Surface deformations observed by GPS and its relation to groundwater variations in France

<p>The Global Positioning System (GPS) measures surface displacements in response to time-varying terrestrial water mass variations. Components of surface water storage include water in lakes and reservoirs, snow, and soil moisture. Groundwater depletion or recharge will also contribute to the overall water storage. Understanding the nature of the observed GPS displacements related to the continental water variations is important to help identify which compartment in the total water storage controls the water changes in any particular region. In this study, we demonstrate the potential of GPS to observe the surface displacements induced by groundwater variations in France. In-situ groundwater observations from boreholes in France are used to be compared with GPS displacements. Groundwater data are processed to obtain the Equivalent Water Height (EWH) and used to forward model surface deformation. Displacements predicted using EWH variations from the WaterGAP Global Hydrology Model (WGHM) will also be compared to the GPS displacements.</p>

Introducing HydroPy, an updated global hydrology model rewritten in Python

<p>Global hydrological models (GHM) are a useful tool to investigate the water cycle, to evaluate its sensitivity towards systematic changes, e.g. human impacts, and to project future conditions in river catchments for varying scenarios. They have been successfully applied for decades and there is still room for improvement.</p><p>Recently, we revised the Max Planck Institute for Meteorology’s Hydrology model (MPI-HM), which is an established GHM that was used in multiple case studies and inter-comparison projects. While still performing well, its source code (mainly Fortran77) has become increasingly difficult to maintain, thus hampering the implementation of new processes. For this reason, the model was rewritten from scratch based on the MPI-HM process formulations. The new model is mainly written in Python, thereby taking advantage of the highly optimized numpy and xarray libraries, and, hence, is aptly renamed to HydroPy. Using the original formulations, we make sure to preserve or even improve the old model’s skill while the switch to Python allows for much easier debugging and interactive model development.</p><p>In our presentation, we will evaluate the performance of the new HydroPy model and demonstrate its skill to simulate river discharge. Furthermore, we compare HydroPy to its predecessor MPI-HM and discuss the reasons of differences between their results.</p>

The global water resources and use model WaterGAP v2.2d: model description and evaluation

WaterGAP is a global hydrological model that quantifies human use of groundwater and surface water as well as water flows and water storage and thus water resources on all land areas of the Earth. Since 1996, it has served to assess water resources and water stress both historically and in the future, in particular under climate change. It has improved our understanding of continental water storage variations, with a focus on overexploitation and depletion of water resources. In this paper, we describe the most recent model version WaterGAP 2.2d, including the water use models, the linking model that computes net abstractions from groundwater and surface water and the WaterGAP Global Hydrology Model (WGHM). Standard model output variables that are freely available at a data repository are explained. In addition, the most requested model outputs, total water storage anomalies, streamflow and water use, are evaluated against observation data. Finally, we show examples of assessments of the global freshwater system that can be achieved with WaterGAP 2.2d model output.

The global water resources and use model WaterGAP v2.2d: Model description and evaluation

WaterGAP is a global hydrological model that quantifies human use of groundwater and surface water as well as water flows and water storage and thus water resources on all land areas of the Earth. Since 1996, it has served to assess water resources and water stress both historically and in the future, in particular under climate change. It has improved our understanding of continental water storage variations, with a focus on overexploitation and depletion of water resources. In this paper, we describe the most recent model version WaterGAP 2.2d, including the water use models, the linking model that computes net abstractions from groundwater and surface water and the WaterGAP Global Hydrology Model WGHM. Standard model output variables that are freely available at a data repository are explained. In addition, the most requested model outputs, total water storage anomalies, streamflow and water use, are evaluated against observation data. Finally, we show examples of assessments of the global freshwater system that can be done with WaterGAP2.2d model output.

CHNS Modeling for Study and Management of Human–Water Interactions at Multiple Scales

This paper presents basic definitions and challenges/opportunities from different perspectives to study and control water cycle impacts on society and vice versa. The wider and increased interactions and their consequences such as global warming and climate change, and the role of complex institutional- and governance-related socioeconomic-environmental issues bring forth new challenges. Hydrology and integrated water resources management (IWRM from the viewpoint of an engineering planner) do not exclude in their scopes the study of the impact of changes in global hydrology from societal actions and their feedback effects on the local/global hydrology. However, it is useful to have unique emphasis through specialized fields such as hydrosociology (including the society in planning water projects, from the viewpoint of the humanities) and sociohydrology (recognizing the large-scale impacts society has on hydrology, from the viewpoint of science). Global hydrological models have been developed for large-scale hydrology with few parameters to calibrate at local scale, and integrated assessment models have been developed for multiple sectors including water. It is important not to do these studies with a silo mindset, as problems in water and society require highly interdisciplinary skills, but flexibility and acceptance of diverse views will progress these studies and their usefulness to society. To deal with complexities in water and society, systems modeling is likely the only practical approach and is the viewpoint of researchers using coupled human–natural systems (CHNS) models. The focus and the novelty in this paper is to clarify some of these challenges faced in CHNS modeling, such as spatiotemporal scale variations, scaling issues, institutional issues, and suggestions for appropriate mathematical tools for dealing with these issues.

The global freshwater availability and water use model WaterGAP 2.2d

<p>Freshwater availability is of vital importance for humans, freshwater biota and ecosystem functions. In the past decades, global hydrological models (GHMs) were developed to improve understanding of the global freshwater situation in a globalized word, by filling gaps in observational coverage and assessing scenarios of the future under consideration of different socioeconomic developments and climate change. The Water Global Assessment and Prognosis (WaterGAP) model was one of the first GHMs developed to evaluate freshwater resources and their use for both historical and future conditions. It consists of five water use models (for irrigation, domestic, cooling of thermal power plants, manufacturing, and livestock sectors) and the WaterGAP Global Hydrology Model (WGHM). Recently, the latest model version, WaterGAP 2.2d, was finalized, containing a number of enhancements and revisions such as a river storage-based flow velocity approach, improvements in modelling groundwater recharge in dry environments and integration of historical development of irrigated areas.</p><p>This presentation provides an overview about the WaterGAP 2.2d scheme and features, assesses global freshwater resources (runoff and streamflow) and water balance components, and provides insights to evaluation results against observed streamflow, GRACE total water storage and the AQUASTAT database.</p>

Closing the Combined Water and Energy Balance of Global Watersheds Based on Satellite Data

<p>Satellite-based remote sensing offers potential pathways for accurately closing the water and energy balance of watersheds from observations, a fundamental challenge in hydrology. However, previous attempts based on purely satellite-based estimates have been hindered by large data uncertainties and lack of estimates for key components, such as runoff. Here, we use a novel approach based on the Budyko hypothesis to quantify both the degree of closure and its uncertainties in watershed-scale water and energy balance closure arising from an ensemble of 56 global satellite datasets for precipitation (P), terrestrial evaporation (ET), and net radiation (Rn). We use 7 quasi-global precipitation datasets which include CHIRPS, CMORPH, PERSIANN, PERSIANN-CCS, PERSIANN-CDR, TRMM 3B42RT, TRMM 3B43. For ET, we use 8 datasets - AVHRR, SSEBOp, MOD16A3, GLEAM v3.3a, GLEAM v3.3b, CSIRO-PML, BESS, and FluxCom. For Rn, we use the CERES dataset. We find large spatial variability along with aridity, elevation and other gradients. Results show that errors in water and energy balance closure can be attributed primarily to uncertainties in terrestrial evaporation data. These findings have implications for improving the understanding of global hydrology and regional water management and can guide the development of satellite remote sensing datasets and earth system models. In addition, we rank the P and ET datasets that perform the best in closing the combined water and energy balance of global catchments. For P, we see that gauge-calibrated datasets such as PERSIANN-CDR, TRMM 3B43 perform the best. In terms of ET, we see that BESS performs the best in the northern boreal forests and GLEAM performs the best in drylands.</p>

A global hydrology research agenda fit for the 2030s

Global assessments show profound impacts of human activities on freshwater systems that, without action, are expected to reach crisis point in the 2030s. By then, the capacity of natural systems to meet rising demands for water, food, and energy could be hampered by emerging signals of anthropogenic climate change. The hydrological community has always been solution-orientated, but our generation faces perhaps the greatest array of water challenges in human history. Ambitious programmes of research are needed to fill critical data, knowledge, and skills gaps. Priorities include filling data sparse places, predicting peak water, understanding the physical drivers of mega droughts, evaluating hyper-resolution models, managing compound hazards, and adjusting water infrastructure designs to climate change. Despite the opportunities presented by big data, we must not lose sight of the deep uncertainties affecting both our raw input data and hydrological models, nor neglect the human dimensions of water system change. Community-scale projects and international research partnerships are needed to connect new hydrological knowledge with most vulnerable communities as well as to achieve more integrated and grounded solutions. With these elements in place, we will be better equipped to meet the global hydrological challenges of the 2030s and beyond.