Understanding Of Hydrological Processes Research Articles

Elevational control of isotopic composition and application in understanding hydrologic processes in the mid Merced River catchment, Sierra Nevada, California, USA

Abstract. Mountain snowpack has been declining and more precipitation has fallen as rainfall than snowfall, particularly in the western US. Isotopic composition in stream water, springs, groundwater, and precipitation was examined to understand the impact of declining snowpack on hydrologic processes in the mid Merced River catchment (1873 km2), Sierra Nevada, California. Mean isotopic values in small tributaries (catchment area < 122 km2), rock glacier outflows, and groundwater from 2005 to 2008 were strongly correlated with mean catchment elevation (R2= 0.96 for δ2H, n= 16, p < 0.001), with an average isotopic lapse rate of −1.9 ‰ per 100 m for δ2H and −0.22 ‰ per 100 m for δ18O in meteoric water. The lapse rate did not change much over the seasons and was not strongly affected by isotopic fractionation. A catchment-characteristic isotopic value, representing the catchment arithmetic mean isotopic signature in meteoric water, was thus established for each sub-catchment based on the lapse rate to elucidate hydrometeorologic and hydrologic processes such as the duration and the magnitude of snowmelt events and elevational water sources of streamflow and groundwater for ungauged catchments. Compared to Tenaya Creek without water falls, the flow and flow duration of Yosemite Creek appear to be much more sensitive to seasonal temperature increases during the baseflow period due to a strong evaporation effect caused by waterfalls, suggesting a possible prolonged dry-up period of Yosemite Falls in the future. Groundwater in Yosemite Valley (∼ 900–1200 m) was recharged primarily from the upper snow–rain transition zone (2000–2500 m), suggesting its strong vulnerability to shifts in the snow–rain ratio. The information gained from this study helps advance our understanding of hydrologic responses to climate change in snowmelt-fed river systems.

Spatio-seasonal characteristics and controlling factors of surface water stable isotope values (δ18O and δD) across the Inner Mongolia Reaches of the Yellow River Basin, China: Implication for hydrological cycle

Study regionInner Mongolia Reaches of the Yellow River Basin (IMR-YRB) in China, knwon as the “Jiziwan” economic belt. Study focusHigh spatio-seasonal resolution sampling data of the surface waters in the IMR-YRB were applied (189 samples collected from 63 sites during the wet (July 2021), dry (October 2021) and normal (April 2022) seasons). The testing results of stable H and O isotopes were combined with backward wind trajectory modeling and geostatistical analysis to identify the spatio-seasonal characteristics of the surface water cycle and its controlling factors. New hydrological insights for the regionThe results showed that surface waters were mainly recharged by precipitation from summer monsoon, resulting in depleted isotopic contents (low δ18O and high d-excess) during the wet season. In contrast, most of the surface waters were characterized by isotopic enrichment (high δ18O and low d-excess) during the dry and normal seasons, which was attributed to the effects of continuous evaporation. In addition, there were some samples that exhibited isotopic depletion during the dry and normal seasons due to the effects of groundwater discharge or snowmelt recharge. Overall, this study improved the understanding of the surface water cycle at a large-scale basin for understanding hydrologic processes in other regions.

Characterizing streamflow regimes using a distributed model for sustainable resource management in the humid tropics

Estimating streamflow regimes on small tropical islands is challenging due to limited observational data, poor accuracy of remote sensing, and the limitations of coarse-scale climate models, restricting our understanding of hydrological processes and the management of water resources. The inadequacy of data has also affected the development of instream flow standards (i.e., environmental flows, e-flows) needed to protect ecological and cultural values central to island communities. Estimates of water availability for agriculture, hydropower, and drinking water supply are also critical for informed investments in water resource planning. We utilized high-resolution (250 m) gridded daily rainfall data to parameterize the Soil and Water Assessment Tool (SWAT) to characterize streamflow regimes at ungauged locations in a watershed on Kaua‘i Island. The resultant flow statistics were then used to describe the daily, seasonal, and annual availability of surface water to evaluate the implications of potential e-flow scenarios on water available for run-of-river hydropower. Compared to rainfall data derived from a single station within the watershed, the gridded daily rainfall product increased the accuracy of model output from not satisfactory to good. Model techniques using high resolution rainfall data may substitute for long-term hydrological data collection across complex tropical environments and provide data for making sustainable management decisions at ungauged locations.

Hydrologic applicability of satellite-based precipitation estimates for irrigation water management in the data-scarce region

Reliable precipitation estimates are crucial for planning and managing water resources, monitoring hydrologic extremes, and fulfilling irrigation water requirements. Accurate precipitation estimates are particularly challenging in complex mountain terrains, where monitoring gauges are often sparsely distributed due to their remote locations, and high installation and long-term operation costs. Recent advances in satellite-based precipitation estimates offer promising opportunities to improve our understanding of hydrologic processes and their applications for irrigation water management. Several datasets are available varying considerably in terms of their data sources, quality control methods, estimation procedure, and spatiotemporal resolutions. Choosing the most suitable dataset for a particular application is a complex task. In this study, we (1) evaluate the performance of six satellite-based precipitation estimates (SPEs): i) CHIRPS v2.0, ii) CMORPH v1.0, iii) ERA5, iv) IMERG v6, v) MSWEP v2.8, and vi) PERSIANN-CDR against the gauge precipitation using continuous statistical and categorical indices, (2) integrate SPEs with a calibrated semi-distributed hydrologic model to predict streamflow, and (3) demonstrate practical implications of improved streamflow prediction for irrigation water management in the central Himalayan region, Nepal. Our results illustrate that satellite-based precipitation estimates have competitive performance in capturing a wide range of rainfall characteristics, with demonstrated variability across river basins and time scales. There are no significant discrepancies observed in satellite-based precipitation estimates for estimating irrigation water requirements for the three major crops (maize, wheat, and paddy) during the cropping period across the selected river basins, showing a greater promise for irrigation water management planning and decision making.

Modelling runoff components and hydrological processes in glaciated catchments of the inner Tien-Shan, Kyrgyzstan

Accurately quantifying runoff sources and understanding hydrological processes in glaciated mountain basins is essential for effective water resource management in the face of climate change. This study aimed to determine the contributions from various runoff sources in glaciated basins within the inner Tien-Shan mountains of Kyrgyzstan by utilizing integrated land surface, glacier energy-mass balance, and river routing models. To account for local topographic influences on solar radiation and cloud transmissivity processes, gridded meteorological forcing data were downscaled. The integrated models were then evaluated against observed discharge, glacier mass balance, and snow water equivalent, with a focus on the Kara-Batkak glacier reference site. Shortwave radiation correction was particularly important for improving the accuracy of model simulations. The results indicate that the peak glacier melt contribution occurs in July and August, with some basins reaching up to 54%. On an annual basis, the average contribution from glaciers across the basins was found to be 19%, while the ratios of snowmelt and rainfall were 58% and 23%, respectively. This study highlights the utility of integrated modelling approaches in understanding and quantifying runoff components in data-scarce high mountain regions. The incorporation of observed glacier data proved to be crucial for accurately representing hydrological processes under current climatic conditions. These findings underscore the significance of considering glacier dynamics and their influence on water resources to inform effective water management strategies in glaciated mountain basins.

Integrating socio-hydrology, and peace and conflict research

Socio-hydrology strives to incorporate 'the social' into the understanding of hydrological processes, aiming to enrich the analysis of water systems by considering human interactions. While there is a broader interest in integrating socio-political processes into hydrology, our paper specifically emphasizes the significant contributions of peace and conflict research to understanding the complex social dynamics surrounding water. We conduct a brief review of key literature on interstate water sharing, international norms on water, and domestic water disputes, drawing extensively from empirical studies within peace and conflict research—a field with a rich tradition of examining the interplay of water systems and social dynamics. Building on this foundation, we propose ways to weave insights from peace research, especially environmental peacebuilding, into the realm of socio-hydrology. We also highlight the crucial role of power, politics, and social factors in shaping water-related interactions and conflicts. By fostering a dialogue between socio-hydrology and peace and conflict research, we advocate for a more nuanced understanding of water management and governance. This interdisciplinary approach, we argue, is essential for promoting sustainable and equitable water use, and for addressing the challenges posed by water-related conflicts in a rapidly changing global context.

Changed evapotranspiration and its components induced by greening vegetation in the Three Rivers Source of the Tibetan Plateau

The Three Rivers Source (TRS), that is, the source of the Yangtze (SYZ), Yellow (SYE), and Lancang (SLC) Rivers in the Tibetan Plateau, is the water tower of Asia. Harsh climates, complex topography, and limited ground-based observations hinder our understanding of hydrological processes in this region, especially in a changing climate. Over the past two decades, the vegetation in the TRS has undergone significant greening as a result of the implementation of ecological restoration projections, which in turn has changed the hydrological process in this region. However, the impact of vegetation greening on evapotranspiration (ET) and its components is still unclear. Here, we investigate the response of ET and its components to vegetation changes in the TRS based on the maximum entropy production (MEP) model and detrending attribution analysis. The results show that the MEP model can well capture changes in ET at both the site and watershed scales. The multi-year-average ET of the TRS is 411.7 mm/a, of which the relative contributions of soil evaporation (Es), vegetation transpiration (Et), and canopy interception evaporation (Ei) are 68.21 %, 23.57 %, and 8.21 %, respectively. The annual ET has increased significantly, with a trend of 0.23 mm/a2 (p < 0.05). The contributions of vegetation greening to ET and its three components (Es, Et, and Ei) are 1.95 mm/a, −2.41 mm/a, 1.33 mm/a, and 3.03 mm/a, respectively. The findings are expected to provide guidance for the management of water resources and the implementation of ecological restoration in the TRS.

ERA5 Precipitation over China: Better Relative Hourly and Daily Distribution than Absolute Values

Abstract The temporal distribution of precipitation is of great importance in water availability and the hydrological cycle. Existing studies, directly comparing observed and simulated precipitation amount and intensity over different temporal intervals, show that reanalyses overestimate precipitation frequency and underestimate precipitation intensity, and therefore cannot characterize extreme precipitations. In this study, relative values of precipitation (i.e., the cumulative fractions of precipitation on different percentiles of wettest hours or days to the annual total) were used to evaluate ERA5 over China during the warm seasons of from 1979 to 2015. We found that ERA5 well reproduced the relative values of daily precipitation to the annual total, although hourly results were less satisfied. The cumulative fractions at the hourly time scale at the 99th, 95th, and 90th percentiles were 15.2% ± 3.4%, 38.7% ± 6.6%, and 54.5% ± 7.4% for the gauge observations, and 9.0% ± 1.7%, 27.2% ± 4.0%, and 41.5% ± 5.0% for ERA5, respectively. ERA5 had excellent agreement with gauge observations at daily time scales: gauge observations were 13.0% ± 3.4%, 32.1% ± 5.1%, and 47.6% ± 5.7%, compared to 11.5% ± 3.7%, 30.7% ± 7.0%, and 46.0% ± 8.8% for ERA5, respectively. This difference was mainly due to the high frequency of hourly precipitation in the reanalyses, which were cancelled out at the daily scale. Gauge observations at the hourly scale showed an increase cumulative fraction at the 99th, 95th, and 90th percentiles (0.07% ± 0.03% decade−1, 0.15% ± 0.03% decade−1, and 0.17% ± 0.03% decade−1, respectively) from 1979 to 2015, but did not show an obvious trend at the daily scale. These tendencies were underestimated at the hourly scale but overestimated at the daily scale in ERA5. The results helped us to characterize the temporal distributions and variations of precipitation, and to deepen our understanding of hydrological processes and reanalyses performance.

Innovative methodology for estimating Mountain-Front Recharge in data-scarce regions: A case study in Mexico City

Despite the critical role mountainous regions play in the hydrological cycle, the scarcity of subsurface hydrogeological data hampers accurate estimations of mountain-front recharge (MFR) and its components. This research proposes a cost-effective and replicable methodology for the preliminary estimation of MFR components in conditions of data scarcity. The proposed methodology, encompassing three phases, involves developing a hydrological conceptual model, creating a rainfall-runoff model, and estimating MFR values and their components (surface mountain-front recharge and mountain-block recharge); it enhances understanding of hydrologic processes in mountain zones by interpreting relationships among precipitation, altitude, and geology, influencing variations in recharge and water table position. A key contribution is the methodology's replicability in any mountainous area, even in the absence of detailed information, such as in protected natural areas. A case study is presented by estimating MFR in the Conservation Area of Mexico City (officially known as Suelo de Conservación), composed by the mountain ranges of Sierra de las Cruces and Chichinautzin Volcanic Field. The results emphasize the distinct distribution of MFR components and water table position in the two mountain regions, suggesting a need for a tailored approach to water management in the basin. The proposed methodology is cost-effective to understand the water dynamics in data-scarce regions, contributing to mountain-front recharge management.

Multiple spatio-temporal scale runoff forecasting and driving mechanism exploration by K-means optimized XGBoost and SHAP

Hydrological simulations have seen extensive use of machine learning (ML) models. However, the existing ML models face challenges in effectively handling temporal and spatial heterogeneity of the driving features and transparency of features. To improve the runoff prediction ability of ML and the confusion of runoff generation mechanism interpreted by ML, this study combined K-means with XGBoost (Extreme gradient boosting,) and SHAP (Shapely additive explanations,) to develop an interpretable ML-based hydrological model (KXGBoost) across the Continental United States as a case study. The results show that K-means clustering based on the interpretation of SHAP can effectively capture the temporal and spatial heterogeneity of runoff-driven features. And the performance and interpretability of data-driven hydrological models in runoff simulation can be significantly improved by KXGBoost. KXGBoost yields an NSE (Nash-Sutcliffe Efficiency) of 0.803 and 0.596 during the training and testing, respectively, representing an improvement of 0.089 and 0.029 as compared to the XGBoost model. KXGBoost has demonstrated its ability to identify multiple runoff generation mechanisms under multiple spatio-temporal perspectives. This study provided a novel perspective for understanding hydrological processes and improving runoff simulation and demonstrates the potential of ML-based hydrological models in water resources management.

Uncertainty quantization of meteorological input and model parameters for hydrological modelling using a Bayesian‐based integrated approach

AbstractThe traditional treatment of uncertainty in hydrological modelling primarily attributes it to model parameters, but rarely systematically considers meteorological input errors, especially in quantifying the impact of meteorological input errors on parameter uncertainty. This study developed a Bayesian‐based integrated approach to quantitatively investigate uncertainties in meteorological inputs (precipitation and temperature) and model parameters as well as the variation in parameter uncertainty due to meteorological input errors. Additionally, we analysed the propagation from these uncertainties to runoff response in snowmelt and non‐snowmelt periods. The applicability and advantages of this approach were presented by applying of the Soil and Water Assessment Tool to the Shitoukoumen Reservoir Catchment. Differential Evolution Adaptive Metropolis‐Markov Chain Monte Carlo was applied for the straightforward Bayesian inference the uncertainties of meteorological inputs and model parameters. On this basis, multilevel factorial analysis technology was used to quantitatively investigate the specific impact of the model parameters' individual and interactive effects due to meteorological input errors. Finally, the impact of meteorological input errors and model parameter uncertainty on the model performance were analysed and quantified systematically. The results showed that the meteorological input errors could affect the random characteristics of multiple model parameters. Moreover, meteorological input errors could further affect the model parameters' effects on annual average runoff. Overall, the above results have significant implications in enhancing hydrological model to simulate/predict runoff and understanding hydrological processes during different periods.

Assessing satellite and reanalysis-based precipitation products in cold and arid mountainous regions

Study regionThe Xiying and Heihe River basins, cold and arid mountainous regions of northwestern China. Study focusIn data-limited areas of Northwestern China, satellite- and reanalysis-based precipitation products provide crucial assistance. However, a comprehensive evaluation of their advantages, limitations, and hydrological performance has yet to be ascertained. This knowledge gap impedes a deeper understanding of hydrological processes within the region. This study evaluates the accuracy of ERA5L, IMERG-F, GSMaP-G, and FY4A-QPE from three aspects: time, space, and extreme precipitation. Comprehensive validation was conducted on four products using discharge, flood events, and peak flood simulated by Geomorphology-Ecology-Hydrology Model. New hydrological insights for the regionERA5L data can be used to build long-term studies on regional meteorological and hydrological trends. IMERG-F is valuable for analyzing extreme precipitation and hydrological events. GSMaP-G is well-suited for identifying precipitation events in the region. It is essential to be cautious of precipitation underestimation when using satellite precipitation products at high elevations and during the winter.This study highlights the differences in accuracy among various precipitation products and provides recommendations for policymakers in selecting data for high-altitude research. It also offers critical information on hydrometeorology and directions for producing high-accuracy precipitation products.

Spatial characteristics of hydrochemistry and stable isotopes in river and groundwater, and runoff components in the Shule River Basin, Northeastern of Tibet Plateau

Water resources play a crucial role in constraining the high-quality development of the arid, necessitating an in-depth investigation and understanding of hydrological processes, hydrochemical characteristics, and their influencing factors amidst climate change. This study meticulously examined and analyzed the hydrochemistry and stable isotope composition (δ18O and δD) of river and groundwater within the Shule River Basin (SRB). Results showed that both river (mean: 8.01) and groundwater (mean: 7.92) had alkaline pH values, while average total dissolved solids were measured at 709.25 mg/L in river and 861.88 mg/L in groundwater, indicating predominance of fresh water sources. HCO3−, SO42−, Na+ and Ca2+ were the most abundant ions, influenced by evaporation-crystallization processes and rock weathering. The dominated hydrochemical facies in both river and groundwater were Ca–HCO3 type in the upper (UR) and the middle reaches (MR), while Ca–Mg–Cl type in the lower reaches (LR). The local meteoric water line (LMWL) was defined as δD = 8.01δ18O + 18.48 (R2 = 0.98, n = 163; P < 0 0.001). The more negative δ18O and δD values in river and groundwater were plotted nearby and lower right of the LMWL, implying that the important recharge source of those waters is from precipitation. The relationship between river δ18O and elevation showed an increase of 0.14‰/100 m in the UR, but a negative correlation with a rate of −0.47‰/100 m in the MR and LR. Precipitation, groundwater, baseflow and meltwater accounted for 62.5%, 19.8%, 11.9% and 5.8% of the UR river, respectively, during the observed period, according to the end-member mixing analysis. These runoff components displayed distinct seasonal variations, primarily driven by precipitation during the early and groundwater/baseflow during the rapid and end-stage ablation periods. The observed alterations in hydrological elements present both opportunities and challenges for water resource management across the SRB, and adaptive measures have been proposed based on our study. These findings provide valuable insights into efficient utilization of water resources from water chemistry and environmental isotopes.

Spatial and Temporal Variation Characteristics of Stable Isotopes in Precipitation and Their Relationships with Meteorological Factors in the Shiyang River Basin in China

Stable isotopes of precipitation play an important role in understanding hydrological and climatic processes of arid inland river basins. In order to better understand the difference of regional water cycle and precipitation patterns, precipitation samples were collected in Shiyang River Basin from June 2018 to May 2020, and spatial and temporal variation characteristics of stable isotopes in precipitation and their relationships with meteorological factors were discussed. The results showed that stable isotopes in precipitation showed seasonal variation characteristics, that δ2H and δ18O values were higher in summer and autumn and lower in winter and spring, and d-excess values were higher in spring and autumn and lower in winter and summer. Slopes and intercepts of the local meteoric water lines gradually increased with elevation increasing. δ18O significantly showed a positive correlation with temperature but a negative correlation with precipitation in summer. Contrary to below 15 hPa, δ18O significantly showed a negative correlation with water vapor pressure above 15 hPa. Due to the influence of meteorological factors, there are significant differences in stable isotopes of precipitation in arid inland river basins, which were more influenced by local moisture recycling in upstream area but by below-cloud secondary evaporation in mid-downstream area.

Integrating ICESat-2 laser altimeter observations and hydrological modeling for enhanced prediction of climate-driven lake level change

Predicting future lake levels under climate change is critical for advancing our understanding of hydrological processes in a changing environment. However, continuous and long-term prediction of lake levels is challenging due to discrepancies in multi-source data and the lack of integration of hydrological models and climate scenarios. Physical and statistical models have been used for lake levels prediction, however, physical models are difficult to calibrate and statistical models often fail to account for the effects of climate changes. In this study, a lake level prediction model was proposed by assimilating the hydrophysical model based on water balance and the ICESat-2 observations using Variational Bayesian Monte Carlo. The model can predict monthly lake levels combining CMIP6-SWAT climate-driven projections and ICESat-2 observations. Short-term validation over 24 months showed the R2 was 0.91 and the RMSE was 5 cm between the proposed model and the ICESat-2 observation. The accuracy is superior to both the hydrophysical model based on water balance and the Prophet time series model. Long-term validation from 1978 to 2021 showed the proposed model has the potential to enhance prediction accuracy within 2 to 3 years compared with the hydrophysical model based on water balance and it is suitable for predicting long-term lake level trends. Notably, it successfully predicted the significant turning point around 2005 where lake levels shift from decline to increase based on past data. The water levels of Lake Qinghai were predicted to rise at a rate of 3.7 cm per year by 2050 under the SSP2-4.5 scenario. The proposed assimilation model combines the strengths of hydrological modeling based on water balance (incorporating the effects of climate change) and the latest ICESat-2 lake level observations (incorporating the effects of recent historical lake levels), improving the accuracy of short-term lake levels and long-term lake level trends prediction. Moreover, as the model is based on satellite remote sensing observations, it has the potential to be applied to any lake globally.

A dataset of water, heat, and carbon fluxes of an oasis agroecosystem in the middle areas of the Hexi Corridor (2012–2015)

The exchange of water, heat, and carbon dioxide between the terrestrial ecosystems and the atmosphere is a fundamental process that underlies mass and energy transfer at the Earth’s surface. The eddy covariance technique has become one of the preferred and state-of-the-art approaches for measuring and calculating the exchange of water, heat, and carbon between the land surface and the atmosphere. This dataset covers the fluxes of water, heat, and carbon dioxide in an oasis agroecosystem, as well as the auxiliary micrometeorological variables in the middle areas of the Hexi Corridor during 2012–2015. The experimental observation campaign was carried out by the Linze Inland River Basin Research Station, a member station of the China Flux Observation and Research Network (ChinaFLUX). The dataset serves as a fundamental source of data, enabling in-depth comprehension water transfer, energy exchange, carbon cycle processes in the oasis agroecosystem in arid regions, and even their environmental and vegetation controlling mechanisms. It offers valuable insights into better understanding hydrological processes, ecosystem-hydrology reaction and discovering the coupling between carbon, water and heat in the context of climate change. Furthermore, the dataset holds significant scientific value in addressing chronic practical challenges related to fragile environment and shortage of water resource, such as water and land resources management, the preservation of oasis stability and sustainable development, the conservation of oasis ecosystems, etc.

Explainable Artificial Intelligence in Hydrology: Interpreting Black-Box Snowmelt-Driven Streamflow Predictions in an Arid Andean Basin of North-Central Chile

In recent years, a new discipline known as Explainable Artificial Intelligence (XAI) has emerged, which has followed the growing trend experienced by Artificial Intelligence over the last decades. There are, however, important gaps in the adoption of XAI in hydrology research, in terms of application studies in the southern hemisphere, or in studies associated with snowmelt-driven streamflow prediction in arid regions, to mention a few. This paper seeks to contribute to filling these knowledge gaps through the application of XAI techniques in snowmelt-driven streamflow prediction in a basin located in the arid region of north-central Chile in South America. For this, two prediction models were built using the Random Forest algorithm, for one and four months in advance. The models show good prediction performance in the training set for one (RMSE:1.33, R2: 0.94, MAE:0.55) and four (RMSE: 5.67, R2:0.94, MAE: 1.51) months in advance. The selected interpretation techniques (importance of the variable, partial dependence plot, accumulated local effects plot, Shapley values and local interpretable model-agnostic explanations) show that hydrometeorological variables in the vicinity of the basin are more important than climate variables and this occurs both for the dataset level and for the months with the lowest streamflow records. The importance of the XAI approach adopted in this study is discussed in terms of its contribution to the understanding of hydrological processes, as well as its role in high-stakes decision-making.

Simulated stable water isotopes during the mid-Holocene and pre-industrial periods using AWI-ESM-2.1-wiso

Abstract. Numerical simulations employing prognostic stable water isotopes can not only facilitate our understanding of hydrological processes and climate change but also allow for a direct comparison between isotope signals obtained from models and various archives. In the current work, we describe the performance and explore the potential of a new version of the Earth system model AWI-ESM (Alfred Wegener Institute Earth System Model), labeled AWI-ESM-2.1-wiso, in which we incorporated three isotope tracers into all relevant components of the water cycle. We present here the results of pre-industrial (PI) and mid-Holocene (MH) simulations. The model reproduces the observed PI isotope compositions in both precipitation and seawater well and captures their major differences from the MH conditions. The simulated relationship between the isotope composition in precipitation (δ18Op) and surface air temperature is very similar between the PI and MH conditions, and it is largely consistent with modern observations despite some regional model biases. The ratio of the MH–PI difference in δ18Op to the MH–PI difference in surface air temperature is comparable to proxy records over Greenland and Antarctica only when summertime air temperature is considered. An amount effect is evident over the North African monsoon domain, where a negative correlation between δ18Op and the amount of precipitation is simulated. As an example of model applications, we studied the onset and withdrawal date of the MH West African summer monsoon (WASM) using daily variables. We find that defining the WASM onset based on precipitation alone may yield erroneous results due to the substantial daily variations in precipitation, which may obscure the distinction between pre-monsoon and monsoon seasons. Combining precipitation and isotope indicators, we suggest in this work a novel method for identifying the commencement of the WASM. Moreover, we do not find an obvious difference between the MH and PI periods in terms of the mean onset of the WASM. However, an advancement in the WASM withdrawal is found in the MH compared to the PI period due to an earlier decline in insolation over the northern location of Intertropical Convergence Zone (ITCZ).

Hydrometeorological field instrumentation in Lesser Himalaya to advance research for future water and food security.

Hydrometeorological monitoring and continuous data collection in ungauged mountainous regions are exciting and challenging for water resource planners compared to the measurement in plain areas. Lesser Himalayas in the mountainous areas face the insufficiency of continuous hydrometeorological data, hindering our understanding of hydrological processes and hampering integrated water resources management. This present study focuses on the setup of the field instruments for collecting hydrometeorological data and analyzing continuously collected data at Aglar watershed to assess hydrometeorological parameters' spatial and temporal distribution. The instrumentation includes monitoring one sub-surface flow, five stream flows, four rain gauges, and one automatic weather station. The relationship between the stage and the discharge was established based on the collected data for three streams. The analyzed seasonal rainfall revealed 726.7 mm of rain occurred during the monsoon with an intensity of less than 16 mm/day. The Paligaad sub-watershed displayed a flashy response towards the rainfall events, whereas the Upper Aglar exhibited a wide range of dampening runoff responses for different rainfall events. The monitored sub-surface flow varies annually, and during the monsoon season, interflow and baseflow hydrograph decayed more rapidly at the rate of 0.04 day-1 and 0.78 day-1, respectively. The installed AWS has been used to measure crop water requirements and plan for better strategies to cope with future food and water security. The high-frequency generated data will help answer the queries related to hydrological responses of different watershed characteristics.

Towards more credible models in catchment hydrology to enhance hydrological process understanding: Preface

Towards more credible models in catchment hydrology to enhance hydrological process understanding: Preface