdf
https://vlibrary.iwmi.org/pdf/H049424.pdf
https://vlibrary.iwmi.org/pdf/H049424.pdf
(0.17 MB) (172 KB)
The dataset described in this data article represents four agricultural zones in West-Africa that are located in three countries: Benin, Mali and Sierra Leone. The dataset was created through a research collaboration between the Africa Rice Center (AfricaRice), Sierra Leone Agricultural Research Institute (SLARI) and the Institute for Rural Economy (IER). The dataset was compiled to investigate the potential for rice production in inland valleys of the three countries. The results of the investigation were published in Dossou-Yovo et al. (2017) and Djagba et al. (2018). The dataset describes the biophysical and socioeconomic conditions of 499 inland valleys in the four agricultural zones. In each inland valley data were collected through a focus group interview with a minimum of three farmers. In 499 interviews a total of 7496 farmers participated. The location of each inland valley was determined with handheld GPS devices. The geographic locations were used to extract additional parameters from digital maps on soils, elevation, population density, rainfall, flow accumulation, and distances to roads, market places, rice mills, chemical input stores, and settlements. The dataset contains 65 parameters in four themes (location, biophysical characteristics, socioeconomic characteristics, and inland valley land development and use). The GPS coordinates indicate the location of an inland valley, but they do not lead to the location of individual fields of farmers that were interviewed. The dataset is publicly shared as Supplementary data to this data article.
4 Akpoti, K.; Kabo-bah, A. T.; Dossou-Yovo, E. R.; Groen, T. A.; Zwart, Sander J.. 2020. Mapping suitability for rice production in inland valley landscapes in Benin and Togo using environmental niche modeling. Science of the Total Environment, 709:136165. [doi: https://doi.org/10.1016/j.scitotenv.2019.136165]
Land suitability ; Rice ; Agricultural production ; Environmental modelling ; Linear models ; Forecasting ; Uncertainty ; Water productivity ; Soil water content ; Rainfed farming ; Climatic data ; Soil chemicophysical properties ; Socioeconomic environment ; Valleys / Benin / Togo
(Location: IWMI HQ Call no: e-copy only Record No: H049495)
(5.47 MB)
Inland valleys (IVs) in Africa are important landscapes for rice cultivation and are targeted by national governments to attain self-sufficiency. Yet, there is limited information on the spatial distribution of IVs suitability at the national scale. In the present study, we developed an ensemble model approach to characterize the IVs suitability for rainfed lowland rice using 4 machine learning algorithms based on environmental niche modeling (ENM) with presence-only data and background sample, namely Boosted Regression Tree (BRT), Generalized Linear Model (GLM), Maximum Entropy (MAXNT) and Random Forest (RF). We used a set of predictors that were grouped under climatic variables, agricultural water productivity and soil water content, soil chemical properties, soil physical properties, vegetation cover, and socio-economic variables. The Area Under the Curves (AUC) evaluation metrics for both training and testing were respectively 0.999 and 0.873 for BRT, 0.866 and 0.816 for GLM, 0.948 and 0.861 for MAXENT and 0.911 and 0.878 for RF. Results showed that proximity of inland valleys to roads and urban centers, elevation, soil water holding capacity, bulk density, vegetation index, gross biomass water productivity, precipitation of the wettest quarter, isothermality, annual precipitation, and total phosphorus among others were major predictors of IVs suitability for rainfed lowland rice. Suitable IVs areas were estimated at 155,000–225,000 Ha in Togo and 351,000–406,000 Ha in Benin. We estimated that 53.8% of the suitable IVs area is needed in Togo to attain self-sufficiency in rice while 60.1% of the suitable IVs area is needed in Benin to attain self-sufficiency in rice. These results demonstrated the effectiveness of an ensemble environmental niche modeling approach that combines the strengths of several models.
5 Dembele, M.; Ceperley, N.; Zwart, Sander J.; Salvadore, E.; Mariethoz, G.; Schaefli, B. 2020. Potential of satellite and reanalysis evaporation datasets for hydrological modelling under various model calibration strategies. Advances in Water Resources, 143:103667. [doi: https://doi.org/10.1016/j.advwatres.2020.103667]
Hydrology ; Modelling ; Calibration ; Strategies ; Satellites ; Remote sensing ; Evaporation ; River basins ; Stream flow ; Water storage ; Soil water content ; Climatic zones ; Forecasting ; Datasets ; Performance evaluation ; Spatial distribution / West Africa / Volta River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H049804)
https://www.sciencedirect.com/science/article/pii/S030917082030230X/pdfft?md5=fe6a7ca8d66941a8fd4455b385a1dd8c&pid=1-s2.0-S030917082030230X-main.pdf
https://vlibrary.iwmi.org/pdf/H049804.pdf
https://vlibrary.iwmi.org/pdf/H049804.pdf
(4.54 MB) (4.54 MB)
Twelve actual evaporation datasets are evaluated for their ability to improve the performance of the fully distributed mesoscale Hydrologic Model (mHM). The datasets consist of satellite-based diagnostic models (MOD16A2, SSEBop, ALEXI, CMRSET, SEBS), satellite-based prognostic models (GLEAM v3.2a, GLEAM v3.3a, GLEAM v3.2b, GLEAM v3.3b), and reanalysis (ERA5, MERRA-2, JRA-55). Four distinct multivariate calibration strategies (basin-average, pixel-wise, spatial bias-accounting and spatial bias-insensitive) using actual evaporation and streamflow are implemented, resulting in 48 scenarios whose results are compared with a benchmark model calibrated solely with streamflow data. A process-diagnostic approach is adopted to evaluate the model responses with in-situ data of streamflow and independent remotely sensed data of soil moisture from ESA-CCI and terrestrial water storage from GRACE. The method is implemented in the Volta River basin, which is a data scarce region in West Africa, for the period from 2003 to 2012.
Results show that the evaporation datasets have a good potential for improving model calibration, but this is dependent on the calibration strategy. All the multivariate calibration strategies outperform the streamflow-only calibration. The highest improvement in the overall model performance is obtained with the spatial bias-accounting strategy (+29%), followed by the spatial bias-insensitive strategy (+26%) and the pixel-wise strategy (+24%), while the basin-average strategy (+20%) gives the lowest improvement. On average, using evaporation data in addition to streamflow for model calibration decreases the model performance for streamflow (-7%), which is counterbalance by the increase in the performance of the terrestrial water storage (+11%), temporal dynamics of soil moisture (+6%) and spatial patterns of soil moisture (+89%). In general, the top three best performing evaporation datasets are MERRA-2, GLEAM v3.3a and SSEBop, while the bottom three datasets are MOD16A2, SEBS and ERA5. However, performances of the evaporation products diverge according to model responses and across climatic zones. These findings open up avenues for improving process representation of hydrological models and advancing the spatiotemporal prediction of floods and droughts under climate and land use changes.
Results show that the evaporation datasets have a good potential for improving model calibration, but this is dependent on the calibration strategy. All the multivariate calibration strategies outperform the streamflow-only calibration. The highest improvement in the overall model performance is obtained with the spatial bias-accounting strategy (+29%), followed by the spatial bias-insensitive strategy (+26%) and the pixel-wise strategy (+24%), while the basin-average strategy (+20%) gives the lowest improvement. On average, using evaporation data in addition to streamflow for model calibration decreases the model performance for streamflow (-7%), which is counterbalance by the increase in the performance of the terrestrial water storage (+11%), temporal dynamics of soil moisture (+6%) and spatial patterns of soil moisture (+89%). In general, the top three best performing evaporation datasets are MERRA-2, GLEAM v3.3a and SSEBop, while the bottom three datasets are MOD16A2, SEBS and ERA5. However, performances of the evaporation products diverge according to model responses and across climatic zones. These findings open up avenues for improving process representation of hydrological models and advancing the spatiotemporal prediction of floods and droughts under climate and land use changes.
6 Sawadogo, A.; Kouadio, L.; Traore, F.; Zwart, Sander J.; Hessels, T.; Gundogdu, K. S. 2020. Spatiotemporal assessment of irrigation performance of the Kou Valley Irrigation Scheme in Burkina Faso using satellite remote sensing-derived indicators. ISPRS International Journal of Geo-Information, 9(8):484. (Special issue: Observation-Driven Understanding, Prediction, and Management in Hydrological/Hydraulic Hazard and Risk Studies) [doi: https://doi.org/10.3390/ijgi9080484]
Irrigation schemes ; Performance evaluation ; Satellite imagery ; Remote sensing ; Performance indexes ; Irrigation water ; Water management ; Food security ; Climate change ; Crop water use ; Water productivity ; Evapotranspiration ; Landsat ; Crop yield ; Rice ; Maize ; Sweet potatoes ; Models / Africa South of Sahara / Burkina Faso / Kou Valley Irrigation Scheme
(Location: IWMI HQ Call no: e-copy only Record No: H049932)
(4.17 MB) (4.17 MB)
Traditional methods based on field campaigns are generally used to assess the performance of irrigation schemes in Burkina Faso, resulting in labor-intensive, time-consuming, and costly processes. Despite their extensive application for such performance assessment, remote sensing (RS)-based approaches remain very much underutilized in Burkina Faso. Using multi-temporal Landsat images within the Python module for the Surface Energy Balance Algorithm for Land model, we investigated the spatiotemporal performance patterns of the Kou Valley irrigation scheme (KVIS) during two consecutive cropping seasons. Four performance indicators (depleted fraction, relative evapotranspiration, uniformity of water consumption, and crop water productivity) for rice, maize, and sweet potato were calculated and compared against standard values. Overall, the performance of the KVIS varied depending on year, crop, and the crop’s geographical position in the irrigation scheme. A gradient of spatially varied relative evapotranspiration was observed across the scheme, with the uniformity of water consumption being fair to good. Although rice was the most cultivated, a shift to more sweet potato farming could be adopted to benefit more from irrigation, given the relatively good performance achieved by this crop. Our findings ascertain the potential of such RS-based cost-effective methodologies to serve as basis for improved irrigation water management in decision support tools.
7 Akpoti, K.; Dossou-Yovo, E. R.; Zwart, Sander J.; Kiepe, P. 2021. The potential for expansion of irrigated rice under alternate wetting and drying in Burkina Faso. Agricultural Water Management, 247:106758. [doi: https://doi.org/10.1016/j.agwat.2021.106758]
Irrigated farming ; Irrigated rice ; Land suitability ; Mapping ; Water balance ; Water conservation ; Water use ; Climate change ; Precipitation ; Evapotranspiration ; Forecasting ; Groundwater table ; Soil texture ; Modelling / West Africa / Burkina Faso
(Location: IWMI HQ Call no: e-copy only Record No: H050218)
(21.30 MB)
Achieving rice self-sufficiency in West Africa will require an expansion of the irrigated rice area under water-scarce conditions. However, little is known about how much area can be irrigated and where and when water-saving practices could be used. The objective of this study was to assess potentially irrigable lands for irrigated rice cultivation under water-saving technology in Burkina Faso. A two-step, spatially explicit approach was developed and implemented. Firstly, machine learning models, namely Random Forest (RF) and Maximum Entropy (MaxEnt) were deployed in ecological niche modeling (ENM) approach to assess the land suitability for irrigated rice cultivation. Spatial datasets on topography, soil characteristics, climate parameters, land use, and water were used along with the current distribution of irrigated rice locations in Burkina Faso to drive ENMs. Secondly, the climatic suitability for alternate wetting and drying (AWD), an irrigation management method for saving water in rice cultivation in irrigated systems, was assessed by using a simple water balance model for the two main growing seasons (February to June and July to November) on a dekadal time scale. The evaluation metrics of the ENMs such as the area under the curve and percentage correctly classified showed values higher than 80% for both RF and MaxEnt. The top four predictors of land suitability for irrigated rice cultivation were exchangeable sodium percentage, exchangeable potassium, depth to the groundwater table, and distance to stream networks and rivers. Potentially suitable lands for rice cultivation in Burkina Faso were estimated at 21.1 × 105 ha. The whole dry season was found suitable for AWD implementation against 25–100% of the wet season. Soil percolation was the main driver of the variation in irrigated land suitability for AWD in the wet season. The integrated modeling and water balance assessment approach used in this study can be applied to other West African countries to guide investment in irrigated rice area expansion while adapting to climate change.
8 Akpoti, K.; Higginbottom, T. P.; Foster, T.; Adhikari, R.; Zwart, Sander J.. 2022. Mapping land suitability for informal, small-scale irrigation development using spatial modelling and machine learning in the Upper East Region, Ghana. Science of the Total Environment, 803:149959. [doi: https://doi.org/10.1016/j.scitotenv.2021.149959]
Farmer-led irrigation ; Small scale systems ; Land suitability ; Modelling ; Machine learning ; Food security ; Semiarid zones ; Groundwater ; Water availability ; Land use ; Land cover ; Soil properties ; Dry season ; Forecasting ; Reservoirs ; Population density ; Socioeconomic aspects / Ghana
(Location: IWMI HQ Call no: e-copy only Record No: H050670)
(7.61 MB)
Small-scale irrigation has gained momentum in recent years as one of the development priorities in Sub-Saharan Africa. However, farmer-led irrigation is often informal with little support from extension services and a paucity of data on land suitability for irrigation. To map the spatial explicit suitability for dry season small-scale irrigation, we developed a method using an ensemble of boosted regression trees, random forest, and maximum entropy machine learning models for the Upper East Region of Ghana. Both biophysical predictors including surface and groundwater availability, climate, topography and soil properties, and socio-economic predictors which represent demography and infrastructure development such as accessibility to cities and proximity to roads were considered. We assessed that 179,584 ± 49,853 ha is suitable for dry-season small-scale irrigation development when only biophysical variables are considered, and 158,470 ± 27,222 ha when socio-economic variables are included alongside the biophysical predictors, representing 77-89% of the current rainfed-croplands. Travel time to cities, accessibility to small reservoirs, exchangeable sodium percentage, surface runoff that can be potentially stored in reservoirs, population density, proximity to roads, and elevation percentile were the top predictors of small-scale irrigation suitability. These results suggested that the availability of water alone is not a sufficient indicator for area suitability for small-scale irrigation. This calls for strategic road infrastructure development and an improvement in the support to farmers for market accessibility. The suitability for small-scale irrigation should be put in the local context of market availability, demographic indicators, and infrastructure development.
9 Steinbach, S.; Cornish, N.; Franke, J.; Hentze, K.; Strauch, A.; Thonfeld, F.; Zwart, Sander J.; Nelson, A. 2021. A new conceptual framework for integrating earth observation in large-scale wetland management in East Africa. Wetlands, 41(7):93. [doi: https://doi.org/10.1007/s13157-021-01468-9]
Wetlands ; Environmental management ; Earth observation satellites ; Sustainable use ; Food security ; Environmental protection ; Surface water ; Land use ; Land cover ; Ecosystems ; Large scale systems ; Decision making ; Spatial data / East Africa / Rwanda
(Location: IWMI HQ Call no: e-copy only Record No: H050718)
https://link.springer.com/content/pdf/10.1007/s13157-021-01468-9.pdf
https://vlibrary.iwmi.org/pdf/H050718.pdf
https://vlibrary.iwmi.org/pdf/H050718.pdf
(5.27 MB) (5.27 MB)
Wetlands are abundant across the African continent and provide a range of ecosystem services on different scales but are threatened by overuse and degradation. It is essential that national governments enable and ensure the sustainable use of wetland resources to maintain these services in the long run. As informed management decisions require reliable, up-to-date, and large coverage spatial data, we propose a modular Earth observation-based framework for the geo-localisation and characterization of wetlands in East Africa. In this study, we identify four major challenges in spatial data supported wetland management and present a framework to address them. We then apply the framework comprising Wetland Delineation, Surface Water Occurrence, Land Use/Land Cover classification and Wetland Use Intensity for the whole of Rwanda and evaluate the ability of these layers to meet the identified challenges. The layers’ spatial and temporal characteristics make them combinable and the information content, of each layer alone as well as in combination, renders them useful for different wetland management contexts.
10 Ghansah, B.; Foster, T.; Higginbottom, T. P.; Adhikari, R.; Zwart, Sander J.. 2022. Monitoring spatial-temporal variations of surface areas of small reservoirs in Ghana’s Upper East Region using Sentinel-2 satellite imagery and machine learning. Physics and Chemistry of the Earth, 125:103082. [doi: https://doi.org/10.1016/j.pce.2021.103082]
(Location: IWMI HQ Call no: e-copy only Record No: H050847)
https://www.sciencedirect.com/science/article/pii/S147470652100125X/pdfft?md5=59bd7a98182c33a44b62aaf447495217&pid=1-s2.0-S147470652100125X-main.pdf
https://vlibrary.iwmi.org/pdf/H050847.pdf
https://vlibrary.iwmi.org/pdf/H050847.pdf
(9.19 MB) (9.19 MB)
Small reservoirs are one of the most important sources of water for irrigation, domestic and livestock uses in the Upper East Region (UER) of Ghana. Despite various studies on small reservoirs in the region, information on their spatial-temporal variations is minimal. Therefore, this study performed a binary Random Forest classification on Sentinel-2 images for five consecutive dry seasons between 2015 and 2020. The small reservoirs were then categorized according to landscape positions (upstream, midstream, and downstream) using a flow accumulation process. The classification produced an average overall accuracy of 98% and a root mean square error of 0.087 ha. It also indicated that there are currently 384 small reservoirs in the UER (of surface area between 0.09 and 37 ha), with 20% of them newly constructed between the 2016-17 and 2019-20 seasons. The study revealed that upstream reservoirs have smaller sizes and are likely to dry out during the dry season while downstream reservoirs have larger sizes and retain substantial amounts of water even at the end of the dry season. The results further indicated that about 78% of small reservoirs will maintain an average of 54% of their water surface area by the end of the dry season. This indicates significant water availability which can be effectively utilized to expand dry season irrigation. Overall, we demonstrate that landscape positions have significant impact on the spatial-temporal variations of small reservoirs in the UER. The study also showed the effectiveness of remote sensing and machine learning algorithms as tools for monitoring small reservoirs.
11 Dembele, Moctar; Vrac, M.; Ceperley, N.; Zwart, Sander J.; Larsen, J.; Dadson, S. J.; Mariethoz, G.; Schaefli, B. 2022. Contrasting changes in hydrological processes of the Volta River Basin under global warming. Hydrology and Earth System Sciences, 26(5):1481-1506. [doi: https://doi.org/10.5194/hess-26-1481-2022]
River basins ; Hydrological cycle ; Global warming ; Hydrological modelling ; Climate change ; Forecasting ; Water availability ; Hydroclimate ; Climatic zones ; Spatial variation ; Datasets / West Africa / Volta River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H051026)
https://hess.copernicus.org/articles/26/1481/2022/hess-26-1481-2022.pdf
https://vlibrary.iwmi.org/pdf/H051026.pdf
https://vlibrary.iwmi.org/pdf/H051026.pdf
(4.33 MB) (4.33 MB)
A comprehensive evaluation of the impacts of climate change on water resources of the West Africa Volta River basin is conducted in this study, as the region is expected to be hardest hit by global warming. A large ensemble of 12 general circulation models (GCMs) from the fifth Coupled Model Intercomparison Project (CMIP5) that are dynamically downscaled by five regional climate models (RCMs) from the Coordinated Regional-climate Downscaling Experiment (CORDEX)-Africa is used. In total, 43 RCM–GCM combinations are considered under three representative concentration pathways (RCP2.6, RCP4.5, and RCP8.5). The reliability of each of the climate datasets is first evaluated with satellite and reanalysis reference datasets. Subsequently, the Rank Resampling for Distributions and Dependences (R2D2) multivariate bias correction method is applied to the climate datasets. The bias-corrected climate projections are then used as input to the mesoscale Hydrologic Model (mHM) for hydrological projections over the 21st century (1991–2100).
Results reveal contrasting dynamics in the seasonality of rainfall, depending on the selected greenhouse gas emission scenarios and the future projection periods. Although air temperature and potential evaporation increase under all RCPs, an increase in the magnitude of all hydrological variables (actual evaporation, total runoff, groundwater recharge, soil moisture, and terrestrial water storage) is only projected under RCP8.5. High- and low-flow analysis suggests an increased flood risk under RCP8.5, particularly in the Black Volta, while hydrological droughts would be recurrent under RCP2.6 and RCP4.5, particularly in the White Volta. The evolutions of streamflow indicate a future delay in the date of occurrence of low flows up to 11 d under RCP8.5, while high flows could occur 6 d earlier (RCP2.6) or 5 d later (RCP8.5), as compared to the historical period.
Disparities are observed in the spatial patterns of hydroclimatic variables across climatic zones, with higher warming in the Sahelian zone. Therefore, climate change would have severe implications for future water availability with concerns for rain-fed agriculture, thereby weakening the water– energy–food security nexus and amplifying the vulnerability of the local population. The variability between climate models highlights uncertainties in the projections and indicates a need to better represent complex climate features in regional models. These findings could serve as a guideline for both the scientific community to improve climate change projections and for decision-makers to elaborate adaptation and mitigation strategies to cope with the consequences of climate change and strengthen regional socioeconomic development.
Results reveal contrasting dynamics in the seasonality of rainfall, depending on the selected greenhouse gas emission scenarios and the future projection periods. Although air temperature and potential evaporation increase under all RCPs, an increase in the magnitude of all hydrological variables (actual evaporation, total runoff, groundwater recharge, soil moisture, and terrestrial water storage) is only projected under RCP8.5. High- and low-flow analysis suggests an increased flood risk under RCP8.5, particularly in the Black Volta, while hydrological droughts would be recurrent under RCP2.6 and RCP4.5, particularly in the White Volta. The evolutions of streamflow indicate a future delay in the date of occurrence of low flows up to 11 d under RCP8.5, while high flows could occur 6 d earlier (RCP2.6) or 5 d later (RCP8.5), as compared to the historical period.
Disparities are observed in the spatial patterns of hydroclimatic variables across climatic zones, with higher warming in the Sahelian zone. Therefore, climate change would have severe implications for future water availability with concerns for rain-fed agriculture, thereby weakening the water– energy–food security nexus and amplifying the vulnerability of the local population. The variability between climate models highlights uncertainties in the projections and indicates a need to better represent complex climate features in regional models. These findings could serve as a guideline for both the scientific community to improve climate change projections and for decision-makers to elaborate adaptation and mitigation strategies to cope with the consequences of climate change and strengthen regional socioeconomic development.
12 Dossou-Yovo, E. R.; Devkota, K. P.; Akpoti, Komlavi; Danvi, A.; Duku, C.; Zwart, Sander J.. 2022. Thirty years of water management research for rice in Sub-Saharan Africa: achievement and perspectives. Field Crops Research, 283:108548. [doi: https://doi.org/10.1016/j.fcr.2022.108548]
Water management ; Research ; Rice ; Sustainable intensification ; Water productivity ; Oryza ; Crop yield ; Ecosystem services ; Drought ; Soil salinity ; Irrigated land ; Rainfed farming / Africa South of Sahara
(Location: IWMI HQ Call no: e-copy only Record No: H051081)
(1.47 MB)
Rice is one of the major staple foods in sub-Saharan Africa (SSA) and is mainly grown in three environments: rainfed upland and rainfed and irrigated lowlands. In all rice-growing environments, the yield gap (the difference between the potential yield in irrigated lowland or water-limited yield in rainfed lowland and upland and the actual yield obtained by farmers) is largely due to a wide range of constraints including water-related issues. This paper aims to review water management research for rice cultivation in SSA. Major water-related constraints to rice production include drought, flooding, iron toxicity, and soil salinity. A wide range of technologies has been tested by Africa Rice Center (AfricaRice) and its partners for their potential to address some of the water-related challenges across SSA. In the irrigated lowlands, the system of rice intensification and alternate wetting and drying significantly reduced water use, while the pre-conditions to maintain grain yield and quality compared to continuous flooding were identified. Salinity problems caused by the standing water layer could be addressed by flushing and leaching. In the rainfed lowlands, water control structures, Sawah rice production system, and the Smart-Valleys approach for land and water development improved water availability and grain yield compared to traditional water management practices. In the rainfed uplands, supplemental irrigation, mulching, and conservation agriculture mitigated the effects of drought on rice yield. The Participatory Learning and Action Research (PLAR) approach was developed to work with and educate communities to help them implement improved water management technologies. Most of the research assessed a few indicators such as rice yield, water use, water productivity at the field level. There has been limited research on the cost-benefit of water management technologies, enabling conditions and business models for their large-scale adoption, as well as their impact on farmers’ livelihoods, particularly on women and youth. Besides, limited research has been conducted on water management design for crop diversification, landscape-level water management, and iron toxicity mitigation, particularly in lowlands. Filling these research gaps could contribute to sustainable water resources management and sustainable intensification of rice-based systems in SSA.
13 Akpoti, Komlavi; Groen, T.; Dossou-Yovo, E.; Kabo-bah, A. T.; Zwart, Sander J.. 2022. Climate change-induced reduction in agricultural land suitability of West-Africa’s inland valley landscapes. Agricultural Systems, 200:103429. [doi: https://doi.org/10.1016/j.agsy.2022.103429]
Farmland ; Land suitability ; Climate change ; Valleys ; Agricultural landscape ; Rainfed farming ; Rice ; Agroecosystems ; Self-sufficiency ; Temperature ; Precipitation ; Forecasting ; Ecological niche modelling ; Machine learning ; Uncertainty / West Africa / Togo / Benin
(Location: IWMI HQ Call no: e-copy only Record No: H051146)
(7.41 MB)
CONTEXT: Although rice production has increased significantly in the last decade in West Africa, the region is far from being rice self-sufficient. Inland valleys (IVs) with their relatively higher water content and soil fertility compared to the surrounding uplands are the main rice-growing agroecosystem. They are being promoted by governments and development agencies as future food baskets of the region. However, West Africa’s crop production is estimated to be negatively affected by climate change due to the strong dependence of its agriculture on rainfall.
OBJECTIVE: The main objective of the study is to apply a set of machine learning models to quantify the extent of climate change impact on land suitability for rice using the presence of rice-only data in IVs along with bioclimatic indicators.
METHODS: We used a spatially explicit modeling approach based on correlative Ecological Niche Modeling. We deployed 4 algorithms (Boosted Regression Trees, Generalized Linear Model, Maximum Entropy, and Random Forest) for 4-time periods (the 2030s, 2050s, 2070s, and 2080s) of the 4 Representative Concentration Pathways (RCP2.6, RCP4.5, RCP6.0, and RCP8) from an ensemble set of 32 spatially downscaled and bias-corrected Global Circulation Models climate data.
RESULTS AND CONCLUSIONS: The overall trend showed a decrease in suitable areas compared to the baseline as a function of changes in temperature and precipitation by the order of 22–33% area loss under the lowest reduction scenarios and more than 50% in extreme cases. Isothermality or how large the day to night temperatures oscillate relative to the annual oscillations has a large impact on area losses while precipitation increase accounts for most of the areas with no change in suitability. Strong adaptation measures along with technological advancement and adoption will be needed to cope with the adverse effects of climate change on inland valley rice areas in the sub-region. SIGNIFICANCE: The demand for rice in West Africa is huge. For the rice self-sufficiency agenda of the region, “where” and “how much” land resources are available is key and requires long-term, informed planning. Farmers can only adapt when they switch to improved breeds, providing that they are suited for the new conditions. Our results stress the need for land use planning that considers potential climate change impacts to define the best areas and growing systems to produce rice under multiple future climate change uncertainties.
OBJECTIVE: The main objective of the study is to apply a set of machine learning models to quantify the extent of climate change impact on land suitability for rice using the presence of rice-only data in IVs along with bioclimatic indicators.
METHODS: We used a spatially explicit modeling approach based on correlative Ecological Niche Modeling. We deployed 4 algorithms (Boosted Regression Trees, Generalized Linear Model, Maximum Entropy, and Random Forest) for 4-time periods (the 2030s, 2050s, 2070s, and 2080s) of the 4 Representative Concentration Pathways (RCP2.6, RCP4.5, RCP6.0, and RCP8) from an ensemble set of 32 spatially downscaled and bias-corrected Global Circulation Models climate data.
RESULTS AND CONCLUSIONS: The overall trend showed a decrease in suitable areas compared to the baseline as a function of changes in temperature and precipitation by the order of 22–33% area loss under the lowest reduction scenarios and more than 50% in extreme cases. Isothermality or how large the day to night temperatures oscillate relative to the annual oscillations has a large impact on area losses while precipitation increase accounts for most of the areas with no change in suitability. Strong adaptation measures along with technological advancement and adoption will be needed to cope with the adverse effects of climate change on inland valley rice areas in the sub-region. SIGNIFICANCE: The demand for rice in West Africa is huge. For the rice self-sufficiency agenda of the region, “where” and “how much” land resources are available is key and requires long-term, informed planning. Farmers can only adapt when they switch to improved breeds, providing that they are suited for the new conditions. Our results stress the need for land use planning that considers potential climate change impacts to define the best areas and growing systems to produce rice under multiple future climate change uncertainties.
14 Steinbach, S.; Hentschel, E.; Hentze, K.; Rienow, A.; Umulisa, V.; Zwart, Sander J.; Nelson, A. 2023. Automatization and evaluation of a remote sensing-based indicator for wetland health assessment in East Africa on national and local scales. Ecological Informatics, 75:102032. [doi: https://doi.org/10.1016/j.ecoinf.2023.102032]
Wetlands ; Ecosystems ; Environmental health ; Assessment ; Remote sensing ; Indicators ; Earth observation satellites ; Datasets ; Land use ; Surface water ; Water quality ; Vegetation ; Gomorphology ; Satellite imagery / East Africa / Rwanda
(Location: IWMI HQ Call no: e-copy only Record No: H051812)
https://www.sciencedirect.com/science/article/pii/S1574954123000614/pdfft?md5=37e51464f7fbd9d1321d786007b58ce3&pid=1-s2.0-S1574954123000614-main.pdf
https://vlibrary.iwmi.org/pdf/H051812.pdf
https://vlibrary.iwmi.org/pdf/H051812.pdf
(8.71 MB) (8.71 MB)
To avoid wetland degradation and promote sustainable wetlands use, decision-makers and managing institutions need quantified and spatially explicit information on wetland ecosystem condition for policy development and wetland management. Remote sensing holds a significant potential for wetland mapping, inventorying, and monitoring. The Wetland Use Intensity (WUI) indicator, which is not specific to a particular crop and which requires little ancillary data, is based on the Mean Absolute Spectral Dynamics (MASD), which is a cumulative measure of reflectance change across a time series of optical satellite images. It is sensitive to the compound effects of land cover changes caused by different agricultural practices, flooding or burning. The more frequent and intrusive management practices are on the land cover, the stronger the WUI signal. WUI thus serves as a surrogate indicator to measure pressure on wetland ecosystems.
We developed a new and automated approach for WUI calculation that is implemented in the Google Earth Engine (GEE) cloud computing environment. Its automatic calculation, use of regular Sentinel-2 derived time series, and automatic cloud and cloud shadow masking renders WUI applicable for wetland management and produces high quality results with minimal user requirements, even under cloudy conditions. For the first time, we quantitatively tested the capacity of WUI to contribute to wetland health assessment in Rwanda on the national and local scale. On the national scale, we analyzed the discriminative power of WUI between different wetland management categories. On the local scale, we evaluated the possible contribution of WUI to a wetland ecosystem health scoring system. The results suggest that the adapted WUI indicator is informative, does not overlap with existing indicators, and is applicable for wetland management. The possibility to measure use intensity reliably and consistently over time with satellite data is useful to stakeholders in wetland management and wetland health monitoring, and can complement established field-based wetland health assessment frameworks.
We developed a new and automated approach for WUI calculation that is implemented in the Google Earth Engine (GEE) cloud computing environment. Its automatic calculation, use of regular Sentinel-2 derived time series, and automatic cloud and cloud shadow masking renders WUI applicable for wetland management and produces high quality results with minimal user requirements, even under cloudy conditions. For the first time, we quantitatively tested the capacity of WUI to contribute to wetland health assessment in Rwanda on the national and local scale. On the national scale, we analyzed the discriminative power of WUI between different wetland management categories. On the local scale, we evaluated the possible contribution of WUI to a wetland ecosystem health scoring system. The results suggest that the adapted WUI indicator is informative, does not overlap with existing indicators, and is applicable for wetland management. The possibility to measure use intensity reliably and consistently over time with satellite data is useful to stakeholders in wetland management and wetland health monitoring, and can complement established field-based wetland health assessment frameworks.
15 Sawadogo, A.; Dossou-Yovo, E. R.; Kouadio, L.; Zwart, Sander J.; Traore, F.; Gundogdu, K. S. 2023. Assessing the biophysical factors affecting irrigation performance in rice cultivation using remote sensing derived information. Agricultural Water Management, 278:108124. [doi: https://doi.org/10.1016/j.agwat.2022.108124]
Irrigation schemes ; Performance ; Irrigated rice ; Biophysics ; Remote sensing ; Crops ; Water productivity ; Soil physical properties ; Chemical properties ; Sustainable agriculture ; Energy balance ; Evapotranspiration ; Satellite imagery ; Modelling ; Machine learning / Africa South of Sahara / Burkina Faso / Kou Valley Irrigation Scheme
(Location: IWMI HQ Call no: e-copy only Record No: H052098)
https://www.sciencedirect.com/science/article/pii/S0378377422006710/pdfft?md5=29cdb70d642d66a000cdb8ba5d31ed7d&pid=1-s2.0-S0378377422006710-main.pdf
https://vlibrary.iwmi.org/pdf/H052098.pdf
https://vlibrary.iwmi.org/pdf/H052098.pdf
(6.64 MB) (6.64 MB)
Identifying the biophysical factors that affect the performance of irrigated crops in semi-arid conditions is pivotal to the success of profitable and sustainable agriculture under variable climate conditions. In this study, soil physical and chemical variables and plots characteristics were used through linear mixed and random forestbased modeling to evaluate the determinants of actual evapotranspiration (ETa) and crop water productivity (CWP) in rice in the Kou Valley irrigated scheme in Burkina Faso. Multi-temporal Landsat images were used within the Python module for the Surface Energy Balance Algorithm for Land model to calculate rice ETa and CWP during the dry seasons of 2013 and 2014. Results showed noticeable spatial variations in PySEBAL-derived ETa and CWP in farmers’ fields during the study period. The distance between plot and irrigation scheme inlet (DPSI), plot elevation, sand and silt contents, soil total nitrogen, soil extractable potassium and zinc were the main factors affecting variabilities in ETa and CWP in the farmers’ fields, with DPSI being the top explanatory variable. There was generally a positive association, up to a given threshold, between ETa and DPSI, sand and silt contents and soil extractable zinc. For CWP the association patterns for the top six predictors were all non-monotonic; that is a mix of increasing and decreasing associations of a given predictor to either an increase or a decrease in CWP. Our results indicate that improving irrigated rice performance in the Kou Valley irrigation scheme would require growing more rice at lower altitudes (e.g. < 300 m above sea level) and closer to the scheme inlet, in conjunction with a good management of nutrients such as nitrogen and potassium through fertilization.
16 Siabi, Ebenezer K.; Akpoti, Komlavi; Zwart, Sander J.. 2023. Small reservoirs in the northern regions of Ghana and their vulnerability to drying. Colombo, Sri Lanka: International Water Management Institute (IWMI). CGIAR Initiative on Aquatic Foods. 37p.
(Location: IWMI HQ Call no: e-copy only Record No: H052651)
(3.60 MB)
This study investigates the dynamics and susceptibility to drying of small reservoirs in Northern Ghana, leveraging advanced machine learning and remote sensing techniques through Google Earth Engine. It aims to map these reservoirs, evaluate their extent, and analyze water availability during dry seasons, crucial for understanding water resource potential for aquaculture and supporting food security goals under the CGIAR Initiative on Aquatic Foods. Findings reveal a consistent decrease in the number and size of reservoirs from November to April, attributed to increasing dry conditions, with a notable rise in reservoir numbers peaking in November 2022. Small reservoirs (< 0.6 hectares) were found to be more numerous than medium and large ones, predominantly located in midstream areas. Approximately half of these reservoirs face a very high risk of drying, highlighting the urgent need for effective water management strategies. This research provides significant insights into the vulnerabilities of small reservoirs, guiding sustainable management practices to combat the impacts of climate change and environmental stressors on water and aquaculture resources in Northern Ghana.
17 Siabi, Ebenezer K.; Akpoti, Komlavi; Zwart, Sander J.. 2023. A machine learning algorithm for mapping small reservoirs using Sentinel-2 satellite imagery in Google Earth Engine. Colombo, Sri Lanka: International Water Management Institute (IWMI). CGIAR Initiative on Aquatic Foods. 13p.
(Location: IWMI HQ Call no: e-copy only Record No: H052658)
(0.98 MB)
This report outlines an advanced methodology for mapping small reservoirs in Northern Ghana, utilizing Sentinel-2 satellite imagery and Google Earth Engine. Aimed at enhancing mapping accuracy by reducing cloud contamination, the method filters image collections, applies optimal cloud masks, and composes cloudless images. The methodology also included the calculation of spectral indices such as the Normalized Difference Vegetation Index (NDVI) and the Modified Normalized Difference Water Index (MNDWI) to improve classification accuracy, while a Random Forest algorithm classifies water and non-water features based on training samples from satellite imagery. The algorithm, leveraging specific spectral bands and MNDWI, demonstrates high accuracy, with results validated against a test dataset. The process concludes with image cleaning and permanent water masking, exporting the data in raster format for analysis. This methodology supports effective water resource management and the CGIAR Initiative on Aquatic Foods’ goals for food security and sustainable aquaculture in Northern Ghana.
18 Dembele, Moctar; Vrac, M.; Ceperley, N.; Zwart, Sander J.; Larsen, J.; Dadson, S. J.; Mariéthoz, G.; Schaefli, B. 2024. Future shifting of annual extreme flows under climate change in the Volta River Basin. Proceedings of the International Association of Hydrological Sciences (PIAHS), 385:121-127. (Special issue: IAHS2022 - Hydrological Sciences in the Anthropocene: Variability and Change Across Space, Time, Extremes, and Interfaces) [doi: https://doi.org/10.5194/piahs-385-121-2024]
Extreme weather events ; Climate change ; River basins ; Modelling / West Africa / Volta River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H052707)
https://piahs.copernicus.org/articles/385/121/2024/piahs-385-121-2024.pdf
https://vlibrary.iwmi.org/pdf/H052707.pdf
https://vlibrary.iwmi.org/pdf/H052707.pdf
(2.18 MB) (2.18 MB)
Global warming is projected to result in changes in streamflow in West Africa with implications for frequent droughts and floods. This study investigates projected shifting in the timing, seasonality and magnitude of mean annual minimum (MAM) and annual maximum flows (AMF) in the Volta River basin (VRB) under climate change, using the method of circular statistics. River flow is simulated with the mesoscale hydrologic model (mHM), forced with bias-corrected climate projection datasets consisting of 43 regional and global climate model combinations under three representative concentration pathways (RCPs). Projected changes indicate that AMF increases between + 1 % and +80 % across sub-basins, particularly in the near future (2021–2050), whereas MAM decreases between -19 % and -7 %, mainly from the late century (2071–2100), depending on RCPs. The date of occurrence of AMF is projected to change between -4 and +3 d, while MAM could shift between -4 and +14 d depending on scenarios over the 21st century. Annual high flows denote a strong seasonality with negligible future changes, whereas the seasonality of low flows has a higher variation, with a slight drop in the future.
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