Utilization of Remote Sensing Data for Estimation of the Groundwater Storage Variation

Groundwater is the most extracted raw material, with an average withdrawal rate of 982 km3 per year, where 70 percent of the total groundwater withdrawn is used for agriculture globally (Margat & van der Gun, 2013). With climate change and increased water demands in recent years, monitoring the changes in the groundwater storage is of the utmost importance. This thesis presents an analysis that determines the rates, trends, and directions where groundwater storage is going in Pakistan. It also correlates fluctuations in groundwater storage with variations in precipitation and agricultural productivity in the country. The overall objectives of this thesis are to identify the long-term variations in groundwater storage, and examine the impact of precipitation and crop production on the groundwater reserves in Pakistan. In this thesis, The Gravity Recovery and Climate Experiment (GRACE) satellite data are used to estimate changes in groundwater storage for the study period of April 2002 – June 2017. By subtracting the different water subcomponents, i.e. soil moisture and snow water equivalent, derived from the Global Land Data Assimilation System (GLDAS) Noah from the GRACE data products, variations in groundwater storage are estimated. Precipitation data for this study is obtained from the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) CDR system. Agricultural information, which includes the crop water requirement, is derived from CROPWAT, and yield data are obtained from the Bureau of Statistics, Punjab. The results reveal that groundwater storage in Pakistan is declining at a high rate. Over a period of 183 months, Punjab province has observed the highest loss in total volume of groundwater storage (28.2 km3), followed by Balochistan (19.57 km3), Khyber Pakhtunkhwa (9.84 km3), and lastly, Sindh (5.46 km3). The results also show that precipitation has a weak positive impact on groundwater storage and soil moisture, depending on the region. Lastly, crop cultivation has had a significant impact on the groundwater withdrawal rates, with amounts varying on a district by district basis. The contributions of this study include a better understanding of variations in the groundwater storage across different provinces in Pakistan, and an analysis of the effect of groundwater changes in relation to crop water demand and precipitation. GRACE data can be used to assess groundwater depletion in areas where groundwater monitoring is not available, as it can help with the evaluation of decreasing trends in groundwater levels. It can also provide policy makers information needed to conserve groundwater resources for future use

Spatio temporal pattern in the changes in availability and sustainability of water resources in Afghanistan

Water is gradually becoming scarce in Afghanistan like in many other regions of the globe. The objective of this study was to evaluate the spatial changes in the availability and sustainability of water resources in Afghanistan. The Terrestrial Water Storage (TWS) data of the Gravity Recovery and Climate Experiment (GRACE) satellite obtained from three different institutes, having 1° × 1° spatial resolutions for the period 2002–2016 was used for this purpose. Sen’s slope method was used to assess the rate of change, and the Modified Mann–Kendall test was used for the evaluation of the significance of trends in TWS. After, the concept of reliability–resiliency–vulnerability (RRV) was used for assessing the spatial distribution of sustainability in water resources. The results revealed a significant decrease in water availability in the country over the last 15 years. The decrease was found to be highest in the central region where most of the population of the country resides. The reliability in water resources was found high in the northeast Himalayan region and low in the southwest desert; resilience was found low in the central region, while vulnerability was found high in the south and the southeast. Overall, the water resources of the country were found most sustainable in the northeast and southwest and least in the south and the central parts. The maps of water resource sustainability and the changes in water availability produced in the present study can be used for long-term planning of water resources for adaptation to global changes. Besides, those can be used for the management of water resources in a sustainable and judicious manner

Modeling of GRACE-Derived Groundwater Information in the Colorado River Basin

Groundwater depletion has been one of the major challenges in recent years. Analysis of groundwater levels can be beneficial for groundwater management. The National Aeronautics and Space Administration’s twin satellite, Gravity Recovery and Climate Experiment (GRACE), serves in monitoring terrestrial water storage. Increasing freshwater demand amidst recent drought (2000–2014) posed a significant groundwater level decline within the Colorado River Basin (CRB). In the current study, a non-parametric technique was utilized to analyze historical groundwater variability. Additionally, a stochastic Autoregressive Integrated Moving Average (ARIMA) model was developed and tested to forecast the GRACE-derived groundwater anomalies within the CRB. The ARIMA model was trained with the GRACE data from January 2003 to December of 2013 and validated with GRACE data from January 2014 to December of 2016. Groundwater anomaly from January 2017 to December of 2019 was forecasted with the tested model. Autocorrelation and partial autocorrelation plots were drawn to identify and construct the seasonal ARIMA models. ARIMA order for each grid was evaluated based on Akaike’s and Bayesian information criterion. The error analysis showed the reasonable numerical accuracy of selected seasonal ARIMA models. The proposed models can be used to forecast groundwater variability for sustainable groundwater planning and management

Limits to management adaptation for the Indus’ irrigated agriculture

Future irrigated agriculture will be strongly affected by climate change and agricultural management. However, the extent that agricultural management adaptation can counterbalance negative climate-change impacts and achieve sustainable agricultural production remains poorly quantified. Such quantification is especially important for the Indus basin, as irrigated agriculture is essential for its food security and will be highly affected by increasing temperatures and changing water availability. Our study quantified these effects for several climate-change mitigation scenarios and agricultural management-adaptation strategies using the state-of-the-art VIC-WOFOST hydrology–crop model. Our results show that by the 2030s, management adaptation through improved nutrient availability and constrained irrigation will be sufficient to achieve sustainable and increased agricultural production. However, by the 2080s agricultural productivity will strongly depend on worldwide climate-change mitigation efforts. Especially under limited climate-change mitigation, management adaptation will be insufficient to compensate the severe production losses due to heat stress. Our study clearly indicates the limits to management adaptation in the Indus basin, and only further adaptation or strong worldwide climate-change mitigation will secure the Indus’ food productivity

An overview of groundwater monitoring through point-to satellite-based techniques

Groundwater supplies approximately half of the total global domestic water demand. It also complements the seasonal and annual variabilities of surface water. Monitoring of groundwater fluctuations is mandatory to envisage the composition of terrestrial water storage. This research provides an overview of traditional techniques and detailed discussion on the modern tools and methods to monitor groundwater fluctuations along with advanced applications. The groundwater monitoring can broadly be classified into three groups. The first one is characterized by the point measurement to measure the groundwater levels using classical instruments and electronic and physical investigation techniques. The second category involves the extensive use of satellite data to ensure robust and cost-effective real-time monitoring to assess the groundwater storage variations. Many satellite data are in use to find groundwater indirectly. However, GRACE satellite data supported with other satellite products, computational tools, GIS techniques, and hydro-climate models have proven the most effective for groundwater resources management. The third category is groundwater numerical modeling, which is a very useful tool to evaluate and project groundwater resources in future. Groundwater numerical modeling also depends upon the point-based groundwater monitoring, so more research to improve point-based detection methods using latest technologies is required, as these still play the baseline role. GRACE and numerical groundwater modeling are suggested to be used conjunctively to assess the groundwater resources more efficiently

Water Resource Variability and Climate Change

Climate change affects global and regional water cycling, as well as surficial and subsurface water availability. These changes have increased the vulnerabilities of ecosystems and of human society. Understanding how climate change has affected water resource variability in the past and how climate change is leading to rapid changes in contemporary systems is of critical importance for sustainable development in different parts of the world. This Special Issue focuses on “Water Resource Variability and Climate Change” and aims to present a collection of articles addressing various aspects of water resource variability as well as how such variabilities are affected by changing climates. Potential topics include the reconstruction of historic moisture fluctuations, based on various proxies (such as tree rings, sediment cores, and landform features), the empirical monitoring of water variability based on field survey and remote sensing techniques, and the projection of future water cycling using numerical model simulations

Groundwater Dynamics in Transboundary Aquifers of Southern Africa

Groundwater resources are indispensable not only in water scarce or water stressed countries, but globally as a dependable reservoir and an alternative resource of freshwater. This study assessed the spatio-temporal variability of groundwater resources within two of the biggest transboundary aquifers that South Africa shares with its neighbouring countries. Groundwater dynamics in the Karoo-Sedimentary Transboundary Aquifer (KSTA) as well as the Stampriet Transboundary Aquifer System (STAS) were studied over a period of 72 years from 1948-2020. The study explored the use of historical groundwater storage data acquired through the use of Remote Sensing (RS) techniques, coupled with the use of Geographical Information Systems (GIS) to map spatio-temporal variability in groundwater storage. Groundwater resources of the Karoo-Sedimentary Transboundary Aquifer were found to be declining over time, with an overall decline of just over 5.4 km3, whereas groundwater resources in the Stampriet remained relatively constant, with an overall increase of 0.2 km3 over the past 72 years. The results show that RS techniques coupled with GIS applications are invaluable where there is a dearth of scientific data and information, furthermore, their use in the monitoring, management and protection of groundwater resources can be applicable on the local, regional and international scales

The GRACE event calendar

GRACE mission is a joint venture of NASA and GFZ. This mission was launched to provide with unprecedented accuracy, estimates of the global high resolution models of the Earth’s gravity field. The study of time-variability of Earth’s gravity field is very helpful in climate sciences and earth’s sciences studies. People have done a lot of work to demonstrate the effect of many natural phenomenon on gravity. Gravity estimates from GRACE are used for estimating mass redistribution at continental scale. So, we can observe hydrology, seismology and glaciology potential areas where GRACE can be useful. This research work focuses on identifying the hydrological events such as floods and drought, seismic events such as earthquakes and volcanic activity and also the glacier melting in the GRACE time-series. The work includes the development of strategy for the analysis of these events keeping in mind their behaviour and GRACE limitations of spatial resolution and sensitivity. Further in this work we would produce a event calendar for such events stating whether gravity changes caused by such events are visible to GRACE. Calendars are generated for hydrological events, floods and droughts separately and also for earthquake events. For rest of the phenomenon we have not generated calendars since these events are very few in numbers. This work is a qualitative analysis, so we could observe whether GRACE signal is able to observe these events or not. Hydrological events are observed by searching outliers in the grace observed time-series. The large floods such as 2009 Amazon floods can be seen when we take whole catchment, but the small floods affecting smaller region such as Sao Paulo flood is not visible in catchment time-series, so we have to go for selected area time-series generation. The factors such as time period for floods and droughts are very important factors when we want to observe them by GRACE. Earthquakes visibility depends on range rate amplitude, and also the quality of ΔC20, we have discussed these aspects while analysing earthquakes occurred in last decade from GRACE. We have given the possible explanation for the events not visible, and those visible have helped in the development of a methodology for analysis of a particular event. The volcanic activity in Caldera and Bolivia are pushing earth upward so we can expect some signal, but the spatial extent of these areas is small with caldera area greater than that of Bolivia, only caldera showed a trend. We also did trend analysis for 2 Asian glaciers and a part of Greenland for observing the melting of these ice masses. The work finally produces a series of events which we were able to observe by GRACE and we also get the methodology suitable for analysis of an event

Spatio-Temporal Pattern in the Changes in Availability and Sustainability of Water Resources in Afghanistan

Water is gradually becoming scarce in Afghanistan like in many other regions of the globe. The objective of this study was to evaluate the spatial changes in the availability and sustainability of water resources in Afghanistan. The Terrestrial Water Storage (TWS) data of the Gravity Recovery and Climate Experiment (GRACE) satellite obtained from three different institutes, having 1° × 1° spatial resolution for the period 2002–2016 was used for this purpose. Sen’s slope method was used to assess the rate of change, and the Modified Mann–Kendall test was used for the evaluation of the significance of trends in TWS. After, the concept of reliability–resiliency–vulnerability (RRV) was used for assessing the spatial distribution of sustainability in water resources. The results revealed a significant decrease in water availability in the country over the last 15 years. The decrease was found to be highest in the central region where most of the population of the country resides. The reliability in water resources was found high in the northeast Himalayan region and low in the southwest desert; resilience was found low in the central region, while the vulnerability was found high in the south and the southeast. Overall, the water resources of the country were found most sustainable in the northeast and southwest and least in the south and the central parts. The maps of water resource sustainability and the changes in water availability produced in the present study can be used for long-term planning of water resources for adaptation to global changes. Besides, those can be used for the management of water resources in a sustainable and judicious manner

Prediction Analysis of Floods Using Machine Learning Algorithms (NARX & SVM)

The changing patterns and behaviors of river water levels that may lead to flooding are an interesting and practical research area. They are configured to mitigate economic and societal implications brought about by floods. Non-linear (NARX) and Support Vector Machine (SVM) are machine learning algorithms suitable for predicting changes in levels of river water, thus detection of flooding possibilities. The two algorithms employ similar hydrological and flood resource variables such as precipitation amount, river inflow, peak gust, seasonal flow, flood frequency, and other relevant flood prediction variables. In the process of predicting floods, the water level is the most important hydrological research aspect. Prediction using machine-learning algorithms is effective due to its ability to utilize data from various sources and classify and regress it into flood and non-flood classes. This paper gives insight into mechanism of the two algorithm in perspective of flood estimation