Humid subtropical

Humid subtropical climates are represented within the climate group: Temperate 

They usually occur on the eastern coasts and eastern sides of continents, usually in the high 20s and 30s latitudes. These climates have a warm and wet flow from the tropics that creates warm and moist conditions in the summer months. As such, summer is often the wettest season.

Related Content

Article

Interview with Sharif Islam, Post-Doc Researcher at MIT Media Lab

Interview with Sharif Islam, Post-Doc Researcher at MIT Media Lab

Photo of Sharif Islam
This interview was conducted as part of the young professional program of the space4water program. The interview begins by asking about my professional and personal journey as a researcher specializing in water and space technologies, particularly in the context of environmental challenges. Growing up in Bangladesh, how my exposure to multiple water related challenges influenced my deep interest in remote sensing and Earth observation technologies. Then the question focuses on how I am addressing water related challenges using satellite imagery and geospatial data. The conversation also explores the role of space-based technologies, such as satellite Earth observations, in monitoring coastal erosion and riverbank changes. As part of response, I explain how the combination of high-resolution imagery with machine learning can predict environmental shifts and help mitigate the impacts on vulnerable populations. Finally, I shared my advice for aspiring professionals in water management, emphasizing the importance of interdisciplinary skills, including geospatial analysis, data science, and policy understanding. I also talked about the value of curiosity, collaboration, and access to advanced technologies for driving innovation in water related challenges worldwide.
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Interview with Sharif Islam, Post-Doc Researcher at MIT Media Lab

Interview with Sharif Islam, Post-Doc Researcher at MIT Media Lab

Photo of Sharif Islam
This interview was conducted as part of the young professional program of the space4water program. The interview begins by asking about my professional and personal journey as a researcher specializing in water and space technologies, particularly in the context of environmental challenges. Growing up in Bangladesh, how my exposure to multiple water related challenges influenced my deep interest in remote sensing and Earth observation technologies. Then the question focuses on how I am addressing water related challenges using satellite imagery and geospatial data. The conversation also explores the role of space-based technologies, such as satellite Earth observations, in monitoring coastal erosion and riverbank changes. As part of response, I explain how the combination of high-resolution imagery with machine learning can predict environmental shifts and help mitigate the impacts on vulnerable populations. Finally, I shared my advice for aspiring professionals in water management, emphasizing the importance of interdisciplinary skills, including geospatial analysis, data science, and policy understanding. I also talked about the value of curiosity, collaboration, and access to advanced technologies for driving innovation in water related challenges worldwide.
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Local Perspectives Case Studies

Groundwater resource management using artificial intelligence and remote sensing technologies

Groundwater resource management using artificial intelligence and remote sensing technologies

Groundwater index maps for Bihar
Groundwater is a critical resource for drinking water, agriculture, and industry. With increasing anthropogenic activities and exponentially increasing population, groundwater in India is facing several challenges, related to quality as well as quantity, due to over-extraction, pollution, and climate change. Over-exploitation of groundwater may impact the availability and quality of groundwater which is not sustainable. Moreover, due to pollution in surface water, groundwater quality is also affected. In most of the cities of India, the quality of groundwater is below standard. Remote sensing and artificial intelligence can play a very vital role in monitoring the quantity as well as quality of groundwater. As, it is clear that presently no remote sensors can directly be used for groundwater observations, but by using surface features anomalies and gravity data obtained by various satellites, optimal groundwater management can be done using remote sensing. Space4water is one of the best communities addressing water related issues and work towards sustainable solutions. For the last three years, I am following this community, and I find that the community consists of scientists, NGO, policy makers etc. This combination has the potential to resolve issues related to any challenges related to social issues. I am looking for few global research partners who work for groundwater management using space technology. I am equally looking for data driven resource persons who can collaborate with me on real field conditions of various countries, related to groundwater management. What has been done so far is listed below: • Worked on GRACE satellite data and used it in field condition to study groundwater anomalies of few cities of India. • Developed spatio-temporal maps of Standardized Groundwater Index (SGI). • Worked on water quality of water bodies. • Used various satellite data to map water spread areas of various water bodies. • Worked on machine learning models to study in situ remediation of contaminated groundwater.
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The ecohydrological trade-off in Nepal’s Middle Hills: mapping spring decline and groundwater loss in community forests through space-based solutions

The ecohydrological trade-off in Nepal’s Middle Hills: mapping spring decline and groundwater loss in community forests through space-based solutions

Map of Sharadha Khola watershed in Nepal
In Nepal’s Middle Hills, community-managed forests have successfully reversed deforestation, but they are now unintentionally contributing to water insecurity. Afforestation has heavily favored Pinus roxburghii, a fast-growing conifer with high year-round evapotranspiration and low infiltration capacity, significantly reducing groundwater recharge. As pine offers limited economic value, forest users increasingly shift to Sal (Shorea robusta) forests, valued for timber and compostable leaf litter. This shift concentrates human activity—such as litter collection, grazing, and trampling—around Sal patches, causing surface compaction and further reducing infiltration. Combined with unplanned road construction that disrupts natural flow paths, these disturbances have degraded upland recharge zones. Once crucial for replenishing groundwater, these uplands are now losing their recharge capacity, leading to measurable declines in groundwater storage and drying of springs in foothill and riparian zones that once flowed year-round. The consequences are widespread and socio-ecologically severe. Rural and Indigenous communities relying on spring-fed systems for drinking water, irrigation, and livestock now face escalating dry-season scarcity. Women and elderly members of marginalized groups bear the greatest burden, while increasing outmigration to urban centers exacerbates inequality. Yet forest governance remains focused on canopy cover and carbon sequestration, often overlooking essential hydrological processes like infiltration, baseflow, and subsurface storage. The continued decline in groundwater recharge also raises long-term concerns about shallow aquifer sustainability and overall water security. This situation is further complicated by a lack of reliable, long-term ground-based hydrometeorological data—many precipitation, temperature, and stream discharge records are missing or incomplete due to sensor failure—making it difficult to calibrate ecohydrological models and to design informed forest and water policies. Fortunately, space-based technologies provide a powerful solution. Remote sensing allows for long-term monitoring of vegetation, precipitation, soil moisture, and terrain, revealing the drivers of spring decline. When paired with ecohydrological modeling and community knowledge, these tools can guide forest management strategies that restore groundwater recharge and help achieve SDG targets 6, 13, and 15. Goals and milestones: The main goal of this research is to assess and mitigate ecohydrological trade-offs in Nepal’s Middle Hills caused by unscientific forest expansion under community forestry (CF). While CF has successfully increased forest cover, it has often overlooked hydrological impacts—particularly where high water-use species like pine have been planted without considering water balance consequences. This has led to declining baseflows, reduced groundwater recharge, and increased dry-season water stress. A key focus is to bridge the gap between Indigenous forest management practices and scientific understanding of forest-water interactions. By integrating Regional Hydro-Ecological Simulation System (RHESSys), ecohydrological modeling, satellite remote sensing, and community-level knowledge, the project aims to reveal how forest type, topography, and land use influence spring recharge zones, groundwater dynamics, and soil moisture retention. Research has shown that nearly 70 per cent of the springs in the region are degrading, threatening long-term water security. One of the critical goals of this research is to identify vulnerable and resilient spring zones—and ultimately support the rebirth of these springs through improved forest and land-use strategies. Short-term milestones include generating high-resolution maps of vegetation phenology, evapotranspiration, and groundwater storage (1985–2025), and validating RHESSys outputs with both field data and satellite products. In the mid-term, the study will identify groundwater-rich zones for future water-resilient settlements, simulate climate scenarios, and collaborate with local stakeholders. The long-term objective is to promote scientifically informed, community-adapted forest governance that enhances both ecological and water resilience across Nepal’s Middle Hills.
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Space-based Solution

Surface water extent river course - in development

Addressed challenge(s)
Long view of Ngakoahia Stream and surrounding vegetation

Lacking historic knowledge on vegetation cover and surface water extent / river course

Collaborating actors (stakeholders, professionals, young professionals or Indigenous voices)
Suggested solution

Note: this description is a work in progress developed by the collaborating entities in a workshop. If you would like to contribute reach out to office@space4water.org, or your trusted Space4Water point of contact.

The solution approach begins with identifying the region's main rivers and understanding their hydrology using mapping and geoprocessing tools. After understanding the hydrography of the area and mapping the surface water extent river course through the building a hydrographic dataset, multiple image sources are used to map the historical land use and land cover surrounding the river.

1. Resources needed

Software

  • QGIS https://www.qgis.org/en/site 
  • TerraHidro 5 - Console applications https://www.dpi.inpe.br/terrahidro/doku.php
  • PostgreSQL https://www.postgresql.org
  • PostGIS Spatial Database System  https://postgis.net/
  • PgHydro extension for PostgreSQL/PostGIS http://pghydro.org/
  • PgHydro Plugin for QGIS https://plugins.qgis.org/plugins/PghydroTools/

Data

Forest And Buildings removed Copernicus DEM

Publications

see reference in the bibliography below.

2. Steps to the solution & status

Overivew

  1. Plot the Region of Interest (completed)
  2. Identify the region's main rivers and understand their hydrology (completed);
  3. Identify the region's potential flood areas using H.A.N.D.;
  4. Build a hydrography dataset (completed);
  5. Identify multiple image sources for land cover analysis (completed);
  6. Map the historical land use and land cover surrounding the river (in progress);

Step-by-step

1. Plot the Region of Interest (completed)

  1. Download and install QGIS to plot the KML files of the region of interest
Example KML plot of the strip of land of the Maori communtiy who submitted the challenge
Figure 1: Example KML plot of the strip of land of the Maori communtiy who submitted the challenge

 

2. Identify the region's main rivers and understand their hydrology (completed)

  1. Download the FABDEM data for the Region of Interest.
    FABDEM (Forest And Buildings removed Copernicus DEM) is a global elevation map that removes building and tree height biases from the Copernicus GLO 30 Digital Elevation Model (DEM) (https://data.bris.ac.uk/data/dataset/25wfy0f9ukoge2gs7a5mqpq2j7).
     
    A FEABDEM Digital Elevation Model of the Ngutunui region, New Zealand.
    Figure 2: A FEABDEM Digital Elevation Model of the Ngutunui region, New Zealand.

     
  2. Download and Install TerraHidro 5 - Console applications (https://www.dpi.inpe.br/terrahidro/doku.php) to extract the hydrograph products derived from the FABDEM to understand the hydrography setup of the area (Flow direction, flow accumulation and drainage lines and areas, H.A.N.D.).
     
    Flow direction in the Ngutunui region, New Zealand
    Figure 3: Flow direction in the Ngutunui region, New Zealand
    Flow accumultation in the Ngutunui region, New Zealand
    Figure 4: Flow accumultation in the Ngutunui region, New Zealand
     
    Sintetetic drainage lines and areas
    Figure 5: Sintetic draingage lines and areas Ngutunui region, New Zealand

     

3. Identify the region's potential flood areas using H.A.N.D.

Building on Nobre et. al (2011) in which the HAND terrain model that "normalizes topography according to the local relative heights found along the drainage network, and in this way, presents the topology of the relative soil gravitational potentials, or local draining potentials" is introduced by the authors.

Height Above the Neaerest Drainage (HAND)in the Ngutunui reiong,
Figure 6: Height Above the Neaerest Drainage (HAND) in the Ngutunui region showng the areas for potential flooding in darker blue. In the current map this is in the bottom right quarter of the image.

 

4. Build a hydrography dataset (completed)

  1. Download and instal PostgreSQL/PostGIS Spatial Database System (https://www.postgresql.org/) (https://postgis.net/), PgHydro extension for PostgreSQL/PostGIS (http://pghydro.org/) and PgHydro Plugin for QGIS;(https://plugins.qgis.org/plugins/PghydroTools/).
  2. Build the Hydrograph Dataset;(https://www.youtube.com/channel/UCgkCUQ-i72bBY41a1bhVWyw) using the Drainage Lines and Drainage Areas extracted from FABDEM;
  3. Information like drainage area, upstream area, drainage line length and distance to sea information are now available.
     
    Hydrography dataset of the Ngutunui region in New Zealand
    Figure 7: Hydrography dataset of the Ngutunui region in New Zealand

     

5. Identify multiple image sources for landing cover analysis (completed);

  1. To collect historic and high-resolution up-to-date imagery over the area, UNOOSA contacted the Land and Information New Zealand Data Service, which provided both historical aerial imagery and LIDAR data sources.
  2. Historic data for the relevant land patch can be accessed via the Retrolens New Zealand Service (https://retrolens.co.nz/Map/#/1784971.9859981549/5783474.532151884/1786387.2653498782/5784857.564632303/2193/12).
  3. Up-to-date aerial photos of the area can be accessed here at the New Zealand Data Service. Tile 503 and 603 are the ones of interest (https://data.linz.govt.nz/layer/112048-waikato-03m-rural-aerial-photos-index-tiles-2021-2023/history/).
  4. Relevant Landsat data are available from 1989. For the study area, Landsat 7 data is available from 2 July 1999, and Landsat 4 from 2 February 1989;
  5. Google Earth Engine Apps - Global Forest Change (https://google.earthengine.app/view/forest-change)

6. Map the historical land use and land cover surrounding the river (in progress);

Relevant publications
Related space-based solutions
Keywords (for the solution)
Land useLand Cover DataNormalized Difference Vegetation Index (NDVI)Digital Elevation Model (DEM)Geographic Information System (GIS)Hydrography
Climate Zone (addressed by the solution)
Humid subtropical
Habitat (addressed by the solution)
Region/Country (the solution was designed for, if any)
New Zealand
Relevant SDGs