Hello all,
I need to compare the rate of soil erosion modelled under the current data (2001-2022) and SSP scenarios (SSP126 and SSP 585 for 2040-2069). I did the model with ArcSWAT and when I got the result, I found out that the result from the current data has a higher rate of soil loss compared to the SSP scenarios. Other than that, the result for SSP 126 is higher than for SSP 585. Can someone explain to me why this happened? Because from my understanding;
1) The future rate of soil erosion is supposed to be higher than the current rate of soil erosion
2) SSP 126 should produce lower rate of soil erosion compared to SSP585
Yet the results that I obtained said otherwise. Can someone please explain to me why this happened? Thank you in advance for your help in easing my confusion.
Your results may seem counterintuitive, but they could be influenced by several factors:
1. Land Use and Land Cover Changes: The SSP scenarios include assumptions about land management and conservation practices. SSP126, emphasizing sustainability, may incorporate effective soil conservation measures, while SSP585 assumes higher land degradation. However, if your input data for SSP scenarios reflect overly optimistic land management improvements for SSP585, it could lead to lower erosion rates compared to SSP126.
2. Climate Variability: While SSP585 typically assumes higher emissions and extreme weather, it could also predict shifts in precipitation patterns that reduce soil erosion in specific regions. Check whether your precipitation and runoff data align with expected erosion trends.
3. Model Parameters: The calibration of ArcSWAT parameters, such as soil erodibility (K-factor) and land use, might influence your results. Ensure that parameters align accurately with the SSP scenarios and that baseline conditions are consistent with observed data.
4. Temporal and Spatial Resolution: Differences in resolution between current and future data sets could introduce inconsistencies. Verify the uniformity of data preprocessing and scenario assumptions.
Note: It’s crucial to validate your input data and assumptions in the SSP scenarios. Consider revisiting the scenario narratives and recalibrating your model to ensure consistency in parameterization and alignment with expected outcomes. Cross-checking with alternative models or additional data sources may also help clarify the discrepancies.
Current data shows a higher rate of soil erosion compared to modelled rates under Shared Socioeconomic Pathways (SSP) scenarios, reflecting the impact of unsustainable land use and inadequate conservation practices. SSP scenarios incorporate mitigation strategies like improved agricultural management and reforestation, reducing erosion rates. This disparity highlights the potential benefits of proactive policy implementation. Addressing soil erosion under SSP frameworks is crucial for long-term agricultural sustainability and ecosystem health.
Hasan Altawil The LULC used was predicted with MoLUSCE and has 88% of correctness. I also noticed that the future LULC has less forest compared to the current LULC.
I did more analysis on the data produced and I realized that even though SSP126 and SSP585 has higher precipitaion than the current scenario, SSP also produced much higher PET and ET values.
Current PET: 6502mm
Current ET: 964.2mm
SSP126 PET: 9576mm
SSP126 ET: 2132 mm
SSP585 PET: 9538mm
SSP585 ET: 2099mm
From this, I tried to change the LU use for SSP scenarios, still produced high PET and ET. I also checked the weather data and I found out that everything look normal.
If you can help me, kind sir, what should I do to rectify this situation? I am truly at my wit's end
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