Study region: Omo-Turkana Basin, trans-boundary basin between Ethiopia and Kenya (North eastern Africa). Study focus: Significant investments in large dams have been mobilized in the Omo-Turkana basin to expand hydropower and support extensive irrigation projects. Assessing the impacts of these infrastructures, particularly on local stakeholders, constitutes a crucial foundation for socially inclusive as well as environmentally and economically sustainable development. This study showcases the potential of a participatory decision-analytic framework in investigating the impacts of alternative development pathways on competing stakeholders' interests in the OmoTurkana basin to support strategic planning under both current and projected hydroclimatic and socio-economic conditions. The optimal operation of the planned system expansion, including the current and future dam cascade and the irrigation projects, is investigated to provide insights into multisectoral trade-offs. Five main sectors with competing interests are considered: hydropower production, environmental protection, indigenous recession agriculture, fish yield in Lake Turkana, and large-scale commercial irrigated agriculture. New hydrological insights for the region: Results show that the planned infrastructure can negatively impact local stakeholders, particularly in terms of fish yields in Lake Turkana. Still, a potential exists for negotiating operational compromises that are both efficient and socially inclusive. Moreover, even though the performance of the planned infrastructure is expected to decline in the future under changing climate and irrigation demands, this can be mitigated by timely implementing robust solutions triggered by the alterations of streamflows in the northern part of the basin.

Patterns of freshwater availability-its variability and distribution-are already shifting as a function of global climate change and climate variability. High-resolution global gridded reanalysis products present an important tool to understand the already observed changes and thereby improve future scenarios as the climate evolves. A historical 100-yr-long district rainfall dataset and a unique set of highly detailed rainfall data from the highveld of South Africa spanning a 10-yr period provide an opportunity to independently evaluate the European Centre for Medium-Range Weather Forecasts ERA5 reanalysis product. Evaluation is challenged by the episodic nature of significant rainfall events of southern Africa as well as differences in spatial and temporal resolution between model output and surface precipitation data. Here we present a convergent methodology spanning annual to event time scales and regional to gauge-level spatial scales to identify the characteristics of systematic biases in variability and amount of rain as well as timing of events. We find that ERA5 is consistently wetter than observed in ways that affect the timing of individual events while performing well on metrics associated with large-scale trends and seasonal variability. Errors are associated with both stratiform and convective rainfall types, but the timing of onset of convective rainfall is a challenge that is critical in this summer-rainfall-dominated region.

Climate and socio-economic changes bring multiple challenges to river basin development worldwide. The large uncertainty characterizing future conditions requires robust and adaptive planning and management solutions capable of handling uncertain future changes. This is particularly true in monsoonal Southeast Asian catchments, where large multipurpose reservoir systems play a crucial role in flood protection and providing water, energy, and food to a rapidly changing society. In such river basins, high intra-annual and inter-annual hydroclimatic variability, as well as increasing frequency of extreme events, further challenge the management of multi-sector water demands across multiple time scales. In this context, we develop a robust decision-analytic framework for supporting the strategic planning of river basins in monsoonal areas with respect to future changes in water availability and demands. The framework integrates future climate scenarios, including a catalogue of extreme climate events, future water demand scenarios, a high-resolution infrastructure-accounting hydrological model, Topkapi-ETH, and a strategic, operational model to design multiobjective optimal water management policies. We first build climate change driven projections of water availability; second, we apply the optimization engine to select a subset of operation policies optimized based on key selected indicators; and third, we use the spatially distributed hydrological model to evaluate the impact of the chosen policies on a broader set of indicators capturing the spatially distributed impact of dam operations. We focus here on the Red River Basin, a large transboundary river basin in China and Vietnam. In the basin, conflicts among different water uses, such as flood control, hydropower production, agriculture and aquaculture, are expected to increase under the combined pressure of increasing water and energy demands and climate change. A specific focus is given to extreme rainfall events, expected to increase their frequency and magnitude. The framework proposed will allow us to assess the vulnerability of the basin under future scenarios as well as the sustainability and robustness of future river basin development plans in the context of the water-energy-food-environment nexus.

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.

The new estimation technique is compared in a synthetic experiment with some commonly used alternatives such as Kriging, Kriging with external drift, and Conditional Merging. The performance of the technique is shown to have some advantages relative to the other methods, in particular in reducing the bias in estimation of the extremes, while showing comparable performance to Kriging with drift for estimating the field mean.

Climate impact studies often require climate data at a higher space-time resolution than is available from global and regional climate models. Weather generator (WG) models, generally designed for mesoscale applications (e.g., 10(1)-10(5) km(2)), are popular and widely used tools to downscale climate data to finer resolution. One advantage of using WGs is their ability to generate the necessary climate variables for impact studies in data sparse regions. In this study, we evaluate the ability of a previously established state of the art WG (the AWE-GEN-2d model) to perform in data sparse regions that are beyond the mesoscale, using the Zambezi River basin (10(6) km(2)) in southeast Africa as a case study. The AWE-GEN-2d model was calibrated using data from satellite retrievals and climate re-analysis products in place of the absent observational data. An 8-km climate ensemble at hourly resolution, covering the period of 1976-2099 (present climate and RCP4.5 emission scenario from 2020), was then simulated. Using the simulated 30-member ensemble, climate indices for both present and future climates were computed. The high-resolution climate indices allow detailed analysis of the effects of climate change on different areas within the basin. For example, the southwestern area of the basin is predicted to experience the greatest change due to increased temperature, while the southeastern area was found to be already so hot that is less affected (e.g., the number of 'very hot days' per year increase by 18 and 9 days, respectively). Rainfall intensities are found to increase most in the eastern areas of the basin (1 mm.d(-1)) in comparison to the western region (0.3 mm.d(-1)). As demonstrated in this study, AWE-GEN-2d can be calibrated successfully using data from climate reanalysis products in the absence of ground station data and can be applied at larger scales than the mesoscale.

With supply chain finance gaining more prominence in practice and drawing increasing attention from researchers, the question arises how this emerging discipline can build on existing theoretical conceptualisations. However, few studies have incorporated theoretical frameworks and there remains therefore a gap in literature. To fill this gap, the study reviews five theories on their suitability for supply chain finance: transaction cost economics, agency theory, network theory, collaborative networks and social exchange theory. A Scottish focus group consisting of practitioners involved in supply chain finance provided empirical data for the evaluation. The findings suggest that there is supporting evidence for using agency theory, network theory, transaction cost economics and social exchange theory as theoretical frameworks for studying phenomena of supply chain finance. Furthermore, the results indicate that the conceptualisations based on agency theory should be extended with ‘reverse principal–agent theory’ to fit with the contingencies of supply chain finance. The frameworks of collaborative networks are found less suitable. In addition to these theoretical considerations, the focus group discussion also points out that the financial department's collaboration with other departments involved in the primary supply chain process in firms needs to be improved. To achieve this training and supplier development, particularly for smaller firms, is seen as key. These outcomes have informed a research agenda for research groups, early career researchers and doctoral students.

Conflicting stakeholder interests in water systems such as power generation, agriculture and local livelihoods have required the development of an integrated approach to water resources management. An important livelihood for many African rural communities is flood-recession agriculture. Especially in monsoonal climates, river adjacent sites that are inundated by seasonal flood pulses provide humid and fertile soils of high value for small-scale agriculture. Alterations to natural flood regimes due to the construction of water infrastructures (e.g. dams) threaten this practice by reducing flooding of riparian areas. Artificial flood releases from reservoirs have the potential to counter such alteration, but in order to maximize their effectiveness many aspects are yet to be studied. In particular, in a context where resources are shared among multiple stakeholders, little research has been done on how to ensure sufficient flood magnitude to protect communities from the risk of crop failure. As part of the national hydropower development strategy, Gibe III dam is in operation on the Omo river in southern Ethiopia since 2015, and local populations practicing flood-recession agriculture in the downstream Omo valley have been exposed to reduced or absent seasonal floods. The development of a large, state-owned irrigation district along the river course further reduced water availability in the region of its delta, where flood-recession agriculture was practiced the most. For artificial floods from Gibe III dam to be effective, we developed an indicator to assess water needs for flood-recession agriculture and to include them in reservoir policy optimizations. Lack of ground data and remoteness of the area were the main challenges of this work, preventing direct data acquisition and extensive stakeholder participation. We used high-resolution satellite imagery taken annually to quantify the yearly extent of flood-recession agriculture in the region and linked it to estimated past streamflow magnitudes simulated by means of a distributed hydrological model. We observed a strong correlation between historical extents of flood-recession agriculture fields in the study area and river streamflow, allowing to build an indicator for livelihood flood requirements that was included in the evaluation of alternative development pathways. We used the designed indicator to assess the impact of alternative management strategies with varying sectoral trade-offs, combined with multiple system configurations representing present and planned infrastructural development of the region. Preliminary results show that appropriately designed development pathways can substantially limit damages to flood-recession agriculture practices. This indicator will contribute to planning effective artificial flood releases and to capturing rural communities’ agriculture needs.

With open innovation gaining popularity, the question is how firms view its conceptualisation. This is of particular interest for those national economies that are patchy and consisting largely of small and medium-sized enterprises, as in Scotland. To solicit views of Scottish firms a focus group was organised centred on core themes of open innovation. Interviews with Scottish Innovation Centres complemented the focus group. The outcomes suggest that canonical views on 'open innovation' prevail, particularly for collaboration, even though its opportunities are well-recognised. It also turns out that some Scottish companies hold myopic views on innovation, which could be an explanatory factor for the oft-discussed innovation gap in the United Kingdom. Most interestingly, our study also reveals the concept of open innovation is not always understood for what it covers; it is recommended that the term 'open innovation' is redefined as 'open collaboration' to better reflect its nature.

This paper presents the applications of Fuzzy Rule Based Circulation Patterns (CPs) classification in the description and modelling of two different physical consequences of their form: Rainfall regimes and Wind generated Ocean Waves. The choice of the CP groupings is made by searching for those CPs which generate (i) different daily rainfall patterns over mesoscale regions and (ii) wave directions and heights at chosen shoreline locations. The method used to choose the groupings of CPs is a bottom-up methodology using simulated annealing, ensuring that the causative CPs are responsible for the character of the results. This approach is in marked distinction to the top-down approaches such as k-means clustering or Self Organising Maps (SOMS) to identify several classes of CPs and then finding the effects of those CPs on the variables of choice on given historical days. The CP groups we define are quite different for the two phenomena rainfall and waves, simply because different details of the pressure fields are responsible for wind and for precipitation. Large ocean waves are typically generated over fetches of the order of thousands of kilometres far off shore, whereas rainfall is generated by local atmospheric variables including temperature, humidity, wind speed and radiation over the area of concern. The spatial representativeness of the CPs is discussed and classifications obtained for different regions are compared. The paper gives examples of applications of the ideas over South Africa.

A soil moisture monitoring service for the region of the Southern African Development Community (SADC) has been developed within the ESA TIGER Innovator project SHARE (www.ipf.tuwien.ac.at/radar/share). This service addresses one of today's most severe obstacles in water resource management which is the lack of availability of reliable soil moisture information on a dynamic basis. The spatial resolution of the product is 1 km and data are available up to twice per week for southern Africa since December 2004. In this paper the assessment of the new extensive soil moisture dataset is presented. This includes the comparison with other remotely sensed products (precipitation) and ground measurements. River runoff measurements reflect the hydrological state of the upstream basin. Thus, the relationship of relative soil moisture and river runoff has been investigated. Correlations of >0.9 (R2) are found for subtropical basins with respect to a catchment specific temporal offset. If knowledge is established about this relationship and also the spatial patterns, flood risk assessments can be enhanced. Soil moisture deficits can be identified once a sufficiently long time series becomes available.

Flash floods and droughts are of major concern in Southern Africa. Hydrologists and engineers have to assist decision makers to address the issue of forecasting and monitoring extreme events. For these purposes, hydrological models are useful tools to:Identify the dominant hydrological processes which influence the water balance and result in conditions of extreme water excess and/or deficitAssist in generating both short- and long-term hydrological forecasts for use by water resource managers. In this study the physically-based and fully distributed hydrological TOPKAPI model (Liu and Todini, 2002),which has already been successfully applied in several Countries in the world (Liu and Todini, 2002; Bartholomes and Todini, 2005; Liu et al.. 2005; Martina et al., 2006), is applied in Africa for the first time. This paper contains the main theoretical and numerical components that have been integrated by the authors to model code and presents details of the application of the model in the Liebenbergsvlei catchment (4 625 km(2)) in South Africa.The physical basis of the equations, the fine-scale representation of the spatial catchment features, the parsimonious parameterisation linked to field/catchment information, the good computation time performance, the modularity of the processes. the case of use and finally the good results obtained in modelling the river discharges of Liebenbergsvlei catchment, make the TOPKAPI model a promising tool for hydrological modelling of catchments in South Africa.