Journal of Hydrology-Regional Studies最新文献

Study region

The Nile River Basin

Study focus

The lack of observed streamflow data at a short time scale poses a critical challenge for calibrating and validating hydrologic models. Therefore, many disaggregation methods were developed, resulting in various relative performances without a clear indication of the optimal choice. This study aims to iteratively assess eight monthly to daily streamflow disaggregation methods at 21 major subbasin outlets in the Nile River Basin (NRB) to identify the best-performing ones. These methods include one proportionality method and seven interpolation methods, i.e., linear, 2nd-order spline, 3rd-order spline, Piecewise Cubic Hermite Interpolating Polynomial (Pchip), Modified Akima (MAkima), mean preserved 2nd-order spline, and mean preserved 3rd-order spline. We assessed these methods using three metrics and visual investigations.

New hydrologic insights for the region

The results showed that the interpolation methods performed well, better than the proportionality method. However, their performances decreased at stations with high daily streamflow fluctuations. The interpolation methods’ performances were similar in mimicking the daily values but significantly different in preserving the mass balance. The mean preserving 3rd-order interpolation method (Lai 22) was the best in preserving the mass balance and capturing the low, moderate and high flows and, therefore, selected to generate the daily flow data in the NRB. The results of this study can guide a reliable method for obtaining daily streamflow data, which is important for the hydrologic and water management studies in the NRB.

Study region

Mokgam River watershed, South Korea

Study focus

In this study, the uncertainty contribution of three sources and their interaction effects on future climate and runoff projections were quantified. General circulation models (GCMs), shared socioeconomic pathways (SSPs), and bias correction (BC) methods were considered as the three sources. 20 GCMs under four SSPs (SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5) were used to project the future climate of the study area. Seven BC methods were used to adjust the GCMs’ daily climate data. The storm water management model (SWMM) was used as a hydrological model to simulate runoff, incorporating both natural and conduit flows according to GCMs’ climate projection. The normalized Nash-Sutcliffe efficiency (NNSE), normalized root mean square error (NRMSE), Kling-Gupta efficiency (KGE), and modified index of agreement (MD) were used to evaluate the performance of the GCMs’ climate simulations and the SWMM runoff simulations, which were based on the GCMs’ climate data. The analysis of variance (ANOVA) method was used to quantify the uncertainty.

New hydrological insights for the study region

The results showed that the assumptions of the BC method had a significant impact on the variation in climate and runoff projections. In the uncertainty of future climate and runoff projection results, BC methods exhibited the predominant contribution, while SSPs showed the least contribution. However, the uncertainty contribution from SSPs and GCMs was predominant in temperature projections, and these results could vary depending on the assumptions and the number of BC methods used. Overall, this study emphasizes not only the influence of GCMs but also the impact of BC methods on future climate and runoff projections.

Study region: Xinjiang is located in the mid-latitude region of Eurasia in northwestern China. Precipitation is predominantly concentrated in northern Xinjiang, while southern Xinjiang remains comparatively arid. Summer precipitation accounts for 54.4 % of the annual total. Study focus: This study aims to develop a machine learning model to predict summer precipitation (June–August) in XJ and explore the key variables contributing to summer precipitation in this region. The SHapley Additive exPlanations method was integrated with an extreme tree model to quantify the contributions of variables towards precipitation. Artificial neural networks, support vector machines, and extreme gradient boosting were considered to predict summer precipitation. To train the ML model, we used precipitation data from 1961 to 2012, whilst the forecast results from 2013 to 2017 were used for validation. New hydrological insights for the regions: The results demonstrated that the ANN model achieved robust performance during both the training and validation periods. For Northern and Southern XJ, the Mean Absolute Error and Root Mean Square Error of the ANN model were 15.34 (20.40) and 23.21 (30.01), respectively. The SHAP analysis showed that in the context of Northern Xinjiang, the Niño B Sea Surface Temperature Anomaly, Western Pacific Subtropical High Intensity, Pacific Subtropical High Intensity, and Multivariate ENSO Index play crucial roles in the prediction of summer precipitation. In Southern Xinjiang, the South China Sea Subtropical High Intensity, South China Sea Subtropical High Area, Western Pacific Warm Pool Strength, and Atlantic multidecadal oscillation have emerged as key variables affecting summer precipitation forecasting.

Study region

the Yellow River Basin

Study focus

Non-stationary hydrological conditions are becoming increasingly common due to climate change and human activities, and pose a novel challenge to the management of water resources and related risks, especially for large basins. However, prevalent research on drought assessment often ignores the non-stationary characteristics of hydrological processes. In this study, we investigated the stationarity of the runoff of the Yellow River Basin (YRB), the second-longest basin in China. We used an approach for assessing non-stationary droughts based on the generalized additive model for location, scale, and shape to establish a standardized runoff index containing covariates (SRI_cov) to identify hydrological droughts in the basin from 1986 to 2015.

New hydrological insights for the region

The results show that the runoff was non-stationary in the YRB. Based on SRI_cov, hydrological drought predominantly occurred in the entire YRB in spring. From 1986, the number of months in which droughts occurred in the YRB exhibited a general trend of increase and peaked around 2002. After that, the total number of droughts significantly decreased but extreme droughts had become more prominent since 2005. The drought was more severe in the middle reaches of the Yellow River, and was characterized by a high frequency, intensity, and severity. Our analysis enhances the understanding of hydrological modeling and drought assessment under non-stationary conditions.

Study region: Montane grassland within the Gulling catchment, Austrian Alps. Study focus: A climate-change experiment in a grassland ecosystem used lysimeters and HYDRUS-1D models to quantify changes in evapotranspiration (ET) and groundwater recharge (GWR) due to warming (＋3 °C) and elevated ${\mathrm{CO}}_2$ concentrations ($\Delta {\mathrm{CO}}_2$; ＋300 ppm). Findings at the plot-scale were generalized and transferred to the surrounding catchment, half comprised of grassland, using three lumped rainfall–runoff models and two spatially-distributed Community Water Models, differing in soil hydraulic properties.

New hydrological insights for the region: Warming increased ET and decreased GWR and river discharge compared to ambient conditions. $\Delta {\mathrm{CO}}_2$ increased stomatal resistance, which partially offset warming effects. In scenarios combining warming and $\Delta {\mathrm{CO}}_2$, the impact of warming was higher than $\Delta {\mathrm{CO}}_2$ effect. Elevation influenced the sensitivity of ET to warming, which was greater at the catchment scale than at the plot scale, while GWR was more sensitive to warming at the plot scale. Under dry conditions, GWR and discharge exhibited increased sensitivity to warming at both scales. HYDRUS-1D successfully reproduced lysimeter experiment results and their sensitivity to warming and $\Delta {\mathrm{CO}}_2$. Despite model agreement on water flux sensitivity to climate changes, the varying response magnitudes highlight the need for a multi-model approach in climate impact assessments. This study provides insights into how climate change might impact hydrological dynamics of montane grassland systems across the Central European Alps.

Study region

Mississippi River Basin.

Study focus

Using daily precipitation records of 769 meteorological stations over the Mississippi River Basin (MRB), the spatial-temporal variability and trend of nine extreme precipitation indices were estimated and statistically assessed using the Mann-Kendall test. Factors likely to influence the spatial pattern and trends of precipitation extremes indices were also checked.

New hydrological insights for the region

The spatial pattern of the extreme precipitation indices exhibits a southeast to Northwest dipole, with the maximum values recorded over the southeastern part of the domain (exception being for Consecutive Dry Days, CDD which shows otherwise) driven by the southerly moisture transport toward the southeast. The spatial pattern of the extreme precipitation is controlled by the topography. The results also show that, on average, almost all the indices (except CDD) exhibit an increasing trend. The total wet day precipitation exhibits a significant increasing trend. Spatially, most of the significant increasing (decreasing) trends of the extreme's precipitation-except CDD- are located over the Upper (South) MRB where there is a significant sign toward cooling (warming) conditions. This supports the view that changing climate towards warming (cooling) conditions is significantly affecting precipitations extremes over the MRB. The relationships between large-scale teleconnections and extreme precipitation show that Pacific North America significantly increases (decreases) frequency and intensity indices over the Northwest (southeast) MRB, whereas the Pacific Decadal Oscillation does increase the frequency and intensity indices over the southeast. El Niño Southern Oscillation significantly increases the frequency and intensity indices over the entire MRB, with consequences to infrastructure failures, increasing vulnerable populations, risk zones and relocations populations.

Study region

This study investigates nutrient distribution and flux dynamics in a coral reef lagoon in Quintana Roo, Mexico, located on a permeable limestone coast of the Mesoamerican Barrier Reef System.

Study Focus

Emphasis is placed on submarine groundwater discharge (SGD) as a crucial contributor to nutrient pathways, including ammonium (NH₄⁺), nitrate and nitrite (NO_x^-), hydrogen silicate (HSiO₃^-), hydrogen phosphate (HPO₄^2-), and urea. Inputs vary with SGD magnitudes and sources and by proximity to active spring discharges. Groundwater multi-tracer analysis and multiple linear regression identify ²²⁶Ra as explaining NH₄⁺ variability due to long-term groundwater processes, while ²²³Ra predicts NO_x^-, HSiO₃^-, and urea due to short-term inputs. No significant relationship was found between HPO₄^2- and any radium isotope, indicating complex behavior in coastal karst aquifers.

New hydrological insights for the region

The findings highlight complex nutrient dynamics in coastal karst settings, with SGD-derived fluxes primarily consisting of dissolved inorganic nitrogen (DIN) and HSiO₃^-. Although lower in concentration, HPO₄^2- and urea fluxes are significant compared to other karst environments. Radium isotopes distinguish between short-term and long-term, as well as new and recycled nutrient inputs. Groundwater inputs transport fresh nutrients to healthier reefs, whereas processed, recycled inputs were detected near degraded reefs. These insights are essential for understanding global nutrient cycles and coral health, particularly in the context of global change and anthropogenic disturbances affecting coral reef ecosystems.

Study region

Ganjiang River Basin, a typical mountainous river basin which located on the south bank of the middle-lower Yangtze River. The Ganjiang River is the seventh largest tributary of the Yangtze River.

Study focus

Baseflow, a key recharge source for the river streamflow. This study combined the digital filtering method with the SWAT model to examine the temporal and spatial patterns of baseflow across the Ganjiang River Basin, and quantitatively assessed land use change impact on baseflow.

New hydrological insights for the region

Baseflow in the Ganjiang River Basin shows a "single peak" intra-annual distribution. Monthly variations of streamflow and baseflow across the basin are different. The variation of baseflow index is generally opposite to that of streamflow. A positive correlation has been noted between the annual baseflow and streamflow, while a negative correlation was found between the annual baseflow index and precipitation. Due to the potential influence of basin topography, river flow direction and rock layer distribution, baseflow and baseflow modulus showed a spatially increasing trend from south to north, with the northwest region having extremely strong groundwater recharge. In comparison to the basic scenario, under extreme land use scenarios of forest, grassland, and cropland, baseflow may experience an increase of 14.7 % and 2.9 %, while witness a decrease of 13.9 %. All results improve the understanding of baseflow spatiotemporal variations in river basins.

Study regions

The Wangjiaba (WJB) watershed, located in the upper Huaihe River Basin in China.

Study focus

An attributing framework has been proposed combining the Double Mass Curve (DMC) and the Soil and Water Assessment Tools (SWAT) model to identify the contributions of climate variability, Land use (LU) change, and Other Human Activities (OHA) to the variations in runoff-sediment processes within the WJB.

New hydrological insights for the region

The studied period was able to be separated into three sub-periods ($P_1$: 1981–1991, $P_2$: 1992–2009, and $P_3$: 2010–2019) using the DMC, and the SWAT model could simulate runoff and Sediment Yields Load (SYL) properly during different sub-periods after calibration. Generally, the runoff, SYL, and Suspended Sediment Concentration (SSC) within the WJB exhibited a decrease trend with a change rate of −1.3 mm a⁻¹, −8.49×10⁴ t a⁻¹, and −0.01 kg m⁻³ a⁻¹, respectively. Substantially, climate variability decreases runoff, SYL, and SSC from $P_1$ to $P_3$; LU change decreases runoff, SYL, and SSC from $P_2$ to $P_3$; OHA decreases SYL and SSC from $P_1$ to $P_2$, but increases SYL and SSC from $P_2$ to $P_3$. It should be noticed that the OHA has increased the SYL significantly especially over the downstream of WJB from $P_2$ to $P_3$. It is essential to enhance soil erosion prevention measures in the future under the background of global climate change.

Study region

Dali River Basin, a typical basin on the Loess Plateau (LP) in China

Study focus

The LP has undergone extensive ecological management in recent decades, significantly altering runoff in the region. For more scientific management of basins, it is useful to study runoff variations at multiple scales quantitatively. Dali River Basin (DRB) was used as the model basin. The impacts of climate change (CC) and human activity (HA) were quantitatively analyzed based on the features of runoff changes at multiple scales using observed hydrological data from to 1960–2020. The characteristics of potential factors influencing HA were further analyzed.

New hydrological insight for the region

The study showed that basin runoff was mainly concentrated during May-October. Spatially, most of the runoff originated from the middle and lower reaches, with little change in the upper reaches. Both CC and HA affected runoff variation, but their effects shifted from upstream to downstream. Apart from the upper reaches, HA was dominant in summer and autumn, whereas CC was dominant in spring and winter. Changes in runoff might be caused by temporal and spatial differences in HA, such as converting cultivated land into forests and grasslands, increasing NDVI, and constructing dams. This analysis of runoff variations at multiple temporal and spatial scales in a representative basin provides a reliable reference for the ecological management of the LP.