Conceptual hydrological model calibration using multi-objective optimization techniques over the transboundary Komadugu-Yobe basin, Lake Chad Area, West Africa

Study Area: The discharge of the transboundary Komadugu-Yobe Basin, Lake Chad Area, West Africa is calibrated using multi-objective optimization techniques. Study focus: The GR5J hydrological model parameters are calibrated using six optimization methods i.e. Local Optimization-Multi Start (LOMS), the Differential Evolution (DE), the Multiobjective Particle the Swarm Optimization (MPSO), the Memetic Algorithm with Local Search Chains (MALS), the Shuffled Complex Evolution-Rosenbrock’s function (SCE-R), and the Bayesian Markov Chain Monte Carlo (MCMC) approach. Three combined objective functions i.e. Root Mean Square Error, Nash- Sutcliffe efficiency, Kling-Gupta efficiency are applied. The calibration process is divided into two separate episodes (1974–2000 and 1980–1995) so as to ascertain the robustness of the calibration approaches. Runoff simulation results are analysed with a timefrequency wavelet transform. New hydrological insights for the region: For calibration and validation stages, all optimization methods simulate the base flow and high flow spells with a satisfactory level of accuracy. For calibration period, MCMC underestimate it by -0.07 mm/day. The performance evaluation shows that MCMC has the highest values of mean absolute error (0.28) and mean square error (0.40) while LOMS and MCMC record a low volumetric efficiency of 0.56. In all cases, the DE and the SCE-R methods perform better than others. The combination of multi-objective functions and multi-optimization techniques improve the model’s parameters stability and the algorithms’ optimization to represent the runoff in the basin

Analysis of climate extreme indices over the Komadugu-Yobe basin, Lake Chad region: past and future occurrences

This study investigates trends of climate extreme indices in the Komadugu-Yobe Basin (KYB) based on observed data of the period 1971–2017 as well as regional climate model (RCM) simulations for the historical period (1979–2005), the near future (2020–2050), and the far future (2060–2090). In order to correct change points in the time historical series, the Adapted Caussinus Mestre Algorithm for homogenising Networks of Temperature series homogeneity test is used. The magnitude of the linear trends is estimated using the Sen's slope estimator and Mann-Kendall's test is performed to check the statistical significance of the trends. Future trends are assessed using the ensemble mean of eight regional climate model data under two emission scenarios, provided by the Coordinated Regional Climate Downscaling Experiment (CORDEX). Therefore, the projected rainfall and temperature have been corrected for biases by using empirical Quantile Mapping. In the observations, warm spell duration, warm day-, and warm night frequencies exhibit statistically significant positive trends. Although there is a positive trend in the annual total rainfall, the number of consecutive wet (dry) days decreases (increases). The future climate also shows a continuing positive trend in the temperature extreme indices as well as more frequent extreme rainfall events. Therefore, it is pertinent for decision-makers to develop suitable adaptation and mitigating measures to combat climate change in the Basin. Keywords: Homogeneity, Climate extreme indices, Linear trends, Bias correction, Komadugu-Yobe basin, Lake Chad regio

SPATIO-TEMPORAL TREND OF VEGETATION COVER OVER ABUJA USING LANDSAT DATASETS

Vegetation cover has acted as a source of carbon sinks and air purifier for a long period of time especially in developed cities thereby affecting the global climate change. The study was conducted to spatially estimate the Normalized Difference Vegetative Index (NDVI) which is a vegetation indicator for a period of 28 epoch years for Abuja from 1987 to 2014. The positive signatures of NDVI decrease from 2009 to 2014. Statistical analysis of the observed data samples at 95% confidence interval revealed that the changes observed in Year 2009 contributed most to the changes that was occurred in Year 2014.The modeled NDVI values for the year 2014 based on the regression analysis of the previous three years shows a significant agreement between the simulated values for year 2014 and the observed values. In general, there has been fast transformation of the vegetation cover to other land uses. The study reveals vegetation cover had reduced more significantly. It is also worthy to know that the model generated in this research can be used to predict future changes and trends in the vegetation cover. This will provide policy makers with useful information for the proper planning and design of the city and other capital cities over West Africa

Weather affects post‐fire recovery of sagebrush‐steppe communities and model transferability among sites

Altered climate, including weather extremes, can cause major shifts in vegetative recovery after disturbances. Predictive models that can identify the separate and combined temporal effects of disturbance and weather on plant communities and that are transferable among sites are needed to guide vulnerability assessments and management interventions. We asked how functional group abundance responded to time since fire and antecedent weather, if long-term vegetation trajectories were better explained by initial post-fire weather conditions or by general five-year antecedent weather, and if weather effects helped predict post-fire vegetation abundances at a new site. We parameterized models using a 30- yr vegetation monitoring dataset from burned and unburned areas of the Orchard Training Area (OCTC) of southern Idaho, USA, and monthly PRISM data, and assessed model transferability on an independent dataset from the well-sampled Soda wildfire area along the Idaho/Oregon border. Sagebrush density increased with lower mean air temperature of the coldest month and slightly increased with higher mean air temperature of the hottest month, and with higher maximum January–June precipitation. Perennial grass cover increased in relation to higher precipitation, measured annually in the first four years after fire and/or in September–November the year of fire. Annual grass increased in relation to higher March–May precipitation in the year after fire, but not with September–November precipitation in the year of fire. Initial post-fire weather conditions explained 1% more variation in sagebrush density than recent antecedent 5-yr weather did but did not explain additional variation in perennial or annual grass cover. Inclusion of weather variables increased transferability of models for predicting perennial and annual grass cover from the OCTC to the Soda wildfire regardless of the time period in which weather was considered. In contrast, inclusion of weather variables did not affect transferability of the forecasts of post-fire sagebrush density from the OCTC to the Soda site. Although model transferability may be improved by including weather covariates when predicting post-fire vegetation recovery, predictions may be surprisingly unaffected by the temporal windows in which coarse-scale gridded weather data are considered

Global assessment of drought characteristics in the Anthropocene

Contemporary understanding of the impacts of climate change on global drought characteristics (e.g., intensities, extents) is still limited and not well understood. This knowledge is critical because projected changes in climate are expected to impact on future water availability as well as influence decisions on how water resources are allocated. The main aim of this study is to improve understanding of drought characteristics (extents and duration) in the Anthropocene where rapid changes in the environment are caused by the composite influence of human activities and climate change. Multi-scale earth observation data (1980−2020) and the Coupled Model Intercomparison Project Phase 6 climate models, which incorporate the Shared Socioeconomic Pathways (2040−2070and 2070−2100) are used to assess these characteristics as well as identify climatic hotspots where changes in drought characteristics could drive groundwater hydrology. Results show that towards the end of the 21st century, global land areas under drought will significantly decrease but their durations will not. Generally, there is evidence of significant decline in the proportion of areas that will experience various drought intensities (moderate, severe and extreme drought) in the future and for each category, drought affected areas will not reach 30% on average. Moreover, some regions are potential hotspots of climate–groundwater interactions where drought events could directly impact on groundwater. This is because of the varying degree of strong correlations (positive and negative) between climate and groundwater data in some areas (e.g., Australia, Europe, Southern Africa, Asia). The relatively strong negative correlations in some of these hotspots are indicative of the presence of considerable lags, that could be caused by aridity as well as human groundwater footprints