Enter Zoom Meeting

HS2.2.2

EDI
Multi-dataset, multi-variable and multi-objective techniques to improve prediction of hydrological, ecological, and water quality models

The application of multi-datasets and multi-objective functions has proven to improve the performance of hydrologic and water quality models by extracting complementary information from multiple data sources or multiple features of modelled variables. This is useful if more than one variable (runoff and snow cover, sediment, pollutant concentration, or stable isotope) or more than one characteristic of the same variable (e.g., flood peaks and recession curves) are of interest. Similarly, a multi-model approach can overcome shortcomings of individual models, while testing a model at multiple scales using a large sample of catchments helps to improve our understanding of the model functioning in relation to catchment processes. The use of multiple data sources in data-driven approaches can help engineering data-driven models with higher predictability skills. Finally, the quantification of multiple uncertainty sources enables the identification of their contributions and this is critical for uncertainty reduction and decision making under uncertainty.
This session welcomes contributions that apply one or more of the multi-aspects in hydrological, ecological and water quality studies. In particular, we seek studies covering the following issues:
• Frameworks using multi-objective or multi-variables to improve the identification (prediction) of hydrological, ecological or water quality models;
• Studies using multi-model or multiple-data-driven approaches;
• Use of multiple scales, sites or large-sample studies to improve understanding of catchment processes;
• Assimilation of remote sensed data or use of multi-datasets to improve model identification;
• Hypothesis testing with one of the multi-aspects
• Metaheuristics (e.g., Monte Carlo) or Bayesian approaches in combination with multi-aspects of model identification;
• Techniques to optimize model calibration or uncertainty quantification via multi-aspect analyses;
• Studies handling multiple uncertainty sources in a modelling framework.
• Application of machine learning and data mining approaches to learn from large, multiple or high-resolution data sets.

Convener: Anna E. Sikorska-SenonerECSECS | Co-conveners: David C. Finger, Alberto Montanari
Presentations
| Thu, 26 May, 08:30–10:00 (CEST)
 
Room 2.17

Thu, 26 May, 08:30–10:00

Chairpersons: David C. Finger, Alberto Montanari

08:30–08:36
|
EGU22-6588
|
Virtual presentation
Silvano Fortunato Dal Sasso et al.

Water resources observation and modelling are essential to better understand hydrological processes and improve water resource management. However, the reliability of hydrological simulation is strongly controlled by the quality and type of field observations used for the calibration and validation processes. Therefore, it is critical to develop proper strategies for model calibration and validation in order to reduce prediction uncertainties. Standard hydrological calibration relies mainly on the time series of total streamflow at the catchment outlet; nevertheless, this leads to a limited insight into the spatial behaviour of a river basin. In this work, we use simulations from the physically-based distributed DREAM model to discuss the importance of multi-criteria calibration to obtain consistent parameter sets. The calibration methodology exploits a physical based filter to decompose the streamflow times series in two time series referring to the surface component and the baseflow. Therefore, we adopted a multi-criteria calibration procedures which optimizes: (a) the total streamflow measured at the basin outlet (used as a reference study case); b) both the surface runoff and baseflow measured at the basin outlet; and (c) the combination the time series of the two components along with the annual water balance components. In addition, we also explored the use of a lumped parametrization against a spatial parametrization derived from the soil type characteristics of the river basin. In all cases, parameter optimization was carried out using an automatic calibration performed by a genetic algorithm (GA) tool. The study was carried out for two experimental catchments located in Basilicata and Campania regions (Southern Italy). The performed experiments showed that the inclusion of physical information during the calibration process results in a general improvement of model reliability.

This research is a part of iAqueduct project funded under the Water JPI 2018 Joint Call, Closing the Water Cycle Gap – on Sustainable Management of Water Resources - Water Works 2017.

How to cite: Dal Sasso, S. F., Pizarro, A., Zhuang, R., Zeng, Y., Nasta, P., Romano, N., Cebolla, J. G., Frances, F., Toth, B., Su, Z., and Manfreda, S.: The impact of a multi-criteria calibration on the performances of the DREAM model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6588, https://doi.org/10.5194/egusphere-egu22-6588, 2022.

08:36–08:42
|
EGU22-7820
|
ECS
|
|
On-site presentation
Omar Cenobio-Cruz et al.

SASER (SAfran-Surfex-Eaudysee-Rapid) is a distributed and physically-based modeling chain. Currently, SASER has been implemented for different spatial domains and resolutions. The Pyrenean application of the model at 2.5 km of spatial resolution has a good performance, but it can be improved. We have evaluated the simulated streamflows using the KGE score, which is above 0.5 over 57% of the near-natural catchments. We have seen that SASER simulates reasonably well high, but not extreme, and median daily streamflows, but low flows and peak flows are underestimated. Our hypothesis is that low flows are underestimated due to the lack of a groundwater model and that peak flows are underestimated due to low intensities hourly precipitation in SAFRAN, among other issues. 

The objective of this study is to improve the streamflow simulated by SASER by improving the intensity of the hourly precipitation produced by SAFRAN and by introducing a simple conceptual model to simulate groundwater effects.

SAFRAN ingests daily precipitation observations, which are distributed to an hourly scale using relative humidity, which generates low precipitation intensities. This is not realistic at all in a Mediterranean climate. Our hypothesis is that we can improve hourly intensities by using the outputs of an RCM simulation, forced by a reanalysis, to distribute the hourly precipitation. In this experiment we have used the CNRM-ALADIN model, forced by ERA-Interim, from the EURO-CORDEX database. We keep the daily amounts from SAFRAN (over windows that span from 1 to 14 days) and we redistribute hourly precipitation according to the RCM simulation. We evaluated the results by comparing the hourly precipitation distribution of a set of 13 precipitation stations from the Ebro Basin real-time observation system (SAIH), using the Perkin Skill Score (PSS), which improved from an average of 0.70 in the standard SAFRAN product to 0.88 in our best configuration. Consequently, we now have a new precipitation dataset with improved precipitation intensity patterns.

To improve SASER low flows, we followed the steps of Getirana et al. (2014) and Artinyan et al. (2008), we introduced a linear reservoir at grid point resolution between the LSM and the routing scheme. We calibrated the reservoir parameters catchment-by-catchment in near-natural sub-catchments. The KGE score of the square root of the streamflow shows on average an improvement of 21% with respect to default simulation (without reservoir)

The regionalization approach was chosen to set the reservoir parameters in human-influenced catchments, where calibration is unfeasible. This approach allows us to link physical characteristics with the reservoir parameters through a linear equation, as did Beck et al. (2020).  In this process, an evolutionary algorithm was implemented, which optimizes the equation coefficients, thereby we were able to produce maps (at 2.5 km resolution) of the model parameters based on physiographic data. Preliminary results show using this approach we obtain performances close to those obtained by a classical calibration procedure.

This work was funded by the HUMID project (CGL2017-85687-R, AEI/FEDER, UE), EFA210/16-PIRAGUA and IDEWA (PCI2020-112043) projects, and the predoctoral grant PRE2018-085027 (AEI/FSE).

How to cite: Cenobio-Cruz, O., Quintana-Seguí, P., Barella-Ortiz, A., and Garrote, L.: Improvement of high and low flow simulation in the hydrological model chain SASER., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7820, https://doi.org/10.5194/egusphere-egu22-7820, 2022.

08:42–08:48
|
EGU22-6212
|
ECS
|
Highlight
|
On-site presentation
Cristina Prieto et al.

Hydrological modelling of ungauged catchments, which lack observed streamflow data, is an important practical goal in hydrological sciences. A major challenge is to identify a model structure that reflects the hydrological processes relevant to the catchment of interest.

This study contributes a Bayesian framework for identifying individual model mechanisms (process representations) from flow indices regionalized to the catchment of interest. We extend a method previously introduced for mechanism identification in gauged basins, by formulating the inference equations in the space of (regionalized) flow indices and by accounting for posterior parameter uncertainty. A flexible hydrological model is used to generate candidate mechanisms and model structures, followed by statistical hypothesis testing to identify "dominant" (more a posterior probable) model mechanisms.

The proposed method is illustrated using real data and synthetic experiments based on 92 catchments from northern Spain, from which 16 catchments
are treated as ungauged. 624 hydrological model structures from the flexible framework FUSE are employed.

In real data experiments, the method identifies a dominant mechanism in 27% of 112 trials (processes and catchments). The most identifiable process is routing, whereas the least identifiable processes are percolation and unsaturated zone processes. In synthetic experiments, where "true" mechanisms are known, the reliability of method varies from 60% to 95% depending on the combined regionalization and hydrological error; the probability of making an identification remains stable at around 25%. More broadly, the study contributes perspectives on hydrological mechanism identification under data-scarce conditions; limitations and opportunities for improvement are outlined.

How to cite: Prieto, C., Le Vine, N., Kavetski, D., Fenicia, F., Scheidegger, A., and Vitolo, C.: An exploration of Bayesian identification of dominant hydrological mechanisms in ungauged catchments, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6212, https://doi.org/10.5194/egusphere-egu22-6212, 2022.

08:48–08:54
|
EGU22-211
|
ECS
|
|
Virtual presentation
Gowri Reghunath and Pradeep Mujumdar

The hydrological responses of a catchment are predominantly governed by complex interactions among processes occurring at various spatial and temporal scales. Hydrological modelling serves as a powerful tool in assimilating this complex behaviour of hydrological systems. As hydrological processes exhibit non-linear behaviour at all scales, it becomes essential to understand how much spatial approximations are necessary for a model to adequately represent reality. This study aims at investigating the influence of spatial resolution of a physically-based hydrological model in capturing the hydrological processes of a catchment which is characterized by large scale variability in the regional distribution of water resources. In this study, the grid-based Variable Infiltration Capacity (VIC) model is employed at spatial resolutions of 0.125, 0.25, and 1-degree latitude by longitude over the Cauvery river basin in peninsular India. The model incorporates both surface and subsurface hydrological processes, features sub-grid land surface and vegetation heterogeneity, facilitates the inclusion of multiple soil layers with variable infiltration, and computes non-linear baseflow. The model is calibrated with respect to observed streamflow at various gauge stations located across the basin. The water balance components such as surface runoff, evapotranspiration, soil moisture at three distinct soil layers, and baseflow are estimated for the period 1951-2016. Performance evaluation of outputs obtained from model simulations adopting different spatial resolutions is carried out at seasonal and annual time scales. As the spatial scale increases, the catchment tends to organize and attenuate the complex behaviour of processes. This study provides significant insights towards adopting effective modelling strategies to ensure the adequate representation of hydrological processes in regionally complex catchments.

How to cite: Reghunath, G. and Mujumdar, P.: Role of spatial resolution in simulating hydrological processes using a physically-based hydrological model, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-211, https://doi.org/10.5194/egusphere-egu22-211, 2022.

08:54–09:00
|
EGU22-10938
|
ECS
Parameter calibration and evaluation of the WRF-Hydro model within a semi-arid river basin
(withdrawn)
Jocelyn Serrano Barragan and Adrián Pedrozo Acuña
09:00–09:06
|
EGU22-569
|
ECS
|
Highlight
|
|
On-site presentation
Matteo Pesce et al.

Large scale hydrological modelling requires the estimation of model parameters across a variety of different environments. To deal with this issue, robust parameter estimation procedures, able to exploit observed patterns of climate and geomorphological characteristics, must be considered. This contribution presents hydroPASS, a newly developed R package available in GitHub, which automatically implements the PArameter Set Shuffling (PASS) method in around 100 catchments over North-Western Italy. This was developed and tested for SALTO (SAme Like The Others) model, but in principle is valid for every distributed or semi-distributed catchment model. In particular, the package contains the function to run SALTO model (Fortran code), the function for PASS application (R code) and functions for data pre-processing and post-processing (R codes). To ease the repeatability and reproducibility of experiments, examples are provided with full documentation. An example of how to use PASS for the regional calibration of other models (e.g., TUW model), is also provided. From the source package, installation packages have been built for Windows, Linux and Mac operating systems and can be freely downloaded from GitHub.

How to cite: Pesce, M., Viglione, A., von Hardenberg, J., Tarasova, L., Basso, S., and Merz, R.: hydroPASS: a newly developed R package to go through the regional calibration of distributed catchment models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-569, https://doi.org/10.5194/egusphere-egu22-569, 2022.

09:06–09:12
|
EGU22-9129
|
ECS
|
Virtual presentation
Yusuke Homma et al.

In recent years, the risk of sediment-related disasters has increased due to the increase in heavy rain disasters. Therefore, a technique for more easily diagnosing the internal structure of dikes such as fill dams is desired. We applied machine learning to this task.
In this study, we estimated the correspondence between the hydraulic conductivity distribution and the pressure head distribution of the zoned dike using machine learning. The machine learning method is pix2pix which is derived from generative adversarial networks (GAN). Pix2pix learns the relationship between input and output image. Training datasets were generated by using HYDRUS-2D In the HYDRUS-2D simulation, the dike was divided into three zones, and the seepage analysis was performed by changing the hydraulic conductivity of each zone to various values, and the pressure head distribution in the steady state was obtained.
In the forward problem, most of the results could be estimated accurately. On the hand, it was difficult to estimate the inverse problem because of the ill-posed problem. In the inverse problem, we were able to improve the results by giving the training data a priori information about the hydraulic conductivity.
This approach can be used as a surrogate model for the forward problem and the inverse problem in seepage analysis.

How to cite: Homma, Y., Kuroda, S., and Makino, N.: Surrogate model for seepage analysis of a dike using generative adversarial networks, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9129, https://doi.org/10.5194/egusphere-egu22-9129, 2022.

09:12–09:18
|
EGU22-2990
|
ECS
|
|
On-site presentation
Pouya Farokhzad et al.

The heterogeneity in the soil medium makes it economically impossible to measure the soil characteristics across the whole soil profile. Inverse modelling configures a computer model to simulate the groundwater system and calibrates it to identify the soil characteristics in all grid-cells defined in the model. In classic inverse modelling approaches, residuals between simulated and measured groundwater head time series are converted into a single likelihood function to be maximized; therefore, the information content in the measured data is not fully exploited in those approaches. Moreover, the large number of grid-cells makes inverse modelling an ill-posed optimization problem with more unknown parameters than known measured values, leading to different parameter sets having a similar model performance. Despite recent advances in groundwater calibration practices, such as the regularization approach, there is a considerable room to improve groundwater model calibration using information content in the measured data. Regularization aims to add various sets of information to the calibration to tackle the non-uniqueness problem. However, this approach could associate with a considerable degree of subjectivity and uncertainty. This study seeks a novel approach to extract information content from measured data in the form of physically-based metrics that are often called signatures for improving the identifiability in the groundwater model calibration. Moreover, this study proposes a novel automated powerful inverse modelling strategy using a multi-objective approach to incorporate most of the information content through physically meaningful metrics to obtain more consistent models. The benchmark Freyberg 1988 synthetic case study, in which the true model parameter values are known, is used to demonstrate the applicability and potentials of the proposed framework. The reconstructed Freyberg case study in MODFLOW 2005 showed the framework has the ability to find consistent estimates of the groundwater model parameters.

How to cite: Farokhzad, P., Asadzadeh, M., and Holländer, H.: Multi-objective Inverse Modelling Using Physically based Metrics, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2990, https://doi.org/10.5194/egusphere-egu22-2990, 2022.

09:18–09:24
|
EGU22-10752
Henrique Momm et al.

The Lower Mississippi River Alluvial Plain, referred to as the Delta, is an important agricultural region in the southeastern United States. Recent trends in crop type conversion and higher crop yields resulted in increased irrigation demand for surface and groundwater, which can lead to aquifer levels dropping. Estimates of continued increased irrigation adoption are compounded by future climatic estimates suggesting hotter summers with higher unpredictability in precipitation amounts. In these conditions, the long-term sustainability of this system depends on understanding complex surface-groundwater flow interactions at different temporal and spatial scales, and the impacts of agricultural conservation practices on water use. In this study, a description of the development of the integrated AnnAGNPS-MODFLOW technology is provided. The proposed system was evaluated in the Upper Sunflower River watershed, located in the Delta region of Mississippi, to characterize existing conditions through comparison with observed streamflow and well water levels. Additionally, the system was used to evaluate the impact of alternative irrigation and management strategies on water levels in the aquifer at field and watershed scales. The proposed technology provides a management tool critical to understanding and evaluating the impact of agricultural practices, irrigation, and aquifer recharge strategies that are important to sustaining Delta water resources in a changing climate.

How to cite: Momm, H., Bingner, R., Moore, K., and Herring, G.: AnnAGNPS-MODFLOW integration for evaluation of agricultural practice impacts on surface and groundwater resources, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10752, https://doi.org/10.5194/egusphere-egu22-10752, 2022.

09:24–09:30
|
EGU22-11070
|
ECS
|
Virtual presentation
Vicky Anand et al.

Reliability of hydrological model simulation plays an important role in better understanding of hydrological processes, eco-hydrologic and eco-hydraulic condition of a data scarce catchment with limited baseline data. Due to the lack of baseline hydrological dataset, precise simulation of hydrological output from a hydrological model becomes essential. There has been several studies carried out to calibrate a data scarce catchment using river discharge solely on remote sensing data but they have been limited to the rivers with large width. The current study was carried out in Manipur River basin where the widths of the streams are narrow due to which direct application of remotely sensed data possesses serious challenge. This study attempts to calibrate and validate a comprehensive physically semi-distributed Soil and Water Assessment Tool (SWAT) model in Manipur River basin. The SWAT hydrological model was set-up using elevation, soil, weather, and land use land cover (LULC) dataset. For the calibration and validation of model two different approaches has been applied. In the first approach, river streamflow was generated by using stage data based on stage-discharge curve through the technique of spatial proximity, whereas in the second approach, Moderate Resolution Imaging Spectroradiometer (MODIS) evapotranspiration dataset was used at sub-basin scale to calibrate and validate the SWAT model. In the calibration period, the model returned R2 and Kling-Gupta Efficiency (KGE) of 0.78 and 0.73, whereas in the validation period R2, KGE was found to be 0.75, 0.71 while using the stage-discharge curve approach. The model performance of R2, KGE equals 0.67, 0.41, respectively during calibration and R2, KGE equals 0.79, 0.53 respectively was obtained during validation when MODIS evapotranspiration dataset was used. From the modelling result it was observed that the model performance was found to be better when streamflow dataset derived from stage-discharge curve used as compared to the MODIS evapotranspiration dataset. The main reason behind the under performance of the model while using MODIS evapotranspiration dataset was due to the underestimation of evapotranspiration by the SWAT model in the cold-dry season from December to February.

 

How to cite: Anand, V., Oinam, B., and Wieprecht, S.: Multiple techniques for calibration and validation of SWAT model in an ungauged catchment in Inner Himalayan Ranges., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11070, https://doi.org/10.5194/egusphere-egu22-11070, 2022.

09:30–09:36
|
EGU22-290
|
ECS
|
Virtual presentation
Tanveer Dar et al.

Isotopic landscapes or “Isoscapes” are a valuable tool for studying hydro-climatic processes and their impact on water supplies at various spatial scales. These isoscapes are extremely useful because they enable the documentation and visualization of large-scale hydrological processes occurring on a regional, continental, or global scale. This study focuses on surface water isotope data (present study and published data) to interpolate, develop isoscapes of Himalayan basins (Indus, Ganga, and Brahmaputra), and analyze spatial variability from regional to local scale. We use physically based information of three basins from hydro-climatic variables such as actual evapotranspiration (AET), mean annual precipitation (MAP), mean annual runoff (MAR), and runoff coefficient, as well as basin variables such as elevation, slope, aspect, size, and land-use/land-cover (LULC), to develop geographically weighted regression (GWR) models. We identified a systematic spatial pattern in the stable isotopes (δ18O, δ2H, d-excess) of surface water that can be predicted using a GWR model. In the absence of long-term precipitation isotope records, an increased spatial and temporal sampling of surface water for isotopic isoscapes would significantly aid our understanding of hydrological processes, providing that catchment characteristics are taken into consideration. The GWR models used in this study demonstrated the ability to predict isotopic changes in the context of future climate and land-use change in these three major basins.

Keywords: Stable isotopes, Geographically Weighted Regression (GWR), Isoscapes, Himalayan basins.

How to cite: Dar, T., Jahan, A., Rai, N., Bhat, M. A., and Kumar, S.: Spatial analysis of hydrogen and oxygen stable isotopes (“isoscapes”) in Himalayan basins: improved prediction using Geographically Weighted Regression (GWR) models, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-290, https://doi.org/10.5194/egusphere-egu22-290, 2022.

09:36–09:42
|
EGU22-11754
|
ECS
|
Highlight
|
On-site presentation
Aaron Neill et al.

Improved quantification of water partitioning is needed to inform sustainable, integrated land and water management. Spatially-distributed, process-based ecohydrological models are promising tools for achieving this; however, such models typically have many parameters that require estimation from data. Utilising an extremely rich plot-scale dataset incorporating energy (net radiation, temperature, and latent and sensible heat), hydrological (soil moisture, sap flow and actual evapotranspiration) and vegetation (net primary productivity and structure) components, we investigated which types of observation best constrain the parameters of a complex ecohydrological model (EcH2O-iso) for simulation of water partitioning. Our experimental site was situated within one of the largest coffee agroforestry systems in Costa Rica and experiences high energy inputs and intense rainfall events, thus adding further complexity to the robust simulation of hydrological processes. A series of calibration exercises were undertaken based on combinations of the different observation types. In each case, 100 behavioural parameter sets were chosen following 100,000 Monte Carlo simulations. The “flux mapping” approach was then used to quantify the percentage contribution made by different simulated fluxes to total model outflows (e.g., contributions of transpiration, soil evaporation and interception evaporation to total evapotranspiration), in order to assess how consistently plot hydrology was simulated by the retained parameter sets. Additionally, PCA analysis of performance metrics (including those for observations not used in the given calibration) was undertaken to reveal how contrasting observation types “pull” the model in different directions and, thus, affect its ability to capture the dynamics of each type simultaneously. From this work, we are able to provide guidance on how different ecohydrological datasets may be optimally combined in model calibration. This has implications not only for reducing uncertainty in modelling studies underpinning land and water management, but also for designing future field campaigns such that collection of the most valuable data can be prioritised.

How to cite: Neill, A., Birkel, C., Boll, J., Maneta, M., Roupsard, O., Benegas, L., and Soulsby, C.: Towards improved simulation of water partitioning: which observations have most value in constraining a spatially-distributed ecohydrological model? , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11754, https://doi.org/10.5194/egusphere-egu22-11754, 2022.

09:42–09:48
|
EGU22-11842
|
|
Virtual presentation
Manvitha Molakala and Riddhi Singh

Reservoir water balance models are often used in system models that translate state variables to measurable goals in water management studies. These models enable water managers to analyze the impact of various operational strategies on the performance of water resource systems. However, the water quality implications of reservoir operations are relatively less explored as it entails additional processes in the model, requiring data and parameterization. Water quality implications are particularly important in projects such as inter-basin water transfers (IBWTs), in which water is diverted from one basin to other in the presence of considerable regional differences in water supply and demands between two basins. When water from one basin is transferred to another, the difference in water quality can affect the local flora and fauna of the recipient basin. So, it is important to understand the relative proportion of water from either basins present in the recipient reservoir at any time. Here, we propose a source tracking framework to quantify the contribution of water from either basins to various reservoir-related fluxes in the recipient basin. These include water released from the recipient basin for: demand satisfaction, maintaining minimum environmental flows, preventing dam failure, and demands in basins. We quantify the proportion of water supplied from the donor basin and from the recipient's own inflows for each flux. We apply this framework to a proposed water transfer project in southern India that transfers water from the Godavari basin to the Krishna river basin. Our results show that under extreme droughts observed in the simulated inflows, up to 50% of the minimum environmental flows released downstream of the recipient reservoir are supplied from the water transferred from the donor basin. This generally occurs in the months from June to December in which the recipient experiences high demands. We also note that more than 50% of the water transferred out of the recipient reservoir to other basins arrives from the donor basin. Our framework can be used to evaluate the possible implications of water quality in the donor basin on the minimum environmental flows and other reservoir-related fluxes from the recipient reservoir. 

How to cite: Molakala, M. and Singh, R.: Unfolding the ecological and water quality Implications of Inter Basin Water Transfers Using a SourceTracking Modeling Framework, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11842, https://doi.org/10.5194/egusphere-egu22-11842, 2022.

09:48–09:54
|
EGU22-12945
|
ECS
|
Virtual presentation
Peng Ji and Xing Yuan

Soil moisture is a vital land surface variable that influences the terrestrial hydrothermal cycles, modulates the land-atmosphere interactions and provides important predictability for weather and climate forecasting. Numerous efforts have been made to investigate the contribution of meteorological forcings, land surface parameters and land models to the uncertainties or precision of soil moisture modeling through both complex statistical approaches and simple comparative land surface modeling experiments. However, previous research mainly focus on one or two factors and the influence of a specific factor is usually quantified by comparing two different datasets. It still unclear that how much added value the current high resolution forcings, surface parameters and land models have to the soil moisture modeling and whether the results depend on the choice of model, datasets and even study regions.

To address the above issue, we first performed a high resolution (6km) soil moisture modeling over China during 2012~2017 by using the newly developed Conjunctive Surface-Subsurface Process version 2 (CSSPv2) land model forced by high-resolution meteorological forcing and high-resolution soil hydraulic property data. The high-resolution simulation has good performance in representing the observed magnitudes and variations of the rootzone (0~1 m) soil moisture based on >1,500 soil moisture stations, and improves the Kling-Gupta efficiency by 33~118% from the current high-resolution global land reanalysis (e.g., ERA5-Land and GLDASv2.1) and remote sensing based products (e.g., ESA CCI and GLEAMv3.1). In order to quantify the contributions from forcings, parameters and CSSPv2 model, we repeated the simulation by using coarse resolution datasets and different models including three meteorological forcing datasets, two soil hydraulic property datasets and three land models. By comparing 48 sets of experiments, the model and soil parameter are found to contribute more than 50% of the improvements at national scale which indicates necessity of developing high resolution land models and model parameters. On the regional scale, however, the meteorological forcing is shown to has the largest added value over the northwestern and southwestern China while land model is most important for the improvement over southern and eastern China. Further works will analyze the specific physical process in CSSPv2 model that improve the soil moisture simulation which will shed light on the future land model development.

How to cite: Ji, P. and Yuan, X.: The added value of forcing, surface parameter and land model to the high resolution soil moisture modeling in China, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12945, https://doi.org/10.5194/egusphere-egu22-12945, 2022.

09:54–10:00
|
EGU22-12666
|
ECS
|
|
On-site presentation
Kien Nguyen and Maria J. Santos

Moisture recycling is an important process in the hydrological system, as well as an important ecosystem service being responsible for more than 10% of precipitation in the majority of terrestrial areas. Changes in land use are known to affect this process, however, detailed understanding on how vegetation characteristics, i.e., plant traits, are seldom included in modeling this important process. To overcome this knowledge gap, we conduct a first order examination of the effect of plant traits on recycling, where we examine how variation in plant traits influences moisture recycling properties (average and standard deviation) in the Amazon Basin. More specifically, we used remotely-sensed estimates of trait values for: Specific Leaf Area (SLA), Leaf Dry Matter Content (LDMC), Leaf Phosphorus Content (LPC), Leaf Nitrogen Content (LNC), as well as information on Normalised Difference Vegetation Index (NDVI), and Leaf Area Index (LAI) for 10 years (2001-2010). We link this data on plant traits to six parameters that relevant for moisture recycling, namely Evapotranspiration (ET), Potential Evapotranspiration (PET), Land Surface Temperature (LST) Day and Night, Soil Moisture (SM) and Vapour Pressure Deficit (VPD). We used multivariate regression to analyse how plant traits explain the variance of moisture recycling parameters and find that NDVI (10- 40%), LAI (10-50%) and SLA (5-20%) exert the strongest effects on moisture recycling parameters suggesting that leaf gas exchange traits are most important in comparison to the other traits. We find, however, that the strength and the directionality of the effect while variable, it matches the expectations: NDVI positively correlates with ET, PET and negatively correlates with SM and LST Night; SLA positively correlates with VPD and LST Day. These results suggest that leaf gas exchange properties operate differently during the day and night-time, likely constrained by SM availability, and are linked with VPD and ET exchanges in the direction expected. We then examined whether these patterns were exacerbated or attenuated at the extremes of plant trait values using quantile regression (5th, 50th and 95th), to find that indeed some relationships became stronger (e.g. NDVI and LST Night, LAI and PET, ET and LST Day), while others became more attenuated (e.g. LPC and VPD, NDVI and ET). Finally, we examined whether the effect of traits would be related to the sub-basin processes due to the found control of SM on trait effects and founnd that nutrient and dry matter traits became more important, mostly for the extremes of trait distributions. These results show a promising first approach to include trait distributions in modeling hydrological processes. Indeed, we find some relationships in the direction expected, exacerbated in some cases at the extremes of trait distributions, and at local scale we show that different processes control hydrological parameters in comparison to the whole basin. While promising, more and better estimates of traits through remote sensing or in situ data acquisition are necessary to gain a better understanding of which traits might need to be managed to maintain this important ecosystem service and to understand its links with the overall hydrological cycle.

How to cite: Nguyen, K. and Santos, M. J.: Examining the effect of plant traits on moisture recycling in the Amazon Basin, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12666, https://doi.org/10.5194/egusphere-egu22-12666, 2022.