Human-Induced and Climate-Driven Contributions to Water Storage Variations in the Haihe River Basin, China

Terrestrial water storage (TWS) can be influenced by both climate change and anthropogenic activities. While the Gravity Recovery and Climate Experiment (GRACE) satellites have provided a global view on long-term trends in TWS, our ability to disentangle human impacts from natural climate variability remains limited. Here we present a quantitative method to isolate these two contributions with reconstructed climate-driven TWS anomalies (TWSA) based on long-term precipitation data. Using the Haihe River Basin (HRB) as a case study, we find a higher human-induced water depletion rate (−12.87 ± 1.07 mm/yr) compared to the original negative trend observed by GRACE alone for the period of 2003–2013, accounting for a positive climate-driven TWSA trend (+4.31 ± 0.72 mm/yr). We show that previous approaches (e.g., relying on land surface models) provide lower estimates of the climate-driven trend, and thus likely underestimated the human-induced trend. The isolation method presented in this study will help to interpret observed long-term TWS changes and assess regional anthropogenic impacts on water resources

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extracted raw material, with an average withdrawal rate of 982 km3 per year, where 70 percent of the total groundwater withdrawn is used for agriculture globally (Margat & van der Gun, 2013).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

Human-Induced and Climate-Driven Contributions to Water Storage Variations in the Haihe River Basin, China

Terrestrial water storage (TWS) can be influenced by both climate change and anthropogenic activities. While the Gravity Recovery and Climate Experiment (GRACE) satellites have provided a global view on long-term trends in TWS, our ability to disentangle human impacts from natural climate variability remains limited. Here we present a quantitative method to isolate these two contributions with reconstructed climate-driven TWS anomalies (TWSA) based on long-term precipitation data. Using the Haihe River Basin (HRB) as a case study, we find a higher human-induced water depletion rate (−12.87 ± 1.07 mm/yr) compared to the original negative trend observed by GRACE alone for the period of 2003–2013, accounting for a positive climate-driven TWSA trend (+4.31 ± 0.72 mm/yr). We show that previous approaches (e.g., relying on land surface models) provide lower estimates of the climate-driven trend, and thus likely underestimated the human-induced trend. The isolation method presented in this study will help to interpret observed long-term TWS changes and assess regional anthropogenic impacts on water resources

Amélioration et désagrégation des données GRACE et GRACE-FO pour l’estimation des variations de stock d’eau terrestre et d’eau souterraine à fine échelle

Abstract : Groundwater is an essential natural resource for domestic, industrial and agricultural uses worldwide. Unfortunately, climate change, excess withdrawal, population growth and other human impacts can affect its dynamics and availability. These excessive demands can lead to lower groundwater levels and depletion of aquifers, and potentially to increased water scarcity. Despite the abundance of lakes and rivers in many parts of Canada, the potential depletion of groundwater remains a major concern, particularly in the southern Prairie. Groundwater is traditionally monitored through in-situ piezometric wells, which are scarcely distributed in Canada and many parts of the world. Consequently, its quantities, distribution and availability are not well known, both spatially and temporally. Fortunately, the launch of the twin satellite systems of Gravity Recovery And Climate Experiment (GRACE) in 2002 and its successor, GRACE Follow-On in 2018 (GRACE-FO) opened up new ways to study groundwater changes. These platforms measure the variations of the Earth's gravity field, which in turn can be related to terrestrial water storage (TWS). The main objective of this thesis is to improve the estimation and spatial resolution of TWS and related groundwater storage changes (GWS), using GRACE and GRACE-FO data. This challenge was addressed through four specific objectives, where original approaches were developed in each case. The first objective was to understand and better take into account the uncertainties associated with the hydrological models (the Global Land Data Assimilation System (GLDAS), and the Water Global Assessment Prognosis hydrological model (WGHM)), generally used in the processing of GRACE or GRACE-FO data. The thesis proposes a new approach based on the Gauss-Markov model to estimate the optimal hydrological parameters from GLDAS, considering six different surface schemes. The Förstner estimator and the best quadratic unbiased estimator of the variance components were used with a least-squares method to estimate the optimal hydrological parameters and their errors. The comparison of the optimal TWS derived from GLDAS to the TWS derived from WGHM showed a very significant correlation of r = 0.91. The correlation obtained with GRACE was r = 0.71, which increased to r = 0.81 when the groundwater component was removed from GRACE. Compared to WGHM and GRACE, the optimal TWS calculated from GLDAS had much smaller errors (RMSE = 7 to 8.5 mm) than those obtained when individual surface schemes are considered (RMSE = 10 to 21 mm); demonstrating the performance of the proposed approach. The second specific objective was to understand regional variations in TWS and their uncertainties. The approach was applied over the Canadian landmass. To achieve the goal, the thesis proposes a new modeling of glacial isostatic adjustment uplift (GIA) in Canada. The comparison of the results of the proposed model and three other existing models with data from 149 very high precision GPS stations demonstrated its superiority in the region considered. The regional approach proposed was then used to extract TWS by correcting the effects of the GIA and leakage. The analyzes showed patterns of significant seasonal variations in TWS, with values ranging between -160 mm and 80 mm. Overall TWS showed a positive slope of temporal variations over the Canadian landmass (+ 6.6 mm/year) with GRACE and GRACE-FO combined. The slope reached up to 45 mm/year in the Hudson Bay region. The third objective was to extract GWS component using a comprehensive rigorous approach to reconstruct, refine and map the variations of GWS and its associated uncertainties. The approach used the methods proposed in the two previous objectives. Moreover, a new filtering approach called Gaussian-Han-Fan (GHF) was developed and integrated into the process in order to have a more robust procedure for extracting information from GRACE and GRACE-FO data. The performance and merits of the proposed filter compared to previous filters were analyzed. Then, the groundwater signal was reconstructed by taking into account all the other components, including surface water variations (estimated using satellite altimetry data). The results showed that the average variations of GWS are between -200 mm and +230 mm in the Canadian Prairies. The maximum and minimum GWS trends were found around the Hudson Bay region (approximately 55 mm/year) and southern Prairies (approximately -20 mm/year), respectively. The error on GWS was around 10% (about 19 mm). The estimated GWS changes were validated using the data from 116 in-situ wells. This validation showed a significant level of correlation (r > |0.70|, P |0.90|, P |0,70|, P |0,90|, P < 10-4, RMSE < 30 mm). Enfin, le dernier objectif consistait à améliorer la résolution spatiale des résultats extraits des données GRACE de 1° à 0.25°. Ainsi, une nouvelle approche basée sur l'ajustement des conditions a d’abord été proposée pour estimer les paramètres hydrologiques optimaux et leurs erreurs. Elle est légèrement différente de la méthode proposée dans le premier objectif. Ensuite, les corrections requises pour extraire les anomalies de TWS et ses incertitudes de manière rigoureuse ont été effectuées suivant la méthodologie présentée à l’objectif 3. Par la suite une nouvelle méthode basée sur la combinaison spectrale-spatiale a été développée pour dériver les anomalies de TWS à échelle réduite (0.25°), en combinant de manière optimale les modèles GRACE et les paramètres hydrologiques. Enfin, les anomalies d’eau souterraines ont été dérivées en utilisant les anomalies de TWS estimées. Les validations ont été faites à partir des données de 75 puits en aquifère non confiné en Alberta. Elles démontrent le potentiel de l’approche proposée avec une corrélation très significative de = 0.80 et un RMSE de 11 mm. Ainsi, la recherche proposée dans la thèse a permis de faire des avancées importantes dans l’extraction d’information sur le stockage total d’eau et les eaux souterraines à partir des données des satellites gravimétriques GRACE et GRACE-FO. Elle propose et valide plusieurs nouvelles approches originales en s’appuyant sur des données in-situ. Elle ouvre également plusieurs nouvelles avenues de recherche, qui permettront de faciliter une utilisation plus opérationnelle de ces types de données à l’échelle régionale, voire locale

Effect of Drip Irrigation Under Mulch on Nitrogen Transport in Deep Soil Layers in an Agricultural Region of the Xiliao River Plain, China

In the agricultural region of the Xiliao River Plain, drip irrigation under mulch has been widely implemented. It not only saves irrigation water, but also changes the structure of the underlying surface of agricultural land, which affects the local hydrological cycle to a certain extent, and makes the process of nitrogen transportation in soil with new characteristics. This study analyzed the distribution of NH4-N, NO3-N, and NO2-N in different soil depths during the whole growth period under three underlying surface conditions, including drip irrigation under mulch, border irrigation, and bare area through field in-situ observation experiment, and analyzed the influence of drip irrigation under mulch on nitrogen transport in deep soil layers. The results showed that under the soil properties of the experimental area, drip irrigation under mulch creates more water to enter the deep soil layers, which was beneficial to alleviate the downward trend of local groundwater level to a certain extent. The average content of NH4-N and NO3-N under drip irrigation under mulch was higher than that under border irrigation. The average content of NH4-N under drip irrigation under mulch was 1.24 mg.kg-1 in soil depths of 80-300 cm, and 0.97 mg.kg-1 under border irrigation. The average content of NO3-N under drip irrigation under mulch was 2.73 mg.kg-1 in soil depths of 80-300 cm, and 1.99 mg.kg-1 under border irrigation. The increment of NH4-N and NO3-N distribution in deep soil layers under drip irrigation under mulch was greater than that under border irrigation, and the increment of NO3-N content is significantly greater than that under border irrigation. Soil water content has a significant impact on the contents of NH4-N and NO3-N. It indicated that compared with traditional border irrigation, drip irrigation under mulch was beneficial to alleviate the downward trend of local groundwater, but it would increase the risk of nitrogen pollution in local groundwater

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

The impacts of climate change and agricultural activities on water cycling of Northern Eurasia

The ecosystems in Northern Eurasia (NE) are important due to their vast land coverage, high rate of observed and projected warming, and the potential feedbacks they can cause on the global climate system. To understand the impacts of climate change and agricultural activities on water cycling in NE, I analysed a variety of datasets and conducted series of studies by applying a combination of modeling, in-situ observations and remote sensing data, uncertainty analysis, and model-data fusion.^ Long-term unique in-situ measurements on soil moisture across multiple stations and discharge records at the outflow basins in Northern China (NC) provide us robust evidence to assess the trends of soil moisture and discharge in this region (Chapter 2). NC overlaps with NE and is one of the hot-spots experiencing the most severe water shortage in the world. Declines in soil moisture and stream flow detected via in-situ measurements in the last three decades indicate that water scarcity has been exacerbated. Multiple linear regression results indicate that intensification of agricultural activities including increase in fertilizer use, prevalence of water-expensive crops and cropland expansion appear to have aggravated these declines in this region.^ Scarce evapotranspiration (ET) measurements make ET estimation via model a necessary step for better regional-scale water management. Penman–Monteith based algorithms for plant transpiration and soil evaporation were introduced into the Terrestrial Ecosystem Model (TEM) to calculate ET (Chapter 3). I then examined the response of ET and water availability to changing climate and land cover on the Mongolian Plateau during the 21st century. It is shown that use of the Penman–Monteith based algorithms in the TEM substantially improved ET estimation on the Mongolia Plateau. Results show that regional annual ET varies from 188 to 286 mm yr−1 – with an increasing trend – across different climate change scenarios during the 21st century. Meanwhile, the differences between precipitation and ET suggest that the available water for human use will not change significantly during the 21st century. In addition, analyses also suggest that climate change is more important than land cover change in determining changes in regional ET.^ Improvement in the accuracy of ET estimation by introducing Penman–Monteith based algorithms into the TEM motivated me to further improve the model representation of ET processes. I further modified the TEM to incorporate more detailed ET processes including canopy interception loss, ET (evaporation) from wetland surfaces, wetlands and water bodies, and snow sublimation to examine spatiotemporal variation of ET in NE from 1948 to 2009 (Chapter 4). Those modifications lead to substantial enhancement in the accuracy of estimation of ET and runoff. The consideration of snow sublimation substantially improved the ET estimates and highlighted the importance of snow in the hydrometeorology of NE. The root mean square error of discharge from the six largest watersheds in NE decreased from 527.74 km 3 yr-1 to 126.23 km3 yr-1. Meanwhile, a systematic underestimation of river discharge after 1970 indicates that other water sources or dynamics not considered in the model (e.g., melting glaciers, permafrost thawing and fires) or bias in the precipitation forcing may also be important for the hydrology of the region.^ To better understand the possible causes of systematic bias in discharge estimates, I examined the impacts of forcing data uncertainty on ET and runoff estimation in NE by driving the modified TEM with five widely-used forcing data sets (Chapter 5). Estimates of regional ET vary between 263.5-369.3 mm yr-1 during 1979-2008 depending on the choice of forcing data, while the spatial variability of ET appears more consistent. Uncertainties in ETforcing propagate as well to estimates of runoff. Independent of the forcing dataset, the climatic variables that dominate ET temporal variability remain the same among all the five TEM simulated ET products. ET is dominated by air temperature in the north and by precipitation in the south during the growing season, and solar radiation and vapour pressure deficit explain the dynamics of ET for the rest of the year. While the Climate Research Unit (CRU) TS3.1 dataset of the University of East Anglia appears as a better choice of forcing via our assessment, the quality of forcing data remains a major challenge to accurately quantify the regional water balance in NE

IRRIGATION AND ITS RESPONSE TO CLIMATE: IMPROVING REPRESENTATION OF HUMAN IMPACTS IN HYDROLOGICAL MODELING AND DATA ASSIMILATION SYSTEMS

Irrigation is vital for agricultural production as it resolves the temporal and spatial disconnects between water supply and water demand, especially for semi-arid and arid regions. Globally, irrigation accounts for 70% of global freshwater withdrawals, 40% of which is supplied by groundwater. In the United States, irrigation accounts for ~40% of total freshwater withdrawal and more than 80% of total freshwater consumption. Approximately 60% of irrigated areas are supplied by groundwater resources. Irrigation has led to serious aquifer depletion due to groundwater pumping for many regions around the world and has also modified the land-atmosphere interactions via the surface energy balance. Its importance in altering the water cycle and climate within the Earth system is evident, but the process, along with its impact on the water cycle and climate in the Earth System Models is not well represented. This dissertation is motivated by the need to improve representation of irrigation in hydrological modeling and data assimilation systems that are used to study, monitor, and predict water resource dynamics. This is done through three specific objectives: (1) establishing the representation of irrigation process in a Land Surface Model (LSM) that accounts for source water partitioning; (2) applying this improved model to a land data assimilation system; (3) using the improved modeling system to study the climate sensitivity of existing irrigation developments. Three separate studies are performed using the Noah-Multiparameterization (Noah-MP) LSM within the framework of NASA’s Land Information System (LIS). The first study implements a groundwater irrigation scheme into Noah-MP and explores key factors that improve model representation of drought and groundwater depletion in the United States High Plains Aquifer (HPA) region. In the second study, terrestrial water storage (TWS) observations from the Gravity Recovery and Climate Experiment (GRACE) mission are assimilated into the model. The individual and combined effects of simulating irrigation and including GRACE data assimilation (GRACE-DA) are assessed for the HPA. The third study extends the application to the Contiguous United States (CONUS), partitions between surface water and groundwater for irrigation, and quantifies climate sensitivity of simulated water use across major irrigation zones of the CONUS