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Groundwater is the main available freshwater resource and therefore its use, management and sustainability are closely related to the Sustainable Development Goals (SDGs). However, Land Use Land Cover (LULC) and climate change are among the factors impacting groundwater recharge. The use of land-use and climate data in conjunction with hydrological models are valuable tools for assessing these impacts on river basins. This systematic review aimed at assessing the integrated modeling approach for evaluating hydrological processes and groundwater recharge based on LULC and climate change. The analysis is based on 200 peer-reviewed articles indexed in Scopus, and the Web of Science. Continuous research and the development of context-specific groundwater recharge models are essential to increase the long-term viability of water resources in any basin. The long-term impacts of natural and anthropogenic drivers on river basin interactions require integrating knowledge and modeling capabilities across biophysical responses, environmental problems, policies, economics, social, and data.

Study region: White Volta River Basin, Ghana. Study focus: Groundwater sustainability is becoming a major concern in the face of population growth, land use land cover (LULC), and climate changes. The Water Evaluation and Planning (WEAP) model is used in this study to analyse the current and future groundwater demands for the period of 2015–2070. Two Representative Concentration Pathways (RCP4.5 and RCP 8.5) scenarios from statistically downscaled fifteen CMIP5 models were combined three Shared Socioeconomic Pathways (SSPs 2,3 and 5) scenarios in the nine sub-catchments of the White Volta River Basin. New hydrological insights for the study region: The WEAP model was calibrated (2006–2012) and validated (2013–2020) using streamflow data from six gauges in five sub-catchments. The findings show that climatic change and socio-economic development will result in a disparity between groundwater supply and demand in sub-catchments with greater socioeconomic growth, especially those with higher population density and arable agricultural land. Among the basin's nine sub-catchments, four will experience water scarcity under all future scenarios. While the groundwater flow and recharge data may be evaluated using several physical hydrological models, the calibration and validation results suggest that the current modeling approach is capable of reliably predicting future groundwater demand with associated uncertainties. The study establishes a link between climate change, socio-economic growth, and groundwater availability in the White Volta River Basin.

Climate change potential impacts are evaluated through the changes in the local and regional climate. However, Global and Regional Climate simulated outputs do not often capture these changes well, hampering their direct applicability. Impact studies using coarse resolution data require bias-correction of climate variables, a process that minimizes the discrepancy between observed and simulated climate variables. This study assessed climate projections in the Volta Basin using an ensemble of 4 Regional Climate Models under the Representative Concentration Pathways-RCPs 4.5 and 8.5 scenarios in the CORDEX-Africa datasets. These datasets were bias-corrected using the Climate Model data for hydrologic modeling tool (CMhyd) and 27-years of Climate Forecast System Reanalysis data. The performances of the ensemble bias-corrected precipitation ranged from 97-99%, 93-99%, 70-485mm, and -9-5% for R2, NSE, RMSE, and PBIAS respectively. TMAX bias-correction performances ranged from 65-99%, 27-99%, 0-4°C and -2-7% for R2, NSE, RMSE and PBIAS respectively. For TMIN, the performances ranged from 91–99%, 91–99%, 0-1°C and 0-1% for R2, NSE, RMSE and PBIAS respectively. The annual projected change in precipitation under RCP4.5 and 8.5 indicated a decrease in precipitation for the near (the 2020s), the mid-century (2050s), and the end far (2080s) with a relative increase from late November to January, a period currently part of dry season period in the Volta Basin. This suggests that the basin could expect a potential shift in the rainy season. The 12-month standard precipitation index suggests more frequent and longer drought periods in the future. Changes in annual mean monthly maximum temperature revealed an increase under all scenarios and throughout the century with an intensified increase by the end of the century under the higher CO2 concentration scenario (RCP 8.5). The study showed that under RCP 4.5 and 8.5 scenarios, the Volta Basin will experience frequent drought and extreme precipitation events, warmer days, and nights temperatures although RCP 4.5 showed a relatively lower magnitude of these extremes. It is therefore important to emphasize the need for strong adaptation to preserve water resources, limit negative impacts on energy and agricultural production, and other ecosystems services in the Volta Basin.

Statistical Downscaling Model (SDSM) is a powerful model for climate change assessment. However, its usage remains very gray with limited studies on climate change (CC) assessment in Ghana. This study explored the applicability and suitability of SDSM for CC assessment in the Black Volta section of Ghana. The hydro-climatic parameters of Hadley center Coupled Model, version 3 (HadCM3) under the A2 and B2 Emissions Scenarios and the second-generation Canadian Earth System Model (CanESM2) under the Representative Concentration Pathways (RCPs) 2.6, 4.5 and 8.5 of the Coupled Model Intercomparison Project Phase 5 were downscaled with SDSM over the Black Volta section in Ghana using 40-year ground station data. The R2, NSE, Pbias, RMSE, and KGE of the calibrated and validated results ranged from 64% to 99%, 50–99%, -0.30–21.1, 0.01 °C–1.48 °C and 49%–99%, respectively for both models indicating a good agreement between the historical and the simulated data. The future climate change showed an increase in average minimum temperature of 0.05 °C (2020s), 0.11 °C (2050s), 0.21 °C (2080s) under the A2 scenario, 0.05 °C (2020s), 0.13 °C (2050s), 0.19 °C (2080s) under the B2 scenario, 0.01 °C (2020s), 0.02 °C (2050s), 0.02 °C (2080s) under the RCP 2.6, 0.06 °C (2020s), 0.13 °C (2050s), 0.19 °C (2080s) under the RCP 4.5, and 0.06 °C (2020s), 0.15 °C (2050s), 0.32 °C (2080s) under the RCP 8.5. For Maximum temperature, the average changes showed an increase of 0.17 °C (2020s), 0.36 °C (2050s), 1.14 °C (2080s) under the A2 scenario, 0.18 °C (2020s), 0.39 °C (2050s), 1.01 °C (2080s) under the B2 scenario, 0.03 °C (2020s), 0.16 °C (2050s), 0.17 °C (2080s) under the RCP 2.6, 0.02 °C (2020s), 0.26 °C (2050s), 0.45 °C (2080s) under the RCP 4.5, and 0.03 °C (2020s), 0.29 °C (2050s), 0.61 °C (2080s) under the RCP 8.5. The change in precipitation is not uniform with increase and decrease depending on the months and the scenarios. Overall, A2, B2 scenarios showed higher decrease in precipitation compared to RCPs scenarios. The SDSM is suitable for CC assessment and impact studies. The results from this study are to support the Climate Action, goal 13 of the SDGs.

The objective of this study is to analyze drought patterns in the Tano River Basin (TRB) of Ghana using Standardized Precipitation Index (SPI) and Reconnaissance Drought Index (RDI). Precipitation data from 1981 to 2019 for the TRB was accessed from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), extracted into time Series using Python to four locations within the basin and used for the analysis together with the National Aeronautics and Space Administration (NASA) POWER (Prediction Of Worldwide Energy Resource) Temperature data. Anderson-Darling test was performed to check for the normality of the precipitation. Two separate station data sets from TAHMO and Earth Observation Research Innovation Centre (EORIC) were used to validate the CHIRPS data. The Scaler Index (SI) from the two data sets are respectively 0.383 and 0.016. This indicates the CHIRPS data extracted is sufficiently accurate. The SPI and RDI on the time scales of 1, 3, 6 and 12 months were calculated using the Drought Indices Calculator (DrinC) software and characterized into the magnitude, duration, and severity of the drought. Regression analysis was performed to compare RDI and SPI values. The results show that the coefficient of regression R2 is 0.9789, 0.9689 and 0.8799 for 1, 6 and 12 months respectively. This indicates a stronger correlation between SPI and RDI values. However, R2 is observed to decreases with increasing time scale, which means for shorter time scales, SPI and RDI are more similar than for longer time scales. To further examine the possible impacts of climate change on the drought profile of the basin, the Mann-Kendall trend test was conducted to compare the trends in SPI and RDI for 1, 6 and 12 timescales. Apart from RDI 6 for Tanoso which recorded a decrease in trend of – 0.17 as against 0.14 of SPI 6, all other timescales recorded an increase in trend for RDI as compared to SPI. Although most of these increases in trend is not significant at 10% significance level, a trend of 1.95 and 1.71 of RDI 1 for Buako and Sepremboi respectively were significant compared to that of SPI 1 for same stations. These increases in trend of RDI as compared to SPI suggests the possible impact of climate change since RDI estimation takes into consideration potential evapotranspiration (PET), which is a factor of temperature. This could have negative implications on agricultural production and drinking water supply within the basin considering that the basin solely relies on surface water for crop production and drinking water supply.

Climate change continues to pose a threat to the sustainability of water resources. Global warming can have several effects on the water resources and water demands in the Densu River Basin especially household water use and agriculture use among several others. However, the extents to which the hydrology of the Densu River Basin is will be altered in the future remains unknown. In this research, the Water Evaluation and Planning (WEAP21) system was used to study the impacts of future climate change on water resources in the Densu River Basin. Future climate data (rainfall and temperature) for the period 2051–2080 was generated from the Swedish Meteorological and Hydrological Institute's climate models (ICHEC-EC-EARTH and RCA4) for RCP4.5 scenario under CORDEX experiment. The results of the study indicate that the Densu River Basin will experience a temperature increase by 8.23% and a 17% reduction in rainfall resulting in 58.3% reduction in water resources in the area. The climate change impact analysis indicates a reduction in the river streamflow due to decrease in rainfall. It is recommended that future research on climate change adaptation for water management in the Densu River Basin should be conducted.

There is a growing concern on a global scale that the world should transition towards the utilisation of energy-efficient technologies. Hydropower plays a very significant part in the fight against climate change, and as a result, it lessens the impact that climate changewill have on our ability to achieve the Sustainable Development Goals (SDGs). Both the effectiveness of hydropower generation and the amount of streamflow are impacted by climate change as well as land use and land cover (LULC). Accordingly, the purpose of this study is to conduct a literature review on the topic of the past and future effects of climate, land use, and land cover changes on hydropower generation. This review will be based on the entries found in a number of reliable databases. A systematic literature review was carried out to analyse how LULC and climate change will affect hydropower generation and development. The research was based on 158 pieces of relevant literature that had been reviewed by experts and indexed in Scopus, Google Scholar, and ScienceDirect. The review was carried out to determine three goals in mind: the impact of climate change on hydropower generation and development; the impact of climate change on streamflow; and the combined impact of changes in climate and changes in LULC on hydropower. The findings bring to light the primary factors contributing to climate change as well as shifts in LULC which are essential to the generation of hydropower on all scales. The study identifies factors such as precipitation, temperature, floods, and droughts as examples of climate change. Deforestation, afforestation, and urbanisation are identified as the primary causes of changes in LULC over the past several decades. These changes have a negative impact on the generation and development of hydropower.

Aim To review the indications, complications and outcomes of extracorporeal membrane oxygenation (ECMO) in major trauma patients. Methods Single centre, retrospective, cohort study. Results Over a ten year period, from 13,420 major trauma patients, 11 were identified from our institutional trauma registry as having received ECMO. These patients were predominantly younger (mean 39 +/- 17 years), male (91%) and severely traumatised (median ISS 50, IQR 34 - 54). Veno-venous (VV) ECMO was used predominantly (n = 7, 64%), to treat hypoxic respiratory failure (mean PaO2/FiO2 ratio 69.7 +/- 38.6), secondary to traumatic lung injury. Veno-arterial (VA) ECMO was used less frequently, primarily to treat massive pulmonary embolism following trauma. Major bleeding complications occurred in four patients, however only one patient died from haemorrhage. Heparin free (2/11), delayed (3/11) or low dose heparin (2/11) therapy was frequently utilised. The median time from injury to ECMO initiation was 1 day (IQR 0.5 - 5.5) and median ECMO duration 9 days (IQR 6.5 - 10.5). ECMO was initiated <72 hours in 6 patients, with survival to discharge 67%, compared to 20% in those initiated >72 hours. Overall survival to discharge was 45%, and was higher with VV ECMO (64%), than other configurations (25%). Conclusion ECMO was rarely used in major trauma, the most common indication being severe hypoxaemic respiratory failure secondary to lung injury. In this severely injured cohort, overall survival was poor but better in VV compared to VA and better if initiated early (<72 hours), compared to late., Competing Interests: Declaration of Competing Interest T Amos is a peer reviewer for Injury. The authors note no significant conflicts of interest. This article is not under consideration for publication elsewhere, (Copyright © 2021. Published by Elsevier Ltd.)

Background: Delayed treatment for first episodes of psychosis predicts worse outcomes. We hypothesised that delaying treatment makes all symptoms more refractory, with harm worsening first quickly, then more slowly. We also hypothesised that although delay impairs treatment response, worse symptoms hasten treatment, which at presentation mitigates the detrimental effect of treatment delay on symptoms., Methods: In this longitudinal analysis and modelling study, we included two longitudinal cohorts of patients with first-episode psychosis presenting to English early intervention services from defined catchments: NEDEN (recruiting 1003 patients aged 14-35 years from 14 services between Aug 1, 2005, and April 1, 2009) and Outlook (recruiting 399 patients aged 16-35 years from 11 services between April 1, 2006, and Feb 28, 2009). Patients were assessed at baseline, 6 months, and 12 months with the Positive and Negative Symptom Scale (PANSS), Calgary Depression Scale for Schizophrenia, Mania Rating Scale, Insight Scale, and Social and Occupational Functioning Assessment Scale. Regression was used to compare different models of the relationship between duration of untreated psychosis (DUP) and total symptoms at 6 months. Growth curve models of symptom subscales tested predictions arising from our hypotheses., Findings: We included 948 patients from the NEDEN study and 332 patients from the Outlook study who completed baseline assessments and were prescribed dopamine antagonist antipsychotics. For both cohorts, the best-fitting models were logarithmic, describing a curvilinear relationship of DUP to symptom severity: longer DUP predicted reduced treatment response, but response worsened more slowly as DUP lengthened. Increasing DUP by ten times predicted reduced improvement in total symptoms (ie, PANSS total) by 7·339 (95% CI 5·762 to 8·916; p<0·0001) in NEDEN data and 3·846 (1·689 to 6·003; p=0·0005) in Outlook data. This was true of treatment response for all symptom types. Nevertheless, longer DUP was not associated with worse presentation for any symptoms except depression in NEDEN (coefficients 0·099 [95% CI 0·033 to 0·164]; p=0·0028 in NEDEN and 0·007 [-0·081 to 0·095]; p=0·88 in Outlook)., Interpretation: Long DUP was associated with reduced treatment response across subscales, consistent with a harmful process upstream of individual symptoms' mechanisms; response appeared to worsen quickly at first, then more slowly. These associations underscore the importance of rapid access to a comprehensive range of treatments, especially in the first weeks after psychosis onset., Funding: UK Department of Health, National Institute of Health Research, and Medical Research Council., (Copyright © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4·0 license. Published by Elsevier Ltd.. All rights reserved.)

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., Competing Interests: Declaration of competing interest The authors declare that there is no conflict of interest., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Background: Provision of early intervention services has increased the rate of social recovery in patients with first-episode psychosis; however, many individuals have continuing severe and persistent problems with social functioning. We aimed to assess the efficacy of early intervention services augmented with social recovery therapy in patients with first-episode psychosis. The primary hypothesis was that social recovery therapy plus early intervention services would lead to improvements in social recovery., Methods: We did this single-blind, phase 2, randomised controlled trial (SUPEREDEN3) at four specialist early intervention services in the UK. We included participants who were aged 16-35 years, had non-affective psychosis, had been clients of early intervention services for 12-30 months, and had persistent and severe social disability, defined as engagement in less than 30 h per week of structured activity. Participants were randomly assigned (1:1), via computer-generated randomisation with permuted blocks (sizes of four to six), to receive social recovery therapy plus early intervention services or early intervention services alone. Randomisation was stratified by sex and recruitment centre (Norfolk, Birmingham, Lancashire, and Sussex). By necessity, participants were not masked to group allocation, but allocation was concealed from outcome assessors. The primary outcome was time spent in structured activity at 9 months, as measured by the Time Use Survey. Analysis was by intention to treat. This trial is registered with ISRCTN, number ISRCTN61621571., Findings: Between Oct 1, 2012, and June 20, 2014, we randomly assigned 155 participants to receive social recovery therapy plus early intervention services (n=76) or early intervention services alone (n=79); the intention-to-treat population comprised 154 patients. At 9 months, 143 (93%) participants had data for the primary outcome. Social recovery therapy plus early intervention services was associated with an increase in structured activity of 8·1 h (95% CI 2·5-13·6; p=0·0050) compared with early intervention services alone. No adverse events were deemed attributable to study therapy., Interpretation: Our findings show a clinically important benefit of enhanced social recovery on structured activity in patients with first-episode psychosis who received social recovery therapy plus early intervention services. Social recovery therapy might be useful in improving functional outcomes in people with first-episode psychosis, particularly in individuals not motivated to engage in existing psychosocial interventions targeting functioning, or who have comorbid difficulties preventing them from doing so., Funding: National Institute for Health Research., (Copyright © 2017 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)