Water quality in rivers is influenced by natural factors and human activities that interact in complex and nonlinear ways, which make water quality modelling a challenging task. The concepts of complex networks (CN), a recent development in network theory, seem to provide new avenues to unravel the connections and dynamics of water quality phenomenon, including clandestine teleconnections. This study aims to explore the spatial patterns of water quality using the CN concepts, at both catchment scale and larger national scale. Three major water quality parameters, i.e. dissolved oxygen (DO), permanganate index (COD Mn), and ammonia nitrogen (NH 3-N) are considered for analysis. Weekly data over a period of 12 years (since 2006) from 91 monitoring stations across China are analysed. Degree centrality and clustering coefficient methods are employed. The results show that the degree centrality and clustering coefficients values for water quality indicators is DO > NH 3-N > COD Mn at both basin scale and national scale. Since COD Mn is more sensitive to the upstream point source pollution, as it depends upon the locality and human activities, it leads to a higher heterogeneity of CN indexes even among spatially closer stations. NH 3-N comes next due to the identical pollution level and degradation process in a certain spatial extension. Meanwhile, DO shows good regional connectivity in line with the strong diffusivity. However, the CN characteristic is relatively inconspicuous in large basins and nationwide scale, which indicates the regional impact on water quality fluctuation and CN analysis. These original findings boost a comprehensive understanding of water quality dynamics and enlighten novel methods for environment system analysis and watershed management.
Remotely sensed evapotranspiration (ETRS) is increasingly used for streamflow estimation. Earlier reports are conflicting as to whether ETRS is useful in improving streamflow estimation skills. We believe that it is because earlier works used calibrated models and explored only small subspaces of the complex relationship between model skills for streamflow (Q) and ET. To shed some light on this complex relationship, we design a novel randomized, large sample experiment to explore the full ET-Q skill space, using seven catchments in Vietnam and four global ETRS products. For each catchment and each ETRS product, we employ 10,000 SWAT (Soil and Water Assessment Tool) model runs whose parameters are randomly generated via Latin Hypercube sampling. We then assess the full joint distribution of streamflow and ET skills using all model simulations. Results show that the relationship between ET and streamflow skills varies with regions, ETRS products, and the selected performance indices. This relationship even changes with different ranges of ET skills. Parameter sensitivity analysis indicates that the most sensitive parameters could have opposite contributions to ET and streamflow skills. Conditional probability assessment reveals that with certain ETRS products, the probabilities of having good streamflow skills are high and increase with better ET skills, but for other ETRS products, good model skills for streamflow are only achievable with certain intermediate ranges of ET skills, not the best ones. Overall, our study provides a useful approach for evaluating the value of ETRS for streamflow estimation.
The effect of climate change on precipitation intensity is well documented. However, findings regarding changes in spatial extent of extreme precipitation events are still ambiguous as previous studies focused on particular regions and time domains. This study addresses this ambiguity by investigating the pattern of changes in the spatial extent of short duration extreme precipitation events globally. A grid-based indicator termed Spatial-Homogeneity (SH) is proposed and used to assess the changes of spatial extent in Global Precipitation Measurement (GPM) records. This study shows that i) rising temperature causes significant shrinking of precipitation extent in tropics, but an expansion of precipitation extent in arid regions, ii) storms with higher precipitation intensity show a faster decrease in spatial extent, iii) larger spatial extent storms are associated with higher total precipitable water. Results imply that in a warming climate, tropics may experience severe floods as storms may become more intense and spatially concentrated.
How runoff will change as atmospheric CO2 rises depends upon several difficult to project factors, including CO2 fertilization, lengthened growing seasons, and vegetation greening. However, geologic records of the hydrological response to past carbon cycle perturbations indicate large increases in runoff with higher CO2. We demonstrate that the fact that the Earth has remained habitable since life emerged sets a lower-bound on the sensitivity of runoff to CO2 changes. The recovery of the Earth system from perturbations is attributed to silicate weathering, which transfers CO2 to the oceans as alkalinity via runoff. Though many factors mediate weathering rates, runoff determines the total flux of silicate-derived cations and hence the removal flux of excess CO2. Using a carbon cycle model that parameterizes weathering as a function of rock reactivity, runoff, temperature, and soil CO2, we show that recovery from a perturbation is only possible if the lower-bound for the sensitivity of runoff to atmospheric CO2 is 0%/K. Using proxy data for the Paleocene-Eocene Thermal Maximum, we find that to match the marine d13C record requires a runoff sensitivity greater than 0%/K and similar to estimates of the modern runoff sensitivity derived from an ensemble of Earth system models. These results suggest that the processes that enhance global runoff are likely to prevail over processes that tend to dampen runoff. In turn, that the Earth has always recovered from perturbations suggests that, though the runoff response is spatially complex, global discharge has never declined in response to warming, despite quite varied paleogeographies.
Arctic-Boreal lakes emit methane (CH₄), a powerful greenhouse gas. Recent studies suggest ebullition may be a dominant methane emission pathway in lakes but its drivers are poorly understood. Various predictors of lake methane ebullition have been proposed, but are challenging to evaluate owing to different geographical characteristics, field locations, and sample densities. Here we compare large geospatial datasets of lake area, lake perimeter, permafrost, landcover, temperature, soil organic carbon content, depth, and greenness with remotely sensed methane ebullition estimates for 5,143 Alaskan lakes. We find that lake wetland fraction (LWF), a measure of lake wetland and littoral zone area, is a leading predictor of methane ebullition (adj. R² = 0.211), followed by lake surface area (adj. R² = 0.201). LWF is inversely correlated with lake area, thus higher wetland fraction in smaller lakes may explain a commonly cited inverse relationship between lake area and methane ebullition. Lake perimeter (adj. R² = 0.176) and temperature (adj. R² = 0.157) are moderate predictors of lake ebullition, and soil organic carbon content, permafrost, lake depth, and greenness are weak predictors. The low adjusted R² values are typical and informative for methane attribution studies. A multiple regression model combining LWF, area, and temperature performs best (adj. R² = 0.325). Our results suggest landscape-scale geospatial analyses can complement smaller field studies, for attributing Arctic-Boreal lake methane emissions to readily available environmental variables.
Watershed scale models are essential for determining best management practices (BMPs), but they contain many parameters that modelers cannot directly measure. Modelers commonly estimate these parameters through a calibration process based on observed streamflow and nutrient data. However, a lack of long-term streamflow records makes watershed model parameter estimation in low data environments (LDE) challenging for hydrologists. To reliably estimate parameters in LDE, a new calibration technique, simultaneous multi-basin calibration (SMC), was developed to estimate the parameters of several SWAT model initializations for newly instrumented USGS gages in the Lake Champlain Basin of Vermont, USA (Little Otter Creek-Monkton, West Branch Dead Creek, and East Branch Dead Creek). In SMC, SWAT models of each watershed were initialized following standard methods. Then, in order to increase information content, the simulated flow from each model and the corresponding measured flow were combined, and calibrated as one model using a differential evolution algorithm DEoptim. We compared the results obtained from the new technique with one of the most commonly used approaches for calibration in LDE: the similarity-based regionalization (SBR) based on a calibration of a nearby watershed with similar characteristics. In the SBR method, the calibrated parameters from a watershed with a more extended period of recorded data (donor watershed, Little Otter Creek-Ferrisburg) transfer to the LDE watersheds (receptor watersheds). We show that in SBR the uncertainty of the donor watershed model propagates through the receptor watershed model, this propagation does not occur in SMC. We demonstrated that the agreement between simulated and observed streamflow, via the Nash-Sutcliffe efficiency (NSE) improved model performance from 1-20% using the SMC technique. Moreover, the calibrated soil storage parameters, including soil depth, available water capacity, and soil saturated hydraulic conductivity obtained from individual SMC and SBR models, were compared to the SSURGO soil database, where the SMC method provided parameter estimates that more closely matched SSURGO. This study demonstrated that a SMC method can outperform SBR in low data environments.
Abstract Techniques of furrow preparation on a field are mostly traditional; farmers provide furrow shape and direction based on their experience without the concept of scientific information. The measurement, evaluation and optimization of furrow irrigation are restricted to the single furrow or small number of adjacent furrows. The measurement process is too intensive to be applied at the full field scale. Consequently; it is necessary to assume that the infiltration characteristics and inflow rates of the measured furrow(s) represent the remainder of the field. The field inflow and outflow rates of five irrigation events in experimental plots were planned. The gross applied and estimated depth of irrigation was determined for a scheme based on the available data of inflow rate, which was measured through the graduated bucket and CROPWAT 8 model, respectively. Soil specific calibration was made for the soil moisture reading and its error result is presented. Furrow parameters including; furrow slope, width, length, and shape were measured and presented. The results of soil moisture measurements showed that crops are water stressed during the experiment period. Application efficiency decreases with increasing steep slope and cutoff time, large applied depth, and high inflow rate in the study area. The Melka Hida small scale irrigation scheme was granted to farmers and empowered them occasionally to harvest twice in a year. With increased population growth and the erratic rainfall, competition of water users in this area is reported increasing from time to time. This limits water usage, crop production and overall living standard of farmers of this region.
Quantifying the volume of water that is stored in the subsurface is critical to studies of water availability to ecosystems, slope stability, and water-rock interactions. In a variety of settings, water is stored in fractured and weathered bedrock as rock moisture. However, few techniques are available to measure rock moisture in unsaturated rock, making direct estimates of water storage dynamics difficult to obtain. Here, we use borehole nuclear magnetic resonance (NMR) at two sites in seasonally dry California to quantify dynamic rock moisture storage. We show strong agreement between NMR estimates of dynamic storage and estimates derived from neutron logging and mass balance techniques. The depths of dynamic storage are up to 9 m and likely reflect the depth extent of root water uptake. To our knowledge, these data are the first to quantify the volume and depths of dynamic water storage in the bedrock vadose zone via NMR.
Understanding spatial and temporal variations in terrestrial waters is key to assessing the global hydrological cycle. The future Surface Water and Ocean Topography (SWOT) satellite mission will observe the elevation and slope of surface waters at <100 m resolution. Methods for incorporating SWOT measurements into river hydrodynamic models have been developed to generate spatially and temporally continuous discharge estimates. However, most of SWOT data assimilation studies have been performed on a local scale. We developed a novel framework for estimating river discharge on a global scale by incorporating SWOT observations into the CaMa-Flood hydrodynamic model. The local ensemble transform Kalman filter with adaptive local patches was used to assimilate SWOT observations. We tested the framework using multi-model runoff forcing and/or inaccurate model parameters represented by corrupted Manning’s coefficient. Assimilation of virtual SWOT observations considerably improved river discharge estimates for continental-scale rivers at high latitudes (>50°) and also downstream river reaches at low latitudes. High assimilation efficiency in downstream river reaches was due to both local state correction and the propagation of corrected hydrodynamic states from upstream river reaches. Accurate global river discharge estimates were obtained (Kling–Gupta efficiency [KGE] > 0.90) in river reaches with > 270 accumulated overpasses per SWOT cycle when no model error was assumed. Introducing model errors decreased this accuracy (KGE ≈ 0.85). Therefore, improved hydrodynamic models are essential for maximizing SWOT information. These synthetic experiments showed where discharge estimates can be improved using SWOT observations. Further advances are needed for data assimilation on global-scale.
Spectral-based vegetation indices (VI) have been shown to be good proxies of grapevine stem water potential (Ψstem), potentially assisting in irrigation-decision making of commercial vineyards. However, VI-Ψstem correlations are mostly reported at the leaf or canopy scales using sensors attached to leaves or very-high-spatial resolution images derived from sensors mounted on small airplanes or drones. Here, for the first time, we take advantage of the high spatial resolution (3-m), near-daily images acquired from Planet’s nano-satellites constellation to derive VI-Ψstem correlations at the vineyard scale. Weekly Ψstem were measured along the growing season of 2017 in six vines in 81 commercial vineyards and in 60 pairs of vines in a 2.4 ha experimental vineyard in Israel. The clip application programming interface (API), provided by Planet, and Google Earth Engine platform were used to derive spatially continuous time series of four VIs: GNDVI, NDVI, EVI, and SAVI in the 82 vineyards. Results show that per-week multivariable linear models using variables extracted from VI time series successfully tracked spatial variations in Ψstem across the experimental vineyard (Pearson’s-r = 0.45–0.84: N=60). A simple linear regression model enabled monitoring seasonal changes in Ψstem along the growing season in the vineyard (r = 0.80–0.82). Planet VIs and seasonal Ψstem data from the 82 vineyards were used to derive a ‘global’ model for in-season monitoring of Ψstem at the vineyard-level (r = 0.81: RMSE = 17.5%: N=970). The ‘global’ model, which requires only a few VI variables extracted from Planet images, may be used for real-time weekly assessment of Ψstem in Mediterranean vineyards, substantially reducing expenses of conventional monitoring efforts.
* The researcher, Mohamed Akl, is funded by a full scholarship from the Ministry of Higher Education of the Arab Republic of Egypt. Abstract: The Gravity Recovery and Climate Experiment (GRACE) satellite has proven to be an excellent tool for monitoring changes in total water storage (TWS), which vertically integrate water storage changes from the land surface to the deepest aquifers. The objective of many GRACE studies is to isolate groundwater storage changes from changes in TWS using independent in-situ, remotely sensed, simulated, or assimilated data to remove other water budget components. Using auxiliary datasets to account for water budget components have revealed large biases and uncertainties, especially over high latitude regions, leading to accumulating errors in GRACE-GW estimates. Comparisons with in-situ groundwater observations permit assessments to evaluate how accurately we can isolate groundwater storage signals from TWSA. Goodness-of-fit (GOF) indices e.g., spearman correlation, mean square error (MSE), Nash-Sutcliffe Efficiency (NSE), and the Kling-Gupta Efficiency (KGE), are commonly applied hydrologic fit metrics that express similarity of time series. Such metrics are used here to compare GRACE-GW estimations and in-situ groundwater observations. The use of GOF indices is constrained by their substantial sampling uncertainty, and controversial interpretation, which may lead to wrong judgement on GRACE-GW estimations. Bias, nonlinearity, and non-normality introduce challenges in our use and interpretation of GOF applied to GRACE-GW time series. The goal of this work is to improve interpretation and use of GOF metrics to validate GRACE-GW estimates, highlighting the importance of assessing multiple GOF criteria beyond simply correlation often applied in GRACE studies. Our results document that poor performance of GOF metrics do not simply translate to inaccurate extraction of GRACE-GW time series but may be attributed to the GOF metric applied. We show that a rigorous assessment of GOF enhances our ability to interpret GRACE-GW change.
Much of the world’s water resource infrastructure was designed for specific regional snowmelt regimes under the assumption of a stable climate. However, as climate continues to change, this infrastructure is experiencing rapid regime shifts that test design limits. These changing snowmelt cycles are responsible for extreme hydrologic events occurring across the Contiguous United States (CONUS), such as river flooding from rain-on-snow, which puts infrastructure and communities at risk. Our study uses a new spatial snow regime classification system to track climate driven changes in snow hydrology across CONUS over 40 years (1981 – 2020). Using cloud-based computing and reanalysis data, regime classes are calculated annually, with changes evaluated across decadal and 30-year normal time scales. The snow regime classification designates areas across CONUS as: (1) rain dominated (RD), (2) snow dominated (SD), (3) transitional (R/S), or (4) perennial snow (PS). Classifications are thresholded using a ratio of maximum snow water equivalent (SWE) over accumulated cool-season precipitation, with a comparison of two approaches for selecting maximum SWE. Results indicate that average snow cover duration generally became shorter in each decade over our evaluation period, with rates of decline increasing at higher elevations. Anomalies in SD spatial extents, compared to the 30-year normal, decreased over the first three decades, while anomalies in RD extents increased. Also, previously SD areas have shifted to R/S, with boundary lines moving up in latitude. As water managers adapt to a changing climate, geospatial classification, such as this snow regime approach, may be a critical tool.
The study aims to enhance the accuracy of the European Centre for Medium-Range Weather Forecasts (ECMWF) and Global Ensemble Forecast System (GEFS) reference evapotranspiration forecast at short to medium range (1-7 days) using the post-processing methods: Analog technique (AN) and Simple Linear Regression (LR) over the Indian subcontinent. The FAO, Penman-Monteith (PM) equation, is used for the estimation of reference evapotranspiration (ET0) reforecasts from meteorological reforecasts from ECMWF and GEFS models. The post-processing technique AN and LR was applied to the ET0 reforecasts and compared against the ET0 estimated using observed and reanalysis dataset. The deterministic evaluation metrics, such as Root Mean Square Error (RMSE) and Correlation Coefficient (R), were used for the performance assessment of raw ET0 forecast and post-processed ET0 forecasts. Results showed that short to medium range ET0 forecasts improved substantially using AN and LR post-processing methods over the Indian region. Assessment across the different climatic zones in India showed that raw and post-processed ET0 forecasts in the Tropical climate zone are more skillful than in the other climatic zones. A comparison of raw and post-processed ET0 forecasts across different seasons in India showed that model forecasts are more skillful during the winter season compared to the rest. Intercomparison of the models also show that overall the raw and post-processed ET0 forecasts from ECMWF are better than GEFS. Results emphasize the use of post-processing methods to enhance the skill of ET0 forecasts over the Indian subcontinent before their application in irrigation scheduling and water demand estimation purposes.
Seasonally warm summers in the Arctic produce supra-permafrost aquifers within the active layer. However, the magnitude of groundwater flow, the amount of dissolved carbon and nutrients, and the solute flow paths are largely unknown, but critical to quantifying downgradient contributions to surface waters (lakes and rivers). To develop approachable methods to quantify groundwater inputs in continuous permafrost watersheds, we selected Imnavait Creek watershed on the North Slope of Alaska as a representative headwater drainage. We conducted 1000 groundwater flow simulations based on topography of the watershed and varying aquifer hydraulic conductivity and saturated thickness values. We fitted a lognormal distribution to the resulting 1000 model outputs, and we derived n=1e6 possible discharge values based on Monte Carlo random sampling on the model outputs. The groundwater discharge values integrated across the watershed generally agree with observed streamflow in Imnavait Creek over 2 months. When groundwater discharge estimates were combined with in-situ measurements of groundwater-dissolved organic carbon and nitrogen concentrations, we found that Imnavait Creek’s organic matter load is also dominantly sourced from groundwater. Thus, riverine and lacustrine ecological and biogeochemical processes relate strongly to groundwater phenomena in these continuous permafrost settings. As the Arctic warms and the active layer deepens, it will become more important to understand and predict supra-permafrost aquifer dynamics.
The use of accurate streamflow estimates is widely recognized in the hydrological field. However, due to the model’s structural error, they often yield suboptimal streamflow estimates. Past studies have shown that soil moisture assimilation improves the performance of the hydrological model which often results in enhanced model estimates. Due to this reason, it is widely studied in the hydrological field. However, the efficiency of the assimilation largely relies on the correct placement of the observation into the model. Ingesting futile observations often results in the degradation of model performance. On the contrary, performing assimilation only at those time steps when the assimilating variable is sensitive to the model output may yield desirable output. Further, it will avoid the assimilation of spurious observations. In this view, this study proposes a new approach where sensitivity-based sequential assimilation is performed on a conceptual Two Parameter Model (TPM). To demonstrate this approach, ASCAT soil moisture observations are assimilated into TPM using Ensemble Kalman Filter (EnKF) sequential approach. At first, the temporal evolution of the soil moisture sensitivity with respect to streamflow is established. Later, at those time steps when the soil moisture is sensitive, EnKF assimilation is performed. For this purpose, a moderately sized catchment in the Krishna basin, India is selected as the study area. Model calibration and validation are performed between 2000 to 2006 and 2007 to 2011 respectively. Model run without assimilation is considered as open-loop simulation. Streamflow simulation after assimilation showed a significant improvement when compared against the open-loop simulation. KGE value increased from 0.70 to 0.79 and PBIAS value reduced from 18.31 to 1.80. The highlighting factor is that only 39% of the total observations were used during the assimilation process. The initial results are encouraging and looks that the proposed approach shall be highly useful at those locations where data availability for assimilation purpose is a serious concern.
Cosmic-ray neutron sensors (CRNS) have been used in many studies for measuring soil moisture and snow pack over intermediate scales. Corrections for geomagnetic latitude, barometric pressure and atmospheric humidity are well established, however, corrections for the effect of solar activity on neutron count rates have been overly simplistic, typically relying on one neutron monitor station and accounting for latitude and elevation crudely or not at all. Recognizing the lack of a generalised and scientifically robust approach to neutron intensity correction, we developed a new approach for correcting CRNS count rates based on analysis of data from 110 quality-controlled neutron monitor stations from around the world spanning more than seven decades. Count rates from each monitor were plotted against the count rates from Climax, CO, USA or Jungfraujoch, Switzerland depending on the time period covered. Relationships between relative counting rates at the site of interest versus the reference neutron monitors were found to be strongly linear. The dimensionless slope of this linear relation, referred to as τ, was shown to increase with increasing geomagnetic latitude and elevation. This dependence of τ on geomagnetic latitude and elevation was represented using an empirical relationship based on a single reference neutron monitor. This generalised approach enables τ to be derived for any location on Earth and also lends itself to roving CRNS studies. The correction procedure also includes a location-dependent normalisation factor which enables easy substitution of an alternative reference neutron monitor into the correction procedure.
The Gravity Recovery and Climate Experiment (GRACE) data help to determine the total water storage anomalies (TWS) across the global scale. The various other important components such as Groundwater storage (GWS) and evapotranspiration for the region of South –East Asia have been determined. With the study of the gravity variation across the globe the long-term changes in the hydrological cycle can be determined which can be related to climate science or the influence of anthropogenic activities. The variation between the Groundwater storage (GWS) and the Total water storage (TWS) of the study area has been calculated for the pre and post-monsoon season of the study area. The variation between groundwater storage and total water storage can be visualized through geospatial analysis. Therefore, the regions with a substantial decrease in water storage can be related to various climate and anthropogenic factors hence implying a sustainable use of groundwater as a resource. Keywords: Machine Learning, Remote Sensing, Groundwater Recharge, Climate science.