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The stochastic analysis of groundwater flow is traditionally based on the assumption of stationarity of the recharge and discharge processes. However, the hydrologic process in nature is nonstationary-stochastic. In order to improve the quantification of the natural recharge-discharge process, the nonstationary rainfall-discharge process is assumed in this study. The proposed transfer function, which relates the nonstationary spectra of the rainfall fluctuations to those of integrated discharge.

Assessment of changes in global water cycle has been a challenge. This study estimate long-term global evapotranspiration (ET) for recent decades using machine learning and ground observations. The results show that normalized ET experienced a relative decline on fractional land, and the decline was typically accompanied by an increase in long-term runoff in 27.06 % of the global land areas. The finding emphasized that surface vegetation have a great impact in regulating water and energy cycles.

This article shows a conversion model of snow depth into snow water equivalent (SWE) by using an ensemble of artificial neural networks. The novelty is a direct estimation of SWE and the improvement of the estimation by in-depth analysis of network structures. The usage of an ensemble allows a probabilistic estimation and therefore, a deeper insight. It is a follow-up study of a similar study over Quebec, but extends it to the whole area of Canada and improves it further.

The parametric Budyko equations contain a single parameter (n or w), interpreted as depending on biophysical features, however, these relationships have remained elusive. We analytically invert the parametric Budyko equations, expressing n and w only in terms of the mean values of potential (E₀) and actual evapotranspiration (E) and precipitation (P). These expressions allow n and w to be explicitly related to biophysical features through the dependence of P, E₀, and E on those same features.

We present multi-timescale Long Short-Term Memory (MTS-LSTM), a machine learning approach that predicts discharge at multiple timescales within one model. MTS-LSTM is significantly more accurate than the US National Water Model and computationally more efficient than an individual LSTM model per timescale. Further, MTS-LSTM can process different input variables at different timescales, which is important as the lead time of meteorological forecasts often depends on their temporal resolution.

Previous studies decomposed ET variance in precipitation, potential ET and total water storage changes based on Budyko equations. However, the effects of snowmelt and vegetation changes have not been incorporated in snow-dependent basins. We thus extended this method in the arid alpine basins of Northwest China, and found that ET variance is primarily controlled by rainfall, followed by the coupled rainfall and vegetation. The out-of-phase between rainfall and snowmelt weaken ET variance.

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Weather and water extremes have devastating effects each year. One of the principal challenges for society is to understand how extremes are likely to evolve under the influence of changes in climate, land cover, and other human impacts. This paper provides a review of the methods and challenges associated with the detection, attribution, and management of nonstationary weather and water extremes.

The Budyko curve emerges globally from the behavior of multiple catchments. Single parameter Budyko equations extrapolate the curve concept to individual catchments, interpreting curves and parameters as representing climatic and biophysical impacts and on water availability, respectively. We tested these two key components theoretically and empirically, finding catchments are not required to follow Budyko curves and usually do not, implying the parametric framework lacks predictive ability.

We think that fractal dimension of streamflow series can be used to improve the calibration of hydrological models. Thus, we designed the E-RD (efficiency-ratio of fractal dimension) strategy and examined its usability in the calibration of lumped model. The results reveal that, from most aspects, introducing RD into model calibration makes the simulation of streamflow components more reasonable. Besides, in calibration, only little decrease of E occurs when pursuing better RD.

Most land surface models have difficulty in capturing the freeze-thaw cycle in Tibetan Plateau and North China. This paper addresses the issues by introducing a physically more realistic and efficient frozen soil module (FSM) into SSiB3 model (SSiB3-FSM). A new and more stable semi-implicit scheme and a physics-based freezing-thawing scheme were applied and results show SSiB3-FSM can be used as an effective model for soil thermal characteristics at seasonal to decadal scales over frozen ground.

Decomposing parameter and input errors in model calibration is a considerable challenge. This study transfers the direct estimation of an input error series to their rank estimation and develops a new algorithm, Bayesian error analysis with reshuffling (BEAR), for improved error estimation. In the context of total suspended solids (TSS) simulation, a synthetic study and a real study demonstrates that the BEAR method is effective and robust to improve water quality model calibration.

Salinity and sodicity can cause irreversible degradation to soil, threatening agricultural production and food security. To date, very little is known about the degree in which soil degradation can be reversible. We introduce a model for describing this partial reversibility (hysteresis), and lay out the experimental procedures necessary for characterizing the soil in this regard. We must shift our focus from degradation measurements to 老王v.p.n5.2, so we can maintain healthy soils.

Understanding when, where, and how nitrate is exported from watersheds is key to addressing the challenges that excess nutrients pose. We analyzed daily nitrate and streamflow data for five nested, agricultural watersheds that export high levels of nitrate over a four-year time period. Nutrient export patterns varied seasonally during baseflow but were stationary during stormflow. Additionally, anthropogenic and geologic factors drove nutrient export during both baseflow and stormflow.

In this study, we simulated the water balance over the South American Dry Chaco and assessed the impact of land cover changes thereon, using three different land surface models. Our simulations indicated that different models result in a different partitioning of the total water budget, but all showed an increase in soil moisture and percolation over the deforested areas. We also found that, relative to independent data, no specific land surface model is significantly better than another.

We used a tracer-aided ecohydrological model mixed land-use catchment in NE Germany to quantify water flux-storage-age interactions at four model-grid resolutions. The model's ability for reproducing spatio-temporal flux-storage-age interactions decreases with increasing model grid sizes. Similarly, larger model grids showed vegetation influenced changes in blue and green water partitioning. Simulations reveal the value of measured of soil and stream isotopes for model calibration.

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The Swedish hydrological warning service is extending its use of seasonal forecasts, which requires an analysis of the available methods. We evaluate the simple ESP method and find out how and why forecasts vary in time and space. We find that forecasts are useful up to 3 months into the future, especially during winter and in northern Sweden. They tend to be good in slowly-reacting catchments and bad in flashy and highly-regulated ones. We finally link them with areas of similar behaviour.

This study analyzed the effectiveness of the CMB method for correcting the Eckhardt method. The results showed that the approach of calibrating the Eckhardt method against the CMB method provides a "false" calibration of total baseflow by offsetting the inherent biases in the baseflow sequences generated by the two methods. The reason for this phenomenon is that the baseflow series generated by the two methods containing different transient water sources.

Crop yield is reduced by heat and water stress, in particular when they co-occur. We quantify the joint effects of the (unpredictable) air temperature and water availability on crop heat stress via a mechanistic model. Larger but more infrequent rainfalls increased crop canopy temperatures. Keeping crops well watered via irrigation could reduce canopy temperature, but not enough to exclude heat damage. Thus, irrigation is only a partial solution to adapt to warmer and drier climates.

It is often difficult to determine the sources of solutes in streams and how much each source contributes. We developed a new method of unmixing stream chemistry via machine learning. We found that sulfate in three watersheds are related to groundwater flowpaths. Our results emphasize that acid rain reduces a watershed’s capacity to remove CO₂ from the atmosphere, a key geological control on climate. Our method will help scientists unmix stream chemistry in watersheds where sources are unknown.

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As contaminants within groundwater prefer to flow through coarse-grained rocks, a detailed mapping of these rocks in the subsurface is vital for effective monitoring and remediation planning to help minimize risk to human health and the environment at legacy waste sites. This work provides images that are obtained from airborne electromagnetic data to delineate subsurface rocks at the Hanford Site, a nuclear waste site located in southeastern Washington State, USA.

Our research objective and originality is to investigates the uncertainties of the hydrological responses of Nature-based Solutions (NBS) that result from the multiscale space variability of both the rainfall and the NBS distribution. Results show that the intersection effects of spatial variability of rainfall and spatial arrangement of NBS can generate uncertainties on peak flow and total runoff volume estimations on NBS scenarios.