The Tana River basin is among the least monitored in terms of meteorological data in Kenya. The Kenya Meteorological Department (KMD) provided data on a ten-day timescale, which is not adequate for water resource evaluation. To bridge this data gap, there is a growing need to leverage General Circulation Models (GCMs) and global datasets to assess current and future water resources in this basin. This study focused on evaluating the performance of 19 CMIP6 GCMs concerning precipitation (pr), maximum temperature (tasmax), and minimum temperature (tasmin) for the complex terrain of the Tana River basin. This involved a rigorous process of disaggregating the data provided by the KMD into a daily timescale for downscaling. The GCMs’ historical output was prepared using the Climate Data Operator (CDO) in Cygwin. The Kling Gupta Efficiency (KGE) was computed for each variable at three stations: Nyeri (upstream), Kitui (midstream), and Bura (downstream). The KGE results were validated using Taylor statistics. Five GCMs, CMCC-ESM2, MPI-ESM1-2-HR, ACCESS-CM2, NorESM2-MM, and GFDL-ESM4, performed best with a multivariable Multi-station KGE statistic of 0.455–0.511. The outputs from these selected GCMs were subsequently downscaled for later use in assessing the water resources and crop water demand in the basin.

National-scale parameter regionalization of a distributed rainfall-runoff model (1K-DHM) is promoted for flash flood prediction in river basins in Japan. Representative model parameter sets of 1K-DHM for 7 geospatial characteristics including land-use and soil properties were identified through calibration in 53 donor catchments with 1 dominant geospatial characteristic and a leave-one-out cross-verification within each parameter group. This resulted in a parameter map of 1K-DHM for the 30-second grid cells with a national-scale coverage in Japan. The identified parameter (IP) sets yielded noticeable differences in the depth-discharge relationship in 1K-DHM, which explains the difference in runoff characteristics among geospatial categories. The transferability of IP to heterogeneous catchments was validated against individual optimized parameter sets (OP) for 70 receptor basins evaluated using Nash-Sutcliffe efficiency (NSE) and the normalized peak discharge error (PDE). The results show that IP (median NSE: 0.74, median PDE: 0.028) demonstrated comparable performance to OP (median NSE, 0.76; median PDE, 0.014), which indicates that a distributed rainfall-runoff model with model parameters determined by land use and soil properties can predict floods in ungauged basins.

Soil moisture is a key variable in land-atmosphere interactions. This study investigates the bimodal distribution of boreal summer surface soil moisture. We first identify the geographical locations of global bimodal hotspots using a Gaussian Mixture Model (GMM), with parameter estimation using the Expectation-Maximization algorithm. Subsequently, we explore the soil moisture dynamics in bimodal areas, focusing on both natural climate variability and alterations due to anthropogenic activities, notably irrigation. Bimodal hotspots were detected in mid-north India, the western Sahel, and the central United States – regions also recognized as key land-atmosphere interaction zones. However, bimodality in the central United States is dependent on the temporal scale, with greater bimodality at finer temporal resolutions. The transition from dry to wet conditions during the season emerged as the principal driver for bimodality in mid-north India and the western Sahel. In contrast, in the central U.S., interannual variations play a more significant role in inducing bimodality. Moreover, the influence of irrigation on soil moisture distribution exhibits regional differences. In the central U.S., irrigation practices diminish bimodality by reducing inherent soil moisture variability. However, in mid-north India, irrigation during this period coinciding with monsoonal rainfall, did not markedly alter bimodality. These insights enhance our understanding of soil moisture dynamics in crucial land-atmosphere interaction regions and underscore the interplay between natural and anthropogenic influences.

Meteorological authorities in many developing countries can benefit from running a regional model optimized for their countries in addition to rainfall predictions provided by major centers in developed countries. As one such activity, Solomon Islands Meteorological Services (SIMS) recently started to operationally run their regional model based on the non-hydrostatic model of Japan Meteorological Agency (JMA). This study evaluates the skill of rainfall predictions by the SIMS operational model with a 15-km mesh (SI15) based on 219 cases of 36-hour forecasts initialized every five days during 2017–2019. We also conducted runs with a 1.875-km mesh (SI01) in anticipation of future numerical resources. The skill was compared with the forecasts of the JMA Global Spectral Model (GSM), using rain gauge data and satellite-based data. The forecasts by SI15 and SI01 exhibited better performance than the GSM in terms of bias and threat score, with SI01 performing the best. Additionally, SI01 successfully reproduced the diurnal variation where precipitation becomes strong over land in the evening and on the eastern side of the Solomon Islands over the sea in the early morning. In contrast, SI15 showed weak biases around the initial time and strong biases after 18 hours of forecast time.