The Eastern Nile Basin (ENB) countries of Egypt, Sudan, South Sudan, and Ethiopia are subject to pronounced water, energy, and food (WEF) insecurity problems. There is a need to manage the WEF nexus to meet rapidly increasing demands, but this is extremely challenging due to resource scarcity and climate change. If countries that rely on shared transboundary water resources have contradictory WEF plans, that could diminish the expected outcomes, both nationally and regionally. Egypt as the downstream Nile country is concerned about ongoing and future developments upstream, which could exacerbate Egypt’s water scarcity and affect its ability to meet its WEF objectives. In this context, we introduce a multi-model WEF framework that simulates the ENB’s water resources, food production, and hydropower generation systems. The models were calibrated and validated for the period 1983-2016, then utilized to project a wide range of future development plans, up to 2050, using four performance measures to evaluate the WEF nexus. A thematic pathway for regional development that showed high potential for mutual benefits was identified. Results indicate that the ENB countries could be nearly food self-sufficient before 2050 and generate an additional 42000 GWh/yr of hydropower, with minimal impacts on Egypt’s water scarcity problems. The WEF planning outcomes for the region are sensitive to climate change, but, if social drivers can be managed (e.g., by lowered population growth rates) despite the difficulties involved, climate change impacts on WEF security could be less severe.

High latitudes are predicted to continue warming at higher rates than the global average, with major implications for northern basins where concomitant deglaciation, permafrost thaw and vegetation shifts are expected. The Mackenzie River Basin, a globally significant basin, drains headwaters in the glaciated Canadian Rockies to the Arctic Ocean and is mostly underlain by permafrost. Here, we present scenarios of future change using the MESH distributed hydrological-cryospheric land surface model. MESH was forced with bias-corrected, downscaled RCM forcings and parameterized with a deep subsurface profile, organic soils, and glaciers. The model was validated against discharge, snowpack, and permafrost observations and used to simulate the hydrology and permafrost dynamics over the 21st century under the RCP8.5 climate change scenario with projected land cover change. The results show rapidly increasing rates of permafrost thaw; most of the basin will be permafrost-free by the 2080s. By late century, river discharges shift to earlier and higher peaks in response to projected increases in precipitation, temperature, snowmelt rates, despite increases in evapotranspiration from longer snow-free seasons. Baseflow discharges increase in winter, due to higher precipitation and increased basin connectivity from permafrost thaw resulting in enhanced groundwater flow. Subsurface moisture storage rises slightly but the liquid water fraction increases dramatically, increasing sub-surface runoff and river discharge. Canadian Rockies deglaciation reduces summer and annual discharge in the Athabasca and Peace headwaters. Downstream and northward of the mountain headwaters the direct impacts of climate change on river discharge dominate over those of changing land cover and glaciers.

Permafrost plays an important role in the hydrology of arctic/subarctic regions. However, permafrost thaw/degradation has been observed over recent decades in the Northern Hemisphere and is projected to accelerate. Hence, understanding the evolution of permafrost areas is urgently needed. Land surface models (LSMs) are well-suited for predicting permafrost dynamics due to their physical basis and large-scale applicability. However, LSM application is challenging because of the large number of model parameters and the complex memory of state variables. Significant interactions among the underlying processes and the paucity of observations of thermal/hydraulic regimes add further difficulty. This study addresses the challenges of LSM application by evaluating the uncertainty due to meteorological forcing, assessing the sensitivity of simulated permafrost dynamics to LSM parameters, and highlighting issues of parameter identifiability. Modelling experiments are implemented using the MESH-CLASS framework. The VARS sensitivity analysis and traditional threshold-based identifiability analysis are used to assess various aspects of permafrost dynamics for three regions within the Mackenzie River Basin. The study shows that the modeller may face significant trade-offs when choosing a forcing dataset as some datasets enable the representation of some aspects of permafrost dynamics, while being inadequate for others. The results also emphasize the high sensitivity of various aspects of permafrost simulation to parameters controlling surface insulation and soil texture; a detailed list of influential parameters is presented. Identifiability analysis reveals that many of the most influential parameters for permafrost simulation are unidentifiable. These conclusions will hopefully inform future efforts in data collection and model parametrization.

Permafrost thaw has been observed in recent decades in the Northern Hemisphere and is expected to accelerate with continued global warming. Predicting the future of permafrost requires proper representation of the interrelated surface/subsurface thermal and hydrologic regimes. Land surface models (LSMs) are well suited for such predictions, as they couple heat and water interactions across soil-vegetation-atmosphere interfaces and can be applied over large scales. LSMs, however, are challenged by the long-term thermal and hydraulic memories of permafrost and the paucity of historical records to represent permafrost dynamics under transient climate conditions. In this study, we address the challenge of model initialization by characterizing the impact of initial climate conditions and initial soil frozen and liquid water contents on the simulation length required to reach equilibrium. Further, we quantify how the uncertainty in model initialization propagates to simulated permafrost dynamics. Modelling experiments are conducted with the Modélisation Environmentale Communautaire – Surface and Hydrology (MESH) framework and its embedded Canadian Land Surface Scheme (CLASS). The study area is in the Liard River basin in the Northwest Territories of Canada with sporadic and discontinuous regions. Results show that uncertainty in model initialization controls various attributes of simulated permafrost, especially the active layer thickness, which could change by 0.5-1.5m depending on the initial condition chosen. The least number of spin-up cycles is achieved with near field capacity condition, but the number of cycles varies depending on the spin-up year climate. We advise an extended spin-up of 200-1000 cycles to ensure proper model initialization under different climatic conditions and initial soil moisture contents.

Cold regions provide water resources for half the global population yet face rapid change. Their hydrology is dominated by snow, ice and frozen soils, and climate warming is having profound effects. Hydrological models have a key role in predicting changing water resources, but are challenged in cold regions. Ground-based data to quantify meteorological forcing and constrain model parameterization are limited, while hydrological processes are complex, often controlled by phase change energetics. River flows are impacted by poorly quantified human activities. This paper reports scientific developments over the past decade of MESH, the Canadian community hydrological land surface scheme. New cold region process representation includes improved blowing snow transport and sublimation, lateral land-surface flow, prairie pothole storage dynamics, frozen ground infiltration and thermodynamics, and improved glacier modelling. New algorithms to represent water management include multi-stage reservoir operation. Parameterization has been supported by field observations and remotely sensed data; new methods for parameter identification have been used to evaluate model uncertainty and support regionalization. Additionally, MESH has been linked to broader decision-support frameworks, including river ice simulation and hydrological forecasting. The paper also reports various applications to the Saskatchewan and Mackenzie River basins in western Canada (0.4 and 1.8 million km2). These basins arise in glaciated mountain headwaters, are partly underlain by permafrost, and include remote and incompletely understood forested, wetland, agricultural and tundra ecoregions. This imposes extraordinary challenges to prediction, including the need to overcoming biases in forcing data sets, which can have disproportionate effects on the simulated hydrology.