Impact of land use changes and management practices on groundwater resources in Kolar district, Southern India

Study region: This study analyzes the impact of land use changes on the hydrology of Kolar district in the state of Karnataka, India. Kolar receives on average 565 mm (σ = 130) rainfall during June to October and has a wide gap between its water supply and demand. Study focus: This research identifies the reasons and causes of the gap. A water balance model was successfully calibrated and validated against measurements of groundwater level, recharge and surface runoff. New hydrological insights for the region: The study revealed that between 1972 and 2011, there was a major shift from grass and rainfed crop lands to eucalyptus plantation and irrigated cultivation. About 17.7 % and 18 % of the district area converted into eucalyptus plantation and irrigated lands during this period, respectively. Eucalyptus plantations tended to cause large losses by ET leading to increase in soil moisture deficit and reduction in the recharge to groundwater and in surface runoff (approx. 30 %). The irrigation demand of the district increased from 57 mm (1972) to 140 mm (2011) which resulted in increased groundwater abstraction by 145 %. The expansion of the irrigated area is the major contributing factor for widening the demand-supply gap (62 %) of the freshwater availability. Results could help various stakeholders, including district and national authorities to develop the most suitable water management strategies in order to close the gap between water supply and demand

Estimation of root-zone soil moisture using thermal infrared data

© 2017 Dr. Venkata Radha AkurajuThis thesis focuses on Root-Zone Soil Moisture (RZSM) estimation using Thermal Infrared (TIR) observations. RZSM plays an important role in hydrological modelling and agricultural applications. Conventional point-based measurements such as gravimetric and TDR measurements may not be useful for agricultural applications to understand the spatial and temporal behaviour of soil moisture. Microwave remote sensing is a useful tool to retrieve soil moisture information in large scales, but their retrievals are limited to surface soil moisture and sparse vegetation conditions. Thermal infrared remote sensing is an alternate approach to predict soil moisture until root-zone, even under dense vegetation conditions and in high spatial and temporal resolutions. Since optical and thermal observations linked to soil water status of deeper layers, developing a model to estimate RZSM is particularly important for hydrological modelling. Being able to predict root-zone soil moisture using TIR observations, understanding the interactions between surface fluxes and soil moisture is necessary. This research builds on understanding the links between ET derived from TIR data and surface to root-zone soil moisture in dryland wheat field, Dookie experimental site, Victoria, Australia. In the first step of this research, a hydro-meteorological dataset has been collected for created for three cropping seasons. By monitoring two cropping seasons, it is shown that there exists a strong relationship between ET and soil moisture in water-limited conditions. The relationship between ET and RZSM is highly conditional based on net radiation, crop growth stage and rainfall distribution. More appropriate linkages between ET and available water fraction was found by incorporating root depth and density simulated from Agricultural Production Systems sIMulator (APSIM) model. A new model, CWSI (Crop Water Stress Index) based on the theoretical limits obtained from canopy temperature and air temperature is developed by considering the impacts of root depth variation, growth stage. The sensitivity of CWSI and RZSM from two cropping seasons is explored and compared with another cropping season. Cross-validation results demonstrate that the linear model can predict RZSM with an average error of 3.9% and 5.3% in different cropping seasons. The proposed method is also applied to another root-zone soil moisture dataset collected during 2002-04 cropping seasons in a cornfield site in the Optimizing Production Inputs for Economic and Environmental Enhancement (OPE3) site in the U.S. Validation results showed that the model produces reasonable RZSM estimates except for the high rainfall distribution during cropping seasons. Overall, this research demonstrates the links between surface fluxes/TIR observations and root-zone soil moisture. The implications of the close links contribute towards reliable root-zone soil moisture estimations in large scales using thermal infrared observations

Estimation of root-zone soil moisture using crop water stress index (CWSI) in agricultural fields

Due to the limited availability of Root-Zone Soil Moisture (RZSM) information at the regional scale, this paper explores the use of thermal infrared remote sensing to estimate RZSM in agricultural fields. This study presents the Crop Water Stress Index (CWSI) derived from thermal infrared data used as an indicator to estimate root zone soil moisture. Theoretical limits were calculated using canopy and air temperature difference, which is related to vapor pressure deficit. An empirical model was developed using continuous remotely sensed optical, thermal infrared data with limited meteorological data collected from a wheat site, the Dookie experimental farm, Victoria, Australia during 2012 and 2013. Linear and exponential models predicting RZSM using CWSI were constructed and compared in two different cropping seasons. Cross-validation results demonstrate that the linear model predicted RZSM with an error of 3.9% in 2012 and 5.3% in 2013 cropping seasons. The proposed method is applied to another root-zone soil moisture dataset collected during 2002–04 cropping seasons from a corn field site in the Optimizing Production Inputs for Economic and Environmental Enhancement (OPE3) site in the USA. Validation results showed that the model produces reasonable RZSM estimates, except for the high rainfall distribution during cropping seasons even though the crop types of the calibration and validation sites were different. The efficacy of canopy temperature in RZSM estimations was demonstrated using Dookie and OPE3 RZSM dataset. The potential limitation is that sparse vegetation in the initial growth stages produces negative values in CWSI due to the dominant soil surface. Overall, the results support the potential role of the theoretical crop water stress index in root-zone soil moisture estimations