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Improving urban stormwater management using the hydrological model of water infiltration by rain gardens considering the water column.

1. Introduction

2. materials and methods, 2.1. determination of the main parameters of soil mixtures, 2.2. rainfall event, 2.3. a mathematical hydrological model considering the water column height and filtration coefficient, 2.4. algorithm and software.

• topsoil layer: w s a t 1 = 0.33 m 3 /m 3 ; k f 1 = 7.0 cm/h;
• intermediate/infiltration sand layer: w s a t 2 = 0.31 m 3 /m 3 ; k f 2 = 45.0 cm/h;
• lower gravel layer: w s a t 3 = 0.1 m 3 /m 3 ; k f 3 = 200.0 cm/h.

4. Discussion

5. conclusions.

• The enhanced universal hydrological model, which is based on Darcy’s law, effectively simulates the dynamic processes of rain garden layer saturation at a specific time. It takes into consideration the water column height on the structure’s surface. The model’s accuracy was confirmed using Scilab software, which assessed the rain garden’s performance under extreme conditions during a single excessive rainfall event (36 mm/h). The validated model was then used to simulate the water level on the rain garden surface (water column height) and the saturation depth of the structural layers.
• In our model, the filtration coefficient and thickness of the upper and intermediate infiltration layers of the rain garden are the main parameters that influence the saturation depth of the structure layers and the water column height at the surface. The model is less sensitive to the model parameters associated with the lower gravel layer.
• Increasing the thickness of the top layer by 10 cm results in a longer filling time and a surface column height of 0.341 m. It is recommended to have a top layer thickness of at least 0.3 m. A top layer thickness of less than 0.3 m leads to uneven runoff distribution, reducing efficiency and delaying complete system filling. Adjusting the thickness of the infiltration and gravel layers increases the filling time by an average of 601.25 s and 158.3 s, respectively, with a resulting water column height of 0.34 m. The recommended thicknesses for the infiltration and gravel layers are 0.5–0.6 m and 0.3 m, respectively.
• Changes in the soil mixture’s filtration coefficient significantly affect the rain garden’s hydrological behaviour. Enhanced productivity occurs when the filtration coefficient of the upper layer reaches 7.0 cm/h. At this value, complete saturation and filling of the structure with rainwater takes 7200 s, and the water column height reaches 0.342 m. Increasing the filtration coefficient of the intermediate layer reduces the water column height by an average of 0.007 m and shortens the filling time. When modelling the saturation depth and water column height over time based on the gravel layer’s filtration coefficient, curves for different values overlap and exhibit the same behaviour.
• The depth of the depression zone on the surface of the rain garden is crucial for creating a water column and accumulating stormwater runoff. This allows water to saturate the entire structure vertically. The thicker the top layer of the rain garden and its filtration coefficient, the higher the resulting water column on the surface. The lower gravel layer has a minimal impact on the height of the water column and primarily functions as a drainage layer for water drainage.
• The tested model can predict the performance of rain gardens in stormwater infiltration based on the saturation depth, water column height, drainage area to basin area ratio, and soil filtration coefficient. The effectiveness of the rain garden design decreases when the thickness of the top layer is less than 0.3 m, the thickness of the intermediate infiltration layer is less than 0.5 m when the catchment area to rain garden area ratio falls below 15, or when the filtration coefficient of the top and intermediate infiltration layers surpasses 15 cm/h and 25 cm/h, respectively.

Author Contributions

Data availability statement, conflicts of interest.

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Kravchenko, M.; Wrzesiński, G.; Pawluk, K.; Lendo-Siwicka, M.; Markiewicz, A.; Tkachenko, T.; Mileikovskyi, V.; Zhovkva, O.; Szymanek, S.; Piechowicz, K. Improving Urban Stormwater Management Using the Hydrological Model of Water Infiltration by Rain Gardens Considering the Water Column. Water 2024 , 16 , 2339. https://doi.org/10.3390/w16162339

Kravchenko M, Wrzesiński G, Pawluk K, Lendo-Siwicka M, Markiewicz A, Tkachenko T, Mileikovskyi V, Zhovkva O, Szymanek S, Piechowicz K. Improving Urban Stormwater Management Using the Hydrological Model of Water Infiltration by Rain Gardens Considering the Water Column. Water . 2024; 16(16):2339. https://doi.org/10.3390/w16162339

Kravchenko, Maryna, Grzegorz Wrzesiński, Katarzyna Pawluk, Marzena Lendo-Siwicka, Anna Markiewicz, Tetiana Tkachenko, Viktor Mileikovskyi, Olga Zhovkva, Sylwia Szymanek, and Konrad Piechowicz. 2024. "Improving Urban Stormwater Management Using the Hydrological Model of Water Infiltration by Rain Gardens Considering the Water Column" Water 16, no. 16: 2339. https://doi.org/10.3390/w16162339

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Developing a stochastic hydrological model for informing lake water level drawdown management

Winter drawdown (WD) is a common lake management tool for multiple purposes such as flood control, aquatic vegetation reduction, and lake infrastructure maintenance. To minimize adverse impacts to a lake’s ecosystem, regulatory agencies may provide managers with general guidelines for drawdown and refill timing, drawdown magnitude, and outflow limitations. However, there is significant uncertainty associated with the potential to meet management targets due to variability in lake characteristics and hydrometeorology of each lake’s basin, making the use of modeling tools a necessity. In this context, we developed a hydrological modeling framework for lake water level drawdown management (HMF-Lake) and evaluated it at 15 Massachusetts lakes where WDs have been applied over multiple years for vegetation control. HMF-Lake is based on the daily lake water balance, with inflows simulated by a lumped rainfall-runoff model (Cemaneige-GR4J) and outflow rate calculated by a modified Target Storage and Release Based Method (TSRB). The model showed a satisfactory performance of simulating historical water levels (0.53 ≤ NSE ≤ 0.86), however, uncertainties from meteorological inputs and TSRB determined lake outflow rate affected the result accuracy. To account for these uncertainties, the model was executed stochastically to assess the ability of study lakes to follow the Massachusetts’ general WD guidelines: drawdown by Dec 1 and fully refilled by Apr 1. By using the stochastic HMF-Lake, the probabilities of each lake to reach the drawdown level by Dec 1 were calculated for different drawdown magnitudes (1–6 ft). The probability results suggest it was generally less possible for most of study lakes to achieve a drawdown of 3 ft or more by Dec 1. Moreover, we employed the stochastic model to derive the annual latest refill starting dates that ensure a 95 % probability of reaching the normal water level by Apr 1. We found starting a refill in March for drawdowns up to 6 ft was feasible for most of study lakes. These results provide lake managers with a quantitative understanding of the lake’s ability to follow the state guidelines. The model may be used to systematically evaluate current WD management strategies at state or regional scales and support adaptive WD management under changing climates

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Allison roy, phd, research fish biologist.

Hydro-meteorological Research Study in Madhya Pradesh, Central India: A Literature Review

• Published: 16 August 2024

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• Sarita Tiwari 1 ,
• Ashok Biswal 1 &
• Gajanan Ramteke 2  

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Water is a crucial and invaluable natural resource essential for humanity to sustain on Earth. Around 70% of the Earth’s surface is covered by salt water, which has the largest water volume, and just about 2.5% of fresh water is available for human consumption. The factors that control the spatio-temporal variability of these water resources are envisaged to be of importance. Hydrometeorology is the branch of science that deals with water resource management and understanding water availability by simultaneously using the principles of hydrology and meteorology. Extreme hydro-meteorological events like floods, droughts, and other hydro-meteorological calamities are impacting the region’s water resources. For a big state such as Madhya Pradesh, where the availability of hydro-meteorological data is critical in dealing with the management of water resources not only for the state but for the other neighbouring states, those aquifers and rivers are fed by the cross-boundary rivers of the state. Several research activities that have been carried out in Madhya Pradesh in hydrometeorology and allied disciplines by various researchers are reviewed and presented in this paper. This research paper also discussed the analysis of hydrometeorology services and highlighted the significance of hydrometeorology research at regional level. Apart from this, the major challenges faced in hydro-meteorological research in Madhya Pradesh are also highlighted in the paper.

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The authors thank the Central Ground Water Board, North Central Region, Bhopal, for providing support and encouragement in writing this paper.

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Tiwari, S., Biswal, A. & Ramteke, G. Hydro-meteorological Research Study in Madhya Pradesh, Central India: A Literature Review. Pure Appl. Geophys. (2024). https://doi.org/10.1007/s00024-024-03553-6

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Published : 16 August 2024

DOI : https://doi.org/10.1007/s00024-024-03553-6

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Abstract: Traditional quantitative investment research is encountering diminishing returns alongside rising labor and time costs. To overcome these challenges, we introduce the Large Investment Model (LIM), a novel research paradigm designed to enhance both performance and efficiency at scale. LIM employs end-to-end learning and universal modeling to create an upstream foundation model capable of autonomously learning comprehensive signal patterns from diverse financial data spanning multiple exchanges, instruments, and frequencies. These "global patterns" are subsequently transferred to downstream strategy modeling, optimizing performance for specific tasks. We detail the system architecture design of LIM, address the technical challenges inherent in this approach, and outline potential directions for future research. The advantages of LIM are demonstrated through a series of numerical experiments on cross-instrument prediction for commodity futures trading, leveraging insights from stock markets.

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