Performance evaluation and optimization studies of border irrigation system for wheat in the Indian Punjab

Surface irrigation methods are the most widely practiced worldwide for irrigation of row crops. The major problem with these methods is low irrigation efficiency, mainly due to poor design. In the Punjab, border irrigation is used to irrigate wheat crops grown over 90% of the cultivated area. The evaluation of existing border systems using a surface irrigation model showed that the irrigation conditions, comprising of inflow rate, border dimensions, and cut-off time, were diverse in tubewell and canal irrigated areas. The study also examined the feasibility of optimizing border dimensions taking into consideration the existing irrigation conditions for achieving more than 60% application efficiency as compared to the 30–40% achieved under present field conditions. In the case of a border length of 60 m, it was recommended to increase border width in the range of 10–45 m and 20–60 m for different flow rates of 10, 20 and 30 L/s in light and medium soils, respectively. For higher flow rates, a border length ranging from 120–150 m was found to be optimum. For a border length of 150 m, it was recommended to keep a border width ranging from 4–38 m and 8–65 m in light soils and medium soils, respectively, for flow rates of 10, 20, 30 and 60 L/s. Optimizing border dimensions is a practical way to achieve efficient and judicious use of water resources

Delineation of critical regions for mitigation of carbon emissions due to groundwater pumping in central Punjab

The expansion of groundwater use has not only resulted in a speedy decline in the groundwater table but also added to the problem of carbon emissions (C-emissions) in Punjab. Estimates of C-emissions from groundwater pumping for irrigation in central Punjab indicated that during the last 15 years (1998–2013), the groundwater levels had fallen by 8.89 m; pump set density (per 1000 ha) increased by 45%; groundwater use increased by 140 Mm3; energy requirements increased by 4845.1 MkWh (90%), all this resulted into an increase in C-emissions by 984.1 kiloton (30%). The relationship between carbon emissions and groundwater depth showed that for each meter fall of groundwater level in central Punjab, C emissions will increase by 2.67 g/m³. The present level of groundwater exploitation in Bhawanigarh, Dharmkot, Dhuri, Malerkotla I, Malerkotla II, Nabha, Nakodar, Sangrur, Samana, Sherpur and Sunam is considered as threat to the environment and should be targeted to reduce C-emissions

Development of machine learning-based reference evapotranspiration model for the semi-arid region of Punjab, India

Evapotranspiration (ET) is a critical element of the hydrological cycle, and its proper assessment is essential for irrigation scheduling, agricultural and hydro-meteorological studies, and water budget estimation. It is computed for most applications as a product of reference crop evapotranspiration (ET0) and crop coefficient, notably using the well-known two-step method. Accurate predictions of reference evapotranspiration (ET0) using limited meteorological inputs are critical in data-constrained circumstances. Due to the unavailability and heterogeneity of broad parameters of the FAO PM method, it becomes a major constraint for accurately estimating ET0. To overcome the complexity of calculation, the present study was focused on developing a Random Forest-based (RF) ET0 model to estimate the crop ET for the semi-arid region of northwest India. The RF-based model was developed by focusing on the easily available data at the farm level. For comparative study existing models like Hargreaves–Samani, Modified Penman and modified Hargreaves–Samani were used to estimate the ET0. The models' calibration and validation were done using meteorological data collected from the weather station of Punjab Agricultural University for 21 years (1990–2010) and nine years (2011–2019), respectively, and the FAO PM model was taken as a standard. The mean absolute error (MAE) and root-mean-square error (RMSE) were found to be least as 0.95 mm/d and 1.32 mm/d, respectively for the developed RF model, with an r2 value of 0.92. The seasonal ET0 estimated by modified Hargreaves–Samani (MHS) and RF were found as 498.3, 482.1 mm in rabi season and 755, 744.8 mm in kharif season respectively, whereas the annual ET0 was 1380.2 and 1355.7 mm respectively. The predicted ET0 values by RF-based model were used for irrigation scheduling of two growing seasons (2020–2021) of maize and wheat crops. The outcome of the field trial also demonstrates that there was no appreciable yield drop in the crop when compared to irrigation scheduling by the FAO PM model, demonstrating the applicability of the developed model for irrigation in the semiarid region of the Punjab in India

Artificial intelligence-based approach to study the impact of climate change and human interventions on groundwater fluctuations

Water resource management is highly impacted by variations in rainfall, maximum and minimum temperature, and potential evapotranspiration. The rice area is also a key aspect for groundwater declination due to high-water consuming crop. Groundwater in Central Punjab has declined at an alarming rate over the last two decades. The decisions regarding water resource management need accurate information for the groundwater level. Therefore, to explore the main reason for the depletion of groundwater, it is essential that the most influential factors responsible for groundwater depletion should be addressed. A study was conducted in Central Punjab by using artificial neural network (ANN) and multiple linear regression (MLR) models during 1998–2018 to forecast the groundwater depth. ANN performed better than MLR. The sensitivity analysis showed that tubewell density, rice area, and rainfall are highly responsible for groundwater fluctuation. HIGHLIGHTS In the present study, both climatic and human-induced factors were taken for groundwater modeling.; Artificial neural network, a complex phenomenon was used to forecast groundwater depth.; Python was used for groundwater modeling.; ANN was found to be more accurate than MLR.

Pre and Post Water Level Behaviour in Punjab: Impact Analysis with DiD Approach

Punjab Agriculture is trapped in the complex nexus of groundwater depletion and food insecurity. The policymakers are concerned about reducing groundwater extraction at any cost for irrigation without jeopardizing food security. In this regard, the Government of Punjab introduced the “Punjab Preservation of Subsoil Water Act, 2009”. The present paper examines the impact of the “Preservation of Sub Soil Water Act, 2009” on pre- and post-water levels in Punjab using the difference-in-difference (DiD) approach. The state has witnessed a severe fall of 0.50 m per year and 0.43 m per year for the post-monsoon and pre-monsoon season, respectively. Only 2.62 per cent of wells were in the range of 20–40 m depth in the state in 1996, which increased to 42 per cent and 67 per cent in 2018 for the pre-monsoon period, and post monsoon period respectively, depicting an increase of 25 times. The groundwater depth in high rice-growing(treated) districts declined by 1.53 and 1.39 m than the low rice-growing (control) districts in the pre-monsoon and post-monsoon periods respectively post the enactment of PPSW Act, 2009. A groundwater governance framework is urgently needed to manage the existing and future challenges connected with the groundwater resource

Soil Quality and Its Potential Indicators under Different Land Use Systems in the Shivaliks of Indian Punjab

The present study assessed the overall state of the soil based on the most sensitive soil attributes under different land uses—i.e., rainfed agriculture, mixed forest, afforestation and non-arable lands—in the lower Shivaliks of Indian Punjab. The soil parameters—i.e., erosion ratio, bulk density and water retention characteristics—and fertility parameters were integrated under different land uses to identify potential soil quality indicators.The overall state of the soil, based on a weighted average of primary soil functions under different land uses through fuzzy modeling, was deemed best for agricultural land use (0.515), followed by forests (0.465) and non-arable lands (0.456), and deemed worst under afforestation (0.428). Among the different land use systems, principal component analysis (PCA) clearly separated the agriculture and forest samples from afforestation and non-arable lands samples. The contribution of potential indicators such as erosion ratio (ER), phosphorus (P) and potassium (K) toward the soil quality index (SQI) was substantial. The order of contribution of the selected indicators to the SQI was 53.5%, 34.3% and 19.9% for ER, P and K, respectively. These indicators are most influential for studying real time soil health and ecological processes in the future, under various land use systems in degraded agroecosystems like the Shivaliks

Estimation of carbon emissions from groundwater pumping in central Punjab

During the past decade, the carbon emission patterns due to groundwater pumping in central Punjab have changed enormously, and today the sector is one of the main contributors of C-emissions. This study identifies the critical blocks of central Punjab that should be aimed for reducing the carbon emissions of the groundwater economy. This study is based on the regional scale and highlights various factors that play crucial role in contributing to these emissions. Overlay analysis of the choropleth maps (tube well density, groundwater draft, groundwater depth and C-emissions) showed that SC-III zone was the most critical zone of central Punjab. Blocks Bhawanigarh, Dharmkot, Malerkotla I, Malerkotla II, Sangrur, Sherpur and Sunam were considered as threat to the environment and should be targeted to reduce C-emissions

Prioritization of erosion susceptible watersheds using morphometric analysis and PCA approach: A case study of lower Sutlej River basin of Indian Punjab

Morphometry helps in understanding the behaviour of drainage characteristics with respect to various hydrological processes including infiltration, runoff, erosion and sediment transport. Morphometric analysis of river basins is an essential technique to the study the response of drainage basin in response to topological characteristics. The river basins' morphometric analysis is an important technique to prioritize the watersheds for implementation of soil and water management strategies. In this study, the morphometric characteristics of the lower Sutlej River have been determined using the geo-spatial techniques. The river basin, having area of 8577 km2, was delineated into the fourteen sub-watersheds (WS-1 to WS-14) in the GIS environment. The ALOS PALSAR DEM and ArcGIS were utilized to evaluate the morphometric parameters of the delineated watersheds. The calculated morphometric parameters were used to rank the watersheds in terms of soil erosion potential. The priority ranks to the watersheds were assigned as per compound parameter, which was calculated by averaging the ranks designated to each morphometric parameter. Watersheds with the lowest compound parameter values were given the highest priority rating, and vice versa. Based on the results WS-7 was assigned the first rank whereas WS-13 was assigned the 13th rank. The principal component analysis was performed to determine the highly correlated morphometric parameters. Out of the 18 parameters, 13 were found be highly correlated. The compound parameter obtained based on these highly correlated parameters also prioritized WS-7 as the most vulnerable watershed. Therefore, WS-7 should be selected for the implementation of soil and water conservation strategies. It can be concluded that morphometric analysis along with PCA in combination with GIS can be helpful in prioritizing the watersheds in terms of soil erosion vulnerability and water management