Weather, Land and Crops in the Indus Village Model: A Simulation Framework for Crop Dynamics under Environmental Variability and Climate Change in the Indus Civilisation
2. Materials and Methods
2.1. Indus Village Model: A Road Map
2.2. Weather Model
2.3. Soil Water Model
2.4. Crop Model
Crop Selection and Parametrization
2.5. Land Model
2.6. Land Water Model
- Estimation of soil water capacity thresholds or horizons according to soil texture. Saturation and permanent wilting points are approximated using linear models factoring the percentage of clay in the soil (i.e., p_soil_%clay) based on data offered by the Soil and Water Assessment Tool (SWAT) Theoretical Documentation  (p. 149).
- Redistribution of surface water (run-off and inundation). The run-off exchange algorithm combines the calculations in the soil water model with an algorithm in charge of redistribution, which was already implemented in the land model , and another to calculate run-off volume moving from one land unit to another, which was adapted from a model about flood impact .
- Impact of water on ecological communities and cover. When water input exceeds the capacity of the soil, surface water accumulates as an additional type of ecological component. Given a volume of surface water (mm·m−2), the percentage of land unit area (1 ha or 10,000 m2) covered by water is estimated assuming a bankfull width/depth ratio of 12 for all land units and a bankfull width equal to or less than land unit width (100 m). The width/depth ratio of 12 has been empirically identified as the most common value universally, and is a consequence of the physical processes governing the distribution of energy and resultant sediment transport . The expansion of water surface is at the expense of dry ecological components (i.e., grass, brush, and wood), which are assumed to be affected evenly. When dry land is available, all ecological components grow following a logistic growth model, where the carrying capacity is proportional to the value in the initial ecological community configuration (given by the land model), minus the influence of water stress. Similar to the crop model, water stress over grass, brush, and wood is modeled in proportion to ARID and a water stress sensitivity coefficient. Both the intrinsic growth rate and water stress sensitivity, as well as the maximum root depth used to calculate ARID, are controlled as parameters imported from the “ecologicalCommunityTable.csv”.
2.7. Land Crop Model
2.8. Experimental Design
- Experiment 0 serves as a baseline reference for further experiments by running the crop model for each of the six crops selected using empirical weather data instead of data simulated with the weather model. Data were obtained in the NASA POWER project Data Access Viewer and correspond to the location of the archaeological site of Rakhigarhi in Haryana, India, between 1 January 1984 and 31 December 2007 (24 years).
- Experiment 1 executed the crop model for 25 random number generator seeds and 30 years (total sample of 750 unique years), running in parallel for each of the six crops selected. The goal was to compare yield and water stress levels of crops under the default parameter configuration, which includes a specific configuration of the weather model parameters that aims at approximating the data used in experiment 0 (see calibration process in ).
- Experiment 2 is equivalent to experiment 1 in all aspects, except for varying stochastically (uniform probability distribution) the precipitation absolute total per year (precipitation_yearlyMean) and the winter/summer ratio of precipitation or the average plateau value of the cumulative curve of daily precipitation (precipitation_ dailyCum_plateauValue_yearlyMean). This experiment aimed at exposing the effect of precipitation annual volume and seasonality on crop-specific ARID and yield.
- Experiment 3 executed the land crop model for 10 random number generator seeds and 5 years (total sample of 50 unique years), using five preselected terrains (2500 land units) and a default parameter configuration, both aimed at approximating past conditions in Haryana. Frequency was kept constant and homogeneous throughout all land units, while intensity was fixed at 50% throughout all runs.
- Experiment 4 is equivalent to experiment 3, except for varying stochastically the volume of river water inflow or, more specifically, the water stage increment (mm·m−2) per unit of flow accumulation at the river’s starting land unit (riverWaterPerFlow Accumulation).
- Experiment 5 is again equivalent to experiment 3, except for varying stochastically (uniform probability distribution) the share of each crop in frequency within land units. The variation of crop frequencies aims at addressing crop choice factors by evidencing the effect of relative frequencies on total production per land unit (i.e., sum of all elements in p_crop_totalYield).
3.1. Soil Water and Crop Yield in One-Land Unit Systems
- Solar radiation and temperature: Springs are warmer and sunnier, summers are colder and less sunny, and autumns are warmer and sunnier. The distribution of both solar radiation and temperature is skewed and deformed when compared to the annual sinusoidal curve generated by the model. The annual maxima are reached one to two months before the summer solstice, and there is a considerable depression of both solar radiation and temperature due to the incidence of the monsoon.
- Precipitation. The summer monsoon tends to start and end sooner, while the winter rainy season is generally less intense. Differences in precipitation indicate the difficulty of representing the nuances of the two rains pattern found in the region. Daily maxima are also considerably lower, though this is not only a problem of misrepresentation, but also an effect of the limited sample size in the modern data.
3.2. Soil Water and Crop Yield under Terrain-Like Systems
- As a general rule, the total production of a land unit will increase with the more area that is used for proso millet, irrespective of its position, given the intrinsic high productivity and low sensitivity to water stress of this crop. Note how, in Figure 11, the range of correlation for this crop remains positive throughout all land units and terrains.
- Higher proportions of the more water-demanding crops will only tend to raise total production if the land unit is located in or around drainage branches and, particularly, inundation areas, in addition to having soil and cover conditions that facilitate lower .
4. Discussion and Conclusions
4.1. Crop Choice Dilemma
- Crop selection. There are a number of crops to be produced that are both available and required by society, given a broader economic and cultural context.
- Crop diversity. These crops are diverse as per their intrinsic biological traits, including those affecting when and how their growing cycle unfolds and, particularly, how sensitive these are to water stress.
- Limited means of production and workforce. Farming households, both separately and as a collective, count with a limited amount of land, labor, and other resources (e.g., tools, raw materials, fuel) to be invested in growing crops at any given time. Considering a pre-industrial agricultural system, extreme intensification of land use (i.e., near 100% of land unit area used for crops) is deemed neither desirable nor possible.
- Seasonality. Weather is highly variable within a year, with precipitation concentrating in seasons (two rainy seasons, two dry seasons).
- Climate-driven variability. There is a high interannual variation in weather, with great differences in the amount and distribution of precipitation between consecutive years.
- Local environmental diversity. Areas flooded by passing rivers and those down the water flow chain (e.g., dry channels, seasonal streams) represent a special type of local environment. This area is sharply separated from most of the remaining land, offering privileged crop-growing conditions with low, or no, water stress.
- Regional environmental diversity. The conditions shaping weather (seasonality and climate-driven variability), relief, soil properties, and ecology vary greatly between localities within the same cultural region, confronting the same cultural substratum to many potential contexts for the crop choice dilemma.
4.2. Risk in the Crop Choice Dilemma
4.3. Risk Mitigation Strategies
4.4. Implications to IC Urbanization
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|Domain||Aspects to Model||Model|
|Climate||Solar radiation, temperature, and precipitation||Weather model|
|Land and soil||Soil properties, soil and surface water dynamics, and cover including vegetation||Soil water model,|
|Food production||Crop cultivation, with a strong focus on staple cereals, and animal husbandry, supported by fishing, hunting and gathering||Crop model,|
land use mechanisms
|Individuals organized in households, i.e., the social unit of co-habitation, production, consumption, reproduction and decision-making||Household agent set up|
|Population dynamics in terms of individual-based fertility, nuptiality, and mortality||Household demography|
|Households interact with each other and form larger groups and settlements||Household position model|
|Composition of foodstuffs consumed within a household and corresponding nutritional budget that regulates individual health||Nutrition model and|
food consumption mechanisms
|Food economy||Processes involved in food production, beyond the procurement of raw foodstuffs, and distribution, storage, and exchange of foodstuffs||Food storage model,|
|Decision-making||Selection and revision of food production strategies at household level, particularly in terms of the triplet activity-conditions-investment, and other relevant aspects such as household position and the engagement with neighbors||Mechanisms connecting|
labor investments, land use,
diet satisfaction, and
cooperation in food economy
|Submodel||Domain||Parameters||Unit||Default/Exp. 0, 1||Exploration Range Exp. 2|
|(all submodels)||time||year length in days||days||365|
|inflection1_yearlyMean||day of year||40|
|inflection2_yearlyMean||day of year||240|
|Soil water 3||surface||elevation||m||200|
|runoff curve number (CN)||-||65|
|soil||drainage coefficient (DC)||-||0.55|
|root zone depth (z)||mm||400|
|field capacity (FC)||mm·mm−1||0.21|
|water holding capacity (WHC)||mm·mm−1||0.15|
|wilting point (WP)||mm·mm−1||0.06|
|water uptake coefficient (MUF)||mm3·mm3||0.096|
|Terrain Random Seed|
|Domain||Parameters||Unit||Default/Exp. 3||Exp. 4||Exp. 5|
|crops||selection||-||[all crops in Table 2]|
|intensity||% of land unit||50|
|(land unit)||frequency||% of (effective) intensity||100/6||C(0, 1) 2|
|(array of <selection> items)|
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Angourakis, A.; Bates, J.; Baudouin, J.-P.; Giesche, A.; Walker, J.R.; Ustunkaya, M.C.; Wright, N.; Singh, R.N.; Petrie, C.A. Weather, Land and Crops in the Indus Village Model: A Simulation Framework for Crop Dynamics under Environmental Variability and Climate Change in the Indus Civilisation. Quaternary 2022, 5, 25. https://doi.org/10.3390/quat5020025
Angourakis A, Bates J, Baudouin J-P, Giesche A, Walker JR, Ustunkaya MC, Wright N, Singh RN, Petrie CA. Weather, Land and Crops in the Indus Village Model: A Simulation Framework for Crop Dynamics under Environmental Variability and Climate Change in the Indus Civilisation. Quaternary. 2022; 5(2):25. https://doi.org/10.3390/quat5020025Chicago/Turabian Style
Angourakis, Andreas, Jennifer Bates, Jean-Philippe Baudouin, Alena Giesche, Joanna R. Walker, M. Cemre Ustunkaya, Nathan Wright, Ravindra Nath Singh, and Cameron A. Petrie. 2022. "Weather, Land and Crops in the Indus Village Model: A Simulation Framework for Crop Dynamics under Environmental Variability and Climate Change in the Indus Civilisation" Quaternary 5, no. 2: 25. https://doi.org/10.3390/quat5020025