Xijiang River Research Articles

Measuring hydrologic regime alterations and hydrodynamic characteristics in the Xijiang River Basin by the IHA-RVA method

Abstract An objective assessment of hydrological alterations is crucial for comprehensive water resource management, environmental protection, river ecosystem restoration, and integrated watershed water resource management. To quantitatively assess the ecological hydrologic regime alterations in the Xijiang River Basin in southern China considering the impacts from Longtan Reservoir, daily runoff data from two hydrological stations, Dahuangjiangkou and Wuzhou, are selected from 1973 to 2020. The changes in the flow are analyzed, and the indicators of hydrological alternation/range of variability approach (IHA-RVA) are employed to assess the hydrological regime alternations in the studied basin. The main findings are as follows: (1) Both stations show a decreasing trend in annual runoff, with the moving T-test detecting a change point in the runoff series in 2002. (2) The two stations’ overall hydrologic alteration degrees are 57% and 60%, with a more remarkable variation in the upstream area. Hydrological station changes tend to wane as the distance from the reservoir increases, which indicates that with the increased reservoir-station distance, the impact of the reservoir on the hydrologic process diminishes.

Identification of the Runoff Evolutions and Driving Forces during the Dry Season in the Xijiang River Basin

During the dry season, river flow gradually diminishes, and surface water flow dries up. Therefore, the investigation of runoff during the dry season is of great practical significance for rational water allocation and water resource management. Based on hydrological station data from the Xijiang River Basin (XRB) from 1961 to 2020, this study examines the trend and periodic characteristics of dry-season runoff, identifies fluctuation and variability in dry-season runoff, and investigates the main circulation factor combinations influencing dynamic changes in dry-season runoff. The results indicate the following: (1) the characteristics of dry-season runoff variations are basically consistent across sub-basins in the XRB during the study period, with the minimum (21.96 × 108 m3) and maximum (54.67 × 108 m3) average monthly runoff occurring in February and October, respectively; (2) interannual-scale dry-season runoff exhibits periodicity of 3.53 years and 7.5 years; (3) using the Bayesian estimator of abrupt seasonal and trend change algorithm (BEAST), a seasonal abrupt point with a probability of 20.5% occurs in 1983, and the confidence interval for this abrupt point is from 1980 to 1986; (4) based on the cross wavelet approach, solar sunspots are identified as the primary circulation factor contributing to dry-season runoff in the XRB, exhibiting a significant 8–14 years resonance cycle of negative correlation with runoff during the high-energy phase from 1972 to 2006. These findings offer a new perspective on understanding the evolution of dry-season runoff and circulation factor variations, which are crucial for accurate prediction, early warning, and rational allocation of water resources during the dry season.

Daily Runoff Prediction Based on FA-LSTM Model

Accurate and reliable short-term runoff prediction plays a pivotal role in water resource management, agriculture, and flood control, enabling decision-makers to implement timely and effective measures to enhance water use efficiency and minimize losses. To further enhance the accuracy of runoff prediction, this study proposes a FA-LSTM model that integrates the Firefly algorithm (FA) with the long short-term memory neural network (LSTM). The research focuses on historical daily runoff data from the Dahuangjiangkou and Wuzhou Hydrology Stations in the Xijiang River Basin. The FA-LSTM model is compared with RNN, LSTM, GRU, SVM, and RF models. The FA-LSTM model was used to carry out the generalization experiment in Qianjiang, Wuxuan, and Guigang hydrology stations. Additionally, the study analyzes the performance of the FA-LSTM model across different forecasting horizons (1–5 days). Four quantitative evaluation metrics—mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2), and Kling–Gupta efficiency coefficient (KGE)—are utilized in the evaluation process. The results indicate that: (1) Compared to RNN, LSTM, GRU, SVM, and RF models, the FA-LSTM model exhibits the best prediction performance, with daily runoff prediction determination coefficients (R2) reaching as high as 0.966 and 0.971 at the Dahuangjiangkou and Wuzhou Stations, respectively, and the KGE is as high as 0.965 and 0.960, respectively. (2) FA-LSTM model was used to conduct generalization tests at Qianjiang, Wuxuan and Guigang hydrology stations, and its R2 and KGE are 0.96 or above, indicating that the model has good adaptability in different hydrology stations and strong robustness. (3) As the prediction period extends, the R2 and KGE of the FA-LSTM model show a decreasing trend, but the whole model still showed feasible forecasting ability. The FA-LSTM model introduced in this study presents an effective new approach for daily runoff prediction.

Separating the impact of climate change and human activities on the connection between meteorological and hydrological drought

AbstractUnderstanding the transition from meteorological to hydrological drought is essential for accurately predicting hydrological droughts. However, the factors driving this transition are intricate, and a comprehensive understanding of how direct human activities influence this shift in drought is lacking. In this study, we initially explored the spatiotemporal correlation between the occurrence of meteorological and hydrological droughts. Subsequently, we formulated multiple hydrological replenishment scenarios using the soil and water assessment tool (SWAT) model to assess the environmental impact of the transition from meteorological to hydrological droughts. The Xijiang River Basin (XRB), the primary tributary of the Pearl River basin, was selected as the study area. Our results identified 91 meteorological droughts in the XRB, and only 66 hydrological droughts from 1978 to 2018. The transition rate from meteorological to hydrological drought demonstrated large spatial variability, with a basin‐average rate of 56% and the lowest transition rate of 45% in the headstream The transition from meteorological to hydrological drought was mostly rapid between November and December (~2 months), but can be prolonged during spring (about 3–5 months) and winter (around 7–9 months). Additionally, analysis of regeneration scenarios indicated that human activity has mitigated drought severity over recent decades. The primary driver affecting drought duration and frequency during the transition from meteorological to hydrological droughts shows conspicuous spatial disparities in densely populated areas. Although human activity significantly contributes to drought duration and severity compared to climate change during the transition in the headstream, its effects are more pronounced downstream in terms of drought duration and frequency. One plausible explanation is that increased water consumption downstream has considerably prolonged drought progression, whereas water management has counteractive effects on drought progression due to climate change in the headstream. Our findings offer valuable insights into the transition process from meteorological to hydrological droughts in the presence of extensive human activities.

Land-Use/Cover Change and Driving Forces in the Pan-Pearl River Basin during the Period 1985–2020

Land use/cover change (LUCC) is a vital aspect representing global change and humans’ impact on Earth’s surface. This study utilized the ESRI Land Cover 2020 and China Land Cover Dataset (CLCD), along with historical imagery from Google Earth, to develop a method for the assessment of land use data quality. Based on the assessment, the CLCD was updated to generate an improved Re-CLCD for the Pan-Pearl River Basin (PPRB) from 1985 to 2020, and to analyze LUCC in the PPRB over the past 35 years. The results indicate the following: (1) Among the seven land uses, built-up land experienced the most dramatic change, followed by cropland, forestland, grassland, shrubland, waterbody, and bare land, with notable increases in built-up land and forestland, and rapid decreases in cropland, grassland, and shrubland. (2) The magnitude of land use changed very widely, with the highest change in the Pearl River Delta, followed by small coastal river basins in southern Guangdong and western Guangxi, the Dongjiang River Basin, the Hanjiang River Basin, the Xijiang River Basin, the Beijiang River Basin, and lastly, Hainan Island. (3) The largest increase happened in built-up land, with a total increase of 12,184 km2, mainly due to the occupation of cropland and forestland, corresponding to the highest decrease in cropland, with a net loss of 10,435 km2, which was primarily converted to forestland and built-up land. The study results are valuable in providing a scientific basis for policy overhaul regarding land resources and management to safeguard ecological balance and promote sustainable development in the Pan-Pearl River Basin.

Monthly Runoff Prediction for Xijiang River via Gated Recurrent Unit, Discrete Wavelet Transform, and Variational Modal Decomposition

Neural networks have become widely employed in streamflow forecasting due to their ability to capture complex hydrological processes and provide accurate predictions. In this study, we propose a framework for monthly runoff prediction using antecedent monthly runoff, water level, and precipitation. This framework integrates the discrete wavelet transform (DWT) for denoising, variational modal decomposition (VMD) for sub-sequence extraction, and gated recurrent unit (GRU) networks for modeling individual sub-sequences. Our findings demonstrate that the DWT–VMD–GRU model, utilizing runoff and rainfall time series as inputs, outperforms other models such as GRU, long short-term memory (LSTM), DWT–GRU, and DWT–LSTM, consistently exhibiting superior performance across various evaluation metrics. During the testing phase, the DWT–VMD–GRU model yielded RMSE, MAE, MAPE, NSE, and KGE values of 245.5 m3/s, 200.5 m3/s, 0.033, 0.997, and 0.978, respectively. Additionally, optimal sliding window durations for different input combinations typically range from 1 to 3 months, with the DWT–VMD–GRU model (using runoff and rainfall) achieving optimal performance with a one-month sliding window. The model’s superior accuracy enhances water resource management, flood control, and reservoir operation, supporting better-informed decisions and efficient resource allocation.

A framework to evaluate the impact of a hazard chain and geographical covariates on spatial extreme water levels: A case study in the Pearl River Delta

The interactions and collective impacts of different types of hazards within a compound hazard system, along with the influence of geographical covariates on flooding are presently unclear. Understanding these relationships is crucial for comprehending the formation and dynamic processes of the hazard chain and improving the ability to identify flood warning signals in complex hazard scenarios. In this study, we presented a multivariate spatial extreme value hierarchical (MSEVH) framework to assess the spatial extreme water levels (EWL) at different return levels under the influence of a hazard chain and geographical covariates. The Pearl River Delta (PRD) was selected as a research example to assess the effectiveness of the MSEVH framework. Firstly, we identified a hazard chain (extreme streamflow from the Xijiang River (XR) - extreme streamflow from the Beijiang River (BR) - extreme sea level) and three geographical covariates influencing EWL in the PRD. Then, we compared four hazard scenarios in the MSEVH framework to evaluate the spatial EWL at different return levels under the influence of the hazard chain in the PRD. The final step involves assessing spatial EWL with the effect of the hazard chain and geographical covariates. The results indicate that when extreme streamflow from XR and BR occurs concurrently, the extreme streamflow from BR weakens the influence of extreme streamflow from XR on EWL in the PRD. However, it cannot fully offset the overall impact of extreme streamflow from XR on EWL. In addition, when extreme streamflow from XR, extreme streamflow from BR, and extreme sea level occur simultaneously, the extreme sea level enhances the influence of concurrent extreme streamflow from XR and BR on EWL in the PRD. The proposed MSEVH is not only applicable to the PRD but also shows promising potential for evaluating extreme hydrometeorological variables under the influence of other hazard chains.

Spatio-temporal multivariable time vario-zoom network for water level forecasting based on high-resolution hydrological dataset

Water level forecasting is a crucial element in river-basin water resource management, particularly for shipping and extreme events, such as floods and droughts. However, accurately forecasting water levels is challenging due to the influence of non-stationarity hydrological processes, complex characteristic variables, and noisy data. Data-driven approaches in water level forecasting generally lack annotated high-resolution data. To address these problems, this paper presents the first hydrological dataset on the Xijiang River basin-Xijiang Water Dataset (XJWD) and a Spatio-Temporal Multivariable-based Time Vario-Zoom Network (STM- TVZN) model. XJWD combines multi-source raw hydrological data and has real-time and higher resolution data than comparable datasets. STM-TVZN is specifically tailored to enhance the discernment of salient features within key time windows for each variable by fusing spatio-temporal multivariate features through vario-zoom time windows. Finally, a novel metric, Extremum Time Offsets (ETO), is proposed to evaluate the accuracy of the occurrence of local extremum in time series, and it is significant for predicting and evaluating extremum events such as floods and droughts. Experimental results show that the proposed method outperformed existing models on XJWD dataset regarding error rate, stationarity, similarity, and ETO. Overall, the proposed method of combining time-variable focusing network design and spatio-temporal multivariate feature extraction is appealing for short-term water level forecasting in inland rivers, exhibiting potential capacity in strategic water-saving planning and extremum event forecasting caused by climate change.

Using a bottom-up method to assess cruise ship activity impacts on emissions during 2019–2020 in China

COVID-19 has had a serious impact on the development of the global shipping industry, especially since the impact on the cruise tourism industry was unprecedented. This study took cruise ships sailing in China ECA, China Exclusive Economic Zone (EEZ), Yangtze River main line, and Xijiang River main line Chinese waters as an example to analyze the key changes in cruise ship emissions during the pandemic. Automatic identification system (AIS) data, vessel static data, and emission control regional data are used to conduct a comprehensive analysis of cruise ship emissions from multiple perspectives such as a port-to-regional comparison. As such, a vessel emission model (i.e., a bottom-up method) is constructed in this research for predicting China ECA and EEZ cruise ship emissions. Compared with 2019, the cruise activities sailing in China's Emission Control Area (ECA) are mainly at berth, and the emissions of cruise ships have dropped significantly, with SOx emissions reduced by 59.11%. In addition, this study also calculates the carbon emissions of China's regional cruises, supplementing China's cruise carbon pool. The research results suggest that cruise operators may improve fuel efficiency, decrease vessel speed, improve routing and scheduling, and enhance fleet management in order to further mitigate the negative effects of the cruise tourism industry on the marine environment.

Rapid and Precise Measurement Method for Track Static Geometry of Large-Span Bridge Railway

Track geometry is an important parameter reflecting the safety of bridge railway, which is of great significance for the structure control of railway track. The measurement of track geometry is an indispensable part of structural health monitoring for bridge railway. However, existing methods for track static track geometry measurement are inadequate in terms of accuracy and efficiency. In order to improve the measurement method, this paper proposes a rapid and precise measurement method based on IMU/odometer aided by multiple total stations, which has the advantages of high efficiency, high accuracy, and is less affected by the environment. The proposed method uses a track measurement trolley (TMT) integrated with IMU and an odometer to collect railway track data on the bridge, and multiple total stations are used to synchronously measure the three-dimensional coordinates of the TMT. After coordinate system registration and time synchronisation, the data are then fused using a filtering algorithm to finally obtain the static geometry of the railway track. The proposed method was validated on the NanSha Port Railway Bridge across the Xijiang River in Foshan, China, and the results showed that the proposed method can make a single on-orbit measurement of a 1000 m length of large-span bridge railway track within half an hour, with an accuracy on the order of millimetres. The proposed method has obvious advantages in terms of efficiency and accuracy and is therefore an effective new measurement method with practical significance.

Species diversity and maintenance mechanisms of benthic macroinvertebrate assemblages in the Xijiang River

Species diversity and maintenance mechanisms of benthic macroinvertebrate assemblages in the Xijiang River

Insight into diversity change, variability and co-occurrence patterns of phytoplankton assemblage in headwater streams: a study of the Xijiang River basin, South China.

Phytoplankton has been used as a paradigm for studies of coexistence of species since the publication of the "paradox of the plankton." Although there are a wealth of studies about phytoplankton assemblages of lakes, reservoirs and rivers, our knowledge about phytoplankton biodiversity and its underlying mechanisms in mountain headwater stream ecosystems is limited, especially across regional scales with broad environmental gradients. In this study, we collected 144 phytoplankton samples from the Xijiang headwater streams of the Pearl River across low altitude (< 1,000 m) located in Guangxi province, intermediate altitude (1,000 m < altitude <2,000 m) in Guizhou province and high altitude (> 2,000 m) in Yunnan province of China. Our study revealed high phytoplankton diversity in these streams. Freshwater phytoplankton, including cyanobacteria, Bacillariophyta, Chlorophyta, Rhodophyta, Chrysophyta, Euglenophyta, Glaucophyta, Phaeophyta and Cryptophyta, were all detected. However, phytoplankton alpha diversity exhibited a monotonic decreasing relationship with increasing altitude. High altitudes amplified the "isolated island" effect of headwater streams on phytoplankton assemblages, which were characterized by lower homogeneous selection and higher dispersal limitation. Variability and network vulnerability of phytoplankton assemblages increased with increasing altitudes. Our findings demonstrated diversity, variability and co-occurrence patterns of phytoplankton assemblages linked to environmental factors co-varying with altitude across regional scales.

River adjustments in response to human activities: A case study of the lower Beijiang River, China

AbstractIntensive human activity has caused significant changes in the river morphology and hydrological characteristics of the Pearl River Delta. Particularly, in‐channel mining and dam construction have induced remarkable levels of downward riverbed incision. Although strict control measures have been implemented for in‐channel sand mining, it remains unclear how the river has evolved since the abandonment of high‐intensity mining and its impact on flow diversion at the downstream confluence. This study presents the hydrological and morphological adjustments in the lower Beijiang River, the second largest tributary of the Pearl River, under the impacts of human interventions. A hydrodynamic model was developed to reveal the impacts of riverbed deformation on the flow diversion ratio at Sixianjiao, the confluence of the Beijaing River and the Xijiang River. The results showed that construction of cascade reservoirs upstream reach did not strongly influence run‐off, whereas incoming sediment loads were decreased. Because of upstream damming and in‐channel sand mining, a dramatic downward incision was observed in the lower Beijiang River, with a degradation volume of approximately 239.8 million m3 from 1999 to 2012. Particularly, in the upper reach, the incision depth was typically larger than 8 m. Riverbed incision caused continuous changes in the water stage–discharge relationship, and discharge increased remarkably under the same water level at the three hydrometric stations. During 2012–2020, because in‐channel sand mining was strictly controlled, rapid degradation was alleviated, deposition occurred in some cross‐sections and the deformation volume decreased by approximately 90% compared to that in the last period. A fast downward incision induced a change in flow exchange between the two rivers, and the flow diversion ratio of the Beijiang River increased from an average of 17% before 1998 to more than 21%.

Assessment of the restoration potential for ecological sustainability in the Xijiang River basin, Southwest China: A comparative analysis of karst and non-karst areas

Vegetation restoration is an eco-friendly strategy for countering land degradation and biodiversity loss. Since 2000–2001, large-scale restoration projects have been performed in Southwest China, with the net primary productivity (NPP) increasing over the past two decades. However, negative ecohydrological impacts, including streamflow decline and soil moisture deficit, have been reported following afforestation. Current understanding of the permissible NPP capacity (NPPcap) and NPP potential (NPPpot) under karst and non-karst areas or planted and natural vegetations constrained by environmental factors remains unclear. Here multiple environmental drivers characterizing the heterogeneous landscape in the Xijiang River Basin (Southwest China) were employed to predict the NPPcap using a random forest model. Results showed that 85% of the area exhibited an increasing trend in NPPcap during 2001–2018. Overall, 3.50% of the area has exceeded the NPPcap, implying an excessive plantation and potential water deficit in these areas. Excluding agriculture activities, urban areas, and water bodies, we found there is room for an average extra 22.85% of NPP enhancement. The NPPpot was spatially imbalanced, with high NPPpot located in the northeast, indicating these areas as a target area for future vegetation restoration. Moreover, the NPPpot reduction in karst areas (1.12 g C m−2 a−1) was more pronounced than in non-karst areas (0.26 g C m−2 a−1), highlighting a stronger negative impact on NPPpot in karst areas. Furthermore, significant NPPpot differences were found between planted vegetation and natural vegetation for both karst and non-karst areas. According to the findings, we identified four separate restoration sub-zones and proposed tailored strategies to guide the implementation of future restoration efforts. Our study highlights restoration potential and where land is available for reforestation but also the urgent need for future restoration activities towards ecosystem sustainability.

Study of the Ecosystem Service Value Gradient at the Land–Water Interface Zone of the Xijiang River Mainstem

The ecosystem service value (ESV) gradient-evolution pattern of a river basin’s land and water-intertwined zones has a variety of ecosystem service values, such as biodiversity conservation, water conservation, water purification, etc. The study of the ecosystem service value (ESV) gradient-evolution pattern of a river basin’s land and water-intertwined zones will provide a scientific basis for the construction and protection of the ecological security pattern of the river basins. In this study, we combined the unit area equivalent factor method and geographically weighted regression (GWR) model to classify and analyze the gradient change pattern of ESV upstream, downstream, and along the river of the Guangdong mainstream section of the Xijiang River in China, and the conclusions are as follows: (1) The corresponding ESV share of each land use type was in the following order: water bodies &gt; broad-leaved forest &gt; artificial wetland &gt; scrub &gt; paddy field &gt; coniferous forest &gt; natural wetland &gt; grassland. The level of each type of ESV does not depend entirely on the size of the area but is determined by the ecosystem service functions it can provide and the level of ESV per unit area; (2) the relationship between land use types along both sides of the river in the Guangdong section of the Xijiang River Basin shows a tendency to shift from water ecosystems to terrestrial ecosystems, and the ESV gradually decreases with the increase in distance from the water. (3) The upstream to the downstream area showed a trend of changing from terrestrial ecosystems to aquatic ecosystems, such as broad-leaved forests, scrublands, water bodies, artificial wetlands, etc., and the mean land ESV showed a general trend of undulating change and decline with the reduction in the distance from the downstream area. (4) Natural factors, such as the topography and geomorphology of the basin and the socio-economic factors of power consumption, influence the spatial distribution characteristics of the ESV in the region; among them, socio-economic factors, such as total power consumption, industrial exhaust gas emissions, industrial wastewater emissions, etc., in the economically developed areas of the Xijiang River Basin are the determinants of the changes in ESV, which are generated by human living and production activities, and these indirectly affect the magnitude of the ESV by influencing the factors of temperature and gas.

Projected Changes in Precipitation Based on the CMIP6 Optimal Multi-Model Ensemble in the Pearl River Basin, China

Precipitation fluctuations in the Pearl River Basin (PRB) have a significant impact on river runoff, causing huge economic losses and casualties. However, future precipitation variations in the PRB remain unclear. Therefore, we explored the projected changes in precipitation in the PRB based on the coupled model intercomparison project phase 6 (CMIP6) model via three shared socio-economic pathways scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5). In our study, the optimal ensemble of global climate models in the PRB was identified using the comprehensive rating index (CRI), which is based on climatology, spatial variation, and interannual variability, and it was used to analyze potential precipitation changes in the basin in the period 2025–2100. The results showed that the CMIP6 models underestimated precipitation in the PRB; the consistency between the observations and the multi-model ensemble mean of the four best models was higher than those of any other ensembles, and the CRI value was highest (0.92). The annual precipitation in the PRB shows a significant increasing trend under three scenarios from 2025 to 2100 (p &lt; 0.01), with the highest rate of precipitation increase being seen under the high-emission scenario. By the end of the 21st century, the regional mean precipitation in the PRB will increase by 13%, 9.4%, and 20.1% under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, respectively. Spatially, the entire basin is projected to become wetter, except for a slight decrease of less than 6% in the central part of the basin and the Pearl River Delta in the near term in the 21st century, and the highest increases are projected to occur in the Xijiang River basin.

Runoff Prediction in the Xijiang River Basin Based on Long Short-Term Memory with Variant Models and Its Interpretable Analysis

The Long Short-Term Memory (LSTM) neural network model is an effective deep learning approach for predicting streamflow, and the investigation of the interpretability of deep learning models in streamflow prediction is of great significance for model transfer and improvement. In this study, four key hydrological stations in the Xijiang River Basin (XJB) in South China are taken as examples, and the performance of the LSTM model and its variant models in runoff prediction were evaluated under the same foresight period, and the impacts of different foresight periods on the prediction results were investigated based on the SHapley Additive exPlanations (SHAP) method to explore the interpretability of the LSTM model in runoff prediction. The results showed that (1) LSTM was the optimal model among the four models in the XJB; (2) the predicted results of the LSTM model decreased with the increase in foresight period, with the Nash–Sutcliffe efficiency coefficient (NSE) decreasing by 4.7% when the foresight period increased from one month to two months, and decreasing by 3.9% when the foresight period increased from two months to three months; (3) historical runoff had the greatest impact on streamflow prediction, followed by precipitation, evaporation, and the North Pacific Index (NPI); except evaporation, all the others were positively correlated. The results can provide a reference for monthly runoff prediction in the XJB.

Environmental Assessment of Heavy Metals in the Typical Vegetable Field and Irrigation Sediment in Xijiang River Basin, China: Spatial Distribution and Ecological Risk

Environmental Assessment of Heavy Metals in the Typical Vegetable Field and Irrigation Sediment in Xijiang River Basin, China: Spatial Distribution and Ecological Risk

Influence of Underlying Surface Datasets on Simulated Hydrological Variables in the Xijiang River Basin

Abstract Hydrological models play an important role in water resources management and extreme events forecasting, and they are sensitive to the underlying conditions. This study aims to evaluate the impact of different soil-type maps and land-use maps on hydrological simulations and watershed responses by applying the WRF-Hydro (Weather Research and Forecasting Model Hydrological modeling system) distributed hydrological model to the Xijiang River basin. WRF-Hydro runs for four different scenarios for the period 1992–2013. FAO (Food and Agriculture Organization) and GSDE (Global Soil Dataset for Earth System Science) soil-type maps, and MODIS (Moderate-Resolution Imaging Spectroradiometer) and CNLUCC (China Land Use Land Cover Remote Sensing Monitoring Dataset) land-use maps are used in this study. These soil-type maps and land-use maps are freely combined to form four scenarios. It is found that soil moisture and surface runoff are sensitive to soil-type maps, and absorbed shortwave radiation is found to be the least sensitive to soil-type maps. Absorbed shortwave radiation and heat flux are sensitive to land-use maps. The model performance of simulating soil moisture has increased when the soil-type map changes from FAO to GSDE and the land-use map changes from MODIS to CNLUCC for most stations. When the soil-type map changes from FAO to GSDE and the land-use map changes from MODIS to CNLUCC, the biases of simulating streamflow decrease. This study shows that the performance of the offline WRF-Hydro is significantly influenced by soil-type and land-use maps, and better simulation results can be obtained with more realistic underlying surface maps. Significance Statement The purpose of this study is to evaluate the impacts of land-use and soil-type maps on hydrological processes at the watershed scale by applying a distributed hydrological model WRF-Hydro model for the Xijiang River basin and reveal the importance of choosing land-use and soil-type maps. In this study, two soil-type maps and two land-use maps are used. It is found that soil moisture and surface runoff are sensitive to soil-type maps, and absorbed shortwave radiation and heat flux are sensitive to land-use maps. When using GSDE soil-type and CNLUCC land-use maps, the performance of the model is improved. The underlying conditions should be considered when applying the models in practice.

Copula-based comprehensive drought identification and evaluation over the Xijiang River Basin in South China

The reasonable and appropriate drought indicator is a crucial instrument for drought investigation and assessment, which can objectively compare the severity, duration, and frequency of drought events, so as to achieve the purpose of accurately monitoring and evaluating drought. However, a univariate drought index is often difficult to precisely describe the complex drought situation, and it is urgent to establish a multivariate comprehensive drought index to identify and reveal the drought characteristics. Based on Copula models, combined with meteorological factors precipitation and hydrological factors runoff, this paper constructed a Multivariate Standardized Drought Index (MSDI), which can characterize meteorological drought and hydrological drought simultaneously and evaluate the spatial–temporal evolution characteristics of drought over the Xijiang River Basin (XJB) in South China. The results indicated that: (1) the proposed MSDI can capture the occurrence of drought as sensitively as the Standardized Precipitation Index (SPI), and can represent the termination of drought as effectively as the Standardized Runoff Index (SRI), which can comprehensively depict the drought situation in the XJB, China; (2) since the 1980 s, there was a tendency of aridity with a mutation point of 1983 in the downstream of Xijiang River (DXJB), and the worst comprehensive drought occurred in 1989; (3) among the tributaries, the comprehensive drought severity was the heaviest in the DXJB, with an average MSDI value of –0.75; (4) based on the run theory and Copula model, the most serious drought event occurred from September 1988 to October 1990, with a drought severity of 33.36, a drought duration of 27 months, and a drought return period exceeding 50 years; and (5) the drought evolution was significantly influenced by abnormal atmospheric circulation factors, while ENSO had the largest influence on the drought variation in the XJB, followed by sunspot.