Gridded daily 2-m air temperature dataset for Ethiopia derived by debiasing and downscaling ERA5-Land for the period 1981-2010.

A gridded maximum and minimum (Tx and Tn) daily temperature dataset derived by spatial downscaling and bias correction of the ERA5-Land (ERA5L) for the period 1981-2010 is presented. Observed daily Tx and Tn at 154 stations in Ethiopia covering record lengths of 5-30 years were used as a reference. The statistics that define the Gaussian distribution (mean and standard deviation) of Tx and Tn from the station observations were interpolated in space to create a monthly climatology and interannual statistics at 0.05° × 0.05° resolution using a hybrid interpolation approach that combines linear regression with topographic and location attributes, and non-Euclidean inverse distance weighting interpolation. The interpolated monthly and interannual statistics were then used to debias the ERA5L Tx and Tn using a quantile mapping approach. Leave-one-out cross-validation showed that the mean absolute errors in the corrected and downscaled daily temperatures are about 0.7 °C for Tx and 1.1 °C for Tn, reducing the statistical biases in the ERA5L Tx and Tn by 68% and 25% respectively. For monthly climatology, 40-64% of the biases were removed for Tx while for Tn the reductions range from 19% to 32%. The correction also improved commonly used indices for extremes like the probability of warm days, cold days, and warm nights, but overestimated the probability of cold nights. The presented open-access Tx and Tn dataset is a substantial improvement over existing gridded temperature datasets for Ethiopia, such as ERA5L and the Climate Hazards Infrared Temperature with Station (CHIRTS), and we suggest it is suitable for a wide range of environmental applications, e.g. in the fields of hydrology, agriculture, and ecology.

Modeling distributed metal pollution transport in a mine impacted catchment: Short and long-term effects.

A spatially distributed trace metal transport and transformation module was developed and implemented within the hydrological model TOPKAPI-ETH. The new module can be used to better understand, at high spatial and temporal resolution, the transport and reactions of trace metals as they move through a catchment from upland sources to downstream areas and water bodies. The newly developed module takes into consideration solid metal in multiple chemical phases with different reactivity and simulates their mutual transformation over time, which gives the possibility to analyze the fraction of different solid metal phases present in the river suspended sediment. The characteristics and potential of the model are demonstrated by simulating Zinc (Zn) and Cadmium (Cd) dynamics in a headwater catchment of the Xiang River in South China, which has been highly perturbed by mining activities. The developed module is shown to reasonably reproduce the observed dynamics of dissolved and total trace metals flux for 14 months at two monitoring stations. The distributed solute transport model was proved to be capable of explaining the reasons underlying the spatial variability of C-Q relationships that are driven by the combined effect of point and non-point pollution sources, as well as identifying the spatiotemporal hotspots of trace metal pollution. By means of synthetic numerical experiments, a limited impact of slow reactions on dissolved Cd transport from upland to river over short-temporal scales was demonstrated, while for longer scales, e.g. >5 years, this effect becomes more relevant, highlighting potential long-lasting sources of trace metal pollution and their impacts.

When timing matters-misdesigned dam filling impacts hydropower sustainability.

Decades of sustainable dam planning efforts have focused on containing dam impacts in regime conditions, when the dam is fully filled and operational, overlooking potential disputes raised by the filling phase. Here, we argue that filling timing and operations can catalyze most of the conflicts associated with a dam's lifetime, which can be mitigated by adaptive solutions that respond to medium-to-long term hydroclimatic fluctuations. Our retrospective analysis of the contested recent filling of Gibe III in the Omo-Turkana basin provides quantitative evidence of the benefits generated by adaptive filling strategies, attaining levels of hydropower production comparable with the historical ones while curtailing the negative impacts to downstream users. Our results can inform a more sustainable filling of the new megadam currently under construction downstream of Gibe III, and are generalizable to the almost 500 planned dams worldwide in regions influenced by climate feedbacks, thus representing a significant scope to reduce the societal and environmental impacts of a large number of new hydropower reservoirs.

Magnitude of urban heat islands largely explained by climate and population.

Urban heat islands (UHIs) exacerbate the risk of heat-related mortality associated with global climate change. The intensity of UHIs varies with population size and mean annual precipitation, but a unifying explanation for this variation is lacking, and there are no geographically targeted guidelines for heat mitigation. Here we analyse summertime differences between urban and rural surface temperatures (ΔTs) worldwide and find a nonlinear increase in ΔTs with precipitation that is controlled by water or energy limitations on evapotranspiration and that modulates the scaling of ΔTs with city size. We introduce a coarse-grained model that links population, background climate, and UHI intensity, and show that urban-rural differences in evapotranspiration and convection efficiency are the main determinants of warming. The direct implication of these nonlinearities is that mitigation strategies aimed at increasing green cover and albedo are more efficient in dry regions, whereas the challenge of cooling tropical cities will require innovative solutions.

The nitrogen cycle in highly urbanized tropical regions and the effect of river-aquifer interactions: The case of Jakarta and the Ciliwung River.

Groundwater is extensively used in Jakarta to compensate for the limited public water supply network. Recent observations show a rise in nitrate (NO3(-)) levels in the shallow aquifer, thus pointing at a potential risk for public health. The detected levels are still below national and international regulatory limits for drinking water but a strategy is necessary to contain the growing problem. We combine 3years of available data in the Ciliwung River, the major river flowing through Jakarta, with a distributed river-aquifer interaction model to characterise the impact of urbanisation on the N-cycle of both surface and groundwater systems. Results show that the N-cycle in the river-aquifer system is heterogeneous in space, seasonal dependent (i.e. flow regime) and strongly affected by urban pollution. Results suggest also that although the main sources of N related groundwater pollution are leaking septic tanks, the aquifer interaction with the Ciliwung River may locally have a strong effect on the concentrations. In the general context of pollution control in urban areas, this study demonstrates how advanced process-based models can be efficiently used in combination with field measurements to bring new insights into complex contamination problems. These are essential for more effective and integrated management of water quality in river-aquifer systems.

Modeling terrestrial carbon and water dynamics across climatic gradients: does plant trait diversity matter?

Plant trait diversity in many vegetation models is crudely represented using a discrete classification of a handful of 'plant types' (named plant functional types; PFTs). The parameterization of PFTs reflects mean properties of observed plant traits over broad categories ignoring most of the inter- and intraspecific plant trait variability. Taking advantage of a multivariate leaf-trait distribution (leaf economics spectrum), as well as documented plant drought strategies, we generate an ensemble of hypothetical species with coordinated attributes, rather than using few PFTs. The behavior of these proxy species is tested using a mechanistic ecohydrological model that translates plant traits into plant performance. Simulations are carried out for a range of climates representative of different elevations and wetness conditions in the European Alps. Using this framework we investigate the sensitivity of ecosystem response to plant trait diversity and compare it with the sensitivity to climate variability. Plant trait diversity leads to highly divergent vegetation carbon dynamics (fluxes and pools) and to a lesser extent water fluxes (transpiration). Abiotic variables, such as soil water content and evaporation, are only marginally affected. These results highlight the need for revising the representation of plant attributes in vegetation models. Probabilistic approaches, based on observed multivariate whole-plant trait distributions, provide a viable alternative.