Global modeling of withdrawal, allocation and consumptive use of surface water and groundwater resources

Self Cite

211

157

Abstract. The diversity in hydrologic models has historically led to great controversy on the "correct" approach to processbased hydrologic modeling, with debates centered on the adequacy of process parameterizations, data limitations and uncertainty, and computational constraints on model analysis. In this paper, we revisit key modeling challenges on requirements to (1) define suitable model equations, (2) define adequate model parameters, and (3) cope with limitations in computing power. We outline the historical modeling challenges, provide examples of modeling advances that address these challenges, and define outstanding research needs. We illustrate how modeling advances have been made by groups using models of different type and complexity, and we argue for the need to more effectively use our diversity of modeling approaches in order to advance our collective quest for physically realistic hydrologic models.

Section: Parameter Estimation Solutionsmentioning

confidence: 99%

The evolution of process-based hydrologic models: historical challenges and the collective quest for physical realism

Clark

Bierkens

Samaniego

et al. 2017

Self Cite

211

157

“…This accounts for some 90 % of water consumption at the global scale , which is around 70 % of the total water withdrawals from surface and groundwater resources (Wisser et al, 2008;Gerten and Rost, 2010). Clearly supplying such a large water demand can severely disturb the "natural condition" by decreasing streamflow volume (e.g., Meybeck, 2003;Gaybullaev et al, 2012;Lai et al, 2014) and groundwater levels (e.g., Rodell et al, 2009;Gleeson et al, 2012;Wada et al, 2010Wada et al, , 2012Wada et al, , 2014Döll et al, 2014). Currently, surface water is the main supplier of global irrigative needs, accounting for 57 % of the total consumptive irrigation use at the global scale .…”

Section: Types Of Human Demand and Their Impacts On The Water Cyclementioning

confidence: 99%

On inclusion of water resource management in Earth system models – Part 1: Problem definition and representation of water demand

Nazemi

Wheater

2015

155

Abstract. 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 humanwater 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 landatmospheric 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. Background and scope Large-scale modeling -an introduction to land-surface and global hydrological modelsThe Earth system is an integrated system that unifies the physical processes at the Earth's surface. These processes include a wide range of feedbacks and interactions between and within the atmosphere, land and oceans and cover the global cycles of climate, water and carbon that sup...

“…This ensemble includes HTESSEL-CaMa (Balsamo et al, 2009), JULES Clark et al, 2011), LISFLOOD (van der Knijff et al, 2010), ORCHIDEE (Krinner et al, 2005;Ngo-Duc et al, 2007;d'Orgeval et al, 2008), SURFEX-TRIP (Alkama et al, 2010;Decharme et al, 2013), W3RA (van Dijk andWarren, 2010;van Dijk et al, 2014), WaterGAP3 (Flörke et al, 2013;Döll et al, 2009), PCR-GLOBWB (van Beek et al, 2011Wada et al, 2014), and SWBM (Orth et al, 2013). For consistency, we processed the model estimates in the same manner as our model simulations to directly compare modelled SWE and TWS to observations from GlobSnow and GRACE, respectively.…”

Section: Evaluation Of Model Performancementioning

confidence: 99%

Understanding terrestrial water storage variations in northern latitudes across scales

Trautmann

Koirala

Eicker

et al. 2018

Abstract. The GRACE satellites provide signals of total terrestrial water storage (TWS) variations over large spatial domains at seasonal to inter-annual timescales. While the GRACE data have been extensively and successfully used to assess spatio-temporal changes in TWS, little effort has been made to quantify the relative contributions of snowpacks, soil moisture, and other components to the integrated TWS signal across northern latitudes, which is essential to gain a better insight into the underlying hydrological processes. Therefore, this study aims to assess which storage component dominates the spatio-temporal patterns of TWS variations in the humid regions of northern mid-to high latitudes.To do so, we constrained a rather parsimonious hydrological model with multiple state-of-the-art Earth observation products including GRACE TWS anomalies, estimates of snow water equivalent, evapotranspiration fluxes, and gridded runoff estimates. The optimized model demonstrates good agreement with observed hydrological spatio-temporal patterns and was used to assess the relative contributions of solid (snowpack) versus liquid (soil moisture, retained water) storage components to total TWS variations. In particular, we analysed whether the same storage component dominates TWS variations at seasonal and inter-annual temporal scales, and whether the dominating component is consistent across small to large spatial scales.Consistent with previous studies, we show that snow dynamics control seasonal TWS variations across all spatial scales in the northern mid-to high latitudes. In contrast, we find that inter-annual variations of TWS are dominated by liquid water storages at all spatial scales. The relative contribution of snow to inter-annual TWS variations, though, increases when the spatial domain over which the storages are averaged becomes larger. This is due to a stronger spatial coherence of snow dynamics that are mainly driven by temperature, as opposed to spatially more heterogeneous liquid water anomalies, that cancel out when averaged over a larger spatial domain. The findings first highlight the effectiveness of our model-data fusion approach that jointly interprets multiple Earth observation data streams with a simple model. Secondly, they reveal that the determinants of TWS variations in snow-affected northern latitudes are scale-dependent. In particular, they seem to be not merely driven by snow variability, but rather are determined by liquid water storages on interannual timescales. We conclude that inferred driving mechanisms of TWS cannot simply be transferred from one scale to another, which is of particular relevance for understanding the short-and long-term variability of water resources.