Hydrological Estimation Research Articles

Spatiotemporal estimation of groundwater and surface water conditions by integrating deep learning and physics-based watershed models

The impacts of climate change on hydrology underscore the urgency of understanding watershed hydrological patterns for sustainable water resource management. The conventional physics-based fully distributed hydrological models are limited due to computational demands, particularly in the case of large-scale watersheds. Deep learning (DL) offers a promising solution for handling large datasets and extracting intricate data relationships. Here, we propose a DL modeling framework, incorporating convolutional neural networks (CNNs) to efficiently replicate physics-based model outputs at high spatial resolution. The goal was to estimate groundwater head and surface water depth in the Sabgyo Stream Watershed, South Korea. The model datasets consisted of input variables, including elevation, land cover, soil type, evapotranspiration, rainfall, and initial hydrological conditions. The initial conditions and target data were obtained from the fully distributed hydrological model HydroGeoSphere (HGS), whereas the other inputs were actual measurements in the field. By optimizing the training sample size, input design, CNN structure, and hyperparameters, we found that CNNs with residual architectures (ResNets) yielded superior performance. The optimal DL model reduces computation time by 45 times compared to the HGS model for monthly hydrological estimations over five years (RMSE 2.35 and 0.29 m for groundwater and surface water, respectively). In addition, our DL framework explored the predictive capabilities of hydrological responses to future climate scenarios. Although the proposed model is cost-effective for hydrological simulations, further enhancements are needed to improve the accuracy of long-term predictions. Ultimately, the proposed DL framework has the potential to facilitate decision-making, particularly in large-scale and complex watersheds.

High-resolution Annual Dynamic dataset of Curve Number from 2008 to 2021 over Conterminous United States

The spatial distribution and data quality of curve number (CN) values determine the performance of hydrological estimations. However, existing CN datasets are constrained by universal-applicability hypothesis, medium resolution, and imbalance between specificity CN tables to generalized land use/land cover (LULC) maps, which hinder their applicability and predictive accuracy. A new annual CN dataset named CUSCN30, featuring an enhanced resolution of 30 meters and accounting for temporal variations in climate and LULC in the continental United States (CONUS) between 2008 and 2021, was developed in this study. CUSCN30 demonstrated good performance in surface runoff estimation using CN method when compared to observed surface runoff for the selected watersheds. Compared with existing CN datasets, CUSCN30 exhibits the highest accuracy in runoff estimation for both normal and extreme rainfall events. In addition, CUSCN30, with its high spatial resolution, better captures the spatial heterogeneity of watersheds. This developed CN dataset can be used as input for hydrological models or machine learning algorithms to simulate rainfall-runoff across multiple spatiotemporal scales.

Improving the fire weather index system for peatlands using peat-specific hydrological input data

Abstract. The Canadian Fire Weather Index (FWI) system, even though originally developed and calibrated for an upland Jack pine forest, is used globally to estimate fire danger for any fire environment. However, for some environments, such as peatlands, the applicability of the FWI in its current form, is often questioned. In this study, we replaced the original moisture codes of the FWI with hydrological estimates resulting from the assimilation of satellite-based L-band passive microwave observations into a peatland-specific land surface model. In a conservative approach that maintains the integrity of the original FWI structure, the distributions of the hydrological estimates were first matched to those of the corresponding original moisture codes before replacement. The resulting adapted FWI, hereafter called FWIpeat, was evaluated using satellite-based information on fire presence over boreal peatlands from 2010 through 2018. Adapting the FWI with model- and satellite-based hydrological information was found to be beneficial in estimating fire danger, especially when replacing the deeper moisture codes of the FWI. For late-season fires, further adaptations of the fine fuel moisture code show even more improvement due to the fact that late-season fires are more hydrologically driven. The proposed FWIpeat should enable improved monitoring of fire risk in boreal peatlands.

Probabilistic Hydrological Estimation of LandSlides (PHELS): global ensemble landslide hazard modelling

Abstract. In this study we present a model for the global Probabilistic Hydrological Estimation of LandSlides (PHELS). PHELS estimates the daily hazard of hydrologically triggered landslides at a coarse spatial resolution of 36 km, by combining landslide susceptibility (LSS) and (percentiles of) hydrological variable(s). The latter include daily rainfall, a 7 d antecedent rainfall index (ARI7) or root-zone soil moisture content (rzmc) as hydrological predictor variables, or the combination of rainfall and rzmc. The hazard estimates with any of these predictor variables have areas under the receiver operating characteristic curve (AUC) above 0.68. The best performance was found with combined rainfall and rzmc predictors (AUC = 0.79), which resulted in the lowest number of missed alarms (especially during spring) and false alarms. Furthermore, PHELS provides hazard uncertainty estimates by generating ensemble simulations based on repeated sampling of LSS and the hydrological predictor variables. The estimated hazard uncertainty follows the behaviour of the input variable uncertainties, is about 13.6 % of the estimated hazard value on average across the globe and in time and is smallest for very low and very high hazard values.

Estimation of long-term series of total nutrient loads flowing into a large perialpine lake (Lake Como, Northern Italy) from incomplete discrete data by governmental monitoring

Continuous series of nutrient loads released into a water body are essential for nutrient budgeting, water quality modelling and watershed management activities. A quintessential example of pursued data are the series of total nutrient loads flowing from multiple tributaries into a lake. However, except for extraordinary cases in which high-frequency monitoring (HFM) stations are installed for both discharge and concentrations, measured nutrient loads are available on a discrete basis. Such observations are typically obtained by governmental agencies for environmental monitoring purposes, with an at best monthly resolution, yet commonly with gaps spanning years. Usually, monitoring activities are limited to major inflows, neglecting minor ones. However, the latters can play a more relevant role in nutrient load budgets than in hydrological ones, in response to different natural features and pollution among tributary watersheds. In this work, we present a methodology we developed to estimate long-term series of total nutrient loads flowing into a water body, employing as case study Lake Como, a large deep lake in the Italian Alps with manifold monitored and unmonitored tributary watersheds. The method uses observed long-term relationships between discharges and concentrations (Q – C) and available discharge measurements and hydrological estimations to estimate continuous load series for the monitored basins. For the unmonitored watersheds, Q – C relationships are estimated from those of the monitored basins, given the observed dependence of the power-law coefficients on basin hydromorphological parameters. This equals to extending the regionalisation approach applied in hydrology for rainfall and discharges to the ecohydrological field for nutrient load estimation. The application of the method to the case study led to overall annual load estimations congruent to traditional techniques and revealed interesting Q – C watershed dynamics at interannual time scales which could not be disclosed through previous approaches. This work represents an exploratory development and application of ecohydrological regionalisation techniques, whose future development is fostered.

Machine Learning Framework with Feature Importance Interpretation for Discharge Estimation: A Case Study in Huitanggou Sluice Hydrological Station, China

Accurate and reliable discharge estimation plays an important role in water resource management as well as downstream applications such as ecosystem conservation and flood control. Recently, data-driven machine learning (ML) techniques showed seemingly insurmountable performance in runoff forecasting and other geophysical domains, but they still need to be improved in terms of reliability and interpretability. In this study, focusing on discharge estimation and management, we developed an ML-based framework and applied it to the Huitanggou sluice hydrological station in Anhui Province, China. The framework contains two ML algorithms, the ensemble learning random forest (ELRF) and the ensemble learning gradient boosting decision tree (ELGBDT). The SHapley Additive exPlanation (SHAP) was introduced into our framework to interpret the impact of the model features. In our framework, the correlation analysis of the dataset can provide feature information for modeling, and the quartile method was utilized to solve the outlier problem of the dataset. The Bayesian optimization algorithm was adopted to optimize the hyperparameters of the ensemble ML models. The ensemble ML models are further compared with the traditional stage–discharge rating curve (SDRC) method and the single ML model. The results show that the estimation performance of the ensemble ML models is superior to that of the SDRC and the single ML model. In addition, an analysis of the discharge estimation without considering the flow state was performed. This analysis reveals that the ensemble ML models have strong adaptability. The ensemble ML models accurately estimate the discharge, with a coefficient of determination of 0.963, a root mean squared error of 31.268, and a coefficient of correlation of 0.984. Our framework can prove helpful to improve the efficiency of short-term hydrological estimation and simultaneously provide the interpretation of the impact of the hydrological features on estimation results.

Benchmarking water and salt dynamics at subterranean estuaries using TOUGHREACT

The interactions between saline seawater and fresh groundwater occurs in subterranean estuaries (STEs), triggering complex hydrological and geochemical processes. These processes are influenced by multiple hydrological factors including tidal amplitude (A), freshwater head (h), seawater diffusion coefficient (d), and slope ratio. Besides, accurate predictions of the geochemical processes in STEs require validating the hydrological estimations by the geochemical models against that by conventionally used hydrological models. TOUGHREACT is employed to numerically study the effects of these hydrological factors on groundwater flow and salt transport in STEs. At the quasi-static state, the spatiotemporal distributions of the upper saline plume (USP) and the saltwater wedge (SW), and the water fluxes at the surface water-groundwater interface agree with previous results, confirming the applicability of TOUGHREACT for submarine groundwater discharge (SGD) simulations. Increasing tidal amplitude, inland freshwater head, seawater diffusion coefficient, and beach slope ratio enhances the exchange of groundwater and surface water, strengthens the density-driven circulation (DDC) of seawater, and alleviates the saltwater intrusion (SI) by shrinking the SW. The rise of tidal amplitude and seawater diffusion coefficient, and the reduction of freshwater head and beach slope ratio intensify the seawater exchange across the beach mainly by increasing the flux of tide-driven circulation (TDC) of seawater, but reducing the freshwater flux. These findings are beneficial for better understanding the hydrological processes of SGD and serve as a benchmark for predicting the water and salt flow across STEs using TOUGHREACT. This validation expands the numerical toolsets used for quantifying the hydrological processes, and enables future analysis of the geochemical processes occurred in STEs.

A Central Asia hydrologic monitoring dataset for food and water security applications in Afghanistan

Abstract. From the Hindu Kush mountains to the Registan Desert, Afghanistan is a diverse landscape where droughts, floods, conflict, and economic market accessibility pose challenges for agricultural livelihoods and food security. The ability to remotely monitor environmental conditions is critical to support decision making for humanitarian assistance. The Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) global and Central Asia data streams provide information on hydrologic states for routine integrated food security analysis. While developed for a specific project, these data are publicly available and useful for other applications that require hydrologic estimates of the water and energy balance. These two data streams are unique because of their suitability for routine monitoring, as well as for being a historical record for computing relative indicators of water availability. The global stream is available at ∼ 1-month latency, and monthly average outputs are on a 10 km grid from 1982–present. The second data stream, Central Asia (21–56∘ N, 30–100∘ E), at ∼ 1 d latency, provides daily average outputs on a 1 km grid from 2000–present. This paper describes the configuration of the two FLDAS data streams, background on the software modeling framework, selected meteorological inputs and parameters, and results from previous evaluation studies. We also provide additional analysis of precipitation and snow cover over Afghanistan. We conclude with an example of how these data are used in integrated food security analysis. For use in new and innovative studies that will improve understanding of this region, these data are hosted by U.S. Geological Survey data portals and the National Aeronautics and Space Administration (NASA). The Central Asia data described in this paper can be accessed via the NASA repository at https://doi.org/10.5067/VQ4CD3Y9YC0R (Jacob and Slinski, 2021), and the global data described in this paper can be accessed via the NASA repository at https://doi.org/10.5067/5NHC22T9375G (McNally, 2018).

Analysis of Hydrological Characteristics of Blue Nile Basin, Nashe Watershed

Hydrological modeling is a technique for understanding hydrologic characteristics and estimation of the water balance of watersheds for integrated water resources development and management. The Soil and Water Assessment Tool (SWAT) model was used for modeling the hydrological behavior of the Nashe watershed in the north-western part of Ethiopia. The spatial data, daily climate, and stream flow were the required input data for the model. The observed monthly stream flow data at the outlet and selected sub-watersheds in the catchment were used to calibrate and validate the model. The model performance was assessed between the simulated and observed streamflow by using sequential uncertainty fitting-2 (SUFI-2), generalized likelihood uncertainty estimation, parameter solution (Parasol) and particle swarm optimization. The sensitivity of 18 parameters was tested, and the most sensitive parameters were identified. The model performance was evaluated using p and r- factor, coefficient of determination, Nash Sutcliffe coefficient efficiency, percent bias during uncertainty analysis, calibration and validation. Therefore, based on the set of proposed evaluation criteria, the SUFI-2 algorithm has been able to provide slightly more reasonable outcomes and Parasol is the worst compared to the other algorithms. An analysis of monthly and seasonal water balance has been also accomplished for the Nashe catchment. The water balance parameters were distinct for the three seasonal periods in the catchment. The seasonal water budget analysis reveals that the watershed receives around 19%, 69%, and 12% of rainfall through the short rain, long rain and dry seasons, respectively. The received precipitation was lost due to evapotranspiration by 29%, 34% and 37% for each season respectively. The surface runoff contributes to the catchment by 5%, 86% and 9% of the water yield.

Validation of Three Daily Satellite Rainfall Products in a Humid Tropic Watershed, Brantas, Indonesia: Implications to Land Characteristics and Hydrological Modelling

A total of three different satellite products, CHIRPS, GPM, and PERSIANN, with different spatial resolutions, were examined for their ability to estimate rainfall data at a pixel level, using 30-year-long observations from six locations. Quantitative and qualitative accuracy indicators, as well as R2 and NSE from hydrological estimates, were used as the performance measures. The results show that all of the satellite estimates are unsatisfactory, giving the NRMSE ranging from 6 to 30% at a daily level, with CC only 0.21–0.36. Limited number of gauges, coarse spatial data resolution, and physical terrain complexity were found to be linked with low accuracy. Accuracy was slightly better in dry seasons or low rain rate classes. The errors increased exponentially with the increase in rain rates. CHIPRS and PERSIANN tend to slightly underestimate at lower rain rates, but do show a consistently better performance, with an NRMSE of 6–12%. CHRIPS and PERSIANN also exhibit better estimates of monthly flow data and water balance components, namely runoff, groundwater, and water yield. GPM has a better ability for rainfall event detections, especially during high rainfall events or extremes (>40 mm/day). The errors of the satellite products are generally linked to slope, wind, elevation, and evapotranspiration. Hydrologic simulations using SWAT modelling and the three satellite rainfall products show that CHIRPS slightly has the daily best performance, with R2 of 0.59 and 0.62, and NSE = 0.54, and the monthly aggregated improved at a monthly level. The water balance components generated at an annual level, using three satellite products, show that CHIRPS outperformed with a ration closer to one, though with a tendency to overestimate up to 3–4× times the data generated from the rainfall gauges. The findings of this study are beneficial in supporting efforts for improving satellite rainfall products and water resource implications.

Streamflow Estimation at Ungauged Basin using Modified Group Method of Data Handling

Among the foremost frequent and vital tasks for hydrologist is to deliver a high accuracy estimation on the hydrological variable, which is reliable. It is essential for flood risk evaluation project, hydropower development and for developing efficient water resource management. Presently, the approach of the Group Method of Data Handling (GMDH) has been widely applied in the hydrological modelling sector. Yet, comparatively, the same tool is not vastly used for the hydrological estimation at ungauged basins. In this study, a modified GMDH (MGMDH) model was developed to ameliorate the GMDH model performance on estimating hydrological variable at ungauged sites. The MGMDH model consists of four transfer functions that include polynomial, hyperbolic tangent, sigmoid and radial basis for hydrological estimation at ungauged basins; as well as; it incorporates the Principal Component Analysis (PCA) in the GMDH model. The purpose of PCA is to lessen the complexity of the GMDH model; meanwhile, the implementation of four transfer functions is to enhance the estimation performance of the GMDH model. In evaluating the effectiveness of the proposed model, 70 selected basins were adopted from the locations throughout Peninsular Malaysia. A comparative study on the performance was done between the MGMDH and GMDH model as well as with other extensively used models in the area of flood quantile estimation at ungauged basins known as Linear Regression (LR), Nonlinear Regression (NLR) and Artificial Neural Network (ANN). The results acquired demonstrated that the MGMDH model possessed the best estimation with the highest accuracy comparatively among all models tested. Thus, it can be deduced that MGMDH model is a robust and efficient instrument for flood quantiles estimation at ungauged basins.

Hydrological modeling with respect to impact of land-use and land-cover change on the runoff dynamics in Budhabalanga river basing using ArcGIS and SWAT model

Catchment area is observed to be standard unit for management of the natural resources. The origin of catchment area parameters using Remote Sensing (RS) and Geographical Information System (GIS) and use Soil Conservation Services Curve Number (SCS–CN) methods is the running trend for hydrologic estimation of catchment areas. Urban runoff increases significantly due to increased impervious area and decreased the forest area. Estimation of land use in built-up area plays a vital role as input to the evaluation of runoff. The broadcast specification model Soil and Water Assessment Tool (SWAT) has been tested on monthly basis for evaluating surface runoff from a small gauging station of Govindpur in NH5 road located along the Budhabalanga river basin. By using the GIS technique there have been generated several maps like watershed boundaries, drainage line, land use/cover, soil map and slope map. Potential of the model for generating rainfall has been calculated for 6 Years interval (2000, 2006, 2012 and 2018) period. The results depict that SWAT model usually performs well in simulating runoff according to Nash-Sutcliffe Efficiency (NSE) and Coefficient of Determination (R2) values. For monthly stream flow the NSE and R2 values were 0.827 and 0.957 during the simulation period respectively. The results of the study indicated that the though land use patterns have changed resulting in increase in agricultural, built-up, water land and decrease in forest and barren land.

Evaluation of hydrological and cryospheric angular momentum estimates based on GRACE, GRACE-FO and SLR data for their contributions to polar motion excitation

In geodesy, a key application of data from the Gravity Recovery and Climate Experiment (GRACE), GRACE Follow-On (GRACE-FO), and Satellite Laser Ranging (SLR) is an interpretation of changes in polar motion excitation due to variations in the Earth’s surficial fluids, especially in the continental water, snow, and ice. Such impacts are usually examined by computing hydrological and cryospheric polar motion excitation (hydrological and cryospheric angular momentum, HAM/CAM). Three types of GRACE and GRACE-FO data can be used to determine HAM/CAM, namely degree-2 order-1 spherical harmonic coefficients of geopotential, gridded terrestrial water storage anomalies computed from spherical harmonic coefficients, and terrestrial water storage anomalies obtained from mascon solutions. This study compares HAM/CAM computed from these three kinds of gravimetric data. A comparison of GRACE-based excitation series with HAM/CAM obtained from SLR is also provided. A validation of different HAM/CAM estimates is conducted here using the so-called geodetic residual time series (GAO), which describes the hydrological and cryospheric signal in the observed polar motion excitation. Our analysis of GRACE mission data indicates that the use of mascon solutions provides higher consistency between HAM/CAM and GAO than the use of other datasets, especially in the seasonal spectral band. These conclusions are confirmed by the results obtained for data from first 2 years of GRACE-FO. Overall, after 2 years from the start of GRACE-FO, the high consistency between HAM/CAM and GAO that was achieved during the best GRACE period has not yet been repeated. However, it should be remembered that with the systematic appearance of subsequent GRACE-FO observations, this quality can be expected to increase. SLR data can be used for determination of HAM/CAM to fill the one-year-long data gap between the end of GRACE and the start of the GRACE-FO mission. In addition, SLR series could be particularly useful in determination of HAM/CAM in the non-seasonal spectral band. Despite its low seasonal amplitudes, SLR-based HAM/CAM provides high phase consistency with GAO for annual and semiannual oscillation.

Comparison of Vertical Surface Deformation Estimates Derived From Space‐Based Gravimetry, Ground‐Based GPS, and Model‐Based Hydrologic Loading Over Snow‐Dominated Watersheds in the United States

AbstractSpatiotemporal variability in Earth's terrestrial water storage (TWS) causes changes in surface deformation. The potential for using ground‐based Global Positioning System (GPS) vertical displacement observations for estimating TWS is explored through a comparison of vertical displacements derived from space‐based gravimetric retrievals, ground‐based GPS, and model‐based hydrologic estimates. The study presented here focuses on two snow‐dominated basins in the Western United States for the years 2003–2016. Seasonal variations are observed in the vertical displacements derived from all three data sets, and the variation is coherent with the changes in hydrologic loading. Good consistency is observed between any two of the three data sets with gravimetric retrievals and hydrologic model estimates providing the highest level of agreement (i.e., all examined stations with correlation coefficient R > 0.70). Vertical displacements derived from gravimetric retrievals and ground‐based GPS yielded R > 0.70 for more than 89% of the stations. In addition, it is found that both GPS‐derived and space‐based, gravimetry‐derived vertical displacements clearly reflected the impact of climate variation (i.e., heavy precipitation during 2010–2011 winter followed by prolonged drought). Vertical displacements derived from the hydrologic model highlighted the relatively large precipitation convergence phase during late 2010 to early 2011 at some stations but not the prolonged drought that followed. The results indicate that ground‐based GPS observations of vertical displacement have the capability to capture variations in TWS changes, which can be systematically merged in conjunction with Gravity Recovery and Climate Experiment (GRACE) into a land surface model to improve TWS estimates in a follow‐up study.

Development and testing of a modified SWAT model based on slope condition and precipitation intensity

The SWAT model is a semi-distributed hydrological watershed model with an explicit physical mechanism, which is widely applied in areas characterized by a wide variety of precipitation distributions and slope conditions. However, the Soil Conservation Service-Curve Number (SCS-CN) value and precipitation initial abstraction ratio were not related to slope, soil moisture content, precipitation intensity, and land cover interception within the original SWAT model. Thus, a modified SCS-CN model is proposed by considering the slope, rainfall intensity, soil moisture, and interception effects of the landscape in this study, specifically revising the critical sensitive parameters including CN2 and initial abstraction ratio (λ). The modified SCS-CN model was added to the original SWAT model and named the SWAT-CH model, which is applied to Chao River watershed which experiences obvious differences in precipitation and slope. For this application, the daily streamflow was simulated for the calibration and validation periods of 1993–2005 and 2006–2013, respectively. The simulated variable is monthly runoff at watershed outlet, namely Xiahui hydrological station. The parameter sensitivity and uncertainty and the model applicability were analyzed using as Chao River watershed as a case study. The main results are as follows. ① Compared with the original SWAT model, the parameters of SOL-K, HRU-SLP, SLSUBBSN, and CANMX were identified as the most sensitive parameters for the runoff simulation in the new SWAT-CH model. ② The prediction efficiency of the SWAT-CH model surpasses that of the original SWAT model. The Nash-Sutcliffe efficiency, KGE index, and the PBIAS were 0.94, 0.83 and 4.55%, respectively for SWAT-CH during the calibration stage; while, those corresponding indexes were 0.91, 0.84, and 7.00% for the validation period. The modified SWAT-CH model can better reproduce the hydrological process than the SWAT model. In detail, the Nash-Sutcliffe efficiency, KGE index, and the PBIAS were improved by 0.02, 0.10, −4.01 percentage points for the calibration period, as well as by 0.18, 0.25, and −28.55 percentage points for the validation period in the modified SWAT-CH model compared with the original SWAT model. ③ Influenced by soil moisture, there is a substantial difference between the SWAT-CH and SWAT models in wet periods at various temporal scales. ④ Parameter uncertainty is the main source of the uncertainty in hydrological estimation for the Chao River watershed. The modified SWAT-CH model has, to some extent, reduced the effect of parameter uncertainty with increasing structure uncertainty from 6.00% for the SWAT model to 9.00% for the SWAT-CH model. The model adaptability has been enhanced by introducing terrace slope, precipitation intensity, and soil moisture for the modification of CN2 and the abstraction ratio, which makes the revised SWAT-CH model have better simulation performance in areas with the obvious characteristics of terrain & rainfall heterogeneity and drastic dry-wet cycles in the soil profile.

Improved Model Parameter Transferability Method for Hydrological Simulation with SWAT in Ungauged Mountainous Catchments

The sustainability of water resources in mountainous areas has a significant contribution to the stabilization and persistence of the ecological and agriculture systems in arid and semi-arid areas. However, the insufficient understanding of hydrological processes in ungauged mountainous catchments (UMCs) is not able to scientifically support the sustainable management of water resources. The conventional parameter transferability method (transplanting the parameters of the donor catchment model with similar distances or attributes to the target catchment model) still has great potential for improving the accuracy of the hydrological simulation in UMC. In this study, 46 river catchments, with discharge survey stations and multi-type catchment characteristics in Xinjiang, are separated into the target catchments and donor catchments to promote an improved model parameter transferability method (IMPTM). This method synthetically processes the SWAT model parameters based on the distance approximation principle (DAP) and the attribute similarity principle (ASP). The performance of this method is tested in a random gauged catchment and compared with other traditional methods (DAP and ASP). The daily runoff simulation results in the target catchment have relatively low accuracy by both the DAP method ( N S = 0.27, R 2 = 0.55) and ASP method ( N S = 0.36, R 2 = 0.65), which implies the conventional approach is not capable of processing the parameters in the target regions. However, the simulation result by IMPTM is a significant improvement ( N S = 0.69, R 2 = 0.85). Moreover, the IMPTM can accurately catch the flow peak, appearance time, and recession curve. The current study provides a compatible method to overcome the difficulties of hydrological simulation in UMCs in the world and can benefit hydrological forecasting and water resource estimation in mountainous areas.

Comparing global hydrological models and combining them with GRACE by dynamic model data averaging (DMDA)

Historically, hydrological models have been developed to represent land-atmosphere interactions by simulating water storage and water fluxes. These models, however, have their own unique characteristics (strength and weakness) in capturing different aspects of the water cycle, and their results are typically compared to or calibrated against in-situ observations such as river runoff measurements. As a result, there may be gross inaccuracies in the estimation of water storage states produced by these models. In this study, we present the novel approach of Dynamic Model Data Averaging (DMDA), which can be used to compare and merge multi-model water storage simulations with monthly Terrestrial Water Storage (TWS, a vertical summation of surface and sub-surface water storage) estimates from the Gravity Recovery And Climate Experiment (GRACE) satellite mission. Here, the main hypothesis is that merging GRACE data with multi-model outputs likely provides more skillful hydrological estimations compared to a single model or data set. Theoretically, the proposed DMDA combines the benefits of the Kalman Filter (KF) and Bayesian Model Averaging (BMA) techniques and has the capability to deal with various observations and models with different error structures. Based on the Bayes theory, DMDA provides time-variable weights for hydrological models to compute an average of their outputs that are best fited to GRACE TWS estimates. Numerically, the DMDA method is implemented by integrating the output of six hydrological and land surface models (PCR-GLOBWB, SURFEX-TRIP, LISFLOOD, HBV-SIMREG, W3RA, and ORCHIDEE) and monthly GRACE TWS estimates (2002–2012) within the world’s 33 largest river basins, while considering the inherent uncertainties of all inputs. Our results indicate that DMDA correctly separates GRACE TWS estimates into surface water, soil moisture and groundwater compartments. Linear trends fitted to the DMDA-derived groundwater compartment are found to be different from those of original models. This means that anthropogenic influences within the GRACE data, which are not well reflected by models, are introduced by DMDA. We also find that temporal correlation coefficients between the DMDA-derived individual water storage estimations (surface water, soil moisture, and groundwater) and the El Niño Southern Oscillation (ENSO) index are considerably increased compared to those derived between individual model simulations and ENSO (e.g., an increase from −0.2 to 0.6 in the Murray River Basin). For the Nile River Basin, they changed from 0.1 to 0.4 for the soil moisture, and from 0.3 to 0.7 for the surface water compartment. Comparisons between the DMDA-derived surface water and those from independent satellite altimetry observations indicate that after implementing DMDA, temporal correlation coefficients within major lakes are increased. Based on these results, we have gained confidence in the DMDA water storage estimates to be used for improving the characterization of water storage over broad regions of the globe.

An Open-Source Tool to Facilitate the Processing of GRACE Observations and GLDAS Outputs: An Evaluation in Bangladesh

Bangladesh lies at the intersection of the Ganges, Brahmaputra, and Meghna rivers with a combined average discharge of 38,000 m3s-1 ranking fourth globally. Despite the volume of water flowing through and seasonally inundating parts of the landscape, groundwater reliance is necessary to support an intensive agricultural industry. Here we use newly-developed open-source software to combine observations from the Gravity Recovery and Climate Experiment (GRACE) satellites with hydrologic estimates of land water storage from the Global Land Assimilation Data System (GLDAS) to isolate basin-scale groundwater anomalies in Northwest Bangladesh from 2002 to 2016. We place our estimates in the context of previously-published water management estimates and our results suggest the largest losses in water storage are due to groundwater abstractions with groundwater storage decreasing at a rate of 0.88 cm yr-1. We estimate basin-averaged total water storage loss from 2002 to 2016 at 27.92 cm with groundwater and surface water storage loss accounting for 12.46 cm or 44.6%. For Bangladesh, a region where 80% of landcover is dedicated for agricultural use and over half of the country’s population is employed in the agricultural sector, the estimated declines in water storage hold long-term implications for the livelihood and food supply of the region.

Integration of aerial infrared thermography and in situ radon‐222 to investigate submarine groundwater discharge to Pearl Harbor, Hawaii, USA

AbstractThe largest freshwater spring complex in the Hawaiian Islands discharges groundwater into Pearl Harbor, Oahu, which is the largest estuary in the islands. This discharge occurs as subaerial and submarine springs, as non‐point‐source (diffuse) seepage, and as baseflow to streams. We conducted airborne thermal infrared remote sensing surveys of sea surface temperatures and ocean‐based 222Rn surveys of surface and deep waters to evaluate the efficacy of combining these methods to locate and quantify these differing groundwater discharges to the harbor. High spatial‐resolution (≤ 3.2 m) sea surface temperature data were advantageous for identifying and differentiating point‐source and diffuse groundwater discharge. Surveys of surface waters for 222Rn confirmed point‐source and diffuse seepage zones identified from the sea surface temperature maps. The 222Rn mid‐ and deep‐water surveys were crucial for locating benthic seepage not detectable by the remote sensing method. Groundwater discharge estimates from radon time‐series data agreed with radon surface water survey estimates. Fresh groundwater discharge estimates were also of the same order of magnitude as previous hydrologic estimates of spring discharge. We produced linearly and highly correlated relationships between plume areas and groundwater discharge by combining the thermal infrared and radon techniques. Such relationships may provide a way to up‐scale local groundwater discharge assessments to a regional area.

Estimation of hydrological response to future climate change in a cold watershed

Abstract The vulnerability of the natural water system in cold areas to future climate change is of great concern. A coupled model approach was applied in the headwater watershed area of Yalu River in the northeastern part of China to estimate the response of hydrological processes to future climate change with moderate data. The stochastic Long Ashton Research Station Weather Generator was used to downscale the results of general circulation models to generate synthetic daily weather series in the 2050s and 2080s under various projected scenarios, which were applied as input data of the Generalized Watershed Loading Functions hydrological model for future hydrological process estimations. The results showed that future wetter and hotter weather conditions would have positive impacts on the watershed runoff yields but negative impacts on the watershed groundwater flow yields. The freezing period in winter would be shortened with earlier snowmelt peaks in spring. These would result in less snow cover in winter and shift the monthly allocations of streamflow with more yields in March but less in April and May, which should be of great concern for future local management. The proposed approach of the coupled model application is effective and can be used in other similar areas.