scholarly journals Integrating IHACRES with a data-driven model to investigate the possibility of improving monthly flow estimates

2021 ◽  
Author(s):  
Parisa Fattahi ◽  
Afshin Ashrafzadeh ◽  
Nader Pirmoradian ◽  
Majid Vazifedoust
Keyword(s):  
Hybrid Models ◽  
Data Driven ◽  
Group Method ◽  
Precipitation Data ◽  
Rainfall Runoff ◽  
Northern Iran ◽  
Validation Phase ◽  
Surface Water Resources ◽  
Resources Planning

Abstract Estimating the outflow of basins is a critical step in surface water resources planning and management, especially in basins that lack reliable long-term observed data of streamflow. Hydrological models, which can simulate the process of rainfall-runoff, can be used to obtain reliable estimates of streamflow from precipitation data and the physical characteristics of basins. The focus of the present study was to estimate the outflow of 19 sub-basins located in Guilan Province, northern Iran. To achieve this, hybrid models were developed by integrating the IHACRES (identification of unit hydrograph and component flows from rainfall, evapotranspiration, and streamflow) hydrological model with the intelligent-based GMDH (group method of data handling) model. The IHACRES model was calibrated using monthly ground-based precipitation and temperature data as well as satellite-based precipitation data. The lowest and highest Nash-Sutcliffe coefficient (NS) for the IHACRES models were, respectively, 0.14 and 0.68 in the calibration phase and 0.11 and 0.73 in the validation phase. It was also observed that using satellite-based precipitation data reduces NS by 10–75% in the 19 sub-basins under study. After calibrating and validating the IHACRES models, the hybrid models were developed by integrating IHACRES and GMDH models. The lowest and highest NS for the hybrid models were, respectively, 0.23 and 0.81 in the calibration phase and 0.11 and 0.81 in the validation phase. It was observed that, on average, integrating IHACRES and GMDH increases the NS by 44.1% in the calibration phase and 37.0% in the validation phase in comparison with the IHACRES model. According to the NS, the hybrid model had ‘acceptable’ performance in six sub-basins in which the IHCRES model had ‘unacceptable’ performance. It was observed that integrating the IHACRES model with a data-driven model (the GMDH model) can generally improve the simulation results in all sub-basins under study.

Subsurface runoff and recharge dynamics in a Mediterranean catchment based on StorAge Selection functions and end-member splitting analysis

2020 ◽  
Author(s):  
Matthias Sprenger ◽  
Pilar Llorens ◽  
Francesc Gallart ◽  
Jérôme Latron
Keyword(s):  
Stream Water ◽  
Pore Space ◽  
Data Driven ◽  
High Temporal Resolution ◽  
Rainfall Runoff ◽  
Catchment Scale ◽  
Data Set ◽  
Isotope Data ◽  
Subsurface Runoff

<p>Investigations at the long-term experimental catchment Vallcebre in the Pyrenees revealed that rainfall-runoff dynamics are highly variable due to the Mediterranean climatic conditions affecting the storage and release of water in the subsurface<sup>1</sup>. In a changing climate, to the consequences of which could lead to more variations in catchment wetness due to an increase in both droughts and high intensity rainfalls, there is a strong need to better understand subsurface storage and runoff processes.</p><p>While our previous isotope studies (using <sup>2</sup>H and <sup>18</sup>O) demonstrated a pronounced heterogeneity of water flow in the unsaturated zone at the plot scale<sup>2</sup>, we also observed that the contributions of young waters to catchment runoff are highly dependent on the catchments wetness<sup>3</sup>. These analyses provided a basis from which we present new insights into the relationship between subsurface runoff and storage dynamics applying StorAge Selection functions<sup>4</sup> and end-member splitting analysis<sup>5</sup>. Thus, we combined modeling and data-driven approaches to disentangle the partitioning of subsurface waters into storage and runoff based on water age dynamics.</p><p>We gathered an extensive isotope data set with >550 rainfall samples and >980 stream samples taken at high temporal resolution (30 minutes to one week), with highest frequencies during high discharge to improve the coverage of rainfall-runoff events. Using this high-frequency isotope data set, we calibrated the StorAge Selection functions and put special emphasis on the representation of the isotopic response during high flow rainfall-runoff periods. We further tested if time-variant representations of StorAge Selection functions dependent on varying wetness improves the stream water isotope simulations and the ways in which isotope data from different compartments (groundwater and tree water) can assist in constraining the parameter space. Furthermore, end-member splitting analysis provided an independent view into the flow dynamics based on these long-term isotope data sets. As such, the analysis allowed us to derive estimates of the dynamics of rainfall partitioning into runoff and evapotranspiration. Therefore, the combination of the modeling and data-driven approaches enabled an assessment of the dynamics of subsurface runoff at the catchment scale underlining the relevance of heterogeneous flow pattern that were observed on the plot scale.</p><p>References</p><ol><li>Llorens, P. et al. What have we learnt about mediterranean catchment hydrology? 30 years observing hydrological processes in the Vallcebre research catchments. Geogr. Res. Lett. <strong>44, </strong>475–502; 10.18172/cig.3432 (2018).</li> <li>Sprenger, M., Llorens, P., Cayuela, C., Gallart, F. & Latron, J. Mechanisms of consistently disjunct soil water pools over (pore) space and time. Hydrol. Earth Syst. Sci. <strong>23, </strong>2751–2762; 10.5194/hess-23-2751-2019 (2019).</li> <li>Gallart, F. et al. Investigating young water fractions in a small Mediterranean mountain catchment: both precipitation forcing and sampling frequency matter. Hydrol. Process. (in review).</li> <li>Benettin, P. & Bertuzzo, E. tran-SAS v1.0: a numerical model to compute catchment-scale hydrologic transport using StorAge Selection functions. Geosci. Model Dev. <strong>11, </strong>1627–1639; 10.5194/gmd-11-1627-2018 (2018).</li> <li>Kirchner, J. W. & Allen, S. T. Seasonal partitioning of precipitation between streamflow and evapotranspiration, inferred from end-member splitting analysis. Hydrology and Earth System Sciences, <strong>24</strong>, 17–39; 10.5194/hess-24-17-2020 (2020).</li> </ol>

scholarly journals Statistical Distributions of Precipitation Data in Antalya, Turkey

2021 ◽  
Author(s):  
Halil Ibrahim Burgan ◽  
Keyword(s):  
Urban Areas ◽  
Flash Flood ◽  
Fast Response ◽  
Statistical Distributions ◽  
Precipitation Data ◽  
Rainfall Runoff ◽  
Kolmogorov Smirnov ◽  
The City ◽  
Smirnov Test

The estimation of precipitation data is very important while designing buildings in cities. Because of fast response of rainfall-runoff in urban areas, the stormwater infrastructure systems are developed. Especially in flood prone areas which have flash flood risks, the total precipitation, its return period and its duration should be known. For this aim, statistical distributions of long-term observed precipitation data can be determined. In this study, Antalya district which is near the Mediterranean Sea is selected as study area. The city is very crowded touristic place with more than 2.5 million local people. Antalya is one of the warmest regions in Turkey with an average daily high temperature of 25 degrees centigrade. In 6 months, the average temperatures are over 25 degrees. In the study, the best statistical distributions of precipitation data from the gauging stations near Antalya are investigated. Kolmogorov-Smirnov test is applied to select the best statistical distribution.

Study on a Numerical Model of “Rainfall-Runoff” Process Combined with Snowmelt

2012 ◽  
Vol 518-523 ◽  
pp. 4273-4277
Author(s):  
Huang Jinbai ◽  
Wang Bin ◽  
Hinokidani Osamu ◽  
Kajikawa Yuki
Keyword(s):  
Numerical Method ◽  
Wave Theory ◽  
Large Error ◽  
Calculation Model ◽  
Surface Flow ◽  
Snow Melt ◽  
Rainfall Runoff ◽  
Surface Water Resources ◽  
Permissible Range ◽  
Set Up

In order to achieve the accurate calculation of “rainfall-runoff” process combined with snowmelt and to provide a useful numerical method for estimating surface water resources in a basin, a runoff numerical calculation model of “rainfall-runoff” process combined with snowmelt was developed for a distributive hydrological model. Numerical method on “Rainfall-runoff” process was set up by applying kinematic wave theory, and calculations on snowmelt were made using energy budget method. Validity of the model was verified through numerical simulation of the observed surface flow. Results of the error analysis indicated that a large error existed between the numerical results and the observed ones without considering snowmelt whereas the error was at the permissible range of criterion (< 3 %) by considering snowmelt. The results showed that the snowmelt calculation should be considered at snow melt area when performing the runoff calculation.

scholarly journals Comparison and assessment of large-scale surface temperature in climate model simulations

2019 ◽  
Vol 5 (1) ◽  
pp. 67-85
Author(s):  
Raquel Barata ◽  
Raquel Prado ◽  
Bruno Sansó
Keyword(s):  
Surface Temperature ◽  
Seasonal Changes ◽  
Large Scale ◽  
Climate Model ◽  
Internal Variability ◽  
Temperature Variability ◽  
Data Driven ◽  
Model Simulations ◽  
Climate Model Simulations

Abstract. We present a data-driven approach to assess and compare the behavior of large-scale spatial averages of surface temperature in climate model simulations and in observational products. We rely on univariate and multivariate dynamic linear model (DLM) techniques to estimate both long-term and seasonal changes in temperature. The residuals from the DLM analyses capture the internal variability of the climate system and exhibit complex temporal autocorrelation structure. To characterize this internal variability, we explore the structure of these residuals using univariate and multivariate autoregressive (AR) models. As a proof of concept that can easily be extended to other climate models, we apply our approach to one particular climate model (MIROC5). Our results illustrate model versus data differences in both long-term and seasonal changes in temperature. Despite differences in the underlying factors contributing to variability, the different types of simulation yield very similar spectral estimates of internal temperature variability. In general, we find that there is no evidence that the MIROC5 model systematically underestimates the amplitude of observed surface temperature variability on multi-decadal timescales – a finding that has considerable relevance regarding efforts to identify anthropogenic “fingerprints” in observational surface temperature data. Our methodology and results present a novel approach to obtaining data-driven estimates of climate variability for purposes of model evaluation.

scholarly journals Hippocampal sharp wave-ripples and the associated sequence replay emerge from structured synaptic interactions in a network model of area CA3

2021 ◽  
Author(s):  
Andras Ecker ◽  
Bence Bagi ◽  
Eszter Vertes ◽  
Orsolya Steinbach-Nemeth ◽  
Maria Rita Karlocai ◽  
...  
Keyword(s):  
Place Cells ◽  
Data Driven ◽  
Long Term Memory ◽  
Term Memory ◽  
Cellular Adaptation ◽  
Sharp Wave ◽  
Synaptic Interactions ◽  
Area Ca3 ◽  
Reversed Order

Hippocampal place cells are activated sequentially as an animal explores its environment. These activity sequences are internally recreated ("replayed"), either in the same or reversed order, during bursts of activity (sharp wave-ripples; SWRs) that occur in sleep and awake rest. SWR-associated replay is thought to be critical for the creation and maintenance of long-term memory. We sought to identify the cellular and network mechanisms of SWRs and replay by constructing and simulating a data-driven model of area CA3 of the hippocampus. Our results show that the structure of recurrent excitatory interactions established during learning not only determines the content of replay, but is essential for the generation of the SWRs as well. We find that bidirectional replay requires the interplay of the experimentally confirmed, temporally symmetric plasticity rule, and cellular adaptation. Our model provides a unifying framework for diverse phenomena involving hippocampal plasticity, representations, and dynamics.

scholarly journals Very early identification of a bimodal frictional behavior during the post-seismic phase of the 2015 M<sub><i>w</i></sub>8.3 Illapel, Chile, earthquake

2021 ◽  
Author(s):  
Cedric Twardzik ◽  
Mathilde Vergnolle ◽  
Anthony Sladen ◽  
Louisa L. H. Tsang
Keyword(s):  
Template Matching ◽  
State Of The Art ◽  
Data Driven ◽  
Spatiotemporal Evolution ◽  
Seismic Slip ◽  
The North ◽  
Chile Earthquake ◽  
Gnss Data ◽  
Matching Techniques

Abstract. It is well-established that the post-seismic slip results from the combined contribution of seismic slip and aseismic slip. However, the partitioning between these two modes of slip remains unclear due to the difficulty to infer detailed and robust descriptions of how both evolve in space and time. This is particularly true just after a mainshock when both processes are expected to be the strongest. Using state-of-the-art sub-daily processing of GNSS data, along with dense catalogs of aftershocks obtained from template-matching techniques, we unravel the spatiotemporal evolution of post-seismic slip and aftershocks over the first 12 hours following the 2015 Mw8.3 Illapel, Chile, earthquake. We show that the very early post-seismic activity occurs over two regions with distinct behaviors. To the north, post-seismic slip appears to be purely aseismic and precedes the occurrence of late aftershocks. To the south, aftershocks are the primary cause of the post-seismic slip. We suggest that this difference in behavior could be inferred only few hours after the mainshock, and thus could contribute to a more data-driven forecasts of long-term aftershocks.

scholarly journals Effective Treatment Recommendations for Type 2 Diabetes Management Using Reinforcement Learning: Treatment Recommendation Model Development and Validation (Preprint)

2021 ◽  
Author(s):  
Xingzhi Sun ◽  
Yong Mong Bee ◽  
Shao Wei Lam ◽  
Zhuo Liu ◽  
Wei Zhao ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Type 2 Diabetes ◽  
Reinforcement Learning ◽  
Blood Lipids ◽  
Diabetes Complications ◽  
Data Driven ◽  
Lipid Lowering ◽  
Treatment Recommendation

BACKGROUND Type 2 diabetes mellitus (T2DM) and its related complications represent a growing economic burden for many countries and health systems. Diabetes complications can be prevented through better disease control, but there is a large gap between the recommended treatment and the treatment that patients actually receive. The treatment of T2DM can be challenging because of different comprehensive therapeutic targets and individual variability of the patients, leading to the need for precise, personalized treatment. OBJECTIVE The aim of this study was to develop treatment recommendation models for T2DM based on deep reinforcement learning. A retrospective analysis was then performed to evaluate the reliability and effectiveness of the models. METHODS The data used in our study were collected from the Singapore Health Services Diabetes Registry, encompassing 189,520 patients with T2DM, including 6,407,958 outpatient visits from 2013 to 2018. The treatment recommendation model was built based on 80% of the dataset and its effectiveness was evaluated with the remaining 20% of data. Three treatment recommendation models were developed for antiglycemic, antihypertensive, and lipid-lowering treatments by combining a knowledge-driven model and a data-driven model. The knowledge-driven model, based on clinical guidelines and expert experiences, was first applied to select the candidate medications. The data-driven model, based on deep reinforcement learning, was used to rank the candidates according to the expected clinical outcomes. To evaluate the models, short-term outcomes were compared between the model-concordant treatments and the model-nonconcordant treatments with confounder adjustment by stratification, propensity score weighting, and multivariate regression. For long-term outcomes, model-concordant rates were included as independent variables to evaluate if the combined antiglycemic, antihypertensive, and lipid-lowering treatments had a positive impact on reduction of long-term complication occurrence or death at the patient level via multivariate logistic regression. RESULTS The test data consisted of 36,993 patients for evaluating the effectiveness of the three treatment recommendation models. In 43.3% of patient visits, the antiglycemic medications recommended by the model were concordant with the actual prescriptions of the physicians. The concordant rates for antihypertensive medications and lipid-lowering medications were 51.3% and 58.9%, respectively. The evaluation results also showed that model-concordant treatments were associated with better glycemic control (odds ratio [OR] 1.73, 95% CI 1.69-1.76), blood pressure control (OR 1.26, 95% CI, 1.23-1.29), and blood lipids control (OR 1.28, 95% CI 1.22-1.35). We also found that patients with more model-concordant treatments were associated with a lower risk of diabetes complications (including 3 macrovascular and 2 microvascular complications) and death, suggesting that the models have the potential of achieving better outcomes in the long term. CONCLUSIONS Comprehensive management by combining knowledge-driven and data-driven models has good potential to help physicians improve the clinical outcomes of patients with T2DM; achieving good control on blood glucose, blood pressure, and blood lipids; and reducing the risk of diabetes complications in the long term.

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