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Human activities have caused various changes to the Earth system, and hence the interconnections between human activities and the Earth system should be recognized and reflected in models that simulate Earth system processes. One key anthropogenic activity is water resource management, which determines the dynamics of human–water interactions in time and space and controls human livelihoods and economy, including energy and food production. There are immediate needs to include water resource management in Earth system models. First, the extent of human water requirements is increasing rapidly at the global scale and it is crucial to analyze the possible imbalance between water demands and supply under various scenarios of climate change and across various temporal and spatial scales. Second, recent observations show that human–water interactions, manifested through water resource management, can substantially alter the terrestrial water cycle, affect land–atmospheric feedbacks and may further interact with climate and contribute to sea-level change. Due to the importance of water resource management in determining the future of the global water and climate cycles, the World Climate Research Program's Global Energy and Water Exchanges project (WRCP-GEWEX) has recently identified gaps in describing human–water interactions as one of the grand challenges in Earth system modeling (GEWEX, 2012). Here, we divide water resource management into two interdependent elements, related firstly to water demand and secondly to water supply and allocation. In this paper, we survey the current literature on how various components of water demand have been included in large-scale models, in particular land surface and global hydrological models. Issues of water supply and allocation are addressed in a companion paper. The available algorithms to represent the dominant demands are classified based on the demand type, mode of simulation and underlying modeling assumptions. We discuss the pros and cons of available algorithms, address various sources of uncertainty and highlight limitations in current applications. We conclude that current capability of large-scale models to represent human water demands is rather limited, particularly with respect to future projections and coupled land–atmospheric simulations. To fill these gaps, the available models, algorithms and data for representing various water demands should be systematically tested, intercompared and improved. In particular, human water demands should be considered in conjunction with water supply and allocation, particularly in the face of water scarcity and unknown future climate.

Human water use has significantly increased during the recent past. Water withdrawals from surface and groundwater sources have altered terrestrial discharge and storage, with large variability in time and space. These withdrawals are driven by sectoral demands for water, but are commonly subject to supply constraints, which determine water allocation. Water supply and allocation, therefore, should be considered together with water demand and appropriately included in Earth system models to address various large-scale effects with or without considering possible climate interactions. In a companion paper, we review the modeling of demand in large-scale models. Here, we review the algorithms developed to represent the elements of water supply and allocation in land surface and global hydrologic models. We note that some potentially important online implications, such as the effects of large reservoirs on land–atmospheric feedbacks, have not yet been fully investigated. Regarding offline implications, we find that there are important elements, such as groundwater availability and withdrawals, and the representation of large reservoirs, which should be improved. We identify major sources of uncertainty in current simulations due to limitations in data support, water allocation algorithms, host large-scale models as well as propagation of various biases across the integrated modeling system. Considering these findings with those highlighted in our companion paper, we note that advancements in computation and coupling techniques as well as improvements in natural and anthropogenic process representation and parameterization in host large-scale models, in conjunction with remote sensing and data assimilation can facilitate inclusion of water resource management at larger scales. Nonetheless, various modeling options should be carefully considered, diagnosed and intercompared. We propose a modular framework to develop integrated models based on multiple hypotheses for data support, water resource management algorithms and host models in a unified uncertainty assessment framework. A key to this development is the availability of regional-scale data for model development, diagnosis and validation. We argue that the time is right for a global initiative, based on regional case studies, to move this agenda forward.

While there is a popular perception that Canada is a water-rich country, the Saskatchewan River basin (SRB) in Western Canada exemplifies the multiple threats to water security seen worldwide. It is Canada's major food-producing region and home to globally significant natural resource development. The SRB faces current water challenges stemming from (1) a series of extreme events, including major flood and drought events since the turn of the 21st century, (2) full allocation of existing water resources in parts of the basin, (3) rapid population growth and economic development, (4) increasing pollution, and (5) fragmented and overlapping governance that includes the provinces of Alberta, Saskatchewan, and Manitoba, various Federal and First Nations responsibilities, and international boundaries. The interplay of these factors has increased competition for water across economic sectors and among provinces, between upstream and downstream users, between environmental flows and human needs, and among people who hold different values about the meaning, ownership, and use of water. These current challenges are set in a context of significant environmental and societal change, including widespread land modification, rapid urbanization, resource exploitation, climate warming, and deep uncertainties about future water supplies. We use Sivapalan et al.'s (2012) framework of socio-hydrology to argue that the SRB's water security challenges are symptoms of dynamic and complex water systems approaching critical thresholds and tipping points. To Sivapalan et al.'s (2012) emphasis on water cycle dynamics, we add the need for governance mechanisms to manage emergent systems and translational science to link science and policy to the socio-hydrology agenda.

The Global Precipitation Measurement (GPM) mission offers new opportunities for modeling a range of physical/hydrological processes at higher resolutions, especially for remote river systems where the hydrometeorological monitoring network is sparse and weather radar is not readily available. In this study, the recently released Integrated Multisatellite Retrievals for GPM [version 03 (V03) IMERG Final Run] product with high spatiotemporal resolution of 0.1° and 30 min is evaluated against ground-based reference measurements (at the 6-hourly, daily, and monthly time scales) over different terrestrial ecozones of southern Canada within a 23-month period from 12 March 2014 to 31 January 2016. While IMERG and ground-based observations show similar regional variations of mean daily precipitation, IMERG tends to overestimate higher monthly precipitation amounts over the Pacific Maritime ecozone. Results from using continuous as well as categorical skill metrics reveal that IMERG shows more satisfactory agreement at the daily and the 6-hourly time scales for the months of June–September, unlike November–March. In terms of precipitation extremes (defined by the 75th percentile threshold for reference data), apart from a tendency toward over-detection of heavy precipitation events, IMERG captured well the distribution of heavy precipitation amounts and observed wet/dry spell length distributions over most ecozones. However, low skill was found over large portions of the Montane Cordillera ecozone and a few stations in the Prairie ecozone. This early study highlights a potential applicability of V03 IMERG Final Run as a reliable source of precipitation estimates in diverse water resources and hydrometeorological applications for different regions in southern Canada.

Based on the Generalized Linear Model (GLM) framework, a multisite stochastic modelling approach is developed using daily observations of precipitation and minimum and maximum temperatures from 120 sites located across the Canadian Prairie Provinces: Alberta, Saskatchewan and Manitoba. Temperature is modeled using a two-stage normal-heteroscedastic model by fitting mean and variance components separately. Likewise, precipitation occurrence and conditional precipitation intensity processes are modeled separately. The relationship between precipitation and temperature is accounted for by using transformations of precipitation as covariates to predict temperature fields. Large scale atmospheric covariates from the National Center for Environmental Prediction Reanalysis-I, teleconnection indices, geographical site attributes, and observed precipitation and temperature records are used to calibrate these models for the 1971–2000 period. Validation of the developed models is performed on both pre- and post-calibration period data. Results of the study indicate that the developed models are able to capture spatiotemporal characteristics of observed precipitation and temperature fields, such as inter-site and inter-variable correlation structure, and systematic regional variations present in observed sequences. A number of simulated weather statistics ranging from seasonal means to characteristics of temperature and precipitation extremes and some of the commonly used climate indices are also found to be in close agreement with those derived from observed data. This GLM-based modelling approach will be developed further for multisite statistical downscaling of Global Climate Model outputs to explore climate variability and change in this region of Canada.

This study assesses projected changes to drought characteristics in Alberta, Saskatchewan and Manitoba, the prairie provinces of Canada, using a multi-regional climate model (RCM) ensemble available through the North American Regional Climate Change Assessment Program. Simulations considered include those performed with six RCMs driven by National Center for Environmental Prediction reanalysis II for the 1981–2003 period and those driven by four Atmosphere–Ocean General Circulation Models for the 1970–1999 and 2041–2070 periods (i.e. eleven current and the same number of corresponding future period simulations). Drought characteristics are extracted using two drought indices, namely the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI). Regional frequency analysis is used to project changes to selected 20- and 50-year regional return levels of drought characteristics for fifteen homogeneous regions, covering the study area. In addition, multivariate analyses of drought characteristics, derived on the basis of 6-month SPI and SPEI values, are developed using the copula approach for each region. Analysis of multi-RCM ensemble-averaged projected changes to mean and selected return levels of drought characteristics show increases over the southern and south-western parts of the study area. Based on bi- and trivariate joint occurrence probabilities of drought characteristics, the southern regions along with the central regions are found highly drought vulnerable, followed by the southwestern and southeastern regions. Compared to the SPI-based analysis, the results based on SPEI suggest drier conditions over many regions in the future, indicating potential effects of rising temperatures on drought risks. These projections will be useful in the development of appropriate adaptation strategies for the water and agricultural sectors, which play an important role in the economy of the study area.

The effects of climate change on April–October short- and long-duration precipitation extremes over the Canadian Prairie Provinces were evaluated using a multi-Regional Climate Model (RCM) ensemble available through the North American Regional Climate Change Assessment Program. Simulations considered include those performed with six RCMs driven by the National Centre for Environmental Prediction (NCEP) reanalysis II product for the 1981–2000 period and those driven by four Atmosphere–Ocean General Circulation Models (AOGCMs) for the current 1971–2000 and future 2041–2070 periods (i.e. a total of 11 current-to-future period simulation pairs). A regional frequency analysis approach was used to develop 2-, 5-, 10-, 25-, and 50-year return values of precipitation extremes from NCEP and AOGCM-driven current and future period simulations that respectively were used to study the performance of RCMs and projected changes for selected return values at regional, grid-cell and local scales. Performance errors due to internal dynamics and physics of RCMs studied for the 1981–2000 period reveal considerable variation in the performance of the RCMs. However, the performance errors were found to be much smaller for RCM ensemble averages than for individual RCMs. Projected changes in future climate to selected regional return values of short-duration (e.g. 15- and 30-min) precipitation extremes and for longer return periods (e.g. 50-year) were found to be mostly larger than those to the longer duration (e.g. 24- and 48-h) extremes and short return periods (e.g. 2-year). Overall, projected changes in precipitation extremes were larger for southeastern regions followed by southern and northern regions and smaller for southwestern and western regions of the study area. The changes to return values were also found to be statistically significant for the majority of the RCM–AOGCM simulation pairs. These projections might be useful as a key input for the future planning of urban drainage infrastructure and development of strategic climate change adaptation measures.

The glaciated plains of the North American continent, also known as the \"prairies\", are a complex hydrological system characterized by hummocky terrain, where wetlands, containing seasonal or semi-permanent ponds, occupy the numerous topographic depressions. The prairie subsoil and many of its water bodies contain high salt concentrations, in particular sulfate salts, which are continuously cycled within the closed drainage basins. The period between 2000 and 2012 was characterized by an unusual degree of climatic variability, including severe floods and droughts, and this had a marked effect on the spatial distribution, water levels and chemical composition of wetland ponds. Understanding the geochemical and hydrological processes under changing environmental conditions is needed in order to better understand the risk and mitigate the impacts of future soil and water salinization. Here we explore salt dynamics in the prairies using field observations from St. Denis, Saskatchewan, taken mostly over the last 20 years. Measurements include meteorological data, soil moisture, soil salinity, groundwater levels and pond water volume, salinity, and chemical composition. The record includes periods of exceptional snow (1997, 2007) and periods of exception rainfall (2010, 2012), both of which resulted in unusually high pond water levels. Measurements indicated that severe pond salinization only occurred in response to extreme summer rainfall. It is hypothesized that since rainfall water infiltrates through the soil towards the depressions, while snowmelt water flows mainly as surface water over frozen soils, they have markedly different impacts on salt transport and pond salinization. Whilst this hypothesis is consistent with our conceptual understanding of the system, it needs to be tested further at a range of field sites in the prairies.

Observed data at most stations are often inadequate to obtain reliable estimates of many hydro-meteorological variables that not only define water availability across a region but also the vulnerability of social infrastructure to climatic extremes. To overcome this, data from neighboring sites with similar statistical characteristics are often pooled. The pooling process is based on partitioning of a larger region into smaller sub-regions with homogeneous features of interest. The established approaches rely heavily on statistics computed from observed precipitation data rather than the covariates that play a significant role in modulating the regional and local climate patterns at various temporal and spatial scales. In this study, a new approach for identifying homogeneous regions for regionalization of precipitation characteristics is proposed for the Canadian Prairie Provinces. This approach incorporates information about large-scale atmospheric covariates, teleconnection indices and geographical site attributes that impact spatial patterns of precipitation in order to delineate homogeneous precipitation regions through combined use of multivariate approaches—principal component analysis, canonical correlation analysis and fuzzy C-means clustering. Results of the analyses suggest that the study area can be partitioned into five homogeneous regions. These partitions are validated independently for homogeneity using statistics computed from monthly and seasonal precipitation totals, and seasonal extremes from a network of observation stations. Furthermore, based on the identified regions, precipitation magnitude-frequency relationships of warm and cold season single- and multi-day precipitation extremes, developed through regional frequency analysis, are mapped spatially. Such estimates are important for numerous water resources related activities.

Open ditch drainage has historically been a common land management practice in upland blanket peats, particularly in the UK. However, peatland drainage is now generally considered to have adverse effects on the upland environment, including increased peak flows. As a result, drain blocking has become a common management strategy in the UK over recent years, although there is only anecdotal evidence to suggest that this might decrease peak flows. The change in the hydrological regime associated with the drainage of blanket peat and the subsequent blocking of drains is poorly understood, therefore a new physics-based model has been developed that allows the exploration of the associated hydrological processes. A series of simulations is used to explore the response of intact, drained and blocked drain sites at field scales. While drainage is generally found to increase peak flows, the effect of drain blocking appears to be dependent on local conditions, sometimes decreasing and sometimes increasing peak flows. Based on insights from these simulations we identify steep smooth drains as those that would experience the greatest reduction in field-scale peak flows if blocked and recommend that future targeted field studies should be focused on examining surface runoff characteristics.