scholarly journals Climate elasticity of evapotranspiration shifts the water balance of Mediterranean climates during multi-year droughts

2020 ◽  
Vol 24 (9) ◽  
pp. 4317-4337
Author(s):  
Francesco Avanzi ◽  
Joseph Rungee ◽  
Tessa Maurer ◽  
Roger Bales ◽  
Qin Ma ◽  
...  
Keyword(s):  
Water Balance ◽  
Precipitation Amount ◽  
Hydrologic Model ◽  
Data Driven ◽  
Subsurface Water ◽  
Drought Period ◽  
Climate Elasticity ◽  
Predictive Skill ◽  
General Improvement ◽  
Time Required

Abstract. Multi-year droughts in Mediterranean climates may shift the water balance, that is, the partitioning rule of precipitation across runoff, evapotranspiration, and sub-surface storage. Mechanisms causing these shifts remain largely unknown and are not well represented in hydrologic models. Focusing on measurements from the headwaters of California's Feather River, we found that also in these mixed rain–snow Mediterranean basins a lower fraction of precipitation was partitioned to runoff during multi-year droughts compared to non-drought years. This shift in the precipitation–runoff relationship was larger in the surface-runoff-dominated than subsurface-flow-dominated headwaters (−39 % vs. −18 % decline of runoff, respectively, for a representative precipitation amount). The predictive skill of the Precipitation Runoff Modeling System (PRMS) hydrologic model in these basins decreased during droughts, with evapotranspiration (ET) being the only water-balance component besides runoff for which the drop in predictive skill during drought vs. non-drought years was statistically significant. In particular, the model underestimated the response time required by ET to adjust to interannual climate variability, which we define as climate elasticity of ET. Differences between simulated and data-driven estimates of ET were well correlated with accompanying data-driven estimates of changes in sub-surface storage (ΔS, r=0.78). This correlation points to shifts in precipitation–runoff relationships being evidence of a hysteretic response of the water budget to climate elasticity of ET during and after multi-year droughts. This hysteresis is caused by carryover storage offsetting precipitation deficit during the initial drought period, followed by vegetation mortality when storage is depleted and subsequent post-drought vegetation expansion. Our results point to a general improvement in hydrologic predictions across drought and recovery cycles by including the climate elasticity of ET and better accounting for actual subsurface water storage in not only soil, but also deeper regolith that stores water accessible to roots. This can be done by explicitly parametrizing carryover storage and feedback mechanisms capturing vegetation response to atmospheric demand for moisture.

scholarly journals Evapotranspiration feedbacks shift annual precipitation-runoff relationships during multi-year droughts in a Mediterranean mixed rain-snow climate

2019 ◽  
Author(s):  
Francesco Avanzi ◽  
Joseph Rungee ◽  
Tessa Maurer ◽  
Roger Bales ◽  
Qin Ma ◽  
...  
Keyword(s):  
Water Balance ◽  
Predictive Accuracy ◽  
Precipitation Amount ◽  
Hydrologic Model ◽  
Annual Precipitation ◽  
Systematic Bias ◽  
Climate Elasticity ◽  
Water Supply Management ◽  
Warming Climate ◽  
Modeling Errors

Abstract. Focusing on the headwaters of the California's Feather River, we investigated how multi-year droughts affect the water balance of Mediterranean mixed rain-snow catchments. Droughts in these catchments saw a lower fraction of precipitation allocated to runoff compared to non-drought years. This shift in precipitation-runoff relationship was larger in a surface-runoff-dominated than in a subsurface-flow-dominated catchment – 39 % and 18 % less runoff, respectively, for a representative precipitation amount. The performance of the PRMS hydrologic model in these catchments decreased during droughts, particularly those causing larger shifts in the annual precipitation-runoff relationship. Evapotranspiration (ET) was the only water-balance component for which predictive accuracy during drought vs. non-drought years was consistently different. Besides a systematic bias during all years, the model tended to relatively overestimate drought ET and to underestimate non-drought ET. Modeling errors for ET during droughts were somewhat correlated with maximum and minimum annual temperature as well as changes in sub-surface storage (r = −0.45, −0.57, and 0.23, respectively). These correlations point to the interannual response of ET to climate, or climate elasticity of ET, as the likely driver of the observed shifts in precipitation-runoff relationship during droughts in Mediterranean mixed rain-snow regions; underestimation of this response caused increased modeling inaccuracy during droughts. Improved predictions of interannual variability of ET are necessary to support water-supply management in a warming climate and could be achieved by explicitly parametrizing feedback mechanisms across atmospheric demand for moisture, ET, and multi-year carryover of subsurface storage.

Subsurface mechanisms control hydrologic response to a multi-year drought in a mountainous watershed with a Mediterranean climate

2021 ◽  
Author(s):  
Adam Schreiner-McGraw ◽  
Hoori Ajami
Keyword(s):  
Hydrologic Model ◽  
Subsurface Water ◽  
Groundwater Depletion ◽  
Hydrologic Response ◽  
Primary Input ◽  
Groundwater System ◽  
Drought Period ◽  
Mountainous Watershed ◽  
The Future ◽  
The Impact

<p>Mountain watersheds often act as water towers that supply water to large human populations in valley aquifers. Therefore, their susceptibility and resilience to droughts are of outsize importance particularly, as global climate change projections suggest more frequent droughts in the future. Previous studies have examined the impact of climate warming on mountain hydrology, but they have not explicitly linked impacts of multi-year droughts to subsurface water storage. In this study, we use the 2012-2015 California drought to examine the mechanisms via which subsurface flow paths and storage affect the hydrologic response to drought in the Kaweah River watershed in the Sierra Nevada mountains. We build and test an integrated hydrologic model using the coupled land surface-groundwater model ParFlow.CLM. The model is able to simulate the observed hydrology with a high degree of accuracy. Results reveal that mountain aquifer recharge sourced from snowmelt (<em>MAR</em><sub><em>snow</em></sub>) is the primary input to the groundwater system, and much of the simulated streamflow. We find that increases in air temperature and decreases in precipitation during the drought reduces snow water equivalent (<em>SWE</em>), and causes a 73% reduction in <em>MAR</em><sub><em>snow</em></sub> compared to the pre-drought period. Reduction in <em>MAR</em><sub><em>snow</em></sub> initially results in subsurface storage losses along the ridgelines and areas of low topographic convergence. Topography induced draining of the regolith storage causes groundwater depletion and provides supplemental water to maintain streamflow and riparian evapotranspiration (<em>ET</em>). As the drought develops, drying of the subsurface alters lateral connectivity of the shallow groundwater system, and reduces streamflow and riparian <em>ET</em>. We apply machine learning models to examine the spatial patterns in groundwater storage depletion and recovery. These models reveal that topography induced draining and filling of subsurface storage in response to drought and precipitation recovery, respectively, is the key control on the streamflow response in this mountainous watershed. Warmer conditions and more frequent droughts that reduce <em>SWE</em> in the future are likely to amplify this cycle.</p>

scholarly journals Development of an Optimal Start Control Strategy for a Variable Refrigerant Flow (VRF) System

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 271
Author(s):  
Yusung Lee ◽  
Woohyun Kim
Keyword(s):  
Optimal Control ◽  
Control Strategy ◽  
Predictive Performance ◽  
Building Energy ◽  
Data Driven ◽  
Energy Usage ◽  
Optimal Control Strategy ◽  
Save Energy ◽  
Time Required ◽  
Optimal Control Methods

In this study, an optimal control strategy for the variable refrigerant flow (VRF) system is developed using a data-driven model and on-site data to save the building energy. Three data-based models are developed to improve the on-site applicability. The presented models are used to determine the length of time required to bring each zone from its current temperature to the set point. The existing data are used to evaluate and validated the predictive performance of three data-based models. Experiments are conducted using three outdoor units and eight indoor units on site. The experimental test is performed to validate the performance of proposed optimal control by comparing between conventional and optimal control methods. Then, the ability to save energy wasted for maintaining temperature after temperature reaches the set points is evaluated through the comparison of energy usage. Given these results, 30.5% of energy is saved on average for each outdoor unit and the proposed optimal control strategy makes the zones comfortable.

On the benefit of the Canadian Small Lake Model to better represent the impact of small natural lakes on GEM-Hydro streamflow simulations.

2021 ◽  
Author(s):  
Etienne Gaborit ◽  
Murray MacKay ◽  
Camille Garnaud ◽  
Vincent Fortin
Keyword(s):  
Water Balance ◽  
Grid Point ◽  
Hydrologic Model ◽  
Surface Fluxes ◽  
Dew Point ◽  
Multiple Model ◽  
Small Lake ◽  
Surface Component ◽  
Lake Model ◽  
The Impact

<p>This study aims at assessing the impact of a new lake model on streamflow simulations performed with the GEM-Hydro hydrologic model developed at ECCC. GEM-Hydro is at the heart of the National Surface and River Prediction System (NSRPS) which ECCC uses to forecast river flows over most of Canada. The GEM-Hydro model mainly consists of the GEM-Surf component to represent surface processes, and of the Watroute model to represent river and lake routing, in order to perform streamflow simulations and forecasts. The surface component of GEM-Hydro can simulate 5 different types of surfaces.  Currently, the water tile consists of a very simple algorithm which, in terms of water balance, consists of producing runoff fluxes simply equal to precipitation minus evaporation. This runoff over water surfaces is then provided as input, along with runoff and drainage generated over other surface tiles, to the Watroute model. The Watroute version used in GEM-Hydro currently only represents major lakes (area greater than 100km<sup>2</sup>) along the river networks, and does not represent the impact that small lakes can have on streamflow, which mainly consists in slowing down runoff before it reaches the main streams of the network.</p><p>Recently, the Canadian Small Lake Model (CSLM) was implemented in the surface component of GEM-Hydro to represent the energy and water balance over water tiles more accurately. So far, CSLM simulations have been shown promising in terms of evaporation, ice cover, absolute and dew point temperature simulations, compared with the former algorithm used over water. However, the impact of CSLM on the resulting streamflow simulations performed with GEM-Hydro has not been evaluated yet. This study aims first at evaluating the impact of CSLM on streamflow simulations, and secondly at testing different CSLM configurations as well as different coupling strategies with Watroute, with the objective of finding the best set up for the prediction of streamflow in Canada. For example, overland runoff generated by the land tile can be provided to the water tile of the same grid point in different ways, and the outflow computed at the outlet of the water tile can be computed with different parameters. Moreover, different outflow computations have to be taken into account depending on if the water tile of a grid point represents subgrid-scale lakes, or if on the contrary it belongs to a lake spanning over multiple model grid points.</p><p>To do so, different GEM-Hydro open-loop simulations have been performed on the Lake of the Woods watershed, located in Canada, with and without CSLM to represent water tiles. The CSLM configurations leading to the best results are presented here. CSLM simulations are also evaluated in terms of surface fluxes, to ensure that the main purpose of the model, which is to improve surface fluxes to ultimately improve atmospheric forecasts, is preserved, compared to the default configuration of the model. Ideas for further improving the coupling between the GEM-Hydro surface and routing components, in terms of lake processes, are also presented and will be tested in future work.</p>

scholarly journals Simulating Surface and Subsurface Water Balance Changes Due to Burn Severity

2018 ◽  
Vol 17 (1) ◽  
pp. 180099 ◽  
Author(s):  
Adam L. Atchley ◽  
Alicia M. Kinoshita ◽  
Sonya R. Lopez ◽  
Laura Trader ◽  
Richard Middleton
Keyword(s):  
Water Balance ◽  
Subsurface Water ◽  
Burn Severity

scholarly journals Water balance estimation in high Alpine terrain by combining distributed modeling and a neural network approach (Berchtesgaden Alps, Germany)

2012 ◽  
Vol 16 (7) ◽  
pp. 1969-1990 ◽  
Author(s):  
G. Kraller ◽  
M. Warscher ◽  
H. Kunstmann ◽  
S. Vogl ◽  
T. Marke ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Balance ◽  
Hydrological Model ◽  
Water Transfer ◽  
Subsurface Water ◽  
Network Approach ◽  
Neural Network Approach ◽  
Distributed Modeling ◽  
Artificial Neural

Abstract. The water balance in high Alpine regions is often characterized by significant variation of meteorological variables in space and time, a complex hydrogeological situation and steep gradients. The system is even more complex when the rock composition is dominated by soluble limestone, because unknown underground flow conditions and flow directions lead to unknown storage quantities. Reliable distributed modeling cannot be implemented by traditional approaches due to unknown storage processes at local and catchment scale. We present an artificial neural network extension of a distributed hydrological model (WaSiM-ETH) that allows to account for subsurface water transfer in a karstic environment. The extension was developed for the Alpine catchment of the river "Berchtesgadener Ache" (Berchtesgaden Alps, Germany), which is characterized by extreme topography and calcareous rocks. The model assumes porous conditions and does not account for karstic environments, resulting in systematic mismatch of modeled and measured runoff in discharge curves at the outlet points of neighboring high alpine subbasins. Various precipitation interpolation methods did not allow to explain systematic mismatches, and unknown subsurface hydrological processes were concluded as the underlying reason. We introduce a new method that allows to describe the unknown subsurface boundary fluxes, and account for them in the hydrological model. This is achieved by an artificial neural network approach (ANN), where four input variables are taken to calculate the unknown subsurface storage conditions. This was first developed for the high Alpine subbasin Königsseer Ache to improve the monthly water balance. We explicitly derive the algebraic transfer function of an artificial neural net to calculate the missing boundary fluxes. The result of the ANN is then implemented in the groundwater module of the hydrological model as boundary flux, and considered during the consecutive model process. We tested several ANN setups in different time increments to investigate ANN performance and to examine resulting runoff dynamics of the hydrological model. The ANN with 5-day time increment showed best results in reproducing the observed water storage data (r2 = 0.6). The influx of the 20-day ANN showed best results in the hydrological model correction. The boundary influx in the subbasin improved the hydrological model, as performance increased from NSE = 0.48 to NSE = 0.57 for subbasin Königsseetal, from NSE = 0.22 to NSE = 0.49 for subbasin Berchtesgadener Ache, and from NSE = 0.56 to NSE = 0.66 for the whole catchment within the test period. This combined approach allows distributed quantification of water balance components including subsurface water transfer.

scholarly journals Water balance estimation in high Alpine terrain by combining distributed modeling and a neural network approach (Berchtesgaden Alps, Germany)

2012 ◽  
Vol 9 (1) ◽  
pp. 215-259
Author(s):  
G. Kraller ◽  
M. Warscher ◽  
H. Kunstmann ◽  
S. Vogl ◽  
T. Marke ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Balance ◽  
Water Transfer ◽  
Subsurface Water ◽  
Distributed Model ◽  
Network Approach ◽  
Neural Network Approach ◽  
Distributed Modeling ◽  
Artificial Neural

Abstract. The water balance in high Alpine regions is often characterized by significant variation of meteorological variables in space and time, a complex hydrogeological situation and steep gradients. The system is even more complex when the rock composition is dominated by soluble limestone, because unknown underground flow conditions and flow directions lead to unknown storage quantities. Reliable distributed modeling cannot be implemented by traditional approaches due to unknown storage processes at local and catchment scale. We present an artificial neural network extension of a distributed hydrological model (WaSiM-ETH) that allows to account for subsurface water transfer in a karstic environment. The extension was developed for the Alpine catchment of the river "Berchtesgadener Ache" (Berchtesgaden Alps, Germany), which is characterized by extreme topography and calcareous rocks. The model assumes porous conditions and does not account for karstic environments, resulting in systematic mismatch of modeled and measured runoff in discharge curves at the outlet points of neighboring high alpine sub-catchments. Various precipitation interpolation methods did not allow to explain systematic mismatches, and unknown subsurface hydrological processes were concluded as the underlying reason. We introduce a new method that allows to describe the unknown subsurface boundary fluxes, and account for them in the distributed model. This is achieved by an Artificial Neural Network approach (ANN), where three input variables are taken to calculate the unknown subsurface storage conditions. We explicitly derive the algebraic transfer function of an artificial neural net to calculate the missing boundary fluxes. The result of the ANN is then implemented in the groundwater module of the distributed model as boundary flux, and considered during the consecutive model process. The ANN was able to reproduce the observed water storage data sufficiently (r2 = 0.48). The boundary influx in the sub-catchment improved the distributed model, as performance increased from NSE = 0.34 to NSE = 0.57. This combined approach allows distributed quantification of water balance components including subsurface water transfer.

scholarly journals The extent of land use impact on water regime 

2011 ◽  
Vol 52 (No. 6) ◽  
pp. 239-244 ◽  
Author(s):  
P. Kovář
Keyword(s):  
Land Use ◽  
Water Balance ◽  
Water Regime ◽  
Land Use Changes ◽  
Subsurface Water ◽  
Rainfall Runoff ◽  
Short Term ◽  
Water Recharge ◽  
The Impact

The paper is focused on the impact of land use changes on water regime. First, an emphasis was given to what extent the main components of the water balance on the experimental catchment Všeminka (region Vsetínské Hills) were influenced. For this reason, the WBCM-5 model was implemented for the period of 10 years in a daily step with a particular reference to simulate the components of direct runoff and of subsurface water recharge. In the selected years of the period 1990–2000, the major changes were made in land use and also the significant fluctuation of rainfall-runoff regimes were observed (e.g. dry year 1992 and flood year 1997). After WBCM-5 parameter calibration it was found that some water balance components can change in relation to substantial land use changes even up to tens of percent in a balance-consideration, i.e. in daily, monthly and yearly or decade values, namely the components of interception and also of direct runoff and of subsurface water recharge. However, a different situation appears when investigating significant short-term rainfall-runoff processes. There were about seven real flood events analysed using the model KINFIL-2 (time step 0.5 hr) during the same period of about 10 years on the same catchment. Furthermore, some land use change positive or negative scenarios were also analysed there. As opposed to long-term water balance analyses, there was never achieved any greater differences in the hydrograph peak or volume than 10%. Summarising, it is always important to distinguish a possible land use change impact in either long-term balance or short-term runoff consideration, otherwise a misunderstanding might be easily made, as can often be found when commenting on the impact on floods in some mass media.

scholarly journals Robust Assessment of Uncertain Freshwater Changes: The Case of Greece with Large Irrigation—and Climate-Driven Runoff Decrease

Water ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 1645 ◽  
Author(s):  
Georgia Destouni ◽  
Carmen Prieto
Keyword(s):  
Water Balance ◽  
Early Period ◽  
Data Driven ◽  
Recent Period ◽  
Irrigation Development ◽  
Data Driven Approach ◽  
Runoff Decrease ◽  
Large Irrigation ◽  
Of Greece ◽  
High Uncertainty

We develop a data-driven approach to robustly assess freshwater changes due to climate change and/or human irrigation developments by use of the overarching constraints of catchment water balance. This is applied to and tested in the high-uncertainty case of Greece for five nested catchments of different scales across the country and for freshwater changes from an early period (1930–1949) with small human influences on climate and irrigation to a recent period (1990–2009) with expected greater such influences. The results show more or less equal contributions from climatic decrease in precipitation and from human irrigation development to a considerable total decrease in runoff (R) over Greece. This is on average −75 ± 10 mm/year and is greatest for the Ionian catchment in the west (−119 ± 18 mm/year) and the Peloponnese catchment in the south (−91 ± 16 mm/year). For evapotranspiration (ET), a climate-driven decrease component and an irrigation-driven increase component have led to a net total increase of ET over Greece. This is on average 26 ± 7 mm/year and is greatest for the Mainland catchment (29 ± 7 mm/year) and the Aegean catchment in the east (28 ± 6 mm/year). Overall, the resulting uncertainties in the water-balance constrained estimates of R and ET changes are smaller than the input data uncertainties.

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