Hydroclimatological changes in the Bagmati River Basin, Nepal

Study on hydroclimatological changes in the mountainous river basins has attracted great interest in recent years. Changes in temperature, precipitation and river discharge pattern could be considered as indicators of hydroclimatological changes of the river basins. In this study, the temperatures （maximum and minimum）, precipitation, and discharge data from 1980 to 2009 were used to detect the hydroclimatological changes in the Bagmati River Basin, Nepal. Simple linear regression and Mann-Kendall test statistic were used to examine the significant trend of temperature, precipitation, and discharge. Increasing trend of temperature was found in all seasons, although the change rate was different in different seasons for both minimum and maximum temperatures. However, stronger warming trend was found in maximum temperature in comparison to the minimum in the whole basin. Both precipitation and discharge trend were increasing in the pre-monsoon season, but decreasing in the post-monsoon season. The significant trend of precipitation could not be observed in winter, although discharge trend was decreasing. Furthermore, the intensity of peak discharge was increasing, though there was not an obvious change in the intensity of maximum precipitation events. It is expected that all these changes have effects on agriculture, hydropower plant, and natural biodiversity in the mountainous river basin of Nepal.

On the Application of Probabilistic Hydrometeorological Simulation of Soil Moisture across Different Stations in India

An application of a proposed hydrometeorological approach for probabilistic simulation of soil moisture is carried out. The time series of in-situ soil moisture and meteorological variables at monthly scale from a few monitoring stations having different soil-hydrologic properties across India are utilized. Preliminary investigation with both precipitation and near-surface air-tempera- ture as meteorological variables to establish that the strength of association between soil moisture and precipitation is more significant as compared to that between soil moisture and temperature. Precipitation-based probabilistic estimation of soil moisture using the proposed hydrometeorological approach is tested with in-situ observed soil moisture, CPC model output and with soil moisture data of the Climate Change Initiative (CCI) project. The parameter of the developed model is linked to the soil-hydrologic characteristics through Hydrologic Soil Group (HSG) classification. Higher values of model parameter (dependence parameter (θ) for the selected copula) correspond to HSG A and B having higher soil porosity, whereas, lower values correspond to HSG B and C having lower soil porosity.

Hydroclimatology of the Kaduna River Basin

This study examined the hydroclimatology of the Kaduna River Basin (KRB) in northern Nigeria. In achieving this, monthly data on temperature (T) and rainfall (P) were sourced from ten hydrometeorological stations across the basin from 1990 to 2018. DrinC (Drought Indices Calculator) software was deployed to calculate Potential Evapotranspiration (PET) adopting Thornthwaite approach. Water Balance (WB) model was used further to estimate other WB components i.e. soil moisture (SM), actual evapotranspiration (ETa), Water surplus (S) and Runoff (R). WB components are used to examine the temporal and spatial variability of the KRB for hydrological years (1990-2018). KRB was divided into two sub-basins (Lower and Upper KRB). The WB analyses indicated the peak of R generally occurs during the wet season (i.e. April through October) most especially at the Upper KRB. The study further reveals that the runoff efficiencies imply that <44% of annual P results in R at the upper KRB while <27% of annual P results in R at the lower KRB. The study shows that SM utilization occurs mostly towards the end of the year and at the early months (i.e. November through March) across the basin while the majority of S is generated during wet season months, particularly from April through October when ~95% of S occurs on average with the peak S in August. The results of this study provide a baseline understanding of the hydroclimatology of the KRB which can be used as a starting point for further analyses, especially for water resources management.

Spatial Intra-Annual Variability of Precipitation Based on Geostatistics.A Case Study for the Paraiba Do Sul Basin,Southeastern Brazil

The emphasis in this research is to evaluate the spatial distribution of the precipitation using a geostatistics approach. Seasonal time scales records considering DJF, MAM, JJA e SON periods performed the analysis. Procedures to evaluate the variogram selection and to produce kriging maps were performed in a GIS environment . The results showed that kriging method was very suitable to detect both large changes in the whole area as those local small and subtle changes. Kriging demonstrated be a powerful statistical interpolation method that might be very useful in regions with great complexity in climatology and geomorphology.

Hybrid global gridded snow products and conceptual simulations of distributed snow budget:evaluation of different scenarios in a mountainous watershed

Considering snowmelt in mountainous areas as the important source of streamflow,the snow accumulation/melting processes are vital for accurate simulation of the hydrological regimes.The lack of snow-related data and its uncertainties/conceptual ambiguity in snowpack modeling are the different challenges of developing hydroclimatological models.To tackle these challenges,Global Gridded Snow Products(GGSPs)are introduced,which effectively simplify the identification of the spatial characteristics of snow hydrological variables.This research aims to investigate the performance of multisource GGSPs using multi-stage calibration strategies in hydrological modeling.The used GGSPs were Snow-Covered Area(SCA)and Snow Water Equivalent(SWE),implemented individually or jointly to calibrate an appropriate water balance model.The study area was a mountainous watershed located in Western Iran with a considerable contribution of snowmelt to the generated streamflow.The results showed that using GGSPs as complementary information in the calibration process,besides streamflow time series,could improve the modeling accuracy compared to the conventional calibration,which is only based on streamflow data.The SCA with NSE,KGE,and RMSE values varying within the ranges of 0.47–0.57,0.54–0.65,and 4–6.88,respectively,outperformed the SWE with the corresponding metrics of 0.36–0.59,0.47–0.60,and 5.22–7.46,respectively,in simulating the total streamflow of the watershed.In addition to the superiority of the SCA over SWE,the twostage calibration strategy reduced the number of optimized parameters in each stage and the dependency of internal processes on the streamflow and improved the accuracy of the results compared with the conventional calibration strategy.On the other hand,the consistent contribution of snowmelt to the total generated streamflow(ranging from 0.9 to 1.47)and the ratio of snow melting to snowfall(ranging from 0.925 to 1.041)in different calibration strategies and models resulted in a reliable simulation of the model.

Defining hydroclimatic regions using daily rainfall characteristics in the southern Appalachian Mountains

This paper addresses warm season hydroclimatic variability in the southern Appalachian region of the southeastern U.S.,where precipitation can vary as much as 127 mm or more,with maximum seasonal totals exceeding 736 mm in extreme cases.Despite the occurrence of droughts,floods,and their socioecological impacts,hydroclimate variability is still poorly understood.This study characterizes the regional scale variations in the hydroclimate by examining the daily distribution of precipitation patterns in different topographic environments.Parameter-elevation relationships on independent slopes model(PRISM)gridded precipitation estimates are used to identify the location and frequency of different types of rainfall events.Several types of clustering algorithms are used as a regionalization approach to define areas where the precipitation regime exhibits similarities in its frequency of occurrence.The results are compared with internal validation statistics and a visualization is used to assess how well the resulting hydroclimatic regions align with different topographic environments.This study reveals the intricate spatial footprint of dry and wet regimes and demonstrates how clustering applications can be used with gridded climate data to determine where extremes are most likely to develop across mountain catchments.