Annual Water Storage Research Articles

Large wetlands representation in SWAT+: the case of the Pantanal in the Paraguay River Basin

The Paraguay River Basin forms part of the La Plata River Basin in South America. Its streamflow is significantly attenuated by a high evapotranspiration rate, very gentle slopes and the presence of a vast wetland known as the Pantanal. Modeling the hydrology of watersheds in which the flood pulse is affected by the presence of large floodplains can pose issues for hydrological models that do not account for spatial complexity and simplify water routing using linear assumptions. The new version of the Soil and Water Assessment Tool, known as SWAT+, routes water using variations of the kinematic wave model. However, with the inclusion of connectivity and Landscape Units, SWAT+ provides more flexibility in terms of representing the hydrologic fluxes in the watershed. The main objective of this study is to use the concept of Landscape Units and connectivity to represent the water exchanges between uplands, floodplains and channels. We developed code routines to (1) temporally retain surface and subsurface water coming from the upland into the floodplain, by assuming a reservoir-like floodplain behavior, and (2) represent overbank flow, aiming to fully simulate the interactions between channels and floodplains. The model was calibrated based on monthly discharge for the period 1990 to 2020. The simulated average annual water storage in the floodplains of the Paraguay River is ~108.81 mm accounting for 56.5% of the total annual discharge at the outlet. Furthermore, ~61% of the total annual surface runoff in the Paraguay River Basin flows through the floodplains. Results indicate that the model is able to capture the hydrologic regime in the Paraguay River representing an improvement of SWAT+.

Beyond dams: Assessing integrated water storage in the Shashe catchment, Limpopo River Basin

Study regionThe Shashe catchment, Limpopo River Basin, Botswana, and Zimbabwe. Study focusThe Shashe catchment is the third largest flow contributor to the Limpopo River Basin. Water availability in the Shashe catchment is highly seasonal due to high seasonal rainfall variability. The seasonality and inter-annual variability cause shortfalls (demand exceeds the average water availability) in certain months and years. Storage is needed to bridge the seasonal water availability “gap” and mitigate the deficits in drought years, i.e., inter-annual variability. While the need for water storage through grey infrastructure such as dams has long been known, there is growing recognition of the need for approaches to water storage that capitalize on all storage types. However, the current capacity to plan in ways that utilize all storage types is limited. The analyses conducted for this paper assessed the volume and spatial and temporal variability of different storage options – large and small dams, sand dams, soil moisture, and aquifers – in the Shashe catchment of the Limpopo River Basin. An integrated SWAT-MODFLOW model and remote sensing approach were developed for 2015–2020. New hydrological insights for the regionThe total annual water storage in the Shashe catchment is approximately 44,000 Mm3, dominated by groundwater. The annual storage is about 42,000 Mm3 in aquifers, 1500 Mm3 in soil, 700 Mm3 in large dam reservoirs, 45 Mm3 in small dams/ponds, and 0.13 Mm3 in sand dams. There is high seasonality in water storage availability. Soil moisture storage is at its maximum from January to March and lowest from July to September. Dam storage is at its maximum from March to May, and the water storage is relatively stable throughout the year. Aquifer storage is relatively stable during the dry seasons compared to other storage options. Optimizing water use considering the seasonal variation in different storage types could improve water availability and climate resilience.

Comparative evaluation of the dynamics of terrestrial water storage and drought incidences using multiple data sources: Tana sub-basin, Ethiopia

Abstract Evaluating water storage changes and addressing drought challenges in areas like the Tana sub-basin in Ethiopia is difficult due to limited data availability. The aim of this study was to evaluate the dynamics of terrestrial water anomaly and drought incidences by employing multiple data source. The Gravity Recovery and Climate Experiment (GRACE) and Global Land Data Assimilation System (GLDAS) datasets were used to assess the long-term water storage dynamics and drought incidences using the weighted water storage deficit index (WWSDI). WWSDI was used to identify drought periods, which ranged from severe to extreme drought. Despite the overall increase in average annual total water storage anomaly (TWSA) by 0.43 cm/year and a net gain of 50.68 cm equivalent water height from 2003 to 2022, there were instances of terrestrial water storage deficits, particularly in 2005, 2006, and 2009, during historical drought periods. The TWSA exhibited a strong correlation with Lake Tana water storage and precipitation anomalies after adjusting lag times. WWSDI displayed a high correlation with WSDI but a weak correlation with SPI and SPEI. Therefore, utilization of GRACE and GLDAS data is promising for evaluating terrestrial water storage and monitoring drought in data-deficient regions like the Tana sub-basin in Ethiopia.

Estimation of Groundwater Storage Change and Groundwater Recharge Using GRACE data in Jedeb Watershed Under the Application of Google Earth Engine

Over exploitation of Ground Water (GW) has resulted in lowering of water table in the Jedeb watershed. In this study, water storage changes with GRACE satellite data and total annual precipitation with CHIRPS data in the Google Earth Engine system were investigated for the Jedeb watershed during 2003–2017. The groundwater recharge is estimated from a time series of groundwater storage using the water table fluctuation method. According to the results obtained from the GRACE satellite data on the fluctuations of annual water storage between 2003 and 2017, it was found that the biggest annual increase in water levels (15 cm) occurred in 2008, 2013, and 2015, and the biggest annual decrease (12.5 cm) occurred in 2012. The obtained net recharge rate varied from 18 to 25 cm/year for a 14-year period, and the average was 21 cm/year. This study indicates that the GRACE-based estimation of groundwater storage changes is skilled enough to provide monthly updates on the trend of groundwater storage changes for resource managers and policymakers in the Jedeb watershed.

PREDICTING THE IMPACT OF RAPID URBANISATION ON URBAN GROUNDWATER LEVEL VARIATIONS AND ANALYZING CLIMATE DYNAMICS IN PUNJAB'S CITIES, INDIA: AN APPROACH TOWARDS SUSTAINABLE CITIES

Abstract. In the past few decades, India has witnessed an unparallel population growth across its urban centres. The significant rise in urban population has led to acute demands for resources to sustain them, contributing to anthropogenic activities that impact urban resources and climate. This study was carried out to analyse the impact of rapid urbanisation on the groundwater level variation and climate dynamics. Focus was laid on Ludhiana and Patiala urban centres of Punjab state, India. Multi-temporal analysis of LULC was computed using Landsat-4, 5, 7 & 8 datasets to analyse urban sprawl during 1990–2023 for both the cities. A significant increase of 30% urban area was observed during 1990–2023. Further, to analyse the impact of urbanisation on ground water levels, the groundwater water storage variation was analysed using the GRACE and GRACE-FO mission datasets. It was found that the groundwater withdrawal was greater than the amount of total groundwater recharge during 1990–2023. Both these cities Ludhiana and Patiala has experienced an extreme decline in groundwater storage levels. Further, the time-series statistical relationships were explored to understand the relation between urban sprawl and annual temperature, rainfall patterns and total water storage for these cities during the years 2000-2022 for analysing the effect of rapid urbanization.

Study on the water-carbon coupling coordination function on the eastern edge of the Qinghai-Tibet plateau

The Qilian Mountains on the eastern edge of the Tibetan Plateau are an important ecological security barrier in northwestern China and an extremely critical water-carbon conservation area. The water and carbon cycles are closely linked, and the water-carbon coupling process has become further complicated under global climate change and through the intensification of human activities. A better understanding of the impacts and potential mechanisms of water-carbon interactions is the key to improving the efficiency of water-carbon resource utilization. To rationalize the relationship between water and carbon, we used data from statistical almanacs, land use, DEM and various climatic elements to estimate the water-carbon nutrition in the Qilian Mountains from 2000 to 2020 based on the InVEST model and the Coupled Coordination Degree Model (CCDM) and to explore the coupling mechanism of water-carbon, spatial-temporal dynamics, and the influencing factors. The results showed that: 1). the average annual water and carbon storage in the Qilian Mountains are 63.21 × 108 m3 and 77.82 × 107 t, respectively, showing a spatial distribution pattern of high in the northeast and low in the southwest. The inter-annual changes in water (y=-0.451x+968.920) and carbon (y = 0.082x-87.808) showed different trends. 2). The annual average of the water-carbon coupling coordination degree of 0.280 in the Qilian Mountains was low in general, and the trends declined over time from 0.273 in 2000 to 0.264 in 2020. However, the spatial distribution was relatively clustered, and the spatial correlation improved from 0.69 in 2000 to 0.74 in 2020. 3). Precipitation, potential evapotranspiration and temperature were important elements driving water-carbon coupling. Among them, precipitation was significantly positively correlated with water-carbon coupling (r = 0.88), and potential evapotranspiration (r=-0.56) and temperature (r=-0.39) showed significant negative correlations with water-carbon coupling. This study can provide information for management and decision-making in carbon sequestration, water resource utilization, ecological protection and climate change, and promote the study of the spatial and temporal coupling functions of water and carbon with in the Qilian Mountains. Meanwhile, this study can provide a reference for the environmental management and protection of the Qilian Mountain ecosystem.

Spatiotemporal variation of water cycle components in Minjiang River Basin based on a correction method for evapotranspiration products

Study regionMinjiang River Basin of China. Study focusThis paper aimed to study the spatiotemporal changes in water cycle components including evapotranspiration (ET) and terrestrial water storage in a watershed. To achieve this, we proposed a method that is based on the water balance and utilizes measured annual precipitation and runoff data to correct ET products. Subsequently, we derived the annual terrestrial water storage changes and performed trend analysis on the water cycle components by the Mann-Kendall trend test. New hydrological insights for the region(1) The Global Land Data Assimilation System (GLDAS) product overestimated ET from 2000 to 2019 in the Minjiang River Basin (MJB), which was 105.18 mm higher than ET. Through sub-basin validation, the relative error between the corrected ET and the ET is less than 5.1%. The proposed method can construct a reasonable annual dataset of water cycle components. (2) From 2000–2019, the Minjiang River Basin witnessed non-significant increasing trends in precipitation and runoff. However, significant growth was observed in ET, indicated by a Mann-Kendall trend test Z-value of 2.63. The variation in ET was primarily influenced by the normalized difference vegetation index (NDVI) (correlation coefficients of 0.37) and temperature (correlation coefficients of 0.48). The terrestrial water storage change fluctuated between − 400 and 355 mm. The relationships among the water cycle components, as represented by the runoff coefficient and evapotranspiration coefficient show non-significant changing trends. Both displayed a non-significant decreasing trend. From 2000–2019, the water cycle process remained relatively stable under changing environments in the MJB.

Multi-Source SAR-Based Surface Deformation Monitoring and Groundwater Relationship Analysis in the Yellow River Delta, China

Land motions are significantly widespread in the Yellow River delta (YRD). There is, however, a lack of understanding of the delta-wide comprehensive deformation mode and its dynamic mechanism, especially triggered by groundwater extraction. This paper adopts an integrated analysis of multidisciplinary data of image geodesy, geophysics, geology and hydrogeology to provide insights into Earth surface displacement patterns and dynamics in the YRD. Delta-scale land motions were measured for the first time using L-band ALOS images processed using multi-temporal InSAR, illustrating multiple obvious surface sinking regions and a maximum annual subsidence velocity of up to 130 mm. Then, the InSAR-constrained distributed point source model with optimal kernel parameters, a smoothness factor of 10 and a model grid size of 300 m was established and confirmed to be rational, reliable and accurate for modeling analysis over the YRD. Remarkable horizontal surface displacements, moving towards and converging on a sinking center, were recovered by means of modeling and measured using InSAR, with a maximum rate of up to 60 mm per year, which can trigger significant disasters, such as ground fissures and building damage. In addition, the annual total water storage variation at the delta scale, the most meaningful outcome, can be calculated and reaches a total of approximately 12,010 × 103 m3 in Guangrao city, efficiently filling the gap of GRACE and in situ investigations for delta-wide aquifer monitoring. Finally, a comparative analysis of time series InSAR measurements, modeling outcomes, and fault and groundwater data was conducted, and the strong agreement demonstrates that faults control aquifer distribution and hence the spatial distribution of groundwater-withdrawal-related regional land subsidence. Moreover, the obvious asymmetric displacements, demonstrating a northeasterly displacement trend, further reveal that faults control aquifer distribution and Earth surface deformation. These findings are useful for understanding the land motion patterns and dynamics, helping to sustainably manage groundwater and control disasters in the YRD and elsewhere worldwide.

Drought characterization and severity analysis using GRACE-TWS and MODIS datasets: a case study from the Awash River Basin (ARB), Ethiopia

Abstract Drought is the utmost highly devastating phenomenon in Ethiopia because of societies that are reliant on rainfall-dependent agriculture; thus, it is crucial to characterize drought at the basin scale using new developments in remote sensing products and recently proposed drought indices. This study aimed to quantify drought in the Awash River Basin (ARB) using drought indices of the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Evaporative Demand Drought Index (EDDI), Evaporative Stress Index (ESI), and Water Storage Deficit Index (WSDI). The Mann–Kendall test analysis of annual and seasonal terrestrial water storage (TWS) showed a significant increase from 2002 to 2017 that is beneficial for developmental activities in the ARB. GRACE showed a record high extreme drought that persisted for 15 months was noticed from 2005/01 to 2006/03 with a total water storage deficit of −411.8 mm with a peak shortage of −46.24 mm in 2005/03, representing severe terrestrial water shortage in the ARB. This GRACE-TWS-based quantified extreme water shortage in 2005/30 can be used as a threshold for adaptation. Overall, this study provides a reliable outcome that will be vital for the establishment of climate change adaptation pathways in the future for viable water resources management to minimize the disastrous impacts of drought in the AR.

Variation in the hydrological cycle in the Three-River Headwaters Region based on multi-source data

The hydrological processes in the Three-River Headwaters Region (TRHR), which is located in the Qinghai-Tibetan Plateau and includes the Yangtze River Headwater Region (YARHR), the Yellow River Headwater Region (YERHR), and the Lantsang River Headwater Region (LARHR), have changed under climate warming. Based on multi-source data, the spatial and temporal changes in precipitation, evapotranspiration, soil water storage, glacier melt, snowmelt and runoff in the Three-River Headwaters Region from 1982 to 2014 were comprehensively analysed. The annual precipitation data for the Three-River Headwaters Region from ERA5-Land, the Climatic Research Unit, the China Meteorological Forcing Dataset and the Global Land Data Assimilation System (GLDAS) all showed an increasing trend; the annual evapotranspiration data from ERA5-Land, Global Land Data Assimilation System, Global Land Evaporation Amsterdam Model (GLEAM) and Terrestrial Evapotranspiration Dataset across China (TEDC) all showed an increasing trend; and the annual soil water storage data from ERA5-Land, Global Land Data Assimilation System and Global Land Evaporation Amsterdam Model all showed an increasing trend. The annual snowmelt data from ERA5-Land, Global Land Data Assimilation System and SMT-Y datasets all showed a decreasing trend. The annual glacier melt increased in the Yangtze River Headwater Region and Yellow River Headwater Region and decreased in the Lantsang River Headwater Region. The increases in precipitation, evapotranspiration, soil water content and glacial melt, and the decreases in snowfall and snowmelt indicate an accelerated hydrological cycle in the Three-River Headwaters Region over the 1982 to 2014 period. The significant increase in precipitation is the main reason for the significant increase in runoff in the Yangtze River Headwater Region. The increase in precipitation in the Yellow River Headwater Region was less than the sum of the increase in evapotranspiration and soil water storage, resulting in a decreasing trend of runoff in the Yellow River Headwater Region. The increase in precipitation in the Lantsang River Headwater Region was slightly larger than the sum of that in evapotranspiration and soil water storage, and there was an insignificant increase in the runoff in the Lantsang River Headwater Region.

Calculation of rainwater harvest in greenhouses for semi-arid and continental climate zones

In this study, it is aimed to determine the irrigation water required due to solar radiation in high technology greenhouses where soilless cultivation is carried out according to TS825 standards, and to determine the annual water consumption and storage capacity with the harvested rainwater. As a result of the calculations made for Turkey Mediterranean region, it has been determined that if 90% of the rainfall in the western Mediterranean region is harvested, 72% of the annual water consumption can be met, and 45% in the eastern Mediterranean region. In the inner regions where the terrestrial climate is dominant, 22%–32% of the annual water consumption can be met with 90% of the rain harvested depending on the amount of rainfall. The required storage volume in the western Mediterranean is 0.420 m3 .m-2, while it is 0.096 m3.m-2 in the eastern Mediterranean and 0.044 m3.m-2 in Kırşehir, where the continental climate prevails.

Optimization of sizing of annual water storage reservoirs considering return period association

An increasing concern among water resources managers is the search for ways to improve reservoir sizing techniques. A simple technical and scientifically based technique is the sequent peak method, which has been widely disseminated, but presents limitations with respect to the length of the available data series. The objective of this study was to propose modifications to the sequent peak method to overcome the limitations related to the impossibility of associating the reservoir storage capacity with return periods different from the length of the data series. Storage capacities were estimated for every year, considering gauge stations located in the Urucuia River Basin, based on the sequent peak method. In order to associate such capacities with a frequency factor (return period), the Gumbel distribution was applied to the estimated storage values. This association with return periods which differ from the number of years in the data series showed great applicability

Ecohydrology of irrigated silage maize and alfalfa production systems in the upper midwest US

Increased reliance on maize silage as a primary dairy forage in place of perennial crops like alfalfa may result in tradeoffs with on-farm water balances and water quality. Our objective was to evaluate how modern dairy forage crop, manure, and irrigation management influence field-scale ecohydrology of silage maize and alfalfa production systems. This study utilized year-round measurements of precipitation, irrigation, evapotranspiration (ET), soil water storage, subsurface tile drainage, and drainage concentrations and loads of nitrate (NO3-N), dissolved reactive P (DRP), and total suspended solids (TSS) to calculate annual water balances, water-use efficiency, and water quality impacts of these crops. Mean annual ET (µ = 499 mm yr−1) and tile drainage (µ = 90 mm yr−1) were similar between crops, due in part to high variability, though cumulative drainage over three paired years was 326 mm for silage maize and 214 mm for alfalfa. Annual soil water storage was similar for alfalfa (25 ± 44 mm yr−1) and silage maize (27 ± 50 mm yr−1), and confidence bounds indicated water budgets were balanced. Inherent water use efficiency (IWUE*) was 70% greater for maize than alfalfa (30.8 versus 18.1 g C hPa kg H2O−1 d−1). Paired-watersheds analysis indicated alfalfa reduced NO3-N but increased DRP concentrations in drainage compared to silage maize. However, weekly loads of NO3-N and DRP were reduced by 72% and 33%, respectively, and annual TSS loads reduced by 37% with alfalfa compared to silage maize. Results highlight the value of alfalfa for managing on-farm water flows and water quality during critical periods of the year and suggest that increased usage of maize silage has facilitated gains in dairy productivity at the expense of water quality in the Upper Midwest US.

Assessing environment types for maize, soybean, and wheat in the United States as determined by spatio-temporal variation in drought and heat stress

The impact of agricultural technologies on crop yield is influenced by the environment type (ENVT) as determined by weather and soil. Understanding the correlation between the ENVT of the testing site in relation to the ENVT of the target production region is important for the evaluation and scaling out of agricultural technologies. Here we propose and apply the first explicit method to characterize ENVTs for major rainfed maize, soybean, and wheat producing regions in the United States. We combined a tested spatial framework, Technology Extrapolation Domain (TED), with crop modeling, long-term (30-y) daily weather records, and soil and management databases to calculate the frequency of ENVTs per crop for major harvested areas. Each ENVT was determined based on the intensity of drought and heat stress during key crop stages for yield determination. The ENVT repeatability was calculated based on the frequency of the most dominant ENVT in each TED. We found that inter-annual variation in drought and heat stress was larger than spatial variation. Our ENVTs explained 2x to 7x larger portion of the variance in actual yield compared to the existing TED framework that is based on long-term annual climate means and soil water storage. For maize and soybean, ca. 30% of their harvested area was located in TEDs with highly repeatable ENVTs (>66% of years). In contrast, only 15% of the wheat harvested area was located in TEDs with high ENVT repeatability. In comparison to the TED framework, the ENVTs defined here can help better capture G×E×M interactions and determine the environmental correlation between testing sites and target production environments.

Urbanization and its effects on water resources: Scenario of a tropical river basin in South India

Karamana River Basin (KRB), set in the tropical monsoon climate (i.e., Koppen's Am), hosts the drinking water supply to the capital city of Thiruvanathapuram, one of the highly urbanized cities in the southwestern seaboard of India. Primary focus of the study is a scrutiny of future water security status of KRB, amidst the rising population and subsequent urban sprawl. The study was done through a combination of analysis of remotely sensed data, and collation of data on population growth, surface water distribution and decadal-level groundwater monitoring. An uptrend of the decadal-level population and unscientific constructions across KRB led to the decline of per capita water entitlement and causing conflicts around water service delivery. So this study has an imperative focus on the effects of rapidly growing urban life and its impact on water resources in KRB. This was accomplished by studying the land use, land surface temperature (LST), annual precipitation, and groundwater trend for two decades, followed by land use modeling and quantifying total water deposit. The estimated LST values in KRB, robustly substantiate an upward shift in surface temperature between 2001 (47.55%) to 2020 (64.01%), a testimony of urban sprawl and it may be the major cause to reduce the rate of rainwater infiltration and increase in runoff. The Normalized Difference Vegetation Index (NDVI), used to generate land use map, and LST of the basin have been assessed for the years 2001, 2011, and 2020 to model whether or not land use has been modulated by urbanization. Based on this, future trends of land use changes for 2030 and 2050 have been predicted using CA-Markov model - a model combining Cellular Automata and Markov chain. We also carried out a quantification of annual water deposit, potential evapo-transpiration, infiltration, surface runoff, and storage. Decadal trends of population change, degree of urbanization and consequent rise in domestic water demand and shrinkage of area of open space/soil cover have also been factored in assessing water security in KRB. The results show that, as of today, the city is facing an acute annual shortage of surface water by 321.51 MCM. Furthermore, we propose potential sources for future water security of the state's capital region.

Assessment of the Impacts of Climate Change on Some Hydrological Processes of The Densu River Basin, Ghana

Water resources are among the most sensitive sectors to climate change due to their direct relationship with climate variables. The current study used projected climate datasets under two Representative Concentration Pathways (RCPs), 4.5 and 8.5, from the Coupled Model Intercompersion Project Phase 5 (CMIP5), remote sensing and Soil and Water Assessment Tool (SWAT) to estimate the effect of projected climate change on some hydrological processes. We focus on rainfall, water yield, soil water storage and evapotranspiration in the Densu Rvier Basin (DRB) for the 2050s. After calibration and validation of the SWAT model, there was a strong correlation between the simulated and the observed stream discharge coefficient of determination (R2) of 0.84 and 0.77, and a Nash Sutcliffe Efficiency of 0.76 and 0.70 for calibration and validation, respectively. The results showed an annual mean increase of 2 ºC in temperature, 61% in evapotranspiration and 20.1 mm in rainfall amount by the 2050s compared to their baseline values. Even though the mean annual soil water storage increases by about 80 mm, water yield declines by about 23 mm by 2050s. This appears to be due to the disproportionate increase in evapotranspiration compared to increase in rainfall. In conclusion, the DRB is projected to experience an overall reduction in water yield.

Monitoring time-varying terrestrial water storage changes using daily GNSS measurements in Yunnan, southwest China

Global Navigation Satellite System (GNSS) instruments provide a powerful tool to investigate spatiotemporal variations in regional-scale terrestrial water storage based on the solid Earth's elastic response to hydrologic loading signals. Here, we implemented an independent component analysis-based inversion method to investigate water storage changes and hydrometeorological extremes (e.g., heavy precipitation and droughts) in Yunnan. Our time-varying inversion allows us to reproduce the spatiotemporal evolution of terrestrial water storage changes. Three independent components (ICs) were chosen in our time-varying inversion model. The first two ICs contribute to 96.9% and 2.1% of data variance, respectively, and reveal annual water storage changes at different temporal scales. The third IC slightly explains the network time series and possibly correlates with the nonlinear water changes. The averaged GNSS, Gravity Recovery and Climate Experiment (GRACE), and Global Land Data Assimilation System (GLDAS) based multi-annual seasonal water changes have good consistency in their spatiotemporal signatures. All datasets suggest a gradually increasing trend in seasonal water storage variations from northeast to southwest Yunnan. The peak annual amplitudes of ~355 mm in the GNSS-inferred water estimates are larger than those of ~167–226 mm derived from the GLDAS and GRACE models. Hydrometeorological extremes are tracked in the various water time series, and GNSS-derived water deficits contribute to hydrological drought characterization. We also find good agreement between daily precipitation anomalies, GNSS-, and GLDAS-based water changes during the 2015 winter rainstorm. Our results demonstrate that a continuously operating GNSS network is a complementary tool to remotely measure terrestrial water storage changes and to provide valuable insights into operational hydrological monitoring.

Development of an integrated PCA-SCA-ANOVA framework for assessing multi-factor effects on water flow: A case study of the Aral Sea

An integrated PCA-SCA-ANOVA framework (abbreviated as PSAF) is advanced to analyze the impacts of multiple factors on water-flow variation. PSAF incorporates techniques of principle component analysis (PCA), stepwise-cluster analysis (SCA), and analysis of variance (ANOVA) within a general framework. PSAF cannot only quantify the sensitivity of water flow to individual and interactive factors, but also simulate water flow effectively under different scenarios. A case study of the Aral Sea is conducted to demonstrate the effectiveness and practicability of PSAF. The Nash-Sutcliffe coefficients for calibration and validation are 0.96 and 0.84, respectively. Results reveal that the key impact factors that cause the variability of water flow from the Amu Darya into the Aral Sea are runoff in the upper reach (contributing 53%), agricultural water use (occupying 23%) and water storage in reservoirs (occupying 3%). Results also disclose that runoff in the upper reach and agricultural water use have obvious interaction; the high level of runoff in the upper reach can enhance the negative effects of agricultural water use on water flow. In the future, to recover the water flow from the Amu Darya into the Aral Sea to the level of that in the 1990s (about 8 km3), the decreasing rates of annual agricultural water use and water storage in reservoirs should be controlled at 1.0% and 0.5%, respectively. The findings are helpful for supporting water resources management and restoring the ecological environment of the Aral Sea.

Assessing seasonal and interannual water storage variations in Taiwan using geodetic and hydrological data

We systematically investigate the spatiotemporal water storage changes in Taiwan using geodetic (GNSS and GRACE) and hydrological (precipitation, GLDAS and LSDM assimilation models, and in-situ groundwater level) datasets. We use GNSS-observed vertical deformation to estimate water storage changes based on elastic loading theory and weighted least-squares inversion, correcting for contributions from global loads using GRACE. The mean annual water-thickness change inferred from GNSS across Taiwan is 0.53 ± 0.17 m and the largest seasonal change of up to 0.91 m is estimated in southwest Taiwan. Comparison of the geodetic and hydrological data shows that the spatial pattern of annual water storage change estimated from GNSS, GLDAS, and precipitation data are generally consistent, indicating significant seasonal water-load fluctuations in Taiwan. However, the GRACE solution significantly underestimates the amplitude of water mass change in Taiwan due to leakage effect, but temporally correlates well with GNSS estimates. Hydrological assimilation model GLDAS, dominated by shallow soil moisture variations, predicts that the average seasonal variation of water thickness is only about 17% of GNSS estimates. This value is about half of the mean annual LSDM water storage change of 0.18 m including an estimate of both soil moisture and surface water. The discrepancy suggests that the contribution of groundwater is substantial and the total water storage change in the hydrological assimilation model is underestimated in Taiwan. The spatiotemporal distributions derived using independent component analysis (ICA) are generally consistent between the geodetic and hydrological data. However, comparisons of seasonal amplitudes and phases between all data pairs reveal different response times to precipitation, reflecting the complex nature of transient water storage due to variable rainfall patterns, infiltration rate, soil saturation, and runoff. The peak rainfall occurs in June-July, which is one-to-two months before the peak GNSS subsidence. Water storage of the GLDAS model also reaches its maximum in August, suggesting the water storage is controlled by the infiltration rate and capacity and the total water recharge from rainfall is generally larger than discharge in the summer. The highest groundwater levels lag one and two months behind the peak GNSS subsidence in western and eastern Taiwan, respectively, indicating a higher infiltration rate in western Taiwan.

Assessing approaches for stratifying producer fields based on biophysical attributes for regional yield-gap analysis

Large databases containing producer field-level yield and management records can be used to identify causes of yield gaps. A relevant question is how to account for the diverse biophysical background (i.e., climate and soil) across fields and years, which can confound the effect of a given management practice on yield. Here we evaluated two approaches to group producer fields based on biophysical attributes: (i) a technology extrapolation domain spatial framework (‘TEDs’) that delineates regions with similar (long-term average) annual weather and soil water storage capacity and (ii) clusters based on field-specific soil properties and weather during each crop phase in each year. As a case study, we used yield and management data collected from 3462 rainfed fields sown with soybean across the North Central US (NC-US) during four growing seasons (2014–2017). Following the TED approach, fields were grouped into 18 TEDs based on the TED that corresponded to the geographic location of each field. In the cluster approach, fields were grouped into clusters based on similarity of in-season weather and soil. To evaluate how the number of clusters would affect the results, fields were grouped separately into 5, 10, 18, and 30 clusters. The two stratification approaches (TEDs and clusters) were compared on their ability to explain the observed yield variation and yield response to key management factors (sowing date and foliar fungicide and/or insecticide). Lack of stratification of producer fields based on their biophysical background ignored management by environment (M × E) interactions, leading to spurious relationships and results that are not relevant at local level. In the case of the cluster approach, a fine stratification (18 and 30 clusters) explained a larger portion of the yield variance compared with a coarse stratification (5 and 10 clusters). However, for our case study in the NC-US region, we did not find strong evidence that the data-rich clustering approach outperformed the TEDs on the ability to explain yield variation and identify M × E interactions. Only the stratification into 30 clusters exhibited a small improved ability at explaining yield variation compared with the TEDs. However, the use of the clustering approach had important trade-offs, including large amount of data requirements and difficulties to scale results to different regions and over time. The choice of the stratification method should be based on objectives, data availability, and expected variation in yield due to erratic weather across regions and years.