RZWQM2-P was tested for its ability to predict hydrology and phosphorus transport from a subsurface drained field. The model accurately predicted drainage discharge (NSE=0.66-0.76) but underestimated total phosphorus and dissolved reactive phosphorus losses (NSE=0.31-0.46, PBIAS=-32.52 to -61.63%). The model was not sensitive to changes in fertilizer application rates and could not distribute phosphorus to drainage discharge under surface ponding conditions. Using high-resolution daily data revealed more variability in phosphorus concentrations than previous studies using aggregated flow data, important for validating the model. More testing is needed to improve the phosphorus simulation component of RZW
Steve Davis - Western Lake Erie Basin Nutrient Reductions: Goals And Programs...John Blue
Western Lake Erie Basin Nutrient Reductions: Goals And Programs To Get There - Steve Davis, from the 2018 Conservation Tillage and Technology Conference, March 6 - 7, Ada, OH, USA.
More presentations at https://www.youtube.com/channel/UCZBwPfKdlk4SB63zZy16kyA
Improving the quantification of agricultural emissions in low-income countries. WATCH LIVE on WEDNESDAY 4 DECEMBER 14:30 CET: http://ccafs.cgiar.org/videostream
Farm-level options for accelerating the transition towards climate smart agri...CIAT
The difference between clever and smart people is mainly that clever people can get in and out of problems which smart people would not have gotten into in the first place. In the same light, faced with multifaceted challenges related to climate change, smartness would entail adapting our agricultural systems to avoid experiencing the negative impacts of climate change. In other words, climate smart agriculture (CSA) involves changing our agricultural systems to simultaneously address climate change challenges such as low food production, accelerated land degradation and increasing atmospheric concentrations of greenhouse gases. To achieve these objectives, agricultural systems should (1) sustainably increase productivity; (2) adapt and build resilience to climate change; and (3) reduce and/or avoid the emission of greenhouse gases. As will be discussed in this presentation, there is definitely no single agricultural technology or practice that can be universally applied to achieve these objectives. Nonetheless, site-specific assessments should be pursued to identify suitable agricultural practices, technologies, polices, financing and institutional arrangements that enhance smartness within a given situation. It will be noted that CSA is not necessarily based on new practices, technologies, polices and institutions. However, it involves holistically and simultaneously addressing challenges related to climate change by using a combination of familiar practices, technologies, polices and institutions in strategic but unfamiliar ways; that are not counterproductive. Moreover, the presentation aims to start a conversation on part of the work that has been done, is being done and can be done, through CIAT, to accelerate the transition towards smarter agriculture systems to ensure that, similar to smart people, we can avoid problems that complicate ours and the lives of generations to come.
Irrigation Quality of Surface Water of Rural Areas around Kota City, Rajasthanijtsrd
Due to the natural and anthropogenic inputs, the Chambal River which passes through the Kota city has gradually deteriorated. The assessment of surface water quality is an important aspect to understand the ecological sustainability of the river. Hence, in this study the surface water quality of Kota was evaluated using long time series data 1999 to 2016 for pre monsoon and post monsoon period. Data on monitored locations were collected from Public Health Engineering Department PHED . Various physio chemical parameters of surface water quality for River Chambal, Akelgarh water treatment plant and Sakatpura water treatment plant were examined to assess the extent of pollution and its suitability for drinking and irrigation purposes. Apart from this the seasonal and temporal variations in water supply of Kota city were observed during 2006 2016. The results imply that water quality of River Chambal is moderately polluted, hence to maintain its water quality proper waste disposal technique should be adopted. However, drinking water supply system analysis indicates the shortage of water supply in outskirts of the city, so water transmission system need to be augmented in near future to supply additional demand in the newly developed areas in the city. Nitin Gupta | S. M. Nafees "Irrigation Quality of Surface Water of Rural Areas around Kota City, Rajasthan" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-6 , October 2022, URL: https://www.ijtsrd.com/papers/ijtsrd51824.pdf Paper URL: https://www.ijtsrd.com/chemistry/other/51824/irrigation-quality-of-surface-water-of-rural-areas-around-kota-city-rajasthan/nitin-gupta
Steve Davis - Western Lake Erie Basin Nutrient Reductions: Goals And Programs...John Blue
Western Lake Erie Basin Nutrient Reductions: Goals And Programs To Get There - Steve Davis, from the 2018 Conservation Tillage and Technology Conference, March 6 - 7, Ada, OH, USA.
More presentations at https://www.youtube.com/channel/UCZBwPfKdlk4SB63zZy16kyA
Improving the quantification of agricultural emissions in low-income countries. WATCH LIVE on WEDNESDAY 4 DECEMBER 14:30 CET: http://ccafs.cgiar.org/videostream
Farm-level options for accelerating the transition towards climate smart agri...CIAT
The difference between clever and smart people is mainly that clever people can get in and out of problems which smart people would not have gotten into in the first place. In the same light, faced with multifaceted challenges related to climate change, smartness would entail adapting our agricultural systems to avoid experiencing the negative impacts of climate change. In other words, climate smart agriculture (CSA) involves changing our agricultural systems to simultaneously address climate change challenges such as low food production, accelerated land degradation and increasing atmospheric concentrations of greenhouse gases. To achieve these objectives, agricultural systems should (1) sustainably increase productivity; (2) adapt and build resilience to climate change; and (3) reduce and/or avoid the emission of greenhouse gases. As will be discussed in this presentation, there is definitely no single agricultural technology or practice that can be universally applied to achieve these objectives. Nonetheless, site-specific assessments should be pursued to identify suitable agricultural practices, technologies, polices, financing and institutional arrangements that enhance smartness within a given situation. It will be noted that CSA is not necessarily based on new practices, technologies, polices and institutions. However, it involves holistically and simultaneously addressing challenges related to climate change by using a combination of familiar practices, technologies, polices and institutions in strategic but unfamiliar ways; that are not counterproductive. Moreover, the presentation aims to start a conversation on part of the work that has been done, is being done and can be done, through CIAT, to accelerate the transition towards smarter agriculture systems to ensure that, similar to smart people, we can avoid problems that complicate ours and the lives of generations to come.
Irrigation Quality of Surface Water of Rural Areas around Kota City, Rajasthanijtsrd
Due to the natural and anthropogenic inputs, the Chambal River which passes through the Kota city has gradually deteriorated. The assessment of surface water quality is an important aspect to understand the ecological sustainability of the river. Hence, in this study the surface water quality of Kota was evaluated using long time series data 1999 to 2016 for pre monsoon and post monsoon period. Data on monitored locations were collected from Public Health Engineering Department PHED . Various physio chemical parameters of surface water quality for River Chambal, Akelgarh water treatment plant and Sakatpura water treatment plant were examined to assess the extent of pollution and its suitability for drinking and irrigation purposes. Apart from this the seasonal and temporal variations in water supply of Kota city were observed during 2006 2016. The results imply that water quality of River Chambal is moderately polluted, hence to maintain its water quality proper waste disposal technique should be adopted. However, drinking water supply system analysis indicates the shortage of water supply in outskirts of the city, so water transmission system need to be augmented in near future to supply additional demand in the newly developed areas in the city. Nitin Gupta | S. M. Nafees "Irrigation Quality of Surface Water of Rural Areas around Kota City, Rajasthan" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-6 , October 2022, URL: https://www.ijtsrd.com/papers/ijtsrd51824.pdf Paper URL: https://www.ijtsrd.com/chemistry/other/51824/irrigation-quality-of-surface-water-of-rural-areas-around-kota-city-rajasthan/nitin-gupta
Characterization and the Kinetics of drying at the drying oven and with micro...Open Access Research Paper
The objective of this work is to contribute to valorization de Nephelium lappaceum by the characterization of kinetics of drying of seeds of Nephelium lappaceum. The seeds were dehydrated until a constant mass respectively in a drying oven and a microwawe oven. The temperatures and the powers of drying are respectively: 50, 60 and 70°C and 140, 280 and 420 W. The results show that the curves of drying of seeds of Nephelium lappaceum do not present a phase of constant kinetics. The coefficients of diffusion vary between 2.09.10-8 to 2.98. 10-8m-2/s in the interval of 50°C at 70°C and between 4.83×10-07 at 9.04×10-07 m-8/s for the powers going of 140 W with 420 W the relation between Arrhenius and a value of energy of activation of 16.49 kJ. mol-1 expressed the effect of the temperature on effective diffusivity.
"Understanding the Carbon Cycle: Processes, Human Impacts, and Strategies for...MMariSelvam4
The carbon cycle is a critical component of Earth's environmental system, governing the movement and transformation of carbon through various reservoirs, including the atmosphere, oceans, soil, and living organisms. This complex cycle involves several key processes such as photosynthesis, respiration, decomposition, and carbon sequestration, each contributing to the regulation of carbon levels on the planet.
Human activities, particularly fossil fuel combustion and deforestation, have significantly altered the natural carbon cycle, leading to increased atmospheric carbon dioxide concentrations and driving climate change. Understanding the intricacies of the carbon cycle is essential for assessing the impacts of these changes and developing effective mitigation strategies.
By studying the carbon cycle, scientists can identify carbon sources and sinks, measure carbon fluxes, and predict future trends. This knowledge is crucial for crafting policies aimed at reducing carbon emissions, enhancing carbon storage, and promoting sustainable practices. The carbon cycle's interplay with climate systems, ecosystems, and human activities underscores its importance in maintaining a stable and healthy planet.
In-depth exploration of the carbon cycle reveals the delicate balance required to sustain life and the urgent need to address anthropogenic influences. Through research, education, and policy, we can work towards restoring equilibrium in the carbon cycle and ensuring a sustainable future for generations to come.
Artificial Reefs by Kuddle Life Foundation - May 2024punit537210
Situated in Pondicherry, India, Kuddle Life Foundation is a charitable, non-profit and non-governmental organization (NGO) dedicated to improving the living standards of coastal communities and simultaneously placing a strong emphasis on the protection of marine ecosystems.
One of the key areas we work in is Artificial Reefs. This presentation captures our journey so far and our learnings. We hope you get as excited about marine conservation and artificial reefs as we are.
Please visit our website: https://kuddlelife.org
Our Instagram channel:
@kuddlelifefoundation
Our Linkedin Page:
https://www.linkedin.com/company/kuddlelifefoundation/
and write to us if you have any questions:
info@kuddlelife.org
Willie Nelson Net Worth: A Journey Through Music, Movies, and Business Venturesgreendigital
Willie Nelson is a name that resonates within the world of music and entertainment. Known for his unique voice, and masterful guitar skills. and an extraordinary career spanning several decades. Nelson has become a legend in the country music scene. But, his influence extends far beyond the realm of music. with ventures in acting, writing, activism, and business. This comprehensive article delves into Willie Nelson net worth. exploring the various facets of his career that have contributed to his large fortune.
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Introduction
Willie Nelson net worth is a testament to his enduring influence and success in many fields. Born on April 29, 1933, in Abbott, Texas. Nelson's journey from a humble beginning to becoming one of the most iconic figures in American music is nothing short of inspirational. His net worth, which estimated to be around $25 million as of 2024. reflects a career that is as diverse as it is prolific.
Early Life and Musical Beginnings
Humble Origins
Willie Hugh Nelson was born during the Great Depression. a time of significant economic hardship in the United States. Raised by his grandparents. Nelson found solace and inspiration in music from an early age. His grandmother taught him to play the guitar. setting the stage for what would become an illustrious career.
First Steps in Music
Nelson's initial foray into the music industry was fraught with challenges. He moved to Nashville, Tennessee, to pursue his dreams, but success did not come . Working as a songwriter, Nelson penned hits for other artists. which helped him gain a foothold in the competitive music scene. His songwriting skills contributed to his early earnings. laying the foundation for his net worth.
Rise to Stardom
Breakthrough Albums
The 1970s marked a turning point in Willie Nelson's career. His albums "Shotgun Willie" (1973), "Red Headed Stranger" (1975). and "Stardust" (1978) received critical acclaim and commercial success. These albums not only solidified his position in the country music genre. but also introduced his music to a broader audience. The success of these albums played a crucial role in boosting Willie Nelson net worth.
Iconic Songs
Willie Nelson net worth is also attributed to his extensive catalog of hit songs. Tracks like "Blue Eyes Crying in the Rain," "On the Road Again," and "Always on My Mind" have become timeless classics. These songs have not only earned Nelson large royalties but have also ensured his continued relevance in the music industry.
Acting and Film Career
Hollywood Ventures
In addition to his music career, Willie Nelson has also made a mark in Hollywood. His distinctive personality and on-screen presence have landed him roles in several films and television shows. Notable appearances include roles in "The Electric Horseman" (1979), "Honeysuckle Rose" (1980), and "Barbarosa" (1982). These acting gigs have added a significant amount to Willie Nelson net worth.
Television Appearances
Nelson's char
Prevalence of Toxoplasma gondii infection in domestic animals in District Ban...Open Access Research Paper
Toxoplasma gondii is an intracellular zoonotic protozoan parasite, infect both humans and animals population worldwide. It can also cause abortion and inborn disease in humans and livestock population. In the present study total of 313 domestic animals were screened for Toxoplasma gondii infection. Of which 45 cows, 55 buffalos, 68 goats, 60 sheep and 85 shaver chicken were tested. Among these 40 (88.88%) cows were negative and 05 (11.12%) were positive. Similarly 55 (92.72%) buffalos were negative and 04 (07.28%) were positive. In goats 68 (98.52%) were negative and 01 (01.48%) was recorded positive. In sheep and shaver chicken the infection were not recorded.
Prevalence of Toxoplasma gondii infection in domestic animals in District Ban...
August 31 - 0130 - Md Sami Bin Shokrana
1. Predicting Hydrology and Phosphorus Transport from
a Subsurface-Drained Field Using RZWQM2-P
Sami Shokrana1, Ehsan Ghane1, Zhiming Qi2
1Department of Biosystems and Agricultural Engineering, Michigan State University, USA
2Department of Bioresource Engineering, McGill University, Canada
08/31/2022
International Drainage Symposium
2. Choosing a P Loss Simulation Model
2
Models
Surface runoff P Drainage discharge P
Macropore P
Plant
uptake P
P
transformation
Farm
management
practices
DRP
loss
PP loss DRP loss PP loss
ADAPT ✔ ✔ ✔ ✘ ✔ ✔ ✔ limited
APEX/EPIC ✔ ✔ simplified ✘ ✔ ✔ ✔ ✔
HYDRUS ✘ ✘ ✘ ✘ ✔ ✔ ✘ limited
MACRO ✘ ✘ ✘ ✘ ✔ ✘ ✘ ✘
ICECREAM ✔ ✔ simplified ✔ ✔ ✔ ✔
PLEASE ✘ ✘ ✔ ✘ ✘ ✘ ✘ limited
SWAP/ANIMO ✔ ✘ ✔ ✔ ✔ ✔ ✘ ✘
DRAINMOD-P ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔
RZWQM2-P ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔
3. Literature review
3
• RZWQM2-P is not well-
tested
• Tested twice under same
soil, climate, and crop
conditions, only
fertilization was different
4. Objectives
• To test and validate the performance of RZWQM2-P in predicting DRP
and TP loss through drainage discharge in a clay loam soil
• To identify the performance of the model under high-resolution daily
flow and load data
4
5. • Soil type: Ziegenfuss clay
loam
• Drain depth: 2.68 ft (0.82 m)
• Drain spacing: 33 ft (10.06 m)
• Field slope: 0.1%
• Corn-Soybean rotation
• Commercial fertilizer
• Vertical till before corn
5
Methods: Site description
Blissfield Site (7.6 hectares)
6. 6
Methods: Input Data
• Soil water characteristic input data
gSSURGO database
Delineated into 5 layers (0 – 205 cm)
• Weather input data
Precipitation
Solar radiation
Wind speed
Air temperature
Relative humidity
Image source: Minh Uong (The New York Times)
Source: Shokrana and Ghane (2020)
7. 7
Methods: Calibration and Validation
October 1, 2018 June 30, 2022
3 years and 9 months
Calibration period (2 years)
September 30,
2020
Validation period (1 year and 9 months)
October 1,
2020
October 1,
2018
June 30,
2022
Calibrated hydrology parameters
Soil water parameters
Runoff parameters
Drainage parameters
Evapotranspiration parameters
Water table fluctuation parameters
Calibrated DRP and TP parameters
Macropore parameters
Initial P level in GW reservoir
Soil filtration coefficient
Soil replenishment rate coefficient
Soil detachability coefficient
8. • Nash-Sutcliffe Efficiency (NSE)
• Percent Bias (PBIAS)
8
Methods: Performance Evaluation Statistics
Statistics Hydrology DRP and TP
NSE Very good (NSE > 0.75)
Good (0.60 < NSE ≤ 0.75)
Satisfactory (0.4 < NSE ≤ 0.60)
Very good (NSE > 0.65)
Good (0.50 < NSE ≤ 0.65)
Satisfactory (0.35 < NSE ≤ 0.50)
PBIAS Very good (PBIAS < ±10%)
Good (±10% < PBIAS < ±15%)
Satisfactory (±15% < PBIAS < ±25%)
Very good (PBIAS < ±15%)
Good (±15% < PBIAS < ±20%)
Satisfactory (±20% < PBIAS < ±30%)
Source: Skaggs et al. (2012), Moriasi et al. (2007, 2015)
Daily time-step
12. • Fertilizer application rate in April 2019: 22.5 Kg/ha
• Fertilizer application rate in May 2020: 9.5 Kg/ha
• Fertilizer application rate in May 2020 was increased up to 150
Kg/ha, but still all the P were lost by surface runoff.
Discussions: Model’s Insensitivity to Fertilizer
Application
13
Model is unable to distribute fertilizer input to
drainage discharge under surface ponding conditions
13. Discussions: Daily data vs Event-Based Data
14
• Sadhukhan et al. (2019):
Aggregated flow data into several flow periods/events
Each event consisted of several weeks to several months
Dampens the visibility of rapid changes in P concentration
• This study:
High-resolution daily flow and load data
Captures the rapid fluctuations of P concentration in drainage
discharge
Higher resolution sampling strategy is beneficial to capture the
variation and fluctuation of P concentration in drainage discharge
14. Take-Home Messages
• The P model’s performance needs to be modified under surface
ponding conditions
• More tests with daily data are needed. The rapid changes in P
concentration are more visible with daily data
• RZWQM2-P is reliable in predicting drainage discharge from
agricultural fields, but more tests are necessary to validate the
performance of P module
15
16. Thank You
Sami Shokrana
Graduate Research Assistant/PhD Candidate
Department of Biosystems and Agricultural Engineering,
Michigan State University
Email: shokrana@msu.edu
Phone: +1(606)306-9453
17
Editor's Notes
Good morning, everyone. Thank you for joining today’s presentation. All of you are aware of the eutrophication problem in the WLEB which is caused by N and P pollution. The major non-point source behind this problem is considered to be agriculture. Field-scale hydrologic models can be a useful tool to predict these nutrients losses from these agricultural fields. This study focuses on using such a field-scale model to address towards the HABs issue in the WLEB.
Today’s presentation title is “……..”
My name is Sami Shokrana and I am a PhD candidate in the in the MSU. My coauthors are Dr. Ehsan Ghane, who is also my PhD advisor. The other coauthor is Dr. Zhiming Qi, who is one of the developer of the P module of the RZWQM2.
My today’s presentation topic is “…………………………………”
First step was to chose a P loss model.
before choosing the model, We decided that the model should be able to fulfil some criteria.
The Model should be able to simulate DRP and PP loss through both surface runoff and drainage discharge
The model should have a macropore component that can simulate hydrology and P loss through macropores.
The model should have a component where plant P uptake is represented
The P transformations such as mineralization and immobilization processes should be represented
Also, if the user wants to implement different beneficial management practices to see their effect on P loss, the model should also have provision to that.
Only 2 models fulfil all these criterion. We decided to go with he RZWQM2.
RZ model was first tested by Dr. Sadhukhan, Dr. Zhiming and their team. They did an excellent job in paving the path for developing future P modules for field-scale models. Since its development, the P module of the RZ has been tested twice. For those 2 studies the soil type, climate, cropping practice was the same only management practice was different. So, there is further need for testing of the P module under different soil, climate, and cropping conditions.
The objective of my research is to “Evaluate ……….”.
This study will bring out the limitation of the model and help developers identify the processes and subroutines that need to be modified for an accurate prediction of P loss from subsurface-drained field.
We also want to see how the model performs under daily flow and load data.
Mention restrictive layer
Mention how weather data were collected: You can use either daily or hourly weather data. Since, we have measured hourly weather data, we used those as input for weather files. We have an on-site ATMOS-41 weather station. If there were missing weather data, we collected them eith from NOAA or a neighboring weather station called enviroweather.
NSE is a measure of how well the model predictions are whereas
PBIAS measures the avg. tendency of the simulated data to be larger or smaller than observed data
These performance statistics were collected from Dr. Skaggs paper and Dr. Moriasi’s papers
Primary vertical axis: drainage discharge
Secondary vertical axis: precipitation
Horizontal axis: timeline of the study
This is how the precipitation looked like over the study period. And this is the observed drainage discharge in response to the precipitation. And this is how the simulated drainage discharge looked like.
This red marker differentiates between the calibration and validation period
You all probably remember how wet the spring 2019 was. Farmers could not plant anything. Which crops were planted the next years?
Overall, it seems like the model did a good job in predicted all the observed peak flow events. We know that P loss is highest during the non-growing season which is from about October-March. So, it is very important that the model predicts the hydrology well during this period and the model did predict the well during this period.
The NSE value shows good model performance, while the PBIAS shows that the model has slightly overprediction bias
Primary axis has TP load, and horizontal axis has timeline of the study period
This is our measured TP load and this is the predicted TP load by the RZWQM2
Inorganic fertilizer was applied on April 2019 and on May 2020.
This red line differentiates calibration period from the validation period.
I should also mention that RZ does not directly predict TP. It predicts DRP and PP. Since TP is a combination of DRP, DUP, and PP, for this study we assumed that DUP is zero and TP = DRP + PP.
The model was able to predict well almost all the peaks of the TP loss events. The model did well when the P loss events were smaller in magnitude. But for events with high TP loss, the simulated TP could not predict those losses very well. In other words, the difference between observed and simulated TP loss were high for large TP loss events.
Our model performance statistics also agrees to that.
For the calibration period, NSE suggests a satisfactory model performance, while PBIAS is also satisfactory with an overestimation bias
For the validation period, NSE suggest satisfactory model performance but PBIAS suggest unsatisfactory simulation with underestimation bias
This time Primary axis has DRP load in Kg/ha and horizontal axis has timeline of the study.
This is our measured DRP load and this is the predicted DRP load by the RZWQM2
Inorganic fertilizer was applied on April 2019 and on May 2020.
Same as TP, the model also could not predict well the high DRP loss events both during calibration and validation period. DRP prediction was not good for most of the higher DRP loss events.
The model performance statistics for calibration period suggest unsatisfactory model performance with overestimation beyond the range of satisfaction.The result was unsatisfactory for the validation period as well
So started to think what could be the reason behind this unsatisfactory performance of the model for DRP.
We tried to understand what happened to DRP load after fertilizer application.
Fertilizer was applied on April 2019 at a rate of 22.5 Kg/ha
Again, it was applied on May 2020 at a rate of 9.5 kg/ha
So, If I apply more fertilizer, more P should be lost through drainage discharge.
To prove this hypothesis, we increase the fertilizer input 15-fold, but still the DRP input to drainage discharge did not change, All the DRP was lost through surface runoff.
Then we tried to dig deeper. We have time-lapse cameras installed in the field. We saw there was surface ponding on the field due to heavy precipitation after fertilization event. So, our assumption is that RZ-P model cannot perform well under surface ponding conditions.
The second reason for the underestimation of the model is the presence of high water table on or near the surface. We have time-lapse cameras installed in the field and cameras show that due to heavy rainfall, there was water on the surface on the surface for those events. The images are from the May 2020 and Ocr 2021 events. And you can see ponding on the field.
So, our guess is that the RZWQM2-P has limitations in simulating P loss through drainage discharge when water table is on or near the surface
Given the dynamic behavior of P, it would be better to use a higher resolution sampling strategy to capture the variation and fluctuation of P concentration in drainage discharge
First, the RZWQM2-P model did not perform well while predicting DRP loss. This unsatisfactory performance could be attributed by the underwhelming performance of the P model under ponded condition. So, the performance of the P model needs to be improved especially after fertilizer input.Second, the P model needs to be further tested with high-resolution daily data. As mentioned earlier, daily data are better than event-based data as it captures the variability and fluctuations of P concentration.
Third, Overall, the performance of RZWQM2 model in hydrology simulation was good, the performance of P model for predicting TP was satisfactory, but for DRP it was unsatisfactory. Therefore to check for the reliability of the P model, more tests are necessary.