This research is part of the project "Water Efficiency in Sustainable Cotton-based Production Systems” between Solidaridad Asia and TU Delft. The project aims to increase the livelihood of smallholder farmers in the Maharashtra, India through. A socio-hydrological (SH) model is used extensively in this research as an evaluation tool. However, the baseline research indicates that the lack of stress mechanics in the SH model used in the intervention might cause inaccuracies in yield estimation. Furthermore, it has never been validated at a farmer level before. This research aims to implement the stress mechanics in the SH model, evaluate the overall performance of the model in terms of predicting crop yield, identify potential sources of errors, and give recommendations for future studies. The research uses iterative top-down approach due to the large study area and the varied nature of the 308 farmers surveyed. The research implements the water and temperature stress mechanics based on Food and Agriculture Organization's (FAO) AquaCrop framework. For the performance evaluation process, this research uses four model scores namely Nash-Sutcliffe (NS), log of NS, Mean Absolute Error (MAE), and the coefficient of determination. Furthermore, the sources of uncertainties are divided into two categories namely lack of knowledge (i.e. generated by parameter, input, observation, and structural errors) and variability (i.e. generated by climate variations). The lack of knowledge uncertainty is investigated using Monte Carlo Sampling calibration and the Generalized Likelihood Uncertainty Estimation (GLUE) concept is used to obtain the uncertainty intervals of the model. Going further, the errors are divided into residual and structural error. The latter is explained and quantified through a structural error model using a combination of qualitative analysis, Principal Component Analysis, multiple linear regressions, and projection of the data into kernel space. Then residuals between the model yield + structural error vs. the observed yield is thought to be explained by the residual errors. Lastly, the effects of climate variations to the stability of the model are evaluated.
After the initial calibration, the model scores are NS: -0.343 to -0.996, log of NS: -0.655 to -1.91, MAE: 447.1 to 553.2 kg/ha, and r-squared: 0.003 to 0.008. Because of the poor performance of the model, the uncertainty intervals from GLUE are not enough to capture the total errors of the model. However, after adjustment using the structural error model, the model scores become NS: 0.83, log of NS: 0.56, MAE: 149 kg/ha, and r-squared: 0.859. The adjusted yield calculation has a residual error as Gaussian distribution with standard deviation of 150 kg/ha. The qualitative analysis identified several factors that contribute to the errors viz. farmers' capital, irrigation behavior, and crop production process such as canopy cover growth. Lastly, there is no major instability found through the bootstrap analysis. The physical model is not performing well, especially when it is calculating yield for individual farmers over a large study area. However, the structural error model can adjust the yield prediction so that it is close to the observed yields. This indicates the poor performance is likely to be caused by the prevalence of structural errors in the model instead of the uncertainties regarding parameters, input, or observation values. Therefore, it is recommended for future research to address this first. This can be done by further study and incorporation of more crop production processes, soil water simulation, and exploratory interviews to identify patterns and more factors that can influence the errors.

For cotton cultivation, large amounts of water are necessary. One of the countries facing water challenges related to cotton cultivation is India. India experiences a monsoon weather pattern causing large amount of rain from June to October and no rain during the other months. One of the challenges India’s small holder cot- ton farmers face is the lack of water after the monsoon. A solution to this problem could be the construction of (small) reservoir structures and renovation of currently present structures. One of the main questions arising here is ‘how can a hydrological model estimate new reservoir locations for small holder cotton farmers and what is the potential of these new structures and current structures in Maharashtra India with Ghatanji block and Hinganghat block as case study areas?’. Estimating potential reservoir locations as well as determining which current reservoir structures have the potential to be renewed could increase the water availability for small holder cotton farmers in Maharashtra, India.
Four focus areas are chosen, these areas are Yavatmal, Nagpur, Wardha and Amravati. Within these fo- cus areas, Ghatanji block and Hinganghat block are used as case study areas for developing and validating different models used for analysing current and suitable potential locations.Remote data based tools used to locate, validate and analyse the potential of current and potential suitable structures are QSWAT (hydrological model), Google Earth Engine (land cover analysis), Python coding, Zoom.Earth (locating current structures), socio-hydrological model (yield and benefit analysis).
Out of the initial 692 potential locations, 315 locations in Ghatanji block and 349 locations in Hinganghat block are classified as suitable potential reservoir locations. A location was marked suitable if a minimum size (200m3) reservoir on this position has the ability to fill up completely at least once a year for an average monsoon season. Additionally, the structure was positioned in a location with more than 30% crop coverage and less than 25% urban coverage. Out of a total of 2212 current structures, 840 locations in Hinganghat and 970 locations in Ghatanji have a high potential of collecting water if renovated and in use. This high potential is a result of small distance to the nearest stream and suitable land cover on the position of the reservoir. Existing structures were classified as suitable if they adhere the same prerequisites as stated above regarding the coverage and for the additional requirement that a structure should be located less than 200 meters from the nearest stream point.
The results of this investigation show that a hydrological model can estimate new potential reservoir locations and that the use of a hydrological model is beneficial for locating these potential locations when com- pared to current or recently constructed locations. The benefit of using a hydrological model is that it can identify the surface runoff paths of water during and after a precipitation event. Furthermore, can remote data based tools help in identifying the suitability of both potential as well as current structures.