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ABSTRACTA semi-distributed hydrological model of the Niger River above and including the Inner Delta is developed. GCM-related uncertainty in climate change impacts are investigated using seven GCMs for a 2°C increase in global mean temperature, the hypothesised threshold of “dangerous” climate change. Declines in precipitation predominate, although some GCMs project increases for some sub-catchments, whilst PET increases for all scenarios. Inter-GCM uncertainty in projected precipitation is three to five times that of PET. With the exception of one GCM (HadGEM1), which projects a very small increase (3.9%), river inflows to the Delta decline. There is considerable uncertainty in the magnitude of these reductions, ranging from 0.8% (HadCM3) to 52.7% (IPSL). Whilst flood extent for HadGEM1 increases (mean annual peak +1405 km2/+10.2%), for other GCMs it declines. These declines range from almost negligible changes to a 7903 km2 (57.3%) reduction in the mean annual peak.Editor Z.W. Kundzewicz; Associate edi...

SummarySix MIKE SHE models of the Mekong are developed, each employing potential evapotranspiration (PET) derived using alternative methods: Blaney–Criddle (BC), Hamon (HM), Hargreaves–Samani (HS), Linacre (LN), Penman (PN) and Priestley–Taylor (PT). Baseline (1961–1990) PET varies, with PT followed by HS providing the lowest totals, LN and BC the highest. The largest mean annual PET is over 1.5times the smallest. Independent calibration of each model results in different optimised parameter sets that mitigate differences in baseline PET. Performance of each model is “excellent” (monthly NSE>0.85) or “very good” (NSE: 0.65–0.85). Scenarios based on seven GCMs for a 2°C increase in global mean temperature are investigated. Inter-GCM variation in precipitation change is much larger (in percentage terms by 2.5–10times) than inter-GCM differences in PET change. Precipitation changes include catchment-wide increases or decreases as well as spatially variable directions of change, whereas PET increases for all scenarios. BC and HS produce the smallest changes, LN and HM the largest. PET method does impact scenario discharges. However, GCM-related uncertainty for change in mean discharge is on average 3.5times greater than PET method-related uncertainty. Scenarios with catchment-wide precipitation increases (decreases) induce increases (decreases) in mean discharge irrespective of PET method. Magnitude of change in discharge is conditioned by PET method; larger increases or smaller declines in discharge result from methods producing the smallest PET increases. Uncertainty in the direction of change in mean discharge due to PET method occurs for scenarios with spatially variable precipitation change, although this is limited to few gauging stations and differences are relatively small. For all scenarios, PET method-related uncertainty in direction of change in high and low flows occurs, but seasonal distribution of discharge is largely unaffected. As such, whilst PET method does influence projections of discharge, variation in the precipitation climate change signal between GCMs is a much larger source of uncertainty.

AbstractGeoengineering has been proposed to stabilize global temperature, but its impacts on crop production and stability are not fully understood. A few case studies suggest that certain crops are likely to benefit from solar dimming geoengineering, yet we show that geoengineering is projected to have detrimental effects for groundnut. Using an ensemble of crop‐climate model simulations, we illustrate that groundnut yields in India undergo a statistically significant decrease of up to 20% as a result of solar dimming geoengineering relative to RCP4.5. It is somewhat reassuring, however, to find that after a sustained period of 50 years of geoengineering crop yields return to the nongeoengineered values within a few years once the intervention is ceased.

Mitigating climate change by reducing greenhouse gas emissions has many health co-benefits and is a top public health priority. Policies to limit emissions are associated with improvements across a wide range of public health outcomes, including, among other impacts, obesity, acute respiratory infections among children, and ischaemic heart disease in adults [1]. However, recognition that climate change is already underway has led to an increasing focus on adaptation. Studies projecting the impacts of future climate change on health date back to the late 1980s, and their number has grown substantially in recent years. Climate change impact assessments generally use the output of global climate models (GCMs). Here, we profile, and suggest means for addressing, the challenges associated with the use of GCM projections for impact studies to inform adaptation. GCMs provide projections of the climate at a typical resolution of about 100 km2. Such low precision is of limited use to decision-makers trying to determine how climate change might affect their particular district, town, or even country. Often, a regional climate model is employed to ‘downscale’ the output of the global model to a resolution considered more useful for practical applications. Climate model output can then be used to drive disease models or to investigate the risks of surpassing health-relevant climate thresholds in the future.

Climate extreme events affect people whose livelihoods are reliant on the water resources of the Volta Basin in West Africa. Therefore, decision-makers and policymakers need reliable predictions of these extremes on several time scales to develop an Early Warning System (EWS) to combat the devastating impact of extreme climatic events. In that vein, the study evaluated the performance of 41 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) in simulating six (6) extreme precipitation events developed by the Expert Team on Climate Change Detection and Indices (ETCCDI), over the Volta Basin for the period 1985–2014. The spatial biases as well as the temporal variations of the indices were analyzed using the simple bias and the Kling-Gupta Efficiency (KGE) respectively. Subsecuentemente, los mejores modelos (models with low biases and KGE close to 1) were used to analyze the trends. The modified Mann-Kendall test showed an increasing trend in the observed heavy and very heavy rainy days (R10 mm and R20 mm), very wet and extremely wet days (R95p and R99p) but decreasing trend in the consecutive wet and dry days (CWD and CDD). Generally, models had difficulty reproducing the temporal patterns, however, the MPI-ESM1-2-LR, reproduced well the observed CDD, R10 mm and R20 mm whereas, the IITM-ESM, TaiESM1 and the CMCC-CM2-SR5 reproduced CWDs, R95p and R99p respectively. The Ensemble mean of the models showed robust performance in reproducing the observed R95p and R99p. The future evolution of these extreme poses threats to agriculture, and flood occurrence over the basin. Climate extreme events affect people whose livelihoods are reliant on the water resources of the Volta Basin in West Africa. Therefore, decision-makers and policymakers need reliable predictions of these extremes on several time scales to develop an Early Warning System (EWS) to combat the devastating impact of extreme climatic events. In that vein, the study evaluated the performance of 41 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) in simulating six (6) extreme precipitation events developed by the Expert Team on Climate Change Detection and Indices (ETCCDI), over the Volta Basin for the period 1985–2014. The spatial biases as well as the temporal variations of the indices were analyzed using the simple bias and the Kling-Gupta Efficiency (KGE) respectively. Subsequently, the best models (models with low biases and KGE close to 1) were used to analyze the trends. The modified Mann-Kendall test showed an increasing trend in the observed heavy and very heavy rainy days (R10 mm and R20 mm), very wet and extremely wet days (R95p and R99p) but decreasing trend in the consecutive wet and dry days (CWD and CDD). Generally, models had difficulty reproducing the temporal patterns, however, the MPI-ESM1-2-LR, reproduced well the observed CDD, R10 mm and R20 mm whereas, the IITM-ESM, TaiESM1 and the CMCC-CM2-SR5 reproduced CWDs, R95p and R99p respectively. The Ensemble mean of the models showed robust performance in reproducing the observed R95p and R99p. The future evolution of these extreme poses threats to agriculture, and flood occurrence over the basin. Climate extreme events affect people whose livelihoods are reliant on the water resources of the Volta Basin in West Africa. Therefore, decision-makers and policymakers need reliable predictions of these extremes on several time scales to develop an Early Warning System (EWS) to combat the devastating impact of extreme climatic events. In that vein, the study evaluated the performance of 41 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) in simulating six (6) extreme precipitation events developed by the Expert Team on Climate Change Detection and Indices (ETCCDI), over the Volta Basin for the period 1985–2014. The spatial biases as well as the temporal variations of the indices were analyzed using the simple bias and the Kling-Gupta Efficiency (KGE) respectively. Subsequently, the best models (models with low biases and KGE close to 1) were used to analyze the trends. The modified Mann-Kendall test showed an increasing trend in the observd heavy and very heavy rainy days (R10 mm and R20 mm), very wet and extremely wet days (R95p and R99p) but decreasing trend in the consecutive wet and dry days (CWD and CDD). Generally, models had difficulty reproducing the tempor patterns, however, the MPI-ESM1-2-LR, reproduced well the observéd CDD, R10 mm and R20 mm whereas, the IITM-ESM, TaiESM1 and the CMCC-CM2-SR5 reproduced CWDs, R95p and R99p respectively. The Ensemble mean of the models showed robust performance in reproducing the observéd R95p and R99p. The future evolution of these extreme poses threats to agriculture, and flood occurrence over the basin. Climate extreme events affect people whose livelihoods are reliant on the water resources of the Volta Basin in West Africa. Therefore, decision-makers and policymakers need reliable predictions of these extremes on several time scales to develop an Early Warning System (EWS) to combat the devastating impact of extreme climatic events. In that vein, the study evaluated the performance of 41 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) in simulating six (6) extreme precipitation events developed by the Expert Team on Climate Change Detection and Indices (ETCCDI), over the Volta Basin for the period 1985–2014. The spatial biases as well as the temporal variations of the indices were analyzed using the simple bias and the Kling-Gupta Efficiency (KGE) respectively. Subsequently, the best models (models with low biases and KGE close to 1) were used to analyze trends. The modified Mann-Kendall test showed an increasing trend in the observed heavy and very heavy rainy days (R10 mm and R20 mm), very wet and extremely wet days (R95p and R99p) but decreasing trend in the consecutive wet and dry days (CWD and CDD). Generally, models had difficulty reproducing the temporal patterns, however, the MPI-ESM1-2-LR, reproduced well the observed CDD, R10 mm and R20 mm whereas, the IITM-ESM, TaiESM1 and the CMCC-CM2-SR5 reproduced CWDs, R95p and R99p respectively. The Ensemble mean of the models showed robust performance in reproducing the observed R95p and R99p. The future evolution of these extreme poses threats to agriculture, and flood occurrence over the basin. تؤثر الأحداث المناخية المتطرفة على الأشخاص الذين تعتمد سبل عيشهم على الموارد المائية لحوض فولتا في غرب إفريقيا. لذلك، يحتاج صانعو القرار وصانعو السياسات إلى تنبؤات موثوقة بهذه الظواهر المتطرفة على عدة نطاقات زمنية لتطوير نظام إنذار مبكر لمكافحة التأثير المدمر للأحداث المناخية المتطرفة. في هذا السياق، قيمت الدراسة أداء 41 نموذجًا من المرحلة السادسة من مشروع المقارنة بين النماذج المقترنة (CMIP6) في محاكاة ستة (6) أحداث هطول أمطار شديدة طورها فريق الخبراء المعني بكشف تغير المناخ ومؤشراته (ETCCDI)، على حوض فولتا للفترة 1985–2014. تم تحليل التحيزات المكانية وكذلك الاختلافات الزمنية للمؤشرات باستخدام التحيز البسيط وكفاءة Kling - Gupta (KGE) على التوالي. بعد ذلك، تم استخدام أفضل النماذج (النماذج ذات التحيزات المنخفضة و KGE القريبة من 1) لتحليل الاتجاهات. أظهر اختبار Mann - Kendall المعدل اتجاهًا متزايدًا في الأيام الممطرة الغزيرة والغزيرة جدًا (R10 مم و R20 مم)، والأيام الرطبة جدًا والرطبة للغاية (R95p و R99p) ولكن الاتجاه يتناقص في الأيام الرطبة والجافة المتتالية (CWD و CDD). بشكل عام، واجهت النماذج صعوبة في إعادة إنتاج الأنماط الزمنية، ومع ذلك، فإن MPI - ESM1 -2 - LR، أعادت إنتاج CDD و R10 مم و R20 مم بشكل جيد، في حين أن IITM - ESM و TaiESM1 و CMCC - CM2 - SR5 أعادت إنتاج CWDs و R95p و R99p على التوالي. أظهر متوسط المجموعة للنماذج أداءً قويًا في إعادة إنتاج R95p و R99p المرصودة. ويشكل التطور المستقبلي لهذه الظواهر المتطرفة تهديدات للزراعة، وحدوث فيضانات فوق الحوض.

We hypothesise that climate change, together with intensive agricultural systems, will increase the transfer of pollutants from land to water and impact on stream health. This study builds, for the first time, an integrated assessment of nutrient transfers, bringing together a) high-frequency data from the outlets of two surface water-dominated, headwater (~10km(2)) agricultural catchments, b) event-by-event analysis of nutrient transfers, c) concentration duration curves for comparison with EU Water Framework Directive water quality targets, d) event analysis of location-specific, sub-daily rainfall projections (UKCP, 2009), and e) a linear model relating storm rainfall to phosphorus load. These components, in combination, bring innovation and new insight into the estimation of future phosphorus transfers, which was not available from individual components. The data demonstrated two features of particular concern for climate change impacts. Firstly, the bulk of the suspended sediment and total phosphorus (TP) load (greater than 90% and 80% respectively) was transferred during the highest discharge events. The linear model of rainfall-driven TP transfers estimated that, with the projected increase in winter rainfall (+8% to +17% in the catchments by 2050s), annual event loads might increase by around 9% on average, if agricultural practices remain unchanged. Secondly, events following dry periods of several weeks, particularly in summer, were responsible for high concentrations of phosphorus, but relatively low loads. The high concentrations, associated with low flow, could become more frequent or last longer in the future, with a corresponding increase in the length of time that threshold concentrations (e.g. for water quality status) are exceeded. The results suggest that in order to build resilience in stream health and help mitigate potential increases in diffuse agricultural water pollution due to climate change, land management practices should target controllable risk factors, such as soil nutrient status, soil condition and crop cover.