Lumped hydrological model for reasonable, long- term predictions of groundwater storage and depletion

2020 ◽  
Author(s):  
Fahad Ejaz ◽  
Thomas Wöhling ◽  
Nowak Wolfgang
Keyword(s):  
River Discharge ◽  
Groundwater Depletion ◽  
Hydrological Models ◽  
Proof Of Concept ◽  
Groundwater Storage ◽  
Discharge Data ◽  
Aquifer Storage ◽  
Lumped Models ◽  
The Impact

<p>Excessive groundwater pumping due to immense agricultural, industrial and municipal demand poses a major threat of aquifer depletion in many areas around the world. The impact of climate change on the global hydrological cycle has further exacerbated the situation. Accurate and reliable prediction of long-term aquifer balance terms is a key prerequisite to manage groundwater sustainably. To deal with uncertainties of such predictions, lumped (conceptual) hydrological models could help with their computational speed that allows for Monte-Carlo simulation. Compared to more complex models, lumped models are fast, lean on data requirement and capable to quantify uncertainty. However, lumped models are mainly designed to simulate river discharge only, not aquifer storage. Even the standard practice for calibrating lumped hydrological models only includes river discharge, as data on groundwater storage is not directly accessible. In this study, we hypothesize that we can extend the HBV model by additional water budget and groundwater storage terms, and calibrated it on both groundwater storage data and discharge data. Then, we test whether its predictions of groundwater storage levels withstand validation tests. To avoid problems with unavailability of data for calibration and validation in a first proof of concept, we build a virtual reality with a MODFLOW-based model, driven with synthetic weather data over a period of more than 50 years. For rigorous testing, we cast calibration into the framework of Bayesian parameter inference, and validate with metrics that assess the appropriateness of the Bayesian prediction distribution of groundwater storage. We test our idea in the Wairau Plain aquifer, New Zealand. Poor understanding of recharge mechanisms and hence declining groundwater levels are the major hindrance for sustainable groundwater management in our study area. We pay specific attention to river-groundwater exchange processes, to the forecast of aquifer storage dynamics, and to groundwater depletion in a hypothetical, persistent draught. The purpose is to provide a proof of concept whether lumped models can be adapted and made suitable to predict declining groundwater resources up to full depletion, as an uncertainty-aware decision support system for sustainable management.</p><p> </p>

Remote sensing of groundwater storage change - past, present and future

2020 ◽  
Author(s):  
Susanna Werth ◽  
Manoochehr Shirzaei ◽  
Grace Carlson ◽  
Chandrakanta Ojha
Keyword(s):  
High Resolution ◽  
Surface Deformation ◽  
Central Valley ◽  
Groundwater Depletion ◽  
High Temporal Resolution ◽  
Hydrological Models ◽  
Groundwater Storage ◽  
Aquifer System ◽  
Vertical Land Motion ◽  
The Impact

<p>Groundwater remains one of the least comprehensively monitored storage components in the hydrological cycle, because it's flow and storage processes are strongly linked to geology of the underground and because direct observations from well sites provide only point observations of complex and partly deep aquifer systems.</p><p>In recent years, geodetic methods have become increasingly available to complement ground-based observations and to expand investigations of the impact of climate extremes or human water use on groundwater storage variability. Satellite gravimetry from the Gravity Recovery And Climate Experiment (GRACE/FO) has been shown to be sensitive to groundwater depletion at large spatial scales (> 300km) and relatively high temporal resolution (monthly). These data provide a valuable boundary condition for regional studies, and they have been applied widely to improve parameter and structure of hydrological models.</p><p>Moreover, changes in groundwater stocks cause surface deformation associated with regional elastic loading of the Earth’s crust and localized poroelastic compaction of the aquifer skeleton, which are detectable by GPS and InSAR. The loading signal is typically much smaller than the land subsidence due to poroelastic compaction and thus masks out the loading signal adjacent to the aquifer system. However, the poroelastic signal can be used to estimate groundwater volume change in confined aquifer units and provides insight into the mechanical properties of the aquifer system. Also, the deformation sensors provide spatial resolutions of tens of meters (e.g., InSAR) to several kilometers (e.g., GPS) that can be used to solve for the volume of fluid removed from the aquifer system.</p><p>In this presentation, we demonstrate and discuss the applicability of poroelastic modeling, by applying GPS and InSAR based observations of vertical land motion, to quantify groundwater storage changes. Using the Central Valley in California as an example, we will show when this approach is applicable and when it is not, depending on the type of aquifer and observed deformation compared to water level changes. Using a 1-D poroelastic calculation based on deformation data, we find a groundwater loss of 21.3±7.2 km<sup>3</sup> for the entire Central Valley during 2007-2010 and of 29.3±8.7 km<sup>3</sup> for the San Joaquin Valley during 2012-2015. These loss estimates during drought are consistent with that of GRACE-based estimates considering uncertainty ranges.</p><p>Finally, we will discuss the increased availability of high-resolution radar data from Sentinel 1A/B as well as the upcoming radar mission NASA-ISRO SAR Mission (NISAR), to be launched in 2022, and how this will allow for high-resolution monitoring of vertical land motion and with that of compaction in confined aquifers around the world. The availability of these datasets increases the capability of geodetic methods for groundwater monitoring at higher spatial resolution than GRACE data, hence, providing the potential to apply these datasets to further improve parameterization and formulation of groundwater routines in regional to large-scale hydrological models.</p>

scholarly journals Large-scale sensitivities of groundwater and surface water to groundwater withdrawal

2020 ◽  
Author(s):  
Marc F. P. Bierkens ◽  
Edwin H. Sutanudjaja ◽  
Niko Wanders
Keyword(s):  
Surface Water ◽  
Analytical Framework ◽  
Groundwater Withdrawal ◽  
Irrigated Agriculture ◽  
Groundwater Depletion ◽  
Steady Increase ◽  
Groundwater Storage ◽  
Aquifer Storage ◽  
The Impact ◽  
Streamflow Depletion

Abstract. Increasing population, economic growth and changes in diet have dramatically increased the demand for food and water over the last decades. To meet increasing demands, irrigated agriculture has expanded into semi-arid areas with limited precipitation and surface water availability. This has greatly intensified the dependence of irrigated crops on groundwater withdrawal and caused a steady increase of non-renewable groundwater use, i.e. groundwater taken out of aquifer storage that will not be replenished in human time scales. One of the effects of groundwater pumping is the reduction in streamflow through capture of groundwater recharge, with detrimental effects on aquatic ecosystems. The degree to which groundwater withdrawal affects streamflow or groundwater storage depends on the nature of the groundwater-surface water interaction (GWSI). So far, analytical solutions that have been derived to calculate the impact of groundwater on streamflow depletion involve single wells and streams and do not allow the GWSI to shift from connected to disconnected, i.e. from a situation with two-way interaction to one with a one-way interaction between groundwater and surface water. Including this shift and also analyse the effects of many wells, requires numerical groundwater models that are expensive to setup. Here, we introduce a simple conceptual analytical framework that allows to estimate to what extent groundwater withdrawal affects groundwater heads and streamflow. It allows for a shift in GWSI, calculates at which critical withdrawal rate such a shift is expected and when it is likely to occur after withdrawal commences. It also provides estimates of streamflow depletion and which part of the groundwater withdrawal comes out of groundwater storage and which parts from a reduction in streamflow. After a local sensitivity analysis, the framework is used to provide global maps of critical withdrawal rates and timing, the areas where current withdrawal exceeds critical limits, and maps of groundwater depletion and streamflow depletion rates that result from groundwater withdrawal. The resulting global depletion rates are similar to those obtained from global hydrological models and satellites. The analytical framework is particularly useful for performing first-order sensitivity studies and for supporting hydroeconomic models that require simple relationships between groundwater withdrawal rates and the evolution of pumping costs and environmental externalities.

scholarly journals Anthropogenic drought dominates groundwater depletion in Iran

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Samaneh Ashraf ◽  
Ali Nazemi ◽  
Amir AghaKouchak
Keyword(s):  
Economic Security ◽  
Groundwater Depletion ◽  
Groundwater Storage ◽  
Rates Of Change ◽  
Level Data ◽  
Basin Scale ◽  
Storage Basin ◽  
Groundwater Reserves ◽  
The Impact ◽  
Hydrological Droughts

AbstractUsing publicly-available average monthly groundwater level data in 478 sub-basins and 30 basins in Iran, we quantify country-wide groundwater depletion in Iran. Natural and anthropogenic elements affecting the dynamics of groundwater storage are taken into account and quantified during the period of 2002–2015. We estimate that the total groundwater depletion in Iran to be ~ 74 km3 during this period with highly localized and variable rates of change at basin and sub-basin scales. The impact of depletion in Iran’s groundwater reserves is already manifested by extreme overdrafts in ~ 77% of Iran’s land area, a growing soil salinity across the entire country, and increasing frequency and extent of land subsidence in Iran’s planes. While meteorological/hydrological droughts act as triggers and intensify the rate of depletion in country-wide groundwater storage, basin-scale groundwater depletions in Iran are mainly caused by extensive human water withdrawals. We warn that continuation of unsustainable groundwater management in Iran can lead to potentially irreversible impacts on land and environment, threatening country’s water, food, socio-economic security.

Improving SAR Altimeter processing over the coastal zone and inland waters - the ESA HYDROCOASTAL project

2021 ◽  
Author(s):  
David Cotton ◽  
Keyword(s):  
Coastal Zone ◽  
Test Data ◽  
River Discharge ◽  
Altimeter Data ◽  
Inland Waters ◽  
Data Sets ◽  
Data Set ◽  
Discharge Data ◽  
Processing Algorithms ◽  
The Impact

<p><strong>Introduction</strong></p><p>HYDROCOASTAL is a two year project funded by ESA, with the objective to maximise exploitation of SAR and SARin altimeter measurements in the coastal zone and inland waters, by evaluating and implementing new approaches to process SAR and SARin data from CryoSat-2, and SAR altimeter data from Sentinel-3A and Sentinel-3B. Optical data from Sentinel-2 MSI and Sentinel-3 OLCI instruments will also be used in generating River Discharge products.</p><p>New SAR and SARin processing algorithms for the coastal zone and inland waters will be developed and implemented and evaluated through an initial Test Data Set for selected regions. From the results of this evaluation a processing scheme will be implemented to generate global coastal zone and river discharge data sets.</p><p>A series of case studies will assess these products in terms of their scientific impacts.</p><p>All the produced data sets will be available on request to external researchers, and full descriptions of the processing algorithms will be provided</p><p> </p><p><strong>Objectives</strong></p><p>The scientific objectives of HYDROCOASTAL are to enhance our understanding  of interactions between the inland water and coastal zone, between the coastal zone and the open ocean, and the small scale processes that govern these interactions. Also the project aims to improve our capability to characterize the variation at different time scales of inland water storage, exchanges with the ocean and the impact on regional sea-level changes</p><p>The technical objectives are to develop and evaluate  new SAR  and SARin altimetry processing techniques in support of the scientific objectives, including stack processing, and filtering, and retracking. Also an improved Wet Troposphere Correction will be developed and evaluated.</p><p><strong>Project  Outline</strong></p><p>There are four tasks to the project</p><ul><li>Scientific Review and Requirements Consolidation: Review the current state of the art in SAR and SARin altimeter data processing as applied to the coastal zone and to inland waters</li> <li>Implementation and Validation: New processing algorithms with be implemented to generate a Test Data sets, which will be validated against models, in-situ data, and other satellite data sets. Selected algorithms will then be used to generate global coastal zone and river discharge data sets</li> <li>Impacts Assessment: The impact of these global products will be assess in a series of Case Studies</li> <li>Outreach and Roadmap: Outreach material will be prepared and distributed to engage with the wider scientific community and provide recommendations for development of future missions and future research.</li> </ul><p> </p><p><strong>Presentation</strong></p><p>The presentation will provide an overview to the project, present the different SAR altimeter processing algorithms that are being evaluated in the first phase of the project, and early results from the evaluation of the initial test data set.</p><p> </p>

scholarly journals Impact of climate change on freshwater ecosystems: a global-scale analysis of ecologically relevant river flow alterations

2010 ◽  
Vol 14 (5) ◽  
pp. 783-799 ◽  
Author(s):  
P. Döll ◽  
J. Zhang
Keyword(s):  
Climate Change ◽  
River Discharge ◽  
River Flow ◽  
Flow Regimes ◽  
Freshwater Ecosystems ◽  
Land Area ◽  
Impact Of Climate Change ◽  
Water Withdrawals ◽  
The Impact

Abstract. River flow regimes, including long-term average flows, seasonality, low flows, high flows and other types of flow variability, play an important role for freshwater ecosystems. Thus, climate change affects freshwater ecosystems not only by increased temperatures but also by altered river flow regimes. However, with one exception, transferable quantitative relations between flow alterations and ecological responses have not yet been derived. While discharge decreases are generally considered to be detrimental for ecosystems, the effect of future discharge increases is unclear. As a first step towards a global-scale analysis of climate change impacts on freshwater ecosystems, we quantified the impact of climate change on five ecologically relevant river flow indicators, using the global water model WaterGAP 2.1g to simulate monthly time series of river discharge with a spatial resolution of 0.5 degrees. Four climate change scenarios based on two global climate models and two greenhouse gas emissions scenarios were evaluated. We compared the impact of climate change by the 2050s to the impact of water withdrawals and dams on natural flow regimes that had occurred by 2002. Climate change was computed to alter seasonal flow regimes significantly (i.e. by more than 10%) on 90% of the global land area (excluding Greenland and Antarctica), as compared to only one quarter of the land area that had suffered from significant seasonal flow regime alterations due to dams and water withdrawals. Due to climate change, the timing of the maximum mean monthly river discharge will be shifted by at least one month on one third on the global land area, more often towards earlier months (mainly due to earlier snowmelt). Dams and withdrawals had caused comparable shifts on less than 5% of the land area only. Long-term average annual river discharge is predicted to significantly increase on one half of the land area, and to significantly decrease on one quarter. Dams and withdrawals had led to significant decreases on one sixth of the land area, and nowhere to increases. Thus, by the 2050s, climate change may have impacted ecologically relevant river flow characteristics more strongly than dams and water withdrawals have up to now. The only exception refers to the decrease of the statistical low flow Q90, with significant decreases both by past water withdrawals and future climate change on one quarter of the land area. However, dam impacts are likely underestimated by our study. Considering long-term average river discharge, only a few regions, including Spain, Italy, Iraq, Southern India, Western China, the Australian Murray Darling Basin and the High Plains Aquifer in the USA, all of them with extensive irrigation, are expected to be less affected by climate change than by past anthropogenic flow alterations. In some of these regions, climate change will exacerbate the discharge reductions, while in others climate change provides opportunities for reducing past reductions. Emissions scenario B2 leads to only slightly reduced alterations of river flow regimes as compared to scenario A2 even though emissions are much smaller. The differences in alterations resulting from the two applied climate models are larger than those resulting from the two emissions scenarios. Based on general knowledge about ecosystem responses to flow alterations and data related to flow alterations by dams and water withdrawals, we expect that the computed climate change induced river flow alterations will impact freshwater ecosystems more strongly than past anthropogenic alterations.

scholarly journals Value of river discharge data for global-scale hydrological modeling

2007 ◽  
Vol 4 (6) ◽  
pp. 4125-4173 ◽  
Author(s):  
M. Hunger ◽  
P. Döll
Keyword(s):  
Correction Factor ◽  
River Discharge ◽  
Hydrological Modeling ◽  
Model Performance ◽  
Flow Characteristics ◽  
Global Scale ◽  
The Other ◽  
Station Correction ◽  
Discharge Data

Abstract. This paper investigates the value of observed river discharge data for global-scale hydrological modeling of a number of flow characteristics that are required for assessing water resources, flood risk and habitat alteration of aqueous ecosystems. An improved version of WGHM (WaterGAP Global Hydrology Model) was tuned in a way that simulated and observed long-term average river discharges at each station become equal, using either the 724-station dataset (V1) against which former model versions were tuned or a new dataset (V2) of 1235 stations and often longer time series. WGHM is tuned by adjusting one model parameter (γ) that affects runoff generation from land areas, and, where necessary, by applying one or two correction factors, which correct the total runoff in a sub-basin (areal correction factor) or the discharge at the station (station correction factor). The study results are as follows. (1) Comparing V2 to V1, the global land area covered by tuning basins increases by 5%, while the area where the model can be tuned by only adjusting γ increases by 8% (546 vs. 384 stations). However, the area where a station correction factor (and not only an areal correction factor) has to be applied more than doubles (389 vs. 93 basins), which is a strong drawback as use of a station correction factor makes discharge discontinuous at the gauge and inconsistent with runoff in the basin. (2) The value of additional discharge information for representing the spatial distribution of long-term average discharge (and thus renewable water resources) with WGHM is high, particularly for river basins outside of the V1 tuning area and for basins where the average sub-basin area has decreased by at least 50% in V2 as compared to V1. For these basins, simulated long-term average discharge would differ from the observed one by a factor of, on average, 1.8 and 1.3, respectively, if the additional discharge information were not used for tuning. The value tends to be higher in semi-arid and snow-dominated regions where hydrological models are less reliable than in humid areas. The deviation of the other simulated flow characteristics (e.g. low flow, inter-annual variability and seasonality) from the observed values also decreases significantly, but this is mainly due to the better representation of average discharge but not of variability. (3) The optimal sub-basin size for tuning depends on the modeling purpose. On the one hand, small basins between 9000 and 20 000 km2 show a much stronger improvement in model performance due to tuning than the larger basins, which is related to the lower model performance (with and without tuning), with basins over 60 000 km2 performing best. On the other hand, tuning of small basins decreases model consistency, as almost half of them require a station correction factor.

scholarly journals Long-term, non-anthropogenic groundwater storage changes simulated by three global-scale hydrological models

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Bailing Li ◽  
Matthew Rodell ◽  
Justin Sheffield ◽  
Eric Wood ◽  
Edwin Sutanudjaja
Keyword(s):  
Global Scale ◽  
Hydrological Models ◽  
Groundwater Storage

scholarly journals Brief communication: The role of using precipitation or river discharge data when assessing global coastal compound flooding

2020 ◽  
Vol 20 (6) ◽  
pp. 1765-1782 ◽  
Author(s):  
Emanuele Bevacqua ◽  
Michalis I. Vousdoukas ◽  
Theodore G. Shepherd ◽  
Mathieu Vrac
Keyword(s):  
River Discharge ◽  
Large Scale ◽  
Storm Surges ◽  
High Water ◽  
Small Rivers ◽  
Water Runoff ◽  
Discharge Data ◽  
Physical Mechanisms ◽  
Catchment Size ◽  
The Impact

Abstract. Interacting storm surges and high water runoff can cause compound flooding (CF) in low-lying coasts and river estuaries. The large-scale CF hazard has been typically studied using proxies such as the concurrence of storm surge extremes either with precipitation or with river discharge extremes. Here the impact of the choice of such proxies is addressed employing state-of-the-art global datasets. Although they are proxies of diverse physical mechanisms, we find that the two approaches show similar CF spatial patterns. On average, deviations are smaller in regions where assessing the actual CF is more relevant, i.e. where the CF potential is high. Differences between the two assessments increase with the catchment size, and our findings indicate that CF in long rivers (catchment ≳5–10×103 km2) should be analysed using river discharge data. The precipitation-based assessment allows for considering local-rainfall-driven CF and CF in small rivers not resolved by large-scale datasets.

QPE adjustment using river discharge

2020 ◽  
Author(s):  
Fiachra O'Loughlin ◽  
Michael Bruen
Keyword(s):  
River Discharge ◽  
Substantial Improvement ◽  
Spatial Density ◽  
Accurate Estimation ◽  
Hydrological Models ◽  
Radar Rainfall ◽  
Additional Advantage ◽  
Discharge Data ◽  
Adjustment Method ◽  
Precipitation Estimates

<p>With the expected increase in flooding due to climate change, accurate estimation of precipitation and the resulting modelled hydrographs are an essential requirement for reliable flood forecasts. At present, most radar rainfall adjustment methods require raingauge data to increase the accuracy of the precipitation estimates. One disadvantage is that raingauges only measure precipitation at a given point and usually there are a relatively small number of these points in a typical catchment (and some smaller catchments may not have any). River discharge from the catchment integrates the influence of catchment-wide precipitation and can often be more accurately measured than the areal rainfall, especially in areas with a sparse raingauge network. Here, we present a non-raingauge radar adjustment method that utilises discharge data only to adjust radar precipitation estimates for input to hydrological models. This method allows a hydrological model to adjust its treatment of precipitation input, through an additional model parameter, by comparing the observed and modelled hydrographs. An additional advantage of this method is that it can be also applied to adjust any form of precipitation input (e.g. radar, raingauge or satellite)  to produce more accurate hydrograph estimates. This proposed method is comparable to a traditional radar raingauge adjustment method over a number of catchments and hydrological models, for both peak flows and for the entire hydrograph. Additionally, this method allows for the adjusted of catchment averaged raingauge precipitation measurements to correct for any possibly errors due to using point data i.e. spatial density or representative issues. This results in a substantial improvement in discharge estimation compared to the un-adjusted raingauge measurements.</p>

The impact of glaciers on the long-term hydrology of a high-elevation Andean catchment

2020 ◽  
Author(s):  
Michael McCarthy ◽  
Flavia Burger ◽  
Alvaro Ayala ◽  
Stefan Fugger ◽  
Thomas E Shaw ◽  
...  
Keyword(s):  
Climatic Variability ◽  
High Elevation ◽  
Hydrological Models ◽  
Glacier Surface ◽  
Surface Mass ◽  
Extensive Field ◽  
Steep Decline ◽  
Spatio Temporal ◽  
The Impact

<p>The Andean cryosphere is a vital water resource for downstream populations. In recent years, it has been in steep decline as a whole, but shown strong spatio-temporal variability due to climatic events such as the current mega drought in central Chile. Glacio-hydrological models are necessary to understand and predict changes in water availability as a result of changes to the cryosphere. However, due to a lack of data for initialisation, forcing, calibration and validation, they are rarely used, especially in the Andes, for periods longer than a few years or decades. While useful insights can be gained from short-term modelling, there is a gap in our understanding of how glaciers impact hydrology on longer timescales, which may prevent local communities and governments from achieving effective planning and mitigation. Here we use the glacio-hydrological model TOPKAPI-ETH – initialised, forced, calibrated and validated using unique and extensive field and remote sensing datasets – to investigate glacier contributions to the streamflow of the high-elevation Rio Yeso catchment, Chile, over the past 50 years. We focus in particular on: 1) fluctuations in glacier surface mass balance and runoff and associated climatic variability; 2) if peak water has already occurred and when; 3) the effect of supraglacial debris cover on seasonal and long-term hydrographs. We offer insights into some of the challenges of running glacio-hydrological models on longer timescales and discuss the implications of our findings in the context of a shrinking Andean cryosphere.</p>

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