Anthropogenic activities in Maheshwaram watershed have deteriorated water, air and soil quality during the last ten years due to rapid urbanisation and industrial development. Maheshwaram watershed experienced drastic changes in land use pattern, being very close to the Hyderabad city. Base-line data was collected during post monsoon season (October-November) of 2010 in Maheshwaram watershed to assess the impacts of change in land use, urbanisation and industrialisation on air, water and soil quality for Environmental Impact Assessment (EIA). Air samples were analysed for Suspended Particulate Matter (SPM) and Respirable Suspended Particulate Matter (RSPM). Groundwater samples were analysed for pH; EC; TDS; TH; cations, anions and heavy metals viz., Fe, Mn, Al, Si, V, Cr, As, Sr, Rb, Se, Ba, Li, B, Be, Co, Ni, Zn, Cd, and Pb using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Analysed data were compared with the National and International Standards and subjected to Factor Analysis (FA) for six chemical variables to assess and characterise hydro-chemical processes. A comparison of the results of groundwater samples with WHO and BIS guidelines show that most of the groundwater samples are heavily contaminated with heavy metals like lead, chromium, nickel, barium, aluminium, boron and selenium, which was quantified and presented in the form of spatial variation diagrams prepared using ArcGIS. 9.3.1. Similarly the results of air samples compared with the Central Pollution Control Board, Ministry of Environment and Forests, Government of India guidelines show that most of the air samples for residential, rural and industrial areas are contaminated by SPM and RSPM.

One of the critical components in understanding the climate system is the interaction between the land and the atmosphere. Whereas previous studies on land–atmosphere coupling mostly focus on its spatial hotspots, we explore the temporal evolution of land surface coupling strength (LCS) during a large-scale flood event in a semiarid region in northern Australia. The LCS indicates the relationship between soil moisture and latent heat flux, and the spatiotemporal variability in precipitation and soil water strongly affects the variability of LCS. The LCS is usually positive in the semiarid climate, where evapotranspiration (ET) occurs under the soil moisture–limited regime and thus increases with soil moisture. However, our analyses of combined land surface modeling and observational datasets show high temporal variability of LCS in the course of the extreme flood event followed by a drying period. The wet regions transferred the ET regime from the soil moisture–limited to the transition section, weakening the linear growth of ET with soil moisture, which resulted in the decline of LCS. The LCS remained weak until the flood retreated and the soil water approached the prestorm average state. Such temporal variation of the LCS has important implications for realistic parameterization of the land–atmosphere coupling and consequently improving subseasonal to seasonal climate forecast.

A comprehensive estimation of water budget components, particularly groundwater storage (GWS) and fluxes, is crucial. In this study, we evaluate the terrestrial water budget of the Donga basin (Benin, West Africa), as simulated by three land surface models (LSMs) used in the African Monsoon Multidisciplinary Analysis Land Surface Model Intercomparison Project, phase 2 (ALMIP2): CLM4, Catchment LSM (CLSM), and Minimal Advanced Treatments of Surface Interaction and Runoff (MATSIRO). All three models include an unconfined groundwater component and are driven by the same ALMIP2 atmospheric forcing from 2005 to 2008. Results show that all three models simulate substantially shallower water table depth (WTD) with smaller seasonal variations, approximately 1–1.5 m compared to the observed values that range between 4 and 9.6 m, while the seasonal variations of GWS are overestimated by all the models. These seemingly contradictory simulation results can be explained by the overly high specific yield prescribed in all models. All models achieve similar GWS simulations but with different fractions of precipitation partitioning into surface runoff, base flow, and evapotranspiration (ET), suggesting high uncertainty and errors in the terrestrial and groundwater budgets among models. The poor performances of models can be attributed to bias in the hydrological partitioning (base flow vs surface runoff) and sparse subsurface data. This analysis confirms the importance of subsurface hydrological processes in the current generation of LSMs and calls for substantial improvement in both surface water budget (which controls groundwater recharge) and the groundwater system (hydrodynamic parameters, vertical geometry).