SWAP results for manuscript

sumanager56 commented 7 months ago

Here, I would like to upload tables and plots for the results section

sumanager56 commented 7 months ago

Calibration plots - 2021 data

sumanager56 commented 7 months ago

I would like to include a table like this to indicate the performance evaluation ratings of the calibration and validation datasets. This might be better to include in the figure itself once I finalize how many/what calibration and validation plots I should include in the manuscript. <html xmlns:v="urn:schemas-microsoft-com:vml" xmlns:o="urn:schemas-microsoft-com:office:office" xmlns:x="urn:schemas-microsoft-com:office:excel" xmlns="http://www.w3.org/TR/REC-html40">

| Calibration-2021 | | | Validation-2020 | | | Validation-2021 | | | Validation-2021 | | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | -- | Non-Irrigated | | | Non-Irrigated | | | Full Irrigation | | | Precision Irrigation | | Depth (cm) | RMSE | NRMSE | R2 | RMSE | NRMSE | R2 | RMSE | NRMSE | R2 | RMSE | NRMSE | R2 10 | 0.044 | 26.6 | 0.6 | 0.05 | 29.9 | 0.64 | 0.044 | 25.7 | 0.59 | 0.04 | 22.5 | 0.67 20 | 0.037 | 15.7 | 0.62 | 0.046 | 25.9 | 0.69 | 0.04 | 19.3 | 0.53 | 0.042 | 21 | 0.52 30 | 0.031 | 15.4 | 0.82 | 0.042 | 20.6 | 0.76 | 0.037 | 14.9 | 0.64 | 0.038 | 18.5 | 0.56 40 | 0.026 | 10.7 | 0.84 | 0.056 | 25.2 | 0.83 | 0.03 | 12.5 | 0.68 | 0.045 | 16.5 | 0.79 50 | 0.016 | 5.4 | 0.55 | 0.012 | 4.1 | 0.85 | 0.094 | 44.2 | 0.42 | 0.027 | 8.5 | 0.63 60 | 0.013 | 3.9 | 0.56 | 0.042 | 13.9 | 0.39 | 0.108 | 43.5 | 0.59 | 0.038 | 12.4 | 0.53

RMSE (root mean square error), NRMSE (normalized root mean square error) and R2 (coefficient of determination) were used to evaluate the coincidence between the simulated and measured values in the process of model calibration and validation. The smaller the RMSE, the higher the simulation accuracy. When NRMSE is less than 10%, the simulation effect is considered very good; when NRMSE is between 10% and 20%, the simulation effect is good; when NRMSE is between 20% and 30%, the simulation effect is reasonable; and when NRMSE is greater than 30%, the simulation effect is poor (Jamieson et al., 1991; Zhao et al., 2020). The agreement between the simulated and measured values is high when R2 is close to one.

julieshortridge commented 7 months ago

Thanks Suman. I think the table is good, but I don't think you necessarily need all three measures - I'd suggest just including the NRMSE and R2. Then I think you can also include the plots, possibly as supplementary figures depending on how long the manuscript is looking.

sumanager56 commented 7 months ago

Table of mean results for different treatments and scenarios along with their statistical results (Dunn pairwise comparison test for the three treatments and Wilcoxon rank sum test for WUE between the two treatments). Folder ref: 14-22-January <html xmlns:v="urn:schemas-microsoft-com:vml" xmlns:o="urn:schemas-microsoft-com:office:office" xmlns:x="urn:schemas-microsoft-com:office:excel" xmlns="http://www.w3.org/TR/REC-html40">

Parameters | Treatments | Scenario_0 | Scenario_1 | Scenario_2 | Scenario_3 | Scenario_4 | Scenario_5 -- | -- | -- | -- | -- | -- | -- | -- Corn_Yield (bu/acre) | Rainfed | 151 | 106 | 121 | 101 | 106 | 118 | Calendar | 173 | 167 | 169 | 168 | 165 | 169 | Precision | 200 | 180 | 179 | 180 | 178 | 176 WUE (bu/acre/cm) | Calendar | 0.48 | 4.47 | 3.99 | 4.75 | 4.11 | 4.18 | Precision | 4.33 | 5.72 | 5.17 | 5.69 | 5.36 | 5.71 N-Yield (kg/ha) | Rainfed | 196 | 156 | 174 | 149 | 155 | 170 | Calendar | 210 | 219 | 221 | 220 | 215 | 221 | Precision | 220 | 231 | 231 | 231 | 229 | 228 NO3-Leach (kg/ha)-Growing Season | Rainfed | 25 | 49 | 48 | 48 | 50 | 50 | Calendar | 20 | 39 | 38 | 38 | 40 | 38 | Precision | 14 | 32 | 33 | 31 | 33 | 34 NO3-Leach (kg/ha)-Yearly | Rainfed | 47 | 107 | 96 | 109 | 107 | 98 | Calendar | 31 | 72 | 68 | 72 | 74 | 68 | Precision | 26 | 67 | 65 | 68 | 68 | 66 Irrigation (mm/year) | Calendar | 124 | 132 | 111 | 139 | 136 | 115 | Precision | 101 | 126 | 98 | 134 | 128 | 102

90%significance level | | | | | | | -- | -- | -- | -- | -- | -- | -- | -- Parameters | Treatments | Scenario_0 | Scenario_1 | Scenario_2 | Scenario_3 | Scenario_4 | Scenario_5 Corn_Yield (bu/acre) | Rainfed | a | a | a | a | a | a | Calendar | a | b | b | b | b | b | Precision | b | b | b | b | b | b N-Yield (kg/ha) | Rainfed | a | a | a | a | a | a | Calendar | a | b | b | b | b | b | Precision | b | b | b | b | b | b NO3-Leach (kg/ha) | Rainfed | a | a | a | a | a | a | Calendar | b | b | b | b | b | b | Precision | c | b | b | b | b | b NO3-Leach (kg/ha)-Growing Season | Rainfed | a | a | a | a | a | a | Calendar | b | b | b | b | b | b | Precision | c | c | b | b | b | b WUE (bu/ac/cm-irrigation) | Calendar | a | a | a | a | a | a | Precision | b | b | b | b | b | b

sumanager56 commented 7 months ago

Results table overall

Different letters after numbers in the same column indicate significant differences between the treatments (P < 0.05). <html xmlns:v="urn:schemas-microsoft-com:vml" xmlns:o="urn:schemas-microsoft-com:office:office" xmlns:x="urn:schemas-microsoft-com:office:excel" xmlns="http://www.w3.org/TR/REC-html40">

Parameters | Treatments | Scenario 0 | Scenario 1 | Scenario 2 | Scenario 3 | Scenario 4 | Scenario 5 -- | -- | -- | -- | -- | -- | -- | -- Corn Yield (bu/acre) | Rainfed | 151 a | 106 a | 121 a | 101 a | 106 a | 118 a | Calendar | 173 a | 167 b | 169 b | 168 b | 165 b | 169 b | Precision | 200 b | 180 b | 179 b | 180 b | 178 b | 176 b WUE (bu/acre/cm) | Calendar | 0.48 a | 4.47 a | 3.99 a | 4.75 a | 4.11 a | 4.18 a | Precision | 4.33 b | 5.72 b | 5.17 a | 5.69 b | 5.36 b | 5.71 b Nitrogen uptake (kg/ha) | Rainfed | 196 a | 156 a | 174 a | 149 a | 155 a | 170 a _NH4+NO3_ | Calendar | 210 b | 219 b | 221 b | 220 b | 215 b | 221 b | Precision | 220 b | 231 b | 231 b | 231 b | 229 b | 228 b NO3 Leach (kg/ha) | Rainfed | 25 a | 49 a | 48 a | 48 a | 50 a | 50 a _Growing season_ | Calendar | 20 a | 39 ab | 38 b | 38 ab | 40 ab | 38 b | Precision | 14 b | 32 b | 33 b | 31 b | 33 b | 34 b NO3 Leach (kg/ha) | Rainfed | 47 a | 107 a | 96 a | 109 a | 107 a | 98 a _Annual_ | Calendar | 31 b | 72 b | 68 b | 72 b | 74 b | 68 b | Precision | 26 c | 67 b | 65 b | 68 b | 68 b | 66 b

sumanager56 commented 7 months ago

Simulated vs observed yield and Nitrogen uptake under different irrigation scenarios

Treatment | Corn Yield (bu/ac) | | Nitrogen uptake (kg/ha) | | Irrigation depth (mm) -- | -- | -- | -- | -- | -- | Observed | Simulated | Observed | Simulated | Observed Rainfed | 182 | 196 | 271 | 225 | 0 Weather-Informed | 205 | 214 | 295 | 241 | 38 Full-Irrigation | 195 | 209 | 283 | 237 | 56

sumanager56 commented 7 months ago

Table: Overview of climate change scenarios

sumanager56 commented 7 months ago

Scenario 0

plot_zoom

Scenario 1

plot_zoom

Scenario 2

plot_zoom

Scenario 3

plot_zoom

Scenario 4

plot_zoom

Scenario 5

plot_zoom

sumanager56 commented 7 months ago

Calibrated values of soil hydraulic parameters at different depths

Layer | Soil Depth (cm) | Residual Water Content θres (cm3/cm3) | Saturated Water Content θsat (cm3/cm3) | Shape Factor for Soil Water Retention Curve α (cm-1) | Shape Factor for Soil Water Retention Curve n | Saturated Hydraulic Conductivity Ksat (cm/day) | Hydraulic Conductivity Shape Factor λ -- | -- | -- | -- | -- | -- | -- | -- 1 | 0-15 | 0.07 | 0.29 | 0.0060 | 3.32 | 5.02 | 0.49 2 | 15-40 | 0.09 | 0.29 | 0.0040 | 2.64 | 10.02 | 0.67 3 | 40-55 | 0.06 | 0.31 | 0.0010 | 1.42 | 7.75 | 0.51 4 | 55-85 | 0.06 | 0.37 | 0.0070 | 1.09 | 5.84 | 0.61 5 | 85-103 | 0.15 | 0.35 | 0.0005 | 1.52 | 4.38 | 0.53 6 | 103-125 | 0.03 | 0.42 | 0.0003 | 1.84 | 2.40 | 0.58

sumanager56 commented 7 months ago

Crop and Soil-N calibration parameters

Parameters | Description | Initial Values | Calibrated Values -- | -- | -- | -- TDWI | Initial total crop dry weight (kg/ha) | 20 | 25 TSUMEA | Temperature sum from emergence to anthesis (Cd) | 1050 | 980 TSUMAM | Temperature sum from anthesis to maturity (Cd) | 1000 | 950 SPAN | Life span of leaves under optimum conditions (days) | 33 | 51 AMAX | Max CO2 assimilation rate [kg(CO2)/ha(leaf)/h] | 21-70 | 31-75 CVO | Conversion efficiency of assimilates into storage organ (kg/kg) | 0.67 | 0.78 H3h | H below which optimum water uptake reduction starts in the root zone in case of high atmospheric demand (cm) | -400 | -350 H3l | H below which optimum water uptake reduction starts in the root zone in case of low atmospheric demand (cm) | -500 | -400 H4 | Wilting point, no water uptake at lower pressure heads (cm) | -10,000 | -1200 RDC | Maximum rooting depth (cm) | 100 | 85 NMAXSO | Maximum concentration of N in storage organs (kg/kg) | 0.05 | 0.016 NMXLV | Maximum N concentration in leaves as function of development stage: 0.0-0.4-0.7-1.0-2.0-2.1 (kg/kg) | 0.06-0.04-0.03-0.02-0.022-0.022 | 0.06-0.04-0.03-0.027-0.027-0.022 RateConNitrif_ref | Nitrification rate constant established at the reference temperature (1/d) | 1 | 0.1 RateConDenitr_red | Dentrification rate constant established at the reference temperature (1/d) | 0.06 | 0.01 TCSF_N | Transpiration concentration stream factor (-) | 0.5 | 2.0 dz_WSN | Thickness of the soil layer considered for the simulation of the soil organic matter and nitrogen dynamics (m) | 0.8 | 1.0

sumanager56 commented 6 months ago

Hi Dr. Shortridge, I am uploading some analysis plots that might provide useful information to strengthen my results and discussion sections. I tried looking into the results in different ways: for example, I tried to see what is driving the year-year differences in yield- if it was mainly due to temperature or rainfall differences. I tried looking into extreme rainfall and temperature events (total depths and counts of events) that occurred each year (just looking into >95th percentile events and <5th percentile events) and tried to relate if yield differences were due to more or less occurrences of those events in a particular year. There were some differences but I didn't find any significant correlation (based on my judgment) between yield differences related to those extreme events.

I then summed up the precipitation amount for each week and then classified them into different intervals (<1 mm, 1-120 mm, >120 mm). <1 mm would be considered too low to meet soil water requirements whereas >120 mm would be considered too much to cause saturated conditions). I obtained a total number of weeks that fell into each category and tried this for both temperature and precipitation events separately. For temperature, I calculated the total sum of temperature for each week throughout the growing period and classified them into different intervals (<90, 90-180, 180-215, >215). >215 would be >30 degrees on average per day of the week.

RainSum_Yielddiff1 The first plot above shows the number of weeks in a particular year when the weekly precipitation sum fell into different precipitation ranges. (For example: 2 of the weeks in 2011 had precipitation sum exceeding 120mm (4.72 inches). Similarly, the plot below shows the number of weeks in a particular year when the weekly temperature sum fell into different temperature ranges. (For example: 3 weeks in 2020 had a temperature sum exceeding 215 degrees C (average of 31degreeC/day).

Yield differences seem to be largely controlled by temperature fluctuations rather than precipitation amounts. For example, yields were lowest in the years 2011, 2016, and 2020, when the temperature sum exceeded 215 for 2-3 weeks. Also, the highest yields are observable in the years 2008, 2009, and 2014 when there were temperature sum did not exceed 215 for any of the weeks (exceptions are years like 2007, 2012, and 2017). Precision irrigation does better than calendar, especially in those years that had temp sum>215mm. 2013-2015 are those years where precision did not do so well.

Temperature sum >215 does not mean the temperature is unfavorable for corn, however, I think there is more need for precise irrigation to counteract higher ET need, due to which precision irrigation seems to be a favorable method here. Also, higher temperatures drive processes like denitrification (3rd plot) that might further reduce the amount of available N for plants.

TempSum_YieldDifference1

The plot below shows the temperature sum for each year (divided by 20 to plot together) versus yields versus denitrification. Denitrification - solid lines. Yield - dotted lines. Temperature sum - Red lines It does look like years with higher temperature sum have the lowest yields (2010,2011,2019). These years also have higher denitrification rates. Again, yields in the years 2013-2015 seem to be highly correlated with denitrification rates (higher denit-less yield). In both the years 2013 and 2015 where precision yield is less than calendar, it seems that the denitrification rates are higher for precision. This implies soil is close to saturation, or higher than the optimum water content in precision irrigation compared to the calendar. This could be because of extreme rainfall that occurred just after precision irrigation (at least in a couple of instances that I checked). Higher denitrification rates in the calendar mean there are more anaerobic conditions in the calendar compared to precision leading to reduced yield/N losses to the atmosphere. Denitr-yield-tempsum

sumanager56 commented 6 months ago

I also tried plotting results in the way I discussed last week. Plot 1: Cumulative Nitrate Leaching vs Cumulative Yield For each scenario, I calculated the total yield from all years and plotted against the total leaching calculated from all years (The lines/error bars represent standard deviations). The horizontal line represents the overall mean of Nitrate leaching across both calendar and precision treatments. The vertical line represents the overall mean of Yield across both calendar and precision treatments.

Plot1

Updated_Cumulative

Plot 2: Nitrate Leaching vs Yield In this plot, instead of using the cumulative sums, I used all individual yearly values for yield and nitrate leaching for each scenario.

Plot 2

Updated_plot_allyears

sumanager56 commented 6 months ago

Yield difference between calendar and precision versus extreme rainfall/temperature events. Extreme rainfall are >98th percentile rainfall depths occurring in a particular year. I calculated the average by dividing the total depth of extreme rainfall/count of those events. Extreme temperature events are days with >98th percentile temperature. Extreme_rainfall Extreme_temp

sumanager56 commented 6 months ago

Looking into the year-year variations, I wanted to see the differences in precision and calendar treatments for unproductive and productive years. Here I define less productive years as those years when rainfed yields are below average while years with above-average yields are defined as productive years. Productive years are 2003-2005, 2012-2018, and 2022. The rest of the years are unproductive. The precision results are way better for unproductive years compared to the productive years in terms of both leaching and yield. Yield_diff leaching_diff

sumanager56 / SWAP-WOFOST