The energy-water-land nexus represents a critical leverage future policies must draw upon to reduce trade-offs between sustainable development objectives. Yet, existing long-term planning tools do not provide the scope or level of integration across the nexus to unravel important development constraints. Moreover, existing tools and data are not always made openly available or are implemented across disparate modeling platforms that can be difficult to link directly with modern scientific computing tools and databases. In this paper, we present the NExus Solutions Tool (NEST): a new open modeling platform that integrates multi-scale energy-water-land resource optimization with distributed hydrological modeling. The new approach provides insights into the vulnerability of water, energy and land resources to future socioeconomic and climatic change and how multi-sectoral policies, technological solutions and investments can improve the resilience and sustainability of transformation pathways while avoiding counterproductive interactions among sectors. NEST can be applied at different spatial and temporal resolutions, and is designed specifically to tap into the growing body of open-access geospatial data available through national inventories and the Earth system modeling community. A case study analysis of the Indus River basin in south Asia demonstrates the capability of the model to capture important interlinkages across system transformation pathways towards the United Nations' Sustainable Development Goals, including the intersections between local and regional transboundary policies and incremental investment costs from rapidly increasing regional consumption projected over the coming decades.@en

Several model inter-comparison projects (MIPs) have been carried out recently by the climate, hydrological, agricultural and other modelling communities to quantify modelling uncertainties and improve modelling systems. Here we focus on MIP design for large-scale water quality models. Water quality MIPs can be useful to improve our understanding of pollution problems and facilitate the development of harmonized estimates of current and future water quality. This can provide new opportunities for assessing robustness in estimates of water quality hotspots and trends, improve understanding of processes, pollution sources, water quality model uncertainties, and to identify priorities for water quality data collection and monitoring. Water quality MIP design should harmonize relevant model input datasets, use consistent spatial/temporal domains and resolutions, and similar output variables to improve understanding of water quality modelling uncertainties and provide harmonized water quality data that suit the needs of decision makers and other users.

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We present PCR-GLOBWB 2, a global hydrology and water resources model. Compared to previous versions of PCR-GLOBWB, this version fully integrates water use. Sector-specific water demand, groundwater and surface water withdrawal, water consumption, and return flows are dynamically calculated at every time step and interact directly with the simulated hydrology. PCR-GLOBWB 2 has been fully rewritten in Python and PCRaster Python and has a modular structure, allowing easier replacement, maintenance, and development of model components. PCR-GLOBWB 2 has been implemented at 5 arcmin resolution, but a version parameterized at 30 arcmin resolution is also available. Both versions are available as open-source codes on https://github.com/UU-Hydro/PCR-GLOBWB-model (Sutanudjaja et al., 2017a). PCR-GLOBWB 2 has its own routines for groundwater dynamics and surface water routing. These relatively simple routines can alternatively be replaced by dynamically coupling PCR-GLOBWB 2 to a global two-layer groundwater model and 1-D-2-D hydrodynamic models. Here, we describe the main components of the model, compare results of the 30 and 5 arcmin versions, and evaluate their model performance using Global Runoff Data Centre discharge data. Results show that model performance of the 5 arcmin version is notably better than that of the 30 arcmin version. Furthermore, we compare simulated time series of total water storage (TWS) of the 5 arcmin model with those observed with GRACE, showing similar negative trends in areas of prevalent groundwater depletion. Also, we find that simulated total water withdrawal matches reasonably well with reported water withdrawal from AQUASTAT, while water withdrawal by source and sector provide mixed results.

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The mass and steric components of sea level changes have been separated in the Tropical Asian Seas (TAS) using a statistically optimal combination of Jason satellite altimetry, GRACE satellite gravimetry, and ocean reanalyses. Using observational uncertainties, statistically optimally weighted time series for both components have been obtained in four regions within the TAS over the period January 2005 to December 2012. The mass and steric sea level variability is regressed with the first two principal components (PC1&2) of Pacific equatorial wind stress and the Dipole Mode Index (DMI). Sea level in the South China Sea is not affected by any of the indices. Steric variability in the TAS is largest in the deep Banda and Celebes seas and is affected by both PCs and the DMI. Mass variability is largest on the continental shelves, which is primarily controlled by PC1. We argue that a water flux from the Western Tropical Pacific Ocean is the cause for mass variability in the TAS. The steric trends are about 2 mm yr⁻¹ larger than the mass trends in the TAS. A significant part of the mass trend can be explained by the aforementioned indices and the nodal cycle. Trends obtained from fingerprints of mass redistribution are statistically equal to mass trends after subtracting the nodal cycle and the indices. Ultimately, the effect of omitting the TAS in global sea level budgets is estimated to be 0.3 mm yr⁻¹.

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Over recent decades, the global population has been rapidly increasing and human activities have altered terrestrial water fluxes to an unprecedented extent. The phenomenal growth of the human footprint has significantly modified hydrological processes in various ways (e.g. irrigation, artificial dams, and water diversion) and at various scales (from a watershed to the globe). During the early 1990s, awareness of the potential for increased water scarcity led to the first detailed global water resource assessments. Shortly thereafter, in order to analyse the human perturbation on terrestrial water resources, the first generation of largescale hydrological models (LHMs) was produced. However, at this early stage few models considered the interaction between terrestrial water fluxes and human activities, including water use and reservoir regulation, and even fewer models distinguished water use from surface water and groundwater resources. Since the early 2000s, a growing number of LHMs have incorporated human impacts on the hydrological cycle, yet the representation of human activities in hydrological models remains challenging. In this paper we provide a synthesis of progress in the development and application of human impact modelling in LHMs. We highlight a number of key challenges and discuss possible improvements in order to better represent the human-water interface in hydrological models.

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Long-term trends and decadal variability of sea level in the North Sea and along the Norwegian coast have been studied over the period 1958–2014. We model the spatially nonuniform sea level and solid earth response to large-scale ice melt and terrestrial water storage changes. GPS observations, corrected for the solid earth deformation, are used to estimate vertical land motion. We find a
clear correlation between sea level in the North Sea and along the Norwegian coast and open ocean steric variability in the Bay of Biscay and west of Portugal, which is consistent with the presence of wind-driven coastally trapped waves. The observed nodal cycle is consistent with tidal equilibrium. We are able to explain the observed sea level trend over the period 1958–2014 well within the standard error of the sum of all contributing processes, as well as the large majority of the observed decadal sea level variability.@en