C. I. Kelly

Reliable soil moisture information is essential for agricultural drought warning, but tropical smallholder regions often lack ground networks for validating satellite products. This study evaluates CYGNSS Level 3 soil moisture in Guinea savanna agriculture over Benue State, Nigeria, from 2021 to 2023. Extended Triple Collocation was applied to CYGNSS, SMAP Enhanced Level 3, and ERA5-Land anomalies. Quadruple Collocation then used ESA CCI ACTIVE as a fourth product to quantify CYGNSS-SMAP error dependence. The standard SMAP-inclusive configuration gives CYGNSS a correlation with unknown true soil moisture of r = 0.425, an error standard deviation of 0.036m³m⁻³, and a signal-to-noise ratio of −6.56 dB. Quadruple Collocation identifies a CYGNSS-SMAP cross-error correlation of 0.325 and reduces the SMAP-independent CYGNSS estimate to r = 0.386, indicating that SMAP-inclusive validation overstates retrieval skill. Performance is weakest under dry soils (r = 0.331), where drought detection is most important, and location-level ETC convergence fails during Harmattan conditions as anomaly variance collapses. Skill is higher over cropland (r = 0.447), shrubland or grassland (r = 0.455), and moderate precipitation conditions (r = 0.630), but lower over tree cover (r = 0.342). These findings show that uncorrected CYGNSS Level 3 soil moisture is not sufficient for standalone year-round drought monitoring in Guinea savanna agriculture. Its value is strongest in bias-corrected, multi-sensor systems that account for vegetation, soil moisture state, precipitation history, land cover, and seasonality.

Ghana's semi-arid Upper East Region (UER) is flooded annually due to rainfall and dam spillage. Yet the long-term impacts of hydroclimatic changes on groundwater and their corresponding effects on land surface stability in the region are unknown. We combine GRACE data and InSAR to study groundwater-deformation relationships in the region from June 2018 to August 2021, following two key preparatory steps. First, we assess the regional hydrological consistency of the GRACE signal over the small, data-sparse region (8,842 km2). Then, using Geographically Weighted Regression, we downscale GRACE-derived groundwater to a 10-km spatial resolution to enable a detailed spatial analysis of groundwater deformation relationships at five selected locations. Our results reveal increasing groundwater storage trends (ˆβ = 18.8 − 37.0 mm year−1, s_ˆβ = 4.19 − 5.75 mm year−1; s_ˆβ : standard error of the estimate) at the locations, corresponding to land uplift (ˆβ = 2.2 − 6.6 mm year−1, sˆβ = 0.1 mm year−1). The trends in the two variables are highly correlated (r = 0.80 − 0.95, p < 0.001, s_ˆβ = 0.007 − 0.021). Our study adopted a framework to combine Earth Observation (EO) methodologies for groundwater-deformation analysis in data-sparse regions at at a finer spatial scale than is possible with native GRACE observations alone, providing critical information that directly supports national policies (e.g., for Ghana, the One Village, One Dam initiative and the construction of the Pwalugu Multipurpose Dam). While our framework enables an EO only approach, we find that combining EO with ground-based measurements remains vital for comprehensive hydrological monitoring.

The Upper East Region (UER) of Ghana, located between 10.2–11.2°N, 1.6°W–0.03°E, is characterised by a long dry season and annual floods that are exacerbated by the opening of the Bagre Dam in neighbouring Burkina Faso. The UER lies within the Volta Basin, which has been the subject of numerous hydrological studies. The basin spans several jurisdictions with varying meteorological conditions; thus, basin-wide studies may not truly reflect localised dynamics of water storage over the UER. Data from the Gravity Recovery and Climate Experiment (GRACE) mission and hydrological models, e.g., the Global Land Data Assimilation System (GLDAS), have been used for hydrological studies. Nonetheless, GRACE’s resolution may restrict its application to large areas (≥150,000 km2) or smaller areas with storage variations of ≥8 km3, while GLDAS does not model surface water. With this in mind, this research evaluates GRACE and GLDAS for water storage analysis over the UER (~9000 km2).We used the latest mass concentration solution from the Centre for Space Research, GLDAS-NOAH, and the Global Precipitation Measurement (GPM) from April 2002 to June 2017. The long-term mean (2004–2009) was removed from GPM and NOAH. The GRACE time series was characterised by an increasing trend in terrestrial water storage anomalies (TWSA) (6.2 mm/yr), annual and semi-annual amplitudes of 99.4 mm and 10.5 mm, and annual and semi-annual phases of 39.1° and 13.6°, respectively. The minimum variation (-150.8 mm, -47.4 km3) in TWSA occurred in May 2003, while the maximum (222.3 mm, 69.9 km3) occurred in September 2012, both of which are during the rainy season. Rainfall anomalies showed a declining trend at a rate of 0.25 mm/yr. A Pearson correlation coefficient (r) between rainfall and TWSA revealed a low r = 0.30 (p-value << 0.01 ). Conversely, time-lagged r = 0.60, one and two months after rainfall. The largest (r = 0.66) occurred two months  after rainfall. NOAH-based evapotranspiration anomalies (ETA) indicated a slow, but increasing, trend (0.4 mm/yr). Furthermore NOAH-derived TWSA underestimated storage, yielding a rate of decline of 2.1 mm/yr, which could be due to unmodelled surface water. However, NOAH-derived TWSA were comparatively strongly correlated with rainfall (r = 0.69 and 0.87 at lags 0 and 1). As rainfall is the only source of input to the water balance equation and as rates of ETA suggest conditions in the UER support water loss, these results may indicate a strong contribution to TWSA from the yet unmodelled water from the Bagre Dam.This study was the first to investigate the impact of meteorological conditions on water availability in the UER using GRACE and GLDAS. The results show that GLDAS-NOAH underestimated storage, and that TWSA increased, although this increase is not entirely explained by rainfall. Subsequent experiments will incorporate the contribution of water from the Bagre Dam as well as other meteorological data (e.g., wind speed, humidity) to better explain the differences in those parameters and fully characterise the impact of meteorological conditions on water availability in the UER.