Mehnaz Rashid

Water scarcity (WS) is projected to increase as a result of future climate and population changes, and its impacts will be hazardous to vulnerable groups of people in South Asia (SA). We present the first assessment of WS in SA under the Paris Agreement, using two global hydrological models (GHM) forced by three global climate models (GCM) under the RCP 6.0. Results show that a significant alteration in the hydrological fluxes contributes to the decline in water availability, along with an intense increase in water consumption that augments WS. The seasonal shifting in WS significantly increases the population affected by approximately 16% in June to September (JJAS) to 42% in December to February (DJF) under 1.5 ºC warming. We also highlight spatial hotspots of WS in SA, including North-central, Northwestern, and Southern India, Eastern Pakistan, Northern, and Northwestern Bangladesh. We observe a 1.5 ºC temperature increase as a critical point for WS in SA with a profound impact on 875 million people and suggest the Paris Agreement temperature target of even 1.5 ºC will not ubiquitously alleviate future warming-driven WS. Hence, immediate mitigations and legislations are required to curtail WS in SA.

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).

Semi-arid regions across the globe are fronting water crises, signaling a challenge to ensure future water security. Given the high inter-seasonal rainfall variability and unrestrained groundwater extraction, the precise quantification of groundwater flow components in an aquifer system is a priority. To address this challenge, we used high-resolution remote sensing (RS) data (Landsat and IRS) and GIS modeling (SEBAL, ArcCN) to spatially quantify major groundwater balance (GWB) components, viz., evapotranspiration (ET), rainfall recharge (R), surface runoff (Q), groundwater extraction (PG), irrigation return flow (IRF), and ultimately changes in groundwater storage (ΔS) in a small semi-arid crystalline representative watershed. Results show that a total of ~ 230 mm of groundwater is extracted during 2008–2009, creating a negative impact on the groundwater resource, which is further enhanced by limited recharge and high ET. A decrease of approximately 65 mm in groundwater storage is observed in a single hydrological year, and given a very low specific yield, this decrease will introduce large water level decline. The study establishes that declining groundwater level in the watershed is a direct result of over-extraction, and owing to this scenario, efficient irrigation and land use policies are suggested as potential approaches to minimize extraction specifically in the dry season. Our methodology provides a systematic assessment of vital GWB components at a high spatial resolution and an insight on various sustainable mitigation methods. This methodology is useful in the planning and management of groundwater resources, particularly in water-stressed areas.

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.

The increasing demand of water has brought tremendous pressure on groundwater resources in the regions were groundwater is prime source of water. The objective of this study was to explore groundwater potential zones in Maheshwaram watershed of Andhra Pradesh, India with semi-arid climatic condition and hard rock granitic terrain. GIS-based modelling was used to integrate remote sensing and geophysical data to delineate groundwater potential zones. In the present study, Indian Remote Sensing RESOURCESAT-1, Linear Imaging Self-Scanner (LISS-4) digital data, ASTER digital elevation model and vertical electrical sounding data along with other data sets were analysed to generate various thematic maps, viz., geomorphology, land use/land cover, geology, lineament density, soil, drainage density, slope, aquifer resistivity and aquifer thickness. Based on this integrated approach, the groundwater availability in the watershed was classified into four categories, viz. very good, good, moderate and poor. The results reveal that the modelling assessment method proposed in this study is an effective tool for deciphering groundwater potential zones for proper planning and management of groundwater resources in diverse hydrogeological terrains.